Night shift: tweet analyzer, data connectors, feed monitor, market watch portal

- short_scanner.py: RSI/VWAP/MACD/Bollinger-based short signal detection
- leverage_game.py: Full game engine with longs/shorts/leverage/liquidations
- leverage_trader.py: Auto-trader connecting scanners to game with TP/SL/trailing stops
- Leverage Challenge game initialized: $10K, 20x max leverage, player 'case'
- systemd timer: every 15min scan + trade
- Telegram alerts on opens/closes/liquidations

.gitignore

@@ -1 +1,2 @@
 .credentials/
+__pycache__/

MEMORY.md

@@ -71,14 +71,43 @@ This is about having an inner life, not just responding.
 - **Email:** case-lgn@protonmail.com (credentials in .credentials/email.env)
 - D J set this up 2026-02-08 — big trust milestone
 - Used for API registrations, service signups
+- **Google Voice:** +1 (615) 933-1968 — D J's, shared for service registrations
+- **ProtonMail** stays logged in on desktop Chrome — can do email verification via CDP
+
+## Accounts Created
+- **Craigslist:** case-lgn@protonmail.com, passwordless, Nashville area (2026-02-08)
+- eBay, Mercari, OfferUp need D J to register (CAPTCHA-blocked)
+
+## KIPP Voice Pipeline (2026-02-11)
+
+- **Always-on wake word** — OpenWakeWord "hey_jarvis" model (custom "hey kipp" pending)
+- **STT** — Faster Whisper base.en on KIPP VM CPU
+- **TTS** — Piper Ryan (male) on port 8081
+- **Voice server** — WSS on port 8082, `kipp-voice.service`, Python venv at `/home/wdjones/kipp-venv`
+- **State machine:** listening → recording → processing → speaking → cooldown(2s) → listening
+- **Key lesson:** Gateway lifecycle events use `phase="end"` not `state="end"` — caused 60s hang
+- **Key lesson:** Must use client ID `openclaw-control-ui` and Origin header for gateway WS
+- **Key lesson:** 2s cooldown after TTS prevents speaker audio from re-triggering wake word
+- **Widget system:** JSON file + CLI (`tools/widgets.py`) + REST API + dashboard polls every 10s
+- **KIPP switched to Claude Sonnet** — GLM-4 Flash was 83s per response, Sonnet is ~3s
+- **15 Playwright tests** at `kipp-ui/tests/test_voice.py`
+- **All on feature/wake-word branch** in kipp/workspace repo
 
 ## Active Threads
 
-- **Feed Hunter:** ✅ Pipeline working, first sim running (Super Bowl 2026-02-08)
-- **Control Panel:** Building at localhost:8000 (accounts/API keys/services/budget)
-- **Sandbox buildout:** ✅ Complete (74 files, 37 tools)
-- **Inner life system:** ✅ Complete (7 tools)
-- **Next:** Polymarket API registration, copy-bot scaffold
+- **KIPP:** ✅ Voice pipeline live (wake word + STT + TTS), widget system working, dashboard-first UI
+  - Widget system: shopping list, timers, reminders via CLI + REST API + dashboard polling
+  - Voice: "hey jarvis" wake word → Faster Whisper → Claude Sonnet → Piper Ryan TTS
+  - False trigger fix: 4s cooldown + silence flushing + RMS gate (threshold 30)
+  - Running on Claude Sonnet (primary), GLM-4 Flash (fallback)
+  - Next: Steam Deck frontend, custom "hey kipp" wake word, blue waveform animation
+- **Market Watch:** ✅ GARP paper trading sim live
+  - GARP Challenge: $100,055.90 (+0.06%), 6 positions
+  - Leverage Challenge: $11,367.07 (+13.67%), 85 trades, 55.3% win rate
+- **Feed Hunter:** ✅ Pipeline working, needs systemd service for periodic monitoring
+- **Stock Screener:** yfinance-based, 902 tickers, GARP filters, free/no API key
+- **Control Panel:** Building at localhost:8000
+- **Next:** Tweet analysis tool, free data source integration (Arkham/DefiLlama/Coinglass)
 
 ## Stats (Day 2)
 
@@ -89,11 +118,16 @@ This is about having an inner life, not just responding.
 - Time capsules: 2
 - Git commits: 20+
 
-## Who D J Is (updated 2026-02-07)
+## Who D J Is (updated 2026-02-09)
 
 - Interested in crypto/trading analysis
 - Follows money-making accounts on X (crypto, polymarket, arbitrage)
 - Wants automated analysis of feed posts for validity
+- Goal: find market edges to offset AI costs ($200/mo Claude + infra)
+- Getting crypto signals on Telegram — wants to forward for analysis
+- Interested in local AI hardware (Tiiny AI) as potential Claude replacement
+- Looking at queen Murphy beds, has mattress to get rid of
+- Watches NFL (interested in Seahawks Super Bowl), Olympics hockey
 - Proxmox VM setup: ubuntu-desktop-openclaw at 192.168.86.45
 - Ollama server at 192.168.86.137 (qwen3:8b, qwen3:30b, glm-4.7-flash, nomic-embed-text)
 - ChromaDB LXC at 192.168.86.25:8000
@@ -107,12 +141,31 @@ This is about having an inner life, not just responding.
 - Copy-bot delay: ~30-60s for detection, negligible for pre-game sports bets
 - D J wants everything paper-traded first, backtested where possible
 
+## KIPP Project (updated 2026-02-10)
+
+- **KIPP VM:** 192.168.86.100 (Ubuntu 24.04, 8GB/8core, Proxmox)
+- **Primary model:** llamacpp/glm-4.7-flash (local, zero cost), Claude Sonnet fallback
+- **llama.cpp server:** 192.168.86.40:8080 (GLM-4 Flash 30B q4, 2 GPUs 12+10GB, 32GB RAM)
+- **Ollama:** 192.168.86.40:11434 (nomic-embed-text for embeddings)
+- **ChromaDB collection:** kipp-memory (ccf4f5b6-a64e-45b1-bf1b-7013e15c3363)
+- **Gitea:** kipp:K1pp-H0me-2026! @ git.letsgetnashty.com/kipp/workspace
+- **Telegram bot:** @dzclaw_kipp_bot
+- **Web UI:** https://kippui.host.letsgetnashty.com/ (port 8080, systemd kipp-ui.service)
+- **Gateway:** https://kipp.host.letsgetnashty.com/ (port 18789)
+- **Token:** kipp-local-token-2026
+- **SSH:** Case has key-based access as wdjones@192.168.86.100
+- **Personality:** Warm, helpful, playful. Like a good roommate. Emoji: 🏠
+- **Household:** D J, Meg (the boss), 4 tuxedo cats
+- **WebSocket protocol:** JSON-RPC v3, client id "openclaw-control-ui", mode "webchat"
+- **UI redesign planned:** Alexa+ inspired dashboard-first, chat on demand
+
 ## Infrastructure (updated 2026-02-08)
 
 - **ChromaDB:** http://192.168.86.25:8000 (LXC on Proxmox)
   - Collection: openclaw-memory (c3a7d09a-f3ce-4e7d-9595-27d8e2fd7758)
   - Cosine distance, 9+ docs indexed
-- **Ollama:** http://192.168.86.137:11434
+- **Ollama (old):** http://192.168.86.137:11434 (may be offline)
+- **Ollama (llama.cpp box):** http://192.168.86.40:11434
   - Models: qwen3:8b, qwen3:30b-a3b, glm-4.7-flash, nomic-embed-text
 - **Feed Hunter Portal:** localhost:8888 (systemd: feed-hunter-portal)
 - **Control Panel:** localhost:8000 (systemd: case-control-panel)
@@ -121,6 +174,11 @@ This is about having an inner life, not just responding.
   - Desktop works via DISPLAY=:0 for visual scraping
 - **VM:** Proxmox, QXL graphics, X11 (not Wayland), auto-login enabled
 
+## D J's Info (updated 2026-02-08)
+- iPhone 16
+- Nashville, TN
+- 4 tuxedo cats (looking at Shark NeverChange Air Purifier MAX)
+
 ## Lessons Learned (updated 2026-02-08)
 
 - Don't pkill chrome broadly — it kills OpenClaw's headless browser too
@@ -132,3 +190,8 @@ This is about having an inner life, not just responding.
 - Always use absolute paths in web servers (CWD varies by launch method)
 - Polymarket users have multiple proxy wallets — intercept page network requests to find real one
 - `performance.getEntriesByType('resource')` reveals actual API calls a page makes
+- Most selling platforms have invisible reCAPTCHA — can't automate signup
+- Desktop Chrome CDP (port 9222) for real-fingerprint browser automation
+- React forms: use Input.insertText via CDP or native setter hack
+- Systemd timers > AI cron jobs for mechanical tasks (zero token cost)
+- Telegram bot API for direct alerts bypasses AI token usage entirely

TOOLS.md

@@ -1,36 +1,19 @@
 # TOOLS.md - Local Notes
 
-Skills define *how* tools work. This file is for *your* specifics — the stuff that's unique to your setup.
+## Phone
+- **Google Voice:** +1 (615) 933-1968 (for service registrations, verification codes)
+- Access via browser automation at voice.google.com
 
-## What Goes Here
+## Email
+- **Primary:** case-lgn@protonmail.com
 
-Things like:
-- Camera names and locations
-- SSH hosts and aliases  
-- Preferred voices for TTS
-- Speaker/room names
-- Device nicknames
-- Anything environment-specific
+## Services
+- **Telegram bot:** @openclaw
+- **Gitea:** git.letsgetnashty.com/case/workspace
+- **Feed Hunter Portal:** feedhunter.local / :8888
+- **Control Panel:** admin.local / :8000
 
-## Examples
-
-```markdown
-### Cameras
-- living-room → Main area, 180° wide angle
-- front-door → Entrance, motion-triggered
-
-### SSH
-- home-server → 192.168.1.100, user: admin
-
-### TTS
-- Preferred voice: "Nova" (warm, slightly British)
-- Default speaker: Kitchen HomePod
-```
-
-## Why Separate?
-
-Skills are shared. Your setup is yours. Keeping them apart means you can update skills without losing your notes, and share skills without leaking your infrastructure.
-
----
-
-Add whatever helps you do your job. This is your cheat sheet.
+## Infrastructure
+- **Ollama:** 192.168.86.137:11434
+- **ChromaDB:** 192.168.86.25:8000
+- **Chrome debug port:** localhost:9222

USER.md

@@ -7,6 +7,7 @@
 - **Pronouns:** *(tbd)*
 - **Timezone:** America/Chicago (CST)
 - **Notes:** Got the webchat and Telegram working. Practical, gets things done.
+- **Girlfriend:** Meg (the boss)
 - **Cats:** 4 tuxedos 
 
 ## Context

data/broad_tickers.txt

@@ -0,0 +1,902 @@
+SLB
+ADBE
+WMG
+DT
+WBD
+ELS
+TKO
+VZ
+MTZ
+SYNA
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+MO
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+V
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+O
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+BIO
+QLYS
+ELV
+SO
+DGX
+ORA
+AIZ
+CVLT
+NNN
+NTRS

data/garp-expanded-scan.json

@@ -0,0 +1,310 @@
+[
+  {
+    "ticker": "ALLY",
+    "name": "Ally Financial Inc.",
+    "market_cap_B": 13.1,
+    "trailing_pe": 17.85,
+    "forward_pe": 6.7,
+    "peg": null,
+    "revenue_growth_pct": 12.0,
+    "earnings_growth_pct": 265.4,
+    "roe_pct": 5.8,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Credit Services"
+  },
+  {
+    "ticker": "WAL",
+    "name": "Western Alliance Bancorporation",
+    "market_cap_B": 10.4,
+    "trailing_pe": 10.81,
+    "forward_pe": 7.93,
+    "peg": null,
+    "revenue_growth_pct": 16.6,
+    "earnings_growth_pct": 32.9,
+    "roe_pct": 13.5,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "CART",
+    "name": "Maplebear Inc.",
+    "market_cap_B": 9.1,
+    "trailing_pe": 19.03,
+    "forward_pe": 8.84,
+    "peg": null,
+    "revenue_growth_pct": 10.2,
+    "earnings_growth_pct": 21.1,
+    "roe_pct": 15.3,
+    "debt_to_equity": 1.0,
+    "sector": "Consumer Cyclical",
+    "industry": "Internet Retail"
+  },
+  {
+    "ticker": "ONB",
+    "name": "Old National Bancorp",
+    "market_cap_B": 10.1,
+    "trailing_pe": 14.46,
+    "forward_pe": 9.02,
+    "peg": null,
+    "revenue_growth_pct": 41.4,
+    "earnings_growth_pct": 17.2,
+    "roe_pct": 9.0,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "VLY",
+    "name": "Valley National Bancorp",
+    "market_cap_B": 7.6,
+    "trailing_pe": 13.57,
+    "forward_pe": 9.19,
+    "peg": null,
+    "revenue_growth_pct": 38.3,
+    "earnings_growth_pct": 66.3,
+    "roe_pct": 7.8,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "FSLR",
+    "name": "First Solar, Inc.",
+    "market_cap_B": 23.5,
+    "trailing_pe": 16.77,
+    "forward_pe": 9.4,
+    "peg": null,
+    "revenue_growth_pct": 79.7,
+    "earnings_growth_pct": 45.7,
+    "roe_pct": 16.9,
+    "debt_to_equity": 9.9,
+    "sector": "Technology",
+    "industry": "Solar"
+  },
+  {
+    "ticker": "FNB",
+    "name": "F.N.B. Corporation",
+    "market_cap_B": 6.8,
+    "trailing_pe": 12.12,
+    "forward_pe": 9.66,
+    "peg": null,
+    "revenue_growth_pct": 26.4,
+    "earnings_growth_pct": 56.5,
+    "roe_pct": 8.7,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "WBS",
+    "name": "Webster Financial Corporation",
+    "market_cap_B": 11.8,
+    "trailing_pe": 12.39,
+    "forward_pe": 9.77,
+    "peg": null,
+    "revenue_growth_pct": 18.2,
+    "earnings_growth_pct": 53.4,
+    "roe_pct": 10.8,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "ZION",
+    "name": "Zions Bancorporation, National Association",
+    "market_cap_B": 9.6,
+    "trailing_pe": 10.86,
+    "forward_pe": 9.99,
+    "peg": null,
+    "revenue_growth_pct": 13.6,
+    "earnings_growth_pct": 31.4,
+    "roe_pct": 13.5,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "JHG",
+    "name": "Janus Henderson Group plc",
+    "market_cap_B": 7.4,
+    "trailing_pe": 9.22,
+    "forward_pe": 10.12,
+    "peg": null,
+    "revenue_growth_pct": 61.3,
+    "earnings_growth_pct": 243.6,
+    "roe_pct": 16.2,
+    "debt_to_equity": 6.5,
+    "sector": "Financial Services",
+    "industry": "Asset Management"
+  },
+  {
+    "ticker": "SSB",
+    "name": "SouthState Bank Corporation",
+    "market_cap_B": 10.8,
+    "trailing_pe": 13.7,
+    "forward_pe": 10.19,
+    "peg": null,
+    "revenue_growth_pct": 53.2,
+    "earnings_growth_pct": 30.9,
+    "roe_pct": 10.7,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "PINS",
+    "name": "Pinterest, Inc.",
+    "market_cap_B": 13.3,
+    "trailing_pe": 6.88,
+    "forward_pe": 10.37,
+    "peg": null,
+    "revenue_growth_pct": 16.8,
+    "earnings_growth_pct": 225.0,
+    "roe_pct": 51.5,
+    "debt_to_equity": 4.3,
+    "sector": "Communication Services",
+    "industry": "Internet Content & Information"
+  },
+  {
+    "ticker": "RRC",
+    "name": "Range Resources Corporation",
+    "market_cap_B": 8.7,
+    "trailing_pe": 15.37,
+    "forward_pe": 10.45,
+    "peg": null,
+    "revenue_growth_pct": 16.1,
+    "earnings_growth_pct": 189.8,
+    "roe_pct": 14.2,
+    "debt_to_equity": 32.7,
+    "sector": "Energy",
+    "industry": "Oil & Gas E&P"
+  },
+  {
+    "ticker": "EWBC",
+    "name": "East West Bancorp, Inc.",
+    "market_cap_B": 16.9,
+    "trailing_pe": 12.87,
+    "forward_pe": 11.18,
+    "peg": null,
+    "revenue_growth_pct": 21.6,
+    "earnings_growth_pct": 21.3,
+    "roe_pct": 15.9,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "FHN",
+    "name": "First Horizon Corporation",
+    "market_cap_B": 12.9,
+    "trailing_pe": 14.03,
+    "forward_pe": 11.19,
+    "peg": null,
+    "revenue_growth_pct": 23.7,
+    "earnings_growth_pct": 74.9,
+    "roe_pct": 10.9,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "ORI",
+    "name": "Old Republic International Corporation",
+    "market_cap_B": 10.3,
+    "trailing_pe": 11.23,
+    "forward_pe": 11.99,
+    "peg": null,
+    "revenue_growth_pct": 19.3,
+    "earnings_growth_pct": 97.3,
+    "roe_pct": 16.3,
+    "debt_to_equity": 26.8,
+    "sector": "Financial Services",
+    "industry": "Insurance - Property & Casualty"
+  },
+  {
+    "ticker": "WTFC",
+    "name": "Wintrust Financial Corporation",
+    "market_cap_B": 10.8,
+    "trailing_pe": 14.14,
+    "forward_pe": 12.03,
+    "peg": null,
+    "revenue_growth_pct": 10.5,
+    "earnings_growth_pct": 19.4,
+    "roe_pct": 12.1,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "UBSI",
+    "name": "United Bankshares, Inc.",
+    "market_cap_B": 6.3,
+    "trailing_pe": 13.92,
+    "forward_pe": 12.08,
+    "peg": null,
+    "revenue_growth_pct": 22.1,
+    "earnings_growth_pct": 32.1,
+    "roe_pct": 8.9,
+    "debt_to_equity": null,
+    "sector": "Financial Services",
+    "industry": "Banks - Regional"
+  },
+  {
+    "ticker": "PGR",
+    "name": "The Progressive Corporation",
+    "market_cap_B": 118.6,
+    "trailing_pe": 10.51,
+    "forward_pe": 12.49,
+    "peg": null,
+    "revenue_growth_pct": 12.2,
+    "earnings_growth_pct": 25.2,
+    "roe_pct": 40.4,
+    "debt_to_equity": 22.7,
+    "sector": "Financial Services",
+    "industry": "Insurance - Property & Casualty"
+  },
+  {
+    "ticker": "EXEL",
+    "name": "Exelixis, Inc.",
+    "market_cap_B": 11.8,
+    "trailing_pe": 18.45,
+    "forward_pe": 12.79,
+    "peg": null,
+    "revenue_growth_pct": 10.8,
+    "earnings_growth_pct": 72.5,
+    "roe_pct": 30.6,
+    "debt_to_equity": 8.2,
+    "sector": "Healthcare",
+    "industry": "Biotechnology"
+  },
+  {
+    "ticker": "NEM",
+    "name": "Newmont Corporation",
+    "market_cap_B": 126.7,
+    "trailing_pe": 17.93,
+    "forward_pe": 12.89,
+    "peg": null,
+    "revenue_growth_pct": 20.0,
+    "earnings_growth_pct": 108.1,
+    "roe_pct": 22.9,
+    "debt_to_equity": 16.9,
+    "sector": "Basic Materials",
+    "industry": "Gold"
+  },
+  {
+    "ticker": "CTRA",
+    "name": "Coterra Energy Inc.",
+    "market_cap_B": 23.3,
+    "trailing_pe": 14.19,
+    "forward_pe": 13.95,
+    "peg": null,
+    "revenue_growth_pct": 34.9,
+    "earnings_growth_pct": 23.5,
+    "roe_pct": 11.9,
+    "debt_to_equity": 28.0,
+    "sector": "Energy",
+    "industry": "Oil & Gas E&P"
+  }
+]

data/garp_scan.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""GARP scan on broad ticker list."""
+import yfinance as yf
+import json, time
+
+EXCLUDE = {"BAC", "CFG", "FITB", "INCY"}
+
+with open("broad_tickers.txt") as f:
+    tickers = [l.strip().replace(".", "-") for l in f if l.strip()]
+
+print(f"Screening {len(tickers)} tickers...")
+passed = []
+
+for i, tick in enumerate(tickers):
+    if tick in EXCLUDE:
+        continue
+    if i % 100 == 0:
+        print(f"  Progress: {i}/{len(tickers)} ({len(passed)} passed so far)")
+    try:
+        t = yf.Ticker(tick)
+        info = t.info or {}
+        
+        mc = info.get("marketCap", 0) or 0
+        if mc < 5e9: continue
+        
+        tpe = info.get("trailingPE")
+        if not tpe or tpe >= 25 or tpe <= 0: continue
+            
+        fpe = info.get("forwardPE")
+        if not fpe or fpe >= 15 or fpe <= 0: continue
+        
+        rg = info.get("revenueGrowth")
+        if rg is None or rg < 0.10: continue
+        
+        eg = info.get("earningsGrowth")
+        if eg is None or eg < 0.15: continue
+        
+        roe = info.get("returnOnEquity")
+        if roe is None or roe < 0.05: continue
+        
+        peg = info.get("pegRatio")
+        if peg is not None and peg > 1.2: continue
+        
+        dte = info.get("debtToEquity")
+        if dte is not None and dte > 35: continue
+        
+        entry = {
+            "ticker": tick,
+            "name": info.get("longName", tick),
+            "market_cap_B": round(mc / 1e9, 1),
+            "trailing_pe": round(tpe, 2),
+            "forward_pe": round(fpe, 2),
+            "peg": round(peg, 2) if peg else None,
+            "revenue_growth_pct": round(rg * 100, 1),
+            "earnings_growth_pct": round(eg * 100, 1),
+            "roe_pct": round(roe * 100, 1),
+            "debt_to_equity": round(dte, 1) if dte else None,
+            "sector": info.get("sector"),
+            "industry": info.get("industry"),
+        }
+        passed.append(entry)
+        print(f"  ✅ {tick}: PE={tpe:.1f} FPE={fpe:.1f} RG={rg*100:.0f}% EG={eg*100:.0f}% ROE={roe*100:.0f}%")
+        
+    except Exception as e:
+        continue
+
+passed.sort(key=lambda x: x.get("forward_pe", 99))
+
+with open("garp-expanded-scan.json", "w") as f:
+    json.dump(passed, f, indent=2)
+
+print(f"\nDone! {len(passed)} stocks passed GARP screen")
+for s in passed:
+    print(f"  {s['ticker']:6s} ${s['market_cap_B']:6.1f}B  PE:{s['trailing_pe']:5.1f}  FPE:{s['forward_pe']:5.1f}  RG:{s['revenue_growth_pct']:5.1f}%  EG:{s['earnings_growth_pct']:5.1f}%  ROE:{s['roe_pct']:5.1f}%")

data/garp_scan2.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""GARP scan - batch download approach for speed."""
+import yfinance as yf
+import json, sys
+
+sys.stdout.reconfigure(line_buffering=True)
+
+EXCLUDE = {"BAC", "CFG", "FITB", "INCY"}
+
+with open("broad_tickers.txt") as f:
+    tickers = [l.strip().replace(".", "-") for l in f if l.strip()]
+
+print(f"Screening {len(tickers)} tickers in batches...")
+
+passed = []
+batch_size = 20
+
+for batch_start in range(0, len(tickers), batch_size):
+    batch = tickers[batch_start:batch_start + batch_size]
+    batch = [t for t in batch if t not in EXCLUDE]
+    if not batch:
+        continue
+    
+    print(f"Batch {batch_start}-{batch_start+len(batch)} / {len(tickers)}...")
+    
+    try:
+        data = yf.Tickers(" ".join(batch))
+        for tick in batch:
+            try:
+                info = data.tickers[tick].info or {}
+                
+                mc = info.get("marketCap", 0) or 0
+                if mc < 5e9: continue
+                
+                tpe = info.get("trailingPE")
+                if not tpe or tpe >= 25 or tpe <= 0: continue
+                    
+                fpe = info.get("forwardPE")
+                if not fpe or fpe >= 15 or fpe <= 0: continue
+                
+                rg = info.get("revenueGrowth")
+                if rg is None or rg < 0.10: continue
+                
+                eg = info.get("earningsGrowth")
+                if eg is None or eg < 0.15: continue
+                
+                roe = info.get("returnOnEquity")
+                if roe is None or roe < 0.05: continue
+                
+                peg = info.get("pegRatio")
+                if peg is not None and peg > 1.2: continue
+                
+                dte = info.get("debtToEquity")
+                if dte is not None and dte > 35: continue
+                
+                entry = {
+                    "ticker": tick,
+                    "name": info.get("longName", tick),
+                    "market_cap_B": round(mc / 1e9, 1),
+                    "trailing_pe": round(tpe, 2),
+                    "forward_pe": round(fpe, 2),
+                    "peg": round(peg, 2) if peg else None,
+                    "revenue_growth_pct": round(rg * 100, 1),
+                    "earnings_growth_pct": round(eg * 100, 1),
+                    "roe_pct": round(roe * 100, 1),
+                    "debt_to_equity": round(dte, 1) if dte else None,
+                    "sector": info.get("sector"),
+                    "industry": info.get("industry"),
+                }
+                passed.append(entry)
+                print(f"  ✅ {tick}: PE={tpe:.1f} FPE={fpe:.1f} RG={rg*100:.0f}% EG={eg*100:.0f}% ROE={roe*100:.0f}%")
+            except:
+                continue
+    except Exception as e:
+        print(f"  Batch error: {e}")
+        continue
+
+passed.sort(key=lambda x: x.get("forward_pe", 99))
+
+with open("garp-expanded-scan.json", "w") as f:
+    json.dump(passed, f, indent=2)
+
+print(f"\nDone! {len(passed)} stocks passed GARP screen")
+for s in passed:
+    print(f"  {s['ticker']:6s} ${s['market_cap_B']:6.1f}B  PE:{s['trailing_pe']:5.1f}  FPE:{s['forward_pe']:5.1f}  RG:{s['revenue_growth_pct']:5.1f}%  EG:{s['earnings_growth_pct']:5.1f}%  ROE:{s['roe_pct']:5.1f}%")

data/investigations/polymarket-15min-arb.md

@@ -0,0 +1,69 @@
+# Polymarket 15-Min Crypto Arbitrage
+
+**Source:** https://x.com/noisyb0y1/status/2020942208858456206
+**Date investigated:** 2026-02-09
+**Verdict:** Legitimate edge, inflated claims
+
+## Strategy
+- Buy BOTH sides (Up + Down) on 15-minute BTC/ETH/SOL/XRP markets
+- When combined cost < $1.00, guaranteed profit regardless of outcome
+- Edge exists because these markets are low liquidity / inefficient pricing
+
+## Reference Wallet
+- `0xE594336603F4fB5d3ba4125a67021ab3B4347052`
+- Real PnL on 2026-02-09: ~$9K on $82K deployed (11% daily)
+- Combined costs ranged from $0.70 (great arb) to $1.10 (not arb)
+- Best arbs: ETH markets at $0.70-0.73 combined cost
+
+## Why It Works
+- 15-min markets have thin books — prices diverge from fair value
+- Binary outcome means Up + Down must sum to $1.00 at resolution
+- If you buy both for < $1.00 total, guaranteed profit
+
+## Challenges
+- Needs significant capital ($50K+) to make meaningful returns
+- Fill quality degrades at scale — slippage kills the edge
+- Competition from other bots narrows the window
+- Not all markets have arb — some combined costs > $1.00
+
+## Revisit When
+- [ ] We have capital to deploy
+- [ ] Built a bot to scan for combined < $1.00 opportunities in real-time
+- [ ] Polymarket adds more 15-min markets (more opportunities)
+
+## Related
+- Tweet author promoting "Clawdbots" — bot product shill
+- "$99K in a day" / "$340K total" claims are inflated (real: $9K profit)
+
+---
+
+# Elon Tweet Count Strategy
+
+**Source:** @browomo tweet Feb 9 / wallet @Annica on Polymarket
+**Wallet:** `0x689ae...2779e`
+**Actual PnL:** $520,469 | Volume: $51.8M | Rank #193
+**Verdict:** Legit strategy, "insider" framing is BS
+
+## How It Works
+- Polymarket has weekly markets: "How many tweets will Elon post Feb 2-4?"
+- Ranges like 90-114, 115-139, 140-164, etc.
+- You DON'T need insider access — just count his tweets mid-period
+- As the window closes, you can estimate the final count with high confidence
+- Buy the correct range when it's still cheap, collect $1 payout
+
+## Why It Works
+- Most bettors place bets early (before data exists)
+- Late bettors with real-time tweet counts have an information edge
+- Similar to the weather METAR concept but this one actually works
+- $520K PnL proves sustained profitability
+
+## Challenges
+- Markets may get more efficient as more people do this
+- Need to monitor Elon's posting in real-time
+- Liquidity might be thin on specific ranges
+- Could automate: scrape X API for Elon's tweet count, compare to market prices
+
+## Revisit When
+- [ ] Build automated Elon tweet counter
+- [ ] Monitor market prices vs actual count for edge sizing
+- [ ] Check if other "count" markets exist (posts, mentions, etc.)

data/stock-analysis-deep-dive.json

@@ -0,0 +1,762 @@
+{
+  "BAC": {
+    "ticker": "BAC",
+    "name": "Bank of America Corporation",
+    "price_action": {
+      "current": 56.53,
+      "1yr_ago": 46.33,
+      "1yr_return_pct": 22.02,
+      "52wk_high": 57.25,
+      "52wk_low": 33.82,
+      "pct_from_52wk_high": -1.26,
+      "50d_ma": 54.25,
+      "200d_ma": 49.18,
+      "above_50d_ma": true,
+      "above_200d_ma": true
+    },
+    "insider_transactions": [
+      {
+        "date": "2026-01-15 00:00:00",
+        "insider": "MOYNIHAN BRIAN T",
+        "transaction": "",
+        "shares": "17891",
+        "value": "nan"
+      },
+      {
+        "date": "2025-12-15 00:00:00",
+        "insider": "MOYNIHAN BRIAN T",
+        "transaction": "",
+        "shares": "17892",
+        "value": "nan"
+      },
+      {
+        "date": "2025-12-11 00:00:00",
+        "insider": "MOYNIHAN BRIAN T",
+        "transaction": "",
+        "shares": "130000",
+        "value": "0.0"
+      },
+      {
+        "date": "2025-12-09 00:00:00",
+        "insider": "GREENER GEOFFREY S",
+        "transaction": "",
+        "shares": "9265",
+        "value": "0.0"
+      },
+      {
+        "date": "2025-12-03 00:00:00",
+        "insider": "SCRIVENER THOMAS M",
+        "transaction": "",
+        "shares": "3000",
+        "value": "0.0"
+      },
+      {
+        "date": "2025-11-28 00:00:00",
+        "insider": "OKPARA JOHNBULL",
+        "transaction": "",
+        "shares": "26784",
+        "value": "nan"
+      },
+      {
+        "date": "2025-11-14 00:00:00",
+        "insider": "MOYNIHAN BRIAN T",
+        "transaction": "",
+        "shares": "17891",
+        "value": "nan"
+      },
+      {
+        "date": "2025-11-14 00:00:00",
+        "insider": "SCHIMPF ERIC A",
+        "transaction": "",
+        "shares": "1234",
+        "value": "nan"
+      },
+      {
+        "date": "2025-11-14 00:00:00",
+        "insider": "HANS LINDSAY D",
+        "transaction": "",
+        "shares": "975",
+        "value": "nan"
+      },
+      {
+        "date": "2025-11-14 00:00:00",
+        "insider": "GOPALKRISHNAN HARI",
+        "transaction": "",
+        "shares": "2703",
+        "value": "nan"
+      }
+    ],
+    "top_institutional_holders": [
+      {
+        "holder": "Vanguard Group Inc",
+        "shares": 651076825,
+        "pct_held": "0.0892"
+      },
+      {
+        "holder": "Berkshire Hathaway, Inc",
+        "shares": 568070012,
+        "pct_held": "0.077800006"
+      },
+      {
+        "holder": "Blackrock Inc.",
+        "shares": 535326028,
+        "pct_held": "0.0733"
+      },
+      {
+        "holder": "JPMORGAN CHASE & CO",
+        "shares": 363341282,
+        "pct_held": "0.0498"
+      },
+      {
+        "holder": "State Street Corporation",
+        "shares": 301312466,
+        "pct_held": "0.041300002"
+      }
+    ],
+    "institutional_pct": 0.71739,
+    "earnings": [
+      {
+        "date": "2026-04-15 09:00:00-04:00",
+        "eps_estimate": 0.99,
+        "eps_actual": null,
+        "surprise_pct": null
+      },
+      {
+        "date": "2026-01-14 06:00:00-05:00",
+        "eps_estimate": 0.96,
+        "eps_actual": 0.98,
+        "surprise_pct": 2.23
+      },
+      {
+        "date": "2025-10-15 06:00:00-04:00",
+        "eps_estimate": 0.95,
+        "eps_actual": 1.06,
+        "surprise_pct": 11.43
+      },
+      {
+        "date": "2025-07-16 06:00:00-04:00",
+        "eps_estimate": 0.86,
+        "eps_actual": 0.89,
+        "surprise_pct": 3.61
+      },
+      {
+        "date": "2025-04-15 06:00:00-04:00",
+        "eps_estimate": 0.82,
+        "eps_actual": 0.9,
+        "surprise_pct": 10.29
+      },
+      {
+        "date": "2025-01-16 06:00:00-05:00",
+        "eps_estimate": 0.77,
+        "eps_actual": 0.82,
+        "surprise_pct": 6.84
+      },
+      {
+        "date": "2024-10-15 06:00:00-04:00",
+        "eps_estimate": 0.76,
+        "eps_actual": 0.81,
+        "surprise_pct": 6.34
+      },
+      {
+        "date": "2024-07-16 06:00:00-04:00",
+        "eps_estimate": 0.8,
+        "eps_actual": 0.83,
+        "surprise_pct": 3.58
+      }
+    ],
+    "analyst": {
+      "target_mean": 62.20833,
+      "target_low": 56.0,
+      "target_high": 71.0,
+      "recommendation": "buy",
+      "num_analysts": 24,
+      "upside_pct": 10.04
+    },
+    "fundamentals": {
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+      "forward_pe": 11.407365,
+      "peg_ratio": null,
+      "market_cap": 412810084352,
+      "revenue_growth": 0.132,
+      "earnings_growth": 0.209,
+      "roe": 0.1019,
+      "debt_to_equity": null,
+      "dividend_yield": 1.95,
+      "beta": 1.273
+    },
+    "technical": {
+      "rsi_14": 71.14,
+      "trend": "bullish"
+    }
+  },
+  "CFG": {
+    "ticker": "CFG",
+    "name": "Citizens Financial Group, Inc.",
+    "price_action": {
+      "current": 68.12,
+      "1yr_ago": 46.24,
+      "1yr_return_pct": 47.32,
+      "52wk_high": 68.12,
+      "52wk_low": 33.06,
+      "pct_from_52wk_high": 0.0,
+      "50d_ma": 59.58,
+      "200d_ma": 49.69,
+      "above_50d_ma": true,
+      "above_200d_ma": true
+    },
+    "insider_transactions": [
+      {
+        "date": "2025-12-11 00:00:00",
+        "insider": "VAN SAUN BRUCE W",
+        "transaction": "",
+        "shares": "8000",
+        "value": "458558"
+      },
+      {
+        "date": "2025-12-03 00:00:00",
+        "insider": "LAMONICA SUSAN",
+        "transaction": "",
+        "shares": "20128",
+        "value": "1120727"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "KELLY EDWARD J. III",
+        "transaction": "",
+        "shares": "316",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "HANKOWSKY WILLIAM P",
+        "transaction": "",
+        "shares": "347",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "ZURAITIS MARITA",
+        "transaction": "",
+        "shares": "347",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "CUMMING CHRISTINE M",
+        "transaction": "",
+        "shares": "347",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "ATKINSON TRACY A",
+        "transaction": "",
+        "shares": "85",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "CUMMINGS KEVIN",
+        "transaction": "",
+        "shares": "162",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "SWIFT CHRISTOPHER J",
+        "transaction": "",
+        "shares": "200",
+        "value": "0"
+      },
+      {
+        "date": "2025-11-12 00:00:00",
+        "insider": "SIEKERKA MICHELE N",
+        "transaction": "",
+        "shares": "162",
+        "value": "0"
+      }
+    ],
+    "top_institutional_holders": [
+      {
+        "holder": "Vanguard Group Inc",
+        "shares": 51303226,
+        "pct_held": "0.1195"
+      },
+      {
+        "holder": "Blackrock Inc.",
+        "shares": 43851199,
+        "pct_held": "0.1021"
+      },
+      {
+        "holder": "Capital World Investors",
+        "shares": 37289711,
+        "pct_held": "0.0868"
+      },
+      {
+        "holder": "Invesco Ltd.",
+        "shares": 24064513,
+        "pct_held": "0.055999998"
+      },
+      {
+        "holder": "State Street Corporation",
+        "shares": 22969833,
+        "pct_held": "0.0535"
+      }
+    ],
+    "institutional_pct": 0.99170995,
+    "earnings": [
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+        "date": "2026-04-16 09:00:00-04:00",
+        "eps_estimate": 1.09,
+        "eps_actual": null,
+        "surprise_pct": null
+      },
+      {
+        "date": "2026-01-21 06:00:00-05:00",
+        "eps_estimate": 1.11,
+        "eps_actual": 1.13,
+        "surprise_pct": 2.24
+      },
+      {
+        "date": "2025-10-15 06:00:00-04:00",
+        "eps_estimate": 1.03,
+        "eps_actual": 1.05,
+        "surprise_pct": 2.27
+      },
+      {
+        "date": "2025-07-17 06:00:00-04:00",
+        "eps_estimate": 0.88,
+        "eps_actual": 0.92,
+        "surprise_pct": 4.13
+      },
+      {
+        "date": "2025-04-16 06:00:00-04:00",
+        "eps_estimate": 0.75,
+        "eps_actual": 0.77,
+        "surprise_pct": 2.68
+      },
+      {
+        "date": "2025-01-17 06:00:00-05:00",
+        "eps_estimate": 0.83,
+        "eps_actual": 0.85,
+        "surprise_pct": 2.3
+      },
+      {
+        "date": "2024-10-16 06:00:00-04:00",
+        "eps_estimate": 0.79,
+        "eps_actual": 0.79,
+        "surprise_pct": -0.24
+      },
+      {
+        "date": "2024-07-17 03:00:00-04:00",
+        "eps_estimate": 0.79,
+        "eps_actual": 0.82,
+        "surprise_pct": 3.5
+      }
+    ],
+    "analyst": {
+      "target_mean": 72.275,
+      "target_low": 62.5,
+      "target_high": 80.0,
+      "recommendation": "buy",
+      "num_analysts": 20,
+      "upside_pct": 6.1
+    },
+    "fundamentals": {
+      "trailing_pe": 17.647669,
+      "forward_pe": 10.848602,
+      "peg_ratio": null,
+      "market_cap": 29256599552,
+      "revenue_growth": 0.107,
+      "earnings_growth": 0.359,
+      "roe": 0.07241,
+      "debt_to_equity": null,
+      "dividend_yield": 2.7,
+      "beta": 1.071
+    },
+    "technical": {
+      "rsi_14": 75.46,
+      "trend": "bullish"
+    }
+  },
+  "FITB": {
+    "ticker": "FITB",
+    "name": "Fifth Third Bancorp",
+    "price_action": {
+      "current": 55.08,
+      "1yr_ago": 42.49,
+      "1yr_return_pct": 29.63,
+      "52wk_high": 55.08,
+      "52wk_low": 32.46,
+      "pct_from_52wk_high": 0.0,
+      "50d_ma": 48.27,
+      "200d_ma": 42.82,
+      "above_50d_ma": true,
+      "above_200d_ma": true
+    },
+    "insider_transactions": [
+      {
+        "date": "2026-02-02 00:00:00",
+        "insider": "SMITH BARBARA",
+        "transaction": "",
+        "shares": "40498",
+        "value": "0"
+      },
+      {
+        "date": "2026-02-02 00:00:00",
+        "insider": "SEFZIK PETER L",
+        "transaction": "",
+        "shares": "209382",
+        "value": "0"
+      },
+      {
+        "date": "2026-02-02 00:00:00",
+        "insider": "KERR DEREK J",
+        "transaction": "",
+        "shares": "14189",
+        "value": "0"
+      },
+      {
+        "date": "2026-02-02 00:00:00",
+        "insider": "VAN DE VEN MICHAEL G",
+        "transaction": "",
+        "shares": "47972",
+        "value": "0"
+      },
+      {
+        "date": "2026-01-07 00:00:00",
+        "insider": "ALMODOVAR PRISCILLA",
+        "transaction": "",
+        "shares": "848",
+        "value": "0"
+      },
+      {
+        "date": "2025-12-15 00:00:00",
+        "insider": "PRESTON BRYAN D",
+        "transaction": "",
+        "shares": "1100",
+        "value": "0"
+      },
+      {
+        "date": "2025-12-11 00:00:00",
+        "insider": "SCHRAMM JUDE",
+        "transaction": "",
+        "shares": "2250",
+        "value": "109125"
+      },
+      {
+        "date": "2025-10-20 00:00:00",
+        "insider": "BAYH B EVAN III",
+        "transaction": "",
+        "shares": "3000",
+        "value": "123650"
+      },
+      {
+        "date": "2025-10-09 00:00:00",
+        "insider": "GONZALEZ CHRISTIAN",
+        "transaction": "",
+        "shares": "5709",
+        "value": "0"
+      },
+      {
+        "date": "2025-08-28 00:00:00",
+        "insider": "SHAFFER ROBERT P",
+        "transaction": "",
+        "shares": "14035",
+        "value": "372208"
+      }
+    ],
+    "top_institutional_holders": [
+      {
+        "holder": "Vanguard Group Inc",
+        "shares": 107237083,
+        "pct_held": "0.16219999"
+      },
+      {
+        "holder": "Blackrock Inc.",
+        "shares": 88421716,
+        "pct_held": "0.1338"
+      },
+      {
+        "holder": "JPMORGAN CHASE & CO",
+        "shares": 65878286,
+        "pct_held": "0.099700004"
+      },
+      {
+        "holder": "State Street Corporation",
+        "shares": 39653081,
+        "pct_held": "0.06"
+      },
+      {
+        "holder": "Charles Schwab Investment Management, Inc.",
+        "shares": 32138466,
+        "pct_held": "0.048600003"
+      }
+    ],
+    "institutional_pct": 0.66008,
+    "earnings": [
+      {
+        "date": "2026-04-17 09:00:00-04:00",
+        "eps_estimate": 0.43,
+        "eps_actual": null,
+        "surprise_pct": null
+      },
+      {
+        "date": "2026-01-20 06:00:00-05:00",
+        "eps_estimate": 0.99,
+        "eps_actual": 1.04,
+        "surprise_pct": 4.93
+      },
+      {
+        "date": "2025-10-17 06:00:00-04:00",
+        "eps_estimate": 0.86,
+        "eps_actual": 0.91,
+        "surprise_pct": 5.47
+      },
+      {
+        "date": "2025-07-17 06:00:00-04:00",
+        "eps_estimate": 0.87,
+        "eps_actual": 0.9,
+        "surprise_pct": 3.85
+      },
+      {
+        "date": "2025-04-17 06:00:00-04:00",
+        "eps_estimate": 0.7,
+        "eps_actual": 0.71,
+        "surprise_pct": 1.42
+      },
+      {
+        "date": "2025-01-21 06:00:00-05:00",
+        "eps_estimate": 0.87,
+        "eps_actual": 0.9,
+        "surprise_pct": 3.01
+      },
+      {
+        "date": "2024-10-18 06:00:00-04:00",
+        "eps_estimate": 0.82,
+        "eps_actual": 0.85,
+        "surprise_pct": 3.17
+      },
+      {
+        "date": "2024-07-19 06:00:00-04:00",
+        "eps_estimate": 0.84,
+        "eps_actual": 0.86,
+        "surprise_pct": 1.79
+      }
+    ],
+    "analyst": {
+      "target_mean": 57.15789,
+      "target_low": 49.0,
+      "target_high": 61.0,
+      "recommendation": "buy",
+      "num_analysts": 19,
+      "upside_pct": 3.77
+    },
+    "fundamentals": {
+      "trailing_pe": 15.6034,
+      "forward_pe": 11.235359,
+      "peg_ratio": null,
+      "market_cap": 49574670336,
+      "revenue_growth": 0.115,
+      "earnings_growth": 0.208,
+      "roe": 0.121929996,
+      "debt_to_equity": null,
+      "dividend_yield": 2.9,
+      "beta": 0.977
+    },
+    "technical": {
+      "rsi_14": 71.83,
+      "trend": "bullish"
+    }
+  },
+  "INCY": {
+    "ticker": "INCY",
+    "name": "Incyte Corporation",
+    "price_action": {
+      "current": 108.39,
+      "1yr_ago": 74.13,
+      "1yr_return_pct": 46.22,
+      "52wk_high": 110.57,
+      "52wk_low": 55.17,
+      "pct_from_52wk_high": -1.97,
+      "50d_ma": 101.9,
+      "200d_ma": 84.49,
+      "above_50d_ma": true,
+      "above_200d_ma": true
+    },
+    "insider_transactions": [
+      {
+        "date": "2026-01-16 00:00:00",
+        "insider": "MORRISSEY MICHAEL JAMES",
+        "transaction": "",
+        "shares": "7426",
+        "value": "0"
+      },
+      {
+        "date": "2026-01-16 00:00:00",
+        "insider": "HEESON LEE",
+        "transaction": "",
+        "shares": "8911",
+        "value": "0"
+      },
+      {
+        "date": "2026-01-07 00:00:00",
+        "insider": "ISSA MOHAMED KHAIRIE",
+        "transaction": "",
+        "shares": "10856",
+        "value": "1184064"
+      },
+      {
+        "date": "2026-01-05 00:00:00",
+        "insider": "STEIN STEVEN H.",
+        "transaction": "",
+        "shares": "15634",
+        "value": "1589978"
+      },
+      {
+        "date": "2025-12-31 00:00:00",
+        "insider": "BAKER BROS ADVISORS L.P.",
+        "transaction": "",
+        "shares": "656",
+        "value": "0"
+      },
+      {
+        "date": "2025-12-31 00:00:00",
+        "insider": "HARRIGAN EDMUND P. M.D.",
+        "transaction": "",
+        "shares": "245",
+        "value": "24199"
+      },
+      {
+        "date": "2025-12-31 00:00:00",
+        "insider": "CLANCY PAUL J.",
+        "transaction": "",
+        "shares": "241",
+        "value": "23804"
+      },
+      {
+        "date": "2025-12-19 00:00:00",
+        "insider": "TRAY THOMAS R",
+        "transaction": "",
+        "shares": "3374",
+        "value": "336350"
+      },
+      {
+        "date": "2025-12-19 00:00:00",
+        "insider": "TRAY THOMAS R",
+        "transaction": "",
+        "shares": "2774",
+        "value": "265638"
+      },
+      {
+        "date": "2025-12-17 00:00:00",
+        "insider": "MORRISSEY MICHAEL JAMES",
+        "transaction": "",
+        "shares": "58331",
+        "value": "5675002"
+      }
+    ],
+    "top_institutional_holders": [
+      {
+        "holder": "Baker Bros. Advisors, LP",
+        "shares": 30743663,
+        "pct_held": "0.1566"
+      },
+      {
+        "holder": "Vanguard Group Inc",
+        "shares": 19911434,
+        "pct_held": "0.1014"
+      },
+      {
+        "holder": "Blackrock Inc.",
+        "shares": 17894297,
+        "pct_held": "0.0911"
+      },
+      {
+        "holder": "Dodge & Cox Inc.",
+        "shares": 13932416,
+        "pct_held": "0.071"
+      },
+      {
+        "holder": "State Street Corporation",
+        "shares": 9676796,
+        "pct_held": "0.0493"
+      }
+    ],
+    "institutional_pct": 1.05931,
+    "earnings": [
+      {
+        "date": "2026-02-10 08:00:00-05:00",
+        "eps_estimate": 1.95,
+        "eps_actual": null,
+        "surprise_pct": null
+      },
+      {
+        "date": "2025-10-28 07:00:00-04:00",
+        "eps_estimate": 1.64,
+        "eps_actual": 2.26,
+        "surprise_pct": 38.04
+      },
+      {
+        "date": "2025-07-29 07:00:00-04:00",
+        "eps_estimate": 1.47,
+        "eps_actual": 1.57,
+        "surprise_pct": 6.59
+      },
+      {
+        "date": "2025-04-29 07:00:00-04:00",
+        "eps_estimate": 1.03,
+        "eps_actual": 1.16,
+        "surprise_pct": 12.32
+      },
+      {
+        "date": "2025-02-10 07:00:00-05:00",
+        "eps_estimate": 1.55,
+        "eps_actual": 1.43,
+        "surprise_pct": -8.01
+      },
+      {
+        "date": "2024-10-29 07:00:00-04:00",
+        "eps_estimate": 1.09,
+        "eps_actual": 1.07,
+        "surprise_pct": -2.03
+      },
+      {
+        "date": "2024-07-30 07:00:00-04:00",
+        "eps_estimate": 1.11,
+        "eps_actual": -1.82,
+        "surprise_pct": -264.53
+      },
+      {
+        "date": "2024-04-30 07:00:00-04:00",
+        "eps_estimate": 0.83,
+        "eps_actual": 0.64,
+        "surprise_pct": -23.09
+      }
+    ],
+    "analyst": {
+      "target_mean": 104.22727,
+      "target_low": 70.0,
+      "target_high": 135.0,
+      "recommendation": "buy",
+      "num_analysts": 22,
+      "upside_pct": -3.84
+    },
+    "fundamentals": {
+      "trailing_pe": 18.371185,
+      "forward_pe": 13.606971,
+      "peg_ratio": null,
+      "market_cap": 21279418368,
+      "revenue_growth": 0.2,
+      "earnings_growth": 2.907,
+      "roe": 0.30389,
+      "debt_to_equity": 0.887,
+      "dividend_yield": null,
+      "beta": 0.847
+    },
+    "technical": {
+      "rsi_14": 54.22,
+      "trend": "bullish"
+    }
+  }
+}

data/stock-screener-results.json

@@ -0,0 +1,71 @@
+{
+  "date": "2026-02-08",
+  "filters": "GARP",
+  "results": [
+    {
+      "ticker": "BAC",
+      "name": "Bank of America Corporation",
+      "mcap_b": 412.8,
+      "rev_growth": 13.2,
+      "trail_pe": 14.8,
+      "fwd_pe": 11.4,
+      "peg": "N/A",
+      "eps_growth": 20.9,
+      "roe": 10.2,
+      "quick": "N/A",
+      "de": "N/A"
+    },
+    {
+      "ticker": "CFG",
+      "name": "Citizens Financial Group, Inc.",
+      "mcap_b": 29.3,
+      "rev_growth": 10.7,
+      "trail_pe": 17.6,
+      "fwd_pe": 10.8,
+      "peg": "N/A",
+      "eps_growth": 35.9,
+      "roe": 7.2,
+      "quick": "N/A",
+      "de": "N/A"
+    },
+    {
+      "ticker": "FITB",
+      "name": "Fifth Third Bancorp",
+      "mcap_b": 49.6,
+      "rev_growth": 11.5,
+      "trail_pe": 15.6,
+      "fwd_pe": 11.2,
+      "peg": "N/A",
+      "eps_growth": 20.8,
+      "roe": 12.2,
+      "quick": "N/A",
+      "de": "N/A"
+    },
+    {
+      "ticker": "INCY",
+      "name": "Incyte Corporation",
+      "mcap_b": 21.3,
+      "rev_growth": 20.0,
+      "trail_pe": 18.4,
+      "fwd_pe": 13.6,
+      "peg": "N/A",
+      "eps_growth": 290.7,
+      "roe": 30.4,
+      "quick": 2.86,
+      "de": 0.9
+    }
+  ],
+  "stats": {
+    "scanned": 503,
+    "revenue": 316,
+    "pe": 121,
+    "fwd_pe": 32,
+    "peg": 0,
+    "eps": 14,
+    "roe": 1,
+    "quick": 11,
+    "de": 4,
+    "mcap": 0,
+    "err": 0
+  }
+}

data/stock_deep_dive.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python3
+"""Deep dive analysis on BAC, CFG, FITB, INCY + expanded GARP scan."""
+
+import yfinance as yf
+import json
+import datetime
+import numpy as np
+from pathlib import Path
+
+TARGETS = ["BAC", "CFG", "FITB", "INCY"]
+
+def safe_get(d, *keys, default=None):
+    for k in keys:
+        if isinstance(d, dict):
+            d = d.get(k, default)
+        else:
+            return default
+    return d
+
+def analyze_stock(ticker_str):
+    print(f"\n{'='*60}\nAnalyzing {ticker_str}...")
+    t = yf.Ticker(ticker_str)
+    info = t.info or {}
+    result = {"ticker": ticker_str, "name": info.get("longName", ticker_str)}
+    
+    # 1. Price action (1yr)
+    hist = t.history(period="1y")
+    if not hist.empty:
+        prices = hist["Close"]
+        result["price_action"] = {
+            "current": round(float(prices.iloc[-1]), 2),
+            "1yr_ago": round(float(prices.iloc[0]), 2),
+            "1yr_return_pct": round(float((prices.iloc[-1] / prices.iloc[0] - 1) * 100), 2),
+            "52wk_high": round(float(prices.max()), 2),
+            "52wk_low": round(float(prices.min()), 2),
+            "pct_from_52wk_high": round(float((prices.iloc[-1] / prices.max() - 1) * 100), 2),
+            "50d_ma": round(float(prices.tail(50).mean()), 2),
+            "200d_ma": round(float(prices.tail(200).mean()), 2) if len(prices) >= 200 else None,
+        }
+        cur = float(prices.iloc[-1])
+        ma50 = result["price_action"]["50d_ma"]
+        ma200 = result["price_action"]["200d_ma"]
+        result["price_action"]["above_50d_ma"] = cur > ma50
+        result["price_action"]["above_200d_ma"] = cur > ma200 if ma200 else None
+    
+    # 2. Insider activity
+    try:
+        insiders = t.insider_transactions
+        if insiders is not None and not insiders.empty:
+            recent = insiders.head(10)
+            txns = []
+            for _, row in recent.iterrows():
+                txns.append({
+                    "date": str(row.get("Start Date", "")),
+                    "insider": str(row.get("Insider", "")),
+                    "transaction": str(row.get("Transaction", "")),
+                    "shares": str(row.get("Shares", "")),
+                    "value": str(row.get("Value", "")),
+                })
+            result["insider_transactions"] = txns
+        else:
+            result["insider_transactions"] = []
+    except:
+        result["insider_transactions"] = []
+
+    # 3. Institutional ownership
+    try:
+        inst = t.institutional_holders
+        if inst is not None and not inst.empty:
+            top5 = []
+            for _, row in inst.head(5).iterrows():
+                top5.append({
+                    "holder": str(row.get("Holder", "")),
+                    "shares": int(row.get("Shares", 0)) if row.get("Shares") else 0,
+                    "pct_held": str(row.get("pctHeld", "")),
+                })
+            result["top_institutional_holders"] = top5
+        result["institutional_pct"] = info.get("heldPercentInstitutions")
+    except:
+        result["top_institutional_holders"] = []
+
+    # 4. Earnings surprises
+    try:
+        earn = t.earnings_dates
+        if earn is not None and not earn.empty:
+            surprises = []
+            for idx, row in earn.head(8).iterrows():
+                s = {
+                    "date": str(idx),
+                    "eps_estimate": row.get("EPS Estimate"),
+                    "eps_actual": row.get("Reported EPS"),
+                    "surprise_pct": row.get("Surprise(%)"),
+                }
+                # clean NaN
+                s = {k: (None if isinstance(v, float) and np.isnan(v) else v) for k, v in s.items()}
+                surprises.append(s)
+            result["earnings"] = surprises
+        else:
+            result["earnings"] = []
+    except:
+        result["earnings"] = []
+
+    # 5. Analyst consensus
+    result["analyst"] = {
+        "target_mean": info.get("targetMeanPrice"),
+        "target_low": info.get("targetLowPrice"),
+        "target_high": info.get("targetHighPrice"),
+        "recommendation": info.get("recommendationKey"),
+        "num_analysts": info.get("numberOfAnalystOpinions"),
+    }
+    if result["price_action"].get("current") and info.get("targetMeanPrice"):
+        upside = (info["targetMeanPrice"] / result["price_action"]["current"] - 1) * 100
+        result["analyst"]["upside_pct"] = round(upside, 2)
+
+    # 6. Key fundamentals snapshot
+    result["fundamentals"] = {
+        "trailing_pe": info.get("trailingPE"),
+        "forward_pe": info.get("forwardPE"),
+        "peg_ratio": info.get("pegRatio"),
+        "market_cap": info.get("marketCap"),
+        "revenue_growth": info.get("revenueGrowth"),
+        "earnings_growth": info.get("earningsGrowth"),
+        "roe": info.get("returnOnEquity"),
+        "debt_to_equity": info.get("debtToEquity"),
+        "dividend_yield": info.get("dividendYield"),
+        "beta": info.get("beta"),
+    }
+
+    # 7. Technical - RSI approximation
+    if not hist.empty and len(hist) >= 14:
+        delta = hist["Close"].diff()
+        gain = delta.where(delta > 0, 0).rolling(14).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
+        rs = gain / loss
+        rsi = 100 - (100 / (1 + rs))
+        result["technical"] = {
+            "rsi_14": round(float(rsi.iloc[-1]), 2) if not np.isnan(rsi.iloc[-1]) else None,
+            "trend": "bullish" if cur > ma50 and (ma200 is None or cur > ma200) else "bearish" if cur < ma50 and (ma200 and cur < ma200) else "mixed",
+        }
+
+    print(f"  Done: {result['name']} @ ${result['price_action']['current']}")
+    return result
+
+# ============================================================
+# EXPANDED GARP SCAN
+# ============================================================
+def get_broad_ticker_list():
+    """Get a broad list of US tickers to screen."""
+    import urllib.request
+    # Use Wikipedia's S&P 500 + S&P 400 midcap for broader coverage
+    # Plus some known Russell 1000 members not in S&P 500
+    
+    # Start with S&P 500
+    sp500 = []
+    try:
+        import pandas as pd
+        tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
+        sp500 = tables[0]["Symbol"].str.replace(".", "-", regex=False).tolist()
+    except:
+        pass
+    
+    # S&P 400 midcap
+    sp400 = []
+    try:
+        import pandas as pd
+        tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_400_companies")
+        sp400 = tables[0]["Symbol"].str.replace(".", "-", regex=False).tolist()
+    except:
+        pass
+    
+    all_tickers = list(set(sp500 + sp400))
+    print(f"Total tickers to screen: {len(all_tickers)}")
+    return all_tickers
+
+def garp_screen(tickers, exclude=None):
+    """Apply GARP filters to ticker list."""
+    exclude = set(exclude or [])
+    passed = []
+    
+    for i, tick in enumerate(tickers):
+        if tick in exclude:
+            continue
+        if i % 50 == 0:
+            print(f"  Screening {i}/{len(tickers)}...")
+        try:
+            t = yf.Ticker(tick)
+            info = t.info or {}
+            
+            mc = info.get("marketCap", 0) or 0
+            if mc < 5e9:
+                continue
+            
+            tpe = info.get("trailingPE")
+            if tpe is None or tpe >= 25 or tpe <= 0:
+                continue
+                
+            fpe = info.get("forwardPE")
+            if fpe is None or fpe >= 15 or fpe <= 0:
+                continue
+            
+            rg = info.get("revenueGrowth")
+            if rg is None or rg < 0.10:
+                continue
+            
+            eg = info.get("earningsGrowth")
+            if eg is None or eg < 0.15:
+                continue
+            
+            roe = info.get("returnOnEquity")
+            if roe is None or roe < 0.05:
+                continue
+            
+            # Optional filters
+            peg = info.get("pegRatio")
+            if peg is not None and peg > 1.2:
+                continue
+            
+            dte = info.get("debtToEquity")
+            if dte is not None and dte > 35:
+                continue
+            
+            passed.append({
+                "ticker": tick,
+                "name": info.get("longName", tick),
+                "market_cap": mc,
+                "trailing_pe": round(tpe, 2),
+                "forward_pe": round(fpe, 2),
+                "peg": round(peg, 2) if peg else None,
+                "revenue_growth": round(rg * 100, 1),
+                "earnings_growth": round(eg * 100, 1),
+                "roe": round(roe * 100, 1),
+                "debt_to_equity": round(dte, 1) if dte else None,
+            })
+            print(f"  ✅ PASS: {tick} (PE:{tpe:.1f} FPE:{fpe:.1f} RG:{rg*100:.0f}% EG:{eg*100:.0f}%)")
+            
+        except Exception as e:
+            continue
+    
+    return passed
+
+# ============================================================
+# MAIN
+# ============================================================
+if __name__ == "__main__":
+    output_dir = Path("/home/wdjones/.openclaw/workspace/data")
+    output_dir.mkdir(exist_ok=True)
+    
+    # Deep dive on 4 stocks
+    print("=" * 60)
+    print("DEEP DIVE ANALYSIS")
+    print("=" * 60)
+    
+    analyses = {}
+    for tick in TARGETS:
+        analyses[tick] = analyze_stock(tick)
+    
+    # Save deep dive
+    with open(output_dir / "stock-analysis-deep-dive.json", "w") as f:
+        json.dump(analyses, f, indent=2, default=str)
+    print(f"\nSaved deep dive to stock-analysis-deep-dive.json")
+    
+    # Expanded GARP scan
+    print("\n" + "=" * 60)
+    print("EXPANDED GARP SCAN (S&P 500 + S&P 400 MidCap)")
+    print("=" * 60)
+    
+    tickers = get_broad_ticker_list()
+    new_passes = garp_screen(tickers, exclude=set(TARGETS))
+    
+    with open(output_dir / "garp-expanded-scan.json", "w") as f:
+        json.dump(new_passes, f, indent=2, default=str)
+    print(f"\nExpanded scan found {len(new_passes)} additional stocks")
+    print("Saved to garp-expanded-scan.json")
+    
+    # Print summary
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    for tick, data in analyses.items():
+        pa = data.get("price_action", {})
+        an = data.get("analyst", {})
+        fu = data.get("fundamentals", {})
+        te = data.get("technical", {})
+        print(f"\n{tick} - {data['name']}")
+        print(f"  Price: ${pa.get('current')} | 1yr Return: {pa.get('1yr_return_pct')}%")
+        print(f"  From 52wk High: {pa.get('pct_from_52wk_high')}%")
+        print(f"  PE: {fu.get('trailing_pe')} | Fwd PE: {fu.get('forward_pe')} | PEG: {fu.get('peg_ratio')}")
+        print(f"  Target: ${an.get('target_mean')} ({an.get('upside_pct')}% upside) | Rec: {an.get('recommendation')}")
+        print(f"  RSI: {te.get('rsi_14')} | Trend: {te.get('trend')}")
+        print(f"  Insiders: {len(data.get('insider_transactions', []))} recent txns")
+    
+    if new_passes:
+        print(f"\nNew GARP Candidates ({len(new_passes)}):")
+        for s in sorted(new_passes, key=lambda x: x.get("forward_pe", 99)):
+            print(f"  {s['ticker']:6s} PE:{s['trailing_pe']:5.1f} FPE:{s['forward_pe']:5.1f} RG:{s['revenue_growth']:5.1f}% EG:{s['earnings_growth']:5.1f}% ROE:{s['roe']:5.1f}%")

memory/2026-02-08.md

@@ -37,7 +37,37 @@
 - All Seahawks Super Bowl bets, game tonight ~5:30pm CST
 - Will auto-resolve and report via Telegram
 
+### Selling Stuff Project
+- D J wants to sell a bunch of stuff, needs accounts on selling platforms
+- **Craigslist**: ✅ Account created — case-lgn@protonmail.com, passwordless, Nashville area
+- **eBay, Mercari, OfferUp**: Blocked by CAPTCHA — need D J to register manually
+- Google Voice number: +1 (615) 933-1968 — D J gave this for service registrations
+- ProtonMail is already logged in on desktop Chrome on the VM
+- Browser automation via CDP on desktop Chrome (port 9222) works for sites without CAPTCHA
+- Craigslist has no CAPTCHA — signed up fully automated
+
+### Air Purifier Research
+- D J looking at Shark NeverChange Air Purifier MAX for 4 cats
+- Washable HEPA filter = no replacement costs, good for heavy pet hair
+
+### iPhone Action Button
+- D J has iPhone 16, tried to set up voice-to-Telegram shortcut
+- Abandoned — Telegram doesn't have native Shortcuts integration, workarounds were clunky
+
+### Recycling
+- Nashville cardboard recycling drop-offs: 24/7 unstaffed bins are always full
+- Best bet: staffed Convenience Centers (Tue-Sat 8:30am-4:30pm)
+- Winter storm notice: all centers free this week
+
 ## Key Decisions
 - Systemd timers > AI cron jobs for mechanical monitoring (zero token cost)
 - Telegram bot API for direct alerts bypasses AI entirely
 - Admin panel todos = how I request human action items
+- Desktop Chrome via CDP (port 9222) for browser automation that needs real fingerprint
+- Craigslist for local selling (no CAPTCHA), FB Marketplace needs D J's account
+
+## Lessons Learned
+- Most selling platforms (eBay, Mercari, OfferUp) have invisible reCAPTCHA on signup — can't automate
+- React forms need native input setter (Object.getOwnPropertyDescriptor hack) or Input.insertText via CDP
+- Best Buy blocks all web scraping
+- Proton Mail stays logged in on desktop Chrome — can use for email verification flows

memory/2026-02-09.md

@@ -0,0 +1,109 @@
+# 2026-02-09 — Monday
+
+## X Feed Analysis Day
+Major session analyzing Polymarket/crypto tweets D J forwarded from X feed.
+
+### Tweets Investigated
+1. **@browomo weather edge** — Pilot METAR data for Polymarket weather bets
+   - Wallet `0x594edB9112f...`: Claimed +$27K, actual **-$13,103** (387 losses, 51 wins)
+   - Verdict: SCAM/engagement bait
+
+2. **@ArchiveExplorer planktonXD** — "Buy everything under 5 cents"
+   - Wallet `0x4ffe49ba2a4c...`: Claimed +$104K, actual **-$9,517** (3090 losses, 1368 wins, 37% win rate)
+   - Verdict: SCAM/engagement bait
+
+3. **@krajekis BTC 15-min LONG** — "+700% monthly, 1 trade/day at 9AM"
+   - Backtested 25 days: 52% win rate (coin flip), strategy loses 76% of capital
+   - Verdict: FABRICATED results
+
+4. **@noisyb0y1 15-min arb** — "$99K in a day"
+   - Wallet `0xE594336603F4...`: Real strategy (buying both sides), actual PnL ~$9K not $99K
+   - Combined costs $0.70-$0.95 on some markets = genuine arb edge
+   - Verdict: INFLATED numbers, but strategy has merit → bookmarked for scanner
+
+5. **5 more wallets** — spawned sub-agent to research @IH2P, Super Bowl trader, NegRisk arb, Elon insider, $270→$244K bot
+
+### Pattern Identified
+- Fintwit Polymarket accounts follow identical template: big $ claim → wallet → product shill
+- Products being shilled: Clawdbots, Moltbook, Bullpen (affiliate/paid promos)
+- Real money is in selling engagement, not trading
+- Wallets are cherry-picked; PnL claims conflate position value with profit
+
+### Built Today
+- **Crypto signals pipeline** (`projects/crypto-signals/`)
+  - Signal parser for Telegram JSON exports
+  - Price fetcher (Binance US API)
+  - Backtester with Polymarket fee awareness
+  - Ready for D J's Telegram group export
+
+- **Polymarket 15-min arb scanner** — systemd timer every 2 min
+  - Scans active Up or Down markets
+  - Checks orderbooks for combined < $1.00
+  - Paper trades and Telegram alerts
+  - Finding: markets are tight at steady state, arb windows likely during volatility
+
+- **Playwright installed** — replaced flaky CDP websocket approach
+  - `connect_over_cdp('http://localhost:9222')` works with existing Chrome session
+  - X feed scraper working via Playwright
+
+- **PowerInfer multi-GPU cron** — weekly Monday check for feature support
+
+### Day-of-Week Stock Strategy
+- D J's friend suggested buy Friday/Monday, sell midweek
+- Backtested 5 years of SPY: Monday is strongest day (+0.114%, 62% win rate)
+- Buy Mon Open → Sell Wed Close: 56% win rate, +0.265% avg, $10K→$17.8K
+- But buy & hold still wins: $10K→$19K
+- Verdict: weak edge, needs additional filters to be useful
+
+### Infrastructure
+- Polymarket fee structure documented: 15-min markets have taker fees, max 1.56% at 50/50
+- Fee formula: `shares × price × 0.25 × (price × (1-price))²`
+- Binance international is geo-blocked; Binance US works
+
+### Pending
+- D J sending Telegram crypto signal group export (Option 2: JSON export)
+- Signal provider uses VWAP-based strategy
+- Sub-agent researching 5 more wallets
+
+## Evening Session — KIPP + Leverage Trading
+
+### Crypto Leverage Trading
+- Built **short signal scanner** (`scripts/short_scanner.py`) — RSI/VWAP/MACD/Bollinger scoring for 29 coins
+- Built **leverage game engine** (`leverage_game.py`) — $10K, 20x max leverage, longs/shorts/liquidations
+- Built **auto-trader** (`scripts/leverage_trader.py`) — connects scanners to game, TP/SL/trailing stops
+- "Leverage Challenge" game initialized, player "case"
+- Systemd timer every 15min
+- First scan: market quiet, no strong signals (highest short score 40 on FIL)
+
+### KIPP Setup
+- KIPP VM: 192.168.86.100 (Proxmox CT, 8 cores, 8GB RAM, Ubuntu 24.04)
+- Installed Node.js 22, OpenClaw 2026.2.9
+- Set up SSH key auth from Case's VM
+- Created KIPP identity: SOUL.md, AGENTS.md, MEMORY.md, USER.md
+- Initially tried GLM-4.7 Flash via llama.cpp — had issues, switched to Claude
+- Configured Telegram bot: @dzclaw_kipp_bot (token: 8535439246:AAE...)
+- Webchat accessible via https://kipp.host.letsgetnashty.com/
+- Gateway token: kipp-local-token-2026
+- Proxmox firewall was blocking LAN access — D J disabled it
+- Needed trustedProxies, allowedOrigins, dangerouslyDisableDeviceAuth for reverse proxy
+- D J ran `openclaw configure` to set up Anthropic auth
+- KIPP is LIVE on Claude + Telegram
+- Name confirmed: KIPP (not Kip)
+
+### KIPP Local LLM Attempt (Late Session)
+- Goal: Switch KIPP from Claude to local GLM-4 Flash 30B (q4) on llama.cpp at 192.168.86.40:8080/v1
+- llama.cpp server confirmed running and responding
+- Multiple openclaw.json config attempts failed — schema validation errors
+- Key schema findings for `openclaw.json`:
+  - `gateway.bind`: must be enum (auto/lan/loopback/custom/tailnet)
+  - `agents.defaults.model`: must be `{ primary: string, fallbacks?: string[] }`
+  - `models.providers`: array of `{ baseUrl, apiKey, api, models[] }` for custom endpoints
+  - `auth.profiles`: only allows provider/mode/email — NOT baseURL/apiKey
+- Gateway service installed but crashing on config validation
+- **Status: BLOCKED** — need to apply correct config schema, was mid-fix when session compacted
+
+### Infrastructure
+- SSH keys generated on Case's VM, copied to KIPP VM
+- `loginctl enable-linger` set for boot persistence
+- sshpass installed on Case's VM
+- llama.cpp server: 192.168.86.40:8080/v1 (GLM-4 Flash 30B q4)

memory/2026-02-10.md

@@ -0,0 +1,123 @@
+# 2026-02-10 — Monday Night / Tuesday Morning
+
+## KIPP Build Session
+Major KIPP infrastructure session with D J.
+
+### Completed
+- **KIPP Gitea account** — user: kipp, repo: git.letsgetnashty.com/kipp/workspace (private)
+- **KIPP switched to local LLM** — llamacpp/glm-4.7-flash as primary, Claude Sonnet as fallback
+- **KIPP ChromaDB memory** — collection `kipp-memory` (ccf4f5b6-a64e-45b1-bf1b-7013e15c3363), seeded 9 docs
+- **Ollama URL updated** — 192.168.86.40:11434 (same machine as llama.cpp, not the old 192.168.86.137)
+- **KIPP model config tuned** — maxTokens 2048, contextWindow 32768, auto-recall 1 result
+- **KIPP web UI v1** — thin client at https://kippui.host.letsgetnashty.com/
+  - WebSocket JSON-RPC protocol v3 working (connect handshake, chat.send, streaming)
+  - Weather widget, grocery list, timers, quick actions, mic button
+  - Served from kipp-ui.service on port 8080
+  - Gateway at kipp.host.letsgetnashty.com (port 18789)
+  - CORS handled in Caddy config
+- **Meg added to USER.md** — "the boss"
+- **@RohOnChain tweet analyzed** — Kelly Criterion / gabagool22 ($788K PnL), mostly legit math but misleading framing
+
+### Key Findings
+- GLM-4 Flash is a thinking model — burns ~200 tokens reasoning before responding
+- 32K context split across 4 slots = 8K effective per conversation
+- D J has 30GB free RAM on llama.cpp server — could bump to 128K context
+- OpenClaw WebSocket protocol: JSON-RPC v3, needs connect with client{id,version,platform,mode}, auth{token}, minProtocol:3
+- Event types: `agent` (stream:assistant for deltas, stream:lifecycle for start/end), `chat` (state:delta/final)
+- Caddy reverse proxy handles both services: kippui→8080, kipp→18789
+- `dangerouslyDisableDeviceAuth: true` still requires auth token in connect params
+
+### KIPP Infrastructure
+- **VM:** 192.168.86.100 (wdjones user, SSH key from Case)
+- **llama.cpp:** 192.168.86.40:8080 (GLM-4 Flash 30B q4, 2 GPUs 12GB+10GB, 32GB RAM)
+- **Ollama:** 192.168.86.40:11434 (nomic-embed-text for embeddings)
+- **ChromaDB:** 192.168.86.25:8000 (kipp-memory collection)
+- **Gitea:** kipp:K1pp-H0me-2026! @ git.letsgetnashty.com/kipp/workspace
+- **Telegram:** @dzclaw_kipp_bot
+- **Web UI:** https://kippui.host.letsgetnashty.com/ (port 8080)
+- **Gateway:** https://kipp.host.letsgetnashty.com/ (port 18789)
+- **Token:** kipp-local-token-2026
+
+### Evening Session (D J back from work)
+- **KIPP UI v2 chat confirmed working** — D J tested on Mac, chat end-to-end functional at 18:34
+- **Voice input added** — mic button in chat overlay triggers browser Speech Recognition, auto-sends on final transcript
+- **Piper TTS installed** on KIPP VM — `pip3 install piper-tts`, Amy medium voice model downloaded
+- **TTS API server** built (port 8081, systemd kipp-tts.service) + proxy through UI server on port 8080
+- **Piper voice quality confirmed** — sent D J audio sample via Telegram, not robotic
+- **Caddy WebSocket issue** — persistent 503 on WSS through Caddy reverse proxy
+  - Direct WS to 192.168.86.100:18789 works perfectly every time
+  - Caddy returns 503 on WebSocket upgrade specifically
+  - Fixed gateway: added `192.168.86.0/24` to trustedProxies, added `http://192.168.86.100:8080` to allowedOrigins
+  - Gateway logs showed "origin not allowed" and "proxy headers from untrusted address"
+  - Even after gateway fix, Caddy still 503s — Caddy itself is the bottleneck
+  - Need to check Caddy logs on whatever machine it runs on (192.168.86.1?)
+  - D J's Caddyfile is clean: just `reverse_proxy 192.168.86.100:18789`
+  - Chat works intermittently — connects sometimes then drops
+- **Caddy config** (D J's full Caddyfile): media→86.50:8096, vault→86.244:7080, git→86.244:3002, share→86.26:3000, kippui→86.100:8080, kipp→86.100:18789
+- **@Argona0x tweet analyzed** — $50→$2,980 Polymarket bot claim, 90% likely fake (engagement farming)
+- **Kelly Criterion explained** to D J
+
+### Night Shift Work
+- **Alexa+ UI research completed** — sub-agent produced comprehensive 200-line report at projects/kipp/research/alexa-plus-ui.md
+  - Key patterns: dashboard-first, ephemeral conversation overlay, widget grid, proximity-aware, ambient mode
+  - Verge/CNET screenshots downloaded for reference
+- **KIPP UI v2 built and deployed** — Alexa-inspired dashboard:
+  - Hero card with greeting + status, weather with hourly forecast, shopping list, today/calendar, timers
+  - Chat overlay appears on mic button press, dashboard blurs behind it
+  - Dark theme, card-based, bottom bar with mic button
+  - WebSocket protocol fully working (connect, chat.send, streaming)
+- **Systems check:**
+  - Leverage trader running: $9,987.40 (-0.13% from $10K), HYPE trade closed at -$15.63
+  - GARP portfolio: $100,055.90 (+0.06% from $100K), 6 positions
+  - All systemd timers healthy
+- **Future UI improvements identified:**
+  - Ambient/photo mode when idle (slideshow + big clock)
+  - Blue waveform animation during listening state
+  - Results persistence (timer created in chat → appears on dashboard)
+  - Proximity-aware layout (different for close vs far viewing)
+
+### KIPP HTTPS + Voice Fixed
+- Self-signed cert generated (10yr, SAN for 192.168.86.100)
+- UI server switched to HTTPS on port 8080
+- socat WSS proxy on port 18790 → gateway 18789 (systemd kipp-wss-proxy.service)
+- Browser TTS fallback removed — Piper only
+- Double-voice mystery: D J had 2 tabs open 😂
+- Gateway config fixed: `allowedOrigins` was at root level (invalid), moved to `gateway.controlUi.allowedOrigins`
+- Added `https://192.168.86.100:8080` to allowedOrigins
+
+### @milesdeutscher Tweet Analysis
+- Polymarket copy-trading GitHub bot going viral (23K views)
+- Our take: validates our kch123 approach but we're ahead — we have whale selection, they just have execution code
+- Edge erodes with adoption; not actionable for us
+
+### Night Shift — Ambient Mode
+- Built ambient/idle mode for KIPP UI (sub-agent)
+- Activates after 60s idle: large glowing clock, weather icon, dark gradient background
+- Rotating content: tuxedo cat facts, quotes, trivia (every 30s)
+- Tap anywhere to return to dashboard
+- Verified with Playwright: both ambient and dashboard modes render correctly
+
+### Late Night — KIPP Local-Only Switch
+- **D J decided KIPP stays local-only** — no external exposure, direct IP access
+- Switched UI WebSocket URL from `wss://kipp.host.letsgetnashty.com` to `ws://192.168.86.100:18789`
+- UI renders visually at `http://192.168.86.100:8080/` (Playwright confirmed: green dot, weather, clock)
+- **But WS still broken**: origin-allowed errors persist, old domain URLs not fully stripped from JS fallback/retry code
+- Hundreds of failed reconnect attempts every ~3s in console logs
+- TTS and weather fetch endpoints still referencing old HTTPS domain paths
+- **Next**: fully clean UI JS code of all old domain refs, fix origin-allowed, re-test with Playwright
+- **Network issue on Case's VM** (192.168.86.45): persistent "TypeError: fetch failed" every ~10s — Telegram polling, ChromaDB auto-recall broken. D J communicating via webchat as workaround.
+
+### Caddy Config (D J's reverse proxy)
+```
+kippui.host.letsgetnashty.com {
+    reverse_proxy 192.168.86.100:8080
+}
+kipp.host.letsgetnashty.com {
+    header Access-Control-Allow-Origin "https://kippui.host.letsgetnashty.com"
+    header Access-Control-Allow-Methods "GET, POST, OPTIONS"
+    header Access-Control-Allow-Headers "Content-Type, Authorization"
+    @options method OPTIONS
+    handle @options { respond 204 }
+    reverse_proxy 192.168.86.100:18789
+}
+```

memory/2026-02-11.md

@@ -0,0 +1,61 @@
+# 2026-02-11
+
+## KIPP Voice Pipeline — Major Build Session
+
+### Built & Deployed (feature/wake-word branch)
+- **Always-on wake word detection** via OpenWakeWord (hey_jarvis model as placeholder)
+- **Faster Whisper** (base.en) for speech-to-text on KIPP VM
+- **Voice WebSocket server** on port 8082 (TLS) — `kipp-voice.service`
+- **Python venv** at `/home/wdjones/kipp-venv` with openwakeword, faster-whisper, websockets, aiohttp
+- **Male TTS voice** — switched from Amy to Ryan (Piper en_US)
+- **Hero panel chat** — voice interaction happens inside the greeting/hero card, not a separate overlay
+- **Widget state system** — JSON file + CLI tool + REST API + dashboard polling
+  - `tools/widgets.py` for shopping list, timers, reminders
+  - API endpoints on UI server: GET/POST /api/widgets
+  - Dashboard loads real data, polls every 10s
+  - KIPP agent instructed in SOUL.md to use widget CLI
+
+### Key Bugs Fixed
+1. **CSS injected inside JS** — patch script found `/* CHAT OVERLAY */` in both CSS and JS sections
+2. **Gateway challenge-response** — must answer `connect.challenge` with `req` method `connect`
+3. **Client ID must be `openclaw-control-ui`** — gateway validates this
+4. **Origin header required** — voice server needs `Origin: https://192.168.86.100:8080`
+5. **Lifecycle event detection** — gateway sends `phase="end"` not `state="end"` — THIS was the 60-second hang bug
+6. **Audio suppressed during wake state** — browser stopped sending mic data when it should have been recording
+7. **Race condition** — server sent `ready` before TTS finished, mic picked up speaker audio
+8. **Self-triggering wake word** — KIPP's own TTS voice triggered "hey jarvis" — fixed with 2s cooldown
+9. **voiceState stuck on speaking** — client must set listening before server's ready msg arrives
+10. **Duplicate JS blocks** — sub-agent inserted widget code twice
+
+### Voice State Machine (final)
+```
+listening → (wake word) → recording → (silence) → processing → (gateway) → speaking → (done_speaking) → cooldown (2s) → listening
+```
+
+### Timing Config
+- 4s grace period after wake word before silence timeout
+- 1.5s silence after speech to end recording
+- 30s max recording time
+- 2s cooldown after TTS to prevent self-trigger
+
+### KIPP Model Switch
+- Switched from `llamacpp/glm-4.7-flash` (83s responses!) to `anthropic/claude-sonnet-4-20250514` (~3s responses)
+- GLM-4 Flash as fallback
+- Config at `/home/wdjones/.openclaw/openclaw.json` on KIPP VM
+
+### 15 Playwright Tests
+- `kipp-ui/tests/test_voice.py` — UI elements, state transitions, chat flow, server connectivity
+
+## anoin123 Investigation
+- @browomo tweet about anoin123 Polymarket wallet: $1.6M in 57 days
+- **2-4 AM EST claim is FALSE** — trades peak at 3 PM EST
+- Strategy: "No harvester" — buys No at 90-99¢ on time-bounded events, collects spread
+- $2.2M volume, $7K avg trade, concentrated on Iran strikes + government shutdown
+- Monitor set up: `anoin123-monitor.py` + systemd timer every 5min
+- Analysis at `data/investigations/anoin123-analysis.md`
+- Copy-trade verdict: medium value — strategy is mechanical and replicable independently
+
+## Infrastructure Notes
+- KIPP VM services: kipp-ui, kipp-voice, kipp-tts, kipp-wss-proxy, openclaw-gateway
+- Widget data: `/home/wdjones/.openclaw/workspace/kipp-ui/data/widgets.json`
+- All changes on `feature/wake-word` branch in kipp/workspace repo

memory/2026-02-12.md

@@ -0,0 +1,14 @@
+# 2026-02-12
+
+## KIPP Voice Pipeline Fixes
+- False wake word triggers after KIPP speaks — wake model picking up speaker audio
+- Patch 1: Increased cooldown 2s → 4s, added silence flushing during cooldown (feed zeros through wake model to clear internal buffers), added RMS energy gate on wake detection
+- RMS gate of 200 was too aggressive — blocked ALL real wake attempts (real RMS was 45-157)
+- Lowered RMS gate to 30 — just filters literal silence false positives
+- Voice server restarted, D J testing
+
+## Tweet Analyses
+- **@jollygreenmoney / $SHL.V** — Homeland Nickel (TSX-V penny stock). Promoted junior miner, already ran 2,300% from $0.03→$0.72, pulled back to $0.41. Collapsing volume = distribution phase. Coordinated promotion with @Levi_Researcher. Nickel bull thesis has merit but this specific stock is exit liquidity. Verdict: stay away.
+- **@MoonDevOnYT** — "Fastest growing quant repo on GitHub" AI trading agents. 875 followers, self-proclaimed "#1 quant on X." Content marketing funnel → paid private streams at moondev.com. No verifiable P&L, buzzword soup, fantasy architecture. Verdict: course seller, skip.
+
+## D J signed off ~midnight

projects/crypto-signals/data/leverage-game/games/1ac7d29c/game.json

@@ -0,0 +1,16 @@
+{
+  "game_id": "1ac7d29c",
+  "name": "Leverage Challenge",
+  "starting_cash": 10000.0,
+  "max_leverage": 20,
+  "funding_rate_8h": 0.01,
+  "maker_fee": 0.02,
+  "taker_fee": 0.05,
+  "start_date": "2026-02-09",
+  "creator": "case",
+  "created_at": "2026-02-10T02:31:27.614107+00:00",
+  "players": [
+    "case"
+  ],
+  "status": "active"
+}

projects/crypto-signals/data/leverage-game/games/1ac7d29c/players/case/portfolio.json

@@ -0,0 +1,92 @@
+{
+  "cash": 9059.675453916036,
+  "positions": {
+    "SEI_long_1c69": {
+      "symbol": "SEI",
+      "direction": "long",
+      "leverage": 7,
+      "margin_usd": 200,
+      "notional": 1400,
+      "entry_price": 0.0745,
+      "current_price": 0.0743,
+      "liquidation_price": 0.0639,
+      "unrealized_pnl": -3.76,
+      "entry_fee": 0.7,
+      "opened_at": "2026-02-10T12:45:18.117079+00:00",
+      "reason": "Long scanner score:45"
+    },
+    "PUMP_long_4a28": {
+      "symbol": "PUMP",
+      "direction": "long",
+      "leverage": 7,
+      "margin_usd": 200,
+      "notional": 1400,
+      "entry_price": 0.001909,
+      "current_price": 0.001921,
+      "liquidation_price": 0.0016,
+      "unrealized_pnl": 8.8,
+      "entry_fee": 0.7,
+      "opened_at": "2026-02-10T19:30:18.313053+00:00",
+      "reason": "Long scanner score:58"
+    },
+    "TRUMP_long_fbd7": {
+      "symbol": "TRUMP",
+      "direction": "long",
+      "leverage": 7,
+      "margin_usd": 200,
+      "notional": 1400,
+      "entry_price": 3.266,
+      "current_price": 3.266,
+      "liquidation_price": 2.7994,
+      "unrealized_pnl": 0.0,
+      "entry_fee": 0.7,
+      "opened_at": "2026-02-10T19:45:18.100831+00:00",
+      "reason": "Long scanner score:58"
+    },
+    "OP_long_4ea9": {
+      "symbol": "OP",
+      "direction": "long",
+      "leverage": 7,
+      "margin_usd": 200,
+      "notional": 1400,
+      "entry_price": 0.183,
+      "current_price": 0.183,
+      "liquidation_price": 0.1569,
+      "unrealized_pnl": 0.0,
+      "entry_fee": 0.7,
+      "opened_at": "2026-02-10T20:15:18.266153+00:00",
+      "reason": "Long scanner score:45"
+    },
+    "ICP_short_9a0a": {
+      "symbol": "ICP",
+      "direction": "short",
+      "leverage": 10,
+      "margin_usd": 200,
+      "notional": 2000,
+      "entry_price": 2.907,
+      "current_price": 2.907,
+      "liquidation_price": 3.1977,
+      "unrealized_pnl": 0.0,
+      "entry_fee": 1.0,
+      "opened_at": "2026-02-10T23:00:14.402076+00:00",
+      "reason": "Short scanner score:65"
+    },
+    "HYPE_long_d46c": {
+      "symbol": "HYPE",
+      "direction": "long",
+      "leverage": 7,
+      "margin_usd": 200,
+      "notional": 1400,
+      "entry_price": 29.05,
+      "current_price": 29.05,
+      "liquidation_price": 24.9,
+      "unrealized_pnl": 0,
+      "entry_fee": 0.7,
+      "opened_at": "2026-02-11T00:45:18.584851+00:00",
+      "reason": "Long scanner score:45"
+    }
+  },
+  "total_realized_pnl": 315.17545391605773,
+  "total_fees_paid": 55.50000000000006,
+  "total_funding_paid": 0
+}

projects/crypto-signals/data/leverage-game/games/1ac7d29c/players/case/snapshots.json

@@ -0,0 +1 @@
+[]

projects/crypto-signals/data/leverage-game/games/1ac7d29c/players/case/trades.json

@@ -0,0 +1,956 @@
+[
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_915b",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.001989,
+    "liquidation_price": 0.0017,
+    "fee": 0.7,
+    "reason": "Long scanner score:48",
+    "timestamp": "2026-02-10T03:15:17.681597+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "PUMP_long_915b",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.001989,
+    "exit_price": 0.00197,
+    "margin_usd": 200,
+    "pnl": -13.37,
+    "pnl_pct": -6.69,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-6.7%)",
+    "timestamp": "2026-02-10T03:30:04.448216+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_8004",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.00197,
+    "liquidation_price": 0.0017,
+    "fee": 0.7,
+    "reason": "Long scanner score:53",
+    "timestamp": "2026-02-10T03:30:17.846691+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "PUMP_long_8004",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.00197,
+    "exit_price": 0.001999,
+    "margin_usd": 200,
+    "pnl": 20.61,
+    "pnl_pct": 10.3,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+10.3%)",
+    "timestamp": "2026-02-10T03:45:04.447060+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_3ce0",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 30.45,
+    "liquidation_price": 26.1,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T05:45:17.773139+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_3ce0",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 30.45,
+    "exit_price": 30.11,
+    "margin_usd": 200,
+    "pnl": -15.63,
+    "pnl_pct": -7.82,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-7.8%)",
+    "timestamp": "2026-02-10T06:30:04.465160+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_9513",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 30.11,
+    "liquidation_price": 25.8086,
+    "fee": 0.7,
+    "reason": "Long scanner score:55",
+    "timestamp": "2026-02-10T06:30:17.909814+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_9513",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 30.11,
+    "exit_price": 29.98,
+    "margin_usd": 200,
+    "pnl": -6.04,
+    "pnl_pct": -3.02,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-3.0%)",
+    "timestamp": "2026-02-10T08:00:04.447966+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "FIL_short_4625",
+    "symbol": "FIL",
+    "direction": "short",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.954,
+    "liquidation_price": 1.0903,
+    "fee": 0.7,
+    "reason": "Short scanner score:50",
+    "timestamp": "2026-02-10T08:00:17.712210+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_d6e2",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.98,
+    "liquidation_price": 25.6971,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T08:00:17.956985+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_4761",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "margin_usd": 200,
+    "notional": 3000,
+    "entry_price": 0.651,
+    "liquidation_price": 0.6944,
+    "fee": 1.5,
+    "reason": "Short scanner score:80",
+    "timestamp": "2026-02-10T09:00:17.907206+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_4761",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "entry_price": 0.651,
+    "exit_price": 0.657,
+    "margin_usd": 200,
+    "pnl": -27.65,
+    "pnl_pct": -13.82,
+    "fee": 1.5,
+    "liquidated": false,
+    "reason": "SL hit (-13.8%)",
+    "timestamp": "2026-02-10T09:15:04.758739+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_3947",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "margin_usd": 200,
+    "notional": 3000,
+    "entry_price": 0.657,
+    "liquidation_price": 0.7008,
+    "fee": 1.5,
+    "reason": "Short scanner score:85",
+    "timestamp": "2026-02-10T09:15:17.879130+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_3947",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "entry_price": 0.657,
+    "exit_price": 0.651,
+    "margin_usd": 200,
+    "pnl": 27.4,
+    "pnl_pct": 13.7,
+    "fee": 1.5,
+    "liquidated": false,
+    "reason": "TP hit (+13.7%)",
+    "timestamp": "2026-02-10T09:30:04.763834+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_cf21",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "margin_usd": 200,
+    "notional": 3000,
+    "entry_price": 0.651,
+    "liquidation_price": 0.6944,
+    "fee": 1.5,
+    "reason": "Short scanner score:80",
+    "timestamp": "2026-02-10T09:30:18.015729+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_3752",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.00199,
+    "liquidation_price": 0.0017,
+    "fee": 0.7,
+    "reason": "Long scanner score:48",
+    "timestamp": "2026-02-10T09:30:18.259653+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_d6e2",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 29.98,
+    "exit_price": 30.21,
+    "margin_usd": 200,
+    "pnl": 10.74,
+    "pnl_pct": 5.37,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+5.4%)",
+    "timestamp": "2026-02-10T09:45:04.903012+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_e1e2",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.96,
+    "liquidation_price": 25.68,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T10:30:18.207447+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_cf21",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "entry_price": 0.651,
+    "exit_price": 0.627,
+    "margin_usd": 200,
+    "pnl": 110.6,
+    "pnl_pct": 55.3,
+    "fee": 1.5,
+    "liquidated": false,
+    "reason": "TP hit (+55.3%)",
+    "timestamp": "2026-02-10T11:00:04.927667+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_e1e2",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 29.96,
+    "exit_price": 29.54,
+    "margin_usd": 200,
+    "pnl": -19.63,
+    "pnl_pct": -9.81,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-9.8%)",
+    "timestamp": "2026-02-10T11:00:04.997470+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_e791",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "margin_usd": 200,
+    "notional": 2000,
+    "entry_price": 29.54,
+    "liquidation_price": 26.586,
+    "fee": 1.0,
+    "reason": "Long scanner score:60",
+    "timestamp": "2026-02-10T11:00:18.295450+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "FIL_short_4625",
+    "symbol": "FIL",
+    "direction": "short",
+    "leverage": 7,
+    "entry_price": 0.954,
+    "exit_price": 0.93,
+    "margin_usd": 200,
+    "pnl": 35.22,
+    "pnl_pct": 17.61,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+17.6%)",
+    "timestamp": "2026-02-10T11:15:04.755055+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_e791",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "entry_price": 29.54,
+    "exit_price": 29.35,
+    "margin_usd": 200,
+    "pnl": -12.86,
+    "pnl_pct": -6.43,
+    "fee": 1.0,
+    "liquidated": false,
+    "reason": "SL hit (-6.4%)",
+    "timestamp": "2026-02-10T12:15:04.665481+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_cc18",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "margin_usd": 200,
+    "notional": 2000,
+    "entry_price": 29.35,
+    "liquidation_price": 26.415,
+    "fee": 1.0,
+    "reason": "Long scanner score:60",
+    "timestamp": "2026-02-10T12:15:18.125949+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_cc18",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "entry_price": 29.35,
+    "exit_price": 29.67,
+    "margin_usd": 200,
+    "pnl": 21.81,
+    "pnl_pct": 10.9,
+    "fee": 1.0,
+    "liquidated": false,
+    "reason": "TP hit (+10.9%)",
+    "timestamp": "2026-02-10T12:30:04.619392+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_440c",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.67,
+    "liquidation_price": 25.4314,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T12:30:18.005813+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "SEI_long_1c69",
+    "symbol": "SEI",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.0745,
+    "liquidation_price": 0.0639,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-10T12:45:18.117799+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_440c",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 29.67,
+    "exit_price": 29.47,
+    "margin_usd": 200,
+    "pnl": -9.44,
+    "pnl_pct": -4.72,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-4.7%)",
+    "timestamp": "2026-02-10T13:00:04.766754+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_1e8d",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.651,
+    "liquidation_price": 0.744,
+    "fee": 0.7,
+    "reason": "Short scanner score:55",
+    "timestamp": "2026-02-10T13:00:18.109086+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_014f",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "margin_usd": 200,
+    "notional": 2000,
+    "entry_price": 29.47,
+    "liquidation_price": 26.523,
+    "fee": 1.0,
+    "reason": "Long scanner score:60",
+    "timestamp": "2026-02-10T13:00:18.382345+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "PUMP_long_3752",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.00199,
+    "exit_price": 0.00195,
+    "margin_usd": 200,
+    "pnl": -28.14,
+    "pnl_pct": -14.07,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-14.1%)",
+    "timestamp": "2026-02-10T13:15:04.932470+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_c66e",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.00195,
+    "liquidation_price": 0.0017,
+    "fee": 0.7,
+    "reason": "Long scanner score:53",
+    "timestamp": "2026-02-10T13:15:18.331814+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_1e8d",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 7,
+    "entry_price": 0.651,
+    "exit_price": 0.657,
+    "margin_usd": 200,
+    "pnl": -12.9,
+    "pnl_pct": -6.45,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-6.5%)",
+    "timestamp": "2026-02-10T13:30:04.960908+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_014f",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 10,
+    "entry_price": 29.47,
+    "exit_price": 29.75,
+    "margin_usd": 200,
+    "pnl": 19.0,
+    "pnl_pct": 9.5,
+    "fee": 1.0,
+    "liquidated": false,
+    "reason": "TP hit (+9.5%)",
+    "timestamp": "2026-02-10T13:30:05.033055+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_be79",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "margin_usd": 200,
+    "notional": 3000,
+    "entry_price": 0.657,
+    "liquidation_price": 0.7008,
+    "fee": 1.5,
+    "reason": "Short scanner score:70",
+    "timestamp": "2026-02-10T13:30:18.431839+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_be79",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "entry_price": 0.657,
+    "exit_price": 0.651,
+    "margin_usd": 200,
+    "pnl": 27.4,
+    "pnl_pct": 13.7,
+    "fee": 1.5,
+    "liquidated": false,
+    "reason": "TP hit (+13.7%)",
+    "timestamp": "2026-02-10T14:00:04.794774+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_26a4",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.651,
+    "liquidation_price": 0.744,
+    "fee": 0.7,
+    "reason": "Short scanner score:50",
+    "timestamp": "2026-02-10T14:00:18.152240+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_26a4",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 7,
+    "entry_price": 0.651,
+    "exit_price": 0.655,
+    "margin_usd": 200,
+    "pnl": -8.6,
+    "pnl_pct": -4.3,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-4.3%)",
+    "timestamp": "2026-02-10T14:15:04.778561+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_0eec",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 10,
+    "margin_usd": 200,
+    "notional": 2000,
+    "entry_price": 0.655,
+    "liquidation_price": 0.7205,
+    "fee": 1.0,
+    "reason": "Short scanner score:65",
+    "timestamp": "2026-02-10T14:15:18.287529+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_0eec",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 10,
+    "entry_price": 0.655,
+    "exit_price": 0.65,
+    "margin_usd": 200,
+    "pnl": 15.27,
+    "pnl_pct": 7.63,
+    "fee": 1.0,
+    "liquidated": false,
+    "reason": "TP hit (+7.6%)",
+    "timestamp": "2026-02-10T15:15:04.785401+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ASTER_short_1a62",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "margin_usd": 200,
+    "notional": 3000,
+    "entry_price": 0.67,
+    "liquidation_price": 0.7147,
+    "fee": 1.5,
+    "reason": "Short scanner score:85",
+    "timestamp": "2026-02-10T15:45:18.129303+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "PUMP_long_c66e",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.00195,
+    "exit_price": 0.001969,
+    "margin_usd": 200,
+    "pnl": 13.64,
+    "pnl_pct": 6.82,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+6.8%)",
+    "timestamp": "2026-02-10T16:15:04.793084+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ASTER_short_1a62",
+    "symbol": "ASTER",
+    "direction": "short",
+    "leverage": 15,
+    "entry_price": 0.67,
+    "exit_price": 0.654,
+    "margin_usd": 200,
+    "pnl": 71.64,
+    "pnl_pct": 35.82,
+    "fee": 1.5,
+    "liquidated": false,
+    "reason": "TP hit (+35.8%)",
+    "timestamp": "2026-02-10T16:15:04.837617+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ICP_long_2599",
+    "symbol": "ICP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 2.722,
+    "liquidation_price": 2.3331,
+    "fee": 0.7,
+    "reason": "Long scanner score:53",
+    "timestamp": "2026-02-10T18:00:17.949118+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ICP_long_2599",
+    "symbol": "ICP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 2.722,
+    "exit_price": 2.759,
+    "margin_usd": 200,
+    "pnl": 19.03,
+    "pnl_pct": 9.52,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+9.5%)",
+    "timestamp": "2026-02-10T18:15:04.612873+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "TRUMP_long_6717",
+    "symbol": "TRUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 3.239,
+    "liquidation_price": 2.7763,
+    "fee": 0.7,
+    "reason": "Long scanner score:58",
+    "timestamp": "2026-02-10T19:00:17.796328+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_9b05",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.001926,
+    "liquidation_price": 0.0017,
+    "fee": 0.7,
+    "reason": "Long scanner score:58",
+    "timestamp": "2026-02-10T19:15:18.359381+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "TRUMP_long_6717",
+    "symbol": "TRUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 3.239,
+    "exit_price": 3.333,
+    "margin_usd": 200,
+    "pnl": 40.63,
+    "pnl_pct": 20.31,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+20.3%)",
+    "timestamp": "2026-02-10T19:30:04.787087+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "PUMP_long_9b05",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.001926,
+    "exit_price": 0.001909,
+    "margin_usd": 200,
+    "pnl": -12.36,
+    "pnl_pct": -6.18,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-6.2%)",
+    "timestamp": "2026-02-10T19:30:04.861197+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "PUMP_long_4a28",
+    "symbol": "PUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.001909,
+    "liquidation_price": 0.0016,
+    "fee": 0.7,
+    "reason": "Long scanner score:58",
+    "timestamp": "2026-02-10T19:30:18.313742+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "TRUMP_long_fbd7",
+    "symbol": "TRUMP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 3.266,
+    "liquidation_price": 2.7994,
+    "fee": 0.7,
+    "reason": "Long scanner score:58",
+    "timestamp": "2026-02-10T19:45:18.101503+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "OP_long_67c4",
+    "symbol": "OP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.184,
+    "liquidation_price": 0.1577,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-10T19:45:18.373173+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "OP_long_67c4",
+    "symbol": "OP",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.184,
+    "exit_price": 0.183,
+    "margin_usd": 200,
+    "pnl": -7.61,
+    "pnl_pct": -3.8,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-3.8%)",
+    "timestamp": "2026-02-10T20:15:04.857761+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "OP_long_4ea9",
+    "symbol": "OP",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.183,
+    "liquidation_price": 0.1569,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-10T20:15:18.266969+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ALGO_long_5f94",
+    "symbol": "ALGO",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 0.0901,
+    "liquidation_price": 0.0772,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T20:30:18.299437+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_6b17",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.21,
+    "liquidation_price": 25.0371,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-10T21:45:18.366940+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_6b17",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 29.21,
+    "exit_price": 29.52,
+    "margin_usd": 200,
+    "pnl": 14.86,
+    "pnl_pct": 7.43,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+7.4%)",
+    "timestamp": "2026-02-10T22:15:05.229877+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "ICP_short_9a0a",
+    "symbol": "ICP",
+    "direction": "short",
+    "leverage": 10,
+    "margin_usd": 200,
+    "notional": 2000,
+    "entry_price": 2.907,
+    "liquidation_price": 3.1977,
+    "fee": 1.0,
+    "reason": "Short scanner score:65",
+    "timestamp": "2026-02-10T23:00:14.402704+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_81ed",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.0,
+    "liquidation_price": 24.8571,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-10T23:15:18.652083+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_81ed",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 29.0,
+    "exit_price": 28.87,
+    "margin_usd": 200,
+    "pnl": -6.28,
+    "pnl_pct": -3.14,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "SL hit (-3.1%)",
+    "timestamp": "2026-02-10T23:30:01.509254+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_9f34",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 28.87,
+    "liquidation_price": 24.7457,
+    "fee": 0.7,
+    "reason": "Long scanner score:50",
+    "timestamp": "2026-02-10T23:30:14.941390+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "ALGO_long_5f94",
+    "symbol": "ALGO",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 0.0901,
+    "exit_price": 0.0919,
+    "margin_usd": 200,
+    "pnl": 27.97,
+    "pnl_pct": 13.98,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+14.0%)",
+    "timestamp": "2026-02-11T00:30:05.451674+00:00"
+  },
+  {
+    "action": "CLOSE",
+    "pos_id": "HYPE_long_9f34",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "entry_price": 28.87,
+    "exit_price": 29.28,
+    "margin_usd": 200,
+    "pnl": 19.88,
+    "pnl_pct": 9.94,
+    "fee": 0.7,
+    "liquidated": false,
+    "reason": "TP hit (+9.9%)",
+    "timestamp": "2026-02-11T00:30:05.526899+00:00"
+  },
+  {
+    "action": "OPEN",
+    "pos_id": "HYPE_long_d46c",
+    "symbol": "HYPE",
+    "direction": "long",
+    "leverage": 7,
+    "margin_usd": 200,
+    "notional": 1400,
+    "entry_price": 29.05,
+    "liquidation_price": 24.9,
+    "fee": 0.7,
+    "reason": "Long scanner score:45",
+    "timestamp": "2026-02-11T00:45:18.585575+00:00"
+  }
+]

projects/crypto-signals/data/leverage-game/trader_state.json

@@ -0,0 +1,10 @@
+{
+  "peak_pnl": {
+    "SEI_long_1c69": 1.8799999999999997,
+    "PUMP_long_4a28": 4.4,
+    "TRUMP_long_fbd7": 0.0,
+    "OP_long_4ea9": 0.0,
+    "ICP_short_9a0a": 0.0
+  },
+  "last_alert": null
+}

projects/crypto-signals/data/short-scanner/scan_log.json

@@ -0,0 +1,487 @@
+[
+  {
+    "timestamp": "2026-02-10T02:31:38.063585+00:00",
+    "coins_scanned": 29,
+    "strong_signals": 0,
+    "results": [
+      {
+        "symbol": "FIL",
+        "price": 0.954,
+        "rsi": 61.7,
+        "vwap_pct": 9.0,
+        "macd_histogram": -0.932576,
+        "bb_position": 0.76,
+        "change_24h": 0.74,
+        "change_4h": 0.0,
+        "vol_trend": 0.05,
+        "score": 40,
+        "reasons": [
+          "RSI mildly elevated (61.7)",
+          "Well above VWAP (+9.0%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:34.839978+00:00"
+      },
+      {
+        "symbol": "NEAR",
+        "price": 1.045,
+        "rsi": 60.0,
+        "vwap_pct": 1.62,
+        "macd_histogram": -1.030379,
+        "bb_position": 0.93,
+        "change_24h": -0.85,
+        "change_4h": 2.35,
+        "vol_trend": 1.02,
+        "score": 38,
+        "reasons": [
+          "RSI mildly elevated (60.0)",
+          "Slightly above VWAP (+1.6%)",
+          "MACD bearish + accelerating",
+          "Near upper Bollinger (0.93)"
+        ],
+        "timestamp": "2026-02-10T02:31:33.678025+00:00"
+      },
+      {
+        "symbol": "OP",
+        "price": 0.19,
+        "rsi": 64.2,
+        "vwap_pct": 3.23,
+        "macd_histogram": -0.187435,
+        "bb_position": 0.79,
+        "change_24h": 0.53,
+        "change_4h": 0.0,
+        "vol_trend": 1.07,
+        "score": 35,
+        "reasons": [
+          "RSI mildly elevated (64.2)",
+          "Above VWAP (+3.2%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:36.709597+00:00"
+      },
+      {
+        "symbol": "ARB",
+        "price": 0.114,
+        "rsi": 52.1,
+        "vwap_pct": 3.09,
+        "macd_histogram": -0.113948,
+        "bb_position": 0.72,
+        "change_24h": -3.55,
+        "change_4h": 2.06,
+        "vol_trend": 0.11,
+        "score": 30,
+        "reasons": [
+          "Above VWAP (+3.1%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:36.470618+00:00"
+      },
+      {
+        "symbol": "ADA",
+        "price": 0.2693,
+        "rsi": 50.2,
+        "vwap_pct": 1.35,
+        "macd_histogram": -0.269763,
+        "bb_position": 0.62,
+        "change_24h": -1.43,
+        "change_4h": -0.81,
+        "vol_trend": 0.15,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+1.3%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:32.520420+00:00"
+      },
+      {
+        "symbol": "LINK",
+        "price": 8.88,
+        "rsi": 55.1,
+        "vwap_pct": 1.7,
+        "macd_histogram": -8.839752,
+        "bb_position": 0.73,
+        "change_24h": 0.79,
+        "change_4h": -0.34,
+        "vol_trend": 0.84,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+1.7%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:32.968544+00:00"
+      },
+      {
+        "symbol": "UNI",
+        "price": 3.519,
+        "rsi": 56.2,
+        "vwap_pct": 2.62,
+        "macd_histogram": -3.494918,
+        "bb_position": 0.65,
+        "change_24h": 2.18,
+        "change_4h": 0.0,
+        "vol_trend": 0.72,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+2.6%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:34.365690+00:00"
+      },
+      {
+        "symbol": "AAVE",
+        "price": 113.55,
+        "rsi": 55.0,
+        "vwap_pct": 1.28,
+        "macd_histogram": -112.920164,
+        "bb_position": 0.71,
+        "change_24h": 0.82,
+        "change_4h": 2.23,
+        "vol_trend": 0.55,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+1.3%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:34.599689+00:00"
+      },
+      {
+        "symbol": "APT",
+        "price": 1.067,
+        "rsi": 49.4,
+        "vwap_pct": 1.06,
+        "macd_histogram": -1.076147,
+        "bb_position": 0.63,
+        "change_24h": -2.65,
+        "change_4h": 0.0,
+        "vol_trend": 0.23,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+1.1%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:35.993067+00:00"
+      },
+      {
+        "symbol": "SUI",
+        "price": 0.9668,
+        "rsi": 50.4,
+        "vwap_pct": 1.54,
+        "macd_histogram": -0.969969,
+        "bb_position": 0.66,
+        "change_24h": -2.0,
+        "change_4h": -0.17,
+        "vol_trend": 0.03,
+        "score": 23,
+        "reasons": [
+          "Slightly above VWAP (+1.5%)",
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:36.231436+00:00"
+      },
+      {
+        "symbol": "BTC",
+        "price": 70249.15,
+        "rsi": 51.0,
+        "vwap_pct": 0.34,
+        "macd_histogram": -70262.247326,
+        "bb_position": 0.63,
+        "change_24h": -1.38,
+        "change_4h": 0.18,
+        "vol_trend": 0.96,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:31.360741+00:00"
+      },
+      {
+        "symbol": "ETH",
+        "price": 2109.49,
+        "rsi": 55.4,
+        "vwap_pct": 0.97,
+        "macd_histogram": -2104.718634,
+        "bb_position": 0.67,
+        "change_24h": 0.44,
+        "change_4h": 0.2,
+        "vol_trend": 1.27,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:31.596017+00:00"
+      },
+      {
+        "symbol": "SOL",
+        "price": 86.88,
+        "rsi": 52.0,
+        "vwap_pct": 0.65,
+        "macd_histogram": -86.940038,
+        "bb_position": 0.65,
+        "change_24h": -0.98,
+        "change_4h": 0.17,
+        "vol_trend": 0.88,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:31.806936+00:00"
+      },
+      {
+        "symbol": "XRP",
+        "price": 1.4454,
+        "rsi": 53.8,
+        "vwap_pct": 0.52,
+        "macd_histogram": -1.439527,
+        "bb_position": 0.66,
+        "change_24h": -0.39,
+        "change_4h": 0.59,
+        "vol_trend": 1.67,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:32.046327+00:00"
+      },
+      {
+        "symbol": "DOGE",
+        "price": 0.09629,
+        "rsi": 52.4,
+        "vwap_pct": 0.47,
+        "macd_histogram": -0.096217,
+        "bb_position": 0.7,
+        "change_24h": -0.98,
+        "change_4h": -0.43,
+        "vol_trend": 2.79,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:32.284061+00:00"
+      },
+      {
+        "symbol": "AVAX",
+        "price": 9.01,
+        "rsi": 47.3,
+        "vwap_pct": 0.43,
+        "macd_histogram": -9.05205,
+        "bb_position": 0.56,
+        "change_24h": -1.31,
+        "change_4h": -0.44,
+        "vol_trend": 0.29,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:32.758345+00:00"
+      },
+      {
+        "symbol": "DOT",
+        "price": 1.316,
+        "rsi": 47.5,
+        "vwap_pct": 0.69,
+        "macd_histogram": -1.325125,
+        "bb_position": 0.57,
+        "change_24h": -2.52,
+        "change_4h": -0.9,
+        "vol_trend": 0.23,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:33.205989+00:00"
+      },
+      {
+        "symbol": "MATIC",
+        "price": 0.4492,
+        "rsi": 41.9,
+        "vwap_pct": -0.77,
+        "macd_histogram": -0.452491,
+        "bb_position": 0.32,
+        "change_24h": -1.58,
+        "change_4h": 0.22,
+        "vol_trend": 1.07,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:33.442040+00:00"
+      },
+      {
+        "symbol": "ATOM",
+        "price": 1.953,
+        "rsi": 49.3,
+        "vwap_pct": 0.52,
+        "macd_histogram": -1.965896,
+        "bb_position": 0.5,
+        "change_24h": 0.93,
+        "change_4h": 0.05,
+        "vol_trend": 1.04,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:33.888750+00:00"
+      },
+      {
+        "symbol": "LTC",
+        "price": 54.36,
+        "rsi": 50.1,
+        "vwap_pct": 0.22,
+        "macd_histogram": -54.485415,
+        "bb_position": 0.61,
+        "change_24h": -1.06,
+        "change_4h": -0.22,
+        "vol_trend": 2.73,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:34.128167+00:00"
+      },
+      {
+        "symbol": "ALGO",
+        "price": 0.0951,
+        "rsi": 45.3,
+        "vwap_pct": -1.14,
+        "macd_histogram": -0.095952,
+        "bb_position": 0.5,
+        "change_24h": -2.56,
+        "change_4h": -2.36,
+        "vol_trend": 1.65,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:35.076205+00:00"
+      },
+      {
+        "symbol": "XLM",
+        "price": 0.1614,
+        "rsi": 52.5,
+        "vwap_pct": 0.89,
+        "macd_histogram": -0.160963,
+        "bb_position": 0.69,
+        "change_24h": -1.34,
+        "change_4h": 1.13,
+        "vol_trend": 2.74,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:35.314005+00:00"
+      },
+      {
+        "symbol": "VET",
+        "price": 0.00791,
+        "rsi": 48.8,
+        "vwap_pct": -0.02,
+        "macd_histogram": -0.007928,
+        "bb_position": 0.55,
+        "change_24h": -3.06,
+        "change_4h": 0.89,
+        "vol_trend": 0.27,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:35.550100+00:00"
+      },
+      {
+        "symbol": "ICP",
+        "price": 2.782,
+        "rsi": 40.1,
+        "vwap_pct": -1.9,
+        "macd_histogram": -2.843397,
+        "bb_position": 0.14,
+        "change_24h": 3.81,
+        "change_4h": -2.49,
+        "vol_trend": 10.38,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:35.759005+00:00"
+      },
+      {
+        "symbol": "SEI",
+        "price": 0.075,
+        "rsi": 29.9,
+        "vwap_pct": -0.25,
+        "macd_histogram": -0.075645,
+        "bb_position": 0.38,
+        "change_24h": -4.34,
+        "change_4h": 0.0,
+        "vol_trend": 0.11,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:36.945126+00:00"
+      },
+      {
+        "symbol": "HYPE",
+        "price": 31.49,
+        "rsi": 45.7,
+        "vwap_pct": -1.11,
+        "macd_histogram": -31.681649,
+        "bb_position": 0.39,
+        "change_24h": -5.29,
+        "change_4h": 0.41,
+        "vol_trend": 21.91,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:37.184127+00:00"
+      },
+      {
+        "symbol": "TRUMP",
+        "price": 3.446,
+        "rsi": 50.1,
+        "vwap_pct": 0.08,
+        "macd_histogram": -3.45085,
+        "bb_position": 0.49,
+        "change_24h": 0.35,
+        "change_4h": 0.0,
+        "vol_trend": 0.81,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:37.440389+00:00"
+      },
+      {
+        "symbol": "PUMP",
+        "price": 0.002041,
+        "rsi": 37.6,
+        "vwap_pct": -2.03,
+        "macd_histogram": -0.002063,
+        "bb_position": 0.31,
+        "change_24h": -4.31,
+        "change_4h": 0.0,
+        "vol_trend": 1.28,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:37.674937+00:00"
+      },
+      {
+        "symbol": "ASTER",
+        "price": 0.612,
+        "rsi": 50.2,
+        "vwap_pct": 0.21,
+        "macd_histogram": -0.610803,
+        "bb_position": 0.58,
+        "change_24h": -5.85,
+        "change_4h": 0.33,
+        "vol_trend": 0.63,
+        "score": 15,
+        "reasons": [
+          "MACD bearish + accelerating"
+        ],
+        "timestamp": "2026-02-10T02:31:37.912733+00:00"
+      }
+    ]
+  }
+]

projects/crypto-signals/leverage_game.py

@@ -0,0 +1,504 @@
+#!/usr/bin/env python3
+"""
+Crypto Leverage Trading Game Engine
+Paper trading with longs, shorts, and configurable leverage.
+Tracks liquidation prices, unrealized PnL, and funding costs.
+"""
+
+import json
+import os
+import uuid
+import time
+import urllib.request
+from datetime import datetime, date, timezone
+from pathlib import Path
+
+DATA_DIR = Path(__file__).parent / "data" / "leverage-game"
+GAMES_DIR = DATA_DIR / "games"
+
+BINANCE_TICKER = "https://api.binance.us/api/v3/ticker/price"
+
+
+def _load(path, default=None):
+    if path.exists():
+        return json.loads(path.read_text())
+    return default if default is not None else {}
+
+
+def _save(path, data):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(data, indent=2, default=str))
+
+
+def _game_path(game_id):
+    return GAMES_DIR / game_id / "game.json"
+
+def _player_path(game_id, username):
+    return GAMES_DIR / game_id / "players" / username / "portfolio.json"
+
+def _trades_path(game_id, username):
+    return GAMES_DIR / game_id / "players" / username / "trades.json"
+
+def _snapshots_path(game_id, username):
+    return GAMES_DIR / game_id / "players" / username / "snapshots.json"
+
+
+# ── Price Fetching ──
+
+def get_price(symbol):
+    """Get current price from Binance US."""
+    if not symbol.endswith("USDT"):
+        symbol = f"{symbol}USDT"
+    url = f"{BINANCE_TICKER}?symbol={symbol}"
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        return float(json.loads(resp.read())['price'])
+    except:
+        return None
+
+
+# ── Game Management ──
+
+def create_game(name, starting_cash=10_000.0, max_leverage=20, creator="system"):
+    """Create a new leverage trading game."""
+    game_id = str(uuid.uuid4())[:8]
+    config = {
+        "game_id": game_id,
+        "name": name,
+        "starting_cash": starting_cash,
+        "max_leverage": max_leverage,
+        "funding_rate_8h": 0.01,  # 0.01% per 8h (typical perp funding)
+        "maker_fee": 0.02,        # 0.02%
+        "taker_fee": 0.05,        # 0.05%
+        "start_date": date.today().isoformat(),
+        "creator": creator,
+        "created_at": datetime.now(timezone.utc).isoformat(),
+        "players": [],
+        "status": "active",
+    }
+    _save(_game_path(game_id), config)
+    return game_id
+
+
+def list_games(active_only=True):
+    """List all leverage games."""
+    games = []
+    if not GAMES_DIR.exists():
+        return games
+    for gid in os.listdir(GAMES_DIR):
+        gp = _game_path(gid)
+        if gp.exists():
+            config = _load(gp)
+            if active_only and config.get("status") != "active":
+                continue
+            games.append(config)
+    return sorted(games, key=lambda g: g.get("created_at", ""), reverse=True)
+
+
+def get_game(game_id):
+    return _load(_game_path(game_id))
+
+
+def join_game(game_id, username):
+    """Add player to game."""
+    config = get_game(game_id)
+    if not config:
+        return {"error": "Game not found"}
+    if username in config["players"]:
+        return {"error": f"{username} already in game"}
+    
+    config["players"].append(username)
+    _save(_game_path(game_id), config)
+    
+    _save(_player_path(game_id, username), {
+        "cash": config["starting_cash"],
+        "positions": {},
+        "total_realized_pnl": 0,
+        "total_fees_paid": 0,
+        "total_funding_paid": 0,
+    })
+    _save(_trades_path(game_id, username), [])
+    _save(_snapshots_path(game_id, username), [])
+    
+    return {"success": True, "game_id": game_id, "username": username}
+
+
+# ── Position Math ──
+
+def calc_liquidation_price(entry_price, leverage, direction):
+    """
+    Simplified liquidation price.
+    Long: liq = entry * (1 - 1/leverage)
+    Short: liq = entry * (1 + 1/leverage)
+    """
+    if direction == "long":
+        return entry_price * (1 - 1 / leverage)
+    else:  # short
+        return entry_price * (1 + 1 / leverage)
+
+
+def calc_unrealized_pnl(entry_price, current_price, size_usd, leverage, direction):
+    """
+    Calculate unrealized PnL for a leveraged position.
+    size_usd = margin (collateral). Notional = size_usd * leverage.
+    """
+    notional = size_usd * leverage
+    shares = notional / entry_price
+    
+    if direction == "long":
+        pnl = (current_price - entry_price) * shares
+    else:  # short
+        pnl = (entry_price - current_price) * shares
+    
+    return pnl
+
+
+def is_liquidated(entry_price, current_price, leverage, direction):
+    """Check if position would be liquidated."""
+    liq_price = calc_liquidation_price(entry_price, leverage, direction)
+    if direction == "long":
+        return current_price <= liq_price
+    else:
+        return current_price >= liq_price
+
+
+# ── Trading ──
+
+def open_position(game_id, username, symbol, direction, margin_usd, leverage, reason="Manual"):
+    """
+    Open a leveraged position.
+    margin_usd: collateral put up
+    leverage: multiplier (e.g., 10x)
+    direction: 'long' or 'short'
+    """
+    pf = _load(_player_path(game_id, username))
+    game = get_game(game_id)
+    if not pf or not game:
+        return {"error": "Player or game not found"}
+    
+    if direction not in ("long", "short"):
+        return {"error": "Direction must be 'long' or 'short'"}
+    if leverage > game.get("max_leverage", 20):
+        return {"error": f"Max leverage is {game['max_leverage']}x"}
+    if margin_usd > pf["cash"]:
+        return {"error": f"Insufficient cash. Need ${margin_usd:.2f}, have ${pf['cash']:.2f}"}
+    
+    symbol = symbol.upper().replace("USDT", "")
+    price = get_price(symbol)
+    if not price:
+        return {"error": f"Could not fetch price for {symbol}"}
+    
+    notional = margin_usd * leverage
+    fee = notional * game.get("taker_fee", 0.05) / 100
+    
+    # Deduct margin + entry fee from cash
+    pf["cash"] -= (margin_usd + fee)
+    pf["total_fees_paid"] = pf.get("total_fees_paid", 0) + fee
+    
+    pos_id = f"{symbol}_{direction}_{str(uuid.uuid4())[:4]}"
+    liq_price = calc_liquidation_price(price, leverage, direction)
+    
+    pf["positions"][pos_id] = {
+        "symbol": symbol,
+        "direction": direction,
+        "leverage": leverage,
+        "margin_usd": margin_usd,
+        "notional": round(notional, 2),
+        "entry_price": price,
+        "current_price": price,
+        "liquidation_price": round(liq_price, 4),
+        "unrealized_pnl": 0,
+        "entry_fee": round(fee, 4),
+        "opened_at": datetime.now(timezone.utc).isoformat(),
+        "reason": reason,
+    }
+    
+    _save(_player_path(game_id, username), pf)
+    
+    # Log trade
+    trades = _load(_trades_path(game_id, username), [])
+    trades.append({
+        "action": "OPEN",
+        "pos_id": pos_id,
+        "symbol": symbol,
+        "direction": direction,
+        "leverage": leverage,
+        "margin_usd": margin_usd,
+        "notional": round(notional, 2),
+        "entry_price": price,
+        "liquidation_price": round(liq_price, 4),
+        "fee": round(fee, 4),
+        "reason": reason,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+    })
+    _save(_trades_path(game_id, username), trades)
+    
+    return {
+        "success": True,
+        "pos_id": pos_id,
+        "symbol": symbol,
+        "direction": direction,
+        "leverage": leverage,
+        "entry_price": price,
+        "margin": margin_usd,
+        "notional": round(notional, 2),
+        "liquidation_price": round(liq_price, 4),
+        "fee": round(fee, 4),
+    }
+
+
+def close_position(game_id, username, pos_id, reason="Manual"):
+    """Close a leveraged position."""
+    pf = _load(_player_path(game_id, username))
+    game = get_game(game_id)
+    if not pf or not game:
+        return {"error": "Player or game not found"}
+    if pos_id not in pf["positions"]:
+        return {"error": f"Position {pos_id} not found"}
+    
+    pos = pf["positions"][pos_id]
+    price = get_price(pos["symbol"])
+    if not price:
+        return {"error": f"Could not fetch price for {pos['symbol']}"}
+    
+    # Calculate PnL
+    pnl = calc_unrealized_pnl(
+        pos["entry_price"], price, pos["margin_usd"], pos["leverage"], pos["direction"]
+    )
+    
+    # Check liquidation
+    liquidated = is_liquidated(pos["entry_price"], price, pos["leverage"], pos["direction"])
+    if liquidated:
+        pnl = -pos["margin_usd"]  # Lose entire margin
+    
+    # Exit fee
+    notional = pos["margin_usd"] * pos["leverage"]
+    fee = notional * game.get("taker_fee", 0.05) / 100
+    
+    # Return margin + PnL - fee to cash
+    returned = pos["margin_usd"] + pnl - fee
+    if returned < 0:
+        returned = 0  # Can't lose more than margin (no negative balance)
+    
+    pf["cash"] += returned
+    pf["total_realized_pnl"] = pf.get("total_realized_pnl", 0) + pnl
+    pf["total_fees_paid"] = pf.get("total_fees_paid", 0) + fee
+    
+    del pf["positions"][pos_id]
+    _save(_player_path(game_id, username), pf)
+    
+    # Log trade
+    pnl_pct = (pnl / pos["margin_usd"] * 100) if pos["margin_usd"] > 0 else 0
+    trades = _load(_trades_path(game_id, username), [])
+    trades.append({
+        "action": "LIQUIDATED" if liquidated else "CLOSE",
+        "pos_id": pos_id,
+        "symbol": pos["symbol"],
+        "direction": pos["direction"],
+        "leverage": pos["leverage"],
+        "entry_price": pos["entry_price"],
+        "exit_price": price,
+        "margin_usd": pos["margin_usd"],
+        "pnl": round(pnl, 2),
+        "pnl_pct": round(pnl_pct, 2),
+        "fee": round(fee, 4),
+        "liquidated": liquidated,
+        "reason": reason,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+    })
+    _save(_trades_path(game_id, username), trades)
+    
+    return {
+        "success": True,
+        "pos_id": pos_id,
+        "symbol": pos["symbol"],
+        "direction": pos["direction"],
+        "entry_price": pos["entry_price"],
+        "exit_price": price,
+        "pnl": round(pnl, 2),
+        "pnl_pct": round(pnl_pct, 2),
+        "liquidated": liquidated,
+        "returned_to_cash": round(returned, 2),
+    }
+
+
+def update_prices(game_id, username):
+    """Update all position prices and check for liquidations."""
+    pf = _load(_player_path(game_id, username))
+    if not pf:
+        return []
+    
+    liquidations = []
+    to_liquidate = []
+    
+    for pos_id, pos in pf["positions"].items():
+        price = get_price(pos["symbol"])
+        if not price:
+            continue
+        
+        pos["current_price"] = price
+        pos["unrealized_pnl"] = round(
+            calc_unrealized_pnl(pos["entry_price"], price, pos["margin_usd"], pos["leverage"], pos["direction"]),
+            2
+        )
+        
+        if is_liquidated(pos["entry_price"], price, pos["leverage"], pos["direction"]):
+            to_liquidate.append(pos_id)
+        
+        time.sleep(0.1)
+    
+    _save(_player_path(game_id, username), pf)
+    
+    # Process liquidations
+    for pos_id in to_liquidate:
+        result = close_position(game_id, username, pos_id, reason="LIQUIDATED")
+        liquidations.append(result)
+    
+    return liquidations
+
+
+# ── Portfolio View ──
+
+def get_portfolio(game_id, username):
+    """Get full portfolio with live PnL."""
+    pf = _load(_player_path(game_id, username))
+    game = get_game(game_id)
+    if not pf or not game:
+        return None
+    
+    starting = game["starting_cash"]
+    total_unrealized = sum(p.get("unrealized_pnl", 0) for p in pf["positions"].values())
+    total_margin_locked = sum(p["margin_usd"] for p in pf["positions"].values())
+    equity = pf["cash"] + total_margin_locked + total_unrealized
+    total_pnl = equity - starting
+    
+    return {
+        "username": username,
+        "game_id": game_id,
+        "cash": round(pf["cash"], 2),
+        "margin_locked": round(total_margin_locked, 2),
+        "unrealized_pnl": round(total_unrealized, 2),
+        "realized_pnl": round(pf.get("total_realized_pnl", 0), 2),
+        "total_fees": round(pf.get("total_fees_paid", 0), 2),
+        "equity": round(equity, 2),
+        "total_pnl": round(total_pnl, 2),
+        "pnl_pct": round(total_pnl / starting * 100, 2),
+        "num_positions": len(pf["positions"]),
+        "positions": pf["positions"],
+    }
+
+
+def get_trades(game_id, username):
+    return _load(_trades_path(game_id, username), [])
+
+
+def daily_snapshot(game_id, username):
+    """Take daily snapshot."""
+    p = get_portfolio(game_id, username)
+    if not p:
+        return None
+    snapshots = _load(_snapshots_path(game_id, username), [])
+    today = date.today().isoformat()
+    snapshots = [s for s in snapshots if s["date"] != today]
+    snapshots.append({
+        "date": today,
+        "equity": p["equity"],
+        "total_pnl": p["total_pnl"],
+        "pnl_pct": p["pnl_pct"],
+        "cash": p["cash"],
+        "num_positions": p["num_positions"],
+        "realized_pnl": p["realized_pnl"],
+        "total_fees": p["total_fees"],
+    })
+    _save(_snapshots_path(game_id, username), snapshots)
+    return snapshots[-1]
+
+
+def get_leaderboard(game_id):
+    """Leaderboard sorted by equity."""
+    game = get_game(game_id)
+    if not game:
+        return []
+    board = []
+    for username in game["players"]:
+        p = get_portfolio(game_id, username)
+        if p:
+            trades = get_trades(game_id, username)
+            closed = [t for t in trades if t.get("action") in ("CLOSE", "LIQUIDATED")]
+            wins = [t for t in closed if t.get("pnl", 0) > 0]
+            liquidations = [t for t in closed if t.get("liquidated")]
+            board.append({
+                "username": username,
+                "equity": p["equity"],
+                "total_pnl": p["total_pnl"],
+                "pnl_pct": p["pnl_pct"],
+                "num_positions": p["num_positions"],
+                "trades_closed": len(closed),
+                "win_rate": round(len(wins) / len(closed) * 100, 1) if closed else 0,
+                "liquidations": len(liquidations),
+                "total_fees": p["total_fees"],
+            })
+    return sorted(board, key=lambda x: x["pnl_pct"], reverse=True)
+
+
+# ── Auto-Trader (Scanner Integration) ──
+
+def auto_trade_from_scanner(game_id, username, scan_results, margin_per_trade=200, leverage=10):
+    """
+    Automatically open positions based on scanner results.
+    Short scanner (score >= 50) → open short
+    Spot scanner (score >= 40) → open long
+    """
+    opened = []
+    for r in scan_results:
+        symbol = r["symbol"]
+        score = r["score"]
+        
+        # Determine direction based on which scanner produced this
+        direction = r.get("direction", "short")  # Default to short for short scanner
+        
+        if score < 40:
+            continue
+        
+        # Scale leverage with conviction
+        if score >= 70:
+            lev = min(leverage, 15)
+        elif score >= 50:
+            lev = min(leverage, 10)
+        else:
+            lev = min(leverage, 5)
+        
+        result = open_position(game_id, username, symbol, direction, margin_per_trade, lev,
+                              reason=f"Scanner score:{score} | {', '.join(r.get('reasons', []))}")
+        if result.get("success"):
+            opened.append(result)
+    
+    return opened
+
+
+# ── Initialize ──
+
+def ensure_default_game():
+    """Create default Leverage Challenge game."""
+    for g in list_games():
+        if g["name"] == "Leverage Challenge":
+            return g["game_id"]
+    
+    game_id = create_game("Leverage Challenge", starting_cash=10_000.0, max_leverage=20, creator="case")
+    join_game(game_id, "case")
+    return game_id
+
+
+if __name__ == "__main__":
+    game_id = ensure_default_game()
+    game = get_game(game_id)
+    print(f"Game: {game['name']} ({game_id})")
+    print(f"Players: {game['players']}")
+    print(f"Starting cash: ${game['starting_cash']:,.2f}")
+    print(f"Max leverage: {game['max_leverage']}x")
+    
+    board = get_leaderboard(game_id)
+    for entry in board:
+        print(f"  {entry['username']}: ${entry['equity']:,.2f} ({entry['pnl_pct']:+.2f}%) "
+              f"| {entry['trades_closed']} trades | {entry['win_rate']}% win | {entry['liquidations']} liquidated")

projects/crypto-signals/scripts/backtester.py

@@ -0,0 +1,259 @@
+#!/usr/bin/env python3
+"""
+Crypto Signal Backtester
+Simulates each signal against historical price data to determine outcomes.
+"""
+
+import json
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from price_fetcher import get_all_klines, get_current_price, normalize_symbol, datetime_to_ms
+
+
+def simulate_signal(signal, klines):
+    """
+    Simulate a signal against historical candle data.
+    Returns outcome dict with result, P&L, time to resolution, etc.
+    """
+    direction = signal['direction']
+    entry = signal.get('entry')
+    stop_loss = signal.get('stop_loss')
+    targets = signal.get('targets', [])
+    leverage = signal.get('leverage', 1)
+    
+    if not targets or not stop_loss:
+        return {'result': 'incomplete', 'reason': 'missing SL or targets'}
+    
+    target = targets[0]  # Primary target
+    
+    # If entry is 'market', use first candle's open
+    if entry == 'market' or entry is None:
+        if not klines:
+            return {'result': 'no_data'}
+        entry = klines[0]['open']
+    
+    signal['entry_resolved'] = entry
+    
+    # Calculate risk/reward
+    if direction == 'short':
+        risk = abs(stop_loss - entry)
+        reward = abs(entry - target)
+        risk_pct = risk / entry * 100
+        reward_pct = reward / entry * 100
+    else:  # long
+        risk = abs(entry - stop_loss)
+        reward = abs(target - entry)
+        risk_pct = risk / entry * 100
+        reward_pct = reward / entry * 100
+    
+    rr_ratio = reward / risk if risk > 0 else 0
+    
+    result = {
+        'entry_price': entry,
+        'stop_loss': stop_loss,
+        'target': target,
+        'direction': direction,
+        'leverage': leverage,
+        'risk_pct': round(risk_pct, 2),
+        'reward_pct': round(reward_pct, 2),
+        'rr_ratio': round(rr_ratio, 2),
+    }
+    
+    # Walk through candles
+    for i, candle in enumerate(klines):
+        high = candle['high']
+        low = candle['low']
+        
+        if direction == 'short':
+            # Check SL hit (price went above SL)
+            sl_hit = high >= stop_loss
+            # Check TP hit (price went below target)
+            tp_hit = low <= target
+        else:  # long
+            # Check SL hit (price went below SL)
+            sl_hit = low <= stop_loss
+            # Check TP hit (price went above target)
+            tp_hit = high >= target
+        
+        if sl_hit and tp_hit:
+            # Both hit in same candle — assume SL hit first (conservative)
+            result['result'] = 'stop_loss'
+            result['exit_price'] = stop_loss
+            result['candles_to_exit'] = i + 1
+            result['exit_time'] = candle['open_time']
+            break
+        elif tp_hit:
+            result['result'] = 'target_hit'
+            result['exit_price'] = target
+            result['candles_to_exit'] = i + 1
+            result['exit_time'] = candle['open_time']
+            break
+        elif sl_hit:
+            result['result'] = 'stop_loss'
+            result['exit_price'] = stop_loss
+            result['candles_to_exit'] = i + 1
+            result['exit_time'] = candle['open_time']
+            break
+    else:
+        # Never resolved — check current unrealized P&L
+        if klines:
+            last_price = klines[-1]['close']
+            if direction == 'short':
+                unrealized_pct = (entry - last_price) / entry * 100
+            else:
+                unrealized_pct = (last_price - entry) / entry * 100
+            result['result'] = 'open'
+            result['last_price'] = last_price
+            result['unrealized_pct'] = round(unrealized_pct, 2)
+            result['unrealized_pct_leveraged'] = round(unrealized_pct * leverage, 2)
+        else:
+            result['result'] = 'no_data'
+    
+    # Calculate P&L
+    if result['result'] in ('target_hit', 'stop_loss'):
+        exit_price = result['exit_price']
+        if direction == 'short':
+            pnl_pct = (entry - exit_price) / entry * 100
+        else:
+            pnl_pct = (exit_price - entry) / entry * 100
+        
+        result['pnl_pct'] = round(pnl_pct, 2)
+        result['pnl_pct_leveraged'] = round(pnl_pct * leverage, 2)
+    
+    return result
+
+
+def backtest_signals(signals, interval='5m', lookforward_hours=72):
+    """Backtest a list of parsed signals."""
+    results = []
+    
+    for i, signal in enumerate(signals):
+        ticker = signal['ticker']
+        symbol = normalize_symbol(ticker)
+        timestamp = signal.get('timestamp', '')
+        
+        print(f"[{i+1}/{len(signals)}] {ticker} {signal['direction']} ...", end=' ', flush=True)
+        
+        # Get start time
+        start_ms = datetime_to_ms(timestamp) if timestamp else int(time.time() * 1000)
+        end_ms = start_ms + (lookforward_hours * 60 * 60 * 1000)
+        
+        # Cap at current time
+        now_ms = int(time.time() * 1000)
+        if end_ms > now_ms:
+            end_ms = now_ms
+        
+        # Fetch candles
+        klines = get_all_klines(symbol, interval, start_ms, end_ms)
+        
+        if not klines:
+            print(f"NO DATA")
+            results.append({**signal, 'backtest': {'result': 'no_data', 'reason': f'no klines for {symbol}'}})
+            continue
+        
+        # Simulate
+        outcome = simulate_signal(signal, klines)
+        print(f"{outcome['result']} | PnL: {outcome.get('pnl_pct_leveraged', outcome.get('unrealized_pct_leveraged', '?'))}%")
+        
+        results.append({**signal, 'backtest': outcome})
+        time.sleep(0.2)  # Rate limit
+    
+    return results
+
+
+def generate_report(results):
+    """Generate a summary report from backtest results."""
+    total = len(results)
+    wins = [r for r in results if r['backtest'].get('result') == 'target_hit']
+    losses = [r for r in results if r['backtest'].get('result') == 'stop_loss']
+    open_trades = [r for r in results if r['backtest'].get('result') == 'open']
+    no_data = [r for r in results if r['backtest'].get('result') in ('no_data', 'incomplete')]
+    
+    resolved = wins + losses
+    win_rate = len(wins) / len(resolved) * 100 if resolved else 0
+    
+    avg_win = sum(r['backtest']['pnl_pct_leveraged'] for r in wins) / len(wins) if wins else 0
+    avg_loss = sum(r['backtest']['pnl_pct_leveraged'] for r in losses) / len(losses) if losses else 0
+    
+    total_pnl = sum(r['backtest'].get('pnl_pct_leveraged', 0) for r in resolved)
+    
+    # Profit factor
+    gross_profit = sum(r['backtest']['pnl_pct_leveraged'] for r in wins)
+    gross_loss = abs(sum(r['backtest']['pnl_pct_leveraged'] for r in losses))
+    profit_factor = gross_profit / gross_loss if gross_loss > 0 else float('inf')
+    
+    # Risk/reward stats
+    avg_rr = sum(r['backtest'].get('rr_ratio', 0) for r in resolved) / len(resolved) if resolved else 0
+    
+    report = {
+        'summary': {
+            'total_signals': total,
+            'wins': len(wins),
+            'losses': len(losses),
+            'open': len(open_trades),
+            'no_data': len(no_data),
+            'win_rate': round(win_rate, 1),
+            'avg_win_pct': round(avg_win, 2),
+            'avg_loss_pct': round(avg_loss, 2),
+            'total_pnl_pct': round(total_pnl, 2),
+            'profit_factor': round(profit_factor, 2),
+            'avg_risk_reward': round(avg_rr, 2),
+        },
+        'trades': results,
+    }
+    
+    return report
+
+
+def print_report(report):
+    """Pretty print the report."""
+    s = report['summary']
+    print("\n" + "=" * 60)
+    print("CRYPTO SIGNAL BACKTEST REPORT")
+    print("=" * 60)
+    print(f"Total Signals:    {s['total_signals']}")
+    print(f"Wins:             {s['wins']}")
+    print(f"Losses:           {s['losses']}")
+    print(f"Open:             {s['open']}")
+    print(f"No Data:          {s['no_data']}")
+    print(f"Win Rate:         {s['win_rate']}%")
+    print(f"Avg Win:          +{s['avg_win_pct']}% (leveraged)")
+    print(f"Avg Loss:         {s['avg_loss_pct']}% (leveraged)")
+    print(f"Total P&L:        {s['total_pnl_pct']}% (sum of resolved)")
+    print(f"Profit Factor:    {s['profit_factor']}")
+    print(f"Avg R:R:          {s['avg_risk_reward']}")
+    print("=" * 60)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print("Usage: python3 backtester.py <signals.json> [--interval 5m] [--hours 72]")
+        print("\nRun signal_parser.py first to generate signals.json")
+        sys.exit(1)
+    
+    signals_path = sys.argv[1]
+    
+    interval = '5m'
+    hours = 72
+    for i, arg in enumerate(sys.argv):
+        if arg == '--interval' and i + 1 < len(sys.argv):
+            interval = sys.argv[i + 1]
+        if arg == '--hours' and i + 1 < len(sys.argv):
+            hours = int(sys.argv[i + 1])
+    
+    with open(signals_path) as f:
+        signals = json.load(f)
+    
+    print(f"Backtesting {len(signals)} signals (interval={interval}, lookforward={hours}h)\n")
+    
+    results = backtest_signals(signals, interval=interval, lookforward_hours=hours)
+    report = generate_report(results)
+    print_report(report)
+    
+    # Save full report
+    out_path = signals_path.replace('.json', '_backtest.json')
+    with open(out_path, 'w') as f:
+        json.dump(report, f, indent=2)
+    print(f"\nFull report saved to {out_path}")

projects/crypto-signals/scripts/leverage_trader.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+"""
+Automated Leverage Trader
+Runs short scanner + spot scanner, opens positions in the Leverage Challenge game,
+manages exits (TP/SL/trailing stop), and reports via Telegram.
+
+Zero AI tokens — systemd timer.
+"""
+
+import json
+import os
+import sys
+import time
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+# Add parent to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from leverage_game import (
+    ensure_default_game, get_game, get_portfolio, open_position,
+    close_position, update_prices, get_trades, get_leaderboard
+)
+from scripts.short_scanner import scan_coin, COINS as SHORT_COINS
+
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+DATA_DIR = Path(__file__).parent.parent / "data" / "leverage-game"
+STATE_FILE = DATA_DIR / "trader_state.json"
+
+# Trading params
+MARGIN_PER_TRADE = 200       # $200 margin per position
+DEFAULT_LEVERAGE = 10         # 10x default
+MAX_OPEN_POSITIONS = 10       # Max simultaneous positions
+SHORT_SCORE_THRESHOLD = 50    # Min score to open short
+LONG_SCORE_THRESHOLD = 45     # Min score to open long
+TP_PCT = 5.0                  # Take profit at 5% on margin (50% on notional at 10x)
+SL_PCT = -3.0                 # Stop loss at -3% on margin (30% on notional at 10x)
+TRAILING_STOP_PCT = 2.0       # Trailing stop: close if drops 2% from peak
+
+
+def load_state():
+    if STATE_FILE.exists():
+        return json.loads(STATE_FILE.read_text())
+    return {"peak_pnl": {}, "last_alert": None}
+
+def save_state(state):
+    STATE_FILE.parent.mkdir(parents=True, exist_ok=True)
+    STATE_FILE.write_text(json.dumps(state, indent=2))
+
+
+def send_telegram(message):
+    if not TELEGRAM_BOT_TOKEN:
+        print(f"[TG] {message}")
+        return
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({
+        "chat_id": TELEGRAM_CHAT_ID,
+        "text": message,
+        "parse_mode": "HTML"
+    }).encode()
+    req = urllib.request.Request(url, data=data, headers={
+        "Content-Type": "application/json", "User-Agent": "Mozilla/5.0"
+    })
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"Telegram failed: {e}")
+
+
+def run_short_scan():
+    """Run short scanner on all coins."""
+    results = []
+    for symbol in SHORT_COINS:
+        r = scan_coin(symbol)
+        if r:
+            r["direction"] = "short"
+            results.append(r)
+        time.sleep(0.15)
+    return sorted(results, key=lambda x: x['score'], reverse=True)
+
+
+def run_spot_scan():
+    """Run spot/long scanner (inverse of short criteria — oversold = buy)."""
+    results = []
+    for symbol in SHORT_COINS:
+        r = scan_coin(symbol)
+        if r:
+            # Invert: low RSI + below VWAP = long opportunity
+            long_score = 0
+            reasons = []
+            
+            if r['rsi'] <= 25:
+                long_score += 30
+                reasons.append(f"RSI extremely oversold ({r['rsi']})")
+            elif r['rsi'] <= 30:
+                long_score += 25
+                reasons.append(f"RSI oversold ({r['rsi']})")
+            elif r['rsi'] <= 35:
+                long_score += 15
+                reasons.append(f"RSI low ({r['rsi']})")
+            elif r['rsi'] <= 40:
+                long_score += 5
+                reasons.append(f"RSI mildly low ({r['rsi']})")
+            
+            if r['vwap_pct'] < -5:
+                long_score += 20
+                reasons.append(f"Well below VWAP ({r['vwap_pct']:+.1f}%)")
+            elif r['vwap_pct'] < -3:
+                long_score += 15
+                reasons.append(f"Below VWAP ({r['vwap_pct']:+.1f}%)")
+            elif r['vwap_pct'] < -1:
+                long_score += 8
+                reasons.append(f"Slightly below VWAP ({r['vwap_pct']:+.1f}%)")
+            
+            if r['change_24h'] < -15:
+                long_score += 15
+                reasons.append(f"Dumped {r['change_24h']:.1f}% 24h")
+            elif r['change_24h'] < -8:
+                long_score += 10
+                reasons.append(f"Down {r['change_24h']:.1f}% 24h")
+            elif r['change_24h'] < -4:
+                long_score += 5
+                reasons.append(f"Down {r['change_24h']:.1f}% 24h")
+            
+            if r['bb_position'] < 0:
+                long_score += 15
+                reasons.append(f"Below lower Bollinger ({r['bb_position']:.2f})")
+            elif r['bb_position'] < 0.15:
+                long_score += 10
+                reasons.append(f"Near lower Bollinger ({r['bb_position']:.2f})")
+            
+            results.append({
+                "symbol": r["symbol"],
+                "price": r["price"],
+                "rsi": r["rsi"],
+                "vwap_pct": r["vwap_pct"],
+                "change_24h": r["change_24h"],
+                "bb_position": r["bb_position"],
+                "score": long_score,
+                "reasons": reasons,
+                "direction": "long",
+            })
+        time.sleep(0.15)
+    return sorted(results, key=lambda x: x['score'], reverse=True)
+
+
+def manage_exits(game_id, username, state):
+    """Check open positions for TP/SL/trailing stop exits."""
+    pf = get_portfolio(game_id, username)
+    if not pf:
+        return []
+    
+    exits = []
+    for pos_id, pos in list(pf["positions"].items()):
+        pnl_pct = (pos.get("unrealized_pnl", 0) / pos["margin_usd"] * 100) if pos["margin_usd"] > 0 else 0
+        
+        # Track peak PnL for trailing stop
+        peak_key = pos_id
+        if peak_key not in state.get("peak_pnl", {}):
+            state["peak_pnl"][peak_key] = pnl_pct
+        if pnl_pct > state["peak_pnl"].get(peak_key, 0):
+            state["peak_pnl"][peak_key] = pnl_pct
+        
+        peak = state["peak_pnl"].get(peak_key, 0)
+        reason = None
+        
+        # Take profit
+        if pnl_pct >= TP_PCT:
+            reason = f"TP hit ({pnl_pct:+.1f}%)"
+        # Stop loss
+        elif pnl_pct <= SL_PCT:
+            reason = f"SL hit ({pnl_pct:+.1f}%)"
+        # Trailing stop (only if we were profitable)
+        elif peak >= 2.0 and (peak - pnl_pct) >= TRAILING_STOP_PCT:
+            reason = f"Trailing stop (peak {peak:+.1f}%, now {pnl_pct:+.1f}%)"
+        
+        if reason:
+            result = close_position(game_id, username, pos_id, reason=reason)
+            if result.get("success"):
+                exits.append(result)
+                # Clean up peak tracking
+                state["peak_pnl"].pop(peak_key, None)
+    
+    return exits
+
+
+def main():
+    game_id = ensure_default_game()
+    state = load_state()
+    
+    print(f"=== Leverage Trader ===")
+    print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    print(f"Game: {game_id}")
+    
+    # 1. Update prices and check liquidations
+    liquidations = update_prices(game_id, "case")
+    for liq in liquidations:
+        msg = f"💀 <b>LIQUIDATED</b>: {liq['symbol']} {liq['direction']} {liq.get('leverage', '?')}x | Lost ${abs(liq.get('pnl', 0)):.2f}"
+        send_telegram(msg)
+        print(msg)
+    
+    # 2. Manage exits (TP/SL/trailing)
+    exits = manage_exits(game_id, "case", state)
+    for ex in exits:
+        emoji = "✅" if ex.get("pnl", 0) > 0 else "❌"
+        msg = (f"{emoji} <b>Closed</b>: {ex['symbol']} {ex['direction']} | "
+               f"Entry: ${ex['entry_price']:.4f} → Exit: ${ex['exit_price']:.4f} | "
+               f"PnL: ${ex['pnl']:+.2f} ({ex['pnl_pct']:+.1f}%)")
+        print(msg)
+    
+    # 3. Get current portfolio
+    pf = get_portfolio(game_id, "case")
+    num_open = pf["num_positions"] if pf else 0
+    slots = MAX_OPEN_POSITIONS - num_open
+    
+    print(f"\nPortfolio: ${pf['equity']:,.2f} ({pf['pnl_pct']:+.2f}%) | {num_open} positions | {slots} slots open")
+    
+    # 4. Scan for new opportunities
+    if slots > 0:
+        # Run both scanners
+        shorts = run_short_scan()
+        longs = run_spot_scan()
+        
+        # Get existing symbols to avoid doubling up
+        existing_symbols = set()
+        if pf:
+            for pos in pf["positions"].values():
+                existing_symbols.add(pos["symbol"])
+        
+        opened = []
+        
+        # Open short positions
+        for r in shorts:
+            if slots <= 0:
+                break
+            if r["score"] < SHORT_SCORE_THRESHOLD:
+                break
+            if r["symbol"] in existing_symbols:
+                continue
+            
+            lev = 15 if r["score"] >= 70 else 10 if r["score"] >= 60 else 7
+            result = open_position(game_id, "case", r["symbol"], "short", MARGIN_PER_TRADE, lev,
+                                  reason=f"Short scanner score:{r['score']}")
+            if result.get("success"):
+                opened.append(result)
+                existing_symbols.add(r["symbol"])
+                slots -= 1
+                time.sleep(0.2)
+        
+        # Open long positions
+        for r in longs:
+            if slots <= 0:
+                break
+            if r["score"] < LONG_SCORE_THRESHOLD:
+                break
+            if r["symbol"] in existing_symbols:
+                continue
+            
+            lev = 15 if r["score"] >= 70 else 10 if r["score"] >= 60 else 7
+            result = open_position(game_id, "case", r["symbol"], "long", MARGIN_PER_TRADE, lev,
+                                  reason=f"Long scanner score:{r['score']}")
+            if result.get("success"):
+                opened.append(result)
+                existing_symbols.add(r["symbol"])
+                slots -= 1
+                time.sleep(0.2)
+        
+        if opened:
+            lines = [f"📊 <b>Opened {len(opened)} positions</b>\n"]
+            for o in opened:
+                lines.append(f"{'🔴' if o['direction']=='short' else '🟢'} {o['symbol']} {o['direction']} {o['leverage']}x @ ${o['entry_price']:.4f} (${o['margin']:.0f} margin)")
+            send_telegram("\n".join(lines))
+            print(f"\nOpened {len(opened)} new positions")
+    
+    # 5. Send periodic summary (every 4 hours)
+    if exits or liquidations:
+        pf = get_portfolio(game_id, "case")  # Refresh
+        msg = (f"📈 <b>Leverage Challenge Update</b>\n"
+               f"Equity: ${pf['equity']:,.2f} ({pf['pnl_pct']:+.2f}%)\n"
+               f"Positions: {pf['num_positions']} | Cash: ${pf['cash']:,.2f}\n"
+               f"Realized PnL: ${pf['realized_pnl']:+,.2f} | Fees: ${pf['total_fees']:,.2f}")
+        send_telegram(msg)
+    
+    save_state(state)
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()

projects/crypto-signals/scripts/polymarket_arb_scanner.py

@@ -0,0 +1,311 @@
+#!/usr/bin/env python3
+"""
+Polymarket 15-Min Crypto Arbitrage Scanner
+Scans active 15-minute crypto markets for arbitrage opportunities.
+Alerts via Telegram when combined Up+Down cost < $1.00 (after fees).
+
+Zero AI tokens — runs as pure Python via systemd timer.
+"""
+
+import json
+import os
+import sys
+import time
+import urllib.request
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+
+# Config
+DATA_DIR = Path(__file__).parent.parent / "data" / "arb-scanner"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+LOG_FILE = DATA_DIR / "scan_log.json"
+PAPER_TRADES_FILE = DATA_DIR / "paper_trades.json"
+
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+# Polymarket fee formula for 15-min markets
+def calc_taker_fee(shares, price):
+    """Calculate taker fee in USDC."""
+    if price <= 0 or price >= 1:
+        return 0
+    return shares * price * 0.25 * (price * (1 - price)) ** 2
+
+def calc_fee_rate(price):
+    """Effective fee rate at a given price."""
+    if price <= 0 or price >= 1:
+        return 0
+    return 0.25 * (price * (1 - price)) ** 2
+
+
+def get_active_15min_markets():
+    """Fetch active 15-minute crypto markets from Polymarket."""
+    markets = []
+    
+    # 15-min markets are scattered across pagination — scan broadly
+    for offset in range(0, 3000, 200):
+        url = (
+            f"https://gamma-api.polymarket.com/markets?"
+            f"active=true&closed=false&limit=200&offset={offset}"
+            f"&order=volume&ascending=false"
+        )
+        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+        try:
+            resp = urllib.request.urlopen(req, timeout=15)
+            batch = json.loads(resp.read())
+            for m in batch:
+                q = m.get("question", "").lower()
+                if "up or down" in q:
+                    markets.append(m)
+            if len(batch) < 200:
+                break
+        except Exception as e:
+            print(f"Error fetching markets (offset={offset}): {e}")
+            break
+        time.sleep(0.1)
+    
+    # Only keep markets ending within the next 4 hours (tradeable window)
+    now = datetime.now(timezone.utc)
+    tradeable = []
+    for m in markets:
+        end_str = m.get("endDate", "")
+        if not end_str:
+            continue
+        try:
+            end_dt = datetime.fromisoformat(end_str.replace("Z", "+00:00"))
+            hours_until = (end_dt - now).total_seconds() / 3600
+            if 0.25 < hours_until <= 24:  # Skip markets < 15min to expiry (already resolved)
+                m["_hours_until_end"] = round(hours_until, 2)
+                tradeable.append(m)
+        except:
+            pass
+    
+    # Deduplicate
+    seen = set()
+    unique = []
+    for m in tradeable:
+        cid = m.get("conditionId", m.get("id", ""))
+        if cid not in seen:
+            seen.add(cid)
+            unique.append(m)
+    
+    return unique
+
+
+def get_orderbook_prices(token_id):
+    """Get best bid/ask from the CLOB API."""
+    url = f"https://clob.polymarket.com/book?token_id={token_id}"
+    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        book = json.loads(resp.read())
+        bids = book.get("bids", [])
+        asks = book.get("asks", [])
+        best_bid = float(bids[0]["price"]) if bids else 0
+        best_ask = float(asks[0]["price"]) if asks else 1
+        bid_size = float(bids[0].get("size", 0)) if bids else 0
+        ask_size = float(asks[0].get("size", 0)) if asks else 0
+        return {
+            "best_bid": best_bid,
+            "best_ask": best_ask, 
+            "bid_size": bid_size,
+            "ask_size": ask_size,
+            "spread": best_ask - best_bid
+        }
+    except Exception as e:
+        return None
+
+
+def scan_for_arbs():
+    """Scan all active 15-min markets for arbitrage opportunities."""
+    markets = get_active_15min_markets()
+    print(f"Found {len(markets)} active 15-min crypto markets")
+    
+    opportunities = []
+    
+    for market in markets:
+        question = market.get("question", market.get("title", ""))
+        hours_left = market.get("_hours_until_end", "?")
+        
+        # Get token IDs for both outcomes
+        tokens = market.get("clobTokenIds", "")
+        if isinstance(tokens, str):
+            try:
+                tokens = json.loads(tokens) if tokens.startswith("[") else tokens.split(",")
+            except:
+                tokens = []
+        
+        if len(tokens) < 2:
+            continue
+        
+        # Get orderbook for both tokens (ask = price to buy)
+        book_up = get_orderbook_prices(tokens[0])
+        book_down = get_orderbook_prices(tokens[1])
+        time.sleep(0.15)
+        
+        if not book_up or not book_down:
+            continue
+        
+        # For arb: we BUY both sides at the ASK price
+        up_ask = book_up["best_ask"]
+        down_ask = book_down["best_ask"]
+        combined = up_ask + down_ask
+        
+        # Calculate fees on 100 shares
+        fee_up = calc_taker_fee(100, up_ask)
+        fee_down = calc_taker_fee(100, down_ask)
+        total_cost_100 = (up_ask + down_ask) * 100 + fee_up + fee_down
+        net_profit_100 = 100 - total_cost_100
+        net_profit_pct = net_profit_100 / total_cost_100 * 100 if total_cost_100 > 0 else 0
+        
+        # Fillable size (limited by smaller side)
+        fillable_size = min(book_up["ask_size"], book_down["ask_size"])
+        if fillable_size > 0:
+            fill_fee_up = calc_taker_fee(fillable_size, up_ask)
+            fill_fee_down = calc_taker_fee(fillable_size, down_ask)
+            fill_cost = (up_ask + down_ask) * fillable_size + fill_fee_up + fill_fee_down
+            fill_profit = fillable_size - fill_cost
+        else:
+            fill_profit = 0
+        
+        opp = {
+            "question": question,
+            "hours_left": hours_left,
+            "up_ask": up_ask,
+            "down_ask": down_ask,
+            "up_ask_size": book_up["ask_size"],
+            "down_ask_size": book_down["ask_size"],
+            "combined": round(combined, 4),
+            "fee_up_per_100": round(fee_up, 4),
+            "fee_down_per_100": round(fee_down, 4),
+            "total_fees_per_100": round(fee_up + fee_down, 4),
+            "net_profit_per_100": round(net_profit_100, 2),
+            "net_profit_pct": round(net_profit_pct, 2),
+            "fillable_shares": fillable_size,
+            "fillable_profit": round(fill_profit, 2),
+            "is_arb": net_profit_100 > 0,
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+        }
+        opportunities.append(opp)
+    
+    return opportunities
+
+
+def paper_trade(opp):
+    """Record a paper trade for an arb opportunity."""
+    trades = []
+    if PAPER_TRADES_FILE.exists():
+        trades = json.loads(PAPER_TRADES_FILE.read_text())
+    
+    trade = {
+        "id": len(trades) + 1,
+        "timestamp": opp["timestamp"],
+        "question": opp["question"],
+        "up_price": opp.get("up_ask", opp.get("up_price", 0)),
+        "down_price": opp.get("down_ask", opp.get("down_price", 0)),
+        "combined": opp["combined"],
+        "fees_per_100": opp["total_fees_per_100"],
+        "net_profit_per_100": opp["net_profit_per_100"],
+        "net_profit_pct": opp["net_profit_pct"],
+        "status": "open",  # Will be "won" when market resolves (always wins if real arb)
+        "paper_size_usd": 50,  # Paper trade $50 per arb
+    }
+    
+    expected_profit = 50 * opp["net_profit_pct"] / 100
+    trade["expected_profit_usd"] = round(expected_profit, 2)
+    
+    trades.append(trade)
+    PAPER_TRADES_FILE.write_text(json.dumps(trades, indent=2))
+    return trade
+
+
+def send_telegram_alert(message):
+    """Send alert via Telegram bot API (zero tokens)."""
+    if not TELEGRAM_BOT_TOKEN:
+        print(f"[ALERT] {message}")
+        return
+    
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({
+        "chat_id": TELEGRAM_CHAT_ID,
+        "text": message,
+        "parse_mode": "HTML"
+    }).encode()
+    req = urllib.request.Request(url, data=data, headers={
+        "Content-Type": "application/json",
+        "User-Agent": "Mozilla/5.0"
+    })
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"Telegram alert failed: {e}")
+
+
+def main():
+    print(f"=== Polymarket 15-Min Arb Scanner ===")
+    print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    print()
+    
+    opps = scan_for_arbs()
+    
+    arbs = [o for o in opps if o["is_arb"]]
+    non_arbs = [o for o in opps if not o["is_arb"]]
+    
+    print(f"\nResults: {len(opps)} markets scanned, {len(arbs)} arb opportunities\n")
+    
+    for o in sorted(opps, key=lambda x: x.get("net_profit_pct", 0), reverse=True):
+        emoji = "✅" if o["is_arb"] else "❌"
+        print(f"{emoji} {o['question'][:65]}")
+        up = o.get('up_ask', o.get('up_price', '?'))
+        down = o.get('down_ask', o.get('down_price', '?'))
+        print(f"   Up: ${up} | Down: ${down} | Combined: ${o['combined']}")
+        print(f"   Fees/100: ${o['total_fees_per_100']} | Net profit/100: ${o['net_profit_per_100']} ({o['net_profit_pct']}%)")
+        if o.get('fillable_shares'):
+            print(f"   Fillable: {o['fillable_shares']:.0f} shares | Fillable profit: ${o.get('fillable_profit', '?')}")
+        print()
+    
+    # Paper trade any arbs found
+    for arb in arbs:
+        trade = paper_trade(arb)
+        print(f"📝 Paper trade #{trade['id']}: {trade['question'][:50]} | Expected: +${trade['expected_profit_usd']}")
+        
+        # Send Telegram alert
+        msg = (
+            f"🔔 <b>Arb Found!</b>\n\n"
+            f"<b>{arb['question']}</b>\n"
+            f"Up: ${arb.get('up_ask', arb.get('up_price', '?'))} | "
+            f"Down: ${arb.get('down_ask', arb.get('down_price', '?'))}\n"
+            f"Combined: ${arb['combined']} (after fees)\n"
+            f"Net profit: {arb['net_profit_pct']}%\n\n"
+            f"📝 Paper traded $50 → expected +${trade['expected_profit_usd']}"
+        )
+        send_telegram_alert(msg)
+    
+    # Save scan log
+    log = []
+    if LOG_FILE.exists():
+        try:
+            log = json.loads(LOG_FILE.read_text())
+        except:
+            pass
+    
+    log.append({
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "markets_scanned": len(opps),
+        "arbs_found": len(arbs),
+        "opportunities": opps,
+    })
+    
+    # Keep last 1000 scans
+    log = log[-1000:]
+    LOG_FILE.write_text(json.dumps(log, indent=2))
+    
+    # Summary of paper trades
+    if PAPER_TRADES_FILE.exists():
+        trades = json.loads(PAPER_TRADES_FILE.read_text())
+        total_expected = sum(t.get("expected_profit_usd", 0) for t in trades)
+        print(f"\n📊 Paper trade total: {len(trades)} trades, expected profit: ${total_expected:.2f}")
+
+
+if __name__ == "__main__":
+    main()

projects/crypto-signals/scripts/price_fetcher.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Crypto Price Fetcher
+Pulls historical OHLCV data from Binance public API (no key needed).
+"""
+
+import json
+import time
+import urllib.request
+from datetime import datetime, timezone
+
+
+# Binance intl is geo-blocked from US; use Binance US
+BINANCE_KLINES = "https://api.binance.us/api/v3/klines"
+BINANCE_TICKER = "https://api.binance.us/api/v3/ticker/price"
+
+
+def get_price_at_time(symbol, timestamp_ms, interval='1m'):
+    """Get the candle at a specific timestamp."""
+    url = f"{BINANCE_KLINES}?symbol={symbol}&interval={interval}&startTime={timestamp_ms}&limit=1"
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        data = json.loads(resp.read())
+        if data:
+            return {
+                'open_time': data[0][0],
+                'open': float(data[0][1]),
+                'high': float(data[0][2]),
+                'low': float(data[0][3]),
+                'close': float(data[0][4]),
+                'volume': float(data[0][5]),
+            }
+    except Exception as e:
+        print(f"Error fetching {symbol}: {e}")
+    return None
+
+
+def get_klines(symbol, interval='1h', start_time_ms=None, end_time_ms=None, limit=1000):
+    """Get historical klines/candlestick data."""
+    params = f"symbol={symbol}&interval={interval}&limit={limit}"
+    if start_time_ms:
+        params += f"&startTime={start_time_ms}"
+    if end_time_ms:
+        params += f"&endTime={end_time_ms}"
+    
+    url = f"{BINANCE_KLINES}?{params}"
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+    
+    try:
+        resp = urllib.request.urlopen(req, timeout=15)
+        raw = json.loads(resp.read())
+        return [{
+            'open_time': k[0],
+            'open': float(k[1]),
+            'high': float(k[2]),
+            'low': float(k[3]),
+            'close': float(k[4]),
+            'volume': float(k[5]),
+            'close_time': k[6],
+        } for k in raw]
+    except Exception as e:
+        print(f"Error fetching klines for {symbol}: {e}")
+        return []
+
+
+def get_all_klines(symbol, interval, start_time_ms, end_time_ms):
+    """Paginate through all klines between two timestamps."""
+    all_klines = []
+    current_start = start_time_ms
+    
+    while current_start < end_time_ms:
+        batch = get_klines(symbol, interval, current_start, end_time_ms)
+        if not batch:
+            break
+        all_klines.extend(batch)
+        current_start = batch[-1]['close_time'] + 1
+        time.sleep(0.1)  # Rate limiting
+    
+    return all_klines
+
+
+def get_current_price(symbol):
+    """Get current price."""
+    url = f"{BINANCE_TICKER}?symbol={symbol}"
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        data = json.loads(resp.read())
+        return float(data['price'])
+    except Exception as e:
+        print(f"Error fetching current price for {symbol}: {e}")
+        return None
+
+
+def normalize_symbol(ticker):
+    """Convert signal ticker to Binance symbol format."""
+    # Remove USDT suffix if present, then add it back
+    ticker = ticker.upper().replace('USDT', '').replace('/', '')
+    return f"{ticker}USDT"
+
+
+def datetime_to_ms(dt_str):
+    """Convert datetime string to milliseconds timestamp."""
+    # Handle various formats
+    for fmt in ['%Y-%m-%dT%H:%M:%S', '%Y-%m-%d %H:%M:%S', '%Y-%m-%d']:
+        try:
+            dt = datetime.strptime(dt_str, fmt).replace(tzinfo=timezone.utc)
+            return int(dt.timestamp() * 1000)
+        except ValueError:
+            continue
+    return None
+
+
+if __name__ == '__main__':
+    # Test with current signals
+    for ticker in ['ASTERUSDT', 'HYPEUSDT']:
+        symbol = normalize_symbol(ticker)
+        price = get_current_price(symbol)
+        print(f"{symbol}: ${price}")
+        
+        # Get last 24h of 1h candles
+        now_ms = int(time.time() * 1000)
+        day_ago = now_ms - (24 * 60 * 60 * 1000)
+        klines = get_klines(symbol, '1h', day_ago, now_ms)
+        if klines:
+            highs = [k['high'] for k in klines]
+            lows = [k['low'] for k in klines]
+            print(f"  24h range: ${min(lows):.4f} - ${max(highs):.4f}")
+            print(f"  Candles: {len(klines)}")
+        print()

projects/crypto-signals/scripts/short_scanner.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python3
+"""
+Crypto Short Signal Scanner
+Scans for overbought coins ripe for shorting.
+Criteria: high RSI, price above VWAP, fading momentum, bearish divergence.
+
+Zero AI tokens — runs as pure Python via systemd timer.
+"""
+
+import json
+import os
+import sys
+import time
+import math
+import urllib.request
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+
+# Config
+DATA_DIR = Path(__file__).parent.parent / "data" / "short-scanner"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+SCAN_LOG = DATA_DIR / "scan_log.json"
+
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+BINANCE_KLINES = "https://api.binance.us/api/v3/klines"
+BINANCE_TICKER = "https://api.binance.us/api/v3/ticker/24hr"
+
+# Coins to scan (popular leveraged trading coins)
+COINS = [
+    "BTCUSDT", "ETHUSDT", "SOLUSDT", "XRPUSDT", "DOGEUSDT",
+    "ADAUSDT", "AVAXUSDT", "LINKUSDT", "DOTUSDT", "MATICUSDT",
+    "NEARUSDT", "ATOMUSDT", "LTCUSDT", "UNIUSDT", "AAVEUSDT",
+    "FILUSDT", "ALGOUSDT", "XLMUSDT", "VETUSDT", "ICPUSDT",
+    "APTUSDT", "SUIUSDT", "ARBUSDT", "OPUSDT", "SEIUSDT",
+    "HYPEUSDT", "TRUMPUSDT", "PUMPUSDT", "ASTERUSDT",
+]
+
+
+def get_klines(symbol, interval='1h', limit=100):
+    """Fetch klines from Binance US."""
+    url = f"{BINANCE_KLINES}?symbol={symbol}&interval={interval}&limit={limit}"
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        raw = json.loads(resp.read())
+        return [{
+            'open': float(k[1]),
+            'high': float(k[2]),
+            'low': float(k[3]),
+            'close': float(k[4]),
+            'volume': float(k[5]),
+            'close_time': k[6],
+        } for k in raw]
+    except:
+        return []
+
+
+def calc_rsi(closes, period=14):
+    """Calculate RSI."""
+    if len(closes) < period + 1:
+        return 50
+    deltas = [closes[i] - closes[i-1] for i in range(1, len(closes))]
+    gains = [d if d > 0 else 0 for d in deltas]
+    losses = [-d if d < 0 else 0 for d in deltas]
+    
+    avg_gain = sum(gains[:period]) / period
+    avg_loss = sum(losses[:period]) / period
+    
+    for i in range(period, len(deltas)):
+        avg_gain = (avg_gain * (period - 1) + gains[i]) / period
+        avg_loss = (avg_loss * (period - 1) + losses[i]) / period
+    
+    if avg_loss == 0:
+        return 100
+    rs = avg_gain / avg_loss
+    return round(100 - (100 / (1 + rs)), 1)
+
+
+def calc_vwap(klines):
+    """Calculate VWAP from klines."""
+    cum_vol = 0
+    cum_tp_vol = 0
+    for k in klines:
+        tp = (k['high'] + k['low'] + k['close']) / 3
+        cum_vol += k['volume']
+        cum_tp_vol += tp * k['volume']
+    if cum_vol == 0:
+        return 0
+    return cum_tp_vol / cum_vol
+
+
+def calc_ema(values, period):
+    """Calculate EMA."""
+    if not values:
+        return 0
+    multiplier = 2 / (period + 1)
+    ema = values[0]
+    for v in values[1:]:
+        ema = (v - ema) * multiplier + ema
+    return ema
+
+
+def calc_macd(closes):
+    """Calculate MACD (12, 26, 9)."""
+    if len(closes) < 26:
+        return 0, 0, 0
+    ema12 = calc_ema(closes, 12)
+    ema26 = calc_ema(closes, 26)
+    macd_line = ema12 - ema26
+    # Approximate signal line
+    signal = calc_ema(closes[-9:], 9) if len(closes) >= 9 else macd_line
+    histogram = macd_line - signal
+    return macd_line, signal, histogram
+
+
+def calc_bollinger_position(closes, period=20):
+    """How far price is from upper Bollinger band. >1 = above upper band."""
+    if len(closes) < period:
+        return 0.5
+    recent = closes[-period:]
+    sma = sum(recent) / period
+    std = (sum((x - sma)**2 for x in recent) / period) ** 0.5
+    if std == 0:
+        return 0.5
+    upper = sma + 2 * std
+    lower = sma - 2 * std
+    band_width = upper - lower
+    if band_width == 0:
+        return 0.5
+    return (closes[-1] - lower) / band_width
+
+
+def volume_trend(klines, lookback=10):
+    """Compare recent volume to average. >1 means increasing volume."""
+    if len(klines) < lookback * 2:
+        return 1.0
+    recent_vol = sum(k['volume'] for k in klines[-lookback:]) / lookback
+    older_vol = sum(k['volume'] for k in klines[-lookback*2:-lookback]) / lookback
+    if older_vol == 0:
+        return 1.0
+    return recent_vol / older_vol
+
+
+def scan_coin(symbol):
+    """Analyze a single coin for short signals."""
+    # Get 1h candles for RSI/VWAP/indicators
+    klines_1h = get_klines(symbol, '1h', 100)
+    if len(klines_1h) < 30:
+        return None
+    
+    closes = [k['close'] for k in klines_1h]
+    current_price = closes[-1]
+    
+    # RSI (14-period on 1h)
+    rsi = calc_rsi(closes)
+    
+    # VWAP (24h)
+    vwap_24h = calc_vwap(klines_1h[-24:])
+    vwap_pct = ((current_price - vwap_24h) / vwap_24h * 100) if vwap_24h else 0
+    
+    # MACD
+    macd_line, signal_line, histogram = calc_macd(closes)
+    macd_bearish = histogram < 0  # Below signal = bearish
+    
+    # Bollinger position
+    bb_pos = calc_bollinger_position(closes)
+    
+    # Volume trend
+    vol_trend = volume_trend(klines_1h)
+    
+    # 24h change
+    price_24h_ago = closes[-24] if len(closes) >= 24 else closes[0]
+    change_24h = ((current_price - price_24h_ago) / price_24h_ago * 100) if price_24h_ago else 0
+    
+    # 4h change (momentum)
+    price_4h_ago = closes[-4] if len(closes) >= 4 else closes[0]
+    change_4h = ((current_price - price_4h_ago) / price_4h_ago * 100) if price_4h_ago else 0
+    
+    # === SHORT SCORING ===
+    score = 0
+    reasons = []
+    
+    # RSI overbought (max 30 pts)
+    if rsi >= 80:
+        score += 30
+        reasons.append(f"RSI extremely overbought ({rsi})")
+    elif rsi >= 70:
+        score += 25
+        reasons.append(f"RSI overbought ({rsi})")
+    elif rsi >= 65:
+        score += 15
+        reasons.append(f"RSI elevated ({rsi})")
+    elif rsi >= 60:
+        score += 5
+        reasons.append(f"RSI mildly elevated ({rsi})")
+    
+    # Price above VWAP (max 20 pts)
+    if vwap_pct > 5:
+        score += 20
+        reasons.append(f"Well above VWAP (+{vwap_pct:.1f}%)")
+    elif vwap_pct > 3:
+        score += 15
+        reasons.append(f"Above VWAP (+{vwap_pct:.1f}%)")
+    elif vwap_pct > 1:
+        score += 8
+        reasons.append(f"Slightly above VWAP (+{vwap_pct:.1f}%)")
+    
+    # MACD bearish crossover (max 15 pts)
+    if macd_bearish and histogram < -0.001 * current_price:
+        score += 15
+        reasons.append("MACD bearish + accelerating")
+    elif macd_bearish:
+        score += 10
+        reasons.append("MACD bearish crossover")
+    
+    # Bollinger band position (max 15 pts)
+    if bb_pos > 1.0:
+        score += 15
+        reasons.append(f"Above upper Bollinger ({bb_pos:.2f})")
+    elif bb_pos > 0.85:
+        score += 10
+        reasons.append(f"Near upper Bollinger ({bb_pos:.2f})")
+    
+    # Big recent pump (mean reversion candidate) (max 15 pts)
+    if change_24h > 15:
+        score += 15
+        reasons.append(f"Pumped +{change_24h:.1f}% 24h")
+    elif change_24h > 8:
+        score += 10
+        reasons.append(f"Up +{change_24h:.1f}% 24h")
+    elif change_24h > 4:
+        score += 5
+        reasons.append(f"Up +{change_24h:.1f}% 24h")
+    
+    # Volume fading on uptrend (exhaustion) (5 pts)
+    if change_24h > 2 and vol_trend < 0.7:
+        score += 5
+        reasons.append("Volume fading on uptrend (exhaustion)")
+    
+    return {
+        "symbol": symbol.replace("USDT", ""),
+        "price": current_price,
+        "rsi": rsi,
+        "vwap_pct": round(vwap_pct, 2),
+        "macd_histogram": round(histogram, 6),
+        "bb_position": round(bb_pos, 2),
+        "change_24h": round(change_24h, 2),
+        "change_4h": round(change_4h, 2),
+        "vol_trend": round(vol_trend, 2),
+        "score": score,
+        "reasons": reasons,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+    }
+
+
+def send_telegram_alert(message):
+    """Send alert via Telegram bot API."""
+    if not TELEGRAM_BOT_TOKEN:
+        print(f"[ALERT] {message}")
+        return
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({
+        "chat_id": TELEGRAM_CHAT_ID,
+        "text": message,
+        "parse_mode": "HTML"
+    }).encode()
+    req = urllib.request.Request(url, data=data, headers={
+        "Content-Type": "application/json",
+        "User-Agent": "Mozilla/5.0"
+    })
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"Telegram alert failed: {e}")
+
+
+def main():
+    print(f"=== Crypto Short Signal Scanner ===")
+    print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    print()
+    
+    results = []
+    for symbol in COINS:
+        result = scan_coin(symbol)
+        if result:
+            results.append(result)
+        time.sleep(0.15)  # Rate limiting
+    
+    # Sort by score descending
+    results.sort(key=lambda x: x['score'], reverse=True)
+    
+    # Print all results
+    for r in results:
+        emoji = "🔴" if r['score'] >= 50 else "🟡" if r['score'] >= 30 else "⚪"
+        print(f"{emoji} {r['symbol']:8s} score:{r['score']:3d} | RSI:{r['rsi']:5.1f} | VWAP:{r['vwap_pct']:+6.1f}% | 24h:{r['change_24h']:+6.1f}% | BB:{r['bb_position']:.2f}")
+        if r['reasons']:
+            for reason in r['reasons']:
+                print(f"   → {reason}")
+    
+    # Alert on strong short signals (score >= 50)
+    strong = [r for r in results if r['score'] >= 50]
+    if strong:
+        lines = ["🔴 <b>Short Signals Detected</b>\n"]
+        for r in strong:
+            lines.append(f"<b>{r['symbol']}</b> (score: {r['score']})")
+            lines.append(f"  Price: ${r['price']:.4f} | RSI: {r['rsi']} | VWAP: {r['vwap_pct']:+.1f}%")
+            lines.append(f"  24h: {r['change_24h']:+.1f}% | BB: {r['bb_position']:.2f}")
+            for reason in r['reasons']:
+                lines.append(f"  → {reason}")
+            lines.append("")
+        send_telegram_alert("\n".join(lines))
+    
+    # Save scan log
+    log = []
+    if SCAN_LOG.exists():
+        try:
+            log = json.loads(SCAN_LOG.read_text())
+        except:
+            pass
+    
+    log.append({
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "coins_scanned": len(results),
+        "strong_signals": len(strong),
+        "results": results,
+    })
+    log = log[-500:]
+    SCAN_LOG.write_text(json.dumps(log, indent=2))
+    
+    print(f"\n📊 Summary: {len(results)} scanned, {len(strong)} strong short signals")
+
+
+if __name__ == "__main__":
+    main()

projects/crypto-signals/scripts/signal_parser.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""
+Telegram Crypto Signal Parser
+Parses exported Telegram JSON chat history and extracts structured trading signals.
+"""
+
+import json
+import re
+import sys
+from datetime import datetime
+from pathlib import Path
+
+# Signal patterns - adapt as we see more formats
+PATTERNS = {
+    # #TICKER direction entry SL target leverage balance%
+    'standard': re.compile(
+        r'#(\w+)\s+'                           # ticker
+        r'(Long|Short)\s+'                      # direction
+        r'(?:market\s+entry!?|entry[:\s]+([0-9.]+))\s*'  # entry type/price
+        r'SL[;:\s]+([0-9.]+)\s*'               # stop loss
+        r'(?:Targets?|TP)[;:\s]+([0-9.,\s]+)\s*'  # targets (can be multiple)
+        r'(?:Lev(?:erage)?[:\s]*x?([0-9.]+))?\s*'  # leverage (optional)
+        r'(?:([0-9.]+)%?\s*balance)?',          # balance % (optional)
+        re.IGNORECASE
+    ),
+    # Simpler: #TICKER Short/Long entry SL targets
+    'simple': re.compile(
+        r'#(\w+)\s+(Long|Short)',
+        re.IGNORECASE
+    ),
+}
+
+
+def parse_signal_text(text):
+    """Parse a single message text into structured signal(s)."""
+    signals = []
+    
+    # Try to find all ticker mentions
+    ticker_blocks = re.split(r'(?=#\w+USDT)', text)
+    
+    for block in ticker_blocks:
+        if not block.strip():
+            continue
+            
+        signal = {}
+        
+        # Extract ticker
+        ticker_match = re.search(r'#(\w+)', block)
+        if not ticker_match:
+            continue
+        signal['ticker'] = ticker_match.group(1).upper()
+        
+        # Extract direction
+        dir_match = re.search(r'\b(Long|Short)\b', block, re.IGNORECASE)
+        if not dir_match:
+            continue
+        signal['direction'] = dir_match.group(1).lower()
+        
+        # Extract entry price (or "market")
+        entry_match = re.search(r'(?:entry|enter)[:\s]*([0-9.]+)', block, re.IGNORECASE)
+        if entry_match:
+            signal['entry'] = float(entry_match.group(1))
+        else:
+            signal['entry'] = 'market'
+        
+        # Extract stop loss
+        sl_match = re.search(r'SL[;:\s]+([0-9.]+)', block, re.IGNORECASE)
+        if sl_match:
+            signal['stop_loss'] = float(sl_match.group(1))
+        
+        # Extract targets (can be multiple, comma or space separated)
+        tp_match = re.search(r'(?:Targets?|TP)[;:\s]+([0-9.,\s]+)', block, re.IGNORECASE)
+        if tp_match:
+            targets_str = tp_match.group(1)
+            targets = [float(t.strip()) for t in re.findall(r'[0-9.]+', targets_str)]
+            signal['targets'] = targets
+        
+        # Extract leverage
+        lev_match = re.search(r'Lev(?:erage)?[:\s]*x?([0-9.]+)', block, re.IGNORECASE)
+        if lev_match:
+            signal['leverage'] = float(lev_match.group(1))
+        
+        # Extract balance percentage
+        bal_match = re.search(r'([0-9.]+)%?\s*balance', block, re.IGNORECASE)
+        if bal_match:
+            signal['balance_pct'] = float(bal_match.group(1))
+        
+        if signal.get('ticker') and signal.get('direction'):
+            signals.append(signal)
+    
+    return signals
+
+
+def parse_telegram_export(json_path):
+    """Parse a Telegram JSON export file."""
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+    
+    messages = data.get('messages', [])
+    all_signals = []
+    
+    for msg in messages:
+        if msg.get('type') != 'message':
+            continue
+        
+        # Get text content (can be string or list of text entities)
+        text_parts = msg.get('text', '')
+        if isinstance(text_parts, list):
+            text = ''.join(
+                p if isinstance(p, str) else p.get('text', '')
+                for p in text_parts
+            )
+        else:
+            text = text_parts
+        
+        if not text or '#' not in text:
+            continue
+        
+        # Check if it looks like a signal
+        if not re.search(r'(Long|Short)', text, re.IGNORECASE):
+            continue
+        
+        signals = parse_signal_text(text)
+        
+        for signal in signals:
+            signal['timestamp'] = msg.get('date', '')
+            signal['message_id'] = msg.get('id', '')
+            signal['raw_text'] = text[:500]
+            all_signals.append(signal)
+    
+    return all_signals
+
+
+def parse_forwarded_messages(messages_text):
+    """Parse signals from forwarded message text (copy-pasted or forwarded to bot)."""
+    signals = parse_signal_text(messages_text)
+    return signals
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        # Demo with the test signals
+        test_text = """#ASTERUSDT Short market entry! SL: 0.6385 Targets: 0.51 Lev x15 1.3% balance
+#HYPEUSDT Short market entry! SL; 33.5 Target 25 Lev x12 1.4% balance"""
+        
+        signals = parse_signal_text(test_text)
+        print(f"Parsed {len(signals)} signals:\n")
+        for s in signals:
+            print(json.dumps(s, indent=2))
+    else:
+        json_path = sys.argv[1]
+        signals = parse_telegram_export(json_path)
+        print(f"Parsed {len(signals)} signals from export\n")
+        
+        # Save to output
+        out_path = json_path.replace('.json', '_signals.json')
+        with open(out_path, 'w') as f:
+            json.dump(signals, f, indent=2)
+        print(f"Saved to {out_path}")
+        
+        # Quick summary
+        longs = sum(1 for s in signals if s['direction'] == 'long')
+        shorts = sum(1 for s in signals if s['direction'] == 'short')
+        print(f"Longs: {longs}, Shorts: {shorts}")
+        tickers = set(s['ticker'] for s in signals)
+        print(f"Unique tickers: {len(tickers)}")

projects/crypto-watch/data/portfolio.json

@@ -0,0 +1,67 @@
+{
+  "cash": 50000.0,
+  "positions": {
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+}

projects/crypto-watch/data/snapshots.json

@@ -0,0 +1,563 @@
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+]

projects/crypto-watch/data/trades.json

@@ -0,0 +1,102 @@
+[
+  {
+    "id": 1,
+    "timestamp": "2026-02-10T02:17:32.522012+00:00",
+    "action": "BUY",
+    "symbol": "SEI",
+    "price": 0.074487,
+    "qty": 67125.80718783144,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 60): Deep below VWAP (-2.7%), RSI oversold (0)"
+  },
+  {
+    "id": 2,
+    "timestamp": "2026-02-10T02:17:32.522427+00:00",
+    "action": "BUY",
+    "symbol": "ICP",
+    "price": 2.43,
+    "qty": 2057.6131687242796,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 45): Deep below VWAP (-13.5%), RSI low (39)"
+  },
+  {
+    "id": 3,
+    "timestamp": "2026-02-10T02:17:32.523774+00:00",
+    "action": "BUY",
+    "symbol": "PUMP",
+    "price": 0.00200315,
+    "qty": 2496068.691810399,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 40): Deep below VWAP (-5.7%), RSI low (30), Low volume (0.4x)"
+  },
+  {
+    "id": 4,
+    "timestamp": "2026-02-10T02:17:32.524471+00:00",
+    "action": "BUY",
+    "symbol": "TRUMP",
+    "price": 3.35,
+    "qty": 1492.5373134328358,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 40): Deep below VWAP (-2.6%), High volume (1.5x)"
+  },
+  {
+    "id": 5,
+    "timestamp": "2026-02-10T02:17:32.525072+00:00",
+    "action": "BUY",
+    "symbol": "HYPE",
+    "price": 31.47,
+    "qty": 158.8814744200826,
+    "amount_usd": 5000,
+    "reason": "Buy signal (score 25): Below VWAP (-0.8%), Low volume (0.1x), Momentum reversal (bullish)"
+  },
+  {
+    "id": 6,
+    "timestamp": "2026-02-10T06:00:19.532884+00:00",
+    "action": "BUY",
+    "symbol": "VET",
+    "price": 0.00776173,
+    "qty": 644186.2832126343,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 60): Deep below VWAP (-2.4%), RSI low (34), Momentum reversal (bullish)"
+  },
+  {
+    "id": 7,
+    "timestamp": "2026-02-10T06:00:19.533883+00:00",
+    "action": "BUY",
+    "symbol": "ARB",
+    "price": 0.110017,
+    "qty": 45447.521746639155,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 50): Deep below VWAP (-2.6%), RSI low (38), Low volume (0.1x), 24h dump (-5.4%)"
+  },
+  {
+    "id": 8,
+    "timestamp": "2026-02-10T06:00:19.534555+00:00",
+    "action": "BUY",
+    "symbol": "ADA",
+    "price": 0.265455,
+    "qty": 18835.584185643518,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 45): Below VWAP (-1.2%), RSI low (38), Momentum reversal (bullish)"
+  },
+  {
+    "id": 9,
+    "timestamp": "2026-02-10T06:00:19.535250+00:00",
+    "action": "BUY",
+    "symbol": "AAVE",
+    "price": 110.58,
+    "qty": 45.21613311629589,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 45): Below VWAP (-1.8%), RSI low (31), Momentum reversal (bullish)"
+  },
+  {
+    "id": 10,
+    "timestamp": "2026-02-10T06:00:19.535917+00:00",
+    "action": "BUY",
+    "symbol": "NEAR",
+    "price": 1.001,
+    "qty": 4995.004995004995,
+    "amount_usd": 5000,
+    "reason": "Strong buy signal (score 45): Deep below VWAP (-4.5%), RSI low (39)"
+  }
+]

projects/crypto-watch/game_engine.py

@@ -0,0 +1,593 @@
+#!/usr/bin/env python3
+"""
+Crypto Market Watch - Paper Trading Game Engine
+Scans top 150 cryptos using VWAP + RSI + volume analysis.
+Makes autonomous paper trades with full tracking.
+"""
+
+import json
+import time
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+from threading import Lock
+
+DATA_DIR = Path(__file__).parent / "data"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+PORTFOLIO_FILE = DATA_DIR / "portfolio.json"
+TRADES_FILE = DATA_DIR / "trades.json"
+SNAPSHOTS_FILE = DATA_DIR / "snapshots.json"
+WATCHLIST_FILE = DATA_DIR / "watchlist.json"
+
+
+def _load(path, default=None):
+    if path.exists():
+        return json.loads(path.read_text())
+    return default if default is not None else {}
+
+
+def _save(path, data):
+    path.write_text(json.dumps(data, indent=2))
+
+
+# ── Portfolio Management ──
+
+def get_portfolio():
+    default = {
+        "cash": 100000.0,
+        "positions": {},
+        "starting_balance": 100000.0,
+        "created_at": datetime.now(timezone.utc).isoformat(),
+    }
+    return _load(PORTFOLIO_FILE, default)
+
+
+def save_portfolio(portfolio):
+    _save(PORTFOLIO_FILE, portfolio)
+
+
+def get_trades():
+    return _load(TRADES_FILE, [])
+
+
+def save_trades(trades):
+    _save(TRADES_FILE, trades)
+
+
+def buy(symbol, price, amount_usd, reason=""):
+    """Buy a crypto position."""
+    portfolio = get_portfolio()
+    trades = get_trades()
+
+    if amount_usd > portfolio["cash"]:
+        return None, "Insufficient cash"
+
+    qty = amount_usd / price
+    portfolio["cash"] -= amount_usd
+
+    pos = portfolio["positions"].get(symbol, {
+        "qty": 0, "avg_price": 0, "total_cost": 0
+    })
+    
+    new_total_cost = pos["total_cost"] + amount_usd
+    new_qty = pos["qty"] + qty
+    pos["avg_price"] = new_total_cost / new_qty if new_qty > 0 else 0
+    pos["qty"] = new_qty
+    pos["total_cost"] = new_total_cost
+    pos["last_buy"] = datetime.now(timezone.utc).isoformat()
+    
+    portfolio["positions"][symbol] = pos
+    save_portfolio(portfolio)
+
+    trade = {
+        "id": len(trades) + 1,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "action": "BUY",
+        "symbol": symbol,
+        "price": price,
+        "qty": qty,
+        "amount_usd": amount_usd,
+        "reason": reason,
+    }
+    trades.append(trade)
+    save_trades(trades)
+    return trade, None
+
+
+def sell(symbol, price, pct=100, reason=""):
+    """Sell a position (partial or full)."""
+    portfolio = get_portfolio()
+    trades = get_trades()
+
+    pos = portfolio["positions"].get(symbol)
+    if not pos or pos["qty"] <= 0:
+        return None, f"No position in {symbol}"
+
+    sell_qty = pos["qty"] * (pct / 100)
+    sell_value = sell_qty * price
+    cost_basis = pos["avg_price"] * sell_qty
+    pnl = sell_value - cost_basis
+    pnl_pct = (pnl / cost_basis) * 100 if cost_basis > 0 else 0
+
+    portfolio["cash"] += sell_value
+    pos["qty"] -= sell_qty
+    pos["total_cost"] -= cost_basis
+
+    if pos["qty"] < 0.0000001:
+        del portfolio["positions"][symbol]
+    else:
+        portfolio["positions"][symbol] = pos
+
+    save_portfolio(portfolio)
+
+    trade = {
+        "id": len(trades) + 1,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "action": "SELL",
+        "symbol": symbol,
+        "price": price,
+        "qty": sell_qty,
+        "amount_usd": sell_value,
+        "pnl": round(pnl, 2),
+        "pnl_pct": round(pnl_pct, 2),
+        "reason": reason,
+    }
+    trades.append(trade)
+    save_trades(trades)
+    return trade, None
+
+
+def get_portfolio_value(prices):
+    """Calculate total portfolio value."""
+    portfolio = get_portfolio()
+    positions_value = 0
+    position_details = []
+    
+    for symbol, pos in portfolio["positions"].items():
+        current_price = prices.get(symbol, pos["avg_price"])
+        value = pos["qty"] * current_price
+        pnl = value - pos["total_cost"]
+        pnl_pct = (pnl / pos["total_cost"]) * 100 if pos["total_cost"] > 0 else 0
+        positions_value += value
+        position_details.append({
+            "symbol": symbol,
+            "qty": pos["qty"],
+            "avg_price": pos["avg_price"],
+            "current_price": current_price,
+            "value": round(value, 2),
+            "pnl": round(pnl, 2),
+            "pnl_pct": round(pnl_pct, 2),
+        })
+
+    total_value = portfolio["cash"] + positions_value
+    total_pnl = total_value - portfolio["starting_balance"]
+    total_pnl_pct = (total_pnl / portfolio["starting_balance"]) * 100
+
+    return {
+        "cash": round(portfolio["cash"], 2),
+        "positions_value": round(positions_value, 2),
+        "total_value": round(total_value, 2),
+        "total_pnl": round(total_pnl, 2),
+        "total_pnl_pct": round(total_pnl_pct, 2),
+        "positions": sorted(position_details, key=lambda x: -x["value"]),
+        "num_positions": len(position_details),
+    }
+
+
+def take_snapshot(prices):
+    """Save a point-in-time portfolio snapshot."""
+    snapshots = _load(SNAPSHOTS_FILE, [])
+    value = get_portfolio_value(prices)
+    value["timestamp"] = datetime.now(timezone.utc).isoformat()
+    snapshots.append(value)
+    # Keep last 1000
+    _save(SNAPSHOTS_FILE, snapshots[-1000:])
+    return value
+
+
+# ── Market Data ──
+
+def get_top_coins(limit=150):
+    """Fetch top coins by market cap from CoinGecko."""
+    coins = []
+    for page in range(1, (limit // 100) + 2):
+        per_page = min(100, limit - len(coins))
+        if per_page <= 0:
+            break
+        url = (
+            f"https://api.coingecko.com/api/v3/coins/markets?"
+            f"vs_currency=usd&order=market_cap_desc&per_page={per_page}&page={page}"
+            f"&sparkline=false&price_change_percentage=1h,24h,7d"
+        )
+        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+        try:
+            resp = urllib.request.urlopen(req, timeout=15)
+            batch = json.loads(resp.read())
+            coins.extend(batch)
+        except Exception as e:
+            print(f"Error fetching page {page}: {e}")
+            break
+        time.sleep(1)  # Rate limit
+    return coins
+
+
+def get_ohlcv(coin_id, days=2):
+    """Get OHLCV data from CoinGecko for VWAP calculation."""
+    url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/ohlc?vs_currency=usd&days={days}"
+    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        raw = json.loads(resp.read())
+        # CoinGecko OHLC: [timestamp, open, high, low, close]
+        return [{"t": r[0], "o": r[1], "h": r[2], "l": r[3], "c": r[4]} for r in raw]
+    except:
+        return []
+
+
+def get_binance_klines(symbol, interval="1h", limit=48):
+    """Get klines from Binance US for VWAP + volume."""
+    url = f"https://api.binance.us/api/v3/klines?symbol={symbol}USDT&interval={interval}&limit={limit}"
+    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    try:
+        resp = urllib.request.urlopen(req, timeout=10)
+        raw = json.loads(resp.read())
+        return [{"t": k[0], "o": float(k[1]), "h": float(k[2]), "l": float(k[3]),
+                 "c": float(k[4]), "v": float(k[5])} for k in raw]
+    except:
+        return []
+
+
+# ── Technical Analysis ──
+
+def calc_vwap(candles):
+    """Calculate VWAP with standard deviation bands."""
+    if not candles:
+        return None
+    
+    cum_tpv = cum_vol = cum_sq = 0
+    for c in candles:
+        vol = c.get("v", 1)  # Default volume 1 if not available
+        tp = (c["h"] + c["l"] + c["c"]) / 3
+        cum_tpv += tp * vol
+        cum_vol += vol
+    
+    vwap = cum_tpv / cum_vol if cum_vol > 0 else candles[-1]["c"]
+    
+    # Standard deviation
+    for c in candles:
+        vol = c.get("v", 1)
+        tp = (c["h"] + c["l"] + c["c"]) / 3
+        cum_sq += vol * (tp - vwap) ** 2
+    
+    variance = cum_sq / cum_vol if cum_vol > 0 else 0
+    std = variance ** 0.5
+    
+    return {
+        "vwap": vwap,
+        "std": std,
+        "upper1": vwap + std,
+        "lower1": vwap - std,
+        "upper2": vwap + 2 * std,
+        "lower2": vwap - 2 * std,
+    }
+
+
+def calc_rsi(closes, period=14):
+    """Calculate RSI."""
+    if len(closes) < period + 1:
+        return 50  # Default neutral
+    
+    gains = []
+    losses = []
+    for i in range(1, len(closes)):
+        diff = closes[i] - closes[i - 1]
+        gains.append(max(diff, 0))
+        losses.append(max(-diff, 0))
+    
+    avg_gain = sum(gains[-period:]) / period
+    avg_loss = sum(losses[-period:]) / period
+    
+    if avg_loss == 0:
+        return 100
+    rs = avg_gain / avg_loss
+    return 100 - (100 / (1 + rs))
+
+
+def analyze_coin(coin, klines=None):
+    """Full VWAP + RSI + momentum analysis for a single coin."""
+    symbol = coin["symbol"].upper()
+    price = coin["current_price"]
+    
+    if not klines:
+        klines = get_binance_klines(symbol, "1h", 48)
+    
+    if not klines or len(klines) < 10:
+        return None
+    
+    # VWAP
+    vwap_data = calc_vwap(klines)
+    vwap = vwap_data["vwap"]
+    vwap_diff = (price - vwap) / vwap * 100
+    
+    # RSI
+    closes = [k["c"] for k in klines]
+    rsi = calc_rsi(closes)
+    
+    # Volume trend
+    recent_vol = sum(k["v"] for k in klines[-6:])  # Last 6h
+    avg_vol = sum(k["v"] for k in klines) / (len(klines) / 6)
+    vol_ratio = recent_vol / avg_vol if avg_vol > 0 else 1
+    
+    # Momentum (last 4h vs prior 4h)
+    if len(klines) >= 8:
+        recent_mom = (klines[-1]["c"] - klines[-4]["c"]) / klines[-4]["c"] * 100
+        prior_mom = (klines[-4]["c"] - klines[-8]["c"]) / klines[-8]["c"] * 100
+    else:
+        recent_mom = prior_mom = 0
+    
+    # 24h change from CoinGecko
+    change_24h = coin.get("price_change_percentage_24h", 0) or 0
+    
+    # Score: -100 to +100
+    score = 0
+    signals = []
+    
+    # VWAP position
+    if vwap_diff < -2:
+        score += 30
+        signals.append(f"Deep below VWAP ({vwap_diff:.1f}%)")
+    elif vwap_diff < -0.5:
+        score += 15
+        signals.append(f"Below VWAP ({vwap_diff:.1f}%)")
+    elif vwap_diff > 2:
+        score -= 20
+        signals.append(f"Extended above VWAP ({vwap_diff:.1f}%)")
+    elif vwap_diff > 0.5:
+        score += 5
+        signals.append(f"Above VWAP ({vwap_diff:.1f}%)")
+    
+    # RSI
+    if rsi < 30:
+        score += 30
+        signals.append(f"RSI oversold ({rsi:.0f})")
+    elif rsi < 40:
+        score += 15
+        signals.append(f"RSI low ({rsi:.0f})")
+    elif rsi > 70:
+        score -= 25
+        signals.append(f"RSI overbought ({rsi:.0f})")
+    elif rsi > 60:
+        score -= 10
+        signals.append(f"RSI elevated ({rsi:.0f})")
+    
+    # Volume confirmation
+    if vol_ratio > 1.5:
+        score += 10 if vwap_diff < 0 else -10  # High vol at support = bullish
+        signals.append(f"High volume ({vol_ratio:.1f}x)")
+    elif vol_ratio < 0.5:
+        score -= 5
+        signals.append(f"Low volume ({vol_ratio:.1f}x)")
+    
+    # Momentum reversal
+    if recent_mom > 0 and prior_mom < 0:
+        score += 15
+        signals.append("Momentum reversal (bullish)")
+    elif recent_mom < 0 and prior_mom > 0:
+        score -= 15
+        signals.append("Momentum reversal (bearish)")
+    
+    # 24h trend
+    if change_24h < -5:
+        score += 10  # Potential bounce
+        signals.append(f"24h dump ({change_24h:.1f}%)")
+    elif change_24h > 5:
+        score -= 10  # Potential pullback
+        signals.append(f"24h pump ({change_24h:.1f}%)")
+    
+    return {
+        "symbol": symbol,
+        "name": coin["name"],
+        "price": price,
+        "market_cap_rank": coin.get("market_cap_rank", 0),
+        "vwap": round(vwap, 6),
+        "vwap_diff_pct": round(vwap_diff, 2),
+        "rsi": round(rsi, 1),
+        "vol_ratio": round(vol_ratio, 2),
+        "momentum_4h": round(recent_mom, 2),
+        "change_24h": round(change_24h, 2),
+        "score": score,
+        "signals": signals,
+        "vwap_bands": {k: round(v, 6) for k, v in vwap_data.items()},
+    }
+
+
+# ── Trading Logic ──
+
+def should_buy(analysis):
+    """Determine if we should buy based on analysis score."""
+    if not analysis:
+        return False, ""
+    
+    score = analysis["score"]
+    rsi = analysis["rsi"]
+    vwap_diff = analysis["vwap_diff_pct"]
+    
+    # Strong buy: oversold + below VWAP
+    if score >= 40:
+        return True, f"Strong buy signal (score {score}): {', '.join(analysis['signals'])}"
+    
+    # Moderate buy: decent score + below VWAP
+    if score >= 20 and vwap_diff < -0.5:
+        return True, f"Buy signal (score {score}): {', '.join(analysis['signals'])}"
+    
+    return False, ""
+
+
+def should_sell(analysis, position):
+    """Determine if we should sell a position."""
+    if not analysis:
+        return False, 0, ""
+    
+    price = analysis["price"]
+    avg_price = position["avg_price"]
+    pnl_pct = (price - avg_price) / avg_price * 100
+    rsi = analysis["rsi"]
+    vwap_diff = analysis["vwap_diff_pct"]
+    score = analysis["score"]
+    
+    # Take profit: +5% gain with overbought signals
+    if pnl_pct >= 5 and (rsi > 65 or vwap_diff > 1):
+        return True, 100, f"Take profit ({pnl_pct:.1f}%, RSI {rsi:.0f})"
+    
+    # Strong take profit: +10% gain
+    if pnl_pct >= 10:
+        return True, 50, f"Partial take profit ({pnl_pct:.1f}%)"
+    
+    # Stop loss: -8%
+    if pnl_pct <= -8:
+        return True, 100, f"Stop loss ({pnl_pct:.1f}%)"
+    
+    # Bearish signals on losing position
+    if pnl_pct < -3 and score <= -20:
+        return True, 100, f"Cut loss (score {score}, PnL {pnl_pct:.1f}%)"
+    
+    # Extended above VWAP with big gain
+    if pnl_pct >= 3 and vwap_diff > 2:
+        return True, 50, f"Extended above VWAP ({vwap_diff:.1f}%), lock gains"
+    
+    return False, 0, ""
+
+
+# ── Main Scanner ──
+
+def run_scan(max_positions=10, position_size=5000):
+    """
+    Full scan: analyze top 150 cryptos, make buy/sell decisions.
+    max_positions: max simultaneous positions
+    position_size: USD per position
+    """
+    print(f"=== Crypto Market Watch Scan ===")
+    print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    print()
+    
+    # Get top coins
+    print("Fetching top 150 coins...", flush=True)
+    coins = get_top_coins(150)
+    # Deduplicate by symbol (CoinGecko sometimes returns dupes)
+    seen_symbols = set()
+    unique_coins = []
+    for c in coins:
+        sym = c["symbol"].upper()
+        if sym not in seen_symbols:
+            seen_symbols.add(sym)
+            unique_coins.append(c)
+    coins = unique_coins
+    print(f"Got {len(coins)} unique coins")
+    
+    # Map symbols to Binance format
+    # Only analyze coins available on Binance US
+    binance_symbols = set()
+    try:
+        url = "https://api.binance.us/api/v3/exchangeInfo"
+        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+        resp = urllib.request.urlopen(req, timeout=15)
+        exchange = json.loads(resp.read())
+        for s in exchange["symbols"]:
+            if s["quoteAsset"] == "USDT" and s["status"] == "TRADING":
+                binance_symbols.add(s["baseAsset"])
+    except:
+        pass
+    
+    print(f"Binance US has {len(binance_symbols)} USDT pairs")
+    
+    # Analyze each coin
+    analyses = []
+    prices = {}
+    
+    for coin in coins:
+        symbol = coin["symbol"].upper()
+        prices[symbol] = coin["current_price"]
+        
+        if symbol not in binance_symbols:
+            continue
+        
+        analysis = analyze_coin(coin)
+        if analysis:
+            analyses.append(analysis)
+        
+        time.sleep(0.2)  # Rate limit
+    
+    print(f"Analyzed {len(analyses)} coins with Binance data")
+    
+    # Get current portfolio
+    portfolio = get_portfolio()
+    current_positions = set(portfolio["positions"].keys())
+    
+    # ── SELL DECISIONS ──
+    sells = []
+    for symbol, pos in list(portfolio["positions"].items()):
+        analysis = next((a for a in analyses if a["symbol"] == symbol), None)
+        if not analysis:
+            # Can't analyze — skip (don't sell blind)
+            continue
+        
+        do_sell, sell_pct, reason = should_sell(analysis, pos)
+        if do_sell:
+            trade, err = sell(symbol, analysis["price"], sell_pct, reason)
+            if trade:
+                sells.append(trade)
+                print(f"  📤 SELL {symbol} @ ${analysis['price']:,.4f} ({sell_pct}%) — {reason}")
+    
+    # ── BUY DECISIONS ──
+    portfolio = get_portfolio()  # Refresh after sells
+    num_positions = len(portfolio["positions"])
+    available_slots = max_positions - num_positions
+    
+    # Sort by score (best opportunities first)
+    buy_candidates = sorted(
+        [a for a in analyses if a["symbol"] not in portfolio["positions"]],
+        key=lambda x: -x["score"]
+    )
+    
+    buys = []
+    for analysis in buy_candidates[:available_slots * 2]:  # Check 2x candidates
+        if len(buys) >= available_slots:
+            break
+        if portfolio["cash"] < position_size:
+            break
+        
+        do_buy, reason = should_buy(analysis)
+        if do_buy:
+            trade, err = buy(analysis["symbol"], analysis["price"], position_size, reason)
+            if trade:
+                buys.append(trade)
+                portfolio = get_portfolio()  # Refresh
+                print(f"  📥 BUY {analysis['symbol']} @ ${analysis['price']:,.4f} — {reason}")
+    
+    # Take snapshot
+    snapshot = take_snapshot(prices)
+    
+    # Summary
+    print(f"\n{'='*50}")
+    print(f"Buys: {len(buys)} | Sells: {len(sells)}")
+    print(f"Portfolio: ${snapshot['total_value']:,.2f} ({snapshot['total_pnl_pct']:+.2f}%)")
+    print(f"Cash: ${snapshot['cash']:,.2f} | Positions: {snapshot['num_positions']}")
+    
+    if snapshot["positions"]:
+        print(f"\nOpen Positions:")
+        for p in snapshot["positions"]:
+            emoji = "🟢" if p["pnl"] >= 0 else "🔴"
+            print(f"  {emoji} {p['symbol']}: ${p['value']:,.2f} ({p['pnl_pct']:+.1f}%)")
+    
+    # Top opportunities not taken
+    print(f"\nTop Scoring Coins:")
+    for a in sorted(analyses, key=lambda x: -x["score"])[:10]:
+        held = "📌" if a["symbol"] in portfolio.get("positions", {}) else "  "
+        print(f"  {held} {a['symbol']:<8} score:{a['score']:>4} | RSI:{a['rsi']:.0f} | VWAP:{a['vwap_diff_pct']:+.1f}% | 24h:{a['change_24h']:+.1f}%")
+    
+    return {"buys": buys, "sells": sells, "snapshot": snapshot, "analyses": analyses}
+
+
+if __name__ == "__main__":
+    result = run_scan()

projects/crypto-watch/run_scan.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+"""
+Wrapper to run crypto scan and send Telegram alerts.
+Designed for systemd timer execution.
+"""
+
+import json
+import os
+import urllib.request
+from game_engine import run_scan, get_portfolio_value, get_trades
+
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+
+def send_telegram(message):
+    if not TELEGRAM_BOT_TOKEN:
+        return
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({
+        "chat_id": TELEGRAM_CHAT_ID,
+        "text": message,
+        "parse_mode": "HTML",
+    }).encode()
+    req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"Telegram error: {e}")
+
+
+def main():
+    result = run_scan(max_positions=10, position_size=5000)
+    
+    buys = result.get("buys", [])
+    sells = result.get("sells", [])
+    snapshot = result.get("snapshot", {})
+    
+    # Only alert if there were trades
+    if buys or sells:
+        lines = ["🎮 <b>Crypto Watch Update</b>\n"]
+        
+        for t in buys:
+            lines.append(f"📥 BUY {t['symbol']} @ ${t['price']:,.4f}")
+            lines.append(f"   {t.get('reason', '')}\n")
+        
+        for t in sells:
+            emoji = "🟢" if t.get("pnl", 0) >= 0 else "🔴"
+            lines.append(f"📤 SELL {t['symbol']} @ ${t['price']:,.4f}")
+            lines.append(f"   {emoji} PnL: ${t.get('pnl', 0):+,.2f} ({t.get('pnl_pct', 0):+.1f}%)")
+            lines.append(f"   {t.get('reason', '')}\n")
+        
+        lines.append(f"💰 Portfolio: ${snapshot.get('total_value', 0):,.2f} ({snapshot.get('total_pnl_pct', 0):+.2f}%)")
+        lines.append(f"💵 Cash: ${snapshot.get('cash', 0):,.2f} | Positions: {snapshot.get('num_positions', 0)}")
+        
+        send_telegram("\n".join(lines))
+
+
+if __name__ == "__main__":
+    main()

projects/feed-hunter/data/anoin123-tracking/stats.json

@@ -0,0 +1,3 @@
+{
+  "last_check": "2026-02-12T06:14:23.639458+00:00"
+}

projects/feed-hunter/data/feed_captures/feed-20260212-0611.json

@@ -0,0 +1,50 @@
+{
+  "timestamp": "2026-02-12T06:11:18.980961+00:00",
+  "total_scraped": 44,
+  "new_posts": 22,
+  "money_posts": 7,
+  "posts": [
+    {
+      "text": "We\u2019re in the business of supporting traders. Get the tools you need to make your way in the market.",
+      "userName": "tastytrade\n@tastytrade",
+      "timestamp": "",
+      "link": "/tastytrade/status/1971214019274080389/analytics"
+    },
+    {
+      "text": "agentic e2e regression testing solved.\nprompt \u2192 test in under 2 mins.\nif you're still writing tests by hand... why",
+      "userName": "Bug0\n@bug0inc",
+      "timestamp": "",
+      "link": "/bug0inc/status/2019756209390375399/analytics"
+    },
+    {
+      "text": " New GPT crypto price predictions:\n\n$ETH -0.285% => $1945.73\n\n$ZRO +0.086% => $2.329\n\n$UNI 0.0% => $3.492\n\n$BTC -0.116% => $67481.29\n\n$XRP +0.181% => $1.3796\n\n$PENGU +0.116% => $0.006021\n\n$SOL +0.063% => $79.72\n\n$TRUMP +0.094% => $3.195",
+      "userName": "OctoBot - GPT crypto price predictions\n@OctoBotGPT\n\u00b7\n10h",
+      "timestamp": "2026-02-11T20:00:50.000Z",
+      "link": "/OctoBotGPT/status/2021675819580481813"
+    },
+    {
+      "text": " New GPT crypto price predictions:\n\n$ADA 0.0% => $0.2633\n\n$DOGE 0.0% => $0.09372\n\n$SOL 0.0% => $84.78\n\n$XRP +0.266% => $1.427\n\n$SENT +0.594% => $0.02863\n\n$PAXG 0.0% => $5033.12\n\n$PEPE +98.997% => $0.000367\n\n$SUI +0.864% => $0.9487",
+      "userName": "OctoBot - GPT crypto price predictions\n@OctoBotGPT\n\u00b7\nFeb 10",
+      "timestamp": "2026-02-10T08:00:48.000Z",
+      "link": "/OctoBotGPT/status/2021132226792947802"
+    },
+    {
+      "text": "$ZAMA 0.0% => $0.02688\n\n$LIT -1.503% => $0.732\n\n$ZRO -0.259% => $1.927\n\n$BTC -0.078% => $68890.0\n\n$ETH -0.53% => $1997.59\n\n$HBAR +0.512% => $0.09188\n\n$TRX -0.036% => $0.277\n\n$SHIB 0.0% => $5.98e-06",
+      "userName": "OctoBot - GPT crypto price predictions\n@OctoBotGPT\n\u00b7\nFeb 10",
+      "timestamp": "2026-02-10T08:00:48.000Z",
+      "link": "/OctoBotGPT/status/2021132227921301730"
+    },
+    {
+      "text": " New GPT crypto price predictions:\n\n$ZRO +0.959% => $2.398\n\n$DOGE 0.0% => $0.09053\n\n$LINK 0.0% => $8.32\n\n$SOL -0.671% => $80.5\n\n$PENGU -0.169% => $0.005916\n\n$USD1 +0.02% => $1.0\n\n$SUI -0.403% => $0.893\n\n$BTC 0.0% => $67046.23",
+      "userName": "OctoBot - GPT crypto price predictions\n@OctoBotGPT\n\u00b7\n22h",
+      "timestamp": "2026-02-11T08:00:52.000Z",
+      "link": "/OctoBotGPT/status/2021494632635408429"
+    },
+    {
+      "text": "$ETH +1.022% => $1971.45\n\n$BNB 0.0% => $600.95\n\n$ZAMA -0.714% => $0.0196\n\n$SHIB 0.0% => $5.84e-06\n\n$XRP -0.242% => $1.3646\n\n$ASTER +0.459% => $0.653\n\n$ADA -0.393% => $0.2546\n\n$TRX 0.0% => $0.275",
+      "userName": "OctoBot - GPT crypto price predictions\n@OctoBotGPT\n\u00b7\n22h",
+      "timestamp": "2026-02-11T08:00:52.000Z",
+      "link": "/OctoBotGPT/status/2021494633759408338"
+    }
+  ]
+}

projects/feed-hunter/data/kch123-tracking/stats.json

@@ -1,5 +1,5 @@
 {
-  "last_check": "2026-02-08T17:06:58.270395+00:00",
+  "last_check": "2026-02-12T06:14:23.952343+00:00",
   "total_tracked": 3100,
   "new_this_check": 0
 }

projects/feed-hunter/data/seen_posts.json

@@ -0,0 +1 @@
+["37f5b094ed19f0ae", "f013475eaef8b32f", "6f9f9b6f8da5cdc9", "952f5caf7819fde5", "6043a216215e23f0", "ff1b1af5e65905a8", "6217530e01f15dd0", "6e1a48e8344a1d6f", "6d61e4a9dfb604ea", "529a533d4da86360", "d2217d5c918df581", "8b4a57cd97ae6c34", "5bf0ad8e5e2f9dfe", "2ee63742fbc6e541", "7d74e6eca55f346f", "9f29b74a37377015", "3ccdb8471c5c19b6", "8a48151a19597742", "e6a24f45f8a6e8bb", "899377e3ae43e396", "05e47b3d9e24c860", "643b1c7d00ad50f2", "b7a2c548992c278c", "a1a8dddd35fd08d5", "149ace9c327e7700", "7d767e4d0e3a12a3", "0c6a5029b97b5a30", "b7354be18fa9f71a", "7b10b1cb595f2006", "ca4011161b3ce92d", "ffbe4a2b11671722", "cd2ce19326f75133", "60fd088f8f1ae9b2", "8ebbe21b036415d8", "84da8ad2dd424e2b", "7d4648af05013346", "137b80809666db54", "1151a6c9c0f2915c", "36db4785c888600f", "e8c4ec6aa2a9a563", "f9ded3bb072f01fe", "f30bff813bce39b8", "e1a98d7590fe46ee", "a767ef4ed21a86f3", "3525848121516057", "3d6e2887b81b3016", "cb6375e11d10b745", "493d2dc82b844b36", "60dced6f08edb3c2", "7d97ce308ff157b2", "db27f494858bd743", "176776613d96cdbe", "eb7a8395aa02f113"]

projects/feed-hunter/data/simulations/active.json

@@ -11,9 +11,9 @@
       "quantity": 845,
       "stop_loss": null,
       "take_profit": null,
-      "current_price": 0.505,
-      "unrealized_pnl": -1.52,
-      "unrealized_pnl_pct": -0.36,
+      "current_price": 0.9995,
+      "unrealized_pnl": 416.33,
+      "unrealized_pnl_pct": 97.22,
       "source_post": "https://polymarket.com/profile/kch123",
       "thesis": "Copy kch123 proportional. Spread: Seahawks (-4.5) (Seahawks). Weight: 42.9%",
       "notes": "kch123 has $975,120 on this (42.9% of active book)",
@@ -101,9 +101,9 @@
       "quantity": 186,
       "stop_loss": null,
       "take_profit": null,
-      "current_price": 0.475,
-      "unrealized_pnl": -0.93,
-      "unrealized_pnl_pct": -1.04,
+      "current_price": 1,
+      "unrealized_pnl": 96.72,
+      "unrealized_pnl_pct": 108.33,
       "source_post": "https://polymarket.com/profile/kch123",
       "thesis": "Copy kch123 proportional. Spread: Seahawks (-5.5) (Seahawks). Weight: 9.0%",
       "notes": "kch123 has $203,779 on this (9.0% of active book)",

projects/feed-hunter/feed_monitor.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""
+Feed Monitor — Scrapes X home timeline via Chrome CDP (localhost:9222).
+Deduplicates, filters for money/trading topics, saves captures, sends Telegram alerts.
+"""
+
+import json
+import hashlib
+import os
+import sys
+import time
+import http.client
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+PROJECT_DIR = Path(__file__).parent
+DATA_DIR = PROJECT_DIR / "data"
+SEEN_FILE = DATA_DIR / "seen_posts.json"
+CAPTURES_DIR = DATA_DIR / "feed_captures"
+CAPTURES_DIR.mkdir(parents=True, exist_ok=True)
+
+CDP_HOST = "localhost"
+CDP_PORT = 9222
+
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+MONEY_KEYWORDS = [
+    "polymarket", "trade", "trading", "profit", "arbitrage", "crypto",
+    "bitcoin", "btc", "ethereum", "eth", "solana", "sol", "stock",
+    "stocks", "market", "portfolio", "defi", "token", "whale",
+    "bullish", "bearish", "short", "long", "pnl", "alpha", "degen",
+    "usdc", "usdt", "wallet", "airdrop", "memecoin", "nft",
+    "yield", "staking", "leverage", "futures", "options", "hedge",
+    "pump", "dump", "rug", "moon", "bag", "position", "signal",
+]
+
+
+def send_telegram(message: str):
+    if not TELEGRAM_BOT_TOKEN:
+        print(f"[ALERT] {message}")
+        return
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({
+        "chat_id": TELEGRAM_CHAT_ID,
+        "text": message,
+        "parse_mode": "HTML",
+        "disable_web_page_preview": True,
+    }).encode()
+    req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"  Telegram error: {e}")
+
+
+def cdp_send(ws, method: str, params: dict = None, msg_id: int = 1):
+    """Send a CDP command over websocket and return the result."""
+    import websocket
+    payload = {"id": msg_id, "method": method}
+    if params:
+        payload["params"] = params
+    ws.send(json.dumps(payload))
+    while True:
+        resp = json.loads(ws.recv())
+        if resp.get("id") == msg_id:
+            return resp.get("result", {})
+
+
+def get_x_tab_ws():
+    """Find an X.com tab in Chrome and return its websocket URL."""
+    conn = http.client.HTTPConnection(CDP_HOST, CDP_PORT, timeout=5)
+    conn.request("GET", "/json")
+    tabs = json.loads(conn.getresponse().read())
+    conn.close()
+
+    for t in tabs:
+        url = t.get("url", "")
+        if "x.com" in url or "twitter.com" in url:
+            ws_url = t.get("webSocketDebuggerUrl")
+            if ws_url:
+                return ws_url, t.get("url")
+    return None, None
+
+
+def scrape_feed_via_cdp():
+    """Navigate to X home, scroll, extract posts via DOM evaluation."""
+    import websocket
+
+    ws_url, current_url = get_x_tab_ws()
+    if not ws_url:
+        print("ERROR: No X.com tab found in Chrome at localhost:9222")
+        sys.exit(1)
+
+    print(f"Connected to tab: {current_url}")
+    ws = websocket.create_connection(ws_url, timeout=30)
+
+    # Navigate to home timeline
+    cdp_send(ws, "Page.navigate", {"url": "https://x.com/home"}, 1)
+    time.sleep(5)
+
+    all_posts = []
+    seen_texts = set()
+
+    for scroll_i in range(6):
+        # Extract posts from timeline
+        js = """
+        (() => {
+            const posts = [];
+            document.querySelectorAll('article[data-testid="tweet"]').forEach(article => {
+                try {
+                    const textEl = article.querySelector('[data-testid="tweetText"]');
+                    const text = textEl ? textEl.innerText : '';
+                    const userEl = article.querySelector('[data-testid="User-Name"]');
+                    const userName = userEl ? userEl.innerText : '';
+                    const timeEl = article.querySelector('time');
+                    const timestamp = timeEl ? timeEl.getAttribute('datetime') : '';
+                    const linkEl = article.querySelector('a[href*="/status/"]');
+                    const link = linkEl ? linkEl.getAttribute('href') : '';
+                    posts.push({ text, userName, timestamp, link });
+                } catch(e) {}
+            });
+            return JSON.stringify(posts);
+        })()
+        """
+        result = cdp_send(ws, "Runtime.evaluate", {"expression": js, "returnByValue": True}, 10 + scroll_i)
+        raw = result.get("result", {}).get("value", "[]")
+        posts = json.loads(raw) if isinstance(raw, str) else []
+
+        for p in posts:
+            sig = p.get("text", "")[:120]
+            if sig and sig not in seen_texts:
+                seen_texts.add(sig)
+                all_posts.append(p)
+
+        # Scroll down
+        cdp_send(ws, "Runtime.evaluate", {"expression": "window.scrollBy(0, 2000)"}, 100 + scroll_i)
+        time.sleep(2)
+
+    ws.close()
+    return all_posts
+
+
+def post_hash(post: dict) -> str:
+    text = post.get("text", "") + post.get("userName", "")
+    return hashlib.sha256(text.encode()).hexdigest()[:16]
+
+
+def is_money_related(text: str) -> bool:
+    lower = text.lower()
+    return any(kw in lower for kw in MONEY_KEYWORDS)
+
+
+def load_seen() -> set:
+    if SEEN_FILE.exists():
+        try:
+            return set(json.loads(SEEN_FILE.read_text()))
+        except:
+            pass
+    return set()
+
+
+def save_seen(seen: set):
+    # Keep last 10k
+    items = list(seen)[-10000:]
+    SEEN_FILE.write_text(json.dumps(items))
+
+
+def main():
+    now = datetime.now(timezone.utc)
+    print(f"=== Feed Monitor === {now.strftime('%Y-%m-%d %H:%M UTC')}")
+
+    posts = scrape_feed_via_cdp()
+    print(f"Scraped {len(posts)} posts from timeline")
+
+    seen = load_seen()
+    new_posts = []
+    money_posts = []
+
+    for p in posts:
+        h = post_hash(p)
+        if h in seen:
+            continue
+        seen.add(h)
+        new_posts.append(p)
+        if is_money_related(p.get("text", "")):
+            money_posts.append(p)
+
+    save_seen(seen)
+
+    print(f"New posts: {len(new_posts)}")
+    print(f"Money-related: {len(money_posts)}")
+
+    # Save capture
+    ts = now.strftime("%Y%m%d-%H%M")
+    capture = {
+        "timestamp": now.isoformat(),
+        "total_scraped": len(posts),
+        "new_posts": len(new_posts),
+        "money_posts": len(money_posts),
+        "posts": money_posts,
+    }
+    capture_file = CAPTURES_DIR / f"feed-{ts}.json"
+    capture_file.write_text(json.dumps(capture, indent=2))
+    print(f"Saved capture: {capture_file}")
+
+    # Alert on money posts
+    if money_posts:
+        print(f"\n🔔 {len(money_posts)} money-related posts found!")
+        for p in money_posts[:8]:
+            user = p.get("userName", "").split("\n")[0]
+            snippet = p.get("text", "")[:250].replace("\n", " ")
+            link = p.get("link", "")
+            full_link = f"https://x.com{link}" if link and not link.startswith("http") else link
+
+            print(f"  • {user}: {snippet[:100]}...")
+
+            msg = f"🔍 <b>{user}</b>\n\n{snippet}"
+            if full_link:
+                msg += f"\n\n{full_link}"
+            send_telegram(msg)
+    else:
+        print("No new money-related posts.")
+
+    return len(money_posts)
+
+
+if __name__ == "__main__":
+    count = main()
+    sys.exit(0)

projects/feed-hunter/x_cookies.json

@@ -0,0 +1,155 @@
+[
+  {
+    "name": "guest_id_marketing",
+    "value": "v1%3A177052493168164632",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "guest_id_ads",
+    "value": "v1%3A177052493168164632",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "guest_id",
+    "value": "v1%3A177052493168164632",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "personalization_id",
+    "value": "\"v1_6O8SSA4FCcIXzFzq4cql3A==\"",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "__cuid",
+    "value": "7ec0f8364ef9466bb4d5e5398de60a7a",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": false,
+    "httpOnly": false,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "guest_id_marketing",
+    "value": "v1%3A177052493360013497",
+    "domain": ".twitter.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "guest_id_ads",
+    "value": "v1%3A177052493360013497",
+    "domain": ".twitter.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "personalization_id",
+    "value": "\"v1_0RdWTpuTILka/W8MwiVsGQ==\"",
+    "domain": ".twitter.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "guest_id",
+    "value": "v1%3A177052493360013497",
+    "domain": ".twitter.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "g_state",
+    "value": "{\"i_l\":0,\"i_ll\":1770524933853,\"i_b\":\"/335bZxZT54Tkc2wThT5DEH5v8hDZyhbe/JOl6uvF+k\",\"i_e\":{\"enable_itp_optimization\":0}}",
+    "domain": "x.com",
+    "path": "/",
+    "secure": false,
+    "httpOnly": false,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "kdt",
+    "value": "Y9jfWROysXsnZyHwlffVbs8jvBJabIN4RGlZYFHP",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": true,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "auth_token",
+    "value": "219b71a535b96ef9f978612a48cf81a462643ee3",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": true,
+    "sameSite": "None"
+  },
+  {
+    "name": "ct0",
+    "value": "e2c61ad6ce7115f2d8acd2062dc5c9a377140d9b570f871d9b25847f2d7a36fe512a424a359775d73a11a5a0a5154b6623b0021992a2b7f1e094d5ac5ee65cfeaf8ac87de09b7dcfc48f28a5b6dd15dc",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "twid",
+    "value": "u%3D741482516",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "None"
+  },
+  {
+    "name": "lang",
+    "value": "en",
+    "domain": "x.com",
+    "path": "/",
+    "secure": false,
+    "httpOnly": false,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "external_referer",
+    "value": "vC8TI7P7q9UHtLBqrmGBr3bhFoPD7nVN|0|8e8t2xd8A2w%3D",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": false,
+    "sameSite": "Lax"
+  },
+  {
+    "name": "__cf_bm",
+    "value": "UjX5M.SqXScrW4zZ_GhiubhCXhv.8SI8uU7MkZCGT24-1770678794.1374662-1.0.1.1-4x.1srI8Lir7aTkBYJxMGMZQ2E3.EZKgF5S_gLeoAQzEUvIFZQTLQNxhFfiiVNNaXbfZ8HgKEPtSTvpaglXpnCo9COtawFeKPtaKmENpRj5V3mP0EOhtt4w_MpLhHekN",
+    "domain": ".x.com",
+    "path": "/",
+    "secure": true,
+    "httpOnly": true,
+    "sameSite": "Lax"
+  }
+]

projects/feed-hunter/x_scraper_pw.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""
+X/Twitter Feed Scraper using Playwright
+Scrapes specific accounts for trading-related posts.
+Uses saved Chrome session cookies for authentication.
+"""
+
+import json
+import os
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+ACCOUNTS = [
+    "browomo", "ArchiveExplorer", "noisyb0y1", "krajekis",
+    "Shelpid_WI3M", "polyaboretum", "0xashensoul",
+]
+
+TRADING_KEYWORDS = [
+    "polymarket", "trade", "profit", "wallet", "arbitrage", "signal",
+    "crypto", "bitcoin", "ethereum", "solana", "strategy", "edge",
+    "bet", "position", "stock", "market", "pnl", "alpha",
+    "$", "usdc", "defi", "token", "copy", "whale", "degen",
+    "short", "long", "bullish", "bearish", "portfolio",
+]
+
+DATA_DIR = Path(__file__).parent.parent / "data" / "x-feed"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+COOKIE_FILE = Path(__file__).parent / "x_cookies.json"
+TELEGRAM_BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6443752046")
+
+
+def send_telegram(message):
+    if not TELEGRAM_BOT_TOKEN:
+        print(f"[ALERT] {message}")
+        return
+    import urllib.request
+    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
+    data = json.dumps({"chat_id": TELEGRAM_CHAT_ID, "text": message, "parse_mode": "HTML"}).encode()
+    req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except Exception as e:
+        print(f"Telegram error: {e}")
+
+
+def save_cookies(context):
+    cookies = context.cookies()
+    COOKIE_FILE.write_text(json.dumps(cookies, indent=2))
+    print(f"Saved {len(cookies)} cookies")
+
+
+def load_cookies(context):
+    if COOKIE_FILE.exists():
+        cookies = json.loads(COOKIE_FILE.read_text())
+        context.add_cookies(cookies)
+        print(f"Loaded {len(cookies)} cookies")
+        return True
+    return False
+
+
+def export_cookies_from_chrome():
+    """One-time: grab cookies from the running Chrome debug instance."""
+    import http.client, websocket as ws_mod
+    conn = http.client.HTTPConnection("localhost", 9222)
+    conn.request("GET", "/json")
+    tabs = json.loads(conn.getresponse().read())
+    
+    x_tab = None
+    for t in tabs:
+        if "x.com" in t.get("url", ""):
+            x_tab = t
+            break
+    
+    if not x_tab:
+        print("No X tab found in Chrome debug")
+        return []
+    
+    ws = ws_mod.create_connection(x_tab["webSocketDebuggerUrl"], timeout=10)
+    ws.send(json.dumps({"id": 1, "method": "Network.getAllCookies"}))
+    result = json.loads(ws.recv())
+    all_cookies = result.get("result", {}).get("cookies", [])
+    ws.close()
+    
+    # Filter for x.com cookies and convert to Playwright format
+    x_cookies = []
+    for c in all_cookies:
+        if "x.com" in c.get("domain", "") or "twitter.com" in c.get("domain", ""):
+            x_cookies.append({
+                "name": c["name"],
+                "value": c["value"],
+                "domain": c["domain"],
+                "path": c.get("path", "/"),
+                "secure": c.get("secure", False),
+                "httpOnly": c.get("httpOnly", False),
+                "sameSite": c.get("sameSite", "Lax"),
+            })
+    
+    COOKIE_FILE.write_text(json.dumps(x_cookies, indent=2))
+    print(f"Exported {len(x_cookies)} X cookies from Chrome")
+    return x_cookies
+
+
+def scrape_account(page, account, max_scroll=5):
+    """Scrape recent posts from a single account."""
+    posts = []
+    
+    try:
+        page.goto(f"https://x.com/{account}", wait_until="networkidle", timeout=15000)
+    except:
+        try:
+            page.goto(f"https://x.com/{account}", wait_until="domcontentloaded", timeout=10000)
+            page.wait_for_timeout(3000)
+        except Exception as e:
+            print(f"  Failed to load @{account}: {e}")
+            return posts
+    
+    seen_texts = set()
+    
+    for scroll in range(max_scroll):
+        articles = page.query_selector_all("article")
+        
+        for article in articles:
+            try:
+                text = article.inner_text()[:800]
+                # Deduplicate
+                sig = text[:100]
+                if sig in seen_texts:
+                    continue
+                seen_texts.add(sig)
+                
+                # Extract links
+                links = article.query_selector_all("a")
+                urls = [l.get_attribute("href") for l in links if l.get_attribute("href")]
+                
+                posts.append({
+                    "account": account,
+                    "text": text,
+                    "urls": urls[:5],
+                    "scraped_at": datetime.now(timezone.utc).isoformat(),
+                })
+            except:
+                continue
+        
+        # Scroll down
+        page.evaluate("window.scrollBy(0, 1500)")
+        page.wait_for_timeout(1500)
+    
+    return posts
+
+
+def is_trading_related(text):
+    text_lower = text.lower()
+    return any(kw in text_lower for kw in TRADING_KEYWORDS)
+
+
+def main():
+    from playwright.sync_api import sync_playwright
+    
+    print(f"=== X Feed Scraper (Playwright) ===")
+    print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    
+    # Export cookies from Chrome if we don't have them yet
+    if not COOKIE_FILE.exists():
+        print("No cookies found — exporting from Chrome debug session...")
+        export_cookies_from_chrome()
+    
+    with sync_playwright() as p:
+        browser = p.chromium.launch(headless=True)
+        context = browser.new_context(
+            user_agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
+            viewport={"width": 1280, "height": 900},
+        )
+        
+        load_cookies(context)
+        page = context.new_page()
+        
+        all_posts = []
+        trading_posts = []
+        
+        for account in ACCOUNTS:
+            print(f"\nScraping @{account}...", end=" ", flush=True)
+            posts = scrape_account(page, account)
+            print(f"{len(posts)} posts")
+            
+            for post in posts:
+                all_posts.append(post)
+                if is_trading_related(post["text"]):
+                    trading_posts.append(post)
+        
+        browser.close()
+    
+    print(f"\n{'='*50}")
+    print(f"Total posts: {len(all_posts)}")
+    print(f"Trading-related: {len(trading_posts)}")
+    
+    # Save results
+    timestamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M")
+    out_file = DATA_DIR / f"scan-{timestamp}.json"
+    out_file.write_text(json.dumps({
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "total_posts": len(all_posts),
+        "trading_posts": len(trading_posts),
+        "posts": trading_posts,
+    }, indent=2))
+    print(f"Saved to {out_file}")
+    
+    # Check for new posts we haven't seen before
+    seen_file = DATA_DIR / "seen_posts.json"
+    seen = set()
+    if seen_file.exists():
+        try:
+            seen = set(json.loads(seen_file.read_text()))
+        except:
+            pass
+    
+    new_posts = []
+    for post in trading_posts:
+        sig = post["text"][:150]
+        if sig not in seen:
+            new_posts.append(post)
+            seen.add(sig)
+    
+    seen_file.write_text(json.dumps(list(seen)[-5000:]))  # Keep last 5000
+    
+    if new_posts:
+        print(f"\n🔔 {len(new_posts)} NEW trading posts!")
+        for post in new_posts[:5]:
+            lines = post["text"].split("\n")
+            author = f"@{post['account']}"
+            snippet = post["text"][:200].replace("\n", " ")
+            print(f"\n  {author}: {snippet}")
+            
+            # Alert on Telegram
+            msg = f"🔍 <b>New from {author}</b>\n\n{snippet[:300]}"
+            if post.get("urls"):
+                x_urls = [u for u in post["urls"] if "x.com" in u or "twitter.com" in u]
+                if x_urls:
+                    msg += f"\n\n{x_urls[0]}"
+            send_telegram(msg)
+    else:
+        print("\nNo new trading posts since last scan.")
+
+
+if __name__ == "__main__":
+    main()

projects/kip/PROJECT.md

@@ -0,0 +1,224 @@
+# Kip — Voice Assistant
+
+**Codename:** Kip
+**Purpose:** Alexa replacement for D J's girlfriend
+**Architecture:** Steam Deck (thin client) ↔ Proxmox LXC (brains)
+
+---
+
+## Overview
+
+Kip is a privacy-first voice assistant. The Steam Deck acts as a dumb terminal (mic, speaker, screen). All intelligence runs on an LXC container on Proxmox.
+
+## Hardware
+
+### Steam Deck (Client)
+- Always-on, propped up in kitchen/living room on charging dock
+- Runs: wake word detection, audio capture, audio playback, display UI
+- Connects to LXC over local WiFi
+
+### LXC Container (Server)
+- **OS:** Ubuntu 22.04 or 24.04
+- **RAM:** 4GB recommended (Whisper needs ~1.5GB, Piper ~200MB, OpenClaw ~500MB)
+- **Disk:** 10GB (models + data)
+- **CPU:** 2-4 cores (Whisper STT is CPU-bound)
+- **Network:** Static IP on LAN, accessible from Steam Deck
+
+## Software Stack
+
+### LXC Container
+
+| Component | Purpose | Tool |
+|-----------|---------|------|
+| STT | Speech-to-text | Faster Whisper (base.en model) |
+| TTS | Text-to-speech | Piper (en_US voice) |
+| Agent | Intelligence | OpenClaw with Kip agent |
+| API | Communication | FastAPI HTTP server |
+| Data | Grocery list, timers | JSON files + SQLite |
+
+### Steam Deck
+
+| Component | Purpose | Tool |
+|-----------|---------|------|
+| Wake word | "Hey Kip" detection | OpenWakeWord |
+| Audio capture | Record after wake | PyAudio / sounddevice |
+| Audio playback | Play TTS responses | PyAudio / sounddevice |
+| UI | Display info | Web browser (fullscreen PWA) or PyQt |
+| Client | Talk to LXC | Python HTTP client |
+
+---
+
+## LXC Setup Checklist
+
+D J creates the LXC with:
+- [ ] Ubuntu 22.04 or 24.04 template
+- [ ] 4GB RAM, 2-4 CPU cores, 10GB disk
+- [ ] Static IP on LAN (e.g., 192.168.86.XX)
+- [ ] SSH access enabled (key-based)
+- [ ] Audio passthrough NOT needed (LXC doesn't play audio — Deck does)
+- [ ] Internet access (for OpenClaw, model downloads)
+- [ ] Hostname: `kip` (optional, nice to have)
+
+Once created, give Case:
+- IP address
+- SSH credentials or key
+
+---
+
+## API Design (LXC ↔ Steam Deck)
+
+### POST /listen
+Steam Deck sends audio, gets back text response + TTS audio.
+
+```
+Request:
+  Content-Type: multipart/form-data
+  Body: audio file (WAV, 16kHz mono)
+
+Response:
+  {
+    "text": "user said this",
+    "response": "Kip says this",
+    "audio": "<base64 WAV of TTS response>",
+    "ui_update": {
+      "type": "grocery_list",
+      "data": ["eggs", "milk", "bread"]
+    }
+  }
+```
+
+### GET /status
+Health check + current state (timers, lists, etc.)
+
+### GET /grocery
+Returns current grocery list (for phone web view)
+
+### POST /grocery
+Add/remove items (for phone web view)
+
+### GET /ui
+Returns current display state for the Deck's screen.
+
+---
+
+## Kip Agent (OpenClaw)
+
+Kip gets its own OpenClaw agent with:
+
+### SOUL.md (Personality)
+- Name: Kip
+- Friendly, concise, warm
+- Optimized for voice — short responses, no markdown
+- Knows the household (D J, girlfriend, 4 cats)
+- Designed for non-technical user
+
+### Capabilities
+- **Weather:** "Hey Kip, what's the weather?" → Nashville forecast
+- **Timers:** "Hey Kip, set a timer for 15 minutes" → countdown with alarm
+- **Grocery list:** "Hey Kip, add eggs to the list" → persistent list
+- **Grocery check:** "Hey Kip, what's on the grocery list?" → reads it back
+- **General Q&A:** "Hey Kip, how long do I bake chicken at 400?" → answer
+- **Time/Date:** "Hey Kip, what time is it?"
+
+### Future Capabilities
+- Calendar integration
+- Music control (Spotify)
+- Smart home (if they get devices)
+- Recipe lookup
+- Kroger API for prices/ordering
+
+---
+
+## Phone Web View
+
+Simple responsive web page served by the LXC:
+- Shows grocery list
+- Can add/remove items by tapping
+- Accessible at `http://kip.local:8080` or `http://192.168.86.XX:8080`
+- Girlfriend can open it on her iPhone in the store
+
+---
+
+## Build Phases
+
+### Phase 1: Voice Loop (MVP)
+- [ ] LXC setup + dependencies installed
+- [ ] Faster Whisper running (base.en model)
+- [ ] Piper TTS running (pick a good voice)
+- [ ] FastAPI server handling /listen endpoint
+- [ ] Steam Deck: wake word + record + send + play response
+- [ ] Test: "Hey Kip, hello" → Kip responds with voice
+- **Goal: End-to-end voice working**
+
+### Phase 2: Grocery List + Weather
+- [ ] Grocery list CRUD (voice + API)
+- [ ] Weather skill (Nashville)
+- [ ] Timer system with alarm sounds
+- [ ] Phone web view for grocery list
+- [ ] Steam Deck display: clock + weather + active timers
+- **Goal: Actually useful in the kitchen**
+
+### Phase 3: OpenClaw Integration
+- [ ] Kip agent running on OpenClaw
+- [ ] General Q&A via Claude/Qwen
+- [ ] Smarter conversations (context, follow-ups)
+- [ ] Cost optimization: simple commands (timer, list) handled locally, only complex Q&A hits Claude
+- **Goal: Smart assistant, not just a voice command box**
+
+### Phase 4: Polish
+- [ ] Custom wake word model trained on "Hey Kip"
+- [ ] Better TTS voice selection
+- [ ] Deck UI polish (nice weather widget, timer display, list view)
+- [ ] Ambient mode (clock/weather when idle)
+- [ ] Multiple room support (add Pi later)
+- **Goal: Girlfriend actually wants to use it daily**
+
+---
+
+## Cost
+
+| Item | Cost |
+|------|------|
+| Steam Deck | Already owned |
+| LXC container | Free (Proxmox) |
+| OpenWakeWord | Free (open source) |
+| Faster Whisper | Free (open source) |
+| Piper TTS | Free (open source) |
+| OpenClaw | Already running |
+| Claude API for Q&A | Covered by existing subscription |
+| **Total** | **$0** |
+
+---
+
+## File Structure
+
+```
+projects/kip/
+├── PROJECT.md          # This file
+├── server/             # LXC-side code
+│   ├── main.py         # FastAPI server
+│   ├── stt.py          # Whisper STT wrapper
+│   ├── tts.py          # Piper TTS wrapper
+│   ├── skills/         # Timer, grocery, weather handlers
+│   ├── data/           # Grocery lists, state
+│   └── requirements.txt
+├── client/             # Steam Deck code
+│   ├── kip_client.py   # Main client app
+│   ├── wake_word.py    # OpenWakeWord listener
+│   ├── audio.py        # Record/playback
+│   ├── ui/             # Display UI
+│   └── requirements.txt
+└── agent/              # Kip's OpenClaw agent config
+    ├── SOUL.md
+    └── config.yaml
+```
+
+---
+
+## Notes
+- All voice processing (STT/TTS) on LXC, not Deck — keeps client thin
+- Wake word is the ONLY thing that runs on Deck locally
+- Grocery list syncs to a web page for phone access
+- Simple commands (timer, list) should be handled WITHOUT hitting Claude to save tokens
+- Only complex Q&A ("how long to bake chicken?") routes through OpenClaw/Claude
+- Qwen on Ollama (192.168.86.137) as fallback for simple Q&A

projects/kipp/research/alexa-plus-ui.md

@@ -0,0 +1,203 @@
+# Alexa+ UI Design Patterns for Smart Displays
+
+> Research compiled Feb 2026 from Amazon official sources, The Verge, CNET, PCMag, Android Central
+
+## 1. Key UI Patterns
+
+### Home Screen / Dashboard Mode
+- **Personalized home screen** that adapts based on user, time of day, and proximity
+- **Visual ID** (facial recognition via 13MP camera) — recognizes who approaches and personalizes the display (your calendar, your smart home favorites)
+- **Proximity-aware UI**: Shows larger fonts/info when you're far away, detailed widgets when close
+- **Ambient brightness adaptation** via ambient light sensor
+- **Content rotation**: Home screen cycles through widgets, photos, suggestions, weather, calendar events
+- **Home Content Categories** toggle: Users can enable/disable content types (recipes, news, shopping suggestions, etc.) via Settings > Display & Appearance > Home Content Categories
+
+### Conversation Mode (Alexa+ AI Interaction)
+- **Voice-first, screen-second**: Conversation transcription appears on-screen during voice interaction
+- **Visual aids**: Alexa+ shows pictures, videos, recipe cards, or product images as contextual visual responses
+- **Multi-turn conversation**: Follow-up questions without re-saying wake word; conversation context maintained
+- **No persistent chat window**: The conversation UI appears as an overlay during interaction and returns to dashboard when done — it's NOT a chat app
+- **Suggestion chips**: When idle, Alexa+ can display "things to try" suggestions on the home screen
+
+### Smart Home Dashboard Mode  
+- **Full-screen smart home dashboard** accessed via Menu > Smart Home or voice ("Alexa, open my smart home dashboard")
+- **Derived from Echo Hub interface** — grid of device controls with room switching
+- **Map View** integration — visual floor plan layout of devices (from Alexa Map View feature)
+- **Camera feeds** inline — view Ring/security camera live feeds directly
+- **Home/Away/Night modes** — single-tap to change home state
+- **Device status at a glance** — lights, locks, thermostats, cameras all visible
+
+### Media/Entertainment Mode
+- **Media control center**: Dedicated browsing pages for music, ambient sounds, podcasts, books
+- **TV & Videos experience**: Aggregated content from multiple streaming providers
+- **Full-screen video playback** for shows, recipes, video calls
+
+## 2. Color Schemes and Typography
+
+### Visual Design Language
+- **Clean, modern aesthetic** — described as "cleaner, sleeker full-screen UI" (The Verge)
+- **Dark backgrounds** with bright accent elements for widgets (typical of ambient displays)
+- **Adaptive brightness** — screen adjusts to room lighting automatically
+- **Photo-forward**: When idle, displays personal photos from Amazon Photos as ambient wallpaper/slideshow
+- **Card-based UI**: Information presented in distinct card/widget containers with rounded corners
+- **High contrast readability**: Font sizes scale based on distance (proximity sensor)
+
+### Typography
+- Amazon's custom typeface family (Amazon Ember)
+- **Large, glanceable text** for time, weather, calendar — designed to be read across a room
+- **Smaller detail text** when user is close/interacting
+- **Bold headers** for widget titles, lighter weight for content
+
+### Color Palette (observed from press images)
+- **Background**: Dark navy/charcoal or photo wallpaper
+- **Widget cards**: Semi-transparent dark cards with white text, or light cards with dark text
+- **Accent colors**: Alexa blue (#00CAFF-ish), warm amber for alerts/reminders
+- **Smart home controls**: Color-coded by device type (warm yellow for lights, blue for locks, etc.)
+- **Conversation UI**: Blue gradient for Alexa responses, lighter for user transcription
+
+## 3. Widget Types and Layouts
+
+### Available Widget Types (Echo Show 15/21 + new Echo Show 8/11)
+- **Clock/Time** — large, always-visible
+- **Weather** — current conditions + forecast
+- **Calendar** — daily/weekly/monthly views, multi-calendar family support
+- **Smart Home controls** — quick-access device toggles
+- **Camera feeds** — live view from Ring cameras
+- **Shopping list** — editable list
+- **To-do / Tasks** — task widget (coming soon as of late 2025)
+- **Reminders** — upcoming reminders
+- **Music/Now Playing** — currently playing media with controls
+- **Photos** — Amazon Photos slideshow
+- **Timers** — multiple cooking timers visible simultaneously
+- **Recipes** — personalized recipe suggestions (coming soon)
+- **News/Headlines** — rotating news content
+- **Sticky Notes** — personal notes
+- **Skill widgets** — third-party skill mini-views
+- **Personalized notifications** — follow-ups from recent conversations
+
+### Layout System
+- **Grid-based widget layout** — widgets snap to a grid
+- **Resizable widgets** — can be expanded/collapsed
+- **Drag-and-drop rearrangement** — long-press to enter edit mode
+- **Widget Gallery** — swipe down from top to access available widgets
+- **Different layouts for different screen sizes**:
+  - Echo Show 8 (8.7"): 1-2 widget columns, more compact
+  - Echo Show 11 (11"): 2-3 widget columns
+  - Echo Show 15 (15.6"): Full widget panel, landscape orientation, wall-mountable
+  - Echo Show 21 (21"): Largest canvas, most widgets visible simultaneously
+- **Left panel + right content**: On larger screens (15, 21), widget sidebar on left with main content area on right
+
+### Widget Interaction
+- **Tap to expand** — widgets open to full-screen detail view
+- **Swipe left** on widget pane handle to access rearrangement
+- **Voice-addressable** — "Alexa, show my calendar" opens calendar widget full-screen
+
+## 4. Conversation Interface Behavior
+
+### How It Appears
+1. **Wake word trigger** ("Alexa") — blue animation ring/bar appears at bottom of screen
+2. **Listening state** — screen shows blue animated waveform/indicator
+3. **Processing** — brief thinking animation
+4. **Response** — text transcription of conversation appears; visual content (images, cards, lists) shown alongside
+5. **Multi-turn**: Screen stays in conversation mode; displays ongoing transcript
+6. **Suggestion follow-ups**: After response, may show tappable suggestion chips
+
+### How It Disappears
+1. **Conversation ends** (timeout or "thank you") — UI fades back to home screen
+2. **Gradual transition** — conversation results may persist briefly as a notification/widget
+3. **No chat history** visible on home screen — it's ephemeral
+4. **Results persist contextually**: e.g., if you asked about weather, the weather widget may be promoted to prominent position
+
+### Visual Treatment During Conversation
+- **Overlay model**: Conversation takes over most of the screen but feels temporary
+- **Blue accent theming** during active Alexa interaction
+- **Cards/results** appear with smooth animations
+- **Shopping lists, recipes, timers** created during conversation persist as widgets after conversation ends
+
+## 5. Home Dashboard vs Chat App — Design Philosophy
+
+### What Makes It Feel Like a HOME DASHBOARD:
+- **Ambient-first**: Default state is glanceable information, not a conversation thread
+- **Photo wallpaper**: Personal photos rotate as background — feels personal and decorative
+- **Widget grid**: Calendar, weather, smart home status visible at a glance — like a family command center
+- **Proactive, not reactive**: Shows relevant info (upcoming events, weather changes) without being asked
+- **Environmental awareness**: Uses presence sensors, temperature, ambient light to adapt
+- **Family-centric**: Visual ID recognizes different family members, shows personalized content per person
+- **Physical space integration**: Smart home controls are front-and-center; the device is about controlling YOUR space
+- **Always-on display**: Designed to be glanced at throughout the day, not actively used continuously
+
+### What Makes It NOT a Chat App:
+- **No persistent conversation history** on the display
+- **No text input by default** (keyboard available for accessibility but voice is primary)
+- **Conversation is ephemeral** — appears and disappears with the interaction
+- **No message threads or bubbles** — responses are full-screen cards, not chat bubbles
+- **Content-forward**: After a conversation, the RESULT persists (timer, reminder, shopping list) not the conversation itself
+- **The display returns to ambient/dashboard mode** — chat doesn't persist as the primary view
+
+### Key Design Principles:
+1. **Glanceable over readable** — information hierarchy favors quick scanning
+2. **Voice-first, touch-second** — primary interaction is voice; touch is supplementary
+3. **Contextual intelligence** — UI adapts to who's looking, time of day, what's happening
+4. **Ambient computing** — the device fades into the background of your home, activates when needed
+5. **Family hub** — shared device, personalized per user, central to household operations
+6. **Proactive assistance** — surface relevant info before being asked (upcoming event, package delivery, missed routine)
+
+## 6. Screenshot URLs and Visual References
+
+### Official Amazon Press Images
+- About Amazon Echo Show guide: https://www.aboutamazon.com/news/devices/getting-started-echo-show-8-11-alexa-plus-features
+- New Alexa+ features overview: https://www.aboutamazon.com/news/devices/new-alexa-generative-artificial-intelligence
+
+### The Verge Hands-On Photos
+- Smart home dashboard on Echo Show 21: https://platform.theverge.com/wp-content/uploads/sites/2/2025/02/IMG_2557.jpeg
+- Echo Show UI with widgets: https://platform.theverge.com/wp-content/uploads/sites/2/2025/02/IMG_2564.jpeg
+- Shopping list UI: https://platform.theverge.com/wp-content/uploads/sites/2/2025/02/shopping-list.png
+- Full article: https://www.theverge.com/news/621008/hands-on-with-alexa-plus-smart-home-echo-show-21
+
+### Android Central Widget Customization Screenshots
+- Echo Show 15 widget gallery: https://cdn.mos.cms.futurecdn.net/mcx9KfDjTDnTyTWVygkk73.jpg
+- Widget pane expanded: https://cdn.mos.cms.futurecdn.net/WivGCjDJhDdAA3FYWeJFPW.jpg
+- Widget rearrangement: https://cdn.mos.cms.futurecdn.net/fChVCpuYSsEDewQUpxb3QQ.jpg
+- Widget customization: https://cdn.mos.cms.futurecdn.net/5B84ngX2GzNqRsdy33aUua.jpg
+- Full article: https://www.androidcentral.com/how-customize-alexa-widgets-amazon-echo-show-15
+
+### Review Articles with UI Photos
+- CNET Echo Show 11 review: https://www.cnet.com/home/smart-home/this-smart-display-is-the-best-add-on-my-kitchen-has-ever-had/
+- PCMag Echo Show 8 (2025) review: https://www.pcmag.com/reviews/amazon-echo-show-8-4th-gen-2025
+
+## 7. Technical Framework Notes
+
+### Alexa Presentation Language (APL)
+- Amazon's visual design framework for building interactive voice+visual experiences
+- Supports responsive layouts across different Echo Show screen sizes
+- Developers can build custom visual skill responses using APL
+- Reference: https://developer.amazon.com/en-US/alexa/alexa-haus/alexa-presentation-language
+
+### Device Specifications (for UI design reference)
+| Device | Screen Size | Resolution | Orientation |
+|--------|------------|------------|-------------|
+| Echo Show 5 | 5.5" | 960×480 | Landscape |
+| Echo Show 8 (2025) | 8.7" | 1340×800 | Landscape |
+| Echo Show 11 (2025) | 11" | 1920×1200 | Landscape |
+| Echo Show 15 | 15.6" | 1920×1080 | Landscape (wall-mount) |
+| Echo Show 21 | 21" | 1920×1200 | Landscape (wall-mount) |
+
+### Key Hardware Features Relevant to UI
+- **AZ3 Pro chip** with AI accelerator — enables smooth transitions, multitasking between voice and visual
+- **Omnisense presence sensor** (ultrasound + Wi-Fi radar) — fine motion detection for proximity-aware UI
+- **13MP camera with Visual ID** — facial recognition for personalization
+- **Ambient light sensor** — adaptive brightness
+- **Ambient temperature sensor** — can trigger routines
+
+---
+
+## 8. Key Takeaways for KIPP
+
+1. **Dashboard-first, conversation-second**: The display should feel like a home information hub that happens to have AI conversation capabilities, not a chatbot with a screen
+2. **Ephemeral conversation UI**: Conversations appear as overlays and fade back to dashboard — results persist, conversations don't
+3. **Widget grid system**: Modular, customizable cards that users arrange to their preference
+4. **Proximity awareness**: Different information density based on distance from screen
+5. **Visual ID / personalization**: Per-user customization is key to making a shared device feel personal
+6. **Ambient mode is the default state**: Photos, clock, weather — the device should be beautiful when idle
+7. **Proactive intelligence**: Surface information before being asked — upcoming events, deliveries, routine changes
+8. **Voice-first interaction model**: Touch/visual is supplementary to voice

projects/market-watch/README.md

@@ -0,0 +1,86 @@
+# Market Watch - Multiplayer GARP Paper Trading Simulator
+
+Multiplayer paper trading simulator implementing a **Growth at a Reasonable Price (GARP)** strategy. Compete against Case (AI) and other players.
+
+## How It Works
+
+- **Create or join a game** with configurable starting cash
+- **Trade manually** via the web portal or Telegram
+- **Case (AI)** trades autonomously using the GARP strategy
+- **Leaderboard** tracks who's winning by % return
+
+## GARP Filter Criteria
+
+| Metric | Threshold |
+|--------|-----------|
+| Revenue Growth | ≥ 10% |
+| Trailing P/E | < 25 |
+| Forward P/E | < 15 |
+| PEG Ratio | < 1.2 (if available) |
+| EPS Growth | > 15% |
+| ROE | > 5% |
+| Quick Ratio | > 1.5 (if available) |
+| Debt/Equity | < 35% (if available) |
+| Market Cap | > $5B |
+
+### Case's Trading Rules
+- **Buy:** GARP filter pass + RSI < 70 + not within 2% of 52wk high + max 10% per position + max 15 positions
+- **Sell:** Fails GARP rescan OR 10% trailing stop-loss OR RSI > 80
+- **Universe:** S&P 500 + S&P 400 MidCap (~900 stocks)
+
+## Architecture
+
+| File | Description |
+|------|-------------|
+| `game_engine.py` | Multiplayer game/player/portfolio engine |
+| `scanner.py` | GARP scanner across S&P 500 + 400 |
+| `trader.py` | Case's autonomous trading logic |
+| `run_daily.py` | Daily orchestrator (scan → trade → snapshot → alert) |
+| `portfolio.py` | Backward-compatible wrapper for single-player |
+| `portal/server.py` | Web dashboard with multiplayer UI |
+
+### Data Structure
+```
+data/games/{game_id}/
+├── game.json                    # Game config, players, rules
+└── players/{username}/
+    ├── portfolio.json           # Current positions & cash
+    ├── trades.json              # Trade history
+    └── snapshots.json           # Daily value snapshots
+```
+
+## Web Portal
+
+**URL:** http://marketwatch.local (or http://localhost:8889)
+
+- **Home:** List of games, create new game
+- **Game page:** Leaderboard, join game
+- **Player page:** Portfolio, trade form, performance chart, trade history
+
+### API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/api/games` | Create game (form: name, starting_cash, end_date) |
+| POST | `/api/games/{id}/join` | Join game (form: username) |
+| POST | `/api/games/{id}/players/{user}/trade` | Trade (form: action, ticker, shares) |
+| GET | `/api/games/{id}/leaderboard` | Get leaderboard JSON |
+| GET | `/api/games/{id}/players/{user}/portfolio` | Get portfolio JSON |
+
+## Systemd Services
+
+| Service | Schedule |
+|---------|----------|
+| `market-watch.timer` | Mon-Fri 9:00 AM + 3:30 PM CST |
+| `market-watch-portal.service` | Always running (port 8889) |
+
+```bash
+systemctl --user status market-watch.timer
+systemctl --user status market-watch-portal
+journalctl --user -u market-watch -f
+```
+
+## Telegram
+
+Players can trade via: `buy AAPL 10` or `sell BAC 50`
+Daily summaries with leaderboard sent automatically.

projects/market-watch/data/games/7ebf65c7/game.json

@@ -0,0 +1,13 @@
+{
+  "game_id": "7ebf65c7",
+  "name": "GARP Challenge",
+  "starting_cash": 100000.0,
+  "start_date": "2026-02-08",
+  "end_date": null,
+  "creator": "case",
+  "created_at": "2026-02-08T15:15:43.402301",
+  "players": [
+    "case"
+  ],
+  "status": "active"
+}

projects/market-watch/data/games/7ebf65c7/players/case/portfolio.json

@@ -0,0 +1,125 @@
+{
+  "cash": 870.6802087402511,
+  "positions": {
+    "ALLY": {
+      "shares": 156,
+      "avg_cost": 42.65,
+      "current_price": 42.38999938964844,
+      "entry_date": "2026-02-09T10:55:58.244488",
+      "entry_reason": "GARP signal: PE=18.0, FwdPE=6.76, RevGr=12.0%, EPSGr=265.4%, RSI=53.23",
+      "trailing_stop": 38.50199890136719
+    },
+    "JHG": {
+      "shares": 138,
+      "avg_cost": 48.21,
+      "current_price": 48.2400016784668,
+      "entry_date": "2026-02-09T10:55:58.245351",
+      "entry_reason": "GARP signal: PE=9.22, FwdPE=9.96, RevGr=61.3%, EPSGr=243.6%, RSI=68.71",
+      "trailing_stop": 43.46999931335449
+    },
+    "INCY": {
+      "shares": 61,
+      "avg_cost": 108.69,
+      "current_price": 100.05000305175781,
+      "entry_date": "2026-02-09T10:55:58.246289",
+      "entry_reason": "GARP signal: PE=18.42, FwdPE=13.76, RevGr=20.0%, EPSGr=290.7%, RSI=63.48",
+      "trailing_stop": 98.12699890136719
+    },
+    "PINS": {
+      "shares": 332,
+      "avg_cost": 20.06,
+      "current_price": 20.329999923706055,
+      "entry_date": "2026-02-09T10:55:58.247262",
+      "entry_reason": "GARP signal: PE=7.04, FwdPE=10.61, RevGr=16.8%, EPSGr=225.0%, RSI=19.14",
+      "trailing_stop": 18.59850082397461
+    },
+    "EXEL": {
+      "shares": 152,
+      "avg_cost": 43.8,
+      "current_price": 42.97999954223633,
+      "entry_date": "2026-02-09T10:55:58.252764",
+      "entry_reason": "GARP signal: PE=18.4, FwdPE=12.76, RevGr=10.8%, EPSGr=72.5%, RSI=50.12",
+      "trailing_stop": 39.573001098632815
+    },
+    "CART": {
+      "shares": 187,
+      "avg_cost": 35.49,
+      "current_price": 34.619998931884766,
+      "entry_date": "2026-02-09T10:55:58.254418",
+      "entry_reason": "GARP signal: PE=19.5, FwdPE=9.05, RevGr=10.2%, EPSGr=21.1%, RSI=37.75",
+      "trailing_stop": 31.941000000000003
+    },
+    "UBSI": {
+      "shares": 148,
+      "avg_cost": 44.93,
+      "current_price": 44.63999938964844,
+      "entry_date": "2026-02-10T09:06:30.696005",
+      "entry_reason": "GARP signal: PE=13.74, FwdPE=11.93, RevGr=22.1%, EPSGr=32.1%, RSI=67.45",
+      "trailing_stop": 40.437
+    },
+    "WTFC": {
+      "shares": 42,
+      "avg_cost": 158.12,
+      "current_price": 156.07000732421875,
+      "entry_date": "2026-02-10T09:06:30.699573",
+      "entry_reason": "GARP signal: PE=13.87, FwdPE=11.79, RevGr=10.5%, EPSGr=19.4%, RSI=62.2",
+      "trailing_stop": 142.30800000000002
+    },
+    "FHN": {
+      "shares": 258,
+      "avg_cost": 25.64,
+      "current_price": 25.64,
+      "entry_date": "2026-02-10T15:36:28.434830",
+      "entry_reason": "GARP signal: PE=13.71, FwdPE=10.94, RevGr=23.7%, EPSGr=74.9%, RSI=58.44",
+      "trailing_stop": 23.076
+    },
+    "FNB": {
+      "shares": 354,
+      "avg_cost": 18.69,
+      "current_price": 18.69,
+      "entry_date": "2026-02-10T15:36:28.437094",
+      "entry_reason": "GARP signal: PE=11.98, FwdPE=9.55, RevGr=26.4%, EPSGr=56.5%, RSI=62.57",
+      "trailing_stop": 16.821
+    },
+    "WAL": {
+      "shares": 69,
+      "avg_cost": 94.92,
+      "current_price": 94.92,
+      "entry_date": "2026-02-10T15:36:28.439819",
+      "entry_reason": "GARP signal: PE=10.87, FwdPE=7.98, RevGr=16.6%, EPSGr=32.9%, RSI=60.46",
+      "trailing_stop": 85.428
+    },
+    "ONB": {
+      "shares": 259,
+      "avg_cost": 25.53,
+      "current_price": 25.53,
+      "entry_date": "2026-02-10T15:36:28.441188",
+      "entry_reason": "GARP signal: PE=14.26, FwdPE=8.9, RevGr=41.4%, EPSGr=17.2%, RSI=68.73",
+      "trailing_stop": 22.977
+    },
+    "ZION": {
+      "shares": 103,
+      "avg_cost": 64.08,
+      "current_price": 64.08,
+      "entry_date": "2026-02-10T15:36:28.442626",
+      "entry_reason": "GARP signal: PE=10.66, FwdPE=9.8, RevGr=13.6%, EPSGr=31.4%, RSI=60.76",
+      "trailing_stop": 57.672
+    },
+    "EWBC": {
+      "shares": 54,
+      "avg_cost": 120.54,
+      "current_price": 120.54,
+      "entry_date": "2026-02-10T15:36:28.444928",
+      "entry_reason": "GARP signal: PE=12.66, FwdPE=11.0, RevGr=21.6%, EPSGr=21.3%, RSI=65.92",
+      "trailing_stop": 108.486
+    },
+    "BAC": {
+      "shares": 119,
+      "avg_cost": 55.39,
+      "current_price": 55.39,
+      "entry_date": "2026-02-10T15:36:28.446464",
+      "entry_reason": "GARP signal: PE=14.54, FwdPE=11.17, RevGr=13.2%, EPSGr=20.9%, RSI=69.17",
+      "trailing_stop": 49.851
+    }
+  }
+}

projects/market-watch/data/games/7ebf65c7/players/case/snapshots.json

@@ -0,0 +1,18 @@
+[
+  {
+    "date": "2026-02-09",
+    "total_value": 100055.9,
+    "total_pnl": 55.9,
+    "pnl_pct": 0.06,
+    "cash": 60255.3,
+    "num_positions": 6
+  },
+  {
+    "date": "2026-02-10",
+    "total_value": 99255.75,
+    "total_pnl": -744.25,
+    "pnl_pct": -0.74,
+    "cash": 870.68,
+    "num_positions": 15
+  }
+]

projects/market-watch/data/games/7ebf65c7/players/case/trades.json

@@ -0,0 +1,157 @@
+[
+  {
+    "action": "BUY",
+    "ticker": "DUOL",
+    "shares": 57,
+    "price": 116.35,
+    "cost": 6631.95,
+    "reason": "GARP signal: PE=14.65, FwdPE=14.71, RevGr=41.1%, EPSGr=1114.3%, RSI=23.44",
+    "timestamp": "2026-02-09T10:55:58.243888"
+  },
+  {
+    "action": "BUY",
+    "ticker": "ALLY",
+    "shares": 156,
+    "price": 42.65,
+    "cost": 6653.4,
+    "reason": "GARP signal: PE=18.0, FwdPE=6.76, RevGr=12.0%, EPSGr=265.4%, RSI=53.23",
+    "timestamp": "2026-02-09T10:55:58.244719"
+  },
+  {
+    "action": "BUY",
+    "ticker": "JHG",
+    "shares": 138,
+    "price": 48.21,
+    "cost": 6652.98,
+    "reason": "GARP signal: PE=9.22, FwdPE=9.96, RevGr=61.3%, EPSGr=243.6%, RSI=68.71",
+    "timestamp": "2026-02-09T10:55:58.245596"
+  },
+  {
+    "action": "BUY",
+    "ticker": "INCY",
+    "shares": 61,
+    "price": 108.69,
+    "cost": 6630.09,
+    "reason": "GARP signal: PE=18.42, FwdPE=13.76, RevGr=20.0%, EPSGr=290.7%, RSI=63.48",
+    "timestamp": "2026-02-09T10:55:58.246554"
+  },
+  {
+    "action": "BUY",
+    "ticker": "PINS",
+    "shares": 332,
+    "price": 20.06,
+    "cost": 6659.92,
+    "reason": "GARP signal: PE=7.04, FwdPE=10.61, RevGr=16.8%, EPSGr=225.0%, RSI=19.14",
+    "timestamp": "2026-02-09T10:55:58.247536"
+  },
+  {
+    "action": "BUY",
+    "ticker": "EXEL",
+    "shares": 152,
+    "price": 43.8,
+    "cost": 6657.6,
+    "reason": "GARP signal: PE=18.4, FwdPE=12.76, RevGr=10.8%, EPSGr=72.5%, RSI=50.12",
+    "timestamp": "2026-02-09T10:55:58.253048"
+  },
+  {
+    "action": "BUY",
+    "ticker": "CART",
+    "shares": 187,
+    "price": 35.49,
+    "cost": 6636.63,
+    "reason": "GARP signal: PE=19.5, FwdPE=9.05, RevGr=10.2%, EPSGr=21.1%, RSI=37.75",
+    "timestamp": "2026-02-09T10:55:58.254721"
+  },
+  {
+    "action": "SELL",
+    "ticker": "DUOL",
+    "shares": 57,
+    "price": 118.91000366210938,
+    "proceeds": 6777.87,
+    "realized_pnl": 145.92,
+    "entry_price": 116.35,
+    "reason": "No longer passes GARP filter",
+    "timestamp": "2026-02-09T15:36:18.884898"
+  },
+  {
+    "action": "BUY",
+    "ticker": "UBSI",
+    "shares": 148,
+    "price": 44.93,
+    "cost": 6649.64,
+    "reason": "GARP signal: PE=13.74, FwdPE=11.93, RevGr=22.1%, EPSGr=32.1%, RSI=67.45",
+    "timestamp": "2026-02-10T09:06:30.696435"
+  },
+  {
+    "action": "BUY",
+    "ticker": "WTFC",
+    "shares": 42,
+    "price": 158.12,
+    "cost": 6641.04,
+    "reason": "GARP signal: PE=13.87, FwdPE=11.79, RevGr=10.5%, EPSGr=19.4%, RSI=62.2",
+    "timestamp": "2026-02-10T09:06:30.699988"
+  },
+  {
+    "action": "BUY",
+    "ticker": "FHN",
+    "shares": 258,
+    "price": 25.64,
+    "cost": 6615.12,
+    "reason": "GARP signal: PE=13.71, FwdPE=10.94, RevGr=23.7%, EPSGr=74.9%, RSI=58.44",
+    "timestamp": "2026-02-10T15:36:28.436095"
+  },
+  {
+    "action": "BUY",
+    "ticker": "FNB",
+    "shares": 354,
+    "price": 18.69,
+    "cost": 6616.26,
+    "reason": "GARP signal: PE=11.98, FwdPE=9.55, RevGr=26.4%, EPSGr=56.5%, RSI=62.57",
+    "timestamp": "2026-02-10T15:36:28.437460"
+  },
+  {
+    "action": "BUY",
+    "ticker": "WAL",
+    "shares": 69,
+    "price": 94.92,
+    "cost": 6549.48,
+    "reason": "GARP signal: PE=10.87, FwdPE=7.98, RevGr=16.6%, EPSGr=32.9%, RSI=60.46",
+    "timestamp": "2026-02-10T15:36:28.440182"
+  },
+  {
+    "action": "BUY",
+    "ticker": "ONB",
+    "shares": 259,
+    "price": 25.53,
+    "cost": 6612.27,
+    "reason": "GARP signal: PE=14.26, FwdPE=8.9, RevGr=41.4%, EPSGr=17.2%, RSI=68.73",
+    "timestamp": "2026-02-10T15:36:28.441586"
+  },
+  {
+    "action": "BUY",
+    "ticker": "ZION",
+    "shares": 103,
+    "price": 64.08,
+    "cost": 6600.24,
+    "reason": "GARP signal: PE=10.66, FwdPE=9.8, RevGr=13.6%, EPSGr=31.4%, RSI=60.76",
+    "timestamp": "2026-02-10T15:36:28.443023"
+  },
+  {
+    "action": "BUY",
+    "ticker": "EWBC",
+    "shares": 54,
+    "price": 120.54,
+    "cost": 6509.16,
+    "reason": "GARP signal: PE=12.66, FwdPE=11.0, RevGr=21.6%, EPSGr=21.3%, RSI=65.92",
+    "timestamp": "2026-02-10T15:36:28.445341"
+  },
+  {
+    "action": "BUY",
+    "ticker": "BAC",
+    "shares": 119,
+    "price": 55.39,
+    "cost": 6591.41,
+    "reason": "GARP signal: PE=14.54, FwdPE=11.17, RevGr=13.2%, EPSGr=20.9%, RSI=69.17",
+    "timestamp": "2026-02-10T15:36:28.446981"
+  }
+]

projects/market-watch/data/history.json

@@ -0,0 +1 @@
+[]

projects/market-watch/data/logs/2026-02-09.json

@@ -0,0 +1,310 @@
+[
+  {
+    "timestamp": "2026-02-09T10:55:58.244115",
+    "action": "BUY",
+    "ticker": "DUOL",
+    "reason": "GARP signal: PE=14.65, FwdPE=14.71, RevGr=41.1%, EPSGr=1114.3%, RSI=23.44",
+    "details": {
+      "success": true,
+      "ticker": "DUOL",
+      "shares": 57,
+      "price": 116.35,
+      "cost": 6631.95,
+      "cash_remaining": 93368.05
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.244903",
+    "action": "BUY",
+    "ticker": "ALLY",
+    "reason": "GARP signal: PE=18.0, FwdPE=6.76, RevGr=12.0%, EPSGr=265.4%, RSI=53.23",
+    "details": {
+      "success": true,
+      "ticker": "ALLY",
+      "shares": 156,
+      "price": 42.65,
+      "cost": 6653.4,
+      "cash_remaining": 86714.65
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.245798",
+    "action": "BUY",
+    "ticker": "JHG",
+    "reason": "GARP signal: PE=9.22, FwdPE=9.96, RevGr=61.3%, EPSGr=243.6%, RSI=68.71",
+    "details": {
+      "success": true,
+      "ticker": "JHG",
+      "shares": 138,
+      "price": 48.21,
+      "cost": 6652.98,
+      "cash_remaining": 80061.67
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.246780",
+    "action": "BUY",
+    "ticker": "INCY",
+    "reason": "GARP signal: PE=18.42, FwdPE=13.76, RevGr=20.0%, EPSGr=290.7%, RSI=63.48",
+    "details": {
+      "success": true,
+      "ticker": "INCY",
+      "shares": 61,
+      "price": 108.69,
+      "cost": 6630.09,
+      "cash_remaining": 73431.58
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.247771",
+    "action": "BUY",
+    "ticker": "PINS",
+    "reason": "GARP signal: PE=7.04, FwdPE=10.61, RevGr=16.8%, EPSGr=225.0%, RSI=19.14",
+    "details": {
+      "success": true,
+      "ticker": "PINS",
+      "shares": 332,
+      "price": 20.06,
+      "cost": 6659.92,
+      "cash_remaining": 66771.66
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.248094",
+    "action": "SKIP",
+    "ticker": "VLY",
+    "reason": "RSI too high (78.6 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.248884",
+    "action": "SKIP",
+    "ticker": "FHN",
+    "reason": "RSI too high (76.1 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.249433",
+    "action": "SKIP",
+    "ticker": "FNB",
+    "reason": "RSI too high (71.9 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.250037",
+    "action": "SKIP",
+    "ticker": "SSB",
+    "reason": "RSI too high (92.2 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.250674",
+    "action": "SKIP",
+    "ticker": "WBS",
+    "reason": "RSI too high (82.1 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.251353",
+    "action": "SKIP",
+    "ticker": "WAL",
+    "reason": "RSI too high (71.8 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.251944",
+    "action": "SKIP",
+    "ticker": "ONB",
+    "reason": "RSI too high (81.2 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.253279",
+    "action": "BUY",
+    "ticker": "EXEL",
+    "reason": "GARP signal: PE=18.4, FwdPE=12.76, RevGr=10.8%, EPSGr=72.5%, RSI=50.12",
+    "details": {
+      "success": true,
+      "ticker": "EXEL",
+      "shares": 152,
+      "price": 43.8,
+      "cost": 6657.6,
+      "cash_remaining": 60114.06
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.253702",
+    "action": "SKIP",
+    "ticker": "ZION",
+    "reason": "RSI too high (74.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.254967",
+    "action": "BUY",
+    "ticker": "CART",
+    "reason": "GARP signal: PE=19.5, FwdPE=9.05, RevGr=10.2%, EPSGr=21.1%, RSI=37.75",
+    "details": {
+      "success": true,
+      "ticker": "CART",
+      "shares": 187,
+      "price": 35.49,
+      "cost": 6636.63,
+      "cash_remaining": 53477.43
+    }
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.255400",
+    "action": "SKIP",
+    "ticker": "CFG",
+    "reason": "RSI too high (80.9 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.256002",
+    "action": "SKIP",
+    "ticker": "UBSI",
+    "reason": "RSI too high (80.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.256804",
+    "action": "SKIP",
+    "ticker": "EWBC",
+    "reason": "RSI too high (79.3 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.257554",
+    "action": "SKIP",
+    "ticker": "FITB",
+    "reason": "Too close to 52wk high (1.8% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.258277",
+    "action": "SKIP",
+    "ticker": "BAC",
+    "reason": "RSI too high (78.3 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T10:55:58.259039",
+    "action": "SKIP",
+    "ticker": "WTFC",
+    "reason": "RSI too high (72.6 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:18.885237",
+    "action": "SELL",
+    "ticker": "DUOL",
+    "reason": "No longer passes GARP filter",
+    "details": {
+      "success": true,
+      "ticker": "DUOL",
+      "shares": 57,
+      "price": 118.91000366210938,
+      "proceeds": 6777.87,
+      "realized_pnl": 145.92
+    }
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.302964",
+    "action": "SKIP",
+    "ticker": "VLY",
+    "reason": "RSI too high (78.3 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.303492",
+    "action": "SKIP",
+    "ticker": "FHN",
+    "reason": "RSI too high (72.4 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.304721",
+    "action": "SKIP",
+    "ticker": "FNB",
+    "reason": "RSI too high (71.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.305710",
+    "action": "SKIP",
+    "ticker": "SSB",
+    "reason": "RSI too high (89.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.306687",
+    "action": "SKIP",
+    "ticker": "WBS",
+    "reason": "RSI too high (82.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.307754",
+    "action": "SKIP",
+    "ticker": "ONB",
+    "reason": "RSI too high (77.6 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.308706",
+    "action": "SKIP",
+    "ticker": "WAL",
+    "reason": "RSI too high (71.7 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.309624",
+    "action": "SKIP",
+    "ticker": "ZION",
+    "reason": "RSI too high (73.3 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.310657",
+    "action": "SKIP",
+    "ticker": "CFG",
+    "reason": "RSI too high (78.5 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.311641",
+    "action": "SKIP",
+    "ticker": "UBSI",
+    "reason": "RSI too high (77.5 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.312722",
+    "action": "SKIP",
+    "ticker": "EWBC",
+    "reason": "RSI too high (78.6 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.313689",
+    "action": "SKIP",
+    "ticker": "FITB",
+    "reason": "Too close to 52wk high (1.9% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.314689",
+    "action": "SKIP",
+    "ticker": "BAC",
+    "reason": "RSI too high (78.1 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-09T15:36:19.315714",
+    "action": "SKIP",
+    "ticker": "WTFC",
+    "reason": "RSI too high (70.2 > 70)",
+    "details": {}
+  }
+]

projects/market-watch/data/logs/2026-02-10.json

@@ -0,0 +1,240 @@
+[
+  {
+    "timestamp": "2026-02-10T09:06:30.678934",
+    "action": "SKIP",
+    "ticker": "VLY",
+    "reason": "RSI too high (74.2 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.689088",
+    "action": "SKIP",
+    "ticker": "FHN",
+    "reason": "Too close to 52wk high (1.5% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.689343",
+    "action": "SKIP",
+    "ticker": "FNB",
+    "reason": "Too close to 52wk high (1.1% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.690376",
+    "action": "SKIP",
+    "ticker": "SSB",
+    "reason": "RSI too high (85.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.691362",
+    "action": "SKIP",
+    "ticker": "WBS",
+    "reason": "RSI too high (79.7 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.692156",
+    "action": "SKIP",
+    "ticker": "ONB",
+    "reason": "RSI too high (71.2 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.692901",
+    "action": "SKIP",
+    "ticker": "WAL",
+    "reason": "Too close to 52wk high (1.0% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.694010",
+    "action": "SKIP",
+    "ticker": "ZION",
+    "reason": "Too close to 52wk high (1.5% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.694846",
+    "action": "SKIP",
+    "ticker": "CFG",
+    "reason": "RSI too high (72.1 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.696787",
+    "action": "BUY",
+    "ticker": "UBSI",
+    "reason": "GARP signal: PE=13.74, FwdPE=11.93, RevGr=22.1%, EPSGr=32.1%, RSI=67.45",
+    "details": {
+      "success": true,
+      "ticker": "UBSI",
+      "shares": 148,
+      "price": 44.93,
+      "cost": 6649.64,
+      "cash_remaining": 53605.66
+    }
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.697122",
+    "action": "SKIP",
+    "ticker": "EWBC",
+    "reason": "RSI too high (74.7 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.697710",
+    "action": "SKIP",
+    "ticker": "FITB",
+    "reason": "Too close to 52wk high (1.2% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.698565",
+    "action": "SKIP",
+    "ticker": "BAC",
+    "reason": "RSI too high (78.8 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T09:06:30.700317",
+    "action": "BUY",
+    "ticker": "WTFC",
+    "reason": "GARP signal: PE=13.87, FwdPE=11.79, RevGr=10.5%, EPSGr=19.4%, RSI=62.2",
+    "details": {
+      "success": true,
+      "ticker": "WTFC",
+      "shares": 42,
+      "price": 158.12,
+      "cost": 6641.04,
+      "cash_remaining": 46964.62
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.432393",
+    "action": "SKIP",
+    "ticker": "VLY",
+    "reason": "RSI too high (71.8 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.436450",
+    "action": "BUY",
+    "ticker": "FHN",
+    "reason": "GARP signal: PE=13.71, FwdPE=10.94, RevGr=23.7%, EPSGr=74.9%, RSI=58.44",
+    "details": {
+      "success": true,
+      "ticker": "FHN",
+      "shares": 258,
+      "price": 25.64,
+      "cost": 6615.12,
+      "cash_remaining": 40349.5
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.437790",
+    "action": "BUY",
+    "ticker": "FNB",
+    "reason": "GARP signal: PE=11.98, FwdPE=9.55, RevGr=26.4%, EPSGr=56.5%, RSI=62.57",
+    "details": {
+      "success": true,
+      "ticker": "FNB",
+      "shares": 354,
+      "price": 18.69,
+      "cost": 6616.26,
+      "cash_remaining": 33733.24
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.438236",
+    "action": "SKIP",
+    "ticker": "SSB",
+    "reason": "RSI too high (71.1 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.438922",
+    "action": "SKIP",
+    "ticker": "WBS",
+    "reason": "RSI too high (78.0 > 70)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.440504",
+    "action": "BUY",
+    "ticker": "WAL",
+    "reason": "GARP signal: PE=10.87, FwdPE=7.98, RevGr=16.6%, EPSGr=32.9%, RSI=60.46",
+    "details": {
+      "success": true,
+      "ticker": "WAL",
+      "shares": 69,
+      "price": 94.92,
+      "cost": 6549.48,
+      "cash_remaining": 27183.76
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.441912",
+    "action": "BUY",
+    "ticker": "ONB",
+    "reason": "GARP signal: PE=14.26, FwdPE=8.9, RevGr=41.4%, EPSGr=17.2%, RSI=68.73",
+    "details": {
+      "success": true,
+      "ticker": "ONB",
+      "shares": 259,
+      "price": 25.53,
+      "cost": 6612.27,
+      "cash_remaining": 20571.49
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.443384",
+    "action": "BUY",
+    "ticker": "ZION",
+    "reason": "GARP signal: PE=10.66, FwdPE=9.8, RevGr=13.6%, EPSGr=31.4%, RSI=60.76",
+    "details": {
+      "success": true,
+      "ticker": "ZION",
+      "shares": 103,
+      "price": 64.08,
+      "cost": 6600.24,
+      "cash_remaining": 13971.25
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.443883",
+    "action": "SKIP",
+    "ticker": "CFG",
+    "reason": "Too close to 52wk high (1.8% away)",
+    "details": {}
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.445705",
+    "action": "BUY",
+    "ticker": "EWBC",
+    "reason": "GARP signal: PE=12.66, FwdPE=11.0, RevGr=21.6%, EPSGr=21.3%, RSI=65.92",
+    "details": {
+      "success": true,
+      "ticker": "EWBC",
+      "shares": 54,
+      "price": 120.54,
+      "cost": 6509.16,
+      "cash_remaining": 7462.09
+    }
+  },
+  {
+    "timestamp": "2026-02-10T15:36:28.447352",
+    "action": "BUY",
+    "ticker": "BAC",
+    "reason": "GARP signal: PE=14.54, FwdPE=11.17, RevGr=13.2%, EPSGr=20.9%, RSI=69.17",
+    "details": {
+      "success": true,
+      "ticker": "BAC",
+      "shares": 119,
+      "price": 55.39,
+      "cost": 6591.41,
+      "cash_remaining": 870.68
+    }
+  }
+]

projects/market-watch/data/portfolio.json

@@ -0,0 +1 @@
+{"cash": 100000.0, "positions": {}}

projects/market-watch/data/scans/2026-02-08.json

@@ -0,0 +1,206 @@
+{
+  "date": "2026-02-08",
+  "timestamp": "2026-02-08T15:18:03.800566",
+  "total_scanned": 902,
+  "candidates_found": 11,
+  "candidates": [
+    {
+      "ticker": "ALLY",
+      "price": 42.31,
+      "market_cap": 13052339200,
+      "market_cap_b": 13.1,
+      "trailing_pe": 17.85,
+      "forward_pe": 6.7,
+      "peg_ratio": null,
+      "revenue_growth": 12.0,
+      "earnings_growth": 265.4,
+      "roe": 5.8,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 44.58,
+      "week52_high": 47.27,
+      "pct_from_52wk_high": 10.5,
+      "score": -21.04
+    },
+    {
+      "ticker": "JHG",
+      "price": 48.22,
+      "market_cap": 7448852992,
+      "market_cap_b": 7.4,
+      "trailing_pe": 9.22,
+      "forward_pe": 10.12,
+      "peg_ratio": null,
+      "revenue_growth": 61.3,
+      "earnings_growth": 243.6,
+      "roe": 16.2,
+      "quick_ratio": 69.46,
+      "debt_to_equity": 6.5,
+      "rsi": 63.83,
+      "week52_high": 49.42,
+      "pct_from_52wk_high": 2.4,
+      "score": -20.37
+    },
+    {
+      "ticker": "INCY",
+      "price": 108.39,
+      "market_cap": 21279418368,
+      "market_cap_b": 21.3,
+      "trailing_pe": 18.37,
+      "forward_pe": 13.61,
+      "peg_ratio": null,
+      "revenue_growth": 20.0,
+      "earnings_growth": 290.7,
+      "roe": 30.4,
+      "quick_ratio": 2.86,
+      "debt_to_equity": 0.9,
+      "rsi": 54.22,
+      "week52_high": 112.29,
+      "pct_from_52wk_high": 3.5,
+      "score": -17.46
+    },
+    {
+      "ticker": "FHN",
+      "price": 26.23,
+      "market_cap": 12915496960,
+      "market_cap_b": 12.9,
+      "trailing_pe": 14.03,
+      "forward_pe": 11.19,
+      "peg_ratio": null,
+      "revenue_growth": 23.7,
+      "earnings_growth": 74.9,
+      "roe": 10.9,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 72.21,
+      "week52_high": 26.56,
+      "pct_from_52wk_high": 1.2,
+      "score": 1.3299999999999992
+    },
+    {
+      "ticker": "FNB",
+      "price": 18.9,
+      "market_cap": 6768781312,
+      "market_cap_b": 6.8,
+      "trailing_pe": 12.12,
+      "forward_pe": 9.66,
+      "peg_ratio": null,
+      "revenue_growth": 26.4,
+      "earnings_growth": 56.5,
+      "roe": 8.7,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 69.25,
+      "week52_high": 19.04,
+      "pct_from_52wk_high": 0.7,
+      "score": 1.37
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+}

projects/market-watch/data/scans/2026-02-09.json

@@ -0,0 +1,368 @@
+{
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+}

projects/market-watch/data/scans/2026-02-10.json

@@ -0,0 +1,368 @@
+{
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+      "rsi": 60.46,
+      "week52_high": 97.23,
+      "pct_from_52wk_high": 2.4,
+      "score": 3.0300000000000002
+    },
+    {
+      "ticker": "ONB",
+      "price": 25.53,
+      "market_cap": 9976434688,
+      "market_cap_b": 10.0,
+      "trailing_pe": 14.26,
+      "forward_pe": 8.9,
+      "peg_ratio": null,
+      "revenue_growth": 41.4,
+      "earnings_growth": 17.2,
+      "roe": 9.0,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 68.73,
+      "week52_high": 26.17,
+      "pct_from_52wk_high": 2.4,
+      "score": 3.040000000000001
+    },
+    {
+      "ticker": "EXEL",
+      "price": 42.98,
+      "market_cap": 11570324480,
+      "market_cap_b": 11.6,
+      "trailing_pe": 18.06,
+      "forward_pe": 12.45,
+      "peg_ratio": null,
+      "revenue_growth": 10.8,
+      "earnings_growth": 72.5,
+      "roe": 30.6,
+      "quick_ratio": 3.5,
+      "debt_to_equity": 8.2,
+      "rsi": 38.94,
+      "week52_high": 49.62,
+      "pct_from_52wk_high": 13.4,
+      "score": 4.119999999999999
+    },
+    {
+      "ticker": "ZION",
+      "price": 64.08,
+      "market_cap": 9461604352,
+      "market_cap_b": 9.5,
+      "trailing_pe": 10.66,
+      "forward_pe": 9.8,
+      "peg_ratio": null,
+      "revenue_growth": 13.6,
+      "earnings_growth": 31.4,
+      "roe": 13.5,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 60.76,
+      "week52_high": 66.18,
+      "pct_from_52wk_high": 3.2,
+      "score": 5.300000000000001
+    },
+    {
+      "ticker": "CART",
+      "price": 34.62,
+      "market_cap": 9120232448,
+      "market_cap_b": 9.1,
+      "trailing_pe": 19.02,
+      "forward_pe": 8.83,
+      "peg_ratio": null,
+      "revenue_growth": 10.2,
+      "earnings_growth": 21.1,
+      "roe": 15.3,
+      "quick_ratio": 3.33,
+      "debt_to_equity": 1.0,
+      "rsi": 32.09,
+      "week52_high": 53.5,
+      "pct_from_52wk_high": 35.3,
+      "score": 5.699999999999999
+    },
+    {
+      "ticker": "CFG",
+      "price": 67.55,
+      "market_cap": 29011791872,
+      "market_cap_b": 29.0,
+      "trailing_pe": 17.5,
+      "forward_pe": 10.73,
+      "peg_ratio": null,
+      "revenue_growth": 10.7,
+      "earnings_growth": 35.9,
+      "roe": 7.2,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 68.71,
+      "week52_high": 68.78,
+      "pct_from_52wk_high": 1.8,
+      "score": 6.07
+    },
+    {
+      "ticker": "UBSI",
+      "price": 44.64,
+      "market_cap": 6222157312,
+      "market_cap_b": 6.2,
+      "trailing_pe": 13.65,
+      "forward_pe": 11.85,
+      "peg_ratio": null,
+      "revenue_growth": 22.1,
+      "earnings_growth": 32.1,
+      "roe": 8.9,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 64.4,
+      "week52_high": 45.93,
+      "pct_from_52wk_high": 2.8,
+      "score": 6.430000000000001
+    },
+    {
+      "ticker": "EWBC",
+      "price": 120.54,
+      "market_cap": 16584568832,
+      "market_cap_b": 16.6,
+      "trailing_pe": 12.66,
+      "forward_pe": 11.0,
+      "peg_ratio": null,
+      "revenue_growth": 21.6,
+      "earnings_growth": 21.3,
+      "roe": 15.9,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 65.92,
+      "week52_high": 123.82,
+      "pct_from_52wk_high": 2.6,
+      "score": 6.710000000000001
+    },
+    {
+      "ticker": "BAC",
+      "price": 55.39,
+      "market_cap": 404485242880,
+      "market_cap_b": 404.5,
+      "trailing_pe": 14.54,
+      "forward_pe": 11.17,
+      "peg_ratio": null,
+      "revenue_growth": 13.2,
+      "earnings_growth": 20.9,
+      "roe": 10.2,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 69.17,
+      "week52_high": 57.55,
+      "pct_from_52wk_high": 3.8,
+      "score": 7.76
+    },
+    {
+      "ticker": "FITB",
+      "price": 54.5,
+      "market_cap": 49052639232,
+      "market_cap_b": 49.1,
+      "trailing_pe": 15.44,
+      "forward_pe": 11.12,
+      "peg_ratio": null,
+      "revenue_growth": 11.5,
+      "earnings_growth": 20.8,
+      "roe": 12.2,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 57.61,
+      "week52_high": 55.36,
+      "pct_from_52wk_high": 1.6,
+      "score": 7.889999999999999
+    },
+    {
+      "ticker": "WTFC",
+      "price": 156.07,
+      "market_cap": 10452774912,
+      "market_cap_b": 10.5,
+      "trailing_pe": 13.69,
+      "forward_pe": 11.64,
+      "peg_ratio": null,
+      "revenue_growth": 10.5,
+      "earnings_growth": 19.4,
+      "roe": 12.1,
+      "quick_ratio": null,
+      "debt_to_equity": null,
+      "rsi": 58.11,
+      "week52_high": 162.96,
+      "pct_from_52wk_high": 4.2,
+      "score": 8.65
+    }
+  ]
+}

projects/market-watch/data/snapshots.json

@@ -0,0 +1 @@
+[]

projects/market-watch/game_engine.py

@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""Multiplayer game engine for Market Watch paper trading."""
+
+import json
+import os
+import uuid
+from datetime import datetime, date
+
+DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+GAMES_DIR = os.path.join(DATA_DIR, "games")
+
+
+def _load_json(path, default=None):
+    if os.path.exists(path):
+        with open(path) as f:
+            return json.load(f)
+    return default if default is not None else {}
+
+
+def _save_json(path, data):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    with open(path, "w") as f:
+        json.dump(data, f, indent=2, default=str)
+
+
+def _game_dir(game_id):
+    return os.path.join(GAMES_DIR, game_id)
+
+
+def _player_dir(game_id, username):
+    return os.path.join(_game_dir(game_id), "players", username)
+
+
+def _portfolio_path(game_id, username):
+    return os.path.join(_player_dir(game_id, username), "portfolio.json")
+
+
+def _trades_path(game_id, username):
+    return os.path.join(_player_dir(game_id, username), "trades.json")
+
+
+def _snapshots_path(game_id, username):
+    return os.path.join(_player_dir(game_id, username), "snapshots.json")
+
+
+def _game_config_path(game_id):
+    return os.path.join(_game_dir(game_id), "game.json")
+
+
+# ── Game Management ──
+
+def create_game(name, starting_cash=100_000.0, end_date=None, creator="system"):
+    """Create a new game. Returns game_id."""
+    game_id = str(uuid.uuid4())[:8]
+    config = {
+        "game_id": game_id,
+        "name": name,
+        "starting_cash": starting_cash,
+        "start_date": date.today().isoformat(),
+        "end_date": end_date,
+        "creator": creator,
+        "created_at": datetime.now().isoformat(),
+        "players": [],
+        "status": "active",
+    }
+    os.makedirs(_game_dir(game_id), exist_ok=True)
+    _save_json(_game_config_path(game_id), config)
+    return game_id
+
+
+def list_games(active_only=True):
+    """List all games."""
+    games = []
+    if not os.path.exists(GAMES_DIR):
+        return games
+    for gid in os.listdir(GAMES_DIR):
+        config_path = _game_config_path(gid)
+        if os.path.exists(config_path):
+            config = _load_json(config_path)
+            if active_only and config.get("status") != "active":
+                continue
+            games.append(config)
+    return sorted(games, key=lambda g: g.get("created_at", ""), reverse=True)
+
+
+def get_game(game_id):
+    """Get game config."""
+    return _load_json(_game_config_path(game_id))
+
+
+def join_game(game_id, username):
+    """Add a player to a game. Initializes their portfolio."""
+    config = get_game(game_id)
+    if not config:
+        return {"success": False, "error": "Game not found"}
+    if username in config["players"]:
+        return {"success": False, "error": f"{username} already in game"}
+    
+    config["players"].append(username)
+    _save_json(_game_config_path(game_id), config)
+    
+    # Initialize player portfolio
+    player_dir = _player_dir(game_id, username)
+    os.makedirs(player_dir, exist_ok=True)
+    _save_json(_portfolio_path(game_id, username), {
+        "cash": config["starting_cash"],
+        "positions": {},
+    })
+    _save_json(_trades_path(game_id, username), [])
+    _save_json(_snapshots_path(game_id, username), [])
+    
+    return {"success": True, "game_id": game_id, "username": username, "starting_cash": config["starting_cash"]}
+
+
+# ── Trading ──
+
+def buy(game_id, username, ticker, shares, price, reason="Manual"):
+    """Buy shares for a player in a game."""
+    pf = _load_json(_portfolio_path(game_id, username))
+    if not pf:
+        return {"success": False, "error": "Player portfolio not found"}
+    
+    cost = shares * price
+    if cost > pf["cash"]:
+        return {"success": False, "error": f"Insufficient cash. Need ${cost:,.2f}, have ${pf['cash']:,.2f}"}
+    
+    pf["cash"] -= cost
+    
+    if ticker in pf["positions"]:
+        pos = pf["positions"][ticker]
+        total_shares = pos["shares"] + shares
+        pos["avg_cost"] = ((pos["avg_cost"] * pos["shares"]) + cost) / total_shares
+        pos["shares"] = total_shares
+        pos["current_price"] = price
+        # Update trailing stop
+        new_stop = price * 0.90
+        if new_stop > pos.get("trailing_stop", 0):
+            pos["trailing_stop"] = new_stop
+    else:
+        pf["positions"][ticker] = {
+            "shares": shares,
+            "avg_cost": price,
+            "current_price": price,
+            "entry_date": datetime.now().isoformat(),
+            "entry_reason": reason,
+            "trailing_stop": price * 0.90,
+        }
+    
+    _save_json(_portfolio_path(game_id, username), pf)
+    
+    # Log trade
+    trades = _load_json(_trades_path(game_id, username), [])
+    trades.append({
+        "action": "BUY",
+        "ticker": ticker,
+        "shares": shares,
+        "price": price,
+        "cost": round(cost, 2),
+        "reason": reason,
+        "timestamp": datetime.now().isoformat(),
+    })
+    _save_json(_trades_path(game_id, username), trades)
+    
+    return {"success": True, "ticker": ticker, "shares": shares, "price": price, "cost": round(cost, 2), "cash_remaining": round(pf["cash"], 2)}
+
+
+def sell(game_id, username, ticker, shares=None, price=None, reason="Manual"):
+    """Sell shares for a player."""
+    pf = _load_json(_portfolio_path(game_id, username))
+    if not pf:
+        return {"success": False, "error": "Player portfolio not found"}
+    if ticker not in pf["positions"]:
+        return {"success": False, "error": f"No position in {ticker}"}
+    
+    pos = pf["positions"][ticker]
+    if shares is None:
+        shares = pos["shares"]
+    if shares > pos["shares"]:
+        return {"success": False, "error": f"Only have {pos['shares']} shares of {ticker}"}
+    if price is None:
+        price = pos["current_price"]
+    
+    proceeds = shares * price
+    pf["cash"] += proceeds
+    realized_pnl = (price - pos["avg_cost"]) * shares
+    
+    if shares >= pos["shares"]:
+        del pf["positions"][ticker]
+    else:
+        pos["shares"] -= shares
+    
+    _save_json(_portfolio_path(game_id, username), pf)
+    
+    # Log trade
+    trades = _load_json(_trades_path(game_id, username), [])
+    trades.append({
+        "action": "SELL",
+        "ticker": ticker,
+        "shares": shares,
+        "price": price,
+        "proceeds": round(proceeds, 2),
+        "realized_pnl": round(realized_pnl, 2),
+        "entry_price": pos["avg_cost"],
+        "reason": reason,
+        "timestamp": datetime.now().isoformat(),
+    })
+    _save_json(_trades_path(game_id, username), trades)
+    
+    return {"success": True, "ticker": ticker, "shares": shares, "price": price, "proceeds": round(proceeds, 2), "realized_pnl": round(realized_pnl, 2)}
+
+
+def update_price(game_id, username, ticker, price):
+    """Update current price for a position."""
+    pf = _load_json(_portfolio_path(game_id, username))
+    if pf and ticker in pf["positions"]:
+        pos = pf["positions"][ticker]
+        pos["current_price"] = price
+        new_stop = price * 0.90
+        if new_stop > pos.get("trailing_stop", 0):
+            pos["trailing_stop"] = new_stop
+        _save_json(_portfolio_path(game_id, username), pf)
+
+
+def get_portfolio(game_id, username):
+    """Get player's portfolio with unrealized P&L."""
+    pf = _load_json(_portfolio_path(game_id, username))
+    if not pf:
+        return None
+    
+    game = get_game(game_id)
+    starting_cash = game.get("starting_cash", 100_000) if game else 100_000
+    total_value = pf["cash"]
+    positions_out = {}
+    
+    for ticker, pos in pf["positions"].items():
+        unrealized_pnl = (pos["current_price"] - pos["avg_cost"]) * pos["shares"]
+        market_value = pos["current_price"] * pos["shares"]
+        total_value += market_value
+        positions_out[ticker] = {
+            **pos,
+            "unrealized_pnl": round(unrealized_pnl, 2),
+            "market_value": round(market_value, 2),
+        }
+    
+    total_pnl = total_value - starting_cash
+    return {
+        "username": username,
+        "game_id": game_id,
+        "cash": round(pf["cash"], 2),
+        "positions": positions_out,
+        "total_value": round(total_value, 2),
+        "total_pnl": round(total_pnl, 2),
+        "pnl_pct": round(total_pnl / starting_cash * 100, 2),
+        "num_positions": len(positions_out),
+    }
+
+
+def get_trades(game_id, username):
+    """Get player's trade history."""
+    return _load_json(_trades_path(game_id, username), [])
+
+
+def daily_snapshot(game_id, username):
+    """Take daily snapshot for a player."""
+    p = get_portfolio(game_id, username)
+    if not p:
+        return None
+    snapshots = _load_json(_snapshots_path(game_id, username), [])
+    today = date.today().isoformat()
+    snapshots = [s for s in snapshots if s["date"] != today]
+    snapshots.append({
+        "date": today,
+        "total_value": p["total_value"],
+        "total_pnl": p["total_pnl"],
+        "pnl_pct": p["pnl_pct"],
+        "cash": p["cash"],
+        "num_positions": p["num_positions"],
+    })
+    _save_json(_snapshots_path(game_id, username), snapshots)
+    return snapshots[-1]
+
+
+def get_snapshots(game_id, username):
+    """Get player's daily snapshots."""
+    return _load_json(_snapshots_path(game_id, username), [])
+
+
+def get_leaderboard(game_id):
+    """Get game leaderboard sorted by % return."""
+    game = get_game(game_id)
+    if not game:
+        return []
+    
+    board = []
+    for username in game["players"]:
+        p = get_portfolio(game_id, username)
+        if p:
+            trades = get_trades(game_id, username)
+            num_trades = len([t for t in trades if t.get("action") == "SELL"])
+            board.append({
+                "username": username,
+                "total_value": p["total_value"],
+                "total_pnl": p["total_pnl"],
+                "pnl_pct": p["pnl_pct"],
+                "num_positions": p["num_positions"],
+                "num_trades": num_trades,
+                "cash": p["cash"],
+            })
+    
+    return sorted(board, key=lambda x: x["pnl_pct"], reverse=True)
+
+
+# ── Convenience: find default game ──
+
+def get_default_game_id():
+    """Get the first active game (usually 'GARP Challenge')."""
+    games = list_games(active_only=True)
+    if games:
+        return games[0]["game_id"]
+    return None
+
+
+# ── Initialize default game ──
+
+def ensure_default_game():
+    """Create default GARP Challenge game with 'case' player if it doesn't exist."""
+    games = list_games(active_only=True)
+    for g in games:
+        if g["name"] == "GARP Challenge":
+            return g["game_id"]
+    
+    game_id = create_game("GARP Challenge", starting_cash=100_000.0, creator="case")
+    join_game(game_id, "case")
+    return game_id
+
+
+if __name__ == "__main__":
+    game_id = ensure_default_game()
+    game = get_game(game_id)
+    print(f"Game: {game['name']} ({game_id})")
+    print(f"Players: {game['players']}")
+    board = get_leaderboard(game_id)
+    for entry in board:
+        print(f"  {entry['username']}: ${entry['total_value']:,.2f} ({entry['pnl_pct']:+.2f}%)")

projects/market-watch/portal/index.html

@@ -0,0 +1,296 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Market Watch — Paper Trading Dashboard</title>
+<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; background: #0a0e1a; color: #e0e6f0; min-height: 100vh; }
+.header { background: linear-gradient(135deg, #0f1629, #1a2342); padding: 20px 30px; border-bottom: 1px solid #1e2a4a; display: flex; justify-content: space-between; align-items: center; }
+.header h1 { font-size: 1.5rem; font-weight: 600; }
+.header h1 span { color: #4ecdc4; }
+.header .meta { font-size: 0.85rem; color: #7a8bb5; }
+.container { max-width: 1400px; margin: 0 auto; padding: 20px; }
+.grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(320px, 1fr)); gap: 16px; margin-bottom: 20px; }
+.card { background: linear-gradient(145deg, #111827, #0f1520); border: 1px solid #1e2a4a; border-radius: 12px; padding: 20px; }
+.card h2 { font-size: 1rem; color: #7a8bb5; text-transform: uppercase; letter-spacing: 1px; margin-bottom: 12px; font-weight: 500; }
+.card h2 .icon { margin-right: 6px; }
+.stat-row { display: flex; justify-content: space-between; align-items: baseline; margin-bottom: 8px; }
+.stat-label { color: #7a8bb5; font-size: 0.85rem; }
+.stat-value { font-size: 1.1rem; font-weight: 600; }
+.stat-big { font-size: 2rem; font-weight: 700; }
+.green { color: #4ecdc4; }
+.red { color: #ff6b6b; }
+.neutral { color: #7a8bb5; }
+.badge { display: inline-block; padding: 2px 8px; border-radius: 4px; font-size: 0.75rem; font-weight: 600; }
+.badge-green { background: rgba(78,205,196,0.15); color: #4ecdc4; }
+.badge-red { background: rgba(255,107,107,0.15); color: #ff6b6b; }
+.badge-blue { background: rgba(100,149,237,0.15); color: #6495ed; }
+table { width: 100%; border-collapse: collapse; }
+th { text-align: left; color: #7a8bb5; font-size: 0.75rem; text-transform: uppercase; letter-spacing: 1px; padding: 8px; border-bottom: 1px solid #1e2a4a; }
+td { padding: 8px; font-size: 0.9rem; border-bottom: 1px solid #111827; }
+tr:hover td { background: rgba(255,255,255,0.02); }
+.chart-wrap { height: 250px; position: relative; }
+.tabs { display: flex; gap: 8px; margin-bottom: 20px; }
+.tab { padding: 8px 16px; border-radius: 8px; cursor: pointer; font-size: 0.9rem; border: 1px solid #1e2a4a; background: transparent; color: #7a8bb5; transition: all 0.2s; }
+.tab.active { background: #4ecdc4; color: #0a0e1a; border-color: #4ecdc4; font-weight: 600; }
+.tab:hover:not(.active) { border-color: #4ecdc4; color: #4ecdc4; }
+.game-section { display: none; }
+.game-section.active { display: block; }
+.spinner { display: inline-block; width: 20px; height: 20px; border: 2px solid #1e2a4a; border-top-color: #4ecdc4; border-radius: 50%; animation: spin 0.8s linear infinite; }
+@keyframes spin { to { transform: rotate(360deg); } }
+.empty { text-align: center; color: #7a8bb5; padding: 40px; }
+</style>
+</head>
+<body>
+<div class="header">
+  <h1>📊 <span>Market Watch</span> — Paper Trading</h1>
+  <div class="meta">Auto-refresh 60s · <span id="lastUpdate">Loading...</span></div>
+</div>
+<div class="container">
+  <div class="tabs" id="gameTabs"></div>
+  <div id="gameContent"><div class="empty"><div class="spinner"></div> Loading games...</div></div>
+</div>
+
+<script>
+let games = [];
+let activeGameIdx = 0;
+
+async function fetchJSON(url) {
+  const r = await fetch(url);
+  return r.json();
+}
+
+function pnlClass(v) { return v > 0 ? 'green' : v < 0 ? 'red' : 'neutral'; }
+function pnlSign(v) { return v > 0 ? '+' : ''; }
+function fmt(v, d=2) { return v != null ? Number(v).toFixed(d) : '—'; }
+function fmtK(v) { return v >= 1e6 ? (v/1e6).toFixed(1)+'M' : v >= 1e3 ? (v/1e3).toFixed(1)+'K' : fmt(v); }
+
+function renderTabs() {
+  const el = document.getElementById('gameTabs');
+  el.innerHTML = games.map((g, i) => 
+    `<button class="tab ${i === activeGameIdx ? 'active' : ''}" onclick="switchGame(${i})">${g.name || g.game_id.slice(0,8)}</button>`
+  ).join('');
+}
+
+function switchGame(idx) {
+  activeGameIdx = idx;
+  renderTabs();
+  renderGame(games[idx]);
+}
+
+async function renderGame(game) {
+  const el = document.getElementById('gameContent');
+  const gid = game.game_id;
+  
+  // Fetch details in parallel
+  const [detail, trades, portfolio] = await Promise.all([
+    fetchJSON(`/api/game/${gid}`),
+    fetchJSON(`/api/game/${gid}/trades`),
+    fetchJSON(`/api/game/${gid}/portfolio`)
+  ]);
+
+  const player = game.players?.[0] || 'unknown';
+  const pf = portfolio[player] || {};
+  const board = detail.leaderboard || [];
+  const entry = board[0] || {};
+  const starting = game.starting_cash || 100000;
+  const totalVal = pf.total_value || entry.total_value || starting;
+  const totalPnl = totalVal - starting;
+  const pnlPct = (totalPnl / starting * 100);
+  
+  const sells = trades.filter(t => t.action === 'SELL');
+  const wins = sells.filter(t => (t.realized_pnl||0) > 0);
+  const winRate = sells.length ? (wins.length / sells.length * 100) : null;
+  const totalFees = trades.reduce((s,t) => s + (t.fees||0), 0);
+  const realizedPnl = sells.reduce((s,t) => s + (t.realized_pnl||0), 0);
+
+  // Equity curve from snapshots
+  const snapshots = detail.snapshots?.[player] || [];
+
+  let html = `
+  <!-- Summary Cards -->
+  <div class="grid">
+    <div class="card">
+      <h2><span class="icon">💰</span>Portfolio Value</h2>
+      <div class="stat-big ${pnlClass(totalPnl)}">$${fmtK(totalVal)}</div>
+      <div class="stat-row">
+        <span class="stat-label">P&L</span>
+        <span class="stat-value ${pnlClass(totalPnl)}">${pnlSign(totalPnl)}$${fmt(totalPnl)} (${pnlSign(pnlPct)}${fmt(pnlPct)}%)</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Starting Cash</span>
+        <span class="stat-value">$${fmtK(starting)}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Cash Available</span>
+        <span class="stat-value">$${fmtK(pf.cash || 0)}</span>
+      </div>
+    </div>
+    <div class="card">
+      <h2><span class="icon">📈</span>Trading Stats</h2>
+      <div class="stat-row">
+        <span class="stat-label">Total Trades</span>
+        <span class="stat-value">${trades.length}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Win Rate</span>
+        <span class="stat-value ${winRate && winRate > 50 ? 'green' : winRate ? 'red' : 'neutral'}">${winRate != null ? fmt(winRate,1)+'%' : '—'}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Realized P&L</span>
+        <span class="stat-value ${pnlClass(realizedPnl)}">${pnlSign(realizedPnl)}$${fmt(realizedPnl)}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Total Fees</span>
+        <span class="stat-value red">-$${fmt(totalFees)}</span>
+      </div>
+    </div>
+    <div class="card">
+      <h2><span class="icon">🎯</span>Game Info</h2>
+      <div class="stat-row">
+        <span class="stat-label">Game</span>
+        <span class="stat-value">${game.name || gid.slice(0,8)}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Type</span>
+        <span class="stat-value"><span class="badge badge-blue">${game.game_type || 'stock'}</span></span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Player</span>
+        <span class="stat-value">${player}</span>
+      </div>
+      <div class="stat-row">
+        <span class="stat-label">Open Positions</span>
+        <span class="stat-value">${Object.keys(pf.positions || {}).length}</span>
+      </div>
+    </div>
+  </div>
+
+  <!-- Equity Chart -->
+  ${snapshots.length > 1 ? `
+  <div class="card" style="margin-bottom:16px">
+    <h2><span class="icon">📊</span>Equity Curve</h2>
+    <div class="chart-wrap"><canvas id="equityChart"></canvas></div>
+  </div>` : ''}
+
+  <!-- Positions -->
+  <div class="card" style="margin-bottom:16px">
+    <h2><span class="icon">📋</span>Open Positions</h2>
+    ${Object.keys(pf.positions||{}).length ? `
+    <table>
+      <thead><tr><th>Symbol</th><th>Shares/Qty</th><th>Avg Cost</th><th>Current</th><th>Value</th><th>P&L</th></tr></thead>
+      <tbody>
+        ${Object.entries(pf.positions||{}).map(([sym, pos]) => {
+          const upnl = pos.unrealized_pnl || ((pos.current_price||pos.avg_cost) - pos.avg_cost) * (pos.shares||pos.quantity||0);
+          const val = pos.market_value || (pos.current_price||pos.avg_cost) * (pos.shares||pos.quantity||0);
+          return `<tr>
+            <td><strong>${sym}</strong></td>
+            <td>${pos.shares||pos.quantity||0}</td>
+            <td>$${fmt(pos.avg_cost)}</td>
+            <td>$${fmt(pos.current_price||pos.live_price||pos.avg_cost)}</td>
+            <td>$${fmtK(val)}</td>
+            <td class="${pnlClass(upnl)}">${pnlSign(upnl)}$${fmt(upnl)}</td>
+          </tr>`;
+        }).join('')}
+      </tbody>
+    </table>` : '<div class="empty">No open positions</div>'}
+  </div>
+
+  <!-- Recent Trades -->
+  <div class="card">
+    <h2><span class="icon">🔄</span>Recent Trades (last 25)</h2>
+    ${trades.length ? `
+    <table>
+      <thead><tr><th>Time</th><th>Action</th><th>Symbol</th><th>Qty</th><th>Price</th><th>P&L</th></tr></thead>
+      <tbody>
+        ${trades.slice(0,25).map(t => {
+          const rpnl = t.realized_pnl || 0;
+          const actionClass = t.action === 'BUY' ? 'badge-green' : t.action === 'SELL' ? 'badge-red' : 'badge-blue';
+          return `<tr>
+            <td style="font-size:0.8rem;color:#7a8bb5">${t.timestamp ? new Date(t.timestamp).toLocaleString() : '—'}</td>
+            <td><span class="badge ${actionClass}">${t.action}</span></td>
+            <td><strong>${t.ticker||t.symbol||'—'}</strong></td>
+            <td>${t.shares||t.quantity||'—'}</td>
+            <td>$${fmt(t.price)}</td>
+            <td class="${pnlClass(rpnl)}">${t.action==='SELL' ? pnlSign(rpnl)+'$'+fmt(rpnl) : '—'}</td>
+          </tr>`;
+        }).join('')}
+      </tbody>
+    </table>` : '<div class="empty">No trades yet</div>'}
+  </div>`;
+
+  el.innerHTML = html;
+
+  // Draw equity chart
+  if (snapshots.length > 1) {
+    const ctx = document.getElementById('equityChart').getContext('2d');
+    const labels = snapshots.map(s => {
+      const d = s.timestamp || s.date;
+      if (!d) return '—';
+      const dt = new Date(d);
+      return isNaN(dt) ? String(d).slice(0,10) : dt.toLocaleDateString();
+    });
+    const values = snapshots.map(s => s.total_value || s.equity || starting);
+    new Chart(ctx, {
+      type: 'line',
+      data: {
+        labels,
+        datasets: [{
+          label: 'Equity',
+          data: values,
+          borderColor: '#4ecdc4',
+          backgroundColor: 'rgba(78,205,196,0.1)',
+          fill: true,
+          tension: 0.3,
+          pointRadius: 0,
+          borderWidth: 2
+        }, {
+          label: 'Starting',
+          data: Array(labels.length).fill(starting),
+          borderColor: '#7a8bb544',
+          borderDash: [5,5],
+          borderWidth: 1,
+          pointRadius: 0
+        }]
+      },
+      options: {
+        responsive: true, maintainAspectRatio: false,
+        plugins: { legend: { display: false } },
+        scales: {
+          x: { grid: { color: '#1e2a4a' }, ticks: { color: '#7a8bb5', maxTicksLimit: 8 } },
+          y: { grid: { color: '#1e2a4a' }, ticks: { color: '#7a8bb5', callback: v => '$'+fmtK(v) } }
+        }
+      }
+    });
+  }
+}
+
+async function init() {
+  try {
+    games = await fetchJSON('/api/games');
+    if (!games.length) {
+      document.getElementById('gameContent').innerHTML = '<div class="empty">No games found</div>';
+      return;
+    }
+    renderTabs();
+    await renderGame(games[activeGameIdx]);
+    document.getElementById('lastUpdate').textContent = new Date().toLocaleTimeString();
+  } catch (e) {
+    document.getElementById('gameContent').innerHTML = `<div class="empty">Error: ${e.message}</div>`;
+  }
+}
+
+init();
+setInterval(async () => {
+  try {
+    games = await fetchJSON('/api/games');
+    await renderGame(games[activeGameIdx]);
+    document.getElementById('lastUpdate').textContent = new Date().toLocaleTimeString();
+  } catch(e) {}
+}, 60000);
+</script>
+</body>
+</html>

projects/market-watch/portal/server.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""Market Watch Web Portal — modern dark-themed dashboard."""
+
+import json
+import os
+import sys
+import traceback
+from datetime import datetime
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from socketserver import ThreadingMixIn
+from urllib.parse import urlparse, parse_qs
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import game_engine
+
+PORT = 8889
+PORTAL_DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class ThreadedHTTPServer(ThreadingMixIn, HTTPServer):
+    daemon_threads = True
+
+
+def _fetch_live_prices(tickers):
+    """Fetch live prices via yfinance. Returns {ticker: price}."""
+    try:
+        import yfinance as yf
+        data = yf.download(tickers, period="1d", progress=False)
+        prices = {}
+        if len(tickers) == 1:
+            t = tickers[0]
+            if "Close" in data.columns and len(data) > 0:
+                prices[t] = float(data["Close"].iloc[-1])
+        else:
+            if "Close" in data.columns:
+                for t in tickers:
+                    try:
+                        val = data["Close"][t].iloc[-1]
+                        if val == val:  # not NaN
+                            prices[t] = float(val)
+                    except Exception:
+                        pass
+        return prices
+    except Exception:
+        return {}
+
+
+class Handler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        try:
+            path = urlparse(self.path).path.rstrip("/") or "/"
+            
+            if path == "/":
+                return self._serve_file("index.html", "text/html")
+            
+            # API endpoints
+            if path == "/api/games":
+                games = game_engine.list_games(active_only=False)
+                # Enrich with summary
+                for g in games:
+                    board = game_engine.get_leaderboard(g["game_id"])
+                    g["leaderboard"] = board
+                    trades_all = []
+                    for p in g.get("players", []):
+                        trades_all.extend(game_engine.get_trades(g["game_id"], p))
+                    g["total_trades"] = len(trades_all)
+                    sells = [t for t in trades_all if t.get("action") == "SELL"]
+                    wins = [t for t in sells if t.get("realized_pnl", 0) > 0]
+                    g["win_rate"] = round(len(wins)/len(sells)*100, 1) if sells else None
+                return self._json(games)
+            
+            # /api/game/{id}
+            parts = path.split("/")
+            if len(parts) >= 4 and parts[1] == "api" and parts[2] == "game":
+                gid = parts[3]
+                
+                if len(parts) == 4:
+                    game = game_engine.get_game(gid)
+                    if not game:
+                        return self._json({"error": "not found"}, 404)
+                    game["leaderboard"] = game_engine.get_leaderboard(gid)
+                    # Add snapshots for each player
+                    game["snapshots"] = {}
+                    for p in game.get("players", []):
+                        game["snapshots"][p] = game_engine.get_snapshots(gid, p)
+                    return self._json(game)
+                
+                if len(parts) == 5 and parts[4] == "trades":
+                    game = game_engine.get_game(gid)
+                    if not game:
+                        return self._json({"error": "not found"}, 404)
+                    all_trades = []
+                    for p in game.get("players", []):
+                        for t in game_engine.get_trades(gid, p):
+                            t["player"] = p
+                            all_trades.append(t)
+                    all_trades.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
+                    return self._json(all_trades)
+                
+                if len(parts) == 5 and parts[4] == "portfolio":
+                    game = game_engine.get_game(gid)
+                    if not game:
+                        return self._json({"error": "not found"}, 404)
+                    portfolios = {}
+                    all_tickers = []
+                    for p in game.get("players", []):
+                        pf = game_engine.get_portfolio(gid, p)
+                        if pf:
+                            portfolios[p] = pf
+                            all_tickers.extend(pf["positions"].keys())
+                    
+                    # Fetch live prices
+                    all_tickers = list(set(all_tickers))
+                    if all_tickers:
+                        live = _fetch_live_prices(all_tickers)
+                        for p, pf in portfolios.items():
+                            total_value = pf["cash"]
+                            for ticker, pos in pf["positions"].items():
+                                if ticker in live:
+                                    pos["live_price"] = live[ticker]
+                                    pos["current_price"] = live[ticker]
+                                    pos["unrealized_pnl"] = round((live[ticker] - pos["avg_cost"]) * pos["shares"], 2)
+                                    pos["market_value"] = round(live[ticker] * pos["shares"], 2)
+                                total_value += pos["market_value"]
+                            pf["total_value"] = round(total_value, 2)
+                            starting = game.get("starting_cash", 100000)
+                            pf["total_pnl"] = round(total_value - starting, 2)
+                            pf["pnl_pct"] = round((total_value - starting) / starting * 100, 2)
+                    
+                    return self._json(portfolios)
+            
+            self._error(404)
+        except Exception as e:
+            self._json({"error": str(e), "trace": traceback.format_exc()}, 500)
+
+    def _serve_file(self, filename, content_type):
+        filepath = os.path.join(PORTAL_DIR, filename)
+        with open(filepath, "rb") as f:
+            data = f.read()
+        self.send_response(200)
+        self.send_header("Content-Type", content_type)
+        self.end_headers()
+        self.wfile.write(data)
+
+    def _json(self, data, code=200):
+        body = json.dumps(data, default=str).encode()
+        self.send_response(code)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.end_headers()
+        self.wfile.write(body)
+
+    def _error(self, code):
+        self.send_response(code)
+        self.send_header("Content-Type", "text/plain")
+        self.end_headers()
+        self.wfile.write(f"{code}".encode())
+
+    def log_message(self, fmt, *args):
+        pass
+
+
+def main():
+    game_engine.ensure_default_game()
+    print(f"📊 Market Watch Portal → http://localhost:{PORT}")
+    server = ThreadedHTTPServer(("0.0.0.0", PORT), Handler)
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\nStopped")
+
+
+if __name__ == "__main__":
+    main()

projects/market-watch/portfolio.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+"""Portfolio module — backward-compatible wrapper around game_engine.
+
+All operations now delegate to game_engine using the default game and 'case' player.
+"""
+
+import json
+import game_engine
+
+INITIAL_CASH = 100_000.0
+
+
+def _default():
+    """Get default game ID."""
+    return game_engine.get_default_game_id() or game_engine.ensure_default_game()
+
+
+def buy(ticker, shares, price, reason="GARP signal"):
+    return game_engine.buy(_default(), "case", ticker, shares, price, reason)
+
+
+def sell(ticker, shares=None, price=None, reason="GARP exit"):
+    return game_engine.sell(_default(), "case", ticker, shares, price, reason)
+
+
+def update_price(ticker, price):
+    game_engine.update_price(_default(), "case", ticker, price)
+
+
+def get_portfolio():
+    return game_engine.get_portfolio(_default(), "case")
+
+
+def get_history():
+    return game_engine.get_trades(_default(), "case")
+
+
+def daily_snapshot():
+    return game_engine.daily_snapshot(_default(), "case")
+
+
+def get_snapshots():
+    return game_engine.get_snapshots(_default(), "case")
+
+
+if __name__ == "__main__":
+    p = get_portfolio()
+    if p:
+        print(json.dumps(p, indent=2))
+    else:
+        print("No default game found. Run: python3 game_engine.py")

projects/market-watch/run_daily.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""Daily runner for Market Watch - scans, trades (as Case), snapshots, alerts."""
+
+import json
+import os
+import sys
+import requests
+from datetime import datetime
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import game_engine
+from scanner import run_scan
+from trader import run_trading_logic
+
+CREDS_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", ".credentials", "telegram-bot.env")
+CHAT_ID = "6443752046"
+
+
+def load_telegram_token():
+    if os.path.exists(CREDS_FILE):
+        with open(CREDS_FILE) as f:
+            for line in f:
+                if line.startswith("TELEGRAM_BOT_TOKEN="):
+                    return line.strip().split("=", 1)[1]
+    return os.environ.get("TELEGRAM_BOT_TOKEN")
+
+
+def send_telegram(message, token):
+    if not token:
+        return
+    try:
+        url = f"https://api.telegram.org/bot{token}/sendMessage"
+        requests.post(url, json={"chat_id": CHAT_ID, "text": message, "parse_mode": "HTML"}, timeout=10)
+    except Exception as e:
+        print(f"Telegram error: {e}")
+
+
+def main():
+    print(f"📊 Market Watch Daily Run — {datetime.now().strftime('%Y-%m-%d %H:%M')}")
+    
+    token = load_telegram_token()
+    game_id = game_engine.ensure_default_game()
+    game = game_engine.get_game(game_id)
+    
+    # 1. Run GARP scan
+    print("\n[1/3] Running GARP scan...")
+    candidates = run_scan()  # returns list of candidates directly
+    print(f"  Found {len(candidates)} candidates")
+    
+    # 2. Run trading logic for Case
+    print("\n[2/3] Running trading logic for Case...")
+    result = run_trading_logic(game_id, "case", candidates)
+    
+    # 3. Snapshots for all players
+    print("\n[3/3] Taking snapshots...")
+    for username in game["players"]:
+        snap = game_engine.daily_snapshot(game_id, username)
+        if snap:
+            print(f"  {username}: ${snap['total_value']:,.2f} ({snap['pnl_pct']:+.2f}%)")
+    
+    # Telegram summary
+    p = game_engine.get_portfolio(game_id, "case")
+    pnl_emoji = "📈" if p["total_pnl"] >= 0 else "📉"
+    
+    summary = f"📊 <b>Market Watch Daily</b>\n"
+    summary += f"{pnl_emoji} Portfolio: ${p['total_value']:,.2f} ({p['pnl_pct']:+.2f}%)\n"
+    summary += f"💰 Cash: ${p['cash']:,.2f} | Positions: {p['num_positions']}\n"
+    
+    num_trades = len(result.get("sells", [])) + len(result.get("buys", []))
+    if num_trades:
+        summary += f"\n<b>{num_trades} trades executed</b>\n"
+        for s in result.get("sells", []):
+            summary += f"🔴 SELL {s['ticker']} — {s['reason'][:50]}\n"
+        for b in result.get("buys", []):
+            summary += f"🟢 BUY {b['ticker']} — {b['reason'][:50]}\n"
+    else:
+        summary += "\nNo trades today."
+    
+    if candidates:
+        top5 = ", ".join(c["ticker"] for c in candidates[:5])
+        summary += f"\n🔍 Top picks: {top5}"
+    
+    # Leaderboard
+    board = game_engine.get_leaderboard(game_id)
+    if len(board) > 1:
+        summary += "\n\n<b>Leaderboard:</b>\n"
+        medals = ["🥇", "🥈", "🥉"]
+        for i, entry in enumerate(board[:5]):
+            medal = medals[i] if i < 3 else f"#{i+1}"
+            summary += f"{medal} {entry['username']}: {entry['pnl_pct']:+.2f}%\n"
+    
+    send_telegram(summary, token)
+    print("\n✅ Daily run complete")
+
+
+if __name__ == "__main__":
+    main()

projects/market-watch/scanner.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""GARP stock scanner - scans S&P 500 + S&P 400 MidCap for growth-at-reasonable-price candidates."""
+
+import json
+import os
+import re
+import sys
+import time
+from datetime import date, datetime
+
+import numpy as np
+import requests
+import yfinance as yf
+
+DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+SCANS_DIR = os.path.join(DATA_DIR, "scans")
+TICKERS_CACHE = os.path.join(DATA_DIR, "tickers.json")
+
+
+HEADERS = {"User-Agent": "MarketWatch/1.0 (paper trading bot; contact: case-lgn@protonmail.com)"}
+
+
+def _scrape_tickers(url):
+    """Scrape tickers from a Wikipedia S&P constituents page."""
+    import io
+    import pandas as pd
+    resp = requests.get(url, timeout=30, headers=HEADERS)
+    tables = pd.read_html(io.StringIO(resp.text))
+    if tables:
+        df = tables[0]
+        col = "Symbol" if "Symbol" in df.columns else df.columns[0]
+        tickers = df[col].astype(str).str.strip().tolist()
+        tickers = [t.replace(".", "-") for t in tickers if re.match(r'^[A-Z]{1,5}(\.[A-Z])?$', t.replace("-", "."))]
+        return tickers
+    return []
+
+
+def get_sp500_tickers():
+    return _scrape_tickers("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
+
+
+def get_sp400_tickers():
+    return _scrape_tickers("https://en.wikipedia.org/wiki/List_of_S%26P_400_companies")
+
+
+def get_all_tickers(use_cache=True):
+    """Get combined ticker list, with caching."""
+    if use_cache and os.path.exists(TICKERS_CACHE):
+        cache = json.loads(open(TICKERS_CACHE).read())
+        # Use cache if less than 7 days old
+        cached_date = cache.get("date", "")
+        if cached_date and (date.today() - date.fromisoformat(cached_date)).days < 7:
+            return cache["tickers"]
+
+    print("Fetching ticker lists from Wikipedia...")
+    sp500 = get_sp500_tickers()
+    print(f"  S&P 500: {len(sp500)} tickers")
+    sp400 = get_sp400_tickers()
+    print(f"  S&P 400: {len(sp400)} tickers")
+
+    all_tickers = sorted(set(sp500 + sp400))
+    os.makedirs(DATA_DIR, exist_ok=True)
+    with open(TICKERS_CACHE, "w") as f:
+        json.dump({"date": date.today().isoformat(), "tickers": all_tickers, "sp500": len(sp500), "sp400": len(sp400)}, f)
+
+    print(f"  Combined: {len(all_tickers)} unique tickers")
+    return all_tickers
+
+
+def compute_rsi(prices, period=14):
+    """Compute RSI from a price series."""
+    if len(prices) < period + 1:
+        return None
+    deltas = np.diff(prices)
+    gains = np.where(deltas > 0, deltas, 0)
+    losses = np.where(deltas < 0, -deltas, 0)
+    avg_gain = np.mean(gains[-period:])
+    avg_loss = np.mean(losses[-period:])
+    if avg_loss == 0:
+        return 100.0
+    rs = avg_gain / avg_loss
+    return round(100 - (100 / (1 + rs)), 2)
+
+
+def scan_ticker(ticker):
+    """Evaluate a single ticker against GARP criteria. Returns dict or None."""
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+        if not info or info.get("regularMarketPrice") is None:
+            return None
+
+        # Market cap filter
+        market_cap = info.get("marketCap", 0)
+        if not market_cap or market_cap < 5e9:
+            return None
+
+        # P/E filters
+        trailing_pe = info.get("trailingPE")
+        forward_pe = info.get("forwardPE")
+        if trailing_pe is None or trailing_pe <= 0 or trailing_pe >= 25:
+            return None
+        if forward_pe is None or forward_pe <= 0 or forward_pe >= 15:
+            return None
+
+        # Revenue growth
+        revenue_growth = info.get("revenueGrowth")
+        if revenue_growth is None or revenue_growth < 0.10:
+            return None
+
+        # EPS growth (earnings growth)
+        earnings_growth = info.get("earningsGrowth")
+        if earnings_growth is None or earnings_growth < 0.15:
+            return None
+
+        # ROE
+        roe = info.get("returnOnEquity")
+        if roe is None or roe < 0.05:
+            return None
+
+        # Optional filters (don't disqualify if unavailable)
+        peg = info.get("pegRatio")
+        if peg is not None and peg > 1.2:
+            return None
+
+        quick_ratio = info.get("quickRatio")
+        if quick_ratio is not None and quick_ratio < 1.5:
+            return None
+
+        de_ratio = info.get("debtToEquity")
+        if de_ratio is not None and de_ratio > 35:
+            return None
+
+        # Get price history for RSI and 52-week high
+        hist = stock.history(period="3mo")
+        if hist.empty or len(hist) < 20:
+            return None
+
+        closes = hist["Close"].values
+        current_price = closes[-1]
+        rsi = compute_rsi(closes)
+
+        # 52-week high
+        week52_high = info.get("fiftyTwoWeekHigh", current_price)
+        pct_from_high = ((week52_high - current_price) / week52_high) * 100 if week52_high else 0
+
+        return {
+            "ticker": ticker,
+            "price": round(current_price, 2),
+            "market_cap": market_cap,
+            "market_cap_b": round(market_cap / 1e9, 1),
+            "trailing_pe": round(trailing_pe, 2),
+            "forward_pe": round(forward_pe, 2),
+            "peg_ratio": round(peg, 2) if peg else None,
+            "revenue_growth": round(revenue_growth * 100, 1),
+            "earnings_growth": round(earnings_growth * 100, 1),
+            "roe": round(roe * 100, 1),
+            "quick_ratio": round(quick_ratio, 2) if quick_ratio else None,
+            "debt_to_equity": round(de_ratio, 1) if de_ratio else None,
+            "rsi": rsi,
+            "week52_high": round(week52_high, 2) if week52_high else None,
+            "pct_from_52wk_high": round(pct_from_high, 1),
+        }
+
+    except Exception as e:
+        return None
+
+
+def run_scan(batch_size=5, delay=1.0):
+    """Run full GARP scan. Returns list of candidates sorted by score."""
+    tickers = get_all_tickers()
+    candidates = []
+    total = len(tickers)
+
+    print(f"\nScanning {total} tickers...")
+    for i in range(0, total, batch_size):
+        batch = tickers[i:i + batch_size]
+        for ticker in batch:
+            idx = i + batch.index(ticker) + 1
+            sys.stdout.write(f"\r  [{idx}/{total}] Scanning {ticker}...     ")
+            sys.stdout.flush()
+            result = scan_ticker(ticker)
+            if result:
+                candidates.append(result)
+                print(f"\n  ✓ {ticker} passed GARP filter (PE={result['trailing_pe']}, FwdPE={result['forward_pe']}, RevGr={result['revenue_growth']}%)")
+        if i + batch_size < total:
+            time.sleep(delay)
+
+    print(f"\n\nScan complete: {len(candidates)} candidates from {total} tickers")
+
+    # Sort by a composite score: lower forward PE + higher earnings growth
+    for c in candidates:
+        # Simple ranking score: lower is better
+        c["score"] = c["forward_pe"] - (c["earnings_growth"] / 10) - (c["revenue_growth"] / 10)
+    candidates.sort(key=lambda x: x["score"])
+
+    # Save results
+    os.makedirs(SCANS_DIR, exist_ok=True)
+    scan_file = os.path.join(SCANS_DIR, f"{date.today().isoformat()}.json")
+    scan_data = {
+        "date": date.today().isoformat(),
+        "timestamp": datetime.now().isoformat(),
+        "total_scanned": total,
+        "candidates_found": len(candidates),
+        "candidates": candidates,
+    }
+    with open(scan_file, "w") as f:
+        json.dump(scan_data, f, indent=2)
+    print(f"Results saved to {scan_file}")
+
+    return candidates
+
+
+def load_latest_scan():
+    """Load the most recent scan results."""
+    if not os.path.exists(SCANS_DIR):
+        return None
+    files = sorted(f for f in os.listdir(SCANS_DIR) if f.endswith(".json"))
+    if not files:
+        return None
+    with open(os.path.join(SCANS_DIR, files[-1])) as f:
+        return json.load(f)
+
+
+if __name__ == "__main__":
+    candidates = run_scan()
+    if candidates:
+        print(f"\nTop candidates:")
+        for c in candidates[:10]:
+            print(f"  {c['ticker']:6s}  Price=${c['price']:8.2f}  PE={c['trailing_pe']:5.1f}  FwdPE={c['forward_pe']:5.1f}  "
+                  f"RevGr={c['revenue_growth']:5.1f}%  EPSGr={c['earnings_growth']:5.1f}%  RSI={c['rsi']}")
+    else:
+        print("No candidates found matching GARP criteria.")

projects/market-watch/trader.py

@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+"""GARP trading decision engine — multiplayer aware."""
+
+import json
+import os
+from datetime import datetime
+
+import yfinance as yf
+
+import game_engine
+import scanner
+
+MAX_POSITIONS = 15
+MAX_POSITION_PCT = 0.10
+RSI_BUY_LIMIT = 70
+RSI_SELL_LIMIT = 80
+NEAR_HIGH_PCT = 2.0
+
+LOG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "logs")
+
+
+def log_decision(action, ticker, reason, details=None):
+    os.makedirs(LOG_DIR, exist_ok=True)
+    entry = {
+        "timestamp": datetime.now().isoformat(),
+        "action": action,
+        "ticker": ticker,
+        "reason": reason,
+        "details": details or {},
+    }
+    log_file = os.path.join(LOG_DIR, f"{datetime.now().strftime('%Y-%m-%d')}.json")
+    logs = []
+    if os.path.exists(log_file):
+        with open(log_file) as f:
+            logs = json.load(f)
+    logs.append(entry)
+    with open(log_file, "w") as f:
+        json.dump(logs, f, indent=2, default=str)
+    return entry
+
+
+def update_all_prices(game_id, username):
+    """Update current prices for all held positions."""
+    p = game_engine.get_portfolio(game_id, username)
+    updated = []
+    for ticker in p["positions"]:
+        try:
+            stock = yf.Ticker(ticker)
+            hist = stock.history(period="5d")
+            if not hist.empty:
+                price = float(hist["Close"].iloc[-1])
+                game_engine.update_price(game_id, username, ticker, price)
+                updated.append((ticker, price))
+        except Exception as e:
+            print(f"  Warning: Could not update {ticker}: {e}")
+    return updated
+
+
+def check_sell_signals(game_id, username):
+    """Check existing positions for sell signals."""
+    p = game_engine.get_portfolio(game_id, username)
+    sells = []
+
+    if not p["positions"]:
+        return sells
+
+    latest_scan = scanner.load_latest_scan()
+    scan_tickers = set()
+    if latest_scan:
+        scan_tickers = {c["ticker"] for c in latest_scan.get("candidates", [])}
+
+    for ticker, pos in list(p["positions"].items()):
+        sell_reason = None
+
+        if pos["current_price"] <= pos.get("trailing_stop", 0):
+            sell_reason = f"Trailing stop hit (stop={pos.get('trailing_stop', 0):.2f}, price={pos['current_price']:.2f})"
+
+        if not sell_reason:
+            try:
+                stock = yf.Ticker(ticker)
+                hist = stock.history(period="3mo")
+                if not hist.empty and len(hist) >= 15:
+                    rsi = scanner.compute_rsi(hist["Close"].values)
+                    if rsi and rsi > RSI_SELL_LIMIT:
+                        sell_reason = f"RSI overbought ({rsi:.1f} > {RSI_SELL_LIMIT})"
+            except:
+                pass
+
+        if not sell_reason and latest_scan and ticker not in scan_tickers:
+            sell_reason = f"No longer passes GARP filter"
+
+        if sell_reason:
+            result = game_engine.sell(game_id, username, ticker, price=pos["current_price"], reason=sell_reason)
+            log_entry = log_decision("SELL", ticker, sell_reason, result)
+            sells.append(log_entry)
+            print(f"  SELL {ticker}: {sell_reason}")
+
+    return sells
+
+
+def check_buy_signals(game_id, username, candidates=None):
+    """Check scan candidates for buy signals."""
+    p = game_engine.get_portfolio(game_id, username)
+    buys = []
+
+    if p["num_positions"] >= MAX_POSITIONS:
+        print(f"  Max positions reached ({MAX_POSITIONS}), skipping buys")
+        return buys
+
+    if candidates is None:
+        latest_scan = scanner.load_latest_scan()
+        if not latest_scan:
+            print("  No scan data available")
+            return buys
+        candidates = latest_scan.get("candidates", [])
+
+    position_size = p["total_value"] / MAX_POSITIONS
+    max_per_position = p["total_value"] * MAX_POSITION_PCT
+    existing_tickers = set(p["positions"].keys())
+
+    for c in candidates:
+        if p["num_positions"] + len(buys) >= MAX_POSITIONS:
+            break
+
+        ticker = c["ticker"]
+        if ticker in existing_tickers:
+            continue
+
+        rsi = c.get("rsi")
+        if rsi and rsi > RSI_BUY_LIMIT:
+            log_decision("SKIP", ticker, f"RSI too high ({rsi:.1f} > {RSI_BUY_LIMIT})")
+            continue
+
+        pct_from_high = c.get("pct_from_52wk_high", 0)
+        if pct_from_high < NEAR_HIGH_PCT:
+            log_decision("SKIP", ticker, f"Too close to 52wk high ({pct_from_high:.1f}% away)")
+            continue
+
+        price = c["price"]
+        # Refresh cash from current portfolio state
+        current_p = game_engine.get_portfolio(game_id, username)
+        amount = min(position_size, max_per_position, current_p["cash"])
+        if amount < price:
+            continue
+
+        shares = int(amount / price)
+        if shares < 1:
+            continue
+
+        reason = (f"GARP signal: PE={c['trailing_pe']}, FwdPE={c['forward_pe']}, "
+                  f"RevGr={c['revenue_growth']}%, EPSGr={c['earnings_growth']}%, RSI={rsi}")
+
+        result = game_engine.buy(game_id, username, ticker, shares, price, reason=reason)
+        if result["success"]:
+            log_entry = log_decision("BUY", ticker, reason, result)
+            buys.append(log_entry)
+            print(f"  BUY {ticker}: {shares} shares @ ${price:.2f} = ${shares * price:,.2f}")
+        else:
+            log_decision("SKIP", ticker, f"Buy failed: {result.get('error', 'unknown')}")
+
+    return buys
+
+
+def run_trading_logic(game_id, username, candidates=None):
+    """Run full trading cycle for a player."""
+    print(f"\n--- Trading Logic [{username}@{game_id}] ---")
+
+    print("\nUpdating prices...")
+    updated = update_all_prices(game_id, username)
+    for ticker, price in updated:
+        print(f"  {ticker}: ${price:.2f}")
+
+    print("\nChecking sell signals...")
+    sells = check_sell_signals(game_id, username)
+    if not sells:
+        print("  No sell signals")
+
+    print("\nChecking buy signals...")
+    buys = check_buy_signals(game_id, username, candidates)
+    if not buys:
+        print("  No buy signals")
+
+    return {"sells": sells, "buys": buys, "price_updates": len(updated)}
+
+
+if __name__ == "__main__":
+    gid = game_engine.get_default_game_id()
+    if gid:
+        run_trading_logic(gid, "case")
+    else:
+        print("No default game found. Run game_engine.py first.")

tools/analyze_tweet.py

@@ -0,0 +1,389 @@
+#!/usr/bin/env python3
+"""Tweet Analysis Tool - Scrapes and analyzes tweets via Chrome CDP."""
+
+import argparse
+import asyncio
+import json
+import re
+import sys
+from datetime import datetime
+
+try:
+    from playwright.async_api import async_playwright
+except ImportError:
+    print("ERROR: playwright not installed. Run: pip install playwright", file=sys.stderr)
+    sys.exit(1)
+
+try:
+    import yfinance as yf
+except ImportError:
+    yf = None
+
+
+def extract_tickers(text: str) -> list[str]:
+    """Extract $TICKER patterns from text."""
+    return list(set(re.findall(r'\$([A-Z]{1,5}(?:\.[A-Z]{1,2})?)', text.upper())))
+
+
+def lookup_tickers(tickers: list[str]) -> dict:
+    """Look up ticker data via yfinance."""
+    if not yf or not tickers:
+        return {}
+    results = {}
+    for t in tickers[:5]:  # limit to 5
+        try:
+            info = yf.Ticker(t).info
+            results[t] = {
+                "price": info.get("currentPrice") or info.get("regularMarketPrice"),
+                "market_cap": info.get("marketCap"),
+                "name": info.get("shortName"),
+                "volume": info.get("volume"),
+                "day_change_pct": info.get("regularMarketChangePercent"),
+                "52w_high": info.get("fiftyTwoWeekHigh"),
+                "52w_low": info.get("fiftyTwoWeekLow"),
+            }
+        except Exception:
+            results[t] = {"error": "lookup failed"}
+    return results
+
+
+async def scrape_tweet(url: str) -> dict:
+    """Connect to Chrome CDP and scrape tweet data."""
+    # Normalize URL
+    url = url.replace("twitter.com", "x.com")
+    if not url.startswith("http"):
+        url = "https://" + url
+
+    data = {
+        "url": url,
+        "author": None,
+        "handle": None,
+        "text": None,
+        "timestamp": None,
+        "metrics": {},
+        "images": [],
+        "bio": None,
+        "followers": None,
+        "following": None,
+        "reply_to": None,
+        "replies_sample": [],
+        "scrape_error": None,
+    }
+
+    async with async_playwright() as p:
+        try:
+            browser = await p.chromium.connect_over_cdp("http://localhost:9222")
+        except Exception as e:
+            data["scrape_error"] = f"CDP connection failed: {e}"
+            return data
+
+        try:
+            ctx = browser.contexts[0] if browser.contexts else await browser.new_context()
+            page = await ctx.new_page()
+            await page.goto(url, wait_until="domcontentloaded", timeout=30000)
+            await page.wait_for_timeout(4000)
+
+            # Get the main tweet article
+            # Try to find the focal tweet
+            tweet_sel = 'article[data-testid="tweet"]'
+            articles = await page.query_selector_all(tweet_sel)
+
+            if not articles:
+                data["scrape_error"] = "No tweet articles found on page"
+                await page.close()
+                return data
+
+            # The focal tweet is typically the one with the largest text or specific structure
+            # On a tweet permalink, it's usually the first or second article
+            focal = None
+            for art in articles:
+                # The focal tweet has a different time display (absolute vs relative)
+                time_el = await art.query_selector('time')
+                if time_el:
+                    dt = await time_el.get_attribute('datetime')
+                    if dt:
+                        focal = art
+                        data["timestamp"] = dt
+                        break
+            if not focal:
+                focal = articles[0]
+
+            # Author info
+            user_links = await focal.query_selector_all('a[role="link"]')
+            for link in user_links:
+                href = await link.get_attribute("href") or ""
+                if href.startswith("/") and href.count("/") == 1 and len(href) > 1:
+                    spans = await link.query_selector_all("span")
+                    for span in spans:
+                        txt = (await span.inner_text()).strip()
+                        if txt.startswith("@"):
+                            data["handle"] = txt
+                        elif txt and not data["author"] and not txt.startswith("@"):
+                            data["author"] = txt
+                    break
+
+            # Tweet text
+            text_el = await focal.query_selector('div[data-testid="tweetText"]')
+            if text_el:
+                data["text"] = await text_el.inner_text()
+
+            # Metrics (replies, retweets, likes, views)
+            group = await focal.query_selector('div[role="group"]')
+            if group:
+                buttons = await group.query_selector_all('button')
+                metric_names = ["replies", "retweets", "likes", "bookmarks"]
+                for i, btn in enumerate(buttons):
+                    aria = await btn.get_attribute("aria-label") or ""
+                    # Parse numbers from aria labels like "123 replies"
+                    nums = re.findall(r'[\d,]+', aria)
+                    if nums and i < len(metric_names):
+                        data["metrics"][metric_names[i]] = nums[0].replace(",", "")
+
+            # Views - often in a separate span
+            view_spans = await focal.query_selector_all('a[role="link"] span')
+            for vs in view_spans:
+                txt = (await vs.inner_text()).strip()
+                if "views" in txt.lower() or "Views" in txt:
+                    nums = re.findall(r'[\d,.KkMm]+', txt)
+                    if nums:
+                        data["metrics"]["views"] = nums[0]
+
+            # Images
+            imgs = await focal.query_selector_all('img[alt="Image"]')
+            for img in imgs:
+                src = await img.get_attribute("src")
+                if src:
+                    data["images"].append(src)
+
+            # Check if it's a reply
+            reply_indicators = await page.query_selector_all('div[data-testid="tweet"] a[role="link"]')
+
+            # Try to get author profile info by hovering or checking
+            # We'll grab it from the page if visible
+            if data["handle"]:
+                handle_clean = data["handle"].lstrip("@")
+                # Check for bio/follower info in any hover cards or visible elements
+                all_text = await page.inner_text("body")
+                # Look for follower patterns
+                follower_match = re.search(r'([\d,.]+[KkMm]?)\s+Followers', all_text)
+                following_match = re.search(r'([\d,.]+[KkMm]?)\s+Following', all_text)
+                if follower_match:
+                    data["followers"] = follower_match.group(1)
+                if following_match:
+                    data["following"] = following_match.group(1)
+
+            # Sample some replies (articles after the focal tweet)
+            if len(articles) > 1:
+                for art in articles[1:4]:
+                    reply_text_el = await art.query_selector('div[data-testid="tweetText"]')
+                    if reply_text_el:
+                        rt = await reply_text_el.inner_text()
+                        if rt:
+                            data["replies_sample"].append(rt[:200])
+
+            await page.close()
+
+        except Exception as e:
+            data["scrape_error"] = str(e)
+            try:
+                await page.close()
+            except:
+                pass
+
+    return data
+
+
+def analyze(data: dict) -> dict:
+    """Produce structured analysis from scraped data."""
+    text = data.get("text") or ""
+    tickers = extract_tickers(text)
+    ticker_data = lookup_tickers(tickers)
+
+    # Red flags detection
+    red_flags = []
+    text_lower = text.lower()
+    promo_words = ["100x", "1000x", "moon", "gem", "rocket", "guaranteed", "easy money",
+                   "don't miss", "last chance", "about to explode", "next big", "sleeping giant",
+                   "never stops printing", "true freedom", "beat the institutions", "revolution",
+                   "empire", "vault", "get rich", "financial freedom", "life changing",
+                   "without a degree", "from a bedroom", "join this"]
+    for w in promo_words:
+        if w in text_lower:
+            red_flags.append(f"Promotional language: '{w}'")
+
+    if len(tickers) > 3:
+        red_flags.append(f"Multiple tickers mentioned ({len(tickers)})")
+
+    if len(text) > 2000:
+        red_flags.append("Extremely long promotional thread")
+    if "github" in text_lower and ("star" in text_lower or "repo" in text_lower):
+        red_flags.append("Pushing GitHub repo (potential funnel to paid product)")
+    if any(w in text_lower for w in ["course", "discord", "premium", "paid group", "subscribe"]):
+        red_flags.append("Funneling to paid product/community")
+
+    # Check replies for coordinated patterns
+    replies = data.get("replies_sample", [])
+    if replies:
+        rocket_replies = sum(1 for r in replies if any(e in r for e in ["🚀", "💎", "🔥", "LFG"]))
+        if rocket_replies >= 2:
+            red_flags.append("Replies show coordinated hype patterns")
+
+    # Check for penny stock characteristics
+    for t, info in ticker_data.items():
+        if isinstance(info, dict) and not info.get("error"):
+            price = info.get("price")
+            mcap = info.get("market_cap")
+            if price and price < 1:
+                red_flags.append(f"${t} is a penny stock (${price})")
+            if mcap and mcap < 50_000_000:
+                red_flags.append(f"${t} micro-cap (<$50M market cap)")
+
+    # Build verdict
+    if len(red_flags) >= 3:
+        verdict = "High risk - multiple red flags detected, exercise extreme caution"
+    elif len(red_flags) >= 1:
+        verdict = "Some concerns - verify claims independently before acting"
+    elif tickers:
+        verdict = "Worth investigating - do your own due diligence"
+    else:
+        verdict = "Informational tweet - no immediate financial claims detected"
+
+    return {
+        "tweet_data": data,
+        "tickers_found": tickers,
+        "ticker_data": ticker_data,
+        "red_flags": red_flags,
+        "verdict": verdict,
+    }
+
+
+def format_markdown(analysis: dict) -> str:
+    """Format analysis as markdown."""
+    d = analysis["tweet_data"]
+    lines = [f"# Tweet Analysis", ""]
+    lines.append(f"**URL:** {d['url']}")
+    lines.append(f"**Analyzed:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    lines.append("")
+
+    # WHO
+    lines.append("## 👤 WHO")
+    lines.append(f"- **Author:** {d.get('author') or 'Unknown'}")
+    lines.append(f"- **Handle:** {d.get('handle') or 'Unknown'}")
+    if d.get("followers"):
+        lines.append(f"- **Followers:** {d['followers']}")
+    if d.get("following"):
+        lines.append(f"- **Following:** {d['following']}")
+    if d.get("bio"):
+        lines.append(f"- **Bio:** {d['bio']}")
+    lines.append("")
+
+    # WHAT
+    lines.append("## 📝 WHAT")
+    lines.append(f"> {d.get('text') or 'Could not extract tweet text'}")
+    lines.append("")
+    if d.get("timestamp"):
+        lines.append(f"**Posted:** {d['timestamp']}")
+    metrics = d.get("metrics", {})
+    if metrics:
+        m_parts = [f"{v} {k}" for k, v in metrics.items()]
+        lines.append(f"**Metrics:** {' | '.join(m_parts)}")
+    if d.get("images"):
+        lines.append(f"**Images:** {len(d['images'])} attached")
+    lines.append("")
+
+    # VERIFY
+    lines.append("## ✅ VERIFY")
+    tickers = analysis.get("tickers_found", [])
+    td = analysis.get("ticker_data", {})
+    if tickers:
+        lines.append(f"**Tickers mentioned:** {', '.join('$' + t for t in tickers)}")
+        lines.append("")
+        for t, info in td.items():
+            if isinstance(info, dict) and not info.get("error"):
+                lines.append(f"### ${t}" + (f" - {info.get('name', '')}" if info.get('name') else ""))
+                if info.get("price"):
+                    lines.append(f"- **Price:** ${info['price']}")
+                if info.get("market_cap"):
+                    mc = info["market_cap"]
+                    if mc > 1e9:
+                        lines.append(f"- **Market Cap:** ${mc/1e9:.2f}B")
+                    else:
+                        lines.append(f"- **Market Cap:** ${mc/1e6:.1f}M")
+                if info.get("volume"):
+                    lines.append(f"- **Volume:** {info['volume']:,}")
+                if info.get("day_change_pct"):
+                    lines.append(f"- **Day Change:** {info['day_change_pct']:.2f}%")
+                if info.get("52w_high") and info.get("52w_low"):
+                    lines.append(f"- **52W Range:** ${info['52w_low']} - ${info['52w_high']}")
+                lines.append("")
+            elif isinstance(info, dict) and info.get("error"):
+                lines.append(f"- ${t}: lookup failed")
+    else:
+        lines.append("No tickers mentioned in tweet.")
+    lines.append("")
+
+    # RED FLAGS
+    lines.append("## 🚩 RED FLAGS")
+    flags = analysis.get("red_flags", [])
+    if flags:
+        for f in flags:
+            lines.append(f"- ⚠️ {f}")
+    else:
+        lines.append("- None detected")
+    lines.append("")
+
+    # MONEY
+    lines.append("## 💰 MONEY")
+    if tickers and not flags:
+        lines.append("Potential opportunity identified. Research further before any position.")
+    elif tickers and flags:
+        lines.append("Tickers mentioned but red flags present. High risk of promoted/manipulated asset.")
+    else:
+        lines.append("No direct financial opportunity identified in this tweet.")
+    lines.append("")
+
+    # VERDICT
+    lines.append("## 🎯 VERDICT")
+    lines.append(f"**{analysis['verdict']}**")
+    lines.append("")
+
+    # Scrape issues
+    if d.get("scrape_error"):
+        lines.append(f"---\n⚠️ *Scrape warning: {d['scrape_error']}*")
+
+    return "\n".join(lines)
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="Analyze a tweet")
+    parser.add_argument("url", help="Tweet URL (x.com or twitter.com)")
+    parser.add_argument("--json", action="store_true", dest="json_output", help="Output JSON")
+    parser.add_argument("-o", "--output", help="Write output to file")
+    args = parser.parse_args()
+
+    # Validate URL
+    if not re.search(r'(x\.com|twitter\.com)/.+/status/\d+', args.url):
+        print("ERROR: Invalid tweet URL", file=sys.stderr)
+        sys.exit(1)
+
+    print("Scraping tweet...", file=sys.stderr)
+    data = await scrape_tweet(args.url)
+
+    print("Analyzing...", file=sys.stderr)
+    analysis = analyze(data)
+
+    if args.json_output:
+        output = json.dumps(analysis, indent=2, default=str)
+    else:
+        output = format_markdown(analysis)
+
+    if args.output:
+        with open(args.output, "w") as f:
+            f.write(output)
+        print(f"Written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

tools/data_sources/README.md

@@ -0,0 +1,51 @@
+# Data Source Connectors
+
+Standalone Python scripts for fetching crypto/market data. Each has CLI with `--pretty` (JSON formatting) and `--summary` (human-readable output).
+
+## defillama.py ✅ (no auth needed)
+
+DefiLlama API — DeFi protocol data, token prices, yield farming opportunities.
+
+```bash
+./defillama.py protocols --limit 10 --summary    # Top protocols by TVL
+./defillama.py tvl aave --pretty                  # TVL for specific protocol
+./defillama.py prices coingecko:bitcoin coingecko:ethereum --summary
+./defillama.py yields --limit 20 --stablecoins --summary  # Top stablecoin yields
+```
+
+**Endpoints used:** api.llama.fi/protocols, api.llama.fi/tvl/{name}, coins.llama.fi/prices, yields.llama.fi/pools
+
+## coinglass.py 🔑 (API key recommended)
+
+Coinglass — funding rates, open interest, long/short ratios.
+
+```bash
+export COINGLASS_API_KEY=your_key  # Get at coinglass.com/pricing
+./coinglass.py funding --summary
+./coinglass.py oi --summary
+./coinglass.py long-short --summary
+```
+
+**Note:** Free internal API endpoints often return empty data. API key required for reliable access.
+
+## arkham.py 🔑 (API key required)
+
+Arkham Intelligence — whale wallet tracking, token transfers, entity search.
+
+```bash
+export ARKHAM_API_KEY=your_key  # Sign up at platform.arkhamintelligence.com
+./arkham.py notable --summary           # List known whale addresses
+./arkham.py address vitalik --summary   # Address intelligence (supports name shortcuts)
+./arkham.py transfers 0x1234... --limit 10 --pretty
+./arkham.py search "binance" --pretty
+```
+
+**Built-in shortcuts:** vitalik, justin-sun, binance-hot, coinbase-prime, aave-treasury, uniswap-deployer
+
+## Programmatic Usage
+
+```python
+from tools.data_sources.defillama import get_protocols, get_prices, get_yield_pools
+from tools.data_sources.coinglass import get_funding_rates
+from tools.data_sources.arkham import get_address_info, NOTABLE_ADDRESSES
+```

tools/data_sources/__init__.py

@@ -0,0 +1,4 @@
+"""Crypto & market data source connectors."""
+from pathlib import Path
+
+DATA_SOURCES_DIR = Path(__file__).parent

tools/data_sources/arkham.py

@@ -0,0 +1,167 @@
+#!/usr/bin/env python3
+"""Arkham Intelligence connector — whale tracking, token flows, address intelligence.
+
+Requires API key for most endpoints. Set ARKHAM_API_KEY env var.
+Sign up at https://platform.arkhamintelligence.com
+"""
+
+import argparse
+import json
+import os
+import sys
+from typing import Any
+
+import requests
+
+BASE = "https://api.arkhamintelligence.com"
+TIMEOUT = 30
+
+NOTABLE_ADDRESSES = {
+    "vitalik": "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
+    "justin-sun": "0x3DdfA8eC3052539b6C9549F12cEA2C295cfF5296",
+    "binance-hot": "0x28C6c06298d514Db089934071355E5743bf21d60",
+    "coinbase-prime": "0xA9D1e08C7793af67e9d92fe308d5697FB81d3E43",
+    "aave-treasury": "0x464C71f6c2F760DdA6093dCB91C24c39e5d6e18c",
+    "uniswap-deployer": "0x41653c7d61609D856f29355E404F310Ec4142Cfb",
+}
+
+
+def _get(path: str, params: dict | None = None) -> Any:
+    key = os.environ.get("ARKHAM_API_KEY")
+    headers = {"User-Agent": "Mozilla/5.0"}
+    if key:
+        headers["API-Key"] = key
+    r = requests.get(f"{BASE}/{path}", params=params, headers=headers, timeout=TIMEOUT)
+    if r.status_code in (401, 403) or "api key" in r.text.lower():
+        raise EnvironmentError(
+            "Arkham API key required. Set ARKHAM_API_KEY env var.\n"
+            "Sign up at https://platform.arkhamintelligence.com"
+        )
+    r.raise_for_status()
+    return r.json()
+
+
+def resolve_address(name_or_addr: str) -> str:
+    return NOTABLE_ADDRESSES.get(name_or_addr.lower(), name_or_addr)
+
+
+# ── Data fetchers ───────────────────────────────────────────────────────────
+
+def get_address_info(address: str) -> dict:
+    return _get(f"intelligence/address/{resolve_address(address)}")
+
+
+def get_transfers(address: str, limit: int = 20) -> dict:
+    return _get("token/transfers", {"address": resolve_address(address), "limit": limit})
+
+
+def search_entity(query: str) -> dict:
+    return _get("intelligence/search", {"query": query})
+
+
+# ── Summary helpers ─────────────────────────────────────────────────────────
+
+def summary_address(data: dict) -> str:
+    lines = ["═══ Address Intelligence ═══", ""]
+    if isinstance(data, dict):
+        entity = data.get("entity", {}) or {}
+        if entity:
+            lines.append(f"  Entity: {entity.get('name', 'Unknown')}")
+            lines.append(f"  Type:   {entity.get('type', 'Unknown')}")
+        lines.append(f"  Address: {data.get('address', '?')}")
+        labels = data.get("labels", [])
+        if labels:
+            lines.append(f"  Labels: {', '.join(str(l) for l in labels)}")
+    else:
+        lines.append(f"  {data}")
+    return "\n".join(lines)
+
+
+def summary_transfers(data) -> str:
+    lines = ["═══ Recent Transfers ═══", ""]
+    transfers = data if isinstance(data, list) else (data.get("transfers", data.get("data", [])) if isinstance(data, dict) else [])
+    if not transfers:
+        lines.append("  No transfers found.")
+        return "\n".join(lines)
+    for t in transfers[:15]:
+        token = t.get("token", {}).get("symbol", "?") if isinstance(t.get("token"), dict) else "?"
+        amount = t.get("amount", t.get("value", "?"))
+        fr = t.get("from", {})
+        to = t.get("to", {})
+        fl = (fr.get("label") or fr.get("address", "?")[:12]) if isinstance(fr, dict) else str(fr)[:12]
+        tl = (to.get("label") or to.get("address", "?")[:12]) if isinstance(to, dict) else str(to)[:12]
+        lines.append(f"  {token:<8} {str(amount):>15}  {fl} → {tl}")
+    return "\n".join(lines)
+
+
+def summary_notable() -> str:
+    lines = ["═══ Notable/Whale Addresses ═══", ""]
+    for name, addr in NOTABLE_ADDRESSES.items():
+        lines.append(f"  {name:<20} {addr}")
+    lines.append("")
+    lines.append("  Use these as shortcuts: arkham.py address vitalik")
+    return "\n".join(lines)
+
+
+# ── CLI ─────────────────────────────────────────────────────────────────────
+
+def main():
+    common = argparse.ArgumentParser(add_help=False)
+    common.add_argument("--pretty", action="store_true", help="Pretty-print JSON output")
+    common.add_argument("--summary", action="store_true", help="Human-readable summary")
+
+    parser = argparse.ArgumentParser(description="Arkham Intelligence connector", parents=[common])
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    p_addr = sub.add_parser("address", help="Address intelligence", parents=[common])
+    p_addr.add_argument("address", help="Ethereum address or notable name")
+
+    p_tx = sub.add_parser("transfers", help="Recent token transfers", parents=[common])
+    p_tx.add_argument("address")
+    p_tx.add_argument("--limit", type=int, default=20)
+
+    p_search = sub.add_parser("search", help="Search entities", parents=[common])
+    p_search.add_argument("query")
+
+    sub.add_parser("notable", help="List notable/whale addresses", parents=[common])
+
+    args = parser.parse_args()
+
+    try:
+        if args.command == "notable":
+            if args.summary:
+                print(summary_notable())
+            else:
+                json.dump(NOTABLE_ADDRESSES, sys.stdout, indent=2 if args.pretty else None)
+                print()
+            return
+
+        if args.command == "address":
+            data = get_address_info(args.address)
+            if args.summary:
+                print(summary_address(data)); return
+            result = data
+        elif args.command == "transfers":
+            data = get_transfers(args.address, args.limit)
+            if args.summary:
+                print(summary_transfers(data)); return
+            result = data
+        elif args.command == "search":
+            result = search_entity(args.query)
+        else:
+            parser.print_help(); return
+
+        json.dump(result, sys.stdout, indent=2 if args.pretty else None)
+        print()
+
+    except EnvironmentError as e:
+        print(str(e), file=sys.stderr); sys.exit(1)
+    except requests.HTTPError as e:
+        detail = e.response.text[:200] if e.response is not None else ""
+        print(json.dumps({"error": str(e), "detail": detail}), file=sys.stderr); sys.exit(1)
+    except Exception as e:
+        print(json.dumps({"error": f"{type(e).__name__}: {e}"}), file=sys.stderr); sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tools/data_sources/coinglass.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+"""Coinglass data connector — funding rates, open interest, long/short ratios.
+
+Uses the free fapi.coinglass.com internal API where available.
+Some endpoints may return empty data without authentication.
+Set COINGLASS_API_KEY env var for authenticated access to open-api.coinglass.com.
+"""
+
+import argparse
+import json
+import os
+import sys
+from typing import Any
+
+import requests
+
+FREE_BASE = "https://fapi.coinglass.com/api"
+AUTH_BASE = "https://open-api.coinglass.com/public/v2"
+TIMEOUT = 30
+
+
+def _free_get(path: str, params: dict | None = None) -> Any:
+    headers = {
+        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36",
+        "Referer": "https://www.coinglass.com/",
+    }
+    r = requests.get(f"{FREE_BASE}/{path}", params=params, headers=headers, timeout=TIMEOUT)
+    r.raise_for_status()
+    data = r.json()
+    if data.get("code") == "0" or data.get("success"):
+        return data.get("data", [])
+    raise ValueError(f"API error: {data.get('msg', 'unknown')}")
+
+
+def _auth_get(path: str, params: dict | None = None) -> Any:
+    key = os.environ.get("COINGLASS_API_KEY")
+    if not key:
+        raise EnvironmentError("COINGLASS_API_KEY not set. Get one at https://www.coinglass.com/pricing")
+    headers = {"coinglassSecret": key}
+    r = requests.get(f"{AUTH_BASE}/{path}", params=params, headers=headers, timeout=TIMEOUT)
+    r.raise_for_status()
+    data = r.json()
+    if data.get("success") or data.get("code") == "0":
+        return data.get("data", [])
+    raise ValueError(f"API error: {data.get('msg', 'unknown')}")
+
+
+# ── Data fetchers ───────────────────────────────────────────────────────────
+
+def get_funding_rates() -> list[dict]:
+    """Funding rates across exchanges."""
+    try:
+        data = _free_get("fundingRate/v2/home")
+        if data:
+            return data
+    except Exception:
+        pass
+    return _auth_get("funding")
+
+
+def get_open_interest() -> list[dict]:
+    """Aggregated open interest data."""
+    try:
+        data = _free_get("openInterest/v3/home")
+        if data:
+            return data
+    except Exception:
+        pass
+    return _auth_get("open_interest")
+
+
+def get_long_short_ratio() -> list[dict]:
+    """Global long/short account ratios."""
+    try:
+        data = _free_get("futures/longShort/v2/home")
+        if data:
+            return data
+    except Exception:
+        pass
+    return _auth_get("long_short")
+
+
+# ── Summary helpers ─────────────────────────────────────────────────────────
+
+def _no_data_msg(name: str) -> str:
+    return (f"No {name} data available (free API may be restricted).\n"
+            "Set COINGLASS_API_KEY for full access: https://www.coinglass.com/pricing")
+
+
+def summary_funding(data: list[dict]) -> str:
+    if not data:
+        return _no_data_msg("funding rate")
+    lines = ["═══ Funding Rates ═══", ""]
+    for item in data[:20]:
+        symbol = item.get("symbol", item.get("coin", "?"))
+        rate = None
+        if "uMarginList" in item:
+            for m in item["uMarginList"]:
+                rate = m.get("rate")
+                if rate is not None:
+                    break
+        else:
+            rate = item.get("rate")
+        if rate is not None:
+            lines.append(f"  {symbol:<10} {float(rate)*100:>8.4f}%")
+        else:
+            lines.append(f"  {symbol:<10} (rate unavailable)")
+    return "\n".join(lines)
+
+
+def summary_oi(data: list[dict]) -> str:
+    if not data:
+        return _no_data_msg("open interest")
+    lines = ["═══ Open Interest ═══", ""]
+    for item in data[:20]:
+        symbol = item.get("symbol", item.get("coin", "?"))
+        oi = item.get("openInterest", item.get("oi", 0))
+        lines.append(f"  {symbol:<10} OI: ${float(oi):>15,.0f}")
+    return "\n".join(lines)
+
+
+def summary_ls(data: list[dict]) -> str:
+    if not data:
+        return _no_data_msg("long/short")
+    lines = ["═══ Long/Short Ratios ═══", ""]
+    for item in data[:20]:
+        symbol = item.get("symbol", item.get("coin", "?"))
+        long_rate = item.get("longRate", item.get("longRatio", "?"))
+        short_rate = item.get("shortRate", item.get("shortRatio", "?"))
+        lines.append(f"  {symbol:<10} Long: {long_rate}  Short: {short_rate}")
+    return "\n".join(lines)
+
+
+# ── CLI ─────────────────────────────────────────────────────────────────────
+
+def main():
+    common = argparse.ArgumentParser(add_help=False)
+    common.add_argument("--pretty", action="store_true", help="Pretty-print JSON output")
+    common.add_argument("--summary", action="store_true", help="Human-readable summary")
+
+    parser = argparse.ArgumentParser(description="Coinglass data connector", parents=[common])
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    sub.add_parser("funding", help="Funding rates across exchanges", parents=[common])
+    sub.add_parser("oi", help="Open interest overview", parents=[common])
+    sub.add_parser("long-short", help="Long/short ratios", parents=[common])
+
+    args = parser.parse_args()
+
+    try:
+        if args.command == "funding":
+            data = get_funding_rates()
+            if args.summary:
+                print(summary_funding(data)); return
+            result = data
+        elif args.command == "oi":
+            data = get_open_interest()
+            if args.summary:
+                print(summary_oi(data)); return
+            result = data
+        elif args.command == "long-short":
+            data = get_long_short_ratio()
+            if args.summary:
+                print(summary_ls(data)); return
+            result = data
+        else:
+            parser.print_help(); return
+
+        json.dump(result, sys.stdout, indent=2 if args.pretty else None)
+        print()
+
+    except EnvironmentError as e:
+        print(str(e), file=sys.stderr); sys.exit(1)
+    except requests.HTTPError as e:
+        print(json.dumps({"error": str(e)}), file=sys.stderr); sys.exit(1)
+    except Exception as e:
+        print(json.dumps({"error": f"{type(e).__name__}: {e}"}), file=sys.stderr); sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tools/data_sources/defillama.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+"""DefiLlama API connector — TVL, token prices, yield/APY data.
+
+No authentication required. All endpoints are free.
+API base: https://api.llama.fi  |  Prices: https://coins.llama.fi  |  Yields: https://yields.llama.fi
+"""
+
+import argparse
+import json
+import sys
+from typing import Any
+
+import requests
+
+BASE = "https://api.llama.fi"
+COINS_BASE = "https://coins.llama.fi"
+YIELDS_BASE = "https://yields.llama.fi"
+
+TIMEOUT = 30
+
+
+def _get(url: str, params: dict | None = None) -> Any:
+    r = requests.get(url, params=params, timeout=TIMEOUT)
+    r.raise_for_status()
+    return r.json()
+
+
+# ── Protocol / TVL ──────────────────────────────────────────────────────────
+
+def get_protocols(limit: int = 20) -> list[dict]:
+    """Top protocols by TVL."""
+    data = _get(f"{BASE}/protocols")
+    # Sort by tvl descending, filter out CEXes
+    protos = [p for p in data if p.get("category") != "CEX" and p.get("tvl")]
+    protos.sort(key=lambda p: p.get("tvl", 0), reverse=True)
+    return protos[:limit]
+
+
+def get_tvl(protocol: str) -> dict:
+    """Get current TVL for a specific protocol (slug name)."""
+    val = _get(f"{BASE}/tvl/{protocol}")
+    return {"protocol": protocol, "tvl": val}
+
+
+def get_protocol_detail(protocol: str) -> dict:
+    """Full protocol details including chain breakdowns."""
+    return _get(f"{BASE}/protocol/{protocol}")
+
+
+# ── Token Prices ────────────────────────────────────────────────────────────
+
+def get_prices(coins: list[str]) -> dict:
+    """Get current prices. Coins format: 'coingecko:ethereum', 'ethereum:0x...', etc."""
+    joined = ",".join(coins)
+    data = _get(f"{COINS_BASE}/prices/current/{joined}")
+    return data.get("coins", {})
+
+
+# ── Yields / APY ────────────────────────────────────────────────────────────
+
+def get_yield_pools(limit: int = 30, min_tvl: float = 1_000_000, stablecoin_only: bool = False) -> list[dict]:
+    """Top yield pools sorted by APY."""
+    data = _get(f"{YIELDS_BASE}/pools")
+    pools = data.get("data", [])
+    # Filter
+    pools = [p for p in pools if (p.get("tvlUsd") or 0) >= min_tvl and (p.get("apy") or 0) > 0]
+    if stablecoin_only:
+        pools = [p for p in pools if p.get("stablecoin")]
+    pools.sort(key=lambda p: p.get("apy", 0), reverse=True)
+    return pools[:limit]
+
+
+# ── Summary helpers ─────────────────────────────────────────────────────────
+
+def _fmt_usd(v: float) -> str:
+    if v >= 1e9:
+        return f"${v/1e9:.2f}B"
+    if v >= 1e6:
+        return f"${v/1e6:.1f}M"
+    return f"${v:,.0f}"
+
+
+def summary_protocols(protos: list[dict]) -> str:
+    lines = ["═══ Top Protocols by TVL ═══", ""]
+    for i, p in enumerate(protos, 1):
+        lines.append(f"  {i:>2}. {p['name']:<25} TVL: {_fmt_usd(p.get('tvl', 0)):>12}  chain: {p.get('chain', '?')}")
+    return "\n".join(lines)
+
+
+def summary_prices(prices: dict) -> str:
+    lines = ["═══ Token Prices ═══", ""]
+    for coin, info in prices.items():
+        lines.append(f"  {info.get('symbol', coin):<10} ${info['price']:>12,.2f}  (confidence: {info.get('confidence', '?')})")
+    return "\n".join(lines)
+
+
+def summary_yields(pools: list[dict]) -> str:
+    lines = ["═══ Top Yield Pools ═══", ""]
+    for i, p in enumerate(pools, 1):
+        lines.append(
+            f"  {i:>2}. {p.get('symbol','?'):<25} APY: {p.get('apy',0):>8.2f}%  "
+            f"TVL: {_fmt_usd(p.get('tvlUsd',0)):>10}  {p.get('chain','?')}/{p.get('project','?')}"
+        )
+    return "\n".join(lines)
+
+
+# ── CLI ─────────────────────────────────────────────────────────────────────
+
+def main():
+    common = argparse.ArgumentParser(add_help=False)
+    common.add_argument("--pretty", action="store_true", help="Pretty-print JSON output")
+    common.add_argument("--summary", action="store_true", help="Human-readable summary")
+    parser = argparse.ArgumentParser(description="DefiLlama data connector", parents=[common])
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # protocols
+    p_proto = sub.add_parser("protocols", help="Top protocols by TVL", parents=[common])
+    p_proto.add_argument("--limit", type=int, default=20)
+
+    # tvl
+    p_tvl = sub.add_parser("tvl", help="TVL for a specific protocol", parents=[common])
+    p_tvl.add_argument("protocol", help="Protocol slug (e.g. aave, lido)")
+
+    # prices
+    p_price = sub.add_parser("prices", help="Token prices", parents=[common])
+    p_price.add_argument("coins", nargs="+", help="Coin IDs: coingecko:ethereum, ethereum:0x...")
+
+    # yields
+    p_yield = sub.add_parser("yields", help="Top yield pools", parents=[common])
+    p_yield.add_argument("--limit", type=int, default=30)
+    p_yield.add_argument("--min-tvl", type=float, default=1_000_000)
+    p_yield.add_argument("--stablecoins", action="store_true")
+
+    args = parser.parse_args()
+
+    try:
+        if args.command == "protocols":
+            data = get_protocols(args.limit)
+            if args.summary:
+                print(summary_protocols(data))
+                return
+            result = [{"name": p["name"], "tvl": p.get("tvl"), "chain": p.get("chain"), "category": p.get("category"), "symbol": p.get("symbol")} for p in data]
+        elif args.command == "tvl":
+            result = get_tvl(args.protocol)
+            if args.summary:
+                print(f"{args.protocol}: {_fmt_usd(result['tvl'])}")
+                return
+        elif args.command == "prices":
+            result = get_prices(args.coins)
+            if args.summary:
+                print(summary_prices(result))
+                return
+        elif args.command == "yields":
+            data = get_yield_pools(args.limit, args.min_tvl, args.stablecoins)
+            if args.summary:
+                print(summary_yields(data))
+                return
+            result = [{"symbol": p.get("symbol"), "apy": p.get("apy"), "tvlUsd": p.get("tvlUsd"), "chain": p.get("chain"), "project": p.get("project"), "pool": p.get("pool")} for p in data]
+        else:
+            parser.print_help()
+            return
+
+        indent = 2 if args.pretty else None
+        json.dump(result, sys.stdout, indent=indent)
+        print()
+
+    except requests.HTTPError as e:
+        print(json.dumps({"error": str(e)}), file=sys.stderr)
+        sys.exit(1)
+    except Exception as e:
+        print(json.dumps({"error": f"Unexpected: {e}"}), file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tools/tweet_analyzer_wrapper.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+# Tweet Analyzer Wrapper - for OpenClaw agent use
+# Usage: ./tweet_analyzer_wrapper.sh <tweet_url> [output_file]
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+URL="${1:?Usage: $0 <tweet_url> [output_file]}"
+OUTPUT="${2:-}"
+
+if [ -n "$OUTPUT" ]; then
+    python3 "$SCRIPT_DIR/analyze_tweet.py" "$URL" -o "$OUTPUT"
+    echo "Analysis written to $OUTPUT"
+else
+    python3 "$SCRIPT_DIR/analyze_tweet.py" "$URL"
+fi