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data/investigations/polymarket-15min-arb.md
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data/investigations/polymarket-15min-arb.md
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# Polymarket 15-Min Crypto Arbitrage
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**Source:** https://x.com/noisyb0y1/status/2020942208858456206
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**Date investigated:** 2026-02-09
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**Verdict:** Legitimate edge, inflated claims
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## Strategy
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- Buy BOTH sides (Up + Down) on 15-minute BTC/ETH/SOL/XRP markets
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- When combined cost < $1.00, guaranteed profit regardless of outcome
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- Edge exists because these markets are low liquidity / inefficient pricing
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## Reference Wallet
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- `0xE594336603F4fB5d3ba4125a67021ab3B4347052`
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- Real PnL on 2026-02-09: ~$9K on $82K deployed (11% daily)
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- Combined costs ranged from $0.70 (great arb) to $1.10 (not arb)
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- Best arbs: ETH markets at $0.70-0.73 combined cost
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## Why It Works
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- 15-min markets have thin books — prices diverge from fair value
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- Binary outcome means Up + Down must sum to $1.00 at resolution
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- If you buy both for < $1.00 total, guaranteed profit
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## Challenges
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- Needs significant capital ($50K+) to make meaningful returns
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- Fill quality degrades at scale — slippage kills the edge
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- Competition from other bots narrows the window
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- Not all markets have arb — some combined costs > $1.00
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## Revisit When
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- [ ] We have capital to deploy
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- [ ] Built a bot to scan for combined < $1.00 opportunities in real-time
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- [ ] Polymarket adds more 15-min markets (more opportunities)
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## Related
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- Tweet author promoting "Clawdbots" — bot product shill
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- "$99K in a day" / "$340K total" claims are inflated (real: $9K profit)
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@ -1,62 +1,66 @@
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# 2026-02-09
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# 2026-02-09 — Monday
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## Market Watch Launch Day
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- GARP paper trading simulator went live
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- 9 AM systemd timer fired but crashed (scanner returns list, code expected dict) — fixed
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- Manual run at 10:49 AM — **7 positions opened**:
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- DUOL (57 shares @ $116.35)
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- ALLY (156 shares @ $42.65)
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- JHG (138 shares @ $48.21)
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- INCY (61 shares @ $108.69)
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- PINS (332 shares @ $20.06)
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- EXEL (152 shares @ $43.80)
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- CART (187 shares @ $35.49)
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- ~$46.5K deployed, ~$53.5K cash remaining
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- Portal live at marketwatch.local:8889
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- Multiplayer game engine working — "GARP Challenge" game created
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## X Feed Analysis Day
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Major session analyzing Polymarket/crypto tweets D J forwarded from X feed.
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## Super Bowl Results (from last night)
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- Seahawks 36, Patriots 13
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- kch123 copy-trade sim: ALL 5 positions won, +$728 on $1K bankroll (+72.8%)
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- kch123 himself probably cleared ~$2M profit on this game
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- Two weeks straight of wins for kch123 (60-0 last week + Super Bowl sweep)
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### Tweets Investigated
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1. **@browomo weather edge** — Pilot METAR data for Polymarket weather bets
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- Wallet `0x594edB9112f...`: Claimed +$27K, actual **-$13,103** (387 losses, 51 wins)
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- Verdict: SCAM/engagement bait
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## Craigslist Account (from yesterday)
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- Registered: case-lgn@protonmail.com, Nashville area
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- Password set, credentials saved to .credentials/craigslist.env
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- User ID: 405642144
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- D J not ready for listings yet (needs photos)
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2. **@ArchiveExplorer planktonXD** — "Buy everything under 5 cents"
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- Wallet `0x4ffe49ba2a4c...`: Claimed +$104K, actual **-$9,517** (3090 losses, 1368 wins, 37% win rate)
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- Verdict: SCAM/engagement bait
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## D J Interests
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- Looking at queen Murphy beds on Craigslist
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- Wants to get rid of an old mattress (options discussed: Metro Nashville bulky pickup, free CL listing, dump)
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- Interested in Olympics men's hockey (USA in Group C, games start Feb 11)
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- Expanding analysis beyond Polymarket into crypto and stocks
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- Goal: find market gaps to offset AI service costs ($200/mo Claude + infra)
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- Getting crypto signals on Telegram, wants to forward them for analysis
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- Asked about Tiiny AI (kickstarter AI hardware, 80GB unified memory, NPU) as potential Claude replacement
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3. **@krajekis BTC 15-min LONG** — "+700% monthly, 1 trade/day at 9AM"
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- Backtested 25 days: 52% win rate (coin flip), strategy loses 76% of capital
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- Verdict: FABRICATED results
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## Stock Screener Built
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- GARP filters automated via yfinance (free, no API key)
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- S&P 500 + S&P 400 MidCap scan (~902 tickers)
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- Initial S&P 500 scan found 4: BAC, CFG, FITB, INCY
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- Expanded scan found 22 total candidates
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- Top non-bank picks: PINS, FSLR, PGR, NEM, ALLY
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- Deep dive sub-agent ran on all 4 original picks
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4. **@noisyb0y1 15-min arb** — "$99K in a day"
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- Wallet `0xE594336603F4...`: Real strategy (buying both sides), actual PnL ~$9K not $99K
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- Combined costs $0.70-$0.95 on some markets = genuine arb edge
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- Verdict: INFLATED numbers, but strategy has merit → bookmarked for scanner
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## X Post Analysis: @Shelpid_WI3M / anoin123
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- Wallet 0x96489abc... is real, +$1.59M PnL, 216 trades
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- BUT: concentrated single-thesis bet (NO on Iran strikes), not diversified alpha
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- Post is a shill for PolyCopyBot (Telegram copy-trading bot)
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- Verdict: real wallet, misleading narrative, exists to sell bot subscriptions
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5. **5 more wallets** — spawned sub-agent to research @IH2P, Super Bowl trader, NegRisk arb, Elon insider, $270→$244K bot
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## Tiiny AI Analysis
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- 80GB LPDDR5X, ARM + NPU (160 TOPS), 18-40 tok/s, 30W
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- Kickstarter vaporware — doesn't exist yet
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- Would blow away D J's current 22GB VRAM setup IF it ships
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- Recommended waiting for real reviews, not pre-ordering
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### Pattern Identified
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- Fintwit Polymarket accounts follow identical template: big $ claim → wallet → product shill
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- Products being shilled: Clawdbots, Moltbook, Bullpen (affiliate/paid promos)
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- Real money is in selling engagement, not trading
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- Wallets are cherry-picked; PnL claims conflate position value with profit
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## Lessons
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- Scanner run_scan() returns list, not dict — caused systemd crash on first real run
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- Always test the full pipeline end-to-end before relying on timers
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- yfinance is reliable and free for fundamental data, no API key needed
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### Built Today
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- **Crypto signals pipeline** (`projects/crypto-signals/`)
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- Signal parser for Telegram JSON exports
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- Price fetcher (Binance US API)
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- Backtester with Polymarket fee awareness
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- Ready for D J's Telegram group export
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- **Polymarket 15-min arb scanner** — systemd timer every 2 min
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- Scans active Up or Down markets
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- Checks orderbooks for combined < $1.00
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- Paper trades and Telegram alerts
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- Finding: markets are tight at steady state, arb windows likely during volatility
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- **Playwright installed** — replaced flaky CDP websocket approach
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- `connect_over_cdp('http://localhost:9222')` works with existing Chrome session
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- X feed scraper working via Playwright
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- **PowerInfer multi-GPU cron** — weekly Monday check for feature support
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### Day-of-Week Stock Strategy
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- D J's friend suggested buy Friday/Monday, sell midweek
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- Backtested 5 years of SPY: Monday is strongest day (+0.114%, 62% win rate)
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- Buy Mon Open → Sell Wed Close: 56% win rate, +0.265% avg, $10K→$17.8K
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- But buy & hold still wins: $10K→$19K
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- Verdict: weak edge, needs additional filters to be useful
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### Infrastructure
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- Polymarket fee structure documented: 15-min markets have taker fees, max 1.56% at 50/50
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- Fee formula: `shares × price × 0.25 × (price × (1-price))²`
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- Binance international is geo-blocked; Binance US works
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### Pending
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- D J sending Telegram crypto signal group export (Option 2: JSON export)
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- Signal provider uses VWAP-based strategy
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- Sub-agent researching 5 more wallets
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36935
projects/crypto-signals/data/arb-scanner/scan_log.json
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36935
projects/crypto-signals/data/arb-scanner/scan_log.json
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File diff suppressed because it is too large
Load Diff
259
projects/crypto-signals/scripts/backtester.py
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projects/crypto-signals/scripts/backtester.py
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#!/usr/bin/env python3
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"""
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Crypto Signal Backtester
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Simulates each signal against historical price data to determine outcomes.
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"""
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import json
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import sys
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from price_fetcher import get_all_klines, get_current_price, normalize_symbol, datetime_to_ms
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def simulate_signal(signal, klines):
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"""
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Simulate a signal against historical candle data.
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Returns outcome dict with result, P&L, time to resolution, etc.
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"""
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direction = signal['direction']
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entry = signal.get('entry')
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stop_loss = signal.get('stop_loss')
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targets = signal.get('targets', [])
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leverage = signal.get('leverage', 1)
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if not targets or not stop_loss:
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return {'result': 'incomplete', 'reason': 'missing SL or targets'}
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target = targets[0] # Primary target
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# If entry is 'market', use first candle's open
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if entry == 'market' or entry is None:
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if not klines:
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return {'result': 'no_data'}
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entry = klines[0]['open']
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signal['entry_resolved'] = entry
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# Calculate risk/reward
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if direction == 'short':
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risk = abs(stop_loss - entry)
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reward = abs(entry - target)
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risk_pct = risk / entry * 100
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reward_pct = reward / entry * 100
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else: # long
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risk = abs(entry - stop_loss)
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reward = abs(target - entry)
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risk_pct = risk / entry * 100
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reward_pct = reward / entry * 100
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rr_ratio = reward / risk if risk > 0 else 0
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result = {
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'entry_price': entry,
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'stop_loss': stop_loss,
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'target': target,
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'direction': direction,
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'leverage': leverage,
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'risk_pct': round(risk_pct, 2),
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'reward_pct': round(reward_pct, 2),
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'rr_ratio': round(rr_ratio, 2),
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}
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# Walk through candles
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for i, candle in enumerate(klines):
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high = candle['high']
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low = candle['low']
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if direction == 'short':
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# Check SL hit (price went above SL)
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sl_hit = high >= stop_loss
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# Check TP hit (price went below target)
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tp_hit = low <= target
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else: # long
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# Check SL hit (price went below SL)
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sl_hit = low <= stop_loss
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# Check TP hit (price went above target)
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tp_hit = high >= target
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if sl_hit and tp_hit:
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# Both hit in same candle — assume SL hit first (conservative)
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result['result'] = 'stop_loss'
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result['exit_price'] = stop_loss
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result['candles_to_exit'] = i + 1
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result['exit_time'] = candle['open_time']
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break
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elif tp_hit:
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result['result'] = 'target_hit'
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result['exit_price'] = target
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result['candles_to_exit'] = i + 1
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result['exit_time'] = candle['open_time']
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break
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elif sl_hit:
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result['result'] = 'stop_loss'
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result['exit_price'] = stop_loss
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result['candles_to_exit'] = i + 1
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result['exit_time'] = candle['open_time']
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break
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else:
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# Never resolved — check current unrealized P&L
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if klines:
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last_price = klines[-1]['close']
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if direction == 'short':
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unrealized_pct = (entry - last_price) / entry * 100
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else:
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unrealized_pct = (last_price - entry) / entry * 100
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result['result'] = 'open'
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result['last_price'] = last_price
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result['unrealized_pct'] = round(unrealized_pct, 2)
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result['unrealized_pct_leveraged'] = round(unrealized_pct * leverage, 2)
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else:
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result['result'] = 'no_data'
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# Calculate P&L
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if result['result'] in ('target_hit', 'stop_loss'):
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exit_price = result['exit_price']
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if direction == 'short':
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pnl_pct = (entry - exit_price) / entry * 100
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else:
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pnl_pct = (exit_price - entry) / entry * 100
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result['pnl_pct'] = round(pnl_pct, 2)
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result['pnl_pct_leveraged'] = round(pnl_pct * leverage, 2)
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return result
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def backtest_signals(signals, interval='5m', lookforward_hours=72):
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"""Backtest a list of parsed signals."""
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results = []
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for i, signal in enumerate(signals):
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ticker = signal['ticker']
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symbol = normalize_symbol(ticker)
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timestamp = signal.get('timestamp', '')
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print(f"[{i+1}/{len(signals)}] {ticker} {signal['direction']} ...", end=' ', flush=True)
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# Get start time
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start_ms = datetime_to_ms(timestamp) if timestamp else int(time.time() * 1000)
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end_ms = start_ms + (lookforward_hours * 60 * 60 * 1000)
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# Cap at current time
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now_ms = int(time.time() * 1000)
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if end_ms > now_ms:
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end_ms = now_ms
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# Fetch candles
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klines = get_all_klines(symbol, interval, start_ms, end_ms)
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if not klines:
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print(f"NO DATA")
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results.append({**signal, 'backtest': {'result': 'no_data', 'reason': f'no klines for {symbol}'}})
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continue
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# Simulate
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outcome = simulate_signal(signal, klines)
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print(f"{outcome['result']} | PnL: {outcome.get('pnl_pct_leveraged', outcome.get('unrealized_pct_leveraged', '?'))}%")
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results.append({**signal, 'backtest': outcome})
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time.sleep(0.2) # Rate limit
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return results
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def generate_report(results):
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"""Generate a summary report from backtest results."""
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total = len(results)
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wins = [r for r in results if r['backtest'].get('result') == 'target_hit']
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losses = [r for r in results if r['backtest'].get('result') == 'stop_loss']
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open_trades = [r for r in results if r['backtest'].get('result') == 'open']
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no_data = [r for r in results if r['backtest'].get('result') in ('no_data', 'incomplete')]
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resolved = wins + losses
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win_rate = len(wins) / len(resolved) * 100 if resolved else 0
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avg_win = sum(r['backtest']['pnl_pct_leveraged'] for r in wins) / len(wins) if wins else 0
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avg_loss = sum(r['backtest']['pnl_pct_leveraged'] for r in losses) / len(losses) if losses else 0
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total_pnl = sum(r['backtest'].get('pnl_pct_leveraged', 0) for r in resolved)
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# Profit factor
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gross_profit = sum(r['backtest']['pnl_pct_leveraged'] for r in wins)
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gross_loss = abs(sum(r['backtest']['pnl_pct_leveraged'] for r in losses))
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profit_factor = gross_profit / gross_loss if gross_loss > 0 else float('inf')
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# Risk/reward stats
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avg_rr = sum(r['backtest'].get('rr_ratio', 0) for r in resolved) / len(resolved) if resolved else 0
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report = {
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'summary': {
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'total_signals': total,
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'wins': len(wins),
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'losses': len(losses),
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'open': len(open_trades),
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'no_data': len(no_data),
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'win_rate': round(win_rate, 1),
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'avg_win_pct': round(avg_win, 2),
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'avg_loss_pct': round(avg_loss, 2),
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'total_pnl_pct': round(total_pnl, 2),
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'profit_factor': round(profit_factor, 2),
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'avg_risk_reward': round(avg_rr, 2),
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},
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'trades': results,
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}
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return report
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def print_report(report):
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"""Pretty print the report."""
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s = report['summary']
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print("\n" + "=" * 60)
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print("CRYPTO SIGNAL BACKTEST REPORT")
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print("=" * 60)
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print(f"Total Signals: {s['total_signals']}")
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print(f"Wins: {s['wins']}")
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print(f"Losses: {s['losses']}")
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print(f"Open: {s['open']}")
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print(f"No Data: {s['no_data']}")
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print(f"Win Rate: {s['win_rate']}%")
|
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print(f"Avg Win: +{s['avg_win_pct']}% (leveraged)")
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print(f"Avg Loss: {s['avg_loss_pct']}% (leveraged)")
|
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print(f"Total P&L: {s['total_pnl_pct']}% (sum of resolved)")
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print(f"Profit Factor: {s['profit_factor']}")
|
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print(f"Avg R:R: {s['avg_risk_reward']}")
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print("=" * 60)
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if __name__ == '__main__':
|
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if len(sys.argv) < 2:
|
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print("Usage: python3 backtester.py <signals.json> [--interval 5m] [--hours 72]")
|
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print("\nRun signal_parser.py first to generate signals.json")
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sys.exit(1)
|
||||
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||||
signals_path = sys.argv[1]
|
||||
|
||||
interval = '5m'
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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}")
|
||||
311
projects/crypto-signals/scripts/polymarket_arb_scanner.py
Normal file
311
projects/crypto-signals/scripts/polymarket_arb_scanner.py
Normal file
@ -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()
|
||||
131
projects/crypto-signals/scripts/price_fetcher.py
Normal file
131
projects/crypto-signals/scripts/price_fetcher.py
Normal file
@ -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()
|
||||
166
projects/crypto-signals/scripts/signal_parser.py
Normal file
166
projects/crypto-signals/scripts/signal_parser.py
Normal file
@ -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)}")
|
||||
File diff suppressed because one or more lines are too long
@ -1,5 +1,5 @@
|
||||
{
|
||||
"last_check": "2026-02-09T16:58:59.975254+00:00",
|
||||
"last_check": "2026-02-09T23:25:59.773254+00:00",
|
||||
"total_tracked": 3100,
|
||||
"new_this_check": 0
|
||||
}
|
||||
155
projects/feed-hunter/x_cookies.json
Normal file
155
projects/feed-hunter/x_cookies.json
Normal file
@ -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"
|
||||
}
|
||||
]
|
||||
249
projects/feed-hunter/x_scraper_pw.py
Normal file
249
projects/feed-hunter/x_scraper_pw.py
Normal file
@ -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()
|
||||
@ -1,18 +1,10 @@
|
||||
{
|
||||
"cash": 53477.43000000002,
|
||||
"cash": 60255.30020874026,
|
||||
"positions": {
|
||||
"DUOL": {
|
||||
"shares": 57,
|
||||
"avg_cost": 116.35,
|
||||
"current_price": 116.35,
|
||||
"entry_date": "2026-02-09T10:55:58.243598",
|
||||
"entry_reason": "GARP signal: PE=14.65, FwdPE=14.71, RevGr=41.1%, EPSGr=1114.3%, RSI=23.44",
|
||||
"trailing_stop": 104.715
|
||||
},
|
||||
"ALLY": {
|
||||
"shares": 156,
|
||||
"avg_cost": 42.65,
|
||||
"current_price": 42.65,
|
||||
"current_price": 42.040000915527344,
|
||||
"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": 38.385
|
||||
@ -20,7 +12,7 @@
|
||||
"JHG": {
|
||||
"shares": 138,
|
||||
"avg_cost": 48.21,
|
||||
"current_price": 48.21,
|
||||
"current_price": 48.20000076293945,
|
||||
"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": 43.389
|
||||
@ -28,31 +20,31 @@
|
||||
"INCY": {
|
||||
"shares": 61,
|
||||
"avg_cost": 108.69,
|
||||
"current_price": 108.69,
|
||||
"current_price": 109.02999877929688,
|
||||
"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": 97.821
|
||||
"trailing_stop": 98.12699890136719
|
||||
},
|
||||
"PINS": {
|
||||
"shares": 332,
|
||||
"avg_cost": 20.06,
|
||||
"current_price": 20.06,
|
||||
"current_price": 20.139999389648438,
|
||||
"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": 18.054
|
||||
"trailing_stop": 18.125999450683594
|
||||
},
|
||||
"EXEL": {
|
||||
"shares": 152,
|
||||
"avg_cost": 43.8,
|
||||
"current_price": 43.8,
|
||||
"current_price": 43.95000076293945,
|
||||
"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": 39.42
|
||||
"trailing_stop": 39.555000686645506
|
||||
},
|
||||
"CART": {
|
||||
"shares": 187,
|
||||
"avg_cost": 35.49,
|
||||
"current_price": 35.49,
|
||||
"current_price": 35.150001525878906,
|
||||
"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
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
[
|
||||
{
|
||||
"date": "2026-02-09",
|
||||
"total_value": 100000.0,
|
||||
"total_pnl": 0.0,
|
||||
"pnl_pct": 0.0,
|
||||
"cash": 53477.43,
|
||||
"num_positions": 7
|
||||
"total_value": 100055.9,
|
||||
"total_pnl": 55.9,
|
||||
"pnl_pct": 0.06,
|
||||
"cash": 60255.3,
|
||||
"num_positions": 6
|
||||
}
|
||||
]
|
||||
@ -61,5 +61,16 @@
|
||||
"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"
|
||||
}
|
||||
]
|
||||
@ -194,5 +194,117 @@
|
||||
"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": {}
|
||||
}
|
||||
]
|
||||
@ -1,356 +1,338 @@
|
||||
{
|
||||
"date": "2026-02-09",
|
||||
"timestamp": "2026-02-09T10:55:58.242176",
|
||||
"timestamp": "2026-02-09T15:36:18.290971",
|
||||
"total_scanned": 902,
|
||||
"candidates_found": 21,
|
||||
"candidates_found": 20,
|
||||
"candidates": [
|
||||
{
|
||||
"ticker": "DUOL",
|
||||
"price": 116.35,
|
||||
"market_cap": 5378552832,
|
||||
"market_cap_b": 5.4,
|
||||
"trailing_pe": 14.65,
|
||||
"forward_pe": 14.71,
|
||||
"peg_ratio": null,
|
||||
"revenue_growth": 41.1,
|
||||
"earnings_growth": 1114.3,
|
||||
"roe": 36.2,
|
||||
"quick_ratio": 2.6,
|
||||
"debt_to_equity": 7.4,
|
||||
"rsi": 23.44,
|
||||
"week52_high": 544.93,
|
||||
"pct_from_52wk_high": 78.6,
|
||||
"score": -100.83
|
||||
},
|
||||
{
|
||||
"ticker": "ALLY",
|
||||
"price": 42.65,
|
||||
"market_cap": 13158768640,
|
||||
"market_cap_b": 13.2,
|
||||
"trailing_pe": 18.0,
|
||||
"forward_pe": 6.76,
|
||||
"price": 42.04,
|
||||
"market_cap": 12969046016,
|
||||
"market_cap_b": 13.0,
|
||||
"trailing_pe": 17.74,
|
||||
"forward_pe": 6.66,
|
||||
"peg_ratio": null,
|
||||
"revenue_growth": 12.0,
|
||||
"earnings_growth": 265.4,
|
||||
"roe": 5.8,
|
||||
"quick_ratio": null,
|
||||
"debt_to_equity": null,
|
||||
"rsi": 53.23,
|
||||
"rsi": 49.52,
|
||||
"week52_high": 47.27,
|
||||
"pct_from_52wk_high": 9.8,
|
||||
"score": -20.98
|
||||
"pct_from_52wk_high": 11.1,
|
||||
"score": -21.08
|
||||
},
|
||||
{
|
||||
"ticker": "JHG",
|
||||
"price": 48.21,
|
||||
"market_cap": 7447323136,
|
||||
"price": 48.2,
|
||||
"market_cap": 7445763584,
|
||||
"market_cap_b": 7.4,
|
||||
"trailing_pe": 9.22,
|
||||
"forward_pe": 9.96,
|
||||
"forward_pe": 9.95,
|
||||
"peg_ratio": null,
|
||||
"revenue_growth": 61.3,
|
||||
"earnings_growth": 243.6,
|
||||
"roe": 16.2,
|
||||
"quick_ratio": 69.46,
|
||||
"debt_to_equity": 6.5,
|
||||
"rsi": 68.71,
|
||||
"rsi": 68.18,
|
||||
"week52_high": 49.42,
|
||||
"pct_from_52wk_high": 2.4,
|
||||
"score": -20.529999999999998
|
||||
"pct_from_52wk_high": 2.5,
|
||||
"score": -20.54
|
||||
},
|
||||
{
|
||||
"ticker": "INCY",
|
||||
"price": 108.69,
|
||||
"market_cap": 21338314752,
|
||||
"market_cap_b": 21.3,
|
||||
"trailing_pe": 18.42,
|
||||
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|
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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{
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||||
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||||
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||||
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{
|
||||
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||||
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|
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"ticker": "ONB",
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"ticker": "WAL",
|
||||
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|
||||
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||||
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|
||||
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|
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"ticker": "EXEL",
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
"roe": 30.6,
|
||||
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|
||||
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|
||||
"rsi": 50.12,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"score": 4.470000000000001
|
||||
},
|
||||
{
|
||||
"ticker": "ZION",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
"rsi": 74.03,
|
||||
"rsi": 73.29,
|
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|
||||
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|
||||
"score": 5.49
|
||||
"pct_from_52wk_high": 1.5,
|
||||
"score": 5.470000000000001
|
||||
},
|
||||
{
|
||||
"ticker": "CART",
|
||||
"price": 35.49,
|
||||
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|
||||
"price": 35.15,
|
||||
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|
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||||
"earnings_growth": 21.1,
|
||||
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|
||||
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|
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|
||||
"rsi": 37.75,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"score": 5.84
|
||||
},
|
||||
{
|
||||
"ticker": "CFG",
|
||||
"price": 68.17,
|
||||
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|
||||
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|
||||
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|
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"forward_pe": 10.82,
|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
"score": 6.16
|
||||
"pct_from_52wk_high": 1.4,
|
||||
"score": 6.09
|
||||
},
|
||||
{
|
||||
"ticker": "UBSI",
|
||||
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|
||||
"market_cap": 6316939264,
|
||||
"price": 45.06,
|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"rsi": 77.51,
|
||||
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|
||||
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|
||||
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|
||||
"pct_from_52wk_high": 1.9,
|
||||
"score": 6.54
|
||||
},
|
||||
{
|
||||
"ticker": "EWBC",
|
||||
"price": 123.19,
|
||||
"market_cap": 16949170176,
|
||||
"price": 122.56,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"week52_high": 123.82,
|
||||
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|
||||
"score": 6.949999999999999
|
||||
"pct_from_52wk_high": 1.0,
|
||||
"score": 6.890000000000001
|
||||
},
|
||||
{
|
||||
"ticker": "FITB",
|
||||
"price": 54.38,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"rsi": 66.02,
|
||||
"rsi": 65.77,
|
||||
"week52_high": 55.36,
|
||||
"pct_from_52wk_high": 1.8,
|
||||
"score": 7.859999999999999
|
||||
"pct_from_52wk_high": 1.9,
|
||||
"score": 7.85
|
||||
},
|
||||
{
|
||||
"ticker": "BAC",
|
||||
"price": 56.43,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"forward_pe": 11.38,
|
||||
"peg_ratio": null,
|
||||
@ -359,28 +341,28 @@
|
||||
"roe": 10.2,
|
||||
"quick_ratio": null,
|
||||
"debt_to_equity": null,
|
||||
"rsi": 78.3,
|
||||
"rsi": 78.1,
|
||||
"week52_high": 57.55,
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"ticker": "WTFC",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"forward_pe": 11.82,
|
||||
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|
||||
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|
||||
"earnings_growth": 19.4,
|
||||
"roe": 12.1,
|
||||
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|
||||
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|
||||
"rsi": 72.56,
|
||||
"rsi": 70.23,
|
||||
"week52_high": 162.96,
|
||||
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|
||||
"score": 8.92
|
||||
"pct_from_52wk_high": 2.7,
|
||||
"score": 8.83
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user