Case
be43231c3f
- Game engine with multiplayer support (create games, join, leaderboard) - GARP stock screener (S&P 500 + 400 MidCap, 900+ tickers) - Automated trading logic for AI player (Case) - Web portal at marketwatch.local:8889 with dark theme - Systemd timer for Mon-Fri market hours - Telegram alerts on trades and daily summary - Stock analysis deep dive data (BAC, CFG, FITB, INCY) - Expanded scan results (22 GARP candidates) - Craigslist account setup + credentials
296 lines
11 KiB
Python
296 lines
11 KiB
Python
#!/usr/bin/env python3
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"""Deep dive analysis on BAC, CFG, FITB, INCY + expanded GARP scan."""
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import yfinance as yf
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import json
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import datetime
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import numpy as np
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from pathlib import Path
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TARGETS = ["BAC", "CFG", "FITB", "INCY"]
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def safe_get(d, *keys, default=None):
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for k in keys:
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if isinstance(d, dict):
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d = d.get(k, default)
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else:
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return default
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return d
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def analyze_stock(ticker_str):
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print(f"\n{'='*60}\nAnalyzing {ticker_str}...")
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t = yf.Ticker(ticker_str)
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info = t.info or {}
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result = {"ticker": ticker_str, "name": info.get("longName", ticker_str)}
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# 1. Price action (1yr)
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hist = t.history(period="1y")
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if not hist.empty:
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prices = hist["Close"]
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result["price_action"] = {
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"current": round(float(prices.iloc[-1]), 2),
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"1yr_ago": round(float(prices.iloc[0]), 2),
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"1yr_return_pct": round(float((prices.iloc[-1] / prices.iloc[0] - 1) * 100), 2),
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"52wk_high": round(float(prices.max()), 2),
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"52wk_low": round(float(prices.min()), 2),
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"pct_from_52wk_high": round(float((prices.iloc[-1] / prices.max() - 1) * 100), 2),
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"50d_ma": round(float(prices.tail(50).mean()), 2),
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"200d_ma": round(float(prices.tail(200).mean()), 2) if len(prices) >= 200 else None,
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}
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cur = float(prices.iloc[-1])
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ma50 = result["price_action"]["50d_ma"]
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ma200 = result["price_action"]["200d_ma"]
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result["price_action"]["above_50d_ma"] = cur > ma50
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result["price_action"]["above_200d_ma"] = cur > ma200 if ma200 else None
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# 2. Insider activity
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try:
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insiders = t.insider_transactions
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if insiders is not None and not insiders.empty:
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recent = insiders.head(10)
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txns = []
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for _, row in recent.iterrows():
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txns.append({
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"date": str(row.get("Start Date", "")),
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"insider": str(row.get("Insider", "")),
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"transaction": str(row.get("Transaction", "")),
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"shares": str(row.get("Shares", "")),
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"value": str(row.get("Value", "")),
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})
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result["insider_transactions"] = txns
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else:
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result["insider_transactions"] = []
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except:
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result["insider_transactions"] = []
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# 3. Institutional ownership
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try:
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inst = t.institutional_holders
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if inst is not None and not inst.empty:
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top5 = []
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for _, row in inst.head(5).iterrows():
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top5.append({
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"holder": str(row.get("Holder", "")),
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"shares": int(row.get("Shares", 0)) if row.get("Shares") else 0,
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"pct_held": str(row.get("pctHeld", "")),
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})
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result["top_institutional_holders"] = top5
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result["institutional_pct"] = info.get("heldPercentInstitutions")
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except:
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result["top_institutional_holders"] = []
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# 4. Earnings surprises
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try:
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earn = t.earnings_dates
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if earn is not None and not earn.empty:
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surprises = []
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for idx, row in earn.head(8).iterrows():
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s = {
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"date": str(idx),
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"eps_estimate": row.get("EPS Estimate"),
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"eps_actual": row.get("Reported EPS"),
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"surprise_pct": row.get("Surprise(%)"),
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}
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# clean NaN
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s = {k: (None if isinstance(v, float) and np.isnan(v) else v) for k, v in s.items()}
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surprises.append(s)
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result["earnings"] = surprises
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else:
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result["earnings"] = []
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except:
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result["earnings"] = []
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# 5. Analyst consensus
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result["analyst"] = {
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"target_mean": info.get("targetMeanPrice"),
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"target_low": info.get("targetLowPrice"),
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"target_high": info.get("targetHighPrice"),
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"recommendation": info.get("recommendationKey"),
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"num_analysts": info.get("numberOfAnalystOpinions"),
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}
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if result["price_action"].get("current") and info.get("targetMeanPrice"):
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upside = (info["targetMeanPrice"] / result["price_action"]["current"] - 1) * 100
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result["analyst"]["upside_pct"] = round(upside, 2)
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# 6. Key fundamentals snapshot
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result["fundamentals"] = {
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"trailing_pe": info.get("trailingPE"),
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"forward_pe": info.get("forwardPE"),
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"peg_ratio": info.get("pegRatio"),
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"market_cap": info.get("marketCap"),
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"revenue_growth": info.get("revenueGrowth"),
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"earnings_growth": info.get("earningsGrowth"),
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"roe": info.get("returnOnEquity"),
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"debt_to_equity": info.get("debtToEquity"),
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"dividend_yield": info.get("dividendYield"),
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"beta": info.get("beta"),
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}
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# 7. Technical - RSI approximation
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if not hist.empty and len(hist) >= 14:
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delta = hist["Close"].diff()
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gain = delta.where(delta > 0, 0).rolling(14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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result["technical"] = {
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"rsi_14": round(float(rsi.iloc[-1]), 2) if not np.isnan(rsi.iloc[-1]) else None,
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"trend": "bullish" if cur > ma50 and (ma200 is None or cur > ma200) else "bearish" if cur < ma50 and (ma200 and cur < ma200) else "mixed",
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}
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print(f" Done: {result['name']} @ ${result['price_action']['current']}")
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return result
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# ============================================================
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# EXPANDED GARP SCAN
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# ============================================================
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def get_broad_ticker_list():
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"""Get a broad list of US tickers to screen."""
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import urllib.request
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# Use Wikipedia's S&P 500 + S&P 400 midcap for broader coverage
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# Plus some known Russell 1000 members not in S&P 500
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# Start with S&P 500
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sp500 = []
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try:
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import pandas as pd
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tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
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sp500 = tables[0]["Symbol"].str.replace(".", "-", regex=False).tolist()
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except:
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pass
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# S&P 400 midcap
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sp400 = []
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try:
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import pandas as pd
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tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_400_companies")
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sp400 = tables[0]["Symbol"].str.replace(".", "-", regex=False).tolist()
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except:
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pass
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all_tickers = list(set(sp500 + sp400))
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print(f"Total tickers to screen: {len(all_tickers)}")
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return all_tickers
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def garp_screen(tickers, exclude=None):
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"""Apply GARP filters to ticker list."""
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exclude = set(exclude or [])
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passed = []
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for i, tick in enumerate(tickers):
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if tick in exclude:
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continue
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if i % 50 == 0:
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print(f" Screening {i}/{len(tickers)}...")
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try:
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t = yf.Ticker(tick)
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info = t.info or {}
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mc = info.get("marketCap", 0) or 0
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if mc < 5e9:
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continue
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tpe = info.get("trailingPE")
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if tpe is None or tpe >= 25 or tpe <= 0:
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continue
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fpe = info.get("forwardPE")
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if fpe is None or fpe >= 15 or fpe <= 0:
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continue
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rg = info.get("revenueGrowth")
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if rg is None or rg < 0.10:
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continue
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eg = info.get("earningsGrowth")
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if eg is None or eg < 0.15:
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continue
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roe = info.get("returnOnEquity")
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if roe is None or roe < 0.05:
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continue
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# Optional filters
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peg = info.get("pegRatio")
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if peg is not None and peg > 1.2:
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continue
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dte = info.get("debtToEquity")
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if dte is not None and dte > 35:
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continue
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passed.append({
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"ticker": tick,
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"name": info.get("longName", tick),
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"market_cap": mc,
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"trailing_pe": round(tpe, 2),
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"forward_pe": round(fpe, 2),
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"peg": round(peg, 2) if peg else None,
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"revenue_growth": round(rg * 100, 1),
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"earnings_growth": round(eg * 100, 1),
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"roe": round(roe * 100, 1),
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"debt_to_equity": round(dte, 1) if dte else None,
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})
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print(f" ✅ PASS: {tick} (PE:{tpe:.1f} FPE:{fpe:.1f} RG:{rg*100:.0f}% EG:{eg*100:.0f}%)")
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except Exception as e:
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continue
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return passed
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# ============================================================
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# MAIN
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# ============================================================
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if __name__ == "__main__":
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output_dir = Path("/home/wdjones/.openclaw/workspace/data")
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output_dir.mkdir(exist_ok=True)
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# Deep dive on 4 stocks
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print("=" * 60)
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print("DEEP DIVE ANALYSIS")
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print("=" * 60)
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analyses = {}
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for tick in TARGETS:
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analyses[tick] = analyze_stock(tick)
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# Save deep dive
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with open(output_dir / "stock-analysis-deep-dive.json", "w") as f:
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json.dump(analyses, f, indent=2, default=str)
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print(f"\nSaved deep dive to stock-analysis-deep-dive.json")
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# Expanded GARP scan
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print("\n" + "=" * 60)
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print("EXPANDED GARP SCAN (S&P 500 + S&P 400 MidCap)")
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print("=" * 60)
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tickers = get_broad_ticker_list()
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new_passes = garp_screen(tickers, exclude=set(TARGETS))
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with open(output_dir / "garp-expanded-scan.json", "w") as f:
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json.dump(new_passes, f, indent=2, default=str)
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print(f"\nExpanded scan found {len(new_passes)} additional stocks")
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print("Saved to garp-expanded-scan.json")
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# Print summary
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print("\n" + "=" * 60)
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print("SUMMARY")
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print("=" * 60)
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for tick, data in analyses.items():
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pa = data.get("price_action", {})
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an = data.get("analyst", {})
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fu = data.get("fundamentals", {})
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te = data.get("technical", {})
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print(f"\n{tick} - {data['name']}")
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print(f" Price: ${pa.get('current')} | 1yr Return: {pa.get('1yr_return_pct')}%")
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print(f" From 52wk High: {pa.get('pct_from_52wk_high')}%")
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print(f" PE: {fu.get('trailing_pe')} | Fwd PE: {fu.get('forward_pe')} | PEG: {fu.get('peg_ratio')}")
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print(f" Target: ${an.get('target_mean')} ({an.get('upside_pct')}% upside) | Rec: {an.get('recommendation')}")
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print(f" RSI: {te.get('rsi_14')} | Trend: {te.get('trend')}")
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print(f" Insiders: {len(data.get('insider_transactions', []))} recent txns")
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if new_passes:
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print(f"\nNew GARP Candidates ({len(new_passes)}):")
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for s in sorted(new_passes, key=lambda x: x.get("forward_pe", 99)):
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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}%")
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