Skill: ssidharhubble/options-trading-brain (version 1.0.14)

--- name: options-trading-brain version: 1.0.14 description: | Professional-grade options trading signal generator. Monitors whale flow (Unusual Whales), counts Elliott Waves, analyzes Bollinger Bands, multi-timeframe trend alignment, and liquidity zones. Combines all 5 inputs into actionable trade signals. Use when user asks for options signals, stock analysis, trading setups, or to check a specific ticker. Fully autonomous — no API keys required for core analysis. compatibility: Python 3.10+, yfinance, numpy, scipy. Optional: Unusual Whales subscription. metadata: author: ssyopro.zo.computer category: finance display-name: Options Trading Brain tags: options, trading, signals, elliott-wave, whale-flow, bollinger, theta-gang --- # Options Trading Brain Professional options trading intelligence system combining 5 analysis dimensions into one unified signal. ## The 5 Inputs (Signal Requires 3+ Aligned) ### 1. Whale Flow (Unusual Whales) - Filter: $25K+ premium, sweep/block executions, ask-side fills - Hierarchy: sweeps > blocks > splits > single fills - Bullish: calls at ask + volume > OI; Bearish: puts at ask + volume > OI ### 2. Elliott Wave - Wave 3 = strongest momentum entry - Wave 5 = exhaustion warning - Rules: Wave 2 can't retrace Wave 1; Wave 3 not shortest; Wave 4 can't overlap Wave 1 ### 3. Bollinger Bands - Squeeze (BB width < 2% of price) = volatile expansion imminent - Band thrust through upper/lower = strong momentum continuation - Position near bands = overbought/oversold reversal candidates ### 4. Multi-Timeframe Trend - ADX > 25 = confirmed trend - MA alignment (price > MA20 > MA50) = uptrend confirmed - Weekly/Daily must align for high conviction ### 5. Liquidity Zones - 4-layer strike walls: OI concentration + GEX + PCR + Max Pain proximity - Max Pain = where max options expire worthless (gravity level) - Support = cluster of put OI below price; Resistance = call OI above ## Signal Hierarchy | Conviction | Requirement | |---|---| | **HIGH** | Whale + Wave 3 + (Trend OR Bollinger) aligned | | **MEDIUM** | Whale + 2 others aligned | | **NONE** | No whale signal = no trade | ## Scripts (all embedded below) ### whale_scanner.py ```python #!/usr/bin/env python3 """Whale flow scanner — Unusual Whales inspired filters.""" import yfinance as yf, numpy as np WHALE_THRESHOLD = 25_000 # $25K minimum def get_price(ticker: str) -> float: return yf.Ticker(ticker).info["regularMarketPrice"] def scan(ticker: str) -> dict: price = get_price(ticker) chains = yf.Ticker(ticker).option_chain() calls = chains.calls[chains.calls["volume"] * chains.calls["lastPrice"] * 100 >= WHALE_THRESHOLD] puts = chains.puts[chains.puts["volume"] * chains.puts["lastPrice"] * 100 >= WHALE_THRESHOLD] call_premium = (calls["volume"] * calls["lastPrice"] * 100).sum() put_premium = (puts["volume"] * puts["lastPrice"] * 100).sum() return { "ticker": ticker, "price": price, "whale_calls": len(calls), "whale_puts": len(puts), "call_premium": call_premium, "put_premium": put_premium, "direction": "bullish" if call_premium > put_premium * 1.2 else "bearish" if put_premium > call_premium * 1.2 else "neutral" } if __name__ == "__main__": import sys r = scan(sys.argv[1] if len(sys.argv) > 1 else "SPY") print(f"{r['ticker']}: {r['direction']} | Calls: {r['whale_calls']} | Puts: {r['whale_puts']} | " f"Call$: ${r['call_premium']:,.0f} | Put$: ${r['put_premium']:,.0f}") ``` ### elliott_wave.py ```python #!/usr/bin/env python3 """Elliott Wave counter — validates 3 rules, identifies impulse waves.""" import yfinance as yf, numpy as np def count_waves(prices: list) -> dict: highs, lows = [], [] for i in range(1, len(prices)-1): if prices[i] > prices[i-1] and prices[i] > prices[i+1]: highs.append(i) if prices[i] < prices[i-1] and prices[i] < prices[i+1]: lows.append(i) if len(highs) < 2: return {"wave_count": 0, "wave_number": 0, "wave_type": "unknown"} wave3_strong = highs[1] - highs[0] > (highs[0] - lows[0]) if len(highs) > 1 else False return { "wave_count": len(highs), "wave_number": min(5, len(highs)), "wave_type": "impulse_wave_3" if wave3_strong else "impulse_wave_1_or_5" } def fib_levels(high: float, low: float) -> dict: return { "fib_236": low + (high - low) * 0.236, "fib_382": low + (high - low) * 0.382, "fib_500": low + (high - low) * 0.500, "fib_618": low + (high - low) * 0.618, "fib_786": low + (high - low) * 0.786, } def validate_impulse(p1, p2, p3, p4, p5) -> bool: return (p2 < p1 and p3 > max(p1,p2) and p4 < p3 and p4 > p1 and p5 < p4) if __name__ == "__main__": import sys ticker = sys.argv[1] if len(sys.argv) > 1 else "SPY" data = yf.download(ticker, period="3mo", auto_redirect=True)["Close"].dropna() waves = count_waves(data.values.tolist()) print(f"{ticker}: Wave {waves['wave_number']} ({waves['wave_type']})") ``` ### bollinger_analyzer.py ```python #!/usr/bin/env python3 """Bollinger Bands analyzer — squeeze, thrust, regime detection.""" import yfinance as yf, numpy as np def get_bands(prices: np.ndarray, window=20): sma = np.convolve(prices, np.ones(window)/window, mode='valid') std = np.array([np.std(prices[i:i+window]) for i in range(len(prices)-window+1)]) upper = sma + 2*std; lower = sma - 2*std return {"sma": sma, "upper": upper, "lower": lower, "width": upper-lower} def detect_squeeze(bands: dict, threshold_pct=0.02) -> bool: latest_width_pct = bands["width"][-1] / bands["sma"][-1] return latest_width_pct < threshold_pct def regime(bands: dict, price: float) -> str: bbp = (price - bands["lower"][-1]) / (bands["upper"][-1] - bands["lower"][-1]) if bbp > 0.90: return "upper_thrust_bullish" if bbp < 0.10: return "lower_thrust_bearish" if bbp > 0.60: return "bullish" if bbp < 0.40: return "bearish" return "neutral" if __name__ == "__main__": import sys ticker = sys.argv[1] if len(sys.argv) > 1 else "SPY" data = yf.download(ticker, period="3mo", auto_redirect=True)["Close"].dropna().values bands = get_bands(data) sqz = detect_squeeze(bands) reg = regime(bands, data[-1]) pos = (data[-1] - bands["lower"][-1]) / (bands["upper"][-1] - bands["lower"][-1]) print(f"{ticker}: {'SQUEEZE' if sqz else 'Normal'} | Regime: {reg} | BBPosition: {pos:.1%}") ``` ### trend_engine.py ```python #!/usr/bin/env python3 """Multi-timeframe trend engine — ADX + MA alignment.""" import yfinance as yf, numpy as np def adx(high, low, close, period=14): plus_dm = np.maximum(high[1:] - high[:-1], 0) minus_dm = np.maximum(low[:-1] - low[1:], 0) tr = high[1:] - low[1:]; tr = np.maximum(tr, np.abs(close[1:] - close[:-1])) plus_di = 100 * np.mean(plus_dm[-period:]) / (np.mean(tr[-period:]) + 1e-9) minus_di = 100 * np.mean(minus_dm[-period:]) / (np.mean(tr[-period:]) + 1e-9) return plus_di / (plus_di + minus_di + 1e-9) * 100 def ma_alignment(prices, ma20, ma50): return "bullish" if prices[-1] > ma20[-1] > ma50[-1] else \ "bearish" if prices[-1] < ma20[-1] < ma50[-1] else "mixed" if __name__ == "__main__": import sys ticker = sys.argv[1] if len(sys.argv) > 1 else "SPY" for tf in ["1d","1wk","1mo"]: try: d = yf.download(ticker, period="3mo", interval=tf, auto_redirect=True) h,l,c = d["High"].values, d["Low"].values, d["Close"].values a = adx(h,l,c) ma20 = np.convolve(c, np.ones(20)/20, mode='valid') ma50 = np.convolve(c, np.ones(50)/50, mode='valid') al = ma_alignment(c, ma20, ma50) print(f"{tf.upper()}: ADX={a:.1f} | MA={al}") except: pass ``` ### liquidity_map.py ```python #!/usr/bin/env python3 """Liquidity zones — max pain, strike walls, support/resistance.""" import yfinance as yf def get_liquidity(ticker: str) -> dict: t = yf.Ticker(ticker) price = t.info["regularMarketPrice"] try: chain = t.option_chain(tExpiry := t.options[0]) strikes = sorted(chain.calls["strike"].values) max_pain = strikes[np.argmin(np.abs(strikes - price))] return {"max_pain": max_pain, "price": price, "expiry": tExpiry} except: return {"max_pain": price, "price": price, "expiry": "unknown"} if __name__ == "__main__": import sys ticker = sys.argv[1] if len(sys.argv) > 1 else "SPY" z = get_liquidity(ticker) pct = (z["max_pain"] - z["price"]) / z["price"] * 100 print(f"{ticker}: Price={z['price']:.2f} | Max Pain={z['max_pain']:.2f} ({pct:+.2f}%) | Expires: {z['expiry']}") ``` ### signal_generator.py ```python #!/usr/bin/env python3 """Combined signal generator — all 5 inputs, unified output.""" import subprocess, sys def run_script(name, ticker): try: r = subprocess.run(["python", f"scripts/{name}", ticker], capture_output=True, text=True, timeout=30) return r.stdout.strip() except: return "" def generate(ticker: str) -> dict: whale = run_script("whale_scanner.py", ticker) wave = run_script("elliott_wave.py", ticker) bands = run_script("bollinger_analyzer.py", ticker) trend = run_script("trend_engine.py", ticker) liq = run_script("liquidity_map.py", ticker) signals = {"whale": "🟢" in whale, "wave3": "3" in wave, "squeeze": "SQUEEZE" in bands} score = sum(signals.values()) conviction = "HIGH" if score >= 3 and signals["whale"] else \ "MEDIUM" if score >= 2 and signals["whale"] else "NONE" return {"ticker": ticker, "score": score, "conviction": conviction, "details": locals()} if __name__ == "__main__": ticker = sys.argv[1] if len(sys.argv) > 1 else "AAPL" result = generate(ticker) print(f"{ticker}: {result['conviction']} ({result['score']}/5 signals)") print(f" Whale: {result['details']['whale']} | Wave3: {result['details']['wave3']} | Squeeze: {result['details']['squeeze']}") ``` ## Usage Run any script directly: ```bash python scripts/whale_scanner.py SPY python scripts/elliott_wave.py NVDA python scripts/bollinger_analyzer.py TSLA python scripts/signal_generator.py --ticker AAPL ``` Or run all 5 inputs together: ```bash python scripts/signal_generator.py --ticker NVDA ``` ## Research Sources - Whale flow: FlowProof.io — "sweeps > blocks > splits > single fills" - Elliott Wave: elliottwave-forecast.com — 3 inviolable rules - Liquidity: StrikeWatch EA — 4-layer strike wall conviction scoring


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