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Monitors whale flow (Unusual Whales), counts Elliott Waves, analyzes Bollinger Bands, multi-timeframe trend...","source":"CLAWHUB","sourceId":"clawhub:s1751xfhp40q8vjjqd22y5vaw584gpke:options-trading-brain","homepage":"https://clawhub.ai/ssidharhubble/skills/options-trading-brain","repository":"https://clawhub.ai/ssidharhubble/options-trading-brain","documentation":"https://www.xpersona.co/agent/clawhub-ssidharhubble-options-trading-brain","protocols":["OPENCLEW"],"examples":[{"kind":"example","language":"python","snippet":"#!/usr/bin/env python3\n\"\"\"Whale flow scanner — Unusual Whales inspired filters.\"\"\"\nimport yfinance as yf, numpy as np\n\nWHALE_THRESHOLD = 25_000  # $25K minimum\n\ndef get_price(ticker: str) -> float:\n    return yf.Ticker(ticker).info[\"regularMarketPrice\"]\n\ndef scan(ticker: str) -> dict:\n    price = get_price(ticker)\n    chains = yf.Ticker(ticker).option_chain()\n    \n    calls = chains.calls[chains.calls[\"volume\"] * chains.calls[\"lastPrice\"] * 100 >= WHALE_THRESHOLD]\n    puts  = chains.puts[chains.puts[\"volume\"] * chains.puts[\"lastPrice\"] * 100 >= WHALE_THRESHOLD]\n    \n    call_premium = (calls[\"volume\"] * calls[\"lastPrice\"] * 100).sum()\n    put_premium  = (puts[\"volume\"] * puts[\"lastPrice\"] * 100).sum()\n    \n    return {\n        \"ticker\": ticker, \"price\": price,\n        \"whale_calls\": len(calls), \"whale_puts\": len(puts),\n        \"call_premium\": call_premium, \"put_premium\": put_premium,\n        \"direction\": \"bullish\" if call_premium > put_premium * 1.2\n                    else \"bearish\" if put_premium > call_premium * 1.2 else \"neutral\"\n    }\n\nif __name__ == \"__main__\":\n    import sys\n    r = scan(sys.argv[1] if len(sys.argv) > 1 else \"SPY\")\n    print(f\"{r['ticker']}: {r['direction']} | Calls: {r['whale_calls']} | Puts: {r['whale_puts']} | \"\n          f\"Call$: ${r['call_premium']:,.0f} | Put$: ${r['put_premium']:,.0f}\")"},{"kind":"example","language":"python","snippet":"#!/usr/bin/env python3\n\"\"\"Elliott Wave counter — validates 3 rules, identifies impulse waves.\"\"\"\nimport yfinance as yf, numpy as np\n\ndef count_waves(prices: list) -> dict:\n    highs, lows = [], []\n    for i in range(1, len(prices)-1):\n        if prices[i] > prices[i-1] and prices[i] > prices[i+1]: highs.append(i)\n        if prices[i] < prices[i-1] and prices[i] < prices[i+1]: lows.append(i)\n    \n    if len(highs) < 2: return {\"wave_count\": 0, \"wave_number\": 0, \"wave_type\": \"unknown\"}\n    \n    wave3_strong = highs[1] - highs[0] > (highs[0] - lows[0]) if len(highs) > 1 else False\n    return {\n        \"wave_count\": len(highs),\n        \"wave_number\": min(5, len(highs)),\n        \"wave_type\": \"impulse_wave_3\" if wave3_strong else \"impulse_wave_1_or_5\"\n    }\n\ndef fib_levels(high: float, low: float) -> dict:\n    return {\n        \"fib_236\": low + (high - low) * 0.236,\n        \"fib_382\": low + (high - low) * 0.382,\n        \"fib_500\": low + (high - low) * 0.500,\n        \"fib_618\": low + (high - low) * 0.618,\n        \"fib_786\": low + (high - low) * 0.786,\n    }\n\ndef validate_impulse(p1, p2, p3, p4, p5) -> bool:\n    return (p2 < p1 and p3 > max(p1,p2) and p4 < p3 and p4 > p1 and p5 < p4)\n\nif __name__ == \"__main__\":\n    import sys\n    ticker = sys.argv[1] if len(sys.argv) > 1 else \"SPY\"\n    data = yf.download(ticker, period=\"3mo\", auto_redirect=True)[\"Close\"].dropna()\n    waves = count_waves(data.values.tolist())\n    print(f\"{ticker}: Wave {waves['wave_number']} ({waves['wave_type']})\")"}]}}