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AI가 evaluate() 메서드 내부에 또 다른 evaluate() 함수를 중첩 정의하는 실수로 생성된 IndentationError 수정. 각 파일별 수정 내용: - v20260220_210124_evolved.py: 중첩 def evaluate 제거, 상수/로직 8칸으로 정규화 - v20260220_210159_evolved.py: 중첩 def evaluate 제거, 16칸→8칸 들여쓰기 수정 - v20260220_210244_evolved.py: 12칸→8칸 들여쓰기 수정 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
115 lines
4.9 KiB
Python
115 lines
4.9 KiB
Python
"""Auto-generated strategy: v20260220_210124
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Generated at: 2026-02-20T21:01:24.706847+00:00
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Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
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"""
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from __future__ import annotations
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from typing import Any
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from src.strategies.base import BaseStrategy
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class Strategy_v20260220_210124(BaseStrategy):
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"""Strategy: v20260220_210124"""
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def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
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import datetime
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# --- Strategy Constants ---
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# Minimum price for a stock to be considered for trading (avoids penny stocks)
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MIN_PRICE = 5.0
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# Momentum signal thresholds (stricter than previous failures)
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MOMENTUM_PRICE_CHANGE_THRESHOLD = 7.0 # % price change
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MOMENTUM_VOLUME_RATIO_THRESHOLD = 4.0 # X times average volume
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# Oversold signal thresholds (more conservative)
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OVERSOLD_RSI_THRESHOLD = 25.0 # RSI value (lower means more oversold)
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# Confidence levels
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CONFIDENCE_HOLD = 30
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CONFIDENCE_BUY_OVERSOLD = 65
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CONFIDENCE_BUY_MOMENTUM = 85
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CONFIDENCE_BUY_STRONG_MOMENTUM = 90 # For higher-priced stocks with strong momentum
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# Market hours in UTC (9:30 AM ET to 4:00 PM ET)
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MARKET_OPEN_UTC = datetime.time(14, 30)
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MARKET_CLOSE_UTC = datetime.time(21, 0)
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# Volatile periods within market hours (UTC) to avoid
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# First hour after open (14:30 UTC - 15:30 UTC)
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VOLATILE_OPEN_END_UTC = datetime.time(15, 30)
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# Last 30 minutes before close (20:30 UTC - 21:00 UTC)
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VOLATILE_CLOSE_START_UTC = datetime.time(20, 30)
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current_price = market_data.get('current_price')
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price_change_pct = market_data.get('price_change_pct')
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volume_ratio = market_data.get('volume_ratio') # Assumed pre-computed indicator
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rsi = market_data.get('rsi') # Assumed pre-computed indicator
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timestamp_str = market_data.get('timestamp')
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action = "HOLD"
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confidence = CONFIDENCE_HOLD
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rationale = "Initial HOLD: No clear signal or conditions not met."
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# --- 1. Basic Data Validation ---
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if current_price is None or price_change_pct is None:
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return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
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"rationale": "Insufficient core data (price or price change) to evaluate."}
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# --- 2. Price Filter: Avoid low-priced/penny stocks ---
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if current_price < MIN_PRICE:
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return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
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"rationale": f"Avoiding low-priced stock (${current_price:.2f} < ${MIN_PRICE:.2f})."}
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# --- 3. Time Filter: Only trade during core market hours ---
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if timestamp_str:
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try:
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dt_object = datetime.datetime.fromisoformat(timestamp_str)
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current_time_utc = dt_object.time()
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if not (MARKET_OPEN_UTC <= current_time_utc < MARKET_CLOSE_UTC):
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return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
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"rationale": f"Avoiding trade outside core market hours ({current_time_utc} UTC)."}
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if (MARKET_OPEN_UTC <= current_time_utc < VOLATILE_OPEN_END_UTC) or \
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(VOLATILE_CLOSE_START_UTC <= current_time_utc < MARKET_CLOSE_UTC):
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return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
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"rationale": f"Avoiding trade during volatile market open/close periods ({current_time_utc} UTC)."}
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except ValueError:
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rationale += " (Warning: Malformed timestamp, time filters skipped)"
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# --- Initialize signal states ---
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has_momentum_buy_signal = False
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has_oversold_buy_signal = False
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# --- 4. Evaluate Enhanced Buy Signals ---
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# Momentum Buy Signal
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if volume_ratio is not None and \
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price_change_pct > MOMENTUM_PRICE_CHANGE_THRESHOLD and \
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volume_ratio > MOMENTUM_VOLUME_RATIO_THRESHOLD:
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has_momentum_buy_signal = True
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rationale = f"Momentum BUY: Price change {price_change_pct:.2f}%, Volume {volume_ratio:.2f}x."
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confidence = CONFIDENCE_BUY_MOMENTUM
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if current_price >= 10.0:
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confidence = CONFIDENCE_BUY_STRONG_MOMENTUM
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# Oversold Buy Signal
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if rsi is not None and rsi < OVERSOLD_RSI_THRESHOLD:
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has_oversold_buy_signal = True
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if not has_momentum_buy_signal:
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rationale = f"Oversold BUY: RSI {rsi:.2f}."
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confidence = CONFIDENCE_BUY_OVERSOLD
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if current_price >= 10.0:
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confidence = min(CONFIDENCE_BUY_OVERSOLD + 5, 80)
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# --- 5. Decision Logic ---
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if has_momentum_buy_signal:
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action = "BUY"
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elif has_oversold_buy_signal:
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action = "BUY"
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return {"action": action, "confidence": confidence, "rationale": rationale}
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