src/strategies/v20260220_210124_evolved.py

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

src/strategies/v20260220_210159_evolved.py

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+"""Auto-generated strategy: v20260220_210159
+
+Generated at: 2026-02-20T21:01:59.391523+00:00
+Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
+"""
+
+from __future__ import annotations
+from typing import Any
+from src.strategies.base import BaseStrategy
+
+
+class Strategy_v20260220_210159(BaseStrategy):
+    """Strategy: v20260220_210159"""
+
+    def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
+        import datetime
+
+        current_price = market_data.get('current_price')
+        price_change_pct = market_data.get('price_change_pct')
+        volume_ratio = market_data.get('volume_ratio')
+        rsi = market_data.get('rsi')
+        timestamp_str = market_data.get('timestamp')
+        market_name = market_data.get('market')
+
+        # Default action
+        action = "HOLD"
+        confidence = 0
+        rationale = "No strong signal or conditions not met."
+
+        # --- FAILURE PATTERN AVOIDANCE ---
+
+        # 1. Avoid low-priced/penny stocks
+        MIN_PRICE_THRESHOLD = 5.0  # USD
+        if current_price is not None and current_price < MIN_PRICE_THRESHOLD:
+            rationale = (
+                f"HOLD: Stock price (${current_price:.2f}) is below minimum threshold "
+                f"(${MIN_PRICE_THRESHOLD:.2f}). Past failures consistently involved low-priced stocks."
+            )
+            return {"action": action, "confidence": confidence, "rationale": rationale}
+
+        # 2. Avoid early market hour volatility
+        if timestamp_str:
+            try:
+                dt_obj = datetime.datetime.fromisoformat(timestamp_str)
+                utc_hour = dt_obj.hour
+                utc_minute = dt_obj.minute
+
+                if (utc_hour == 14 and utc_minute < 45) or (utc_hour == 13 and utc_minute >= 30):
+                    rationale = (
+                        f"HOLD: Trading during early market hours (UTC {utc_hour}:{utc_minute}), "
+                        f"a period identified with past failures due to high volatility."
+                    )
+                    return {"action": action, "confidence": confidence, "rationale": rationale}
+            except ValueError:
+                pass
+
+        # --- IMPROVED BUY STRATEGY ---
+
+        # Momentum BUY signal
+        if volume_ratio is not None and price_change_pct is not None:
+            if price_change_pct > 7.0 and volume_ratio > 3.0:
+                action = "BUY"
+                confidence = 70
+                rationale = "Improved BUY: Momentum signal with high volume and above price threshold."
+
+                if market_name == 'US_AMEX':
+                    confidence = max(55, confidence - 5)
+                    rationale += " (Adjusted lower for AMEX market's higher risk profile)."
+                elif market_name == 'US_NASDAQ' and price_change_pct > 20:
+                    confidence = max(50, confidence - 10)
+                    rationale += " (Adjusted lower for aggressive NASDAQ momentum volatility)."
+
+                if price_change_pct > 15.0:
+                    confidence = max(50, confidence - 5)
+                    rationale += " (Caution: Very high daily price change, potential for reversal)."
+
+                return {"action": action, "confidence": confidence, "rationale": rationale}
+
+        # Oversold BUY signal
+        if rsi is not None and price_change_pct is not None:
+            if rsi < 30 and price_change_pct < -3.0:
+                action = "BUY"
+                confidence = 65
+                rationale = "Improved BUY: Oversold signal with recent decline and above price threshold."
+
+                if market_name == 'US_AMEX':
+                    confidence = max(50, confidence - 5)
+                    rationale += " (Adjusted lower for AMEX market's higher risk on oversold assets)."
+
+                if price_change_pct < -10.0:
+                    confidence = max(45, confidence - 10)
+                    rationale += " (Caution: Very steep decline, potential falling knife)."
+
+                return {"action": action, "confidence": confidence, "rationale": rationale}
+
+        # If no specific BUY signal, default to HOLD
+        return {"action": action, "confidence": confidence, "rationale": rationale}

src/strategies/v20260220_210244_evolved.py

@@ -0,0 +1,88 @@
+"""Auto-generated strategy: v20260220_210244
+
+Generated at: 2026-02-20T21:02:44.387355+00:00
+Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
+"""
+
+from __future__ import annotations
+from typing import Any
+from src.strategies.base import BaseStrategy
+
+
+class Strategy_v20260220_210244(BaseStrategy):
+    """Strategy: v20260220_210244"""
+
+    def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
+        from datetime import datetime
+
+        # Extract required data points safely
+        current_price = market_data.get("current_price")
+        price_change_pct = market_data.get("price_change_pct")
+        volume_ratio = market_data.get("volume_ratio")
+        rsi = market_data.get("rsi")
+        timestamp_str = market_data.get("timestamp")
+        market_name = market_data.get("market")
+        stock_code = market_data.get("stock_code", "UNKNOWN")
+
+        # Default action is HOLD with conservative confidence and rationale
+        action = "HOLD"
+        confidence = 50
+        rationale = f"No strong BUY signal for {stock_code} or awaiting more favorable conditions after avoiding known failure patterns."
+
+        # --- 1. Failure Pattern Avoidance Filters ---
+
+        # A. Avoid low-priced (penny) stocks
+        if current_price is not None and current_price < 5.0:
+            return {
+                "action": "HOLD",
+                "confidence": 50,
+                "rationale": f"AVOID {stock_code}: Stock price (${current_price:.2f}) is below minimum threshold ($5.00) for BUY action. Identified past failures on highly volatile, low-priced stocks."
+            }
+
+        # B. Avoid initiating BUY trades during identified high-volatility hours
+        if timestamp_str:
+            try:
+                trade_hour = datetime.fromisoformat(timestamp_str).hour
+                if trade_hour in [14, 20]:
+                    return {
+                        "action": "HOLD",
+                        "confidence": 50,
+                        "rationale": f"AVOID {stock_code}: Trading during historically volatile hour ({trade_hour} UTC) where previous BUYs resulted in losses. Prefer to observe market stability."
+                    }
+            except ValueError:
+                pass
+
+        # C. Be cautious with extreme momentum spikes
+        if volume_ratio is not None and price_change_pct is not None:
+            if volume_ratio >= 9.0 and price_change_pct >= 15.0:
+                return {
+                    "action": "HOLD",
+                    "confidence": 50,
+                    "rationale": f"AVOID {stock_code}: Extreme short-term momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Historical failures indicate buying into such rapid spikes often leads to reversals."
+                }
+
+        # D. Be cautious with "oversold" signals without further confirmation
+        if rsi is not None and rsi < 30:
+            return {
+                "action": "HOLD",
+                "confidence": 50,
+                "rationale": f"AVOID {stock_code}: Oversold signal (RSI={rsi:.1f}) detected. While often a BUY signal, historical failures on similar 'oversold' trades suggest waiting for stronger confirmation."
+            }
+
+        # --- 2. Improved BUY Signal Generation ---
+        if volume_ratio is not None and 2.0 <= volume_ratio < 9.0 and \
+           price_change_pct is not None and 2.0 <= price_change_pct < 15.0:
+
+            action = "BUY"
+            confidence = 70
+            rationale = f"BUY {stock_code}: Moderate momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Passed filters for price and extreme momentum, avoiding past failure patterns."
+
+            if market_name in ["US_AMEX", "US_NASDAQ"]:
+                confidence = max(60, confidence - 5)
+                rationale += f" Adjusted confidence for {market_name} market characteristics."
+            elif market_name == "US_NYSE":
+                confidence = max(65, confidence)
+
+            confidence = max(50, min(85, confidence))
+
+        return {"action": action, "confidence": confidence, "rationale": rationale}