Update risk_engine.py

risk_engine.py

@@ -1,81 +1,114 @@
-import numpy as np
 from typing import Dict, Any
+
+import numpy as np
+
 from config import (
     MAX_RISK_PER_TRADE,
-    HIGH_VOLATILITY_THRESHOLD,
+    HIGH_VOL_THRESHOLD,
+    LOW_VOL_THRESHOLD,
     REDUCED_RISK_FACTOR,
+    ATR_STOP_MULT,
+    RR_RATIO,
+    DEFAULT_ACCOUNT_EQUITY,
 )
 
 
-def compute_stop_distance(atr: float, multiplier: float = 2.0) -> float:
-    return atr * multiplier
+def compute_dynamic_risk_fraction(
+    vol_ratio: float,
+    regime_score: float,
+    volume_score: float,
+    base_risk: float = MAX_RISK_PER_TRADE,
+) -> float:
+    risk = base_risk
+
+    if vol_ratio > HIGH_VOL_THRESHOLD:
+        risk *= REDUCED_RISK_FACTOR
+    elif vol_ratio > HIGH_VOL_THRESHOLD * 0.75:
+        risk *= 0.75
+
+    if regime_score < 0.3:
+        risk *= REDUCED_RISK_FACTOR
+    elif regime_score < 0.5:
+        risk *= 0.7
+
+    if volume_score < 0.3:
+        risk *= 0.8
+
+    return float(np.clip(risk, 0.002, base_risk))
 
 
 def compute_position_size(
     account_equity: float,
     entry_price: float,
     stop_distance: float,
-    risk_fraction: float = MAX_RISK_PER_TRADE,
+    risk_fraction: float,
 ) -> float:
     if stop_distance <= 0 or entry_price <= 0:
         return 0.0
     dollar_risk = account_equity * risk_fraction
     units = dollar_risk / stop_distance
-    notional = units * entry_price
-    return notional
-
-
-def compute_risk_fraction(
-    vol_ratio: float,
-    regime_score: float,
-    base_risk: float = MAX_RISK_PER_TRADE,
-) -> float:
-    risk = base_risk
-
-    if vol_ratio > HIGH_VOLATILITY_THRESHOLD:
-        risk *= REDUCED_RISK_FACTOR
-
-    if regime_score < 0.4:
-        risk *= REDUCED_RISK_FACTOR
-    elif regime_score < 0.6:
-        risk *= 0.75
-
-    return float(np.clip(risk, 0.001, base_risk))
+    return units * entry_price
 
 
 def evaluate_risk(
-    df_last_close: float,
+    close: float,
     atr: float,
     atr_pct: float,
     regime_score: float,
     vol_ratio: float,
-    account_equity: float = 10000.0,
-    stop_multiplier: float = 2.0,
+    volume_score: float = 0.5,
+    account_equity: float = DEFAULT_ACCOUNT_EQUITY,
+    stop_mult: float = ATR_STOP_MULT,
+    rr_ratio: float = RR_RATIO,
 ) -> Dict[str, Any]:
-    stop_distance = compute_stop_distance(atr, stop_multiplier)
-    risk_fraction = compute_risk_fraction(vol_ratio, regime_score)
+    stop_distance = atr * stop_mult
+
+    risk_fraction = compute_dynamic_risk_fraction(
+        vol_ratio=vol_ratio,
+        regime_score=regime_score,
+        volume_score=volume_score,
+    )
+
     position_notional = compute_position_size(
-        account_equity, df_last_close, stop_distance, risk_fraction
+        account_equity=account_equity,
+        entry_price=close,
+        stop_distance=stop_distance,
+        risk_fraction=risk_fraction,
     )
 
-    stop_price_long = df_last_close - stop_distance
-    stop_price_short = df_last_close + stop_distance
-    risk_reward_target = stop_distance * 2.0
+    dollar_at_risk = account_equity * risk_fraction
+    reward_distance = stop_distance * rr_ratio
+
+    stop_long = close - stop_distance
+    stop_short = close + stop_distance
+    target_long = close + reward_distance
+    target_short = close - reward_distance
 
-    target_long = df_last_close + risk_reward_target
-    target_short = df_last_close - risk_reward_target
+    leverage_implied = position_notional / account_equity if account_equity > 0 else 1.0
+
+    quality_deduction = 0.0
+    if vol_ratio > HIGH_VOL_THRESHOLD:
+        quality_deduction += 0.2
+    if regime_score < 0.4:
+        quality_deduction += 0.15
+    risk_quality = float(np.clip(1.0 - quality_deduction, 0.0, 1.0))
 
     return {
-        "entry_price": df_last_close,
-        "atr": atr,
-        "atr_pct": atr_pct,
-        "stop_distance": stop_distance,
-        "stop_price_long": stop_price_long,
-        "stop_price_short": stop_price_short,
-        "target_long": target_long,
-        "target_short": target_short,
-        "risk_fraction": risk_fraction,
-        "position_notional": position_notional,
-        "vol_ratio": vol_ratio,
-        "regime_score": regime_score,
+        "entry_price": close,
+        "atr": round(atr, 8),
+        "atr_pct": round(atr_pct * 100, 3),
+        "stop_distance": round(stop_distance, 8),
+        "stop_long": round(stop_long, 8),
+        "stop_short": round(stop_short, 8),
+        "target_long": round(target_long, 8),
+        "target_short": round(target_short, 8),
+        "reward_distance": round(reward_distance, 8),
+        "rr_ratio": rr_ratio,
+        "risk_fraction": round(risk_fraction * 100, 3),
+        "dollar_at_risk": round(dollar_at_risk, 2),
+        "position_notional": round(position_notional, 2),
+        "leverage_implied": round(leverage_implied, 2),
+        "vol_ratio": round(vol_ratio, 3),
+        "regime_score": round(regime_score, 4),
+        "risk_quality": round(risk_quality, 3),
     }