feat: math core v3 engine upgrade

services/trading/position_sizer.py

@@ -4,11 +4,14 @@ Computes dollar allocation and share quantity for a trade by applying
 a sequential adjustment pipeline: confidence gate, correlation reduction,
 sector exposure, diversification bonus, earnings proximity, portfolio
 heat check, active-pool minimum, absolute cap, and share rounding.
+
+Also provides v3 fractional Kelly position sizing (Requirements 14.1–14.7).
 """
 
 from __future__ import annotations
 
 import math
+from dataclasses import dataclass
 from datetime import datetime, timezone
 
 from services.trading.models import (
@@ -345,3 +348,111 @@ class PositionSizer:
             return new_dollar, new_pct
 
         return dollar_amount, allocation_pct
+
+
+
+# ===========================================================================
+# v3 Fractional Kelly Position Sizing (Requirements 14.1–14.7)
+# ===========================================================================
+
+
+@dataclass(frozen=True)
+class KellySizingResult:
+    """Result of v3 fractional Kelly position sizing computation."""
+
+    portfolio_pct: float
+    f_kelly: float
+    reward_ratio: float
+    downgrade: bool
+    downgrade_reason: str
+
+
+def _clamp(value: float, lo: float, hi: float) -> float:
+    """Clamp value to [lo, hi]."""
+    return max(lo, min(hi, value))
+
+
+def compute_reward_ratio(
+    confidence: float, strength: float, contradiction: float
+) -> float:
+    """Compute reward ratio b = clamp(1.2 + 2.0*confidence + 1.0*strength - contradiction, 1.2, 3.0).
+
+    Requirements: 14.2
+    """
+    raw = 1.2 + 2.0 * confidence + 1.0 * strength - contradiction
+    return _clamp(raw, 1.2, 3.0)
+
+
+def compute_kelly_sizing(
+    p_win: float,
+    b: float,
+    confidence: float,
+    data_quality: float,
+    contradiction: float,
+    max_position_pct: float,
+    available_caps: dict[str, float],
+) -> KellySizingResult:
+    """Compute fractional Kelly position sizing.
+
+    f_kelly = (p_win * b - (1 - p_win)) / b
+    portfolio_pct = clamp(max(0, f_kelly) * 0.25 * confidence * data_quality * (1 - contradiction), 0, max_position_pct)
+
+    Apply min of all capacity constraints from available_caps:
+      - sector_capacity
+      - correlation_capacity (0 if avg corr > 0.80)
+      - heat_capacity
+
+    Downgrade rules:
+      - If f_kelly <= 0 → portfolio_pct = 0, downgrade with reason "negative_edge"
+      - If portfolio_pct < 0.005 → downgrade with reason "position_below_minimum"
+
+    Requirements: 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7
+    """
+    # Compute Kelly fraction (Req 14.3)
+    f_kelly = (p_win * b - (1.0 - p_win)) / b
+
+    # Negative edge → immediate downgrade (Req 14.7)
+    if f_kelly <= 0.0:
+        return KellySizingResult(
+            portfolio_pct=0.0,
+            f_kelly=f_kelly,
+            reward_ratio=b,
+            downgrade=True,
+            downgrade_reason="negative_edge",
+        )
+
+    # Apply fractional Kelly with dampening factors (Req 14.4)
+    raw_pct = f_kelly * 0.25 * confidence * data_quality * (1.0 - contradiction)
+    portfolio_pct = _clamp(max(0.0, raw_pct), 0.0, max_position_pct)
+
+    # Apply capacity constraints (Req 14.5)
+    sector_capacity = available_caps.get("sector_capacity", max_position_pct)
+    correlation_capacity = available_caps.get("correlation_capacity", max_position_pct)
+    heat_capacity = available_caps.get("heat_capacity", max_position_pct)
+
+    # Correlation capacity of 0 means avg corr > 0.80 → force zero
+    portfolio_pct = min(
+        portfolio_pct,
+        max_position_pct,
+        sector_capacity,
+        correlation_capacity,
+        heat_capacity,
+    )
+
+    # Position below minimum threshold → downgrade (Req 14.6)
+    if portfolio_pct < 0.005:
+        return KellySizingResult(
+            portfolio_pct=0.0,
+            f_kelly=f_kelly,
+            reward_ratio=b,
+            downgrade=True,
+            downgrade_reason="position_below_minimum",
+        )
+
+    return KellySizingResult(
+        portfolio_pct=portfolio_pct,
+        f_kelly=f_kelly,
+        reward_ratio=b,
+        downgrade=False,
+        downgrade_reason="",
+    )