feat: use playbook allocation_pct in position sizing (#172)

- Add playbook_allocation_pct and scenario_confidence parameters to
  _determine_order_quantity() with playbook-based sizing taking priority
  over volatility-score fallback when provided
- Confidence scaling: confidence/80 multiplier (confidence 96 → 1.2x)
  clipped to [POSITION_MIN_ALLOCATION_PCT, POSITION_MAX_ALLOCATION_PCT]
- Pass matched_scenario.allocation_pct and match.confidence from
  trading_cycle so AI's allocation decisions reach order execution
- Add 4 new tests: playbook priority, confidence scaling, max clamp,
  and fallback behavior

Closes #172

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/main.py

@@ -190,8 +190,15 @@ def _determine_order_quantity(
     candidate: ScanCandidate | None,
     settings: Settings | None,
     broker_held_qty: int = 0,
+    playbook_allocation_pct: float | None = None,
+    scenario_confidence: int = 80,
 ) -> int:
-    """Determine order quantity using volatility-aware position sizing."""
+    """Determine order quantity using volatility-aware position sizing.
+
+    Priority:
+    1. playbook_allocation_pct (AI-specified) scaled by scenario_confidence
+    2. Fallback: volatility-score-based allocation from scanner candidate
+    """
     if action == "SELL":
         return broker_held_qty
     if current_price <= 0 or total_cash <= 0:
@@ -200,6 +207,22 @@ def _determine_order_quantity(
     if settings is None or not settings.POSITION_SIZING_ENABLED:
         return 1
 
+    # Use AI-specified allocation_pct if available
+    if playbook_allocation_pct is not None:
+        # Confidence scaling: confidence 80 → 1.0x, confidence 95 → 1.19x
+        confidence_scale = scenario_confidence / 80.0
+        effective_pct = min(
+            settings.POSITION_MAX_ALLOCATION_PCT,
+            max(
+                settings.POSITION_MIN_ALLOCATION_PCT,
+                playbook_allocation_pct * confidence_scale,
+            ),
+        )
+        budget = total_cash * (effective_pct / 100.0)
+        quantity = int(budget // current_price)
+        return max(0, quantity)
+
+    # Fallback: volatility-score-based allocation
     target_score = max(1.0, settings.POSITION_VOLATILITY_TARGET_SCORE)
     observed_score = candidate.score if candidate else target_score
     observed_score = max(1.0, min(100.0, observed_score))
@@ -568,6 +591,7 @@ async def trading_cycle(
             if decision.action == "SELL"
             else 0
         )
+        matched_scenario = match.matched_scenario
         quantity = _determine_order_quantity(
             action=decision.action,
             current_price=current_price,
@@ -575,6 +599,8 @@ async def trading_cycle(
             candidate=candidate,
             settings=settings,
             broker_held_qty=broker_held_qty,
+            playbook_allocation_pct=matched_scenario.allocation_pct if matched_scenario else None,
+            scenario_confidence=match.confidence,
         )
         if quantity <= 0:
             logger.info(

tests/test_main.py

@@ -205,6 +205,84 @@ class TestDetermineOrderQuantity:
         )
         assert result == 2
 
+    def test_determine_order_quantity_uses_playbook_allocation_pct(self) -> None:
+        """playbook_allocation_pct should take priority over volatility-based sizing."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # playbook says 20%, confidence 80 → scale=1.0 → 20%
+        # 1,000,000 * 20% = 200,000 // 50,000 price = 4 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=20.0,
+            scenario_confidence=80,
+        )
+        assert result == 4
+
+    def test_determine_order_quantity_confidence_scales_allocation(self) -> None:
+        """Higher confidence should produce a larger allocation (up to max)."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # confidence 96 → scale=1.2 → 10% * 1.2 = 12%
+        # 1,000,000 * 12% = 120,000 // 50,000 price = 2 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=10.0,
+            scenario_confidence=96,
+        )
+        # scale = 96/80 = 1.2 → effective_pct = 12.0
+        # budget = 1_000_000 * 0.12 = 120_000 → qty = 120_000 // 50_000 = 2
+        assert result == 2
+
+    def test_determine_order_quantity_confidence_clamped_to_max(self) -> None:
+        """Confidence scaling should not exceed POSITION_MAX_ALLOCATION_PCT."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 15.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # playbook 20% * scale 1.5 = 30% → clamped to 15%
+        # 1,000,000 * 15% = 150,000 // 50,000 price = 3 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=20.0,
+            scenario_confidence=120,  # extreme → scale = 1.5
+        )
+        assert result == 3
+
+    def test_determine_order_quantity_fallback_when_no_playbook(self) -> None:
+        """Without playbook_allocation_pct, falls back to volatility-based sizing."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_VOLATILITY_TARGET_SCORE = 50.0
+        settings.POSITION_BASE_ALLOCATION_PCT = 10.0
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # Same as test_buy_with_position_sizing_calculates_correctly (no playbook)
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=None,  # explicit None → fallback
+        )
+        assert result == 2
+
 
 class TestSafeFloat:
     """Test safe_float() helper function."""