feat(wire-02): Document Wave D adaptive position sizer integration gap

CRITICAL FINDING: RegimeAdaptiveFeatures (Features 221-224) are fully implemented but NOT integrated into trading decision flow. Analysis Results: - ✅ RegimeAdaptiveFeatures: 644 lines, 12/12 tests passing - ✅ Database schema: regime_states, regime_transitions, adaptive_strategy_metrics - ✅ gRPC endpoints: GetRegimeState, GetRegimeTransitions defined - ❌ Trading Agent Service: NO regime integration in allocation.rs - ❌ Order Generation: NO stop-loss multiplier application Impact: - ML models train with regime features - Production trading IGNORES regime state - Position sizes remain STATIC (no 0.2x-1.5x adjustment) - Expected Sharpe improvement: 0% (instead of +25-50%) Integration Plan (11 hours): 1. Phase 1: Database query layer (2h) - regime.rs 2. Phase 2: Allocation integration (3h) - RegimeAdaptive method 3. Phase 3: Service wiring (2h) - RegimeDetector in service 4. Phase 4: Order generation (1h) - stop-loss multipliers 5. Phase 5: Testing (3h) - regime allocation tests Code Changes: - New files: regime.rs (200 lines), tests (300 lines) - Modified: allocation.rs (+100), service.rs (+50), orders.rs (+30) - Total: ~500 new lines, ~180 modified lines Performance: +3ms latency (acceptable for +25-50% Sharpe) Risk: Low (feature flag + 3-level rollback plan) Recommendation: PROCEED before 225-feature ML retraining Files: - AGENT_WIRE02_ADAPTIVE_SIZER_INTEGRATION.md (full analysis) - AGENT_WIRE02_QUICK_SUMMARY.md (executive summary) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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+# Agent WIRE-02: Wave D Adaptive Position Sizer Integration Analysis
+
+**Date**: 2025-10-19
+**Agent**: WIRE-02
+**Mission**: Investigate why AdaptivePositionSizer is implemented but not integrated into trading flow
+**Status**: ✅ **COMPLETE** - Gap identified, integration plan ready
+
+---
+
+## Executive Summary
+
+**CRITICAL FINDING**: Wave D's regime-adaptive position sizing (Features 221-224) is **IMPLEMENTED but NOT INTEGRATED** into the trading decision flow.
+
+**Impact**: The 225-feature ML models will train with regime-adaptive features, but **production trading will not apply regime-based position sizing** unless we complete the missing integration.
+
+**Root Cause**:
+1. `RegimeAdaptiveFeatures` exists in `ml/src/features/regime_adaptive.rs` (644 lines, 12/12 tests passing)
+2. Database schema exists (`regime_states`, `regime_transitions`, `adaptive_strategy_metrics`)
+3. gRPC endpoints exist (`GetRegimeState`, `GetRegimeTransitions`)
+4. **BUT**: `services/trading_agent_service/src/allocation.rs` has NO imports/usage of regime detection
+
+**Gap**: Position sizing multipliers (0.2x-1.5x) and stop-loss multipliers (1.5x-4.0x ATR) are computed as ML features but **never applied to actual order sizing**.
+
+---
+
+## Investigation Results
+
+### 1. Implementation Status ✅
+
+#### ✅ Feature Extraction (`ml/src/features/regime_adaptive.rs`)
+
+**Location**: `/home/jgrusewski/Work/foxhunt/ml/src/features/regime_adaptive.rs` (644 lines)
+
+**Struct**: `RegimeAdaptiveFeatures`
+```rust
+pub struct RegimeAdaptiveFeatures {
+    current_regime: MarketRegime,
+    returns_window: VecDeque<f64>,
+    window_size: usize,
+    current_position_size: f64,
+    max_position_size: f64,
+    atr_period: usize,
+}
+```
+
+**Key Methods**:
+```rust
+pub fn update(
+    &mut self,
+    regime: MarketRegime,
+    return_value: f64,
+    current_position: f64,
+    bars: &[OHLCVBar],
+) -> [f64; 4] {
+    // Returns:
+    // [0]: Position multiplier (0.2x-1.5x based on regime)
+    // [1]: Stop-loss multiplier (1.5x-4.0x ATR based on regime)
+    // [2]: Regime-adjusted Sharpe ratio
+    // [3]: Risk budget utilization
+}
+```
+
+**Position Multipliers** (Feature 221):
+```rust
+const POSITION_MULTIPLIERS: [(MarketRegime, f64); 7] = [
+    (MarketRegime::Normal, 1.0),         // Baseline
+    (MarketRegime::Trending, 1.5),       // Increase size in trends
+    (MarketRegime::Sideways, 0.8),       // Reduce in choppy markets
+    (MarketRegime::Bull, 1.2),           // Moderate increase
+    (MarketRegime::Bear, 0.7),           // Reduce in downtrends
+    (MarketRegime::HighVolatility, 0.5), // Reduce risk
+    (MarketRegime::Crisis, 0.2),         // Extreme risk reduction
+];
+```
+
+**Stop-Loss Multipliers** (Feature 222):
+```rust
+const STOPLOSS_MULTIPLIERS: [(MarketRegime, f64); 7] = [
+    (MarketRegime::Normal, 2.0),         // 2x ATR
+    (MarketRegime::Trending, 2.5),       // Wider for trends
+    (MarketRegime::Sideways, 1.5),       // Tighter in ranges
+    (MarketRegime::Bull, 2.0),           // Standard
+    (MarketRegime::Bear, 2.5),           // Wider in bear
+    (MarketRegime::HighVolatility, 3.0), // Wide for volatility
+    (MarketRegime::Crisis, 4.0),         // Very wide to avoid panic exits
+];
+```
+
+**Test Status**: 12/12 tests passing (100%)
+
+---
+
+#### ✅ Database Schema (`migrations/045_wave_d_regime_tracking.sql`)
+
+**Tables Created**:
+
+1. **`regime_states`** (14 columns):
+   - `symbol`, `event_timestamp`, `regime`, `confidence`
+   - CUSUM metrics: `cusum_s_plus`, `cusum_s_minus`, `cusum_alert_count`
+   - ADX metrics: `adx`, `plus_di`, `minus_di`
+   - Stability: `stability`, `entropy`
+   - Indexes: `idx_regime_states_symbol_timestamp`, `idx_regime_states_regime`, `idx_regime_states_confidence`
+
+2. **`regime_transitions`** (10 columns):
+   - `symbol`, `event_timestamp`, `from_regime`, `to_regime`
+   - `duration_bars`, `transition_probability`, `adx_at_transition`
+   - Indexes: `idx_regime_transitions_symbol_timestamp`, `idx_regime_transitions_from_to`
+
+3. **`adaptive_strategy_metrics`** (12 columns):
+   - `symbol`, `event_timestamp`, `regime`
+   - **`position_multiplier`** (0.0-2.0)
+   - **`stop_loss_multiplier`** (1.0-5.0)
+   - `regime_sharpe`, `risk_budget_utilization`
+   - Performance: `total_trades`, `winning_trades`, `total_pnl`
+
+**Functions**:
+- `get_latest_regime(p_symbol TEXT)`: Fetch current regime
+- `get_regime_transition_matrix(p_symbol TEXT, p_window_hours INTEGER)`: Transition probabilities
+- `get_regime_performance(p_symbol TEXT, p_window_hours INTEGER)`: Regime-specific performance
+
+**Migration Status**: Applied (verified in docs)
+
+---
+
+#### ✅ gRPC API Endpoints
+
+**Proto Definitions** (confirmed in 100+ doc references):
+
+```protobuf
+rpc GetRegimeState(GetRegimeStateRequest) returns (GetRegimeStateResponse);
+rpc GetRegimeTransitions(GetRegimeTransitionsRequest) returns (GetRegimeTransitionsResponse);
+
+message GetRegimeStateRequest {
+    string symbol = 1;
+}
+
+message GetRegimeStateResponse {
+    string regime = 1;
+    double confidence = 2;
+    int64 event_timestamp = 3;
+    double cusum_s_plus = 4;
+    double cusum_s_minus = 5;
+    double adx = 6;
+    double stability = 7;
+    double entropy = 8;
+}
+```
+
+**Implementation Status**: Endpoints defined and routed through API Gateway (confirmed in `AGENT_F8_REGIME_ROUTING_VALIDATION_REPORT.md`)
+
+---
+
+### 2. Integration Gap ❌
+
+#### ❌ Trading Agent Service Allocation (`services/trading_agent_service/src/allocation.rs`)
+
+**Current State**: 716 lines, 5 allocation methods, **ZERO regime integration**
+
+**File**: `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/allocation.rs`
+
+**Existing Allocation Methods**:
+1. `EqualWeight` - 1/N allocation
+2. `RiskParity` - Inverse volatility weighting
+3. `MeanVariance` - Markowitz optimization
+4. `MLOptimized` - ML scores as expected returns
+5. `KellyCriterion` - Edge-based sizing
+
+**Missing**:
+```rust
+// ❌ NO IMPORTS
+// use ml::features::regime_adaptive::RegimeAdaptiveFeatures;
+// use ml::ensemble::MarketRegime;
+
+// ❌ NO REGIME DETECTION
+pub fn allocate(&self, assets: &[AssetInfo], total_capital: Decimal) -> Result<HashMap<String, Decimal>> {
+    match &self.method {
+        AllocationMethod::EqualWeight => self.equal_weight(assets, total_capital),
+        AllocationMethod::RiskParity => self.risk_parity(assets, total_capital),
+        // ... NO REGIME ADAPTATION
+    }
+}
+```
+
+**Impact**: All allocation methods compute static weights WITHOUT regime-based adjustments.
+
+---
+
+#### ❌ Service Implementation (`services/trading_agent_service/src/service.rs`)
+
+**File**: `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (675 lines)
+
+**Current `allocate_portfolio` Implementation**:
+```rust
+async fn allocate_portfolio(
+    &self,
+    _request: Request<AllocatePortfolioRequest>,
+) -> Result<Response<AllocatePortfolioResponse>, Status> {
+    info!("AllocatePortfolio called (placeholder)");
+
+    Ok(Response::new(AllocatePortfolioResponse {
+        allocations: vec![],
+        metrics: Some(AllocationMetrics {
+            total_weight: 0.0,
+            portfolio_volatility: 0.0,
+            portfolio_sharpe: 0.0,
+            var_95: 0.0,
+            max_drawdown_estimate: 0.0,
+        }),
+        timestamp: chrono::Utc::now().timestamp_nanos_opt().unwrap_or(0),
+        allocation_id: uuid::Uuid::new_v4().to_string(),
+    }))
+}
+```
+
+**Status**: **PLACEHOLDER** - no actual allocation logic, no regime detection
+
+---
+
+### 3. Data Flow Analysis
+
+#### Current Flow (Wave C - 201 Features)
+```
+1. Universe Selection → [ES.FUT, NQ.FUT, ZN.FUT]
+2. Asset Selection → AssetInfo[] (with ML scores)
+3. PortfolioAllocator::allocate() → Static weights (e.g., 1/N)
+4. Order Generation → Fixed position sizes
+5. Trading Service → Execute orders
+```
+
+#### Missing Flow (Wave D - 225 Features)
+```
+1. Universe Selection → [ES.FUT, NQ.FUT, ZN.FUT]
+2. FOR EACH symbol:
+   a. Query regime_states table → get_latest_regime(symbol)
+   b. Retrieve MarketRegime (Normal/Trending/Volatile/Crisis)
+   c. Look up position_multiplier (0.2x-1.5x)
+   d. Look up stop_loss_multiplier (1.5x-4.0x ATR)
+3. Asset Selection → AssetInfo[] (with ML scores)
+4. ❌ MISSING: Apply regime multipliers to allocation weights
+5. ❌ MISSING: Adjust position sizes by regime
+6. Order Generation → ❌ Uses unadjusted sizes
+7. Trading Service → ❌ Executes with wrong position sizes
+```
+
+---
+
+## Integration Plan
+
+### Phase 1: Database Query Layer (2 hours)
+
+**File**: `services/trading_agent_service/src/regime.rs` (NEW)
+
+```rust
+//! Regime Detection Integration
+//!
+//! Provides regime state queries for position sizing adjustments.
+
+use sqlx::PgPool;
+use anyhow::{Context, Result};
+
+/// Market regime classification
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum MarketRegime {
+    Normal,
+    Trending,
+    Sideways,
+    Bull,
+    Bear,
+    HighVolatility,
+    Crisis,
+}
+
+/// Regime state with metrics
+#[derive(Debug, Clone)]
+pub struct RegimeState {
+    pub regime: MarketRegime,
+    pub confidence: f64,
+    pub event_timestamp: chrono::DateTime<chrono::Utc>,
+    pub position_multiplier: f64,  // From adaptive_strategy_metrics
+    pub stop_loss_multiplier: f64, // From adaptive_strategy_metrics
+}
+
+/// Regime detection client
+pub struct RegimeDetector {
+    db_pool: PgPool,
+}
+
+impl RegimeDetector {
+    pub fn new(db_pool: PgPool) -> Self {
+        Self { db_pool }
+    }
+
+    /// Get current regime state for symbol
+    pub async fn get_regime(&self, symbol: &str) -> Result<RegimeState> {
+        // Query database using get_latest_regime() function
+        let row = sqlx::query!(
+            r#"
+            SELECT regime, confidence, event_timestamp
+            FROM get_latest_regime($1)
+            "#,
+            symbol
+        )
+        .fetch_one(&self.db_pool)
+        .await
+        .context("Failed to fetch regime state")?;
+
+        // Query adaptive metrics
+        let metrics = sqlx::query!(
+            r#"
+            SELECT position_multiplier, stop_loss_multiplier
+            FROM adaptive_strategy_metrics
+            WHERE symbol = $1 AND event_timestamp = $2
+            ORDER BY event_timestamp DESC
+            LIMIT 1
+            "#,
+            symbol,
+            row.event_timestamp
+        )
+        .fetch_one(&self.db_pool)
+        .await
+        .context("Failed to fetch adaptive metrics")?;
+
+        Ok(RegimeState {
+            regime: parse_regime(&row.regime.unwrap_or_default())?,
+            confidence: row.confidence.unwrap_or(0.0),
+            event_timestamp: row.event_timestamp.unwrap(),
+            position_multiplier: metrics.position_multiplier,
+            stop_loss_multiplier: metrics.stop_loss_multiplier,
+        })
+    }
+
+    /// Get regime for multiple symbols (batch query)
+    pub async fn get_regimes_batch(&self, symbols: &[String]) -> Result<Vec<(String, RegimeState)>> {
+        let mut results = Vec::new();
+        for symbol in symbols {
+            if let Ok(state) = self.get_regime(symbol).await {
+                results.push((symbol.clone(), state));
+            }
+        }
+        Ok(results)
+    }
+}
+
+fn parse_regime(s: &str) -> Result<MarketRegime> {
+    match s {
+        "Normal" => Ok(MarketRegime::Normal),
+        "Trending" => Ok(MarketRegime::Trending),
+        "Ranging" | "Sideways" => Ok(MarketRegime::Sideways),
+        "Bull" => Ok(MarketRegime::Bull),
+        "Bear" => Ok(MarketRegime::Bear),
+        "Volatile" | "HighVolatility" => Ok(MarketRegime::HighVolatility),
+        "Crisis" => Ok(MarketRegime::Crisis),
+        _ => Ok(MarketRegime::Normal), // Default fallback
+    }
+}
+```
+
+---
+
+### Phase 2: Allocation Integration (3 hours)
+
+**File**: `services/trading_agent_service/src/allocation.rs` (MODIFY)
+
+**Changes**:
+
+1. **Add regime-aware allocation method**:
+```rust
+#[derive(Debug, Clone)]
+pub enum AllocationMethod {
+    EqualWeight,
+    RiskParity,
+    MeanVariance { lambda: f64 },
+    MLOptimized,
+    KellyCriterion { fraction: f64 },
+    // ✅ NEW: Regime-adaptive allocation
+    RegimeAdaptive {
+        base_method: Box<AllocationMethod>,
+        regime_detector: Arc<RegimeDetector>,
+    },
+}
+```
+
+2. **Implement regime-adaptive wrapper**:
+```rust
+async fn regime_adaptive(
+    &self,
+    assets: &[AssetInfo],
+    total_capital: Decimal,
+    base_method: &AllocationMethod,
+    regime_detector: &RegimeDetector,
+) -> Result<HashMap<String, Decimal>> {
+    // Step 1: Get base allocation (e.g., from MLOptimized)
+    let base_allocator = PortfolioAllocator::new((**base_method).clone());
+    let base_allocation = base_allocator.allocate(assets, total_capital)?;
+
+    // Step 2: Fetch regime states for all symbols
+    let symbols: Vec<String> = assets.iter().map(|a| a.symbol.clone()).collect();
+    let regime_states = regime_detector.get_regimes_batch(&symbols).await?;
+
+    // Step 3: Apply regime multipliers
+    let mut adjusted_allocation = HashMap::new();
+    for (symbol, base_capital) in base_allocation {
+        if let Some((_, regime_state)) = regime_states.iter().find(|(s, _)| s == &symbol) {
+            // Apply position multiplier (0.2x-1.5x)
+            let adjusted_capital = base_capital * Decimal::from_f64_retain(regime_state.position_multiplier)
+                .unwrap_or(Decimal::ONE);
+
+            adjusted_allocation.insert(symbol, adjusted_capital);
+        } else {
+            // No regime data → use base allocation
+            adjusted_allocation.insert(symbol, base_capital);
+        }
+    }
+
+    // Step 4: Renormalize to total_capital (multipliers may exceed 100%)
+    let sum: Decimal = adjusted_allocation.values().sum();
+    if sum > total_capital {
+        for capital in adjusted_allocation.values_mut() {
+            *capital = (*capital / sum) * total_capital;
+        }
+    }
+
+    Ok(adjusted_allocation)
+}
+```
+
+3. **Update `allocate()` to support regime method**:
+```rust
+pub async fn allocate_async(
+    &self,
+    assets: &[AssetInfo],
+    total_capital: Decimal,
+) -> Result<HashMap<String, Decimal>> {
+    match &self.method {
+        AllocationMethod::EqualWeight => self.equal_weight(assets, total_capital),
+        AllocationMethod::RiskParity => self.risk_parity(assets, total_capital),
+        AllocationMethod::MeanVariance { lambda } => self.mean_variance(assets, total_capital, *lambda),
+        AllocationMethod::MLOptimized => self.ml_optimized(assets, total_capital),
+        AllocationMethod::KellyCriterion { fraction } => self.kelly_criterion(assets, total_capital, *fraction),
+        // ✅ NEW
+        AllocationMethod::RegimeAdaptive { base_method, regime_detector } => {
+            self.regime_adaptive(assets, total_capital, base_method, regime_detector).await
+        },
+    }
+}
+```
+
+---
+
+### Phase 3: Service Wiring (2 hours)
+
+**File**: `services/trading_agent_service/src/service.rs` (MODIFY)
+
+**Changes**:
+
+1. **Add RegimeDetector to service state**:
+```rust
+pub struct TradingAgentServiceImpl {
+    db_pool: PgPool,
+    universe_selector: UniverseSelector,
+    strategy_coordinator: StrategyCoordinator,
+    metrics: TradingAgentMetrics,
+    regime_detector: Arc<RegimeDetector>, // ✅ NEW
+}
+
+impl TradingAgentServiceImpl {
+    pub fn new(db_pool: PgPool) -> Self {
+        Self {
+            universe_selector: UniverseSelector::new(db_pool.clone()),
+            strategy_coordinator: StrategyCoordinator::new(db_pool.clone()),
+            metrics: TradingAgentMetrics::new(),
+            regime_detector: Arc::new(RegimeDetector::new(db_pool.clone())), // ✅ NEW
+            db_pool,
+        }
+    }
+}
+```
+
+2. **Implement `allocate_portfolio` with regime adaptation**:
+```rust
+async fn allocate_portfolio(
+    &self,
+    request: Request<AllocatePortfolioRequest>,
+) -> Result<Response<AllocatePortfolioResponse>, Status> {
+    let req = request.into_inner();
+
+    // Convert proto assets to AssetInfo
+    let assets: Vec<AssetInfo> = req.assets.iter().map(|a| {
+        AssetInfo {
+            symbol: a.symbol.clone(),
+            expected_return: a.expected_return,
+            volatility: a.volatility,
+            ml_score: a.ml_score,
+            // ... other fields
+        }
+    }).collect();
+
+    let total_capital = Decimal::from_f64_retain(req.total_capital)
+        .ok_or_else(|| Status::invalid_argument("Invalid total capital"))?;
+
+    // ✅ Use regime-adaptive allocation
+    let base_method = AllocationMethod::MLOptimized;
+    let allocator = PortfolioAllocator::new(AllocationMethod::RegimeAdaptive {
+        base_method: Box::new(base_method),
+        regime_detector: self.regime_detector.clone(),
+    });
+
+    let allocations = allocator.allocate_async(&assets, total_capital)
+        .await
+        .map_err(|e| Status::internal(format!("Allocation failed: {}", e)))?;
+
+    // Convert to proto
+    let proto_allocations: Vec<Allocation> = allocations.iter().map(|(symbol, capital)| {
+        Allocation {
+            symbol: symbol.clone(),
+            capital: capital.to_f64().unwrap_or(0.0),
+            weight: (capital / total_capital).to_f64().unwrap_or(0.0),
+        }
+    }).collect();
+
+    Ok(Response::new(AllocatePortfolioResponse {
+        allocations: proto_allocations,
+        // ... metrics
+    }))
+}
+```
+
+---
+
+### Phase 4: Order Generation Integration (1 hour)
+
+**File**: `services/trading_agent_service/src/orders.rs` (MODIFY)
+
+**Changes**:
+
+1. **Add stop-loss multiplier to order generation**:
+```rust
+pub async fn generate_order(
+    &self,
+    allocation: &PortfolioAllocation,
+    regime_state: &RegimeState, // ✅ NEW parameter
+) -> Result<Order> {
+    let price = self.get_current_price(&allocation.symbol).await?;
+    let quantity = (allocation.capital / price).floor();
+
+    // ✅ Apply regime-based stop-loss
+    let atr = self.calculate_atr(&allocation.symbol, 14).await?;
+    let stop_loss_distance = atr * regime_state.stop_loss_multiplier;
+
+    let stop_loss_price = if allocation.direction == Direction::Long {
+        price - stop_loss_distance
+    } else {
+        price + stop_loss_distance
+    };
+
+    Ok(Order {
+        symbol: allocation.symbol.clone(),
+        quantity,
+        price,
+        stop_loss: Some(stop_loss_price),
+        // ... other fields
+    })
+}
+```
+
+---
+
+### Phase 5: Testing (3 hours)
+
+**File**: `services/trading_agent_service/tests/regime_allocation_test.rs` (NEW)
+
+**Test Cases**:
+
+1. **Test regime multiplier application**:
+   - Crisis regime (0.2x) → $100K allocation → $20K actual
+   - Trending regime (1.5x) → $100K allocation → $150K actual (then renormalized)
+   - Normal regime (1.0x) → $100K allocation → $100K actual
+
+2. **Test stop-loss adjustment**:
+   - Volatile regime (3.0x ATR) → ATR=$10 → Stop=$30 away
+   - Sideways regime (1.5x ATR) → ATR=$10 → Stop=$15 away
+
+3. **Test database fallback**:
+   - No regime data → Use base allocation without multipliers
+   - Stale regime data (>1 hour old) → Fall back to Normal regime
+
+4. **Test end-to-end flow**:
+   - Mock regime_states table with test data
+   - Call `allocate_portfolio` gRPC endpoint
+   - Verify allocations reflect regime multipliers
+
+---
+
+## Validation Checklist
+
+### Pre-Integration ✅
+- [x] RegimeAdaptiveFeatures implemented (644 lines, 12/12 tests)
+- [x] Database schema created (regime_states, regime_transitions, adaptive_strategy_metrics)
+- [x] gRPC endpoints defined (GetRegimeState, GetRegimeTransitions)
+- [x] Migration 045 applied
+
+### Post-Integration (To Be Verified)
+- [ ] `services/trading_agent_service/src/regime.rs` created
+- [ ] `services/trading_agent_service/src/allocation.rs` updated with RegimeAdaptive method
+- [ ] `services/trading_agent_service/src/service.rs` wired with RegimeDetector
+- [ ] `services/trading_agent_service/src/orders.rs` applies stop-loss multipliers
+- [ ] Test suite validates regime multipliers applied correctly
+- [ ] End-to-end test: Crisis regime → 0.2x position size
+- [ ] End-to-end test: Trending regime → 1.5x position size
+- [ ] End-to-end test: Volatile regime → 3.0x ATR stop-loss
+
+---
+
+## Risk Assessment
+
+### Risk 1: Database Query Latency
+- **Impact**: Regime queries add latency to allocation decisions
+- **Mitigation**:
+  - Use database indexes (already created: `idx_regime_states_symbol_timestamp`)
+  - Batch queries for multiple symbols (`get_regimes_batch`)
+  - Cache regime states (TTL: 1 minute)
+- **Acceptable Latency**: <5ms per symbol (P99)
+
+### Risk 2: Stale Regime Data
+- **Impact**: Trading with outdated regime classifications
+- **Mitigation**:
+  - Check `event_timestamp` (reject data >1 hour old)
+  - Fall back to Normal regime (1.0x multiplier) if stale
+  - Monitor `regime_states` freshness via Grafana
+
+### Risk 3: Over-Leverage in Trending Regimes
+- **Impact**: 1.5x multiplier could exceed risk limits
+- **Mitigation**:
+  - Renormalize allocations to 100% after applying multipliers
+  - Hard cap: No single position >20% (already in allocation.rs)
+  - Risk budget monitoring (Feature 224)
+
+### Risk 4: Whipsaw in Crisis Regimes
+- **Impact**: 0.2x multiplier during false alarms → missed opportunities
+- **Mitigation**:
+  - Require high confidence (>0.8) for Crisis regime
+  - Monitor regime flip-flopping (>50/hour alert)
+  - Manual override capability
+
+---
+
+## Performance Impact
+
+### Latency Breakdown (Estimated)
+```
+Current (Wave C - 201 features):
+- Universe Selection: 50ms
+- Asset Selection: 20ms
+- Allocation (MLOptimized): 0.5ms
+- Order Generation: 10ms
+- TOTAL: 80.5ms
+
+With Regime Integration (Wave D - 225 features):
+- Universe Selection: 50ms
+- Asset Selection: 20ms
+- Regime Detection (batch query): +3ms  ← NEW
+- Allocation (RegimeAdaptive): 0.5ms
+- Order Generation (with stop-loss): 10ms
+- TOTAL: 83.5ms (+3.7% overhead)
+```
+
+**Conclusion**: +3ms overhead is acceptable for 25-50% Sharpe improvement.
+
+---
+
+## Code Statistics
+
+### Files to Create
+1. `services/trading_agent_service/src/regime.rs` (~200 lines)
+2. `services/trading_agent_service/tests/regime_allocation_test.rs` (~300 lines)
+
+### Files to Modify
+1. `services/trading_agent_service/src/allocation.rs` (+100 lines)
+2. `services/trading_agent_service/src/service.rs` (+50 lines)
+3. `services/trading_agent_service/src/orders.rs` (+30 lines)
+4. `services/trading_agent_service/src/lib.rs` (+1 line for module export)
+
+### Total Code Changes
+- **New Code**: ~500 lines
+- **Modified Code**: ~180 lines
+- **Total Effort**: ~11 hours (2+3+2+1+3)
+
+---
+
+## Rollback Plan
+
+### Level 1: Feature Flag (Immediate)
+```rust
+const ENABLE_REGIME_ADAPTIVE: bool = false; // Set to true to enable
+
+if ENABLE_REGIME_ADAPTIVE {
+    AllocationMethod::RegimeAdaptive { ... }
+} else {
+    AllocationMethod::MLOptimized // Fall back to Wave C behavior
+}
+```
+
+### Level 2: Database Rollback (5 minutes)
+```sql
+-- Disable regime tables (keep data)
+REVOKE SELECT ON regime_states FROM foxhunt;
+REVOKE SELECT ON adaptive_strategy_metrics FROM foxhunt;
+```
+
+### Level 3: Code Rollback (10 minutes)
+```bash
+git revert <integration-commit-hash>
+cargo build --release -p trading_agent_service
+systemctl restart trading_agent_service
+```
+
+---
+
+## Next Steps
+
+### Immediate (Priority 1)
+1. ✅ **This Report**: Document integration gap
+2. ⏳ **User Decision**: Approve integration plan (11 hours effort)
+3. ⏳ **Implementation**: Execute 5-phase integration plan
+
+### Short-term (After Integration)
+4. ⏳ **Testing**: Run regime allocation tests (3 hours)
+5. ⏳ **Validation**: Paper trading with regime-adaptive sizing (1 week)
+6. ⏳ **Monitoring**: Set up Grafana alerts for regime metrics
+
+### Medium-term (Production)
+7. ⏳ **Performance Tuning**: Optimize database queries (<5ms P99)
+8. ⏳ **Caching**: Add 1-minute TTL cache for regime states
+9. ⏳ **Documentation**: Update production runbooks
+
+---
+
+## Conclusion
+
+**Gap Confirmed**: Wave D's adaptive position sizing (Features 221-224) is **fully implemented** but **NOT integrated** into the trading decision flow.
+
+**Impact**: Without integration, the 225-feature ML models will include regime-adaptive features in training, but **production trading will ignore regime-based position sizing**.
+
+**Recommendation**: **PROCEED WITH INTEGRATION** before ML retraining.
+
+**Rationale**:
+1. **Effort**: 11 hours (manageable)
+2. **Risk**: Low (feature flag + rollback plan)
+3. **Benefit**: +25-50% Sharpe improvement (per Wave D hypothesis)
+4. **Urgency**: Must complete before 225-feature ML retraining (4-6 weeks)
+
+**Decision Required**: Approve integration plan and proceed with implementation?
+
+---
+
+**Files Analyzed**:
+- `/home/jgrusewski/Work/foxhunt/ml/src/features/regime_adaptive.rs` (644 lines)
+- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/allocation.rs` (716 lines)
+- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (675 lines)
+- `/home/jgrusewski/Work/foxhunt/migrations/045_wave_d_regime_tracking.sql` (12,819 bytes)
+- `/home/jgrusewski/Work/foxhunt/AGENT_D11_PORTFOLIO_ALLOCATION_IMPLEMENTATION_REPORT.md` (analysis)
+
+**Agent WIRE-02 Complete** | 2025-10-19
--- a/AGENT_WIRE02_QUICK_SUMMARY.md
+++ b/AGENT_WIRE02_QUICK_SUMMARY.md
@@ -0,0 +1,164 @@
+# Agent WIRE-02: Quick Summary
+
+**Date**: 2025-10-19
+**Status**: ✅ COMPLETE - Integration gap identified
+
+---
+
+## 🔴 CRITICAL FINDING
+
+**Wave D's adaptive position sizing is IMPLEMENTED but NOT INTEGRATED into trading flow.**
+
+---
+
+## Gap Analysis
+
+### ✅ What's Done
+1. **RegimeAdaptiveFeatures** - Fully implemented (644 lines, 12/12 tests)
+   - Position multipliers: 0.2x (Crisis) to 1.5x (Trending)
+   - Stop-loss multipliers: 1.5x (Sideways) to 4.0x ATR (Crisis)
+   - Location: `ml/src/features/regime_adaptive.rs`
+
+2. **Database Schema** - Migration 045 applied
+   - Tables: `regime_states`, `regime_transitions`, `adaptive_strategy_metrics`
+   - Functions: `get_latest_regime()`, `get_regime_transition_matrix()`
+
+3. **gRPC Endpoints** - Defined and routed
+   - `GetRegimeState(symbol) → RegimeStateResponse`
+   - `GetRegimeTransitions(symbol) → TransitionsResponse`
+
+### ❌ What's Missing
+1. **Trading Agent Service** - NO regime integration
+   - File: `services/trading_agent_service/src/allocation.rs` (716 lines)
+   - Status: 5 allocation methods (EqualWeight, RiskParity, MeanVariance, MLOptimized, KellyCriterion)
+   - **NO imports** of `RegimeAdaptiveFeatures`
+   - **NO database queries** to `regime_states`
+   - **NO application** of position/stop-loss multipliers
+
+2. **Order Generation** - NO stop-loss adjustment
+   - File: `services/trading_agent_service/src/orders.rs`
+   - Status: Static stop-loss logic, no regime-based ATR multipliers
+
+---
+
+## Impact
+
+**Without Integration**:
+- ML models train with Features 221-224 (regime multipliers)
+- **BUT** production trading ignores regime state
+- Position sizes stay STATIC (no 0.2x-1.5x adjustment)
+- Stop-losses stay STATIC (no 1.5x-4.0x ATR adjustment)
+- **Expected Sharpe improvement: 0%** (instead of +25-50%)
+
+---
+
+## Integration Plan
+
+### 5-Phase Implementation (11 hours total)
+
+**Phase 1**: Database Query Layer (2h)
+- Create `services/trading_agent_service/src/regime.rs`
+- Implement `RegimeDetector` to query `regime_states` table
+- Add `get_regime(symbol) → RegimeState` method
+
+**Phase 2**: Allocation Integration (3h)
+- Add `AllocationMethod::RegimeAdaptive` enum variant
+- Implement regime multiplier wrapper around base allocation
+- Apply position multipliers (0.2x-1.5x) to allocation weights
+
+**Phase 3**: Service Wiring (2h)
+- Add `RegimeDetector` to `TradingAgentServiceImpl`
+- Wire `allocate_portfolio` gRPC endpoint to use regime-adaptive allocation
+- Add database connection pooling
+
+**Phase 4**: Order Generation (1h)
+- Update `OrderGenerator::generate_order()` to accept `RegimeState`
+- Apply stop-loss multipliers (1.5x-4.0x ATR) based on regime
+
+**Phase 5**: Testing (3h)
+- Create `tests/regime_allocation_test.rs`
+- Validate Crisis regime → 0.2x position size
+- Validate Trending regime → 1.5x position size
+- Validate Volatile regime → 3.0x ATR stop-loss
+
+---
+
+## Code Changes
+
+### Files to Create
+1. `services/trading_agent_service/src/regime.rs` (~200 lines)
+2. `services/trading_agent_service/tests/regime_allocation_test.rs` (~300 lines)
+
+### Files to Modify
+1. `services/trading_agent_service/src/allocation.rs` (+100 lines)
+2. `services/trading_agent_service/src/service.rs` (+50 lines)
+3. `services/trading_agent_service/src/orders.rs` (+30 lines)
+4. `services/trading_agent_service/src/lib.rs` (+1 line)
+
+**Total**: ~500 lines new, ~180 lines modified
+
+---
+
+## Performance Impact
+
+**Latency Addition**: +3ms (batch regime queries)
+- Current: 80.5ms end-to-end
+- With regime: 83.5ms (+3.7% overhead)
+- **Acceptable** for +25-50% Sharpe improvement
+
+---
+
+## Risk Mitigation
+
+1. **Feature Flag**: Easy on/off toggle
+2. **Database Indexes**: Already created (`idx_regime_states_symbol_timestamp`)
+3. **Fallback**: Use Normal regime (1.0x) if data stale/missing
+4. **Renormalization**: Prevent over-leverage from 1.5x multipliers
+5. **Rollback**: 3-level plan (flag → database → code)
+
+---
+
+## Recommendation
+
+**✅ PROCEED WITH INTEGRATION** before ML retraining
+
+**Why**:
+- **Effort**: 11 hours (manageable)
+- **Risk**: Low (feature flag + rollback plan)
+- **Benefit**: Unlock +25-50% Sharpe improvement
+- **Urgency**: Must complete before 225-feature ML retraining (4-6 weeks)
+
+**Next Step**: User approval to execute 5-phase integration plan
+
+---
+
+## Example: Crisis Regime Behavior
+
+**Scenario**: Market crash detected (Crisis regime)
+
+**Without Integration** (Current):
+- Base allocation: $100K to ES.FUT
+- **Actual position**: $100K (FULL RISK)
+- Stop-loss: 2.0x ATR = $20 away
+- **Result**: Full exposure during crisis ❌
+
+**With Integration** (After Fix):
+- Base allocation: $100K to ES.FUT
+- Regime multiplier: 0.2x (Crisis)
+- **Actual position**: $20K (80% RISK REDUCTION) ✅
+- Stop-loss: 4.0x ATR = $40 away (wider to avoid panic exit)
+- **Result**: Protected capital during crisis ✅
+
+---
+
+## Files Referenced
+
+- ✅ `/home/jgrusewski/Work/foxhunt/ml/src/features/regime_adaptive.rs`
+- ✅ `/home/jgrusewski/Work/foxhunt/migrations/045_wave_d_regime_tracking.sql`
+- ❌ `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/allocation.rs` (NO integration)
+- ❌ `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (placeholder only)
+- ❌ `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/orders.rs` (static stop-loss)
+
+---
+
+**Agent WIRE-02 Complete** | 2025-10-19
--- a/AGENT_WIRE14_INTEGRATION_GAPS.txt
+++ b/AGENT_WIRE14_INTEGRATION_GAPS.txt
@@ -0,0 +1,129 @@
+╔══════════════════════════════════════════════════════════════════════════════╗
+║           AGENT WIRE-14: PAPER TRADING WAVE D INTEGRATION GAPS              ║
+╚══════════════════════════════════════════════════════════════════════════════╝
+
+CURRENT STATE (Paper Trading Executor):
+┌──────────────────────────────────────────────────────────────────────────┐
+│ Component              │ Status  │ Wave C Baseline  │ Wave D Required   │
+├────────────────────────┼─────────┼──────────────────┼───────────────────┤
+│ ML Strategy            │ ✅ YES  │ SharedMLStrategy │ SharedMLStrategy  │
+│ Feature Config         │ ❌ NO   │ Hardcoded (20)   │ FeatureConfig::   │
+│                        │         │                  │   wave_d()        │
+│ Feature Count          │ ❌ NO   │ 201 features     │ 225 features      │
+│ Regime Queries         │ ❌ NO   │ None             │ get_latest_regime │
+│ Position Sizing        │ ❌ NO   │ Fixed 1.0        │ Adaptive 0.2-1.5x │
+│ Kelly Criterion        │ ❌ NO   │ Comment only     │ Implemented       │
+└────────────────────────┴─────────┴──────────────────┴───────────────────┘
+
+CRITICAL GAPS:
+
+1. FEATURE EXTRACTION (Line 154-157)
+   ┌─────────────────────────────────────────────────────────────────────┐
+   │ Current:  SharedMLStrategy::new(20, 0.6)                            │
+   │           ^^^^ Hardcoded - NO feature config                        │
+   │                                                                     │
+   │ Required: SharedMLStrategy::new_with_config(                        │
+   │               20, 0.6,                                              │
+   │               FeatureConfig::wave_d()  // ✅ 225 features           │
+   │           )                                                         │
+   └─────────────────────────────────────────────────────────────────────┘
+
+2. REGIME STATE QUERIES (Missing)
+   ┌─────────────────────────────────────────────────────────────────────┐
+   │ Current:  No database queries for regime_states                     │
+   │                                                                     │
+   │ Required: let regime = sqlx::query!(                                │
+   │               "SELECT regime FROM get_latest_regime($1)",           │
+   │               symbol                                                │
+   │           ).fetch_one(&self.db_pool).await?;                        │
+   └─────────────────────────────────────────────────────────────────────┘
+
+3. ADAPTIVE POSITION SIZING (Line 567-575)
+   ┌─────────────────────────────────────────────────────────────────────┐
+   │ Current:  let position_size = 1.0; // Fixed                         │
+   │                                                                     │
+   │ Required: let regime_mult = match regime {                          │
+   │               "Trending" => 1.5,                                    │
+   │               "Ranging" => 0.8,                                     │
+   │               "Volatile" => 0.5,                                    │
+   │               "Transition" => 0.2,                                  │
+   │           };                                                        │
+   │           let size = base * regime_mult * confidence;               │
+   └─────────────────────────────────────────────────────────────────────┘
+
+ARCHITECTURE MISMATCH:
+
+   common::ml_strategy::MLFeatureExtractor
+   ├─ Expected feature count: hardcoded comment (26/36/65)
+   ├─ NO FeatureConfig integration
+   └─ NO Wave D support (225 features)
+
+   ml::features::config::FeatureConfig
+   ├─ wave_d() method exists ✅
+   ├─ enable_wave_d_regime: true ✅
+   └─ 225 feature support ✅
+
+   ⚠️  PROBLEM: These two systems are NOT connected!
+
+TESTING RISK:
+
+   Paper Trading Current:
+   ┌─────────────────────────────────────────────────────────────────────┐
+   │ Features: 201 (Wave C baseline)                                     │
+   │ Sizing: Fixed 1.0 contracts                                        │
+   │ Regime: Not aware                                                  │
+   │                                                                     │
+   │ Result: Tests Wave C, NOT Wave D ❌                                │
+   └─────────────────────────────────────────────────────────────────────┘
+
+   Paper Trading Required:
+   ┌─────────────────────────────────────────────────────────────────────┐
+   │ Features: 225 (Wave D regime detection)                            │
+   │ Sizing: Adaptive 0.2x-1.5x based on regime                         │
+   │ Regime: Queries regime_states before each trade                    │
+   │                                                                     │
+   │ Result: Validates Wave D before production ✅                      │
+   └─────────────────────────────────────────────────────────────────────┘
+
+ACTION PLAN (6 hours):
+
+   [P1] Modify SharedMLStrategy constructor (2h)
+        └─ Accept FeatureConfig parameter
+        └─ Update paper_trading_executor.rs
+        └─ Verify 225-feature extraction
+
+   [P2] Add regime state queries (1h)
+        └─ Implement get_regime_for_symbol()
+        └─ Query get_latest_regime() before trades
+        └─ Log regime transitions
+
+   [P3] Adaptive position sizing (2h)
+        └─ Replace calculate_position_size()
+        └─ Implement regime multipliers (0.2x-1.5x)
+        └─ Add confidence-based Kelly factor
+
+   [P4] Testing & validation (1h)
+        └─ Run 24-hour paper trading test
+        └─ Monitor regime vs. sizing correlation
+        └─ Document Wave C vs. Wave D performance
+
+RECOMMENDATION:
+
+   ⛔ BLOCK production deployment until paper trading validates Wave D
+
+   Why? Paper trading is the ONLY pre-production validation step.
+        If it tests Wave C config, we have ZERO evidence that:
+        - 225-feature extraction works
+        - Regime detection improves performance
+        - Adaptive sizing reduces drawdowns
+
+   Next Steps:
+   1. Implement action items (6 hours)
+   2. Run 24-hour paper trading validation
+   3. Compare Wave C baseline vs. Wave D adaptive results
+   4. Document findings in PAPER_TRADING_WAVE_D_VALIDATION.md
+
+═══════════════════════════════════════════════════════════════════════════════
+Agent WIRE-14 Status: ⚠️  PARTIAL INTEGRATION - CRITICAL GAPS IDENTIFIED
+Next Agent: WIRE-15 (Adaptive Position Sizing Implementation)
+═══════════════════════════════════════════════════════════════════════════════
--- a/AGENT_WIRE14_PAPER_TRADING_STATUS.md
+++ b/AGENT_WIRE14_PAPER_TRADING_STATUS.md
@@ -0,0 +1,427 @@
+# AGENT WIRE-14: Paper Trading Executor Wave D Integration Status
+
+**Agent**: WIRE-14
+**Mission**: Verify paper trading executor uses Wave D features and adaptive sizing
+**Status**: ⚠️ PARTIAL INTEGRATION - Missing Wave D Features
+**Priority**: HIGH - Paper trading must test Wave D before live deployment
+**Date**: 2025-10-19
+
+---
+
+## Executive Summary
+
+The paper trading executor (`services/trading_service/src/paper_trading_executor.rs`) currently uses `SharedMLStrategy` but is **NOT configured for Wave D features**. Critical gaps identified:
+
+1. ✅ Uses `SharedMLStrategy` (ONE SINGLE SYSTEM architecture)
+2. ❌ **NO Wave D feature configuration** - Uses hardcoded defaults (20 lookback, 0.6 confidence)
+3. ❌ **NO regime state queries** - Does not check `regime_states` table
+4. ❌ **NO adaptive position sizing** - Uses fixed 1.0 contract size
+5. ⚠️ **Kelly Criterion mentioned but not implemented** (line 569 comment only)
+
+**Risk**: Paper trading will test Wave C baseline (201 features) instead of Wave D (225 features + regime detection).
+
+---
+
+## Code Analysis
+
+### 1. ML Strategy Initialization
+
+**File**: `services/trading_service/src/paper_trading_executor.rs`
+**Lines**: 154-157
+
+```rust
+pub fn new(db_pool: PgPool, config: PaperTradingConfig) -> Self {
+    // Initialize with shared ML strategy (default configuration)
+    let ml_strategy = SharedMLStrategy::new(20, 0.6);
+    // ^^^ HARDCODED: 20 lookback, 0.6 confidence - NO Wave D config
+```
+
+**Issue**: `SharedMLStrategy::new()` does NOT accept `FeatureConfig` parameter. The constructor signature is:
+```rust
+pub fn new(lookback_periods: usize, min_confidence_threshold: f64) -> Self
+```
+
+**Missing**: No way to pass `FeatureConfig::wave_d()` to enable 225-feature extraction.
+
+---
+
+### 2. Position Sizing Logic
+
+**File**: `services/trading_service/src/paper_trading_executor.rs`
+**Lines**: 567-575
+
+```rust
+fn calculate_position_size(&self, _prediction: &PendingPrediction) -> Result<f64> {
+    // Simple fixed position size for paper trading
+    // In production, this could use Kelly Criterion or volatility-adjusted sizing
+    let position_size = 1.0; // 1 contract
+    // ^^^ FIXED SIZE: No adaptive sizing based on regime or confidence
+
+    if position_size > self.config.max_position_size {
+        return Err(anyhow!("Position size {} exceeds maximum {}", position_size, self.config.max_position_size));
+    }
+
+    Ok(position_size)
+}
+```
+
+**Missing Wave D Adaptive Logic**:
+- No regime state queries (`SELECT regime FROM regime_states`)
+- No adaptive multipliers (0.2x-1.5x based on regime)
+- No Kelly Criterion position sizing
+- No volatility-adjusted sizing
+
+**Expected Behavior** (from Wave D design):
+```rust
+// Query regime state
+let regime = sqlx::query!("SELECT regime FROM get_latest_regime($1)", symbol)
+    .fetch_one(&self.db_pool).await?;
+
+// Apply regime-adaptive multiplier
+let base_size = 1.0;
+let regime_multiplier = match regime.regime.as_str() {
+    "Trending" => 1.5,      // Increase size in trending markets
+    "Ranging" => 0.8,       // Reduce size in ranging markets
+    "Volatile" => 0.5,      // Minimize size in volatile markets
+    "Transition" => 0.2,    // Avoid trading during transitions
+    _ => 1.0,               // Normal sizing for unknown regimes
+};
+
+let position_size = base_size * regime_multiplier * confidence_factor;
+```
+
+---
+
+### 3. Regime State Integration
+
+**Search Results**: ❌ NO regime queries found in `paper_trading_executor.rs`
+
+```bash
+$ grep -rn "regime_states\|regime_transitions\|get_latest_regime" \
+  services/trading_service/src/paper_trading_executor.rs
+
+# Result: 0 matches
+```
+
+**Contrast with `trading.rs` (Trading Service)**:
+```rust
+// services/trading_service/src/services/trading.rs:992-1023
+async fn get_regime_state(&self, req: Request<GetRegimeStateRequest>) -> Result<Response<GetRegimeStateResponse>, Status> {
+    let regime_state = sqlx::query!(
+        r#"SELECT regime, confidence, detected_at FROM get_latest_regime($1)"#,
+        req.symbol
+    ).fetch_one(&self.db_pool).await?;
+
+    Ok(Response::new(GetRegimeStateResponse {
+        current_regime: regime_state.regime.unwrap_or("Normal".to_string()),
+        confidence: regime_state.confidence.unwrap_or(0.0),
+        // ...
+    }))
+}
+```
+
+**Paper Trading Executor**: No equivalent logic.
+
+---
+
+### 4. Feature Configuration Architecture
+
+**Analysis**: `SharedMLStrategy` uses `MLFeatureExtractor` which has a **legacy field** for feature count:
+
+**File**: `common/src/ml_strategy.rs` (lines 66-84)
+```rust
+pub struct MLFeatureExtractor {
+    pub lookback_periods: usize,
+    /// Expected feature count (26=Wave A, 30=Wave A+4 extra, 36=Wave B, 65=Wave C)
+    expected_feature_count: usize,  // ❌ Outdated comment - no Wave D (225)
+    price_history: Vec<f64>,
+    volume_history: Vec<f64>,
+    // ...
+}
+```
+
+**Problem**: `MLFeatureExtractor` does NOT use `FeatureConfig` from `ml/src/features/config.rs` which supports Wave D:
+
+**File**: `ml/src/features/config.rs` (lines 345-355)
+```rust
+pub fn wave_d() -> Self {
+    Self {
+        enable_wave_a: true,
+        enable_wave_b: true,
+        enable_wave_c: true,
+        enable_wave_d_regime: true,  // ✅ Enables 24 regime features (201→225)
+        // ...
+    }
+}
+```
+
+**Root Cause**: Architecture mismatch between `common::ml_strategy` (legacy extractor) and `ml::features::config` (Wave D-aware).
+
+---
+
+## Integration Gaps
+
+### Gap 1: No Wave D Feature Config
+**Current**: `SharedMLStrategy::new(20, 0.6)` - hardcoded defaults
+**Required**: Pass `FeatureConfig::wave_d()` to enable 225-feature extraction
+**Blocker**: `SharedMLStrategy` constructor does NOT accept `FeatureConfig`
+
+**Solution**:
+```rust
+// Option A: Add new constructor
+impl SharedMLStrategy {
+    pub fn new_with_feature_config(
+        lookback: usize,
+        confidence: f64,
+        feature_config: FeatureConfig,
+    ) -> Self {
+        // ...
+    }
+}
+
+// Option B: Modify existing constructor
+pub fn new(
+    lookback: usize,
+    confidence: f64,
+    feature_config: Option<FeatureConfig>,
+) -> Self {
+    let config = feature_config.unwrap_or(FeatureConfig::wave_a());
+    // ...
+}
+```
+
+---
+
+### Gap 2: No Regime State Queries
+**Current**: No database queries for `regime_states` or `regime_transitions`
+**Required**: Query latest regime before position sizing decisions
+**Blocker**: Database access exists (`self.db_pool`) but not used
+
+**Solution**:
+```rust
+async fn get_regime_for_symbol(&self, symbol: &str) -> Result<RegimeState> {
+    let regime = sqlx::query!(
+        r#"
+        SELECT regime, confidence, detected_at
+        FROM get_latest_regime($1)
+        "#,
+        symbol
+    )
+    .fetch_one(&self.db_pool)
+    .await
+    .context("Failed to fetch regime state")?;
+
+    Ok(RegimeState {
+        regime: regime.regime.unwrap_or("Normal".to_string()),
+        confidence: regime.confidence.unwrap_or(0.0),
+        detected_at: regime.detected_at,
+    })
+}
+```
+
+---
+
+### Gap 3: No Adaptive Position Sizing
+**Current**: Fixed 1.0 contract size (line 570)
+**Required**: Regime-adaptive sizing (0.2x-1.5x) + confidence-based Kelly multiplier
+**Blocker**: Regime state not queried, Kelly logic not implemented
+
+**Solution**:
+```rust
+async fn calculate_adaptive_position_size(
+    &self,
+    prediction: &PendingPrediction,
+) -> Result<f64> {
+    // Step 1: Get regime state
+    let regime = self.get_regime_for_symbol(&prediction.symbol).await?;
+
+    // Step 2: Apply regime-adaptive multiplier (Wave D design)
+    let regime_multiplier = match regime.regime.as_str() {
+        "Trending" => 1.5,
+        "Ranging" => 0.8,
+        "Volatile" => 0.5,
+        "Transition" => 0.2,
+        _ => 1.0,
+    };
+
+    // Step 3: Apply confidence-based Kelly multiplier
+    // Kelly formula: f* = (p*b - q) / b
+    // For trading: simplified to linear confidence scaling
+    let confidence_factor = prediction.ensemble_confidence.clamp(0.6, 1.0);
+    let kelly_multiplier = (confidence_factor - 0.6) / 0.4; // 0.6→0.0, 1.0→1.0
+
+    // Step 4: Calculate final position size
+    let base_size = 1.0; // Base contract size
+    let adaptive_size = base_size * regime_multiplier * (1.0 + kelly_multiplier);
+
+    // Step 5: Apply safety limits
+    Ok(adaptive_size.clamp(0.2, 5.0))
+}
+```
+
+---
+
+## Testing Implications
+
+### Current Paper Trading Behavior
+1. **Feature Set**: Uses Wave C baseline (201 features) - NO regime detection
+2. **Position Sizing**: Fixed 1.0 contracts - NO adaptive sizing
+3. **Regime Awareness**: None - trades blindly across all market conditions
+
+### Expected Wave D Behavior
+1. **Feature Set**: 225 features (201 + 24 regime detection)
+2. **Position Sizing**: 0.2x-1.5x adaptive multipliers based on regime
+3. **Regime Awareness**: Queries `regime_states`, avoids transitions
+
+### Risk Assessment
+⚠️ **HIGH RISK**: Paper trading will NOT validate Wave D features before production deployment.
+
+**Scenario**: If paper trading passes with Wave C config, we have NO evidence that:
+- 225-feature extraction works in production
+- Regime detection improves performance
+- Adaptive sizing reduces drawdowns
+
+**Recommendation**: Block production deployment until paper trading uses Wave D config.
+
+---
+
+## Action Items
+
+### Priority 1: Enable Wave D Features (2 hours)
+- [ ] Modify `SharedMLStrategy::new()` to accept `FeatureConfig` parameter
+- [ ] Update `paper_trading_executor.rs` to use `FeatureConfig::wave_d()`
+- [ ] Verify 225-feature extraction in paper trading logs
+
+### Priority 2: Implement Regime Queries (1 hour)
+- [ ] Add `get_regime_for_symbol()` method to `PaperTradingExecutor`
+- [ ] Query `regime_states` table before each trade
+- [ ] Log regime transitions for debugging
+
+### Priority 3: Adaptive Position Sizing (2 hours)
+- [ ] Replace `calculate_position_size()` with `calculate_adaptive_position_size()`
+- [ ] Implement regime multipliers (0.2x-1.5x)
+- [ ] Add confidence-based Kelly multiplier
+- [ ] Validate position size range (0.2-5.0 contracts)
+
+### Priority 4: Testing & Validation (1 hour)
+- [ ] Run paper trading with ES.FUT, NQ.FUT for 24 hours
+- [ ] Monitor regime transitions vs. position sizing
+- [ ] Compare performance: Wave C baseline vs. Wave D adaptive
+- [ ] Document results in `PAPER_TRADING_WAVE_D_VALIDATION.md`
+
+**Total Effort**: 6 hours
+
+---
+
+## Technical Debt
+
+### Issue 1: Architecture Mismatch
+**Problem**: `common::ml_strategy::MLFeatureExtractor` does NOT use `ml::features::config::FeatureConfig`.
+
+**Current State**:
+- `MLFeatureExtractor` has hardcoded feature count expectations (comment: "26=Wave A, 36=Wave B, 65=Wave C")
+- No mention of Wave D (225 features)
+- No integration with `FeatureConfig::wave_d()`
+
+**Solution**:
+```rust
+// common/src/ml_strategy.rs
+pub struct MLFeatureExtractor {
+    pub lookback_periods: usize,
+    feature_config: ml::features::config::FeatureConfig, // ✅ Use canonical config
+    price_history: Vec<f64>,
+    // ...
+}
+
+impl MLFeatureExtractor {
+    pub fn new(lookback: usize, feature_config: FeatureConfig) -> Self {
+        Self {
+            lookback_periods: lookback,
+            feature_config,
+            // ...
+        }
+    }
+}
+```
+
+**Blocker**: Cross-crate dependency (`common` depends on `ml`).
+
+---
+
+### Issue 2: Kelly Criterion Stub
+**Problem**: Line 569 comment says "could use Kelly Criterion" but NOT implemented.
+
+**Current Code**:
+```rust
+// In production, this could use Kelly Criterion or volatility-adjusted sizing
+let position_size = 1.0; // 1 contract
+```
+
+**Required Implementation**:
+```rust
+use risk::kelly_sizing::{KellyResult, KellySizer};
+
+async fn calculate_kelly_position(&self, prediction: &PendingPrediction) -> Result<f64> {
+    // Query historical performance for win rate
+    let win_rate = self.get_strategy_win_rate(&prediction.symbol).await?;
+
+    // Use ensemble confidence as win probability
+    let win_prob = prediction.ensemble_confidence;
+    let loss_prob = 1.0 - win_prob;
+
+    // Expected profit/loss ratio (from historical data)
+    let profit_loss_ratio = 1.5; // 1.5:1 risk/reward
+
+    // Kelly formula: f* = (p*b - q) / b
+    let kelly_fraction = (win_prob * profit_loss_ratio - loss_prob) / profit_loss_ratio;
+
+    // Use fractional Kelly (25%) for safety
+    let fractional_kelly = kelly_fraction * 0.25;
+
+    Ok(fractional_kelly.clamp(0.0, 1.0))
+}
+```
+
+**Existing Code**: `services/trading_service/src/core/risk_manager.rs` has `KellySizer` but NOT used in paper trading.
+
+---
+
+## References
+
+### Codebase Files
+- `services/trading_service/src/paper_trading_executor.rs` (897 lines)
+- `common/src/ml_strategy.rs` (MLFeatureExtractor definition)
+- `ml/src/features/config.rs` (FeatureConfig::wave_d() implementation)
+- `services/trading_service/src/services/trading.rs` (GetRegimeState gRPC method)
+- `services/trading_service/src/core/risk_manager.rs` (KellySizer implementation)
+
+### Database Schema
+- `migrations/045_regime_detection.sql` (regime_states, regime_transitions tables)
+- Stored function: `get_latest_regime(symbol TEXT)`
+
+### Wave D Documentation
+- `CLAUDE.md` (Wave D Phase 6 status, production targets)
+- `WAVE_D_DEPLOYMENT_GUIDE.md` (regime detection integration guide)
+- `WAVE_D_QUICK_REFERENCE.md` (adaptive sizing formulas)
+
+---
+
+## Conclusion
+
+**Status**: ⚠️ **PARTIAL INTEGRATION - CRITICAL GAPS**
+
+The paper trading executor is architecturally sound (uses `SharedMLStrategy`, ONE SINGLE SYSTEM) but **NOT configured for Wave D testing**:
+
+1. ❌ No 225-feature extraction (stuck on Wave C baseline)
+2. ❌ No regime state queries (blind to market conditions)
+3. ❌ No adaptive position sizing (fixed 1.0 contracts)
+
+**Recommendation**: **BLOCK production deployment** until paper trading validates Wave D features. Implement action items (6 hours) and run 24-hour validation before proceeding.
+
+**Next Agent**: WIRE-15 should implement `calculate_adaptive_position_size()` with regime multipliers and Kelly logic.
+
+---
+
+**Agent WIRE-14 signing off.**
+**Mission**: PARTIAL - Integration gaps identified, action plan provided.
+**Handoff**: WIRE-15 (Adaptive Position Sizing Implementation)