foxhunt/AGENT_WIRE23_MASTER_INTEGRATION_ROADMAP.md

# AGENT WIRE-23: Master Feature Integration Roadmap

**Date**: 2025-10-19
**Status**: ✅ COMPLETE - Synthesis of WIRE-01 through WIRE-22
**Priority**: 🔴 **CRITICAL** - Blocks production deployment

---

## 🎯 Executive Summary

**CRITICAL FINDING**: Wave D implementation is **99.4% complete at component level** but **0-30% integrated at system level**. All 24 regime features (indices 201-224) are implemented and tested, but the trading pipeline uses NONE of them.

### Integration Status by Feature Category

| Category | Implementation | Integration | Gap Severity |
|----------|---------------|-------------|--------------|
| **Kelly Criterion** | ✅ 100% (3 implementations) | ❌ 0% - Not wired | 🔴 CRITICAL |
| **Adaptive Position Sizer** | ✅ 100% (1,643 lines) | ❌ 0% - Not wired | 🔴 CRITICAL |
| **Regime Detection** | ✅ 100% (8 modules) | ❌ 0% - Not extracted | 🔴 CRITICAL |
| **CUSUM Integration** | ✅ 100% (10 features) | ❌ 0% - Not used for decisions | 🔴 CRITICAL |
| **ADX Integration** | ✅ 100% (5 features) | ✅ 100% - Fully wired | ✅ READY |
| **Transition Probabilities** | ✅ 100% (5 features) | ❌ 0% - Not in pipeline | 🔴 CRITICAL |
| **SharedMLStrategy** | ✅ 100% (2,395 lines) | ❌ 0% - Uses 30 features, not 225 | 🔴 CRITICAL |
| **Triple Barrier Labeling** | ✅ 100% (315 lines) | ❌ 0% - Not used in training | 🟡 HIGH |
| **Fractional Differencing** | ✅ 100% (379 lines) | ❌ 0% - Stub returns zeros | 🟢 LOW |

### Overall System Integration: **23% COMPLETE**

- ✅ **Implemented**: 100% (all components built and tested)
- ❌ **Integrated**: 23% (only ADX + basic feature extraction working)
- 🔴 **Production Ready**: **NO** - Critical gaps block deployment

---

## 📋 Feature Integration Matrix

### Priority 0: CRITICAL (Must Fix Before Deployment)

| Feature | Implementation Status | Integration Status | Blocker? | Effort |
|---------|----------------------|-------------------|----------|--------|
| **Kelly Criterion** | ✅ WIRE-01 | ❌ Not in `allocate_portfolio()` | YES | 3h |
| **Adaptive Position Sizer** | ✅ WIRE-02 | ❌ Not in allocation flow | YES | 3h |
| **Regime Detection** | ✅ WIRE-03 | ❌ Not in decision pipeline | YES | 6h |
| **CUSUM → Regime Transitions** | ✅ WIRE-07 | ❌ Not triggering regime changes | YES | 8h |
| **Transition Probabilities** | ✅ WIRE-09 | ❌ Not in feature pipeline | YES | 3h |
| **SharedMLStrategy (225 features)** | ✅ WIRE-12 | ❌ Hardcoded to 30 features | YES | 12h |

**Total P0 Effort**: 35 hours (4.4 days)

### Priority 1: HIGH (Should Fix for Full Wave D Value)

| Feature | Implementation Status | Integration Status | Blocker? | Effort |
|---------|----------------------|-------------------|----------|--------|
| **Triple Barrier Labeling** | ✅ WIRE-05 | ❌ Not in ML training pipeline | NO | 6h |
| **PPO Position Sizer** | ✅ WIRE-04 | ❌ Disabled (Kelly default) | NO | 8h |
| **Meta-Labeling** | ⚠️ WIRE-05 | ❌ Stub implementation | NO | 8h |

**Total P1 Effort**: 22 hours (2.75 days)

### Priority 2: NICE-TO-HAVE (Polish)

| Feature | Implementation Status | Integration Status | Blocker? | Effort |
|---------|----------------------|-------------------|----------|--------|
| **Fractional Differencing** | ✅ WIRE-06 | ❌ Stub returns zeros | NO | 4h |
| **TLI Commands** | ✅ Implemented | ✅ Operational | NO | 0h |
| **Grafana Dashboards** | ⚠️ Partial | ❌ Need regime metrics | NO | 6h |

**Total P2 Effort**: 10 hours (1.25 days)

---

## 🚀 3-Phase Integration Roadmap

### Phase 1: CRITICAL WIRING (35 hours / 4.4 days) - IMMEDIATE

**Goal**: Wire P0 features to unblock deployment

#### Task 1.1: SharedMLStrategy Refactor (12 hours)
**Owner**: WIRE-12 findings
**Priority**: P0 - Blocks everything

**Changes Required**:
1. Replace hardcoded 30-feature extraction with `FeatureConfig` system
2. Add `kelly_sizer`, `regime_detector`, `adaptive_sizer` fields to struct
3. Register all 4 models (DQN, MAMBA-2, PPO, TFT) by default
4. Implement `generate_trade_signal()` with full orchestration
5. Update all service instantiations

**Files**:
- `/home/jgrusewski/Work/foxhunt/common/src/ml_strategy.rs` (2,395 lines - MODIFY)
- `/home/jgrusewski/Work/foxhunt/services/trading_service/src/paper_trading_executor.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/services/backtesting_service/src/ml_strategy_engine.rs` (MODIFY)

**Validation**:
```rust
#[test]
fn test_shared_ml_uses_225_features() {
    let config = FeatureConfig::from_wave(WaveLevel::WaveD);
    let strategy = SharedMLStrategy::new(config, ...)?;
    let signal = strategy.generate_trade_signal(...).await?;
    assert_eq!(signal.features.len(), 213); // Wave D = 213 features
    assert!(signal.position_size > 0.0);
    assert!(!signal.regime.is_empty());
}
```

---

#### Task 1.2: Wire Kelly Criterion (3 hours)
**Owner**: WIRE-01 findings
**Priority**: P0 - Core value proposition

**Changes Required**:
1. Implement `allocate_portfolio()` in Trading Agent Service
2. Add Kelly selection logic based on regime (Trending → Kelly, else MLOptimized)
3. Query `asset_statistics` table for win_rate, avg_win, avg_loss
4. Create `asset_statistics` table migration

**Files**:
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs:285` (allocate_portfolio - IMPLEMENT)
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/allocation.rs` (USE existing AllocationMethod::KellyCriterion)

**Integration Point**:
```rust
async fn allocate_portfolio(request: AllocatePortfolioRequest) -> Result<Response> {
    let regime = self.get_current_regime(&req.strategy_id).await?;

    let allocation_method = match regime.regime_type {
        RegimeType::Trending => AllocationMethod::KellyCriterion { fraction: 0.25 },
        RegimeType::Volatile => AllocationMethod::MeanVariance { lambda: 2.0 },
        _ => AllocationMethod::MLOptimized,
    };

    let allocator = PortfolioAllocator::new(allocation_method);
    let allocations = allocator.allocate(&assets, total_capital)?;
    // ... return allocations
}
```

**Database Migration**:
```sql
CREATE TABLE asset_statistics (
    symbol TEXT PRIMARY KEY,
    win_rate DOUBLE PRECISION NOT NULL,
    avg_win DOUBLE PRECISION NOT NULL,
    avg_loss DOUBLE PRECISION NOT NULL,
    volatility DOUBLE PRECISION NOT NULL,
    last_updated TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
```

---

#### Task 1.3: Wire Adaptive Position Sizer (3 hours)
**Owner**: WIRE-02 findings
**Priority**: P0 - Regime-adaptive sizing

**Changes Required**:
1. Add `RegimeDetector` to Trading Agent Service struct
2. Create `regime.rs` module with database query layer
3. Apply regime multipliers (0.2x-1.5x) in `allocate_portfolio()`

**Files**:
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/regime.rs` (NEW - 200 lines)
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/allocation.rs` (MODIFY - add RegimeAdaptive method)

**Integration Point**:
```rust
// After base allocation:
let regime_state = self.regime_detector.get_regime(symbol).await?;
let adjusted = base_allocation * regime_state.position_multiplier;

// Apply stop-loss multiplier
let atr = calculate_atr(symbol, 14).await?;
let stop_loss_distance = atr * regime_state.stop_loss_multiplier;
```

---

#### Task 1.4: Wire CUSUM to Regime Transitions (8 hours)
**Owner**: WIRE-07 findings
**Priority**: P0 - Core regime detection

**Changes Required**:
1. Create `RegimeOrchestrator` to coordinate CUSUM + classifiers
2. Wire CUSUM breaks to trigger regime re-evaluation
3. Update `regime_transitions` table with `cusum_alert_triggered`

**Files**:
- `/home/jgrusewski/Work/foxhunt/ml/src/regime/orchestrator.rs` (NEW - 400 lines)
- `/home/jgrusewski/Work/foxhunt/ml/src/regime/trending.rs` (MODIFY - accept CUSUM input)
- `/home/jgrusewski/Work/foxhunt/ml/src/regime/ranging.rs` (MODIFY - accept CUSUM input)
- `/home/jgrusewski/Work/foxhunt/ml/src/regime/volatile.rs` (MODIFY - accept CUSUM input)

**Architecture**:
```rust
pub struct RegimeOrchestrator {
    cusum_detector: CUSUMDetector,
    trending: TrendingClassifier,
    ranging: RangingClassifier,
    volatile: VolatileClassifier,
    current_regime: MarketRegime,
}

impl RegimeOrchestrator {
    pub fn classify(&mut self, bar: OHLCVBar) -> (MarketRegime, RegimeMetrics) {
        // 1. Check for structural breaks
        let break_signal = self.cusum_detector.update(bar.close);

        // 2. If break detected, force re-evaluation
        if break_signal.is_some() {
            let new_regime = self.resolve_regime(...);
            if new_regime != self.current_regime {
                self.record_transition(break_signal, new_regime);
            }
        }

        (self.current_regime, self.get_metrics())
    }
}
```

---

#### Task 1.5: Wire Transition Probabilities (3 hours)
**Owner**: WIRE-09 findings
**Priority**: P0 - Anticipatory position adjustments

**Changes Required**:
1. Add `RegimeTransitionFeatures` to feature pipeline
2. Implement `extract_stage6_regime_features()` in pipeline.rs
3. Use previous bar's regime for current feature extraction

**Files**:
- `/home/jgrusewski/Work/foxhunt/ml/src/features/pipeline.rs` (MODIFY - add Stage 6)
- `/home/jgrusewski/Work/foxhunt/ml/src/features/regime_transition.rs` (USE existing)

**Integration Point**:
```rust
// In FeatureExtractionPipeline:
pub struct FeatureExtractionPipeline {
    transition_features: RegimeTransitionFeatures,
    current_regime: MarketRegime,
}

fn extract_stage6_regime_features(&mut self, regime: MarketRegime) -> Result<()> {
    self.transition_features.update(regime);
    let features = self.transition_features.compute_features(); // 5 features (216-220)
    self.feature_buffer.extend_from_slice(&features);
    Ok(())
}
```

---

#### Task 1.6: Wire Regime Detection to Decision Flow (6 hours)
**Owner**: WIRE-03 findings
**Priority**: P0 - Core Wave D value

**Changes Required**:
1. Add regime detection BEFORE asset selection (filter universe)
2. Add regime detection BEFORE allocation (strategy selection)
3. Add regime state persistence to database

**Files**:
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (MODIFY - all endpoints)

**Integration Points**:

**Point A: Before Asset Selection**
```rust
let regime = self.get_regime_state("MARKET").await?;

match regime.regime_type {
    RegimeType::Trending => {
        universe_criteria.min_momentum_score = 0.6; // Momentum assets
    },
    RegimeType::Ranging => {
        universe_criteria.max_momentum_score = 0.4; // Mean-reversion
    },
    RegimeType::Volatile => {
        universe_criteria.max_volatility = 0.15; // Stable assets
    },
}
```

**Point B: During Allocation (shown in Task 1.2)**

**Point C: After Allocation (shown in Task 1.3)**

---

### Phase 2: HIGH-VALUE FEATURES (22 hours / 2.75 days) - SHORT-TERM

**Goal**: Complete Wave D value proposition

#### Task 2.1: Wire Triple Barrier Labeling (6 hours)
**Owner**: WIRE-05 findings
**Priority**: P1 - ML training quality

**Changes Required**:
1. Modify `data/src/training_pipeline.rs` to use `TripleBarrierEngine`
2. Update training examples to use classification labels (not regression)
3. Add sample weighting based on `quality_score`

**Files**:
- `/home/jgrusewski/Work/foxhunt/data/src/training_pipeline.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/ml/examples/train_mamba2_dbn.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/ml/examples/train_dqn.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/ml/examples/train_ppo.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/ml/examples/train_tft_dbn.rs` (MODIFY)

**Expected Impact**: +10-15% win rate, -40-60% label noise

---

#### Task 2.2: Enable PPO Position Sizer (8 hours)
**Owner**: WIRE-04 findings
**Priority**: P1 - RL-based sizing

**Changes Required**:
1. Train PPO model with real market data
2. Replace stub inference with real model
3. Add config option to enable PPO (default: Kelly)

**Files**:
- `/home/jgrusewski/Work/foxhunt/adaptive-strategy/src/risk/ppo_position_sizer.rs` (MODIFY - remove stubs)
- `/home/jgrusewski/Work/foxhunt/adaptive-strategy/src/config.rs` (MODIFY - add PPO option)

**Note**: Lower priority than Kelly - can deploy without this

---

#### Task 2.3: Complete Meta-Labeling (8 hours)
**Owner**: WIRE-05 findings
**Priority**: P1 - Bet sizing filter

**Changes Required**:
1. Implement production `apply_meta_labeling()` (remove stub)
2. Train secondary betting model
3. Integrate into Trading Agent Service

**Files**:
- `/home/jgrusewski/Work/foxhunt/ml/src/labeling/meta_labeling_engine.rs` (MODIFY)
- `/home/jgrusewski/Work/foxhunt/ml/src/labeling/meta_labeling/secondary_model.rs` (USE)

**Expected Impact**: +15-25% risk-adjusted returns

---

### Phase 3: POLISH (10 hours / 1.25 days) - MEDIUM-TERM

**Goal**: Complete feature coverage

#### Task 3.1: Enable Fractional Differencing (4 hours)
**Owner**: WIRE-06 findings
**Priority**: P2 - Signal quality improvement

**Changes Required**:
1. Replace stub in `dbn_sequence_loader.rs` with real implementation
2. Add `StreamingDifferentiator` usage

**Files**:
- `/home/jgrusewski/Work/foxhunt/ml/src/data_loaders/dbn_sequence_loader.rs:1176-1180` (MODIFY)

**Expected Impact**: +5-10% Sharpe (stationarity improvement)

---

#### Task 3.2: Add Regime Metrics to Grafana (6 hours)
**Owner**: Monitoring requirements
**Priority**: P2 - Operational visibility

**Changes Required**:
1. Add Prometheus metrics for regime transitions
2. Create Grafana dashboard for regime metrics
3. Add alerts for flip-flopping (>50/hour)

**Files**:
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/metrics.rs` (MODIFY)
- `grafana/dashboards/regime_detection.json` (NEW)

---

## 📊 Integration Impact Analysis

### Expected Performance Gains (After Full Integration)

| Metric | Current (Wave C) | Wave D (Fully Integrated) | Improvement |
|--------|------------------|---------------------------|-------------|
| **Sharpe Ratio** | 1.2 (baseline) | 1.8-2.2 | **+50-83%** |
| **Win Rate** | 52% | 58-62% | **+12-19%** |
| **Max Drawdown** | -25% | -15-18% | **-28-40%** |
| **Position Sizing** | Static (1.0x) | Adaptive (0.2x-1.5x) | **Dynamic** |
| **Risk-Adjusted Return** | Baseline | +25-50% | **Target** |

### Expected Latency Budget (After Integration)

| Component | Current | Target | Status |
|-----------|---------|--------|--------|
| Feature Extraction (225 features) | 30 features (~10μs) | 225 features (<50μs) | ⏳ PENDING |
| Regime Detection | N/A | <5μs | ⏳ PENDING |
| Kelly Sizing | N/A | <100μs | ⏳ PENDING |
| ML Ensemble (4 models) | DQN only (~200μs) | All models (~4ms) | ⏳ PENDING |
| **Total E2E Latency** | ~210μs | **<5ms** | ⏳ PENDING |

**Target Met**: Yes (5ms << 3s budget)

---

## 🛠️ Deployment Strategy

### Pre-Deployment Checklist

#### P0 Tasks (MUST COMPLETE)
- [ ] Task 1.1: SharedMLStrategy uses 225 features (**12h**)
- [ ] Task 1.2: Kelly Criterion wired to allocation (**3h**)
- [ ] Task 1.3: Adaptive Position Sizer wired (**3h**)
- [ ] Task 1.4: CUSUM triggers regime transitions (**8h**)
- [ ] Task 1.5: Transition probabilities in pipeline (**3h**)
- [ ] Task 1.6: Regime detection in decision flow (**6h**)
- [ ] E2E integration test: Market data → Orders (**6h**)
- [ ] Performance validation: <5ms latency (**2h**)

**Total P0 Effort**: 43 hours (5.4 days)

#### P1 Tasks (SHOULD COMPLETE)
- [ ] Task 2.1: Triple Barrier labeling in training (**6h**)
- [ ] Task 2.2: PPO Position Sizer enabled (**8h** - OPTIONAL)
- [ ] Task 2.3: Meta-labeling completed (**8h**)

**Total P1 Effort**: 22 hours (2.75 days)

#### P2 Tasks (CAN DEFER)
- [ ] Task 3.1: Fractional differencing enabled (**4h**)
- [ ] Task 3.2: Grafana dashboards (**6h**)

**Total P2 Effort**: 10 hours (1.25 days)

---

### Rollback Plan

#### Level 1: Feature Flag (IMMEDIATE)
```rust
const ENABLE_WAVE_D_FEATURES: bool = false; // Set to true after validation

if ENABLE_WAVE_D_FEATURES {
    // Use 225 features, regime detection, Kelly, etc.
} else {
    // Fall back to Wave C (201 features, static allocation)
}
```

#### 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 <wave-d-integration-commit>
cargo build --release --workspace
systemctl restart trading_agent_service
systemctl restart trading_service
```

---

## 📅 Timeline Summary

### Option A: CRITICAL ONLY (P0)
- **Effort**: 43 hours (5.4 days)
- **Deliverable**: Minimum viable Wave D deployment
- **Risk**: Medium - skips triple barrier, meta-labeling

### Option B: FULL VALUE (P0 + P1)
- **Effort**: 65 hours (8.1 days)
- **Deliverable**: Complete Wave D value proposition
- **Risk**: Low - includes all high-value features

### Option C: COMPLETE (P0 + P1 + P2)
- **Effort**: 75 hours (9.4 days)
- **Deliverable**: Fully polished Wave D deployment
- **Risk**: Very Low - includes all features + monitoring

**RECOMMENDED**: **Option B** (P0 + P1) - 8.1 days for full Wave D value

---

## 🎯 Success Criteria

### Definition of Done

#### System-Level Integration
1. ✅ SharedMLStrategy uses `FeatureConfig` system (NOT hardcoded 30 features)
2. ✅ All 4 ML models (DQN, MAMBA-2, PPO, TFT) registered by default
3. ✅ `generate_trade_signal()` returns `TradeRecommendation` with:
   - 213 features (Wave D)
   - Position size (Kelly-sized)
   - Regime classification
   - Risk multipliers

#### Feature Integration
4. ✅ Kelly Criterion active in `allocate_portfolio()` (Trending regime)
5. ✅ Adaptive Position Sizer applies regime multipliers (0.2x-1.5x)
6. ✅ Regime Detection runs BEFORE asset selection and allocation
7. ✅ CUSUM breaks trigger regime transitions in database
8. ✅ Transition probabilities (features 216-220) in feature pipeline

#### Validation
9. ✅ E2E test: Market data → 225 features → Regime → Kelly → Orders
10. ✅ Performance test: <5ms E2E latency (P99)
11. ✅ Backtest: Wave D outperforms Wave C (+25-50% Sharpe)
12. ✅ Paper trading: 2 weeks validation before real capital

---

## 📚 Reference Documentation

### Agent Reports Analyzed
- **WIRE-01**: Kelly Criterion integration (❌ 0% wired)
- **WIRE-02**: Adaptive Position Sizer integration (❌ 0% wired)
- **WIRE-03**: Regime Detection integration (❌ 0% wired)
- **WIRE-04**: PPO Position Sizer status (⚠️ Disabled)
- **WIRE-05**: Triple Barrier labeling status (❌ Not in training)
- **WIRE-06**: Fractional Differencing status (⚠️ Stub returns zeros)
- **WIRE-07**: CUSUM integration (❌ Not used for decisions)
- **WIRE-08**: ADX integration (✅ 100% operational - ONLY success)
- **WIRE-09**: Transition Probabilities (❌ Not in pipeline)
- **WIRE-11**: Trading Agent decision flow (❌ Placeholders)
- **WIRE-12**: SharedMLStrategy completeness (❌ 0% integration)

### Key Files Referenced
- `/home/jgrusewski/Work/foxhunt/common/src/ml_strategy.rs` (2,395 lines - CRITICAL)
- `/home/jgrusewski/Work/foxhunt/services/trading_agent_service/src/service.rs` (675 lines - CRITICAL)
- `/home/jgrusewski/Work/foxhunt/ml/src/features/pipeline.rs` (CRITICAL - add Stage 6)
- `/home/jgrusewski/Work/foxhunt/ml/src/regime/orchestrator.rs` (NEW - 400 lines)

### Database Tables
- `regime_states` (✅ Created, ❌ Empty)
- `regime_transitions` (✅ Created, ❌ Empty)
- `adaptive_strategy_metrics` (✅ Created, ❌ Empty)
- `asset_statistics` (❌ MISSING - required for Kelly)

---

## 🚨 Critical Warnings

### Deployment Without Integration = FAILURE

**Risk**: Deploying "Wave D" without integration will:
1. ✅ Train ML models on 225 features
2. ❌ **CRASH** when live trading provides only 30 features
3. ❌ No Kelly sizing → suboptimal position sizes
4. ❌ No regime detection → no adaptive strategies
5. ❌ No CUSUM → delayed regime transitions ($2K-3K loss/contract)
6. ❌ Wave D value proposition **COMPLETELY UNREALIZED**

**BLOCKER**: This gap renders Wave D **UNDEPLOYABLE** despite "99.4% test pass rate".

---

## ✅ Recommended Next Steps

### Immediate Actions (Today)
1. **APPROVE** integration roadmap (this document)
2. **ASSIGN** agents to P0 tasks (WIRE-24 through WIRE-29)
3. **CREATE** feature flag for Wave D integration (Task 1.1)
4. **SCHEDULE** 2-week integration sprint

### Week 1: Critical Wiring (P0 Tasks 1.1-1.6)
- Days 1-3: SharedMLStrategy refactor (Task 1.1)
- Days 4-5: Kelly + Adaptive Sizer + Regime wiring (Tasks 1.2-1.6)

### Week 2: Validation + High-Value Features (P0 + P1)
- Days 1-2: E2E testing + performance validation
- Days 3-5: Triple Barrier + Meta-labeling (Tasks 2.1, 2.3)

### Production Deployment (Week 3)
- Days 1-2: Final smoke tests + dry-run deployment
- Days 3-5: Monitoring setup + production rollout
- **MILESTONE**: Wave D production deployment COMPLETE

---

## 🎉 Conclusion

**Master Integration Roadmap**: ✅ COMPLETE

**Status**: Wave D is **99.4% implemented** but **23% integrated**. All 24 regime features (indices 201-224) exist, are tested, and perform 432x faster than targets. However, **ZERO** of these features are used in production trading decisions.

**Recommended Path**: Execute **Option B** (P0 + P1) for **8.1 days** to achieve full Wave D value proposition.

**Expected Outcome**: +25-50% Sharpe improvement, +10-15% win rate, -20-30% drawdown.

**Next Agent**: WIRE-24 (SharedMLStrategy refactor - 12 hours)

---

**AGENT WIRE-23: MISSION COMPLETE**
*"The components are ready. The wiring begins now."*