foxhunt/AGENT_WIRE02_ADAPTIVE_SIZER_INTEGRATION.md

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

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:

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):

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):

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):

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:

// ❌ 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:

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)

//! 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:

#[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:

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:

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:

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:

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:

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 ✅

• RegimeAdaptiveFeatures implemented (644 lines, 12/12 tests)
• Database schema created (regime_states, regime_transitions, adaptive_strategy_metrics)
• gRPC endpoints defined (GetRegimeState, GetRegimeTransitions)
• 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)

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)

-- 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)

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