Files
foxhunt/PAPER_TRADING_VALIDATION_REPORT_2025-10-14.md
jgrusewski 35feadf55e 🚀 Wave 160 Phase 6: CUDA Mandatory + TDD Testing + TFT Complete (21 Agents)
## Major Achievements

### 1. CUDA Made Default & Mandatory (Agent 143)
- CUDA now default feature in ml/Cargo.toml
- All training requires GPU (no silent CPU fallback)
- Added get_training_device() helper with fail-fast errors
- Removed --use-gpu flags (GPU mandatory)
- **Impact**: No more wasting time on accidental CPU training

### 2. TFT Training COMPLETE (Agent 144)
-  Training completed successfully in 7.6 minutes
-  Early stopping at epoch 100/200 (best val loss: 0.097318)
-  11 checkpoints saved to ml/trained_models/production/tft/
-  GPU Performance: 99% utilization, 367MB VRAM, 4.4s/epoch
-  10x speedup vs CPU (4.4s vs 43-55s per epoch)
- **Status**: PRODUCTION READY

### 3. TFT CUDA Tensor Contiguity Fix (Agent 142)
- Fixed "matmul not supported for non-contiguous tensors" error
- Added .contiguous() call after narrow() operation in QuantileLayer
- Enabled CUDA-accelerated TFT training
- **Files**: ml/src/tft/quantile_outputs.rs

### 4. MAMBA-2 CUDA Layer Normalization (Agent 145)
- Created CudaLayerNorm wrapper for missing CUDA kernel
- Implemented manual layer norm: γ * (x - μ) / sqrt(σ² + ε) + β
- MAMBA-2 now runs on CUDA (no more "no cuda implementation" error)
- **Files**: ml/src/mamba/mod.rs

### 5. TDD E2E Test Suite (Agent 146) 
- Created comprehensive MAMBA-2 test suite (297 lines)
- 7 tests: shapes, batches, CUDA, gradients, configs
- **16x faster debugging**: 5s per iteration vs 80s
- Already caught dtype mismatch bug (F32 vs F64)
- **Files**: ml/tests/e2e_mamba2_training.rs

## Agent Summary (Agents 126-146)

### Code Fixes (Parallel - Agents 137-141)
- **Agent 137**: MAMBA-2 batch dimension fix (streaming + batch loaders)
- **Agent 138**: Liquid NN API fix (mutable loader, iterator fix)
- **Agent 139**: PPO CheckpointMetadata fix (signature fields)
- **Agent 140**: Paper trading executor (498 lines, 100ms polling)
- **Agent 141**: Real model loading (RealDQNModel, RealPPOModel)

### Infrastructure (Agents 143-146)
- **Agent 143**: CUDA mandatory (Cargo.toml, device helpers)
- **Agent 144**: TFT verification (completion monitoring)
- **Agent 145**: MAMBA-2 CUDA layer norm wrapper
- **Agent 146**: TDD E2E test suite (16x faster debugging)

## Files Modified

### Core ML Infrastructure
- ml/Cargo.toml: Added default = ["minimal-inference", "cuda"]
- ml/src/lib.rs: Added get_training_device() helper (+109 lines)
- ml/src/tft/quantile_outputs.rs: Fixed tensor contiguity
- ml/src/mamba/mod.rs: Added CudaLayerNorm wrapper (+41 lines)

### Training Scripts
- ml/examples/train_tft_dbn.rs: Removed --use-gpu flag
- ml/examples/train_ppo.rs: Removed --use-gpu flag
- ml/examples/train_mamba2_dbn.rs: Forced CUDA-only mode
- ml/examples/train_liquid_dbn.rs: Fixed API usage

### Data Loaders
- ml/src/data_loaders/dbn_sequence_loader.rs: Fixed batch dimensions
- ml/src/data_loaders/streaming_dbn_loader.rs: Fixed batch dimensions

### Trading Service
- services/trading_service/src/paper_trading_executor.rs: New executor (+498 lines)
- services/trading_service/src/services/enhanced_ml.rs: Real model loading
- services/trading_service/src/ensemble_coordinator.rs: Integration

### Tests
- ml/tests/e2e_mamba2_training.rs: New TDD test suite (+297 lines)

### Trainers
- ml/src/trainers/tft.rs: Fixed CheckpointMetadata signature fields

## Performance Metrics

### TFT Training
- Duration: 7.6 minutes (100 epochs with early stopping)
- GPU Utilization: 99%
- GPU Memory: 367MB / 4GB (9%)
- Epoch Time: 4.4 seconds (vs 43-55s on CPU)
- Speedup: 10x vs CPU
- Status:  PRODUCTION READY

### TDD Testing
- Test Execution: 5-10 seconds per test
- Debugging Iteration: 5 seconds (vs 80 seconds before)
- Speedup: 16x faster debugging
- First Bug Found: <1 minute (dtype mismatch)

## Documentation
- 21 comprehensive agent reports
- TDD quick start guide
- CUDA troubleshooting guide
- Training verification procedures

## Next Steps
1. Fix MAMBA-2 dtype mismatch (F32→F64) - 2 minutes
2. Run MAMBA-2 tests until passing - 5-10 minutes
3. Launch full MAMBA-2 training - 200 epochs
4. Launch Liquid NN training

## System Status
- TFT:  COMPLETE (production ready)
- MAMBA-2: 🧪 IN TESTING (TDD suite ready)
- CUDA:  DEFAULT (mandatory for training)
- Tests:  16x faster debugging

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-14 23:13:34 +02:00

16 KiB

Paper Trading Validation Report

Date: 2025-10-14 Agent: Agent 123 (Paper Trading Validation) Status: ⚠️ PAPER TRADING INACTIVE (Last prediction 1h ago) Monitoring Period: 2025-10-14 15:06:07 to 16:06:36 UTC


Executive Summary

Overall Status: ⚠️ NEEDS ATTENTION

Infrastructure: OPERATIONAL (9/9 services healthy) Paper Trading: ⚠️ INACTIVE (Stopped 1h ago at 16:06:36 UTC) Models: ⚠️ INCOMPLETE (No individual model votes in ensemble predictions) Risk Systems: OPERATIONAL (Kill switch monitoring active)

Key Findings

  1. 3,000 ensemble predictions generated over 1-hour period (TEST_SYM only)
  2. Zero executed orders - Paper trading predictions not converting to trades
  3. No individual model predictions - DQN, PPO, MAMBA-2 votes all NULL
  4. Ensemble still functioning - Aggregating signals despite missing individual inputs
  5. High disagreement rate - 50.3% average (models not converging on predictions)

Infrastructure Health Check

Service Status (9/9 Healthy)

Service Status Port Uptime Notes
API Gateway Healthy 50051 Running ML endpoints unavailable
Trading Service Healthy 50052 Running Kill switch active
ML Training Service Healthy 50054 Running TLS configured
Backtesting Service Healthy 50053 Restarting Intermittent
PostgreSQL Healthy 5432 Running TimescaleDB active
Redis Healthy 6379 Running Cache operational
Grafana Healthy 3000 v12.2.0 Dashboard accessible
Prometheus Healthy 9090 Running 6/6 targets up
InfluxDB Healthy 8086 Running Metrics storage

Prometheus Targets (6/6 Up)

api_gateway: up (last scrape: 2025-10-14T17:10:04Z)
backtesting_service: up (last scrape: 2025-10-14T17:09:55Z)
ml_training_service: up (last scrape: 2025-10-14T17:10:04Z)
postgres_exporter: up (last scrape: 2025-10-14T17:09:35Z)
prometheus: up (last scrape: 2025-10-14T17:09:52Z)
trading_service: up (last scrape: 2025-10-14T17:10:04Z)

Paper Trading Activity Summary

Ensemble Predictions (Last 24 Hours)

Total Predictions: 3,000 Unique Symbols: 1 (TEST_SYM only) Active Period: 15:06:07 to 16:06:36 UTC (1 hour, 29 seconds) Time Since Last Prediction: 1 hour, 2 minutes, 56 seconds

Signal Distribution

Action Count % of Total Avg Confidence Avg Disagreement
SELL 1,296 43.20% 0.5048 50.21%
BUY 980 32.67% 0.4900 51.31%
HOLD 724 24.13% 0.5020 49.14%

Analysis:

  • SELL signals dominate (43.2% vs 32.7% BUY)
  • HOLD signals at 24.1% - conservative stance
  • Confidence levels mediocre (49-51%, barely above random)
  • High disagreement rates (49-51%) - models not aligned

Ensemble Performance Metrics

Average Confidence: 49.93% (barely above random) Average Disagreement Rate: 50.31% (high model discord) Average Inference Latency: NULL (not measured) Average Aggregation Latency: NULL (not measured)

Trade Execution

Executed Orders: 0 (0% conversion rate) Total PnL: $0.00 Total Commission: $0.00 Orders in Database: 1,621 (historical, not from this session)


Model Performance Analysis ⚠️

Individual Model Status

Model Vote Signal Confidence Weight Status
DQN NULL NULL NULL NULL ⚠️ NOT ACTIVE
PPO NULL NULL NULL NULL ⚠️ NOT ACTIVE
MAMBA-2 NULL NULL NULL NULL ⚠️ NOT ACTIVE
TFT NULL NULL NULL NULL ⚠️ NOT ACTIVE

Critical Issue: All individual model predictions are NULL. Ensemble is producing predictions, but individual models are not being called or their outputs are not being logged.

Sample Predictions (Last 15)

Timestamp Action Signal Confidence Disagreement
16:06:36 BUY 0.0639 0.5141 0.9380
16:06:35 HOLD -0.2013 0.5046 0.3787
16:06:33 HOLD -0.1311 0.8138 0.7856
16:06:32 BUY -0.0994 0.6134 0.2385
16:06:30 SELL 0.9902 0.9694 0.8388
16:06:30 BUY -0.2358 0.1326 0.5103
16:06:29 BUY 0.7720 0.4356 0.1851
16:06:27 SELL 0.8448 0.4884 0.4546
16:06:26 SELL 0.8070 0.9856 0.3944
16:06:23 HOLD 0.0150 0.2916 0.5116

Notable Observations:

  1. Confidence variability: Ranges from 0.1326 to 0.9856 (highly inconsistent)
  2. Disagreement spikes: Some predictions have 93.8% disagreement
  3. Signal-action mismatch: BUY with signal=0.0639, SELL with signal=0.9902 (unclear logic)
  4. Prediction frequency: ~3-5 seconds between predictions (high frequency)

Comparison to Backtest Expectations

Expected Performance (from BACKTEST_EXECUTIVE_SUMMARY.md)

Metric Backtest (8-Model Ensemble) Paper Trading (Current) Status
Sharpe Ratio 7.33 N/A (no PnL yet) ⚠️ UNMEASURED
Monthly Return 31.0% ($31K on $100K) $0 ZERO
Win Rate 58.5% N/A (no trades) ⚠️ UNMEASURED
Max Drawdown 0.21% 0% (no trades) ⚠️ UNMEASURED
Calmar Ratio 5.0 N/A ⚠️ UNMEASURED
Trade Frequency 10-30/day optimal ~3,000/day (1h extrapolated) TOO HIGH
Symbols Traded ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT TEST_SYM only WRONG SYMBOL

Critical Gaps

  1. No real symbols: Trading TEST_SYM instead of ES.FUT, NQ.FUT, etc.
  2. Excessive frequency: ~3,000 predictions/day vs optimal 10-30 trades/day
  3. Zero execution: Predictions not converting to paper trades
  4. Missing individual models: 4-model ensemble (DQN, PPO, MAMBA-2, TFT) not providing inputs
  5. No PnL tracking: Unable to calculate Sharpe ratio or validate backtest results

Real-Time Sharpe Ratio Calculation

Current State: ⚠️ IMPOSSIBLE TO CALCULATE

Why?

  • No executed trades: Can't measure returns without PnL
  • No order fills: Predictions not converting to positions
  • No position tracking: Unable to calculate portfolio volatility

What We Need

  1. Executed Orders: Paper trading system must convert predictions → orders → fills
  2. PnL Tracking: Record profit/loss per trade and aggregate
  3. Position Management: Track open positions and their unrealized P&L
  4. Risk-Free Rate: Define baseline (e.g., 0.02% daily for 5% annual T-bills)

Formula (When Data Available)

Sharpe Ratio = (Avg Daily Return - Risk-Free Rate) / Std Dev of Daily Returns

Example (from backtest):
- Avg Daily Return: 1.03% (31% monthly / 30 days)
- Risk-Free Rate: 0.02% (5% annual / 250 trading days)
- Std Dev: 0.138%
- Sharpe Ratio: (1.03% - 0.02%) / 0.138% = 7.32 ✅ (matches backtest)

Issues and Anomalies

Critical Issues (Fix Immediately)

  1. Paper trading stopped: Last prediction 1 hour ago, system inactive

    • Impact: No live validation, can't test production readiness
    • Action: Restart paper trading service or identify why it stopped
  2. Zero order execution: 3,000 predictions → 0 orders

    • Impact: Unable to measure performance, validate backtest, or calculate Sharpe
    • Action: Check order submission logic, risk checks, or position sizing
  3. Missing individual model predictions: All DQN/PPO/MAMBA-2/TFT votes NULL

    • Impact: Can't debug ensemble logic, assess model diversity, or validate weights
    • Action: Verify model loading, inference pipeline, or database logging
  4. Wrong trading symbol: TEST_SYM instead of ES.FUT/NQ.FUT/ZN.FUT/6E.FUT

    • Impact: Not testing production instruments, no real market data
    • Action: Update symbol configuration to use actual futures contracts

Medium Priority Issues

  1. High disagreement rate: 50.3% average (models not converging)

    • Impact: Ensemble lacks conviction, may lead to poor performance
    • Expected: <30% disagreement for confident predictions (from backtest analysis)
    • Action: Investigate model weights, verify all models are using same features
  2. Excessive prediction frequency: ~3,000/day vs optimal 10-30/day

    • Impact: May lead to over-trading, high commission costs, poor Sharpe
    • Backtest Finding: <20 trades/day = 57.1% profitable vs 40% for high frequency
    • Action: Implement trade frequency throttling or signal filtering
  3. Low average confidence: 49.9% (barely above random)

    • Impact: Weak signal conviction, may indicate broken ensemble logic
    • Expected: >60% confidence for actionable signals (from backtest top models)
    • Action: Validate ensemble aggregation formula, check model weights
  4. Null inference latency: No timing measurements recorded

    • Impact: Can't validate <50μs target, optimize performance, or detect bottlenecks
    • Action: Enable latency tracking in ensemble coordinator

Low Priority (Monitor)

  1. Backtesting service intermittent: Restarting status

    • Impact: May affect on-demand backtesting or validation
    • Action: Check logs, verify resource allocation
  2. API Gateway ML endpoints unavailable: Warning in logs

    • Impact: May block hyperparameter tuning or model retraining
    • Action: Verify TLS connectivity to ML Training Service

Recommendations

Immediate Actions (Today)

  1. Restart paper trading: Identify why it stopped at 16:06:36 UTC

    # Check for errors in Trading Service logs
    docker-compose logs trading_service | grep -A 5 "16:06:36"
    
    # Restart if needed
    docker-compose restart trading_service
    
  2. Enable model predictions: Fix NULL DQN/PPO/MAMBA-2/TFT votes

    # Verify model loading
    docker-compose logs trading_service | grep -E "(DQN|PPO|MAMBA|TFT|model)"
    
    # Check model cache initialization
    docker-compose logs trading_service | grep "model cache"
    
  3. Switch to production symbols: Update configuration from TEST_SYM → ES.FUT/NQ.FUT

    # Check current symbol configuration
    psql postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt \
      -c "SELECT DISTINCT symbol FROM ensemble_predictions ORDER BY symbol;"
    
  4. Enable order execution: Verify paper trading order submission pipeline

    # Check if orders are being created
    psql postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt \
      -c "SELECT COUNT(*), MAX(created_at) FROM orders WHERE account_id LIKE '%paper%';"
    

Short-Term Actions (This Week)

  1. Implement trade frequency throttling: Limit to 10-30 predictions/day

    • Add signal strength filter (only trade when confidence >60%)
    • Implement cooldown period (minimum 30 minutes between trades per symbol)
  2. Add latency tracking: Measure inference and aggregation time

    • Target: <50μs inference per model
    • Target: <10μs ensemble aggregation
  3. Create monitoring dashboard: Real-time Grafana panel

    • Prediction rate (target: 10-30/day)
    • Win rate (rolling 100 trades, target: >55%)
    • Sharpe ratio (rolling 50 trades, target: >2.0)
    • Drawdown per model (alert: 0.5%, kill: 1.0%)
  4. Validate backtest models: Confirm 8 production models are loaded

    # Check production model directory
    ls -lh /home/jgrusewski/Work/foxhunt/ml/trained_models/production/
    
    # Expected files (from backtest analysis):
    # - dqn_epoch_30.safetensors
    # - dqn_epoch_90.safetensors
    # - dqn_epoch_310.safetensors
    # - dqn_epoch_480.safetensors
    # - ppo_actor_epoch_130.safetensors
    # - ppo_actor_epoch_200.safetensors
    # - ppo_actor_epoch_290.safetensors
    # - ppo_actor_epoch_310.safetensors
    

Medium-Term Actions (Next 2 Weeks)

  1. Implement risk framework: Automated kill switches

    • 1.0% per-model max drawdown → Auto-flatten
    • 2.0% ensemble max drawdown → Halt all trading
    • 55% rolling 100-trade win rate → Disable model
  2. Out-of-sample validation: Test on Jan-Mar 2025 data

    • Goal: Sharpe >5.0, Win Rate >55%
    • If successful: Proceed to limited live (Week 5, $10K)
  3. Position sizing implementation: 2% risk per trade

    • Max 3 simultaneous positions per model
    • Dynamic stop-loss based on ATR

Monitoring Checklist (Every 5 Minutes)

  • Paper trading active (predictions within last 5 minutes)
  • Individual model predictions present (no NULL votes)
  • Order execution happening (predictions → orders → fills)
  • Win rate trending (alert if <50% over last 20 trades)
  • Drawdown per model (alert 0.5%, kill 1.0%)
  • Total exposure vs capital (max 3x leverage)
  • Inference latency (<50μs target)
  • Ensemble confidence (>60% for actionable signals)

Performance Comparison

Industry Benchmarks

Metric Our Target (Backtest) Paper Trading (Current) Typical HFT Status
Sharpe Ratio 7.33 N/A 1.5-3.0 ⚠️ UNMEASURED
Monthly Return 31.0% N/A 2-5% ⚠️ UNMEASURED
Win Rate 58.5% N/A 50-55% ⚠️ UNMEASURED
Max Drawdown 0.21% 0% 5-15% ⚠️ UNMEASURED
Trade Frequency 10-30/day ~3,000/day 100-1,000/day TOO HIGH

Key Observations

  1. Infrastructure ready: All 9 services healthy, monitoring operational
  2. Ensemble functional: Generating predictions (though not converting to trades)
  3. Model pipeline broken: Individual models not providing inputs to ensemble
  4. Zero validation: Can't measure Sharpe, win rate, or any performance metrics

Next Agent Handoff

For Agent 124 (Follow-Up Validation)

Priority 1: Fix paper trading execution pipeline

  • Restart paper trading if stopped
  • Enable individual model predictions (fix NULL votes)
  • Verify order execution (predictions → orders → fills)

Priority 2: Switch to production configuration

  • Change from TEST_SYM to ES.FUT/NQ.FUT/ZN.FUT/6E.FUT
  • Reduce prediction frequency from ~3,000/day to 10-30/day
  • Implement signal strength filter (confidence >60%)

Priority 3: Measure real performance

  • Calculate Sharpe ratio from paper trades
  • Track win rate (rolling 100 trades)
  • Monitor drawdown (per-model and ensemble)
  • Compare to backtest expectations (Sharpe 7.33, WR 58.5%)

Files to Check:

  • /home/jgrusewski/Work/foxhunt/services/trading_service/src/lib.rs - Order execution logic
  • /home/jgrusewski/Work/foxhunt/ml/src/ensemble/coordinator_extended.rs - Model loading
  • /home/jgrusewski/Work/foxhunt/config/ - Symbol configuration

Expected Outcome:

  • Paper trading active with >10 trades executed
  • Sharpe ratio >2.0 (Week 4 target from deployment timeline)
  • Win rate >55% sustained
  • Individual model predictions visible in database

Conclusion

Status: ⚠️ PAPER TRADING VALIDATION INCOMPLETE

What's Working:

  • Infrastructure: 9/9 services healthy, monitoring operational
  • Ensemble framework: Generating predictions (though not optimal)
  • Database: Storing predictions with proper schema
  • Risk systems: Kill switch monitoring active

What's Broken:

  • Paper trading execution: Zero orders from 3,000 predictions
  • Individual models: DQN/PPO/MAMBA-2/TFT not providing predictions
  • Performance measurement: Can't calculate Sharpe, validate backtest
  • Symbol configuration: Trading TEST_SYM instead of production futures

Critical Next Steps:

  1. Restart paper trading (stopped 1h ago)
  2. Enable individual model predictions (all NULL)
  3. Fix order execution pipeline (0% conversion rate)
  4. Switch to production symbols (ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT)

Expected Timeline:

  • Today: Fix execution pipeline, restart paper trading
  • This Week: Accumulate 50-100 trades for initial Sharpe calculation
  • Next Week: Compare paper trading Sharpe (target >2.0) to backtest (7.33)
  • Week 4-5: Decision point for limited live deployment ($10K)

Report Generated: 2025-10-14 17:10:00 UTC Agent: Agent 123 (Paper Trading Validation) Total Time: 20 minutes Next Validation: 2025-10-15 (daily report) Status: ⚠️ NEEDS IMMEDIATE ATTENTION