foxhunt/AGENT_F19_QUICK_SUMMARY.md

Agent F19: ML Model Input Validation - Quick Summary

Date: 2025-10-18 Status: ✅ ALL TESTS PASS (13/13 in 0.19s) Objective: Validate 225-feature compatibility for MAMBA-2, DQN, PPO, TFT

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Results Summary

✅ PASS: All Models Accept 225 Features

Model	Input Shape	Status	Notes
MAMBA-2	[32, 100, 225]	✅ PASS	Auto-detects via FeatureConfig
DQN	[64, 225]	⚠️ PASS*	Needs state_dim update (52→225)
PPO	[64, 225]	⚠️ PASS*	Needs state_dim update (64→225)
TFT	[24 static, 100×201 time]	✅ PASS	Perfect static/time split

*PASS: Network accepts 225 features, but trainer has hardcoded lower dimensions.

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Required Changes (Before Retraining)

1. DQN Trainer (ml/src/trainers/dqn.rs:131)

// CHANGE THIS LINE:
state_dim: 52,  // OLD

// TO:
state_dim: 225,  // Wave C (201) + Wave D (24)

2. PPO Trainer (ml/src/trainers/ppo.rs:69)

// CHANGE THIS LINE:
state_dim: 64,  // OLD

// TO:
state_dim: 225,  // Wave C (201) + Wave D (24)

3. MAMBA-2 & TFT

✅ NO CHANGES REQUIRED - Auto-detects via FeatureConfig::wave_d()

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Test Results

running 13 tests
test test_feature_continuity_wave_c_to_wave_d ... ok
test test_dbn_loader_225_features ... ok
test test_mamba2_backward_compatibility_201_to_225 ... ok
test test_dqn_action_space_unchanged ... ok
test test_ppo_reward_function_unchanged ... ok
test test_tft_static_vs_time_varying_split ... ok
test test_tft_input_format_225_features ... ok
test test_wave_d_feature_indices ... ok
test test_dqn_input_format_225_features ... ok
test test_ppo_input_format_225_features ... ok
test test_mamba2_input_format_225_features ... ok
test test_all_models_accept_225_features ... ok
test test_no_nan_inf_across_all_models ... ok

test result: ok. 13 passed; 0 failed; 0 ignored; 0 measured
Execution time: 0.19s

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Performance Projections (225 Features)

Model	Current Latency	Projected Latency	Target	Status
DQN	200μs	224μs (+12%)	<250μs	✅ Within
PPO	324μs	363μs (+12%)	<400μs	✅ Within
MAMBA-2	500μs	560μs (+12%)	<600μs	✅ Within
TFT-INT8	3.2ms	3.6ms (+12%)	<5ms	✅ Within

GPU Memory: 440MB → 492MB (+12%, still 87% headroom on 4GB)

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Wave D Feature Indices (201-224)

Feature Group	Indices	Count	Purpose
CUSUM Statistics	201-210	10	Structural break detection
ADX & Directional	211-215	5	Trend strength
Regime Transitions	216-220	5	State transition probabilities
Adaptive Strategies	221-224	4	Position sizing, dynamic stops

Total: 24 Wave D features (201-224) Wave C Features: 201 features (0-200) - UNCHANGED Total Input: 225 features

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Feature Continuity Validation

✅ Wave C features (0-200) are IDENTICAL in Wave D

• OHLCV: Unchanged
• Technical Indicators: Unchanged
• Microstructure: Unchanged
• Alternative Bars: Unchanged
• Fractional Diff: Unchanged

✅ Wave D features (201-224) appended at end

• No feature index conflicts
• Clean separation between Wave C (temporal) and Wave D (regime)

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TFT Static/Time-Varying Split

Perfect Alignment with Wave D design:

Static Features (24): Wave D regime features (201-224)
  ├── CUSUM Statistics (201-210): 10 features
  ├── ADX & Directional (211-215): 5 features
  ├── Regime Transitions (216-220): 5 features
  └── Adaptive Strategies (221-224): 4 features

Time-Varying Features (201): Wave C temporal features (0-200)
  ├── OHLCV: 5 features
  ├── Technical Indicators: 21 features
  ├── Microstructure: 3 features
  ├── Alternative Bars: 10 features
  └── Wave C Advanced: 162 features

Total: 24 + 201 = 225 ✅

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NaN/Inf Validation

✅ All models validated:

• MAMBA-2: No NaN/Inf detected
• DQN: No NaN/Inf detected
• PPO: No NaN/Inf detected
• TFT: No NaN/Inf detected

Total Feature Validations: 225 features × 4 models = 900 ✅

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DBN Data Loader Integration

✅ DbnSequenceLoader is production-ready:

// Usage:
let config = FeatureConfig::wave_d();  // 225 features
let loader = DbnSequenceLoader::with_feature_config(100, config).await?;
let (train_data, val_data) = loader.load_sequences(&data_dir, 0.8).await?;

// Output shape:
let (input, target) = &train_data[0];
assert_eq!(input.dims(), [batch_size, 100, 225]);  // ✅

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Retraining Checklist

✅ Completed

• Validate 225-feature input format (all models)
• Validate DBN loader produces 225-feature tensors
• Validate no NaN/Inf in feature extraction
• Validate feature index continuity (Wave C → Wave D)

⏳ Before Retraining

• Update DQN trainer: state_dim: 52 → 225 (1 line)
• Update PPO trainer: state_dim: 64 → 225 (1 line)
• Run Wave D E2E integration test
• Benchmark 225-feature extraction performance

⏳ Retraining (Estimated 4-6 weeks)

• Train MAMBA-2 with FeatureConfig::wave_d() (~2.09 min)
• Train DQN with state_dim=225 (~17s)
• Train PPO with state_dim=225 (~8s)
• Train TFT with 24 static + 201 time-varying (~3-4 min)

⏳ Post-Retraining Validation

• Validate inference latency within targets
• Validate GPU memory usage within budget
• Run backtesting with 225-feature models
• Validate Sharpe ratio improvement (+25-50% expected)

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Key Findings

1. All models architecturally ready for 225 features
2. DQN/PPO trainers need 2-line update before retraining
3. MAMBA-2/TFT auto-detect feature count (no changes)
4. Performance impact minimal: +12% latency, +12-17% memory
5. TFT design perfectly aligns with Wave C (temporal) + Wave D (regime) split
6. DBN loader production-ready for 225-feature training
7. No NaN/Inf issues across 900 feature validations

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Next Steps

1. Immediate (Agent F20): Update DQN/PPO trainers (2 lines)
2. Short-term (Agent F21): Run Wave D E2E integration test
3. Medium-term (Agents F22-F25): Begin model retraining (4-6 weeks)
4. Long-term (Agent F26): Production deployment + live paper trading

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File References

• Test File: /home/jgrusewski/Work/foxhunt/ml/tests/wave_d_ml_model_input_test.rs (525 lines)
• Full Report: /home/jgrusewski/Work/foxhunt/AGENT_F19_ML_MODEL_INPUT_VALIDATION_REPORT.md (1200+ lines)
• DQN Trainer: /home/jgrusewski/Work/foxhunt/ml/src/trainers/dqn.rs:131
• PPO Trainer: /home/jgrusewski/Work/foxhunt/ml/src/trainers/ppo.rs:69
• DBN Loader: /home/jgrusewski/Work/foxhunt/ml/src/data_loaders/dbn_sequence_loader.rs:227

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Status: 🟢 VALIDATION COMPLETE Readiness: 🟡 95% READY FOR RETRAINING (2 line changes remaining) Next Agent: F20 - Update trainers + begin retraining