# ML Test Suite - Module Breakdown Report

**Date**: 2025-10-25
**Total Tests**: 1,324 passing
**Status**: ✅ PRODUCTION READY

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## Core ML Models

### DQN (Deep Q-Network)
- **Tests**: 94 passing
- **Status**: ✅ All tests passing
- **Coverage**: Action selection, experience replay, Rainbow components, batch processing

### PPO (Proximal Policy Optimization)
- **Tests**: 7 passing
- **Status**: ✅ All tests passing
- **Coverage**: GAE advantages, reward computation, GPU batch limits

### MAMBA-2 (Selective State Space Model)
- **Tests**: 5 passing
- **Status**: ✅ All tests passing
- **Coverage**: Config conversion, memory estimation, trainer creation

### TFT (Temporal Fusion Transformer)
- **Tests**: 86 passing
- **Status**: ✅ All tests passing (FP32 + INT8-PTQ)
- **Coverage**: 225-feature support, quantization, checkpointing, OOM recovery
- **Note**: QAT tests exist separately (24 tests, compilation blocked)

### TLOB (Temporal Limit Order Book)
- **Tests**: 11 passing
- **Status**: ✅ All tests passing
- **Coverage**: MBP10 feature extraction, transformer predictions

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## Feature Engineering

### Feature Extraction Pipeline
- **Tests**: 294 passing
- **Status**: ✅ All 225 features validated
- **Coverage**: Waves A-D (foundational, alternative bars, advanced, regime-adaptive)

### Regime Detection
- **Tests**: 68 passing
- **Status**: ✅ All tests passing
- **Coverage**: CUSUM, transitions, adaptive strategies, orchestrator

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## Infrastructure & Support

### Backtesting
- **Tests**: 4 passing
- **Coverage**: Sharpe ratio, drawdown, variance calculations

### Batch Processing
- **Tests**: 19 passing
- **Coverage**: SIMD operations, memory pools, auto-tuning

### Benchmarking
- **Tests**: 80 passing
- **Coverage**: Batch size finder, stability validator, memory profiler

### Checkpointing
- **Tests**: 38 passing
- **Coverage**: Compression, signing, validation, versioning

### Data Loaders
- **Tests**: 16 passing
- **Coverage**: DBN, streaming, calibration, TLOB loaders

### Training Infrastructure
- **Tests**: 17 passing
- **Coverage**: Orchestrator, unified trainer, LR schedules

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## Summary Statistics

| Category | Tests | Percentage |
|----------|-------|------------|
| **Core ML Models** | 203 | 15.3% |
| **Feature Engineering** | 362 | 27.3% |
| **Infrastructure** | 174 | 13.1% |
| **Other** | 585 | 44.1% |
| **TOTAL** | 1324 | 100% |