4d0efa82df
Wave 1 (Architecture & Design - 5 agents): - Multi-model training orchestration (DQN, PPO, MAMBA-2, TFT-INT8) - Sequential training strategy (95.9% GPU headroom, 6.3min total) - Hybrid multi-asset strategy (2x parallel, 22% GPU usage, 12-18min) - Backward compatible gRPC API design with oneof pattern - TDD test pyramid (67 tests: 24 unit + 28 integration + 15 E2E) - Implementation roadmap (20 agents, 2.5 weeks, 13,280 LOC) Wave 2 (Core TLI Commands - 5 agents): - tli train start: Multi-model, multi-asset job submission (14 tests ✅) - tli train watch: Real-time streaming with weighted progress (10 tests ✅) - tli train status: Color-coded formatted status display (10 tests ✅) - tli train list: Filtering, sorting, pagination support (12 tests ✅) - tli train stop: Graceful cancellation with checkpoints (11 tests ✅) Status: - 57/57 tests passing (100% TDD compliance) - ~4,095 LOC (tests + implementation + docs) - 3.5 hours actual vs 15-20 hours estimated (78% faster) - Zero compilation errors, production-ready code - Full documentation: WAVE_2_TLI_COMMANDS_COMPLETE.md Next: Wave 3 (Multi-Asset Multi-Model Backend Logic - 5 agents) 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
81 lines
2.9 KiB
Markdown
81 lines
2.9 KiB
Markdown
# MAMBA-2 Parquet Training Example
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This example demonstrates how to train the MAMBA-2 State Space Model using Parquet market data files.
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## Quick Start
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```bash
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# Default: 200 epochs using ES.FUT data
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cargo run -p ml --example train_mamba2_parquet --release
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# Custom Parquet file and epochs
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cargo run -p ml --example train_mamba2_parquet --release -- \
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--parquet-file test_data/NQ_FUT_180d.parquet \
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--epochs 50
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# Custom lookback window (sequence length)
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cargo run -p ml --example train_mamba2_parquet --release -- \
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--parquet-file test_data/ES_FUT_180d.parquet \
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--lookback-window 120 \
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--epochs 100
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```
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## Available Arguments
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- `--parquet-file <path>`: Path to Parquet file (default: test_data/ES_FUT_180d.parquet)
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- `--epochs <n>`: Number of training epochs (default: 200)
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- `--lookback-window <n>`: Sequence length/lookback window (default: 60)
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- `--batch-size <n>`: Batch size for training (default: 32)
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- `--learning-rate <f>`: Learning rate (default: 0.0001)
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- `--hidden-dim <n>`: Hidden dimension (default: 225, Wave D feature count)
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- `--state-dim <n>`: SSM state dimension (default: 16)
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--output-dir <path>`: Output directory for checkpoints (default: ml/checkpoints/mamba2_parquet)
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## Available Parquet Files
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- `test_data/ES_FUT_180d.parquet` - E-mini S&P 500 (180 days)
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- `test_data/NQ_FUT_180d.parquet` - E-mini NASDAQ (180 days)
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- `test_data/6E_FUT_180d.parquet` - Euro FX (180 days)
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- `test_data/ZN_FUT_90d.parquet` - 10-Year T-Note (90 days)
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## Output
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Training outputs are saved to `ml/checkpoints/mamba2_parquet/`:
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- `best_model_epoch_*.ckpt` - Best model based on validation loss
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- `checkpoint_epoch_*.ckpt` - Periodic checkpoints (every 10 epochs)
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- `final_model.ckpt` - Final model after training
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- `training_losses.csv` - Training/validation loss curves
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- `training_metrics.json` - Summary metrics and configuration
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## Features
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- **225 Wave D Features**: Includes 201 Wave C features + 24 Wave D regime features
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- **GPU Training**: CUDA acceleration (RTX 3050 Ti optimized)
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- **Early Stopping**: Automatic stopping after 20 epochs of no improvement
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- **Checkpointing**: Saves best models and periodic snapshots
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- **Monitoring**: Real-time loss, perplexity, and training speed metrics
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## Expected Training Time
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- 50 epochs: ~30-45 minutes (pilot run)
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- 200 epochs: ~2-3 hours (full training)
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- GPU utilization: ~60-70% (memory-bound on RTX 3050 Ti)
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## Requirements
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- CUDA GPU (required - no CPU fallback)
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- Minimum 50 bars in Parquet file (for feature warmup period)
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- ~4GB VRAM available
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## Data Format
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Parquet files should contain OHLCV market data with these columns:
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- `timestamp_ns`: Nanosecond timestamp
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- `open`, `high`, `low`: Price data (optional, defaults to `price`)
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- `price`: Close price (required)
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- `quantity`: Volume (optional, defaults to 0)
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- `symbol`, `venue`, `event_type`, `sequence`: Metadata columns
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See `data/src/providers/databento/dbn_to_parquet_converter.rs` for conversion utilities.
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