01c19662cb
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
76 lines
3.1 KiB
Python
Executable File
76 lines
3.1 KiB
Python
Executable File
"""
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Offline training script.
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Run once to build bucket assignments and train continuation models:
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cd /mnt/dolphinng5_predict
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siloqy-env python adaptive_exit/train.py
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Artifacts written:
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adaptive_exit/models/bucket_assignments.pkl
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adaptive_exit/models/continuation_models.pkl
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adaptive_exit/models/training_data.parquet (optional, for audit)
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"""
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import argparse
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import os
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import sys
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sys.path.insert(0, "/mnt/dolphinng5_predict")
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from adaptive_exit.bucket_engine import build_buckets
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from adaptive_exit.continuation_model import ContinuationModelBank
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from adaptive_exit.data_pipeline import build_training_data
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_MODELS_DIR = os.path.join(os.path.dirname(__file__), "models")
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_TRAIN_DATA_PATH = os.path.join(_MODELS_DIR, "training_data.parquet")
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def main():
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parser = argparse.ArgumentParser(description="Train adaptive exit models")
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parser.add_argument("--k", type=int, default=None, help="Force bucket count (default: auto)")
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parser.add_argument("--save-data", action="store_true", help="Save training parquet for audit")
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parser.add_argument("--force-rebuild", action="store_true", help="Rebuild buckets even if cached")
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parser.add_argument("--vbt-dir", default="/mnt/dolphinng5_predict/vbt_cache",
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help="VBT parquet dir for training data generation")
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parser.add_argument("--klines-dir", default="/mnt/dolphin_training/data/vbt_cache_klines",
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help="1m klines dir for asset bucketing")
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args = parser.parse_args()
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os.makedirs(_MODELS_DIR, exist_ok=True)
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# ── Step 1: Build asset buckets ──────────────────────────────────────────
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print("\n=== STEP 1: Asset Bucketing ===")
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bucket_data = build_buckets(
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klines_dir=args.klines_dir,
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k_override=args.k,
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force_rebuild=args.force_rebuild,
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)
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print(f"Buckets: {bucket_data['n_buckets']} | Assets: {len(bucket_data['assignments'])}")
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# ── Step 2: Build training data from price series ────────────────────────
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print("\n=== STEP 2: Generate MAE/MFE Training Data ===")
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df = build_training_data(
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bucket_assignments=bucket_data["assignments"],
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vbt_dir=args.vbt_dir,
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use_obf_ch=False, # OBF is live-only (13 days); zero-fill training, bolt on at Phase 2
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)
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print(f"Training data shape: {df.shape}")
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print(f"Bucket distribution:\n{df.groupby('bucket_id').size().describe()}")
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print(f"Continuation rate: {df['continuation'].mean():.3f}")
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if args.save_data:
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df.to_parquet(_TRAIN_DATA_PATH)
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print(f"Training data saved → {_TRAIN_DATA_PATH}")
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# ── Step 3: Train continuation models ────────────────────────────────────
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print("\n=== STEP 3: Train Continuation Models ===")
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bank = ContinuationModelBank()
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bank.train(df)
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bank.save()
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print(f"\nModel summary: {bank.summary()}")
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print("\nDone.")
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if __name__ == "__main__":
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main()
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