245 lines
11 KiB
Python
245 lines
11 KiB
Python
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from __future__ import annotations
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import logging
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import shutil
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from pathlib import Path
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from typing import Any
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import pandas as pd
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from trader_training.io_utils import read_json, read_parquet, run_root, sha256_file, sha256_json, utc_now_text, write_json
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from trader_training.pm import default_pm_config
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from trader_training.schemas import (
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CALIBRATION_BUNDLE_VERSION,
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FEATURE_ORDER,
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FEATURE_VERSION,
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FIT_SPLIT,
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LABEL_VERSION,
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MODEL_BUNDLE_VERSION,
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MODEL_OUTPUTS,
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OUTPUT_MAPPING,
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OUTPUT_SCHEMA,
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PM_CONFIG_VERSION,
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SPLIT_VERSION,
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TUNE_SPLIT,
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VALIDATION_LOCKED_SPLIT,
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)
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REQUIRED_MODELS = ("DIRECTION", "ENTRY", "CONTINUE", "EXIT", "RISK")
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EXPORT_STATUS = "CANDIDATE"
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def export_artifact_bundle(args: Any) -> None:
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root = run_root(args)
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export_root = args.export_root or root / "export" / f"trader-model-bundle-{args.run_id}"
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artifact_root = export_root / "artifact_bundle"
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for folder in ("models", "schemas", "calibrators", "manifests", "examples"):
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(artifact_root / folder).mkdir(parents=True, exist_ok=True)
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feature_schema_path = root / "feature" / "feature_schema.json"
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feature_order_path = root / "feature" / "feature_order.json"
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output_schema_path = artifact_root / "schemas" / "output_schema.json"
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shutil.copy2(feature_schema_path, artifact_root / "schemas" / "feature_schema.json")
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shutil.copy2(feature_order_path, artifact_root / "schemas" / "feature_order.json")
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write_json(output_schema_path, OUTPUT_SCHEMA)
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sample_input = _sample_input(root)
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write_json(artifact_root / "examples" / "sample_input.json", sample_input)
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write_json(artifact_root / "examples" / "sample_output.json", _sample_output())
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price_plan = read_json(root / "label" / "price_plan_context.json")
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write_json(artifact_root / "price_plan_context.json", price_plan)
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model_manifest_rows = []
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calibration_manifest_rows = []
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model_hashes = {}
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train_start = _split_time(root, FIT_SPLIT, "start")
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train_end = _split_time(root, FIT_SPLIT, "end")
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validation_start = _split_time(root, VALIDATION_LOCKED_SPLIT, "start")
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validation_end = _split_time(root, VALIDATION_LOCKED_SPLIT, "end")
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calibration_train_manifest = read_json(root / "calibration" / "calibration_train_manifest.json")
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calibration_quality = {row["model_name"]: row for row in calibration_train_manifest.get("calibrators", [])}
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for model_name in REQUIRED_MODELS:
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src = root / "model" / model_name.lower() / f"{model_name.lower()}.onnx"
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dst = artifact_root / "models" / f"{model_name.lower()}.onnx"
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shutil.copy2(src, dst)
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model_hashes[model_name] = sha256_file(dst)
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cal_src = root / "calibration" / model_name.lower() / "calibrator.json"
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cal_dst = artifact_root / "calibrators" / model_name.lower() / "calibrator.json"
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cal_dst.parent.mkdir(parents=True, exist_ok=True)
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shutil.copy2(cal_src, cal_dst)
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cal_hash = sha256_file(cal_dst)
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metrics = read_json(root / "model" / model_name.lower() / "model_train_result.json")
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model_manifest_rows.append(
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_model_manifest_row(
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model_name,
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f"models/{model_name.lower()}.onnx",
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model_hashes[model_name],
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sha256_file(artifact_root / "schemas" / "feature_schema.json"),
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sha256_file(artifact_root / "schemas" / "feature_order.json"),
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sha256_file(output_schema_path),
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metrics.get("metrics", {}),
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metrics.get("quality_status", "UNKNOWN"),
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metrics.get("quality_reasons", []),
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train_start,
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train_end,
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validation_start,
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validation_end,
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EXPORT_STATUS,
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)
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)
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calibration_manifest_rows.append(
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{
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"calibration_bundle_version": CALIBRATION_BUNDLE_VERSION,
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"model_bundle_version": MODEL_BUNDLE_VERSION,
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"model_name": model_name,
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"calibrator_version": f"{model_name.lower()}-cal-v4-btc-p0",
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"calibration_method": "BINNING",
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"calibrator_path": f"calibrators/{model_name.lower()}/calibrator.json",
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"calibrator_hash_sha256": cal_hash,
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"calibration_window_from": _split_time(root, TUNE_SPLIT, "start"),
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"calibration_window_to": _split_time(root, TUNE_SPLIT, "end"),
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"calibration_metrics_json": {},
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"bucket_metrics_json": {},
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"output_after_calibration_schema_hash": sha256_file(output_schema_path),
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"quality_status": calibration_quality.get(model_name, {}).get("quality_status", "UNKNOWN"),
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"quality_reasons_json": calibration_quality.get(model_name, {}).get("quality_reasons", []),
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"status": EXPORT_STATUS,
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}
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)
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pm_payload = read_json(root / "pm-search" / "position_manager_config.json") if (root / "pm-search" / "position_manager_config.json").is_file() else {"config": default_pm_config(), "threshold_stability_json": {}}
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pm_config = pm_payload["config"]
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pm_hash = sha256_json(pm_config)
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backtest_manifest = read_json(root / "backtest" / "backtest_manifest.json")
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write_json(
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artifact_root / "manifests" / "position_manager_manifest.json",
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{
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"pm_config_version": PM_CONFIG_VERSION,
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"model_bundle_version": MODEL_BUNDLE_VERSION,
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"calibration_bundle_version": CALIBRATION_BUNDLE_VERSION,
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"threshold_stability_json": pm_payload.get("threshold_stability_json", {}),
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"allowed_run_modes_json": ["REPLAY_SIM", "SHADOW"],
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"config_json": pm_config,
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"config_hash_sha256": pm_hash,
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"status": EXPORT_STATUS,
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},
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)
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write_json(artifact_root / "manifests" / "model_manifest.json", model_manifest_rows)
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write_json(artifact_root / "manifests" / "calibration_manifest.json", calibration_manifest_rows)
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bundle_hash = sha256_json({"models": model_hashes, "feature_order_hash": sha256_file(artifact_root / "schemas" / "feature_order.json"), "pm_hash": pm_hash})
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write_json(
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artifact_root / "manifests" / "model_bundle_manifest.json",
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{
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"manifest_schema_version": "trader-model-bundle-manifest-v4-p0",
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"model_bundle_version": MODEL_BUNDLE_VERSION,
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"calibration_bundle_version": CALIBRATION_BUNDLE_VERSION,
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"feature_version": FEATURE_VERSION,
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"label_version": LABEL_VERSION,
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"split_version": SPLIT_VERSION,
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"training_run_id": args.run_id,
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"training_export_id": f"export-{args.run_id}",
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"backtest_manifest_id": f"backtest-{args.run_id}",
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"required_models_json": list(REQUIRED_MODELS),
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"provided_models_json": list(REQUIRED_MODELS),
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"missing_models_json": [],
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"allowed_run_modes_json": ["REPLAY_SIM", "SHADOW"],
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"bundle_hash_sha256": bundle_hash,
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"model_quality_status_json": {row["model_name"]: row["quality_status"] for row in model_manifest_rows},
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"calibration_quality_status_json": {row["model_name"]: row["quality_status"] for row in calibration_manifest_rows},
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"backtest_status": backtest_manifest.get("status"),
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"backtest_status_reasons_json": backtest_manifest.get("status_reasons", []),
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"backtest_metrics_json": backtest_manifest.get("metrics", {}),
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"complete": True,
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"status": EXPORT_STATUS,
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},
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)
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write_json(
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artifact_root / "manifests" / "training_export_manifest.json",
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{"created_at": utc_now_text(), "status": EXPORT_STATUS, "artifact_root": str(artifact_root), "bundle_hash_sha256": bundle_hash},
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)
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logging.info("trader.training.artifact_exported runId=%s status=%s bundleHash=%s path=%s", args.run_id, EXPORT_STATUS, bundle_hash, artifact_root)
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def _model_manifest_row(
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model_name: str,
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artifact_path: str,
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artifact_hash: str,
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feature_schema_hash: str,
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feature_order_hash: str,
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output_schema_hash: str,
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metrics: dict,
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quality_status: str,
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quality_reasons: list[str],
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train_start: str,
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train_end: str,
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validation_start: str,
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validation_end: str,
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status: str,
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) -> dict:
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return {
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"model_bundle_version": MODEL_BUNDLE_VERSION,
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"calibration_bundle_version": CALIBRATION_BUNDLE_VERSION,
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"model_name": model_name,
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"model_type": model_name,
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"side": "BOTH",
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"symbol_scope_json": ["BTC-USDT-PERP"],
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"bar_interval": "1m",
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"horizon_minutes": 45,
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"model_format": "ONNX",
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"model_runtime": "ONNX_RUNTIME_JAVA",
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"model_runtime_version": "1.22.0",
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"onnx_opset_version": 17,
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"producer_name": "trader-training",
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"producer_version": "v4-p0",
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"feature_version": FEATURE_VERSION,
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"feature_schema_path": "schemas/feature_schema.json",
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"feature_schema_hash": feature_schema_hash,
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"feature_order_path": "schemas/feature_order.json",
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"feature_order_hash": feature_order_hash,
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"input_tensor_name": "features",
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"input_dtype": "FLOAT32",
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"input_shape_json": {"features": len(FEATURE_ORDER), "batch": 1},
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"input_example_path": "examples/sample_input.json",
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"output_schema_path": "schemas/output_schema.json",
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"output_schema_hash": output_schema_hash,
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"output_tensor_names_json": ["prediction"],
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"output_mapping_json": OUTPUT_MAPPING[model_name],
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"output_value_rules_json": {"clip_by_output_schema": True},
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"label_version": LABEL_VERSION,
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"split_version": SPLIT_VERSION,
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"training_fold": "fold_01",
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"train_start": train_start,
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"train_end": train_end,
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"validation_start": validation_start,
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"validation_end": validation_end,
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"test_start": validation_start,
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"test_end": validation_end,
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"metrics_json": metrics,
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"quality_status": quality_status,
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"quality_reasons_json": quality_reasons,
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"artifact_path": artifact_path,
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"artifact_hash_sha256": artifact_hash,
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"source_hash": artifact_hash,
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"status": status,
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}
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def _sample_input(root) -> dict:
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features = read_parquet(root / "feature" / "feature_frame.parquet")
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row = features[features["data_quality_flag"].isin(["OK", "PARTIAL_OPTIONAL"])].iloc[0]
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return {feature: float(row[feature]) for feature in FEATURE_ORDER}
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def _sample_output() -> dict:
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return {model: {field: 0.0 for field in fields} for model, fields in MODEL_OUTPUTS.items()}
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def _split_time(root, split_id: str, key: str) -> str:
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manifest = read_json(root / "split" / "split_manifest.json")
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for item in manifest["splits"]:
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if item["split_id"] == split_id:
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return item[key]
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return "2026-01-01T00:00:00Z"
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