433 lines
20 KiB
Python
433 lines
20 KiB
Python
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from __future__ import annotations
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import json
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import logging
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import HuberRegressor, LogisticRegression
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from sklearn.metrics import brier_score_loss, mean_absolute_error, roc_auc_score
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from sklearn.preprocessing import StandardScaler
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from trader_training.io_utils import read_json, read_parquet, run_root, sha256_json, write_json, write_parquet, write_text
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from trader_training.labels import _build_path_stats
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from trader_training.schemas import FEATURE_ORDER, FIT_SPLIT, LATEST_STRESS_SPLIT, TUNE_SPLIT, VALIDATION_LOCKED_SPLIT
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STATE_FEATURES = [
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"position_side_sign",
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"time_in_position_minutes",
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"unrealized_pnl_bps",
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"mfe_since_entry_bps",
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"mae_since_entry_bps",
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"distance_to_stop_bps",
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"distance_to_target_bps",
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]
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EVAL_SPLITS = (TUNE_SPLIT, VALIDATION_LOCKED_SPLIT, LATEST_STRESS_SPLIT)
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ALL_SPLITS = (FIT_SPLIT, TUNE_SPLIT, VALIDATION_LOCKED_SPLIT, LATEST_STRESS_SPLIT)
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def run_state_continue_experiment(args: Any) -> None:
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root = run_root(args)
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baseline_root = args.data_root / "trader-v4" / "runs" / args.baseline_run_id
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out_dir = root / "experiments" / "state_continue"
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ages = _parse_ages(args.ages_minutes)
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logging.info(
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"trader.training.state_continue_experiment_started runId=%s baselineRunId=%s ages=%s",
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args.run_id,
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args.baseline_run_id,
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ages,
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)
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feature = _load_feature_frame(baseline_root)
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entry = _load_entry_labels(baseline_root)
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replay = _load_replay(baseline_root)
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plan = read_json(baseline_root / "label" / "price_plan_context.json")
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stop_bps = float(plan["stopDistanceBps"])
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target_bps = float(plan["targetDistanceBps"])
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cost_bps = float(plan["costBps"])
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state_frame = _build_state_frame(feature, entry, replay, ages, stop_bps, target_bps, cost_bps)
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if args.max_rows_per_split:
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state_frame = _cap_rows_per_split(state_frame, int(args.max_rows_per_split))
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dataset_hash = write_parquet(out_dir / "state_continue_train.parquet", state_frame)
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logging.info(
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"trader.training.state_continue_dataset_written runId=%s rowCount=%s splitCounts=%s path=%s",
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args.run_id,
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len(state_frame),
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state_frame["split_id"].value_counts().to_dict(),
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out_dir / "state_continue_train.parquet",
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)
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source_manifest = _source_manifest(args, baseline_root, ages, stop_bps, target_bps, cost_bps, state_frame, dataset_hash)
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write_json(out_dir / "experiment_manifest.json", source_manifest)
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write_json(out_dir / "position_state_feature_schema.json", _state_feature_schema())
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order_hash = write_json(out_dir / "position_state_feature_order.json", STATE_FEATURES)
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write_json(
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out_dir / "position_state_source_manifest.json",
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{
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"entry_predicted_edge_bps": "NOT_USED_IN_THIS_DIAGNOSTIC",
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"entry_direction_prob": "NOT_USED_IN_THIS_DIAGNOSTIC",
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"out_of_fold_used": False,
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"frozen_model_output_used": False,
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"replay_decision_trace_used": False,
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"state_feature_order_hash": order_hash,
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"row_count": len(state_frame),
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"split_counts": state_frame["split_id"].value_counts().to_dict(),
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},
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)
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feature_sets = {
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"market_only": FEATURE_ORDER,
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"market_plus_state": [*FEATURE_ORDER, *STATE_FEATURES],
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}
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results: dict[str, Any] = {}
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prediction_frames: list[pd.DataFrame] = []
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for side in ("LONG", "SHORT"):
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side_frame = state_frame[state_frame["position_side"].eq(side)].copy()
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for feature_set_name, feature_columns in feature_sets.items():
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key = f"{side.lower()}_{feature_set_name}"
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result, predictions = _train_side_models(side_frame, side, feature_columns)
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results[key] = result
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predictions["side"] = side
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predictions["feature_set"] = feature_set_name
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prediction_frames.append(predictions)
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logging.info(
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"trader.training.state_continue_model_trained runId=%s side=%s featureSet=%s tuneAuc=%s tuneMaeRatio=%s",
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args.run_id,
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side,
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feature_set_name,
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result.get(TUNE_SPLIT, {}).get("continue_auc"),
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result.get(TUNE_SPLIT, {}).get("edge_mae_vs_constant_ratio"),
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)
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predictions = pd.concat(prediction_frames, ignore_index=True) if prediction_frames else pd.DataFrame()
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write_parquet(out_dir / "state_continue_predictions.parquet", predictions)
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write_json(out_dir / "state_continue_result.json", results)
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write_text(out_dir / "state_continue_experiment_report.md", _report(args, baseline_root, source_manifest, results))
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logging.info("trader.training.state_continue_experiment_finished runId=%s report=%s", args.run_id, out_dir / "state_continue_experiment_report.md")
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def _parse_ages(raw: str) -> list[int]:
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ages = [int(item.strip()) for item in raw.split(",") if item.strip()]
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if not ages or any(age <= 0 for age in ages):
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raise ValueError(f"invalid ages-minutes: {raw}")
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return sorted(set(ages))
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def _load_feature_frame(baseline_root: Path) -> pd.DataFrame:
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feature = read_parquet(baseline_root / "feature" / "feature_frame.parquet")
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required = {"sample_id", "symbol", "event_time", "open_time_ms", "split_id", "walk_forward_fold", "data_quality_flag", *FEATURE_ORDER}
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missing = sorted(required.difference(feature.columns))
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if missing:
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raise ValueError(f"baseline feature frame missing columns: {missing}")
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feature = feature[feature["data_quality_flag"].isin(["OK", "PARTIAL_OPTIONAL"])].copy()
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feature = feature[feature["split_id"].isin(ALL_SPLITS)].copy()
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return feature
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def _load_entry_labels(baseline_root: Path) -> pd.DataFrame:
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entry = read_parquet(baseline_root / "label" / "entry_labels.parquet")
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required = {"sample_id", "symbol", "event_time", "side", "entry_target", "split_id", "walk_forward_fold"}
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missing = sorted(required.difference(entry.columns))
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if missing:
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raise ValueError(f"baseline entry labels missing columns: {missing}")
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entry = entry[(entry["entry_target"] == 1) & (entry["side"].isin(["LONG", "SHORT"]))].copy()
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entry["entry_open_time_ms"] = pd.to_datetime(entry["event_time"], utc=True).astype("int64") // 1_000_000
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return entry[["sample_id", "symbol", "event_time", "side", "entry_open_time_ms"]].copy()
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def _load_replay(baseline_root: Path) -> pd.DataFrame:
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split_manifest = read_json(baseline_root / "split" / "split_manifest.json")
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replay_path = Path(split_manifest["source_replay_path"])
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replay = read_parquet(replay_path)
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required = {"symbol", "event_time", "open_time_ms", "high", "low", "close", "spread_bps"}
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missing = sorted(required.difference(replay.columns))
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if missing:
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raise ValueError(f"source replay missing columns: {missing}")
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return replay.sort_values(["symbol", "open_time_ms"]).reset_index(drop=True)
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def _build_state_frame(
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feature: pd.DataFrame,
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entry: pd.DataFrame,
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replay: pd.DataFrame,
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ages: list[int],
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stop_bps: float,
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target_bps: float,
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cost_bps: float,
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) -> pd.DataFrame:
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future_stats = _build_path_stats(replay, horizon=30, target_bps=target_bps, stop_bps=stop_bps)
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future_stats = future_stats.rename(columns={"open_time_ms": "current_open_time_ms"})
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current_feature = feature.rename(columns={"sample_id": "current_sample_id", "event_time": "current_event_time", "open_time_ms": "current_open_time_ms"})
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replay_state_source = _state_source_by_age(replay, ages)
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frames: list[pd.DataFrame] = []
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for age in ages:
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candidates = entry.copy()
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candidates["time_in_position_minutes"] = age
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candidates["current_open_time_ms"] = candidates["entry_open_time_ms"] + age * 60_000
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candidates = candidates.merge(
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replay_state_source[replay_state_source["time_in_position_minutes"].eq(age)],
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on=["symbol", "current_open_time_ms", "time_in_position_minutes"],
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how="inner",
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)
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candidates = candidates.merge(current_feature, on=["symbol", "current_open_time_ms"], how="inner")
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candidates = candidates.merge(
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future_stats,
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left_on=["symbol", "current_open_time_ms", "side"],
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right_on=["symbol", "current_open_time_ms", "side"],
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how="inner",
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)
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if candidates.empty:
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continue
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frames.append(_state_rows_for_age(candidates, stop_bps, target_bps, cost_bps))
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logging.info("trader.training.state_continue_age_built ageMinutes=%s rowCount=%s", age, len(candidates))
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if not frames:
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raise ValueError("state continue experiment produced no rows")
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out = pd.concat(frames, ignore_index=True)
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out = out.replace([np.inf, -np.inf], np.nan)
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required = [*FEATURE_ORDER, *STATE_FEATURES, "continue_target", "expected_continue_edge_bps"]
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out = out.dropna(subset=required).copy()
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return out
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def _state_source_by_age(replay: pd.DataFrame, ages: list[int]) -> pd.DataFrame:
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frames: list[pd.DataFrame] = []
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for _, group in replay.groupby("symbol", sort=False, observed=False):
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group = group.sort_values("open_time_ms").copy()
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for age in ages:
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rolling_high = group["high"].rolling(age + 1, min_periods=age + 1).max()
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rolling_low = group["low"].rolling(age + 1, min_periods=age + 1).min()
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frame = pd.DataFrame(
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{
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"symbol": group["symbol"],
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"current_open_time_ms": group["open_time_ms"],
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"time_in_position_minutes": age,
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"entry_price": group["close"].shift(age),
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"current_price": group["close"],
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"high_since_entry": rolling_high,
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"low_since_entry": rolling_low,
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}
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)
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frames.append(frame.dropna())
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return pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
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def _state_rows_for_age(frame: pd.DataFrame, stop_bps: float, target_bps: float, cost_bps: float) -> pd.DataFrame:
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side_sign = np.where(frame["side"].eq("LONG"), 1.0, -1.0)
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entry_price = frame["entry_price"].astype(float)
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current_price = frame["current_price"].astype(float)
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high_since = frame["high_since_entry"].astype(float)
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low_since = frame["low_since_entry"].astype(float)
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long_mask = frame["side"].eq("LONG")
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unrealized = np.where(long_mask, (current_price / entry_price - 1.0) * 10000.0, (entry_price / current_price - 1.0) * 10000.0) - cost_bps
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mfe = np.where(long_mask, (high_since / entry_price - 1.0) * 10000.0, (entry_price / low_since - 1.0) * 10000.0)
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mae = np.where(long_mask, (entry_price / low_since - 1.0) * 10000.0, (high_since / entry_price - 1.0) * 10000.0)
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stop_price = np.where(long_mask, entry_price * (1.0 - stop_bps / 10000.0), entry_price * (1.0 + stop_bps / 10000.0))
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target_price = np.where(long_mask, entry_price * (1.0 + target_bps / 10000.0), entry_price * (1.0 - target_bps / 10000.0))
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distance_to_stop = np.where(long_mask, (current_price / stop_price - 1.0) * 10000.0, (stop_price / current_price - 1.0) * 10000.0)
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distance_to_target = np.where(long_mask, (target_price / current_price - 1.0) * 10000.0, (current_price / target_price - 1.0) * 10000.0)
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expected_edge = frame["future_return_bps"].astype(float) - cost_bps
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continue_target = ((expected_edge >= 2.0) & (frame["mae_bps"].astype(float) < stop_bps)).astype("int8")
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out = frame[
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[
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"current_sample_id",
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"symbol",
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"current_event_time",
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"current_open_time_ms",
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"side",
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"split_id",
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"walk_forward_fold",
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*FEATURE_ORDER,
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]
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].copy()
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out = out.rename(
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columns={
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"current_sample_id": "sample_id",
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"current_event_time": "event_time",
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"current_open_time_ms": "open_time_ms",
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"side": "position_side",
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}
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)
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out["position_side_sign"] = side_sign.astype("float32")
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out["time_in_position_minutes"] = frame["time_in_position_minutes"].astype("float32")
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out["unrealized_pnl_bps"] = unrealized.astype("float32")
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out["mfe_since_entry_bps"] = np.maximum(mfe, 0.0).astype("float32")
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out["mae_since_entry_bps"] = np.maximum(mae, 0.0).astype("float32")
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out["distance_to_stop_bps"] = distance_to_stop.astype("float32")
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out["distance_to_target_bps"] = distance_to_target.astype("float32")
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out["continue_target"] = continue_target
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out["expected_continue_edge_bps"] = expected_edge.astype("float32")
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return out
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def _cap_rows_per_split(frame: pd.DataFrame, max_rows_per_split: int) -> pd.DataFrame:
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capped = []
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for split_id, part in frame.sort_values("event_time").groupby("split_id", sort=False, observed=False):
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if len(part) > max_rows_per_split:
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part = part.tail(max_rows_per_split).copy()
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capped.append(part)
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logging.info("trader.training.state_continue_split_capped splitId=%s rowCount=%s maxRows=%s", split_id, len(part), max_rows_per_split)
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return pd.concat(capped, ignore_index=True)
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def _train_side_models(frame: pd.DataFrame, side: str, feature_columns: list[str]) -> tuple[dict[str, Any], pd.DataFrame]:
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train = frame[frame["split_id"].eq(FIT_SPLIT)].copy()
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if train.empty:
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raise ValueError(f"state continue {side} has no fit_inner rows")
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scaler = StandardScaler()
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x_train = scaler.fit_transform(train[feature_columns].astype("float32"))
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y_train_cls = train["continue_target"].astype(int).to_numpy()
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y_train_reg = train["expected_continue_edge_bps"].astype(float).to_numpy()
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clf = LogisticRegression(max_iter=500)
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clf.fit(x_train, y_train_cls)
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reg = HuberRegressor(alpha=0.001, epsilon=1.35, max_iter=300)
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reg.fit(x_train, y_train_reg)
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metrics: dict[str, Any] = {}
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|
prediction_frames: list[pd.DataFrame] = []
|
||
|
|
for split_id in ALL_SPLITS:
|
||
|
|
part = frame[frame["split_id"].eq(split_id)].copy()
|
||
|
|
if part.empty:
|
||
|
|
continue
|
||
|
|
x = scaler.transform(part[feature_columns].astype("float32"))
|
||
|
|
y_cls = part["continue_target"].astype(int).to_numpy()
|
||
|
|
y_reg = part["expected_continue_edge_bps"].astype(float).to_numpy()
|
||
|
|
proba = clf.predict_proba(x)[:, 1]
|
||
|
|
pred_edge = reg.predict(x)
|
||
|
|
metrics[split_id] = _split_metrics(y_train_cls, y_train_reg, y_cls, y_reg, proba, pred_edge)
|
||
|
|
pred_frame = part[["sample_id", "symbol", "event_time", "split_id", "position_side", "continue_target", "expected_continue_edge_bps"]].copy()
|
||
|
|
pred_frame["continue_prob"] = proba.astype("float32")
|
||
|
|
pred_frame["predicted_continue_edge_bps"] = pred_edge.astype("float32")
|
||
|
|
prediction_frames.append(pred_frame)
|
||
|
|
metrics["row_count"] = int(len(frame))
|
||
|
|
metrics["feature_count"] = len(feature_columns)
|
||
|
|
metrics["feature_hash"] = sha256_json(feature_columns)
|
||
|
|
return metrics, pd.concat(prediction_frames, ignore_index=True)
|
||
|
|
|
||
|
|
|
||
|
|
def _split_metrics(
|
||
|
|
y_train_cls: np.ndarray,
|
||
|
|
y_train_reg: np.ndarray,
|
||
|
|
y_cls: np.ndarray,
|
||
|
|
y_reg: np.ndarray,
|
||
|
|
proba: np.ndarray,
|
||
|
|
pred_edge: np.ndarray,
|
||
|
|
) -> dict[str, Any]:
|
||
|
|
train_rate = float(np.mean(y_train_cls))
|
||
|
|
constant_proba = np.full(len(y_cls), train_rate)
|
||
|
|
train_median = float(np.median(y_train_reg))
|
||
|
|
constant_edge = np.full(len(y_reg), train_median)
|
||
|
|
out: dict[str, Any] = {
|
||
|
|
"row_count": int(len(y_cls)),
|
||
|
|
"positive_rate": float(np.mean(y_cls)),
|
||
|
|
"brier": float(brier_score_loss(y_cls, proba)),
|
||
|
|
"constant_brier": float(brier_score_loss(y_cls, constant_proba)),
|
||
|
|
"edge_mae": float(mean_absolute_error(y_reg, pred_edge)),
|
||
|
|
"edge_constant_mae": float(mean_absolute_error(y_reg, constant_edge)),
|
||
|
|
}
|
||
|
|
if len(np.unique(y_cls)) == 2:
|
||
|
|
out["continue_auc"] = float(roc_auc_score(y_cls, proba))
|
||
|
|
out["brier_vs_constant_ratio"] = float(out["brier"] / out["constant_brier"]) if out["constant_brier"] > 0 else None
|
||
|
|
out["edge_mae_vs_constant_ratio"] = float(out["edge_mae"] / out["edge_constant_mae"]) if out["edge_constant_mae"] > 0 else None
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def _source_manifest(
|
||
|
|
args: Any,
|
||
|
|
baseline_root: Path,
|
||
|
|
ages: list[int],
|
||
|
|
stop_bps: float,
|
||
|
|
target_bps: float,
|
||
|
|
cost_bps: float,
|
||
|
|
state_frame: pd.DataFrame,
|
||
|
|
dataset_hash: str,
|
||
|
|
) -> dict[str, Any]:
|
||
|
|
return {
|
||
|
|
"experiment": "state_continue_diagnostic_v1",
|
||
|
|
"run_id": args.run_id,
|
||
|
|
"baseline_run_id": args.baseline_run_id,
|
||
|
|
"baseline_root": str(baseline_root),
|
||
|
|
"ages_minutes": ages,
|
||
|
|
"target_bps": target_bps,
|
||
|
|
"stop_bps": stop_bps,
|
||
|
|
"cost_bps": cost_bps,
|
||
|
|
"dataset_hash_sha256": dataset_hash,
|
||
|
|
"row_count": int(len(state_frame)),
|
||
|
|
"split_counts": state_frame["split_id"].value_counts().to_dict(),
|
||
|
|
"side_counts": state_frame["position_side"].value_counts().to_dict(),
|
||
|
|
"feature_inputs": {
|
||
|
|
"market_feature_count": len(FEATURE_ORDER),
|
||
|
|
"state_features": STATE_FEATURES,
|
||
|
|
"state_feature_count": len(STATE_FEATURES),
|
||
|
|
},
|
||
|
|
"leakage_policy": {
|
||
|
|
"uses_future_entry_label_as_feature": False,
|
||
|
|
"uses_same_round_model_prediction_as_feature": False,
|
||
|
|
"entry_predicted_edge_bps": "not used",
|
||
|
|
"entry_direction_prob": "not used",
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def _state_feature_schema() -> list[dict[str, Any]]:
|
||
|
|
return [
|
||
|
|
{"name": "position_side_sign", "unit": "-1/1", "source": "synthetic position state", "leakage_check": "known at current position time"},
|
||
|
|
{"name": "time_in_position_minutes", "unit": "minute", "source": "entry time to current time", "leakage_check": "known at current position time"},
|
||
|
|
{"name": "unrealized_pnl_bps", "unit": "bps", "source": "entry price and current close", "leakage_check": "uses <= current time price"},
|
||
|
|
{"name": "mfe_since_entry_bps", "unit": "bps", "source": "high since entry", "leakage_check": "uses only entry..current high"},
|
||
|
|
{"name": "mae_since_entry_bps", "unit": "bps", "source": "low/high since entry", "leakage_check": "uses only entry..current low/high"},
|
||
|
|
{"name": "distance_to_stop_bps", "unit": "bps", "source": "price plan and current close", "leakage_check": "uses fixed plan and current price"},
|
||
|
|
{"name": "distance_to_target_bps", "unit": "bps", "source": "price plan and current close", "leakage_check": "uses fixed plan and current price"},
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def _report(args: Any, baseline_root: Path, manifest: dict[str, Any], results: dict[str, Any]) -> str:
|
||
|
|
baseline = read_json(baseline_root / "model" / "model_train_manifest.json")
|
||
|
|
continue_metrics = baseline["CONTINUE"]["metrics"]
|
||
|
|
lines = [
|
||
|
|
"# State Continue Experiment Report",
|
||
|
|
"",
|
||
|
|
f"- run_id: `{args.run_id}`",
|
||
|
|
f"- baseline_run_id: `{args.baseline_run_id}`",
|
||
|
|
f"- row_count: `{manifest['row_count']}`",
|
||
|
|
f"- ages_minutes: `{manifest['ages_minutes']}`",
|
||
|
|
"",
|
||
|
|
"## Baseline run-10 Continue",
|
||
|
|
"",
|
||
|
|
"| head | auc | mae_vs_constant |",
|
||
|
|
"| --- | ---: | ---: |",
|
||
|
|
f"| long_continue_prob | {continue_metrics['long_continue_prob'].get('auc')} | |",
|
||
|
|
f"| short_continue_prob | {continue_metrics['short_continue_prob'].get('auc')} | |",
|
||
|
|
f"| long_expected_continue_edge_bps | | {continue_metrics['long_expected_continue_edge_bps'].get('mae_vs_constant_ratio')} |",
|
||
|
|
f"| short_expected_continue_edge_bps | | {continue_metrics['short_expected_continue_edge_bps'].get('mae_vs_constant_ratio')} |",
|
||
|
|
"",
|
||
|
|
"## Diagnostic Result",
|
||
|
|
"",
|
||
|
|
"| side | feature_set | split | rows | auc | brier_ratio | mae_ratio |",
|
||
|
|
"| --- | --- | --- | ---: | ---: | ---: | ---: |",
|
||
|
|
]
|
||
|
|
for key, item in results.items():
|
||
|
|
side, feature_set = key.split("_", 1)
|
||
|
|
for split_id in EVAL_SPLITS:
|
||
|
|
metric = item.get(split_id, {})
|
||
|
|
lines.append(
|
||
|
|
f"| {side.upper()} | {feature_set} | {split_id} | {metric.get('row_count')} | {metric.get('continue_auc')} | {metric.get('brier_vs_constant_ratio')} | {metric.get('edge_mae_vs_constant_ratio')} |"
|
||
|
|
)
|
||
|
|
lines.extend(
|
||
|
|
[
|
||
|
|
"",
|
||
|
|
"## Verdict Rule",
|
||
|
|
"",
|
||
|
|
"状态特征只有在 `market_plus_state` 同时好过 `market_only`，并且 validation_locked / latest_stress 没有反向变差时，才进入正式链路。",
|
||
|
|
"",
|
||
|
|
]
|
||
|
|
)
|
||
|
|
return "\n".join(lines)
|