quant-trader-service/training/trader_training/nonlinear_pm_probe.py

from __future__ import annotations

import itertools
import logging
from typing import Any

import numpy as np
import pandas as pd
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor

from trader_training.io_utils import read_parquet, run_root, write_json, write_parquet, write_text
from trader_training.pm import _pm_config_from_thresholds, _pm_frame, _price_plan_context, _simulate_open_trades, _trade_metrics
from trader_training.schemas import FEATURE_ORDER, FIT_SPLIT, LATEST_STRESS_SPLIT, TUNE_SPLIT, VALIDATION_LOCKED_SPLIT


EVAL_SPLITS = (TUNE_SPLIT, VALIDATION_LOCKED_SPLIT, LATEST_STRESS_SPLIT)


def probe_nonlinear_pm(args: Any) -> None:
    root = run_root(args)
    direction_dataset = read_parquet(root / "dataset" / "direction_train.parquet")
    entry_dataset = read_parquet(root / "dataset" / "entry_train.parquet")
    probe_mode = _probe_mode(args)
    entry_train_filter = _entry_train_filter(args)
    entry_opportunity_bps = float(getattr(args, "entry_opportunity_bps", 40.0) or 40.0)
    direction_model = _fit_direction_model(direction_dataset) if probe_mode == "direction_entry_tree" else None
    entry_models = _fit_entry_models(direction_dataset, entry_dataset, entry_train_filter, entry_opportunity_bps)
    frames = {
        split_id: _prediction_frame(root, split_id, direction_dataset, entry_dataset, direction_model, entry_models)
        for split_id in EVAL_SPLITS
    }
    price_plan = _price_plan_context(root)
    candidates = _expanded_threshold_candidates()
    tune_rows: list[dict[str, Any]] = []
    best_thresholds: dict[str, float] | None = None
    best_tune_metrics: dict[str, Any] | None = None
    best_score = -float("inf")

    for thresholds in candidates:
        trades = _simulate_open_trades(frames[TUNE_SPLIT], thresholds, _pm_config_from_thresholds(thresholds), price_plan)
        metrics = _trade_metrics(trades)
        score = _probe_score(metrics)
        tune_rows.append({**thresholds, **metrics, "score": score})
        if score > best_score:
            best_score = score
            best_thresholds = thresholds
            best_tune_metrics = metrics

    if best_thresholds is None or best_tune_metrics is None:
        raise ValueError("nonlinear PM probe did not evaluate any threshold candidate")

    split_metrics: dict[str, Any] = {}
    split_trade_frames: dict[str, pd.DataFrame] = {}
    for split_id, frame in frames.items():
        trades = _simulate_open_trades(frame, best_thresholds, _pm_config_from_thresholds(best_thresholds), price_plan)
        trades = trades.copy()
        trades["eval_split"] = split_id
        split_trade_frames[split_id] = trades
        split_metrics[split_id] = _trade_metrics(trades)
    side_metrics = _side_metrics(split_trade_frames)

    tune_frame = pd.DataFrame(tune_rows).sort_values("score", ascending=False).reset_index(drop=True)
    result = {
        "run_id": args.run_id,
        "purpose": "diagnostic_only_not_exported",
        "model_family": "sklearn_hist_gradient_boosting",
        "probe_mode": probe_mode,
        "entry_train_filter": entry_train_filter,
        "entry_opportunity_bps": entry_opportunity_bps,
        "candidate_count": len(candidates),
        "candidate_summary": _candidate_summary(tune_frame),
        "best_thresholds": best_thresholds,
        "best_tune_metrics": best_tune_metrics,
        "split_metrics": split_metrics,
        "side_metrics": side_metrics,
        "verdict": _verdict(split_metrics),
    }
    out_dir = root / "diagnostics"
    output_stem = _output_stem(probe_mode)
    trade_parts = [trades for trades in split_trade_frames.values() if not trades.empty]
    best_trade_frame = pd.concat(trade_parts, ignore_index=True) if trade_parts else pd.DataFrame()
    write_json(out_dir / f"{output_stem}_result.json", _jsonable(result))
    write_text(out_dir / f"{output_stem}_candidates.csv", tune_frame.head(200).to_csv(index=False))
    write_parquet(out_dir / f"{output_stem}_best_trades.parquet", best_trade_frame)
    write_text(out_dir / f"{output_stem}_side_metrics.csv", _side_metrics_frame(side_metrics).to_csv(index=False))
    write_text(out_dir / f"{output_stem}_report.md", _markdown_report(result, tune_frame.head(20)))
    logging.info(
        "trader.training.nonlinear_pm_probe_written runId=%s probeMode=%s entryTrainFilter=%s verdict=%s tuneTrades=%s validationTrades=%s stressTrades=%s",
        args.run_id,
        probe_mode,
        entry_train_filter,
        result["verdict"]["status"],
        split_metrics[TUNE_SPLIT]["trade_count"],
        split_metrics[VALIDATION_LOCKED_SPLIT]["trade_count"],
        split_metrics[LATEST_STRESS_SPLIT]["trade_count"],
    )


def _probe_mode(args: Any) -> str:
    mode = str(getattr(args, "probe_mode", "direction_entry_tree") or "direction_entry_tree").strip().lower()
    allowed = {"direction_entry_tree", "entry_tree_only"}
    if mode not in allowed:
        raise ValueError(f"unsupported nonlinear PM probe mode: {mode}")
    return mode


def _entry_train_filter(args: Any) -> str:
    value = str(getattr(args, "entry_train_filter", "direction_label") or "direction_label").strip().lower()
    allowed = {"direction_label", "side_opportunity"}
    if value not in allowed:
        raise ValueError(f"unsupported nonlinear Entry train filter: {value}")
    return value


def _output_stem(probe_mode: str) -> str:
    return "nonlinear_pm_probe" if probe_mode == "direction_entry_tree" else f"nonlinear_pm_probe_{probe_mode}"


def _fit_direction_model(dataset: pd.DataFrame) -> HistGradientBoostingClassifier:
    train = dataset[dataset["split_id"].eq(FIT_SPLIT)].copy()
    y = train[["long_target", "short_target", "neutral_target"]].to_numpy().argmax(axis=1)
    model = HistGradientBoostingClassifier(
        max_iter=160,
        learning_rate=0.04,
        max_leaf_nodes=31,
        l2_regularization=0.02,
        early_stopping=True,
        random_state=41,
    )
    model.fit(_x(train), y)
    return model


def _fit_entry_models(direction_dataset: pd.DataFrame, entry_dataset: pd.DataFrame, entry_train_filter: str, opportunity_bps: float) -> dict[str, Any]:
    long_train = _entry_side_fit_frame(direction_dataset, entry_dataset, "LONG", entry_train_filter, opportunity_bps)
    short_train = _entry_side_fit_frame(direction_dataset, entry_dataset, "SHORT", entry_train_filter, opportunity_bps)
    return {
        "long_entry_prob": _fit_binary_head(long_train, "long_entry_target", seed=43),
        "short_entry_prob": _fit_binary_head(short_train, "short_entry_target", seed=47),
        "long_expected_net_edge_bps": _fit_regression_head(long_train, "long_actual_plan_net_edge_bps", seed=53),
        "short_expected_net_edge_bps": _fit_regression_head(short_train, "short_actual_plan_net_edge_bps", seed=59),
    }


def _entry_side_fit_frame(
    direction_dataset: pd.DataFrame,
    entry_dataset: pd.DataFrame,
    side: str,
    entry_train_filter: str,
    opportunity_bps: float,
) -> pd.DataFrame:
    side_lower = side.lower()
    train = entry_dataset[entry_dataset["split_id"].eq(FIT_SPLIT)].copy()
    if entry_train_filter == "direction_label":
        label_column = f"{side_lower}_target"
        required = {"sample_id", label_column}
        missing = sorted(required - set(direction_dataset.columns))
        if missing:
            raise ValueError(f"direction dataset missing columns for nonlinear Entry filter: {missing}")
        train = train.merge(direction_dataset[list(required)], on="sample_id", how="inner", validate="one_to_one")
        if len(train) == 0:
            raise ValueError(f"nonlinear Entry {side} direction-label filter produced no rows")
        mask = pd.to_numeric(train[label_column], errors="coerce").fillna(0).astype(int).eq(1)
        filter_name = f"DIRECTION_LABEL_{side}_FIT_ROWS"
    elif entry_train_filter == "side_opportunity":
        opportunity_column = f"{side_lower}_max_achievable_net_edge_bps"
        if opportunity_column not in train.columns:
            raise ValueError(f"entry dataset missing {opportunity_column} for nonlinear Entry side-opportunity filter")
        mask = pd.to_numeric(train[opportunity_column], errors="coerce").ge(opportunity_bps).fillna(False)
        filter_name = f"SIDE_OPPORTUNITY_{side}_GE_{opportunity_bps:g}_BPS_FIT_ROWS"
    else:
        raise ValueError(f"unsupported nonlinear Entry train filter: {entry_train_filter}")
    out = train.loc[mask].copy()
    logging.info(
        "trader.training.nonlinear_entry_fit_frame side=%s filter=%s rows=%s totalFitRows=%s",
        side,
        filter_name,
        len(out),
        len(train),
    )
    return out


def _fit_binary_head(train: pd.DataFrame, target: str, seed: int) -> HistGradientBoostingClassifier:
    if len(train) < 1000:
        raise ValueError(f"not enough rows to train nonlinear Entry head {target}: {len(train)}")
    y = train[target].astype(int).to_numpy()
    if len(np.unique(y)) < 2:
        raise ValueError(f"nonlinear Entry head {target} has only one class")
    model = HistGradientBoostingClassifier(
        max_iter=180,
        learning_rate=0.04,
        max_leaf_nodes=31,
        l2_regularization=0.02,
        early_stopping=True,
        random_state=seed,
    )
    model.fit(_x(train), y)
    return model


def _fit_regression_head(train: pd.DataFrame, target: str, seed: int) -> HistGradientBoostingRegressor:
    if len(train) < 1000:
        raise ValueError(f"not enough rows to train nonlinear Entry head {target}: {len(train)}")
    model = HistGradientBoostingRegressor(
        max_iter=180,
        learning_rate=0.04,
        max_leaf_nodes=31,
        l2_regularization=0.02,
        early_stopping=True,
        random_state=seed,
    )
    model.fit(_x(train), train[target].astype(float).to_numpy())
    return model


def _prediction_frame(
    root,
    split_id: str,
    direction_dataset: pd.DataFrame,
    entry_dataset: pd.DataFrame,
    direction_model: HistGradientBoostingClassifier | None,
    entry_models: dict[str, Any],
) -> pd.DataFrame:
    frame = _pm_frame(root, split_id).copy()
    entry_split = entry_dataset[entry_dataset["split_id"].eq(split_id)].copy()

    entry_pred = entry_split[["sample_id"]].copy()
    entry_pred["long_entry_prob"] = entry_models["long_entry_prob"].predict_proba(_x(entry_split))[:, 1]
    entry_pred["short_entry_prob"] = entry_models["short_entry_prob"].predict_proba(_x(entry_split))[:, 1]
    entry_pred["pred_long_expected_net_edge_bps"] = entry_models["long_expected_net_edge_bps"].predict(_x(entry_split))
    entry_pred["pred_short_expected_net_edge_bps"] = entry_models["short_expected_net_edge_bps"].predict(_x(entry_split))
    replacements = entry_pred
    drop_columns = [
        "long_entry_prob",
        "short_entry_prob",
        "pred_long_expected_net_edge_bps",
        "pred_short_expected_net_edge_bps",
    ]
    if direction_model is not None:
        direction_split = direction_dataset[direction_dataset["split_id"].eq(split_id)].copy()
        direction_proba = direction_model.predict_proba(_x(direction_split))
        direction_pred = direction_split[["sample_id"]].copy()
        direction_pred["long_prob"] = direction_proba[:, 0]
        direction_pred["short_prob"] = direction_proba[:, 1]
        direction_pred["neutral_prob"] = direction_proba[:, 2]
        replacements = direction_pred.merge(entry_pred, on="sample_id", how="inner", validate="one_to_one")
        drop_columns.extend(["long_prob", "short_prob", "neutral_prob"])
    out = frame.drop(columns=drop_columns, errors="ignore").merge(replacements, on="sample_id", how="inner", validate="one_to_one")
    if len(out) != len(frame):
        raise ValueError(f"nonlinear prediction frame lost rows for {split_id}: before={len(frame)} after={len(out)}")
    return out


def _expanded_threshold_candidates() -> list[dict[str, float]]:
    # 多头和空头在不同市场段里的可靠性可能完全不同；这里分开搜，
    # 1.01 表示这一侧不开仓，用来检查只做多或只做空是否更稳。
    values = itertools.product(
        [0.20, 0.30, 0.40, 0.50, 0.60, 1.01],
        [0.20, 0.30, 0.40, 0.50, 0.60, 1.01],
        [0.05, 0.10, 0.20, 0.30, 0.40, 0.50],
        [0.45, 0.65, 0.85, 1.00],
        [-5.0, 0.0, 3.0, 5.0, 8.0],
        [0.00, 0.01, 0.02, 0.05],
    )
    return [
        {
            "long_open_prob": long_prob,
            "short_open_prob": short_prob,
            "min_entry_prob": entry_prob,
            "max_market_risk_prob": risk_prob,
            "min_expected_edge_bps": edge_bps,
            "min_direction_margin": margin,
        }
        for long_prob, short_prob, entry_prob, risk_prob, edge_bps, margin in values
    ]


def _probe_score(metrics: dict[str, Any]) -> float:
    if metrics["trade_count"] == 0:
        return -1_000_000.0
    sample_penalty = max(0, 80 - int(metrics["trade_count"])) * 2.0
    return (
        float(metrics["avg_weighted_edge_bps"]) * np.sqrt(float(metrics["trade_count"]))
        + float(metrics["total_weighted_edge_bps"]) * 0.03
        - float(metrics["max_drawdown_bps"]) * 0.20
        - sample_penalty
    )


def _side_metrics(split_trade_frames: dict[str, pd.DataFrame]) -> dict[str, dict[str, dict[str, Any]]]:
    metrics: dict[str, dict[str, dict[str, Any]]] = {}
    for split_id, trades in split_trade_frames.items():
        metrics[split_id] = {}
        for side in ("LONG", "SHORT"):
            side_trades = trades[trades["side"].eq(side)].copy() if not trades.empty else trades.copy()
            metrics[split_id][side] = {**_trade_metrics(side_trades), **_exit_metrics(side_trades)}
    return metrics


def _exit_metrics(trades: pd.DataFrame) -> dict[str, float]:
    if trades.empty:
        return {
            "target_hit_rate": 0.0,
            "stop_hit_rate": 0.0,
            "timeout_exit_rate": 0.0,
            "avg_time_to_exit_min": 0.0,
            "p50_time_to_exit_min": 0.0,
        }
    target_hit = pd.to_numeric(trades["target_hit"], errors="coerce").fillna(0).astype(int)
    stop_hit = pd.to_numeric(trades["stop_hit"], errors="coerce").fillna(0).astype(int)
    time_to_exit_min = pd.to_numeric(trades["time_to_exit_ms"], errors="coerce").fillna(0.0).astype(float) / 60_000.0
    return {
        "target_hit_rate": float(target_hit.eq(1).mean()),
        "stop_hit_rate": float(stop_hit.eq(1).mean()),
        "timeout_exit_rate": float((target_hit.ne(1) & stop_hit.ne(1)).mean()),
        "avg_time_to_exit_min": float(time_to_exit_min.mean()),
        "p50_time_to_exit_min": float(time_to_exit_min.median()),
    }


def _side_metrics_frame(side_metrics: dict[str, dict[str, dict[str, Any]]]) -> pd.DataFrame:
    rows: list[dict[str, Any]] = []
    for split_id, split_metrics in side_metrics.items():
        for side, metrics in split_metrics.items():
            rows.append({"split_id": split_id, "side": side, **metrics})
    return pd.DataFrame(rows)


def _candidate_summary(tune_frame: pd.DataFrame) -> dict[str, Any]:
    if tune_frame.empty:
        return {
            "positive_avg_weighted_candidates": 0,
            "positive_total_weighted_candidates": 0,
            "best_avg_weighted_edge_bps": 0.0,
            "best_total_weighted_edge_bps": 0.0,
            "min_viable_trade_count": 80,
            "positive_avg_weighted_viable_candidates": 0,
            "positive_total_weighted_viable_candidates": 0,
            "best_viable_avg_weighted_edge_bps": 0.0,
            "best_viable_total_weighted_edge_bps": 0.0,
        }
    viable = tune_frame[tune_frame["trade_count"] >= 80]
    return {
        "positive_avg_weighted_candidates": int((tune_frame["avg_weighted_edge_bps"] > 0).sum()),
        "positive_total_weighted_candidates": int((tune_frame["total_weighted_edge_bps"] > 0).sum()),
        "best_avg_weighted_edge_bps": float(tune_frame["avg_weighted_edge_bps"].max()),
        "best_total_weighted_edge_bps": float(tune_frame["total_weighted_edge_bps"].max()),
        "min_viable_trade_count": 80,
        "positive_avg_weighted_viable_candidates": int((viable["avg_weighted_edge_bps"] > 0).sum()),
        "positive_total_weighted_viable_candidates": int((viable["total_weighted_edge_bps"] > 0).sum()),
        "best_viable_avg_weighted_edge_bps": float(viable["avg_weighted_edge_bps"].max()) if not viable.empty else 0.0,
        "best_viable_total_weighted_edge_bps": float(viable["total_weighted_edge_bps"].max()) if not viable.empty else 0.0,
    }


def _verdict(metrics: dict[str, Any]) -> dict[str, Any]:
    tune = metrics[TUNE_SPLIT]
    validation = metrics[VALIDATION_LOCKED_SPLIT]
    stress = metrics[LATEST_STRESS_SPLIT]
    passed = (
        tune["trade_count"] >= 80
        and validation["trade_count"] >= 40
        and stress["trade_count"] >= 10
        and tune["avg_weighted_edge_bps"] > 0
        and validation["avg_weighted_edge_bps"] > 0
        and stress["avg_weighted_edge_bps"] > -1.0
    )
    return {
        "status": "PROMISING_DIAGNOSTIC_ONLY" if passed else "NO_STABLE_NONLINEAR_PM_EDGE",
        "reason": "只用于判断树模型方向是否值得继续工程化，不代表可上线。",
    }


def _x(frame: pd.DataFrame) -> np.ndarray:
    return frame[FEATURE_ORDER].apply(pd.to_numeric, errors="coerce").replace([np.inf, -np.inf], np.nan).astype("float32").to_numpy()


def _markdown_report(result: dict[str, Any], top_candidates: pd.DataFrame) -> str:
    mode_text = "只替换 Entry，Direction 使用当前模型输出。" if result["probe_mode"] == "entry_tree_only" else "Direction 和 Entry 都替换成树模型。"
    lines = [
        "# Nonlinear PM Probe Report",
        "",
        "这份报告只做诊断，不导出上线模型。它回答：不加新特征，换成树模型后，PM 能不能筛出稳定正收益。",
        "",
        f"- run_id: `{result['run_id']}`",
        f"- probe_mode: `{result['probe_mode']}`",
        f"- 说明: {mode_text}",
        f"- Entry 训练人群: `{result['entry_train_filter']}`",
        f"- Entry 机会阈值: `{result['entry_opportunity_bps']}` bps",
        f"- verdict: `{result['verdict']['status']}`",
        f"- candidate_count: `{result['candidate_count']}`",
        f"- 正收益候选数: `{result['candidate_summary']['positive_avg_weighted_candidates']}`",
        f"- 至少 80 单的正收益候选数: `{result['candidate_summary']['positive_avg_weighted_viable_candidates']}`",
        f"- 至少 80 单的最好单笔加权收益: `{result['candidate_summary']['best_viable_avg_weighted_edge_bps']:.4f}` bps",
        f"- best_thresholds: `{result['best_thresholds']}`",
        "",
        "## Split Metrics",
        "",
        "| split | trades | win_rate | avg_actual_bps | avg_weighted_bps | total_weighted_bps | profit_factor |",
        "| --- | ---: | ---: | ---: | ---: | ---: | ---: |",
    ]
    for split_id, metrics in result["split_metrics"].items():
        lines.append(
            f"| {split_id} | {metrics['trade_count']} | {metrics['win_rate']:.4f} | "
            f"{metrics['avg_actual_edge_bps']:.4f} | {metrics['avg_weighted_edge_bps']:.4f} | "
            f"{metrics['total_weighted_edge_bps']:.4f} | {metrics['profit_factor']:.4f} |"
        )
    lines.extend(
        [
            "",
            "## Side Breakdown",
            "",
            "| split | side | trades | win_rate | avg_actual_bps | avg_weighted_bps | target_hit_rate | stop_hit_rate | timeout_rate | avg_exit_min |",
            "| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |",
        ]
    )
    for split_id, side_metrics in result["side_metrics"].items():
        for side, metrics in side_metrics.items():
            lines.append(
                f"| {split_id} | {side} | {metrics['trade_count']} | {metrics['win_rate']:.4f} | "
                f"{metrics['avg_actual_edge_bps']:.4f} | {metrics['avg_weighted_edge_bps']:.4f} | "
                f"{metrics['target_hit_rate']:.4f} | {metrics['stop_hit_rate']:.4f} | "
                f"{metrics['timeout_exit_rate']:.4f} | {metrics['avg_time_to_exit_min']:.2f} |"
            )
    lines.extend(["", "## Top Tune Candidates", "", _candidate_table(top_candidates), ""])
    return "\n".join(lines)


def _candidate_table(frame: pd.DataFrame) -> str:
    if frame.empty:
        return "无候选。"
    columns = [
        "long_open_prob",
        "short_open_prob",
        "min_entry_prob",
        "max_market_risk_prob",
        "min_expected_edge_bps",
        "min_direction_margin",
        "trade_count",
        "avg_weighted_edge_bps",
        "total_weighted_edge_bps",
        "profit_factor",
        "score",
    ]
    available = [column for column in columns if column in frame.columns]
    lines = [
        "| " + " | ".join(available) + " |",
        "| " + " | ".join(["---" for _ in available]) + " |",
    ]
    for _, row in frame[available].iterrows():
        values = []
        for column in available:
            value = row[column]
            if isinstance(value, (float, np.floating)):
                values.append(f"{float(value):.6f}")
            else:
                values.append(str(value))
        lines.append("| " + " | ".join(values) + " |")
    return "\n".join(lines)


def _jsonable(value: Any) -> Any:
    if isinstance(value, dict):
        return {str(key): _jsonable(item) for key, item in value.items()}
    if isinstance(value, list):
        return [_jsonable(item) for item in value]
    if isinstance(value, (np.integer,)):
        return int(value)
    if isinstance(value, (np.floating,)):
        return float(value)
    if isinstance(value, np.ndarray):
        return value.tolist()
    return value