Improve nonlinear PM diagnostics

training/scripts/29_probe_nonlinear_pm.py

@@ -8,8 +8,21 @@ from trader_training.nonlinear_pm_probe import probe_nonlinear_pm
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Run diagnostic nonlinear Direction + Entry PM probe.")
+    parser = argparse.ArgumentParser(description="Run diagnostic nonlinear model PM probe.")
     add_common_args(parser)
+    parser.add_argument(
+        "--probe-mode",
+        choices=("direction_entry_tree", "entry_tree_only"),
+        default="direction_entry_tree",
+        help="诊断模式：同时替换 Direction+Entry，或只替换 Entry、保留当前 Direction 输出。",
+    )
+    parser.add_argument(
+        "--entry-train-filter",
+        choices=("direction_label", "side_opportunity"),
+        default="direction_label",
+        help="树模型 Entry 的训练人群来源。",
+    )
+    parser.add_argument("--entry-opportunity-bps", type=float, default=40.0)
     args = parser.parse_args()
     setup_logging()
     probe_nonlinear_pm(args)

training/tests/test_training_contract.py

@@ -23,7 +23,7 @@ from trader_training.entry_mae_label_diagnostic import diagnose_entry_mae_labels
 from trader_training.good_trade_structure import _side_frame, _top_fraction_metrics
 from trader_training.io_utils import read_json, write_json
 from trader_training.labels import ENTRY_LABEL_METHOD, _path_stats_for_group, build_entry_labels
-from trader_training.nonlinear_pm_probe import _expanded_threshold_candidates
+from trader_training.nonlinear_pm_probe import _entry_side_fit_frame, _exit_metrics, _expanded_threshold_candidates
 from trader_training.ofi_feature_experiment import _load_entry_dataset, l1_snapshot_diff_ofi_quote
 from trader_training.promote import promote_artifact_bundle
 from trader_training.replay import build_splits
@@ -58,6 +58,57 @@ class TrainingContractTest(unittest.TestCase):
             },
             candidates,
         )
+        self.assertIn(
+            {
+                "long_open_prob": 1.01,
+                "short_open_prob": 0.2,
+                "min_entry_prob": 0.05,
+                "max_market_risk_prob": 0.45,
+                "min_expected_edge_bps": -5.0,
+                "min_direction_margin": 0.0,
+            },
+            candidates,
+        )
+
+    def test_nonlinear_entry_tree_probe_can_use_side_opportunity_rows(self) -> None:
+        direction = pd.DataFrame(
+            {
+                "sample_id": ["s1", "s2", "s3", "s4"],
+                "long_target": [1, 0, 0, 0],
+                "short_target": [0, 1, 0, 0],
+            }
+        )
+        entry = pd.DataFrame(
+            {
+                "sample_id": ["s1", "s2", "s3", "s4"],
+                "split_id": ["fit_inner", "fit_inner", "fit_inner", "fit_inner"],
+                "long_max_achievable_net_edge_bps": [45.0, 10.0, 65.0, 39.0],
+                "short_max_achievable_net_edge_bps": [8.0, 41.0, 15.0, 70.0],
+            }
+        )
+
+        long_frame = _entry_side_fit_frame(direction, entry, "LONG", "side_opportunity", 40.0)
+        short_frame = _entry_side_fit_frame(direction, entry, "SHORT", "side_opportunity", 40.0)
+
+        self.assertEqual(["s1", "s3"], long_frame["sample_id"].tolist())
+        self.assertEqual(["s2", "s4"], short_frame["sample_id"].tolist())
+
+    def test_nonlinear_pm_probe_exit_metrics_describe_trade_outcomes(self) -> None:
+        trades = pd.DataFrame(
+            {
+                "target_hit": [1, 0, 0],
+                "stop_hit": [0, 1, 0],
+                "time_to_exit_ms": [300_000, 600_000, 2_700_000],
+            }
+        )
+
+        metrics = _exit_metrics(trades)
+
+        self.assertAlmostEqual(1 / 3, metrics["target_hit_rate"])
+        self.assertAlmostEqual(1 / 3, metrics["stop_hit_rate"])
+        self.assertAlmostEqual(1 / 3, metrics["timeout_exit_rate"])
+        self.assertAlmostEqual(20.0, metrics["avg_time_to_exit_min"])
+        self.assertAlmostEqual(10.0, metrics["p50_time_to_exit_min"])
 
     def test_entry_feature_screen_prefers_actual_plan_edge(self) -> None:
         dataset = pd.DataFrame(

training/trader_training/nonlinear_pm_probe.py

@@ -8,7 +8,7 @@ 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_text
+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
 
@@ -20,8 +20,11 @@ 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")
-    direction_model = _fit_direction_model(direction_dataset)
-    entry_models = _fit_entry_models(direction_dataset, entry_dataset)
+    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
@@ -47,28 +50,45 @@ def probe_nonlinear_pm(args: Any) -> 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"
-    write_json(out_dir / "nonlinear_pm_probe_result.json", _jsonable(result))
-    write_text(out_dir / "nonlinear_pm_probe_candidates.csv", tune_frame.head(200).to_csv(index=False))
-    write_text(out_dir / "nonlinear_pm_probe_report.md", _markdown_report(result, tune_frame.head(20)))
+    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 verdict=%s tuneTrades=%s validationTrades=%s stressTrades=%s",
+        "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"],
@@ -76,6 +96,26 @@ def probe_nonlinear_pm(args: Any) -> None:
     )
 
 
+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)
@@ -91,22 +131,56 @@ def _fit_direction_model(dataset: pd.DataFrame) -> HistGradientBoostingClassifie
     return model
 
 
-def _fit_entry_models(direction_dataset: pd.DataFrame, entry_dataset: pd.DataFrame) -> dict[str, Any]:
-    merged = entry_dataset.merge(
-        direction_dataset[["sample_id", "long_target", "short_target"]],
-        on="sample_id",
-        how="inner",
-        validate="one_to_one",
-    )
-    train = merged[merged["split_id"].eq(FIT_SPLIT)].copy()
+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(train[train["long_target"].eq(1)], "long_entry_target", seed=43),
-        "short_entry_prob": _fit_binary_head(train[train["short_target"].eq(1)], "short_entry_target", seed=47),
-        "long_expected_net_edge_bps": _fit_regression_head(train[train["long_target"].eq(1)], "long_actual_plan_net_edge_bps", seed=53),
-        "short_expected_net_edge_bps": _fit_regression_head(train[train["short_target"].eq(1)], "short_actual_plan_net_edge_bps", seed=59),
+        "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)}")
@@ -145,44 +219,45 @@ def _prediction_frame(
     split_id: str,
     direction_dataset: pd.DataFrame,
     entry_dataset: pd.DataFrame,
-    direction_model: HistGradientBoostingClassifier,
+    direction_model: HistGradientBoostingClassifier | None,
     entry_models: dict[str, Any],
 ) -> pd.DataFrame:
     frame = _pm_frame(root, split_id).copy()
-    direction_split = direction_dataset[direction_dataset["split_id"].eq(split_id)].copy()
     entry_split = entry_dataset[entry_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]
 
     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 = direction_pred.merge(entry_pred, on="sample_id", how="inner", validate="one_to_one")
-    out = frame.drop(
-        columns=[
-            "long_prob",
-            "short_prob",
-            "neutral_prob",
-            "long_entry_prob",
-            "short_entry_prob",
-            "pred_long_expected_net_edge_bps",
-            "pred_short_expected_net_edge_bps",
-        ],
-        errors="ignore",
-    ).merge(replacements, on="sample_id", how="inner", validate="one_to_one")
+    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, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.60],
+        [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],
@@ -190,14 +265,14 @@ def _expanded_threshold_candidates() -> list[dict[str, float]]:
     )
     return [
         {
-            "long_open_prob": direction_prob,
-            "short_open_prob": direction_prob,
+            "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 direction_prob, entry_prob, risk_prob, edge_bps, margin in values
+        for long_prob, short_prob, entry_prob, risk_prob, edge_bps, margin in values
     ]
 
 
@@ -213,6 +288,72 @@ def _probe_score(metrics: dict[str, Any]) -> float:
     )
 
 
+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]
@@ -236,14 +377,22 @@ def _x(frame: pd.DataFrame) -> np.ndarray:
 
 
 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，只换成树模型后，PM 能不能筛出稳定正收益。",
+        "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",
@@ -257,6 +406,23 @@ def _markdown_report(result: dict[str, Any], top_candidates: pd.DataFrame) -> st
             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)
 

training/trader_training/pm.py

@@ -400,6 +400,8 @@ def _simulate_open_trades(
     trades["actual_edge_bps"] = np.where(is_long, trades["long_trade_net_edge_bps"], trades["short_trade_net_edge_bps"])
     trades["label_actual_plan_edge_bps"] = np.where(is_long, trades["actual_long_plan_edge_bps"], trades["actual_short_plan_edge_bps"])
     trades["entry_target"] = np.where(is_long, trades["long_entry_target"], trades["short_entry_target"])
+    trades["target_hit"] = np.where(is_long, trades["long_target_hit"], trades["short_target_hit"]).astype(int)
+    trades["stop_hit"] = np.where(is_long, trades["long_stop_hit"], trades["short_stop_hit"]).astype(int)
     effective_pm_config = pm_config or _pm_config_from_thresholds(thresholds)
     effective_price_plan = price_plan or DEFAULT_BACKTEST_PRICE_PLAN
     trades["time_to_exit_ms"] = _time_to_exit_ms(trades, is_long, effective_price_plan)
@@ -424,6 +426,8 @@ def _simulate_open_trades(
             "label_actual_plan_edge_bps",
             "actual_edge_bps",
             "entry_target",
+            "target_hit",
+            "stop_hit",
             "time_to_exit_ms",
             "planned_ratio",
             "weighted_edge_bps",
@@ -447,6 +451,8 @@ def _empty_trade_frame() -> pd.DataFrame:
             "label_actual_plan_edge_bps",
             "actual_edge_bps",
             "entry_target",
+            "target_hit",
+            "stop_hit",
             "time_to_exit_ms",
             "planned_ratio",
             "weighted_edge_bps",