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Improve nonlinear PM diagnostics

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This commit is contained in:
Codex
2026-06-28 09:59:36 +08:00
parent 6be4bb976a
commit 0fe3bd864e
4 changed files with 282 additions and 46 deletions
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training/trader_training/nonlinear_pm_probe.py
+210 -44
View File
@@ -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
+6
View File
@@ -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",