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Support conditional Entry training

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This commit is contained in:
Codex
2026-06-28 08:40:30 +08:00
parent 0323fb3caf
commit 5ad77ffe90
3 changed files with 102 additions and 10 deletions
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training/trader_training/training.py
+85 -9
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@@ -89,6 +89,8 @@ def train_small_models(args: Any) -> None:
model_manifest: dict[str, Any] = {}
for model_name, spec in TARGETS.items():
dataset = read_parquet(root / "dataset" / spec["dataset"])
if model_name == "ENTRY" and _conditional_entry_enabled(args):
dataset = _attach_direction_fit_labels(root, dataset)
if args.max_rows and len(dataset) > args.max_rows:
dataset = dataset.sort_values("event_time").tail(args.max_rows).copy()
if dataset.empty:
@@ -116,7 +118,9 @@ def train_small_models(args: Any) -> None:
heads: list[LinearHead] = []
head_results: list[HeadResult] = []
for item in spec["heads"]:
head_results.extend(_fit_head(item, x_train_scaled, x_tune_scaled, train, tune, scaler))
head_name = item[0]
head_train_mask, head_filter = _head_train_mask(model_name, head_name, train, args)
head_results.extend(_fit_head(item, x_train_scaled, x_tune_scaled, train, tune, scaler, head_train_mask, head_filter, args))
for result in head_results:
logging.info(
"trader.training.model_head_trained runId=%s model=%s head=%s kind=%s targetSource=%s metrics=%s",
@@ -184,20 +188,83 @@ def train_small_models(args: Any) -> None:
write_json(root / "model" / "model_train_manifest.json", model_manifest)
def _fit_head(item, x_train, x_tune, train: pd.DataFrame, tune: pd.DataFrame, scaler: StandardScaler) -> list[HeadResult]:
def _conditional_entry_enabled(args: Any) -> bool:
return bool(getattr(args, "conditional_entry_direction_labels", False))
def _attach_direction_fit_labels(root: Path, entry_dataset: pd.DataFrame) -> pd.DataFrame:
direction = read_parquet(root / "dataset" / "direction_train.parquet")
required = {"sample_id", "long_target", "short_target"}
missing = sorted(required - set(direction.columns))
if missing:
raise ValueError(f"direction_train is missing columns required by conditional Entry training: {missing}")
merged = entry_dataset.merge(direction[list(required)], on="sample_id", how="inner", validate="one_to_one")
if len(merged) != len(entry_dataset):
raise ValueError(
f"conditional Entry training lost rows while attaching direction labels: before={len(entry_dataset)} after={len(merged)}"
)
logging.info(
"trader.training.entry_direction_labels_attached rowCount=%s longDirectionRows=%s shortDirectionRows=%s",
len(merged),
int(pd.to_numeric(merged["long_target"], errors="coerce").fillna(0).astype(int).sum()),
int(pd.to_numeric(merged["short_target"], errors="coerce").fillna(0).astype(int).sum()),
)
return merged
def _head_train_mask(model_name: str, head_name: str, train: pd.DataFrame, args: Any) -> tuple[np.ndarray, str]:
if model_name != "ENTRY" or not _conditional_entry_enabled(args):
return np.ones(len(train), dtype=bool), "ALL_FIT_ROWS"
if head_name.startswith("long_"):
condition_column = "long_target"
filter_name = "DIRECTION_LABEL_LONG_FIT_ROWS"
elif head_name.startswith("short_"):
condition_column = "short_target"
filter_name = "DIRECTION_LABEL_SHORT_FIT_ROWS"
else:
return np.ones(len(train), dtype=bool), "ALL_FIT_ROWS"
if condition_column not in train.columns:
raise ValueError(f"conditional Entry training requires {condition_column} for head {head_name}")
mask = pd.to_numeric(train[condition_column], errors="coerce").fillna(0).astype(int).eq(1).to_numpy()
return mask, filter_name
def _fit_head(
item,
x_train,
x_tune,
train: pd.DataFrame,
tune: pd.DataFrame,
scaler: StandardScaler,
head_train_mask: np.ndarray | None = None,
head_filter: str = "ALL_FIT_ROWS",
args: Any | None = None,
) -> list[HeadResult]:
name, kind, target, fields, target_names = item
if head_train_mask is None:
head_train_mask = np.ones(len(train), dtype=bool)
head_train_mask = np.asarray(head_train_mask, dtype=bool)
if len(head_train_mask) != len(train):
raise ValueError(f"head train mask length mismatch for {name}: mask={len(head_train_mask)} train={len(train)}")
min_fit_rows = int(getattr(args, "conditional_entry_min_fit_rows", 1000) or 1000) if head_filter != "ALL_FIT_ROWS" else 1
head_fit_rows = int(head_train_mask.sum())
if head_fit_rows < min_fit_rows:
raise ValueError(f"{name} has too few fit rows after {head_filter}: {head_fit_rows} < {min_fit_rows}")
head_train = train.loc[head_train_mask].copy()
x_head_train = x_train[head_train_mask]
if kind == "multiclass":
y_train = train[target].to_numpy().argmax(axis=1)
y_train = head_train[target].to_numpy().argmax(axis=1)
y_val = tune[target].to_numpy().argmax(axis=1)
model = LogisticRegression(max_iter=500)
model.fit(x_train, y_train)
model.fit(x_head_train, y_train)
proba = model.predict_proba(x_tune)
weight, bias = _fold_scaler(model.coef_.T, model.intercept_, scaler)
train_prior = train[target].to_numpy().mean(axis=0)
train_prior = head_train[target].to_numpy().mean(axis=0)
metrics = _multiclass_metrics(y_train, y_val, proba, train_prior)
_add_fit_filter_metrics(metrics, head_filter, head_fit_rows, len(train))
return [HeadResult("direction", target_names[0], "softmax", weight, bias, metrics, proba, y_val)]
if kind == "binary":
y_train = pd.to_numeric(train[target], errors="coerce").fillna(0).astype(int).to_numpy()
y_train = pd.to_numeric(head_train[target], errors="coerce").fillna(0).astype(int).to_numpy()
y_val = pd.to_numeric(tune[target], errors="coerce").fillna(0).astype(int).to_numpy()
if len(np.unique(y_train)) < 2:
prevalence = float(np.clip(y_train.mean(), 1e-6, 1 - 1e-6))
@@ -206,7 +273,7 @@ def _fit_head(item, x_train, x_tune, train: pd.DataFrame, tune: pd.DataFrame, sc
proba = np.full(len(y_val), prevalence, dtype=np.float32)
else:
model = LogisticRegression(max_iter=500)
model.fit(x_train, y_train)
model.fit(x_head_train, y_train)
coef = model.coef_
intercept = model.intercept_
proba = model.predict_proba(x_tune)[:, 1]
@@ -214,20 +281,29 @@ def _fit_head(item, x_train, x_tune, train: pd.DataFrame, tune: pd.DataFrame, sc
metrics = _binary_metrics(y_train, y_val, proba)
if len(np.unique(y_val)) == 2:
metrics["auc"] = float(roc_auc_score(y_val, proba))
_add_fit_filter_metrics(metrics, head_filter, head_fit_rows, len(train))
return [HeadResult(fields[0], target_names[0], "sigmoid", weight, bias, metrics, proba.reshape(-1, 1), y_val)]
if kind == "regression":
y_train = pd.to_numeric(train[target], errors="coerce").fillna(0.0).to_numpy()
y_train = pd.to_numeric(head_train[target], errors="coerce").fillna(0.0).to_numpy()
y_val = pd.to_numeric(tune[target], errors="coerce").fillna(0.0).to_numpy()
model = HuberRegressor(alpha=0.001, epsilon=1.35, max_iter=500)
model.fit(x_train, y_train)
model.fit(x_head_train, y_train)
pred = model.predict(x_tune)
weight, bias = _fold_scaler(model.coef_.reshape(1, -1).T, np.array([model.intercept_]), scaler)
metrics = _regression_metrics(y_train, y_val, pred)
metrics["target_source"] = target
_add_fit_filter_metrics(metrics, head_filter, head_fit_rows, len(train))
return [HeadResult(fields[0], None, "identity", weight, bias, metrics, pred.reshape(-1, 1), y_val)]
raise ValueError(f"unsupported head kind: {kind}")
def _add_fit_filter_metrics(metrics: dict[str, Any], fit_filter: str, fit_rows: int, total_fit_rows: int) -> None:
metrics["fit_filter"] = fit_filter
metrics["fit_rows"] = int(fit_rows)
metrics["fit_total_rows"] = int(total_fit_rows)
metrics["fit_row_ratio"] = float(fit_rows / total_fit_rows) if total_fit_rows else 0.0
def _fold_scaler(weight_scaled: np.ndarray, bias_scaled: np.ndarray, scaler: StandardScaler) -> tuple[np.ndarray, np.ndarray]:
scale = np.where(scaler.scale_ == 0, 1.0, scaler.scale_)
weight = weight_scaled / scale.reshape(-1, 1)