File size: 6,468 Bytes
bf4b95a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | from __future__ import annotations
import argparse
import json
import sys
from dataclasses import asdict, dataclass
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import joblib
import numpy as np
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, brier_score_loss, log_loss, roc_auc_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from nifty_backend.runtime import (
DECISION_OVERLAYS,
MODEL_DIR,
MODEL_PATH,
OPENING_DATASET_PATH,
ProbabilityBlend,
apply_decision_overlays,
directional_confidence,
predict_proba_up,
)
DEFAULT_TRAIN_END = pd.Timestamp("2023-12-31")
DEFAULT_VALID_END = pd.Timestamp("2025-08-17")
RANDOM_SEED = 42
@dataclass
class RetrainSummary:
model_name: str
threshold: float
train_rows: int
valid_rows: int
test_rows: int
validation_accuracy: float
test_accuracy: float
validation_auc: float
test_auc: float
test_brier: float
latest_prediction: str
latest_prob_up: float
latest_confidence: float
feature_count: int
def feature_columns(frame: pd.DataFrame) -> list[str]:
excluded = {
"date",
"first5_start",
"first5_end",
"target",
"day_open",
"day_high",
"day_low",
"day_close",
"day_volume",
"day_return",
}
cols = []
for col in frame.columns:
if col in excluded:
continue
if pd.api.types.is_numeric_dtype(frame[col]) and frame[col].notna().mean() >= 0.40:
if frame[col].nunique(dropna=True) > 1:
cols.append(col)
return cols
def best_threshold(y_true: np.ndarray, prob_up: np.ndarray) -> tuple[float, float]:
thresholds = np.linspace(0.35, 0.65, 301)
scores = ((prob_up[:, None] >= thresholds[None, :]) == y_true[:, None]).mean(axis=0)
idx = int(np.argmax(scores))
return float(thresholds[idx]), float(scores[idx])
def score_auc(y_true: np.ndarray, prob_up: np.ndarray) -> float:
if len(np.unique(y_true)) < 2:
return float("nan")
return float(roc_auc_score(y_true, prob_up))
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Retrain the compact NIFTY opening-direction model from Parquet data.")
parser.add_argument("--train-end", default=DEFAULT_TRAIN_END.date().isoformat())
parser.add_argument("--valid-end", default=DEFAULT_VALID_END.date().isoformat())
return parser.parse_args()
def main() -> None:
args = parse_args()
train_end = pd.Timestamp(args.train_end)
valid_end = pd.Timestamp(args.valid_end)
frame = pd.read_parquet(OPENING_DATASET_PATH)
frame["date"] = pd.to_datetime(frame["date"], errors="coerce")
model_frame = frame.dropna(subset=["target"]).sort_values("date").reset_index(drop=True)
features = feature_columns(model_frame)
train_df = model_frame[model_frame["date"] <= train_end]
valid_df = model_frame[(model_frame["date"] > train_end) & (model_frame["date"] <= valid_end)]
test_df = model_frame[model_frame["date"] > valid_end]
if train_df.empty or valid_df.empty or test_df.empty:
raise RuntimeError("Training, validation, and test windows must all contain rows.")
x_train = train_df[features]
y_train = train_df["target"].to_numpy(dtype="int64")
x_valid = valid_df[features]
y_valid = valid_df["target"].to_numpy(dtype="int64")
x_test = test_df[features]
y_test = test_df["target"].to_numpy(dtype="int64")
extra_trees = make_pipeline(
SimpleImputer(strategy="median"),
ExtraTreesClassifier(
n_estimators=800,
max_depth=4,
min_samples_leaf=28,
max_features=0.60,
class_weight="balanced_subsample",
random_state=RANDOM_SEED + 13,
n_jobs=-1,
),
)
logit = make_pipeline(
SimpleImputer(strategy="median"),
StandardScaler(),
LogisticRegression(C=0.25, class_weight="balanced", max_iter=2000, random_state=RANDOM_SEED),
)
extra_trees.fit(x_train, y_train)
logit.fit(x_train, y_train)
model = ProbabilityBlend([extra_trees, logit], np.array([0.75, 0.25]))
valid_prob = predict_proba_up(model, x_valid)
test_prob = predict_proba_up(model, x_test)
threshold, _ = best_threshold(y_valid, valid_prob)
valid_pred = apply_decision_overlays((valid_prob >= threshold).astype("int64"), valid_df, DECISION_OVERLAYS)
test_pred = apply_decision_overlays((test_prob >= threshold).astype("int64"), test_df, DECISION_OVERLAYS)
latest = frame.iloc[[-1]].copy()
latest_prob = predict_proba_up(model, latest[features])
latest_pred = apply_decision_overlays((latest_prob >= threshold).astype("int64"), latest, DECISION_OVERLAYS)
latest_conf = directional_confidence(latest_prob, latest_pred, threshold)
payload = {
"model": model,
"features": features,
"threshold": threshold,
"target": "same-day NIFTY 50 close > same-day NIFTY 50 open after first five 1-minute bars",
"model_name": "compact_extra_trees_logit_overlay",
"decision_overlays": DECISION_OVERLAYS,
}
joblib.dump(payload, MODEL_PATH)
summary = RetrainSummary(
model_name=payload["model_name"],
threshold=float(threshold),
train_rows=int(len(train_df)),
valid_rows=int(len(valid_df)),
test_rows=int(len(test_df)),
validation_accuracy=float(accuracy_score(y_valid, valid_pred)),
test_accuracy=float(accuracy_score(y_test, test_pred)),
validation_auc=score_auc(y_valid, valid_prob),
test_auc=score_auc(y_test, test_prob),
test_brier=float(brier_score_loss(y_test, np.clip(test_prob, 1e-6, 1 - 1e-6))),
latest_prediction="UP" if int(latest_pred[0]) == 1 else "DOWN",
latest_prob_up=float(latest_prob[0]),
latest_confidence=float(latest_conf[0]),
feature_count=int(len(features)),
)
(MODEL_DIR / "summary.json").write_text(json.dumps(asdict(summary), indent=2), encoding="utf-8")
pd.DataFrame([asdict(summary)]).to_csv(MODEL_DIR / "retrain_summary.csv", index=False)
print(asdict(summary))
if __name__ == "__main__":
main()
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