Indicator คือกฎการวัด ไม่ใช่คำทำนาย และไม่ใช่คำสั่งซื้อขาย

import numpy as np
import pandas as pd

def build_features(df: pd.DataFrame) -> pd.DataFrame:
    x = df.sort_values(["instrument_id", "trading_date"]).copy()
    g = x.groupby("instrument_id", group_keys=False)

    x["log_return_1d"] = g["adjusted_close"].transform(
        lambda s: np.log(s / s.shift(1)))
    x["momentum_20"] = g["adjusted_close"].transform(
        lambda s: s / s.shift(20) - 1)
    x["sma_distance_20"] = x["adjusted_close"] / g["adjusted_close"].transform(
        lambda s: s.rolling(20, min_periods=20).mean()) - 1
    x["volatility_20"] = g["log_return_1d"].transform(
        lambda s: s.rolling(20, min_periods=20).std())
    x["volume_z_20"] = g["volume"].transform(
        lambda s: (s - s.rolling(20).mean()) / s.rolling(20).std())
    mid = (x["best_bid"] + x["best_ask"]) / 2
    x["relative_spread"] = (x["best_ask"] - x["best_bid"]) / mid

    feature_cols = ["log_return_1d", "momentum_20", "sma_distance_20",
                    "volatility_20", "volume_z_20", "relative_spread"]
    # Example contract: a decision at date t uses features available through t-1.
    x[feature_cols] = g[feature_cols].shift(1)
    x["feature_version"] = "daily_market_v1"
    return x