thinkingEverytime/QuantOracle

1"""Feature engineering (EOD) - minimal but useful."""2 3from __future__ import annotations4 5import pandas as pd6 7 8def build_features(h: pd.DataFrame) -> pd.DataFrame:9    """Return per-date features. Expects OHLCV with Date index and Close column."""10    if h is None or h.empty or "Close" not in h:11        return pd.DataFrame()12 13    close = h["Close"].astype(float)14    ret1 = close.pct_change(1)15    ret5 = close.pct_change(5)16    ret20 = close.pct_change(20)17    vol20 = close.pct_change().rolling(20).std()18 19    sma20 = close.rolling(20).mean()20    sma50 = close.rolling(50).mean()21    delta = close.diff()22    gain = delta.where(delta > 0, 0.0)23    loss = (-delta).where(delta < 0, 0.0)24    avg_gain = gain.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()25    avg_loss = loss.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()26    rs = avg_gain / avg_loss27    rsi14 = 100 - (100 / (1 + rs))28 29    out = pd.DataFrame(30        {31            "ret_1d": ret1,32            "ret_5d": ret5,33            "ret_20d": ret20,34            "vol_20d": vol20,35            "price_sma20": close / sma20 - 1,36            "price_sma50": close / sma50 - 1,37            "rsi_14": rsi14,38        },39        index=h.index,40    )41    return out.dropna()42 43 44def build_targets(close: pd.Series, horizon: int = 5) -> pd.Series:45    """Forward return over horizon."""46    close = close.astype(float)47    return close.shift(-horizon) / close - 1.048