Delete features_t5.py

features_t5.py

@@ -1,167 +0,0 @@
-"""
-Feature extraction for T+5 model from 1-min OHLCV DataFrames.
-Uses the 09:15-09:20 candle window to predict the day's close.
-"""
-
-import numpy as np
-import pandas as pd
-
-def _safe_fill(arr):
-    return pd.Series(arr).ffill().bfill().values
-
-def extract_semantic_features_t5(df):
-    df = df.copy()
-    df["time"] = df.index.time
-    df["date_only"] = df.index.date
-    required_times = (
-        pd.date_range("09:15", "09:20", freq="min").time.tolist()
-        + [pd.to_datetime("15:10").time()]
-    )
-    df = df[~df.index.duplicated(keep="first")]
-
-    daily_close = df.groupby("date_only")["close"].last()
-    prev_daily_close = daily_close.shift(1)
-
-    time_0921 = pd.to_datetime("09:21").time()
-    time_1200 = pd.to_datetime("12:00").time()
-    df_morning = df[(df["time"] >= time_0921) & (df["time"] <= time_1200)]
-    morning_low_per_date = df_morning.groupby("date_only")["low"].min()
-    morning_high_per_date = df_morning.groupby("date_only")["high"].max()
-
-    df_filtered = df[df["time"].isin(required_times)].copy()
-    pivot_close = df_filtered.pivot(index="date_only", columns="time", values="close")
-    pivot_open  = df_filtered.pivot(index="date_only", columns="time", values="open")
-    pivot_high  = df_filtered.pivot(index="date_only", columns="time", values="high")
-    pivot_low   = df_filtered.pivot(index="date_only", columns="time", values="low")
-    pivot_vol   = df_filtered.pivot(index="date_only", columns="time", values="volume")
-
-    time_0920 = pd.to_datetime("09:20").time()
-    time_1510 = pd.to_datetime("15:10").time()
-
-    if time_0920 not in pivot_close.columns:
-        return None, None, None
-
-    pivot_close = pivot_close.dropna(subset=[time_0920])
-    valid_dates = pivot_close.index
-    times_6m = pd.date_range("09:15", "09:20", freq="min").time
-
-    feature_dicts = []
-    metadata = {}
-
-    for date in valid_dates:
-        f = {}
-        c_series = _safe_fill(pivot_close.loc[date, times_6m].values.astype(float))
-        o_series = _safe_fill(pivot_open.loc[date, times_6m].values.astype(float))
-        h_series = _safe_fill(pivot_high.loc[date, times_6m].values.astype(float))
-        l_series = _safe_fill(pivot_low.loc[date, times_6m].values.astype(float))
-        v_series = pd.Series(pivot_vol.loc[date, times_6m].values.astype(float)).fillna(0).values
-
-        o_915 = o_series[0]
-        c_920 = c_series[-1]
-
-        pdc = prev_daily_close.get(date, np.nan)
-        f["gap"] = 0 if (pd.isna(pdc) or pdc == 0) else (o_915 / pdc) - 1.0
-        f["return_6m"] = (c_920 / o_915) - 1.0 if o_915 != 0 else 0
-        f["std_dev"] = np.std(c_series / (o_915 + 1e-8))
-
-        max_h = np.max(h_series)
-        min_l = np.min(l_series)
-        f["range_pct"] = (max_h - min_l) / (o_915 + 1e-8)
-        f["upper_shadow"] = (max_h - max(o_915, c_920)) / (o_915 + 1e-8)
-        f["lower_shadow"] = (min(o_915, c_920) - min_l) / (o_915 + 1e-8)
-        f["total_vol"] = np.sum(v_series)
-
-        vwap = np.sum(((h_series + l_series + c_series) / 3.0) * v_series) / (np.sum(v_series) + 1e-8)
-        f["vwap_dev"] = (c_920 / vwap) - 1.0 if vwap != 0 else 0
-        f["price_momentum"] = (c_series[-1] - c_series[0]) / (c_series[0] + 1e-8)
-        f["morning_trend"] = np.polyfit(np.arange(len(c_series)), c_series, 1)[0]
-
-        feature_dicts.append(f)
-
-        metadata[date] = {
-            "c_0920": c_920,
-            "h_0920": float(pivot_high.loc[date, time_0920]) if time_0920 in pivot_high.columns else c_920,
-            "l_0920": float(pivot_low.loc[date, time_0920]) if time_0920 in pivot_low.columns else c_920,
-            "v_0920": float(pivot_vol.loc[date, time_0920]) if time_0920 in pivot_vol.columns else 0,
-            "c_1510": float(pivot_close.loc[date, time_1510]) if time_1510 in pivot_close.columns else c_920,
-            "h_1510": float(pivot_high.loc[date, time_1510]) if time_1510 in pivot_high.columns else c_920,
-            "l_1510": float(pivot_low.loc[date, time_1510]) if time_1510 in pivot_low.columns else c_920,
-            "dip_low": float(morning_low_per_date.get(date, c_920)),
-            "peak_high": float(morning_high_per_date.get(date, c_920)),
-        }
-
-    X = pd.DataFrame(feature_dicts, index=valid_dates).fillna(0)
-
-    if time_1510 in pivot_close.columns:
-        target = (pivot_close[time_1510] > pivot_close[time_0920]).astype(int)
-    else:
-        target = pd.Series(0, index=valid_dates)
-
-    return X, target, metadata
-
-def extract_sequential_features_t5(df):
-    df = df.copy()
-    df["time"] = df.index.time
-    df["date_only"] = df.index.date
-    required_times = (
-        pd.date_range("09:15", "09:20", freq="min").time.tolist()
-        + [pd.to_datetime("15:10").time()]
-    )
-    df = df[~df.index.duplicated(keep="first")]
-
-    time_0921 = pd.to_datetime("09:21").time()
-    time_1200 = pd.to_datetime("12:00").time()
-    df_morning = df[(df["time"] >= time_0921) & (df["time"] <= time_1200)]
-    morning_low_per_date = df_morning.groupby("date_only")["low"].min()
-    morning_high_per_date = df_morning.groupby("date_only")["high"].max()
-
-    df_filtered = df[df["time"].isin(required_times)].copy()
-    pivot_close = df_filtered.pivot(index="date_only", columns="time", values="close")
-    pivot_high  = df_filtered.pivot(index="date_only", columns="time", values="high")
-    pivot_low   = df_filtered.pivot(index="date_only", columns="time", values="low")
-    pivot_vol   = df_filtered.pivot(index="date_only", columns="time", values="volume")
-
-    time_0920 = pd.to_datetime("09:20").time()
-    time_1510 = pd.to_datetime("15:10").time()
-
-    if time_0920 not in pivot_close.columns:
-        return None, None, None
-
-    pivot_close = pivot_close.dropna(subset=[time_0920])
-    valid_dates = pivot_close.index
-    times_6m = pd.date_range("09:15", "09:20", freq="min").time
-
-    feature_dicts = []
-    metadata = {}
-
-    for date in valid_dates:
-        f = {}
-        c_series = _safe_fill(pivot_close.loc[date, times_6m].values.astype(float))
-        v_series = pd.Series(pivot_vol.loc[date, times_6m].values.astype(float)).fillna(0).values
-        c_ref = c_series[-1]
-
-        for i, t in enumerate(times_6m):
-            f[f"ret_c_{i}"] = (c_series[i] / (c_ref + 1e-8)) - 1.0
-            f[f"raw_vol_{i}"] = v_series[i]
-        feature_dicts.append(f)
-
-        metadata[date] = {
-            "c_0920": c_ref,
-            "h_0920": float(pivot_high.loc[date, time_0920]) if time_0920 in pivot_high.columns else c_ref,
-            "l_0920": float(pivot_low.loc[date, time_0920]) if time_0920 in pivot_low.columns else c_ref,
-            "v_0920": float(pivot_vol.loc[date, time_0920]) if time_0920 in pivot_vol.columns else 0,
-            "c_1510": float(pivot_close.loc[date, time_1510]) if time_1510 in pivot_close.columns else c_ref,
-            "h_1510": float(pivot_high.loc[date, time_1510]) if time_1510 in pivot_high.columns else c_ref,
-            "l_1510": float(pivot_low.loc[date, time_1510]) if time_1510 in pivot_low.columns else c_ref,
-            "dip_low": float(morning_low_per_date.get(date, c_ref)),
-            "peak_high": float(morning_high_per_date.get(date, c_ref)),
-        }
-
-    X = pd.DataFrame(feature_dicts, index=valid_dates).fillna(0)
-
-    if time_1510 in pivot_close.columns:
-        target = (pivot_close[time_1510] > pivot_close[time_0920]).astype(int)
-    else:
-        target = pd.Series(0, index=valid_dates)
-
-    return X, target, metadata