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Feature extraction from 1-min OHLCV DataFrames.
Two pipelines:
- semantic: Hand-crafted morning indicators (gap, VWAP deviation, momentum, etc.)
- sequential: Raw normalised return and volume vectors for the 09:15-09:30 window.
Both return (X, y, metadata) where:
X : pd.DataFrame of features, indexed by date
y : pd.Series of binary labels (1 = price up 09:30->15:10)
metadata : dict[date] -> { c_0930, h_0930, l_0930, v_0930, c_1510, h_1510, l_1510 }
"""
import numpy as np
import pandas as pd
# ── Helpers ──────────────────────────────────────────────────────────────────
def _safe_fill(arr):
"""Forward-fill then back-fill NaNs in a 1-D array."""
return pd.Series(arr).ffill().bfill().values
# ── Semantic features ────────────────────────────────────────────────────────
def extract_semantic_features(df):
"""
Compute high-level morning indicators from the 09:15-09:30 candle window.
Features: gap, return_16m, return_first_5m, return_next_11m, std_dev,
range_pct, upper_shadow, lower_shadow, total_vol, vwap_dev,
price_momentum, vol_momentum, morning_trend.
"""
df = df.copy()
df["time"] = df.index.time
df["date_only"] = df.index.date
required_times = (
pd.date_range("09:15", "09:30", 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)
# Compute morning session dip/peak (09:31 to 12:00) for limit-order entries
time_0931 = pd.to_datetime("09:31").time()
time_1200 = pd.to_datetime("12:00").time()
df_morning = df[(df["time"] >= time_0931) & (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_0930 = pd.to_datetime("09:30").time()
time_1510 = pd.to_datetime("15:10").time()
if time_0930 not in pivot_close.columns:
return None, None, None
pivot_close = pivot_close.dropna(subset=[time_0930])
valid_dates = pivot_close.index
times_15m = pd.date_range("09:15", "09:30", freq="min").time
feature_dicts = []
metadata = {}
for date in valid_dates:
f = {}
c_series = _safe_fill(pivot_close.loc[date, times_15m].values.astype(float))
o_series = _safe_fill(pivot_open.loc[date, times_15m].values.astype(float))
h_series = _safe_fill(pivot_high.loc[date, times_15m].values.astype(float))
l_series = _safe_fill(pivot_low.loc[date, times_15m].values.astype(float))
v_series = pd.Series(pivot_vol.loc[date, times_15m].values.astype(float)).fillna(0).values
o_915 = o_series[0]
c_930 = c_series[-1]
c_919 = c_series[4] if len(c_series) > 4 else 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_16m"] = (c_930 / o_915) - 1.0 if o_915 != 0 else 0
f["return_first_5m"] = (c_919 / o_915) - 1.0 if o_915 != 0 else 0
f["return_next_11m"] = (c_930 / c_919) - 1.0 if c_919 != 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_930)) / (o_915 + 1e-8)
f["lower_shadow"] = (min(o_915, c_930) - 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_930 / vwap) - 1.0 if vwap != 0 else 0
f["price_momentum"] = (c_series[-1] - c_series[-3]) / (c_series[-3] + 1e-8)
f["vol_momentum"] = (v_series[-1] - v_series[-3]) / (v_series[-3] + 1e-8)
f["morning_trend"] = np.polyfit(np.arange(len(c_series)), c_series, 1)[0]
feature_dicts.append(f)
metadata[date] = {
"c_0930": c_930,
"h_0930": float(pivot_high.loc[date, time_0930]) if time_0930 in pivot_high.columns else c_930,
"l_0930": float(pivot_low.loc[date, time_0930]) if time_0930 in pivot_low.columns else c_930,
"v_0930": float(pivot_vol.loc[date, time_0930]) if time_0930 in pivot_vol.columns else 0,
"c_1510": float(pivot_close.loc[date, time_1510]) if time_1510 in pivot_close.columns else c_930,
"h_1510": float(pivot_high.loc[date, time_1510]) if time_1510 in pivot_high.columns else c_930,
"l_1510": float(pivot_low.loc[date, time_1510]) if time_1510 in pivot_low.columns else c_930,
"dip_low": float(morning_low_per_date.get(date, c_930)),
"peak_high": float(morning_high_per_date.get(date, c_930)),
}
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_0930]).astype(int)
else:
target = pd.Series(0, index=valid_dates)
return X, target, metadata
# ── Sequential features ──────────────────────────────────────────────────────
def extract_sequential_features(df):
"""
Compute normalised return and raw volume vectors for each minute
in the 09:15-09:30 window, relative to the 09:30 close.
"""
df = df.copy()
df["time"] = df.index.time
df["date_only"] = df.index.date
required_times = (
pd.date_range("09:15", "09:30", freq="min").time.tolist()
+ [pd.to_datetime("15:10").time()]
)
df = df[~df.index.duplicated(keep="first")]
# Compute morning session dip/peak (09:31 to 12:00) for limit-order entries
time_0931 = pd.to_datetime("09:31").time()
time_1200 = pd.to_datetime("12:00").time()
df_morning = df[(df["time"] >= time_0931) & (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_0930 = pd.to_datetime("09:30").time()
time_1510 = pd.to_datetime("15:10").time()
if time_0930 not in pivot_close.columns:
return None, None, None
pivot_close = pivot_close.dropna(subset=[time_0930])
valid_dates = pivot_close.index
times_15m = pd.date_range("09:15", "09:30", freq="min").time
feature_dicts = []
metadata = {}
for date in valid_dates:
f = {}
c_series = _safe_fill(pivot_close.loc[date, times_15m].values.astype(float))
v_series = pd.Series(pivot_vol.loc[date, times_15m].values.astype(float)).fillna(0).values
c_ref = c_series[-1]
for i, t in enumerate(times_15m):
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_0930": c_ref,
"h_0930": float(pivot_high.loc[date, time_0930]) if time_0930 in pivot_high.columns else c_ref,
"l_0930": float(pivot_low.loc[date, time_0930]) if time_0930 in pivot_low.columns else c_ref,
"v_0930": float(pivot_vol.loc[date, time_0930]) if time_0930 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_0930]).astype(int)
else:
target = pd.Series(0, index=valid_dates)
return X, target, metadata
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