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feat: add OI derived features (oi_change_ma5, oi_price_spread) to dataset builder and ML features

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Add two new OI-derived features to improve ML model's market microstructure
understanding:
- oi_change_ma5: 5-candle moving average of OI change rate (short-term trend)
- oi_price_spread: z-scored OI minus z-scored price return (divergence signal)

Both features use 96-candle rolling z-score window. FEATURE_COLS expanded from
24 to 26. Existing tests updated to reflect new feature counts.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
21in7
2026-03-04 20:07:40 +09:00
parent 676ec6ef5e
commit ff9e639142
4 changed files with 128 additions and 13 deletions
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15
src/ml_features.py
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@@ -9,8 +9,9 @@ FEATURE_COLS = [
"eth_ret_1", "eth_ret_3", "eth_ret_5",
"xrp_btc_rs", "xrp_eth_rs",
# 시장 미시구조: OI 변화율(z-score), 펀딩비(z-score)
# parquet에 oi_change/funding_rate 컬럼이 없으면 dataset_builder에서 0으로 채움
"oi_change", "funding_rate",
# OI 파생 피처
"oi_change_ma5", "oi_price_spread",
"adx",
]
@@ -37,12 +38,14 @@ def build_features(
eth_df: pd.DataFrame | None = None,
oi_change: float | None = None,
funding_rate: float | None = None,
oi_change_ma5: float | None = None,
oi_price_spread: float | None = None,
) -> pd.Series:
"""
기술 지표가 계산된 DataFrame의 마지막 행에서 ML 피처를 추출한다.
btc_df, eth_df가 제공되면 24개 피처를, 없으면 16개 피처를 반환한다.
btc_df, eth_df가 제공되면 26개 피처를, 없으면 18개 피처를 반환한다.
signal: "LONG" | "SHORT"
oi_change, funding_rate: 실제 값이 제공되면 사용, 없으면 0.0으로 채운다.
oi_change, funding_rate, oi_change_ma5, oi_price_spread: 실제 값이 제공되면 사용, 없으면 0.0으로 채운다.
"""
last = df.iloc[-1]
close = last["close"]
@@ -132,8 +135,10 @@ def build_features(
})
# 실시간에서 실제 값이 제공되면 사용, 없으면 0으로 채운다
base["oi_change"] = float(oi_change) if oi_change is not None else 0.0
base["funding_rate"] = float(funding_rate) if funding_rate is not None else 0.0
base["oi_change"] = float(oi_change) if oi_change is not None else 0.0
base["funding_rate"] = float(funding_rate) if funding_rate is not None else 0.0
base["oi_change_ma5"] = float(oi_change_ma5) if oi_change_ma5 is not None else 0.0
base["oi_price_spread"] = float(oi_price_spread) if oi_price_spread is not None else 0.0
base["adx"] = float(last.get("adx", 0))
return pd.Series(base)