gihyeon/cointrader
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: implement LightGBM model improvement plan with feature normalization and walk-forward validation

Browse Source 
- Added a new markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation.
- Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes.
- Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance.
- Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility.
- Adjusted model training parameters for better performance and included detailed validation results in the training log.
This commit is contained in:
21in7
2026-03-01 22:02:32 +09:00
parent c6428af64e
commit a6697e7cca
7 changed files with 487 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
src/dataset_builder.py
View File

@@ -115,6 +115,16 @@ def _calc_signals(d: pd.DataFrame) -> np.ndarray:
return signal_arr
def _rolling_zscore(arr: np.ndarray, window: int = 200) -> np.ndarray:
"""rolling window z-score 정규화. window 미만 구간은 0으로 채운다.
절대값 피처(수익률, ATR 등)를 레짐 변화에 무관하게 만든다."""
s = pd.Series(arr.astype(np.float64))
mean = s.rolling(window, min_periods=window).mean()
std = s.rolling(window, min_periods=window).std()
z = (s - mean) / std.where(std > 0, other=np.nan)
return z.fillna(0).values.astype(np.float32)
def _calc_features_vectorized(
d: pd.DataFrame,
signal_arr: np.ndarray,
@@ -158,6 +168,13 @@ def _calc_features_vectorized(
ret_3 = close.pct_change(3).fillna(0).values
ret_5 = close.pct_change(5).fillna(0).values
# 절대값 피처를 rolling z-score로 정규화 (레짐 변화에 강하게)
atr_pct_z = _rolling_zscore(atr_pct)
vol_ratio_z = _rolling_zscore(vol_ratio)
ret_1_z = _rolling_zscore(ret_1)
ret_3_z = _rolling_zscore(ret_3)
ret_5_z = _rolling_zscore(ret_5)
prev_macd = macd.shift(1).fillna(0).values
prev_macd_sig = macd_sig.shift(1).fillna(0).values
@@ -190,11 +207,11 @@ def _calc_features_vectorized(
"ema_align": ema_align,
"stoch_k": stoch_k.values.astype(np.float32),
"stoch_d": stoch_d.values.astype(np.float32),
"atr_pct": atr_pct.astype(np.float32),
"vol_ratio": vol_ratio.astype(np.float32),
"ret_1": ret_1.astype(np.float32),
"ret_3": ret_3.astype(np.float32),
"ret_5": ret_5.astype(np.float32),
"atr_pct": atr_pct_z,
"vol_ratio": vol_ratio_z,
"ret_1": ret_1_z,
"ret_3": ret_3_z,
"ret_5": ret_5_z,
"signal_strength": strength,
"side": side,
"_signal": signal_arr, # 레이블 계산용 임시 컬럼
@@ -223,13 +240,18 @@ def _calc_features_vectorized(
eth_r5 = _align(eth_ret_5, n).astype(np.float32)
xrp_r1 = ret_1.astype(np.float32)
xrp_btc_rs = np.where(btc_r1 != 0, xrp_r1 / btc_r1, 0.0).astype(np.float32)
xrp_eth_rs = np.where(eth_r1 != 0, xrp_r1 / eth_r1, 0.0).astype(np.float32)
xrp_btc_rs_raw = np.where(btc_r1 != 0, xrp_r1 / btc_r1, 0.0).astype(np.float32)
xrp_eth_rs_raw = np.where(eth_r1 != 0, xrp_r1 / eth_r1, 0.0).astype(np.float32)
extra = pd.DataFrame({
"btc_ret_1": btc_r1, "btc_ret_3": btc_r3, "btc_ret_5": btc_r5,
"eth_ret_1": eth_r1, "eth_ret_3": eth_r3, "eth_ret_5": eth_r5,
"xrp_btc_rs": xrp_btc_rs, "xrp_eth_rs": xrp_eth_rs,
"btc_ret_1": _rolling_zscore(btc_r1),
"btc_ret_3": _rolling_zscore(btc_r3),
"btc_ret_5": _rolling_zscore(btc_r5),
"eth_ret_1": _rolling_zscore(eth_r1),
"eth_ret_3": _rolling_zscore(eth_r3),
"eth_ret_5": _rolling_zscore(eth_r5),
"xrp_btc_rs": _rolling_zscore(xrp_btc_rs_raw),
"xrp_eth_rs": _rolling_zscore(xrp_eth_rs_raw),
}, index=d.index)
result = pd.concat([result, extra], axis=1)