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

feat(ml): parameterize SL/TP multipliers in dataset_builder

Browse Source 
Add atr_sl_mult and atr_tp_mult parameters to _calc_labels_vectorized
and generate_dataset_vectorized, defaulting to existing constants (1.5,
2.0) for full backward compatibility. Callers (train scripts, backtester)
can now pass symbol-specific multipliers without modifying module-level
constants.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
21in7
2026-03-21 18:03:24 +09:00
parent 41b0aa3f28
commit 75d1af7fcc
2 changed files with 68 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
src/dataset_builder.py
View File

@@ -323,6 +323,8 @@ def _calc_labels_vectorized(
d: pd.DataFrame,
feat: pd.DataFrame,
sig_idx: np.ndarray,
atr_sl_mult: float = ATR_SL_MULT,
atr_tp_mult: float = ATR_TP_MULT,
) -> tuple[np.ndarray, np.ndarray]:
"""
label_builder.py build_labels() 로직을 numpy 2D 배열로 벡터화한다.
@@ -348,11 +350,11 @@ def _calc_labels_vectorized(
continue
if signal == "LONG":
sl = entry - atr * ATR_SL_MULT
tp = entry + atr * ATR_TP_MULT
sl = entry - atr * atr_sl_mult
tp = entry + atr * atr_tp_mult
else:
sl = entry + atr * ATR_SL_MULT
tp = entry - atr * ATR_TP_MULT
sl = entry + atr * atr_sl_mult
tp = entry - atr * atr_tp_mult
end = min(idx + 1 + LOOKAHEAD, n_total)
fut_high = highs[idx + 1 : end]
@@ -391,6 +393,8 @@ def generate_dataset_vectorized(
signal_threshold: int = 3,
adx_threshold: float = 25,
volume_multiplier: float = 2.5,
atr_sl_mult: float = ATR_SL_MULT,
atr_tp_mult: float = ATR_TP_MULT,
) -> pd.DataFrame:
"""
전체 시계열을 1회 계산해 학습 데이터셋을 생성한다.
@@ -435,7 +439,10 @@ def generate_dataset_vectorized(
print(f" 신호 발생 인덱스: {len(sig_idx):,}개")
print(" [3/3] 레이블 계산...")
labels, valid_mask = _calc_labels_vectorized(d, feat_all, sig_idx)
labels, valid_mask = _calc_labels_vectorized(
d, feat_all, sig_idx,
atr_sl_mult=atr_sl_mult, atr_tp_mult=atr_tp_mult,
)
final_sig_idx = sig_idx[valid_mask]
available_feature_cols = [c for c in FEATURE_COLS if c in feat_all.columns]