7e4e9315c2
- Added MLFilter class to load and evaluate LightGBM model for trading signals. - Introduced retraining mechanism to update the model daily based on new data. - Created feature engineering and label building utilities for model training. - Updated bot logic to incorporate ML filter for signal validation. - Added scripts for data fetching and model training. Made-with: Cursor
30 lines
842 B
Python
30 lines
842 B
Python
from typing import Optional
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def build_labels(
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future_closes: list[float],
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future_highs: list[float],
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future_lows: list[float],
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take_profit: float,
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stop_loss: float,
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side: str,
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) -> Optional[int]:
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"""
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진입 이후 미래 캔들을 순서대로 확인해 TP/SL 도달 여부를 판단한다.
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LONG: high >= TP → 1, low <= SL → 0
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SHORT: low <= TP → 1, high >= SL → 0
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둘 다 미도달 → None (학습 데이터에서 제외)
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"""
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for high, low in zip(future_highs, future_lows):
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if side == "LONG":
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if high >= take_profit:
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return 1
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if low <= stop_loss:
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return 0
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else: # SHORT
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if low <= take_profit:
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return 1
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if high >= stop_loss:
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return 0
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return None
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