db144750a3
- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training. - Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance. - Adjusted deployment scripts to support new backend options and improved error handling for model file transfers. - Added new entries to the training log for better tracking of model performance metrics over time. - Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.
26 lines
699 B
Python
26 lines
699 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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for high, low in zip(future_highs, future_lows):
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if side == "LONG":
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# 보수적 접근: 손절(SL)을 먼저 체크
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if low <= stop_loss:
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return 0
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if high >= take_profit:
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return 1
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else: # SHORT
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# 보수적 접근: 손절(SL)을 먼저 체크
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if high >= stop_loss:
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return 0
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if low <= take_profit:
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return 1
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return None
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