mirror of
https://github.com/freqtrade/freqtrade.git
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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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import logging
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from typing import Tuple
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import numpy as np
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import numpy.typing as npt
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import torch
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from pandas import DataFrame
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from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class PyTorchRegressor(BasePyTorchModel):
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"""
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A PyTorch implementation of a regressor.
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User must implement fit method
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def predict(
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self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
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) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
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"""
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Filter the prediction features data and predict with it.
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:param unfiltered_df: Full dataframe for the current backtest period.
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:return:
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:pred_df: dataframe containing the predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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dk.find_features(unfiltered_df)
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filtered_df, _ = dk.filter_features(
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unfiltered_df, dk.training_features_list, training_filter=False
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)
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filtered_df = dk.normalize_data_from_metadata(filtered_df)
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dk.data_dictionary["prediction_features"] = filtered_df
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self.data_cleaning_predict(dk)
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x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
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.float()\
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.to(self.device)
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y = self.model.model(x)
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pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
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return (pred_df, dk.do_predict)
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