145 lines
5.1 KiB
Python
145 lines
5.1 KiB
Python
import logging
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from typing import Any, Dict, Tuple
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from pandas import DataFrame
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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import tensorflow as tf
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from freqtrade.freqai.base_models.BaseTensorFlowModel import BaseTensorFlowModel, WindowGenerator
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from tensorflow.keras.layers import Input, Conv1D, Dense
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from tensorflow.keras.models import Model
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import numpy as np
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logger = logging.getLogger(__name__)
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# tf.config.run_functions_eagerly(True)
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# tf.data.experimental.enable_debug_mode()
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MAX_EPOCHS = 10
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class CNNPredictionModel(BaseTensorFlowModel):
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"""
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User created prediction model. The class needs to override three necessary
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functions, predict(), fit().
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"""
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def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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train_df = data_dictionary["train_features"]
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train_labels = data_dictionary["train_labels"]
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test_df = data_dictionary["test_features"]
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test_labels = data_dictionary["test_labels"]
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n_labels = len(train_labels.columns)
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if n_labels > 1:
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raise OperationalException(
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"Neural Net not yet configured for multi-targets. Please "
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" reduce number of targets to 1 in strategy."
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)
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n_features = len(data_dictionary["train_features"].columns)
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BATCH_SIZE = self.freqai_info.get("batch_size", 64)
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input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
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w1 = WindowGenerator(
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input_width=self.CONV_WIDTH,
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label_width=1,
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shift=1,
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train_df=train_df,
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val_df=test_df,
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train_labels=train_labels,
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val_labels=test_labels,
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batch_size=BATCH_SIZE,
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)
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model = self.create_model(input_dims, n_labels)
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steps_per_epoch = np.ceil(len(test_df) / BATCH_SIZE)
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lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
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0.001, decay_steps=steps_per_epoch * 1000, decay_rate=1, staircase=False
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)
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early_stopping = tf.keras.callbacks.EarlyStopping(
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monitor="loss", patience=3, mode="min", min_delta=0.0001
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)
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model.compile(
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loss=tf.losses.MeanSquaredError(),
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optimizer=tf.optimizers.Adam(lr_schedule),
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metrics=[tf.metrics.MeanAbsoluteError()],
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)
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model.fit(
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w1.train,
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epochs=MAX_EPOCHS,
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shuffle=False,
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validation_data=w1.val,
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callbacks=[early_stopping],
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verbose=1,
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)
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return model
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
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) -> Tuple[DataFrame, DataFrame]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:predictions: np.array of 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_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dk, filtered_dataframe)
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if first:
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full_df = dk.data_dictionary["prediction_features"]
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w1 = WindowGenerator(
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input_width=self.CONV_WIDTH,
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label_width=1,
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shift=1,
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test_df=full_df,
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batch_size=len(full_df),
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)
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predictions = self.model.predict(w1.inference)
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len_diff = len(dk.do_predict) - len(predictions)
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if len_diff > 0:
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dk.do_predict = dk.do_predict[len_diff:]
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else:
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data = dk.data_dictionary["prediction_features"]
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data = tf.expand_dims(data, axis=0)
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predictions = self.model(data, training=False)
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predictions = predictions[:, 0, 0]
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pred_df = DataFrame(predictions, columns=dk.label_list)
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pred_df = dk.denormalize_labels_from_metadata(pred_df)
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return (pred_df, np.ones(len(pred_df)))
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def create_model(self, input_dims, n_labels) -> Any:
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input_layer = Input(shape=(input_dims[1], input_dims[2]))
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Layer_1 = Conv1D(filters=32, kernel_size=(self.CONV_WIDTH,), activation="relu")(input_layer)
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Layer_3 = Dense(units=32, activation="relu")(Layer_1)
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output_layer = Dense(units=n_labels)(Layer_3)
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return Model(inputs=input_layer, outputs=output_layer)
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