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