stable/freqtrade/templates/ExamplePredictionModel.py

140 lines
6.0 KiB
Python

import numpy as np
import pandas as pd
from catboost import CatBoostRegressor, Pool
from pandas import DataFrame
from typing import Any, Dict, Tuple
from freqtrade.freqai.freqai_interface import IFreqaiModel
class ExamplePredictionModel(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def make_labels(self, dataframe: DataFrame) -> DataFrame:
"""
User defines the labels here (target values).
:params:
:dataframe: the full dataframe for the present training period
"""
dataframe['s'] = (dataframe['close'].shift(-self.feature_parameters['period']).rolling(
self.feature_parameters['period']).max() / dataframe['close'] - 1)
self.dh.data['s_mean'] = dataframe['s'].mean()
self.dh.data['s_std'] = dataframe['s'].std()
print('label mean',self.dh.data['s_mean'],'label std',self.dh.data['s_std'])
return dataframe['s']
def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and managed.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
print("--------------------Starting training--------------------")
# create the full feature list based on user config info
self.dh.training_features_list = self.dh.build_feature_list(self.config)
unfiltered_labels = self.make_labels(unfiltered_dataframe)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = self.dh.filter_features(unfiltered_dataframe,
self.dh.training_features_list, unfiltered_labels, training_filter=True)
# split data into train/test data.
data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered)
# standardize all data based on train_dataset only
data_dictionary = self.dh.standardize_data(data_dictionary)
# optional additional data cleaning
if self.feature_parameters['principal_component_analysis']:
self.dh.principal_component_analysis()
if self.feature_parameters["remove_outliers"]:
self.dh.remove_outliers(predict=False)
if self.feature_parameters['DI_threshold']:
self.dh.data['avg_mean_dist'] = self.dh.compute_distances()
print("length of train data", len(data_dictionary['train_features']))
model = self.fit(data_dictionary)
print('Finished training')
print(f'--------------------done training {metadata["pair"]}--------------------')
return model
def fit(self, data_dictionary: Dict) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(
data=data_dictionary['train_features'],
label=data_dictionary['train_labels'],
weight=data_dictionary['train_weights']
)
test_data = Pool(
data=data_dictionary['test_features'],
label=data_dictionary['test_labels'],
weight=data_dictionary['test_weights']
)
model = CatBoostRegressor(verbose=100, early_stopping_rounds=400,
**self.model_training_parameters)
model.fit(X=train_data, eval_set=test_data)
return model
def predict(self, unfiltered_dataframe: DataFrame) -> 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)
"""
print("--------------------Starting prediction--------------------")
original_feature_list = self.dh.build_feature_list(self.config)
filtered_dataframe, _ = self.dh.filter_features(unfiltered_dataframe, original_feature_list, training_filter=False)
filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
self.dh.data_dictionary['prediction_features'] = filtered_dataframe
# optional additional data cleaning
if self.feature_parameters['principal_component_analysis']:
pca_components = self.dh.pca.transform(filtered_dataframe)
self.dh.data_dictionary['prediction_features'] = pd.DataFrame(data=pca_components,
columns = ['PC'+str(i) for i in range(0,self.dh.data['n_kept_components'])],
index = filtered_dataframe.index)
if self.feature_parameters["remove_outliers"]:
self.dh.remove_outliers(predict=True) # creates dropped index
if self.feature_parameters['DI_threshold']:
self.dh.check_if_pred_in_training_spaces() # sets do_predict
predictions = self.model.predict(self.dh.data_dictionary['prediction_features'])
# compute the non-standardized predictions
predictions = predictions * self.dh.data['labels_std'] + self.dh.data['labels_mean']
print("--------------------Finished prediction--------------------")
return (predictions, self.dh.do_predict)