flake8 passing, use pathlib in lieu of os.path to accommodate windows/mac OS
This commit is contained in:
robcaulk
@@ -1,15 +1,17 @@
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import numpy as np
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from typing import Any, Dict, Tuple
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import pandas as pd
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from catboost import CatBoostRegressor, Pool
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from pandas import DataFrame
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from typing import Any, Dict, Tuple
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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class ExamplePredictionModel(IFreqaiModel):
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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(), train(), fit(). The class inherits ModelHandler which
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has its own DataHandler where data is held, saved, loaded, and managed.
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def make_labels(self, dataframe: DataFrame) -> DataFrame:
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@@ -19,15 +21,20 @@ class ExamplePredictionModel(IFreqaiModel):
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:dataframe: the full dataframe for the present training period
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"""
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dataframe['s'] = (dataframe['close'].shift(-self.feature_parameters['period']).rolling(
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self.feature_parameters['period']).max() / dataframe['close'] - 1)
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self.dh.data['s_mean'] = dataframe['s'].mean()
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self.dh.data['s_std'] = dataframe['s'].std()
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dataframe["s"] = (
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dataframe["close"]
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.shift(-self.feature_parameters["period"])
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.rolling(self.feature_parameters["period"])
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.max()
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/ dataframe["close"]
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- 1
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)
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self.dh.data["s_mean"] = dataframe["s"].mean()
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self.dh.data["s_std"] = dataframe["s"].std()
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print('label mean',self.dh.data['s_mean'],'label std',self.dh.data['s_std'])
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return dataframe['s']
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print("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
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return dataframe["s"]
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
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"""
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@@ -35,7 +42,7 @@ class ExamplePredictionModel(IFreqaiModel):
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for storing, saving, loading, and managed.
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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@@ -46,8 +53,12 @@ class ExamplePredictionModel(IFreqaiModel):
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unfiltered_labels = self.make_labels(unfiltered_dataframe)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = self.dh.filter_features(unfiltered_dataframe,
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self.dh.training_features_list, unfiltered_labels, training_filter=True)
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features_filtered, labels_filtered = self.dh.filter_features(
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unfiltered_dataframe,
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self.dh.training_features_list,
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unfiltered_labels,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered)
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@@ -55,46 +66,47 @@ class ExamplePredictionModel(IFreqaiModel):
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data_dictionary = self.dh.standardize_data(data_dictionary)
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# optional additional data cleaning
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if self.feature_parameters['principal_component_analysis']:
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if self.feature_parameters["principal_component_analysis"]:
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self.dh.principal_component_analysis()
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if self.feature_parameters["remove_outliers"]:
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self.dh.remove_outliers(predict=False)
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if self.feature_parameters['DI_threshold']:
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self.dh.data['avg_mean_dist'] = self.dh.compute_distances()
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if self.feature_parameters["DI_threshold"]:
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self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
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print("length of train data", len(data_dictionary['train_features']))
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print("length of train data", len(data_dictionary["train_features"]))
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model = self.fit(data_dictionary)
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print('Finished training')
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print("Finished training")
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print(f'--------------------done training {metadata["pair"]}--------------------')
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return model
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def fit(self, data_dictionary: Dict) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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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_data = Pool(
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data=data_dictionary['train_features'],
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label=data_dictionary['train_labels'],
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weight=data_dictionary['train_weights']
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data=data_dictionary["train_features"],
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label=data_dictionary["train_labels"],
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weight=data_dictionary["train_weights"],
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)
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test_data = Pool(
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data=data_dictionary['test_features'],
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label=data_dictionary['test_labels'],
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weight=data_dictionary['test_weights']
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"],
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weight=data_dictionary["test_weights"],
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)
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model = CatBoostRegressor(verbose=100, early_stopping_rounds=400,
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**self.model_training_parameters)
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model = CatBoostRegressor(
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verbose=100, early_stopping_rounds=400, **self.model_training_parameters
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)
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model.fit(X=train_data, eval_set=test_data)
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return model
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@@ -103,7 +115,7 @@ class ExamplePredictionModel(IFreqaiModel):
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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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: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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@@ -112,27 +124,31 @@ class ExamplePredictionModel(IFreqaiModel):
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print("--------------------Starting prediction--------------------")
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original_feature_list = self.dh.build_feature_list(self.config)
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filtered_dataframe, _ = self.dh.filter_features(unfiltered_dataframe, original_feature_list, training_filter=False)
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filtered_dataframe, _ = self.dh.filter_features(
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unfiltered_dataframe, original_feature_list, training_filter=False
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)
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filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
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self.dh.data_dictionary['prediction_features'] = filtered_dataframe
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self.dh.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning
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if self.feature_parameters['principal_component_analysis']:
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# optional additional data cleaning
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if self.feature_parameters["principal_component_analysis"]:
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pca_components = self.dh.pca.transform(filtered_dataframe)
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self.dh.data_dictionary['prediction_features'] = pd.DataFrame(data=pca_components,
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columns = ['PC'+str(i) for i in range(0,self.dh.data['n_kept_components'])],
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index = filtered_dataframe.index)
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self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
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data=pca_components,
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columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])],
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index=filtered_dataframe.index,
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)
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if self.feature_parameters["remove_outliers"]:
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self.dh.remove_outliers(predict=True) # creates dropped index
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self.dh.remove_outliers(predict=True) # creates dropped index
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if self.feature_parameters['DI_threshold']:
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self.dh.check_if_pred_in_training_spaces() # sets do_predict
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if self.feature_parameters["DI_threshold"]:
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self.dh.check_if_pred_in_training_spaces() # sets do_predict
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predictions = self.model.predict(self.dh.data_dictionary['prediction_features'])
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predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])
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# compute the non-standardized predictions
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predictions = predictions * self.dh.data['labels_std'] + self.dh.data['labels_mean']
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predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"]
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print("--------------------Finished prediction--------------------")
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@@ -1,61 +1,59 @@
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import logging
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from functools import reduce
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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import pandas as pd
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from technical import qtpylib
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import numpy as np
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from freqtrade.strategy import (merge_informative_pair)
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from freqtrade.strategy.interface import IStrategy
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from freqtrade.freqai.strategy_bridge import CustomModel
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from functools import reduce
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from freqtrade.strategy import merge_informative_pair
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from freqtrade.strategy.interface import IStrategy
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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"""
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Example strategy showing how the user connects their own
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Example strategy showing how the user connects their own
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IFreqaiModel to the strategy. Namely, the user uses:
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self.model = CustomModel(self.config)
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self.model.bridge.start(dataframe, metadata)
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to make predictions on their data. populate_any_indicators() automatically
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to make predictions on their data. populate_any_indicators() automatically
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generates the variety of features indicated by the user in the
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canonical freqtrade configuration file under config['freqai'].
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"""
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minimal_roi = {
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"0": 0.01,
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"240": -1
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}
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minimal_roi = {"0": 0.01, "240": -1}
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plot_config = {
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'main_plot': {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"target_roi": {
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"target_roi": {"color": "brown"},
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},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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'subplots': {
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"prediction":{
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'prediction':{'color':'blue'}
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},
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"target_roi":{
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'target_roi':{'color':'brown'},
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},
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"do_predict":{
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'do_predict':{'color':'brown'},
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},
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}
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}
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stoploss = -0.05
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use_sell_signal = True
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startup_candle_count: int = 1000
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startup_candle_count: int = 1000
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def informative_pairs(self):
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pairs = self.freqai_info['corr_pairlist']
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pairs = self.freqai_info["corr_pairlist"]
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informative_pairs = []
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for tf in self.timeframes:
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informative_pairs.append([(pair, tf) for pair in pairs])
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return informative_pairs
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def populate_any_indicators(self, pair, df, tf, informative=None,coin=''):
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def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User can add
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@@ -70,110 +68,116 @@ class FreqaiExampleStrategy(IStrategy):
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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informative[coin+'rsi'] = ta.RSI(informative, timeperiod=14)
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informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25)
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informative[coin+'adx'] = ta.ADX(informative, window=20)
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informative[coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin + "adx"] = ta.ADX(informative, window=20)
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informative[coin+'20sma'] = ta.SMA(informative,timeperiod=20)
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informative[coin+'21ema'] = ta.EMA(informative,timeperiod=21)
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informative[coin+'bmsb'] = np.where(informative[coin+'20sma'].lt(informative[coin+'21ema']),1,0)
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informative[coin+'close_over_20sma'] = informative['close']/informative[coin+'20sma']
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informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20)
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informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "bmsb"] = np.where(
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informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0
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)
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informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"]
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informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25)
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin+'ema21'] = ta.EMA(informative, timeperiod=21)
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informative[coin+'sma20'] = ta.SMA(informative, timeperiod=20)
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informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20)
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stoch = ta.STOCHRSI(informative, 15, 20, 2, 2)
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informative[coin+'srsi-fk'] = stoch['fastk']
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informative[coin+'srsi-fd'] = stoch['fastd']
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informative[coin + "srsi-fk"] = stoch["fastk"]
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informative[coin + "srsi-fd"] = stoch["fastd"]
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
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informative[coin+'bb_lowerband'] = bollinger['lower']
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informative[coin+'bb_middleband'] = bollinger['mid']
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informative[coin+'bb_upperband'] = bollinger['upper']
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informative[coin+'bb_width'] = ((informative[coin+"bb_upperband"] - informative[coin+"bb_lowerband"]) / informative[coin+"bb_middleband"])
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informative[coin+'close-bb_lower'] = informative['close'] / informative[coin+'bb_lowerband']
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informative[coin + "bb_lowerband"] = bollinger["lower"]
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informative[coin + "bb_middleband"] = bollinger["mid"]
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informative[coin + "bb_upperband"] = bollinger["upper"]
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informative[coin + "bb_width"] = (
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informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
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) / informative[coin + "bb_middleband"]
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informative[coin + "close-bb_lower"] = (
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informative["close"] / informative[coin + "bb_lowerband"]
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)
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informative[coin+'roc'] = ta.ROC(informative, timeperiod=3)
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informative[coin+'adx'] = ta.ADX(informative, window=14)
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informative[coin + "roc"] = ta.ROC(informative, timeperiod=3)
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informative[coin + "adx"] = ta.ADX(informative, window=14)
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macd = ta.MACD(informative)
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informative[coin+'macd'] = macd['macd']
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informative[coin+'pct-change'] = informative['close'].pct_change()
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informative[coin+'relative_volume'] = informative['volume'] / informative['volume'].rolling(10).mean()
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informative[coin + "macd"] = macd["macd"]
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informative[coin + "pct-change"] = informative["close"].pct_change()
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informative[coin + "relative_volume"] = (
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informative["volume"] / informative["volume"].rolling(10).mean()
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)
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informative[coin+'pct-change'] = informative['close'].pct_change()
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informative[coin + "pct-change"] = informative["close"].pct_change()
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indicators = [col for col in informative if col.startswith(coin)]
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for n in range(self.freqai_info['feature_parameters']['shift']+1):
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if n==0: continue
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for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix('_shift-'+str(n))
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informative = pd.concat((informative,informative_shift),axis=1)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config['timeframe'], tf, ffill=True)
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skip_columns = [(s + '_'+tf) for s in
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['date', 'open', 'high', 'low', 'close', 'volume']]
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]]
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df = df.drop(columns=skip_columns)
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# the configuration file parameters are stored here
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self.freqai_info = self.config['freqai']
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self.freqai_info = self.config["freqai"]
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# the model is instantiated here
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self.model = CustomModel(self.config)
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print('Populating indicators...')
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print("Populating indicators...")
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# the following loops are necessary for building the features
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# the following loops are necessary for building the features
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# indicated by the user in the configuration file.
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for tf in self.freqai_info['timeframes']:
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dataframe = self.populate_any_indicators(metadata['pair'],
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dataframe.copy(), tf)
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for i in self.freqai_info['corr_pairlist']:
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dataframe = self.populate_any_indicators(i,
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dataframe.copy(), tf, coin=i.split("/")[0]+'-')
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for tf in self.freqai_info["timeframes"]:
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dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf)
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for i in self.freqai_info["corr_pairlist"]:
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dataframe = self.populate_any_indicators(
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i, dataframe.copy(), tf, coin=i.split("/")[0] + "-"
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)
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# the model will return 4 values, its prediction, an indication of whether or not the prediction
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# should be accepted, the target mean/std values from the labels used during each training period.
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(dataframe['prediction'], dataframe['do_predict'],
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dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)
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# the model will return 4 values, its prediction, an indication of whether or not the
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# prediction should be accepted, the target mean/std values from the labels used during
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# each training period.
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(
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dataframe["prediction"],
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dataframe["do_predict"],
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dataframe["target_mean"],
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dataframe["target_std"],
|
||||
) = self.model.bridge.start(dataframe, metadata)
|
||||
|
||||
dataframe['target_roi'] = dataframe['target_mean']+dataframe['target_std']*0.5
|
||||
dataframe['sell_roi'] = dataframe['target_mean']-dataframe['target_std']*1.5
|
||||
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5
|
||||
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5
|
||||
return dataframe
|
||||
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
buy_conditions = [
|
||||
(dataframe['prediction'] > dataframe['target_roi'])
|
||||
&
|
||||
(dataframe['do_predict'] == 1)
|
||||
(dataframe["prediction"] > dataframe["target_roi"]) & (dataframe["do_predict"] == 1)
|
||||
]
|
||||
|
||||
if buy_conditions:
|
||||
dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'buy'] = 1
|
||||
dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
|
||||
# sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
|
||||
sell_conditions = [
|
||||
(dataframe['prediction'] < dataframe['sell_roi'])
|
||||
&
|
||||
(dataframe['do_predict'] == 1)
|
||||
(dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1)
|
||||
]
|
||||
if sell_conditions:
|
||||
dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), 'sell'] = 1
|
||||
dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def get_ticker_indicator(self):
|
||||
return int(self.config['timeframe'][:-1])
|
||||
return int(self.config["timeframe"][:-1])
|
||||
|
||||
Reference in New Issue
Block a user