Source code for tensortrade.features.indicators.simple_moving_average

# Copyright 2019 The TensorTrade Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import pandas as pd
import numpy as np

from gym import Space
from copy import copy
from typing import Union, List, Tuple, Dict

from tensortrade.features.feature_transformer import FeatureTransformer


[docs]class SimpleMovingAverage(FeatureTransformer): """A transformer to add the simple moving average of a column to a feature pipeline."""
[docs] def __init__(self, columns: Union[List[str], str, None] = None, window_size: int = 20, inplace: bool = True, **kwargs): """ Arguments: columns (optional): A list of column names to normalize. window_size (optional): The length of the moving average window. Defaults to 20. inplace (optional): If `False`, a new column will be added to the output for each input column. """ super().__init__(columns=columns, inplace=inplace) self._window_size = window_size
[docs] def transform(self, X: pd.DataFrame, input_space: Space) -> pd.DataFrame: if self.columns is None: self.columns = list(X.columns) for column in self.columns: moving_average = X[column].rolling(self._window_size).mean() if not self._inplace: column = '{}_sma_{}'.format(column, self._window_size) args = dict(zip(column, moving_average)) X.assign(**args) return X