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The AverageStrategy is a trading strategy implemented in Python for backtesting purposes. It is a basic proof of concept strategy that focuses on buying and selling based on moving average crossovers. Here's a breakdown of how the strategy works:
The strategy calculates various technical indicators using the TA-Lib library and assigns them to the input dataframe.
It calculates the MACD indicator.
It calculates two exponential moving averages (EMAs) with different time periods and assigns them to 'maShort' and 'maMedium' columns in the dataframe.
The strategy determines the buy signals based on the crossover of the shorter-term moving average (maShort) above the longer-term moving average (maMedium). It uses the qtpylib library to identify the crossover condition. When the crossover condition is met, a 'buy' signal of value 1 is assigned to the corresponding row in the 'buy' column of the dataframe. The strategy determines the sell signals based on the crossover of the longer-term moving average (maMedium) above the shorter-term moving average (maShort). It uses the qtpylib library to identify the crossover condition. When the crossover condition is met, a 'sell' signal of value 1 is assigned to the corresponding row in the 'sell' column of the dataframe. The strategy also includes some predefined parameters: minimal_roi: Specifies the minimal return on investment (ROI) for the strategy. In this case, it is set to 0.5, meaning a trade will be closed when it reaches a 0.5 ROI. stoploss: Specifies the stop-loss value for the strategy. In this case, it is set to -0.2, meaning a trade will be closed if it reaches a 0.2 loss. ticker_interval: Specifies the interval at which the strategy operates. In this case, it is set to '4h', indicating a 4-hour interval. Please note that the strategy is described as a basic proof of concept and may not perform well in actual trading scenarios.
It calculates the MACD indicator.
It calculates two exponential moving averages (EMAs) with different time periods and assigns them to 'maShort' and 'maMedium' columns in the dataframe.
The strategy determines the buy signals based on the crossover of the shorter-term moving average (maShort) above the longer-term moving average (maMedium). It uses the qtpylib library to identify the crossover condition. When the crossover condition is met, a 'buy' signal of value 1 is assigned to the corresponding row in the 'buy' column of the dataframe. The strategy determines the sell signals based on the crossover of the longer-term moving average (maMedium) above the shorter-term moving average (maShort). It uses the qtpylib library to identify the crossover condition. When the crossover condition is met, a 'sell' signal of value 1 is assigned to the corresponding row in the 'sell' column of the dataframe. The strategy also includes some predefined parameters: minimal_roi: Specifies the minimal return on investment (ROI) for the strategy. In this case, it is set to 0.5, meaning a trade will be closed when it reaches a 0.5 ROI. stoploss: Specifies the stop-loss value for the strategy. In this case, it is set to -0.2, meaning a trade will be closed if it reaches a 0.2 loss. ticker_interval: Specifies the interval at which the strategy operates. In this case, it is set to '4h', indicating a 4-hour interval. Please note that the strategy is described as a basic proof of concept and may not perform well in actual trading scenarios.
stoploss: -0.2 timeframe: 4h hash(sha256): 74fb3d5649c1300d08d34d2eac976906a3122c1709163ab38dfd41c47f6410a4
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last change: 2022-07-02 19:54:08