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The AwesomeMacd strategy is a backtesting strategy that combines the use of the MACD (Moving Average Convergence Divergence) indicator and the Awesome Oscillator. Here's a short description of what the strategy does:
It calculates the ADX (Average Directional Index) indicator with a time period of 14 and adds it to the dataframe. It calculates the Awesome Oscillator (AO) using the qtpylib library and adds it to the dataframe.
It calculates the MACD indicator using the talib library and adds the MACD line, signal line, and histogram to the dataframe.
In the "populate_buy_trend" function, it identifies a buy signal when the MACD is greater than 0, the Awesome Oscillator is greater than 0, and the previous Awesome Oscillator value was less than 0.
In the "populate_sell_trend" function, it identifies a sell signal when the MACD is less than 0, the Awesome Oscillator is less than 0, and the previous Awesome Oscillator value was greater than 0. The strategy sets a minimal ROI (Return on Investment) target of 0.1 and a stop loss level of -0.25. It uses a ticker interval of 1 hour for analyzing the data. Please note that this is a simplified description of the strategy. Further details may be present in the original source code or strategy documentation.
It calculates the MACD indicator using the talib library and adds the MACD line, signal line, and histogram to the dataframe.
In the "populate_buy_trend" function, it identifies a buy signal when the MACD is greater than 0, the Awesome Oscillator is greater than 0, and the previous Awesome Oscillator value was less than 0.
In the "populate_sell_trend" function, it identifies a sell signal when the MACD is less than 0, the Awesome Oscillator is less than 0, and the previous Awesome Oscillator value was greater than 0. The strategy sets a minimal ROI (Return on Investment) target of 0.1 and a stop loss level of -0.25. It uses a ticker interval of 1 hour for analyzing the data. Please note that this is a simplified description of the strategy. Further details may be present in the original source code or strategy documentation.
stoploss: -0.25 timeframe: 1h hash(sha256): b78b7a6c0167275fb9ccb71de8a264606dd113c9902f2d65ee0ee8f338a9d47e
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last change: 2022-07-02 19:54:08