The "GodStra" strategy is a backtesting strategy that implements a buy and sell logic based on user-defined parameters. Here's a breakdown of what the strategy does:
populate_indicators: This method takes a DataFrame containing historical market data and adds technical analysis indicators to it using the add_all_ta_features function. The indicators are calculated based on the "open," "high," "low," "close," and "volume" columns of the DataFrame.
The resulting DataFrame is returned.
populate_buy_trend: This method takes the DataFrame with indicators and applies buy conditions based on user-defined parameters.
It iterates through the buy parameters and checks each condition against the corresponding indicator values in the DataFrame. The conditions can be a comparison operator (>, =, <) between an indicator value and a constant, or a crossed above/below relationship between two indicators. If a condition is met, it adds a value of 1 to the "buy" column of the DataFrame. The resulting DataFrame is returned. populate_sell_trend: This method is similar to populate_buy_trend but applies sell conditions instead. It iterates through the sell parameters and checks each condition against the indicator values in the DataFrame. If a condition is met, it adds a value of 1 to the "sell" column of the DataFrame. The resulting DataFrame is returned. Overall, the strategy calculates technical analysis indicators, applies buy conditions based on user-defined parameters, and applies sell conditions based on separate user-defined parameters. The resulting DataFrame will contain additional columns indicating when to buy and sell based on the defined conditions.
The resulting DataFrame is returned.
populate_buy_trend: This method takes the DataFrame with indicators and applies buy conditions based on user-defined parameters.
It iterates through the buy parameters and checks each condition against the corresponding indicator values in the DataFrame. The conditions can be a comparison operator (>, =, <) between an indicator value and a constant, or a crossed above/below relationship between two indicators. If a condition is met, it adds a value of 1 to the "buy" column of the DataFrame. The resulting DataFrame is returned. populate_sell_trend: This method is similar to populate_buy_trend but applies sell conditions instead. It iterates through the sell parameters and checks each condition against the indicator values in the DataFrame. If a condition is met, it adds a value of 1 to the "sell" column of the DataFrame. The resulting DataFrame is returned. Overall, the strategy calculates technical analysis indicators, applies buy conditions based on user-defined parameters, and applies sell conditions based on separate user-defined parameters. The resulting DataFrame will contain additional columns indicating when to buy and sell based on the defined conditions.
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last change: 2024-07-27 23:20:30