The "GodStra" strategy is a trading strategy implemented as a class that extends the "IStrategy" interface. It has three main functions: "populate_indicators," "populate_buy_trend," and "populate_sell_trend."
In the "populate_indicators" function, the strategy takes a DataFrame of financial data and adds technical analysis indicators to it. It uses the "add_all_ta_features" function to calculate various indicators based on the open, high, low, close, and volume data columns of the DataFrame.
The "populate_buy_trend" function populates the "buy" column of the DataFrame based on a set of buy parameters.
It iterates over the buy parameters and checks the conditions specified for each parameter.
The conditions involve comparing indicator values with other indicators, constants, or thresholds. Based on the condition results, the "buy" column is updated accordingly. The "populate_sell_trend" function works similarly to the "populate_buy_trend" function but populates the "sell" column of the DataFrame instead. It iterates over the sell parameters and checks the conditions specified for each parameter. Based on the condition results, the "sell" column is updated. Overall, the strategy calculates technical indicators, determines buy and sell signals based on specified conditions, and marks the corresponding rows in the DataFrame with values in the "buy" and "sell" columns. This information can be used for further analysis or decision-making in backtesting or live trading scenarios.
The "populate_buy_trend" function populates the "buy" column of the DataFrame based on a set of buy parameters.
It iterates over the buy parameters and checks the conditions specified for each parameter.
The conditions involve comparing indicator values with other indicators, constants, or thresholds. Based on the condition results, the "buy" column is updated accordingly. The "populate_sell_trend" function works similarly to the "populate_buy_trend" function but populates the "sell" column of the DataFrame instead. It iterates over the sell parameters and checks the conditions specified for each parameter. Based on the condition results, the "sell" column is updated. Overall, the strategy calculates technical indicators, determines buy and sell signals based on specified conditions, and marks the corresponding rows in the DataFrame with values in the "buy" and "sell" columns. This information can be used for further analysis or decision-making in backtesting or live trading scenarios.
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last change: 2024-07-26 23:49:00