The "GodStraOpt" strategy is a backtesting strategy implemented as a class in Python. It inherits from the "IStrategy" class. Here's a breakdown of what the strategy does:
populate_indicators function:
This function takes a DataFrame containing trading data and a metadata dictionary as input.
It preprocesses the DataFrame by dropping any rows with missing values and adds technical analysis indicators to the DataFrame using the "add_all_ta_features" function.
The indicators added include open, high, low, close, and volume.
The function returns the updated DataFrame. populate_buy_trend function: This function takes a DataFrame containing trading data and a metadata dictionary as input. It iterates over a range determined by the size of the "buy_params" attribute. Inside the loop, it retrieves various parameters related to buying conditions from the "buy_params" dictionary. It then checks the conditions based on the values of these parameters and appends the resulting condition to a list. The conditions can involve comparisons between DataFrame columns, crossed above or below checks, and comparisons with integer or real values. Finally, the function updates the "buy" column of the DataFrame with a value of 1 where all the conditions are satisfied. The updated DataFrame is returned. populate_sell_trend function: This function is similar to the populate_buy_trend function but applies the conditions to the "sell" column of the DataFrame. It iterates over a range determined by the size of the "sell_params" attribute. Inside the loop, it retrieves various parameters related to selling conditions from the "sell_params" dictionary. It then checks the conditions based on the values of these parameters and appends the resulting condition to a list. The conditions can involve comparisons between DataFrame columns, crossed above or below checks, and comparisons with integer or real values. Finally, the function updates the "sell" column of the DataFrame with a value of 1 where all the conditions are satisfied. The updated DataFrame is returned. Overall, the strategy takes a DataFrame with trading data, adds technical analysis indicators, and applies buy and sell conditions based on the specified parameters. It updates the DataFrame with buy and sell signals, which can be used for further analysis and backtesting.
It preprocesses the DataFrame by dropping any rows with missing values and adds technical analysis indicators to the DataFrame using the "add_all_ta_features" function.
The indicators added include open, high, low, close, and volume.
The function returns the updated DataFrame. populate_buy_trend function: This function takes a DataFrame containing trading data and a metadata dictionary as input. It iterates over a range determined by the size of the "buy_params" attribute. Inside the loop, it retrieves various parameters related to buying conditions from the "buy_params" dictionary. It then checks the conditions based on the values of these parameters and appends the resulting condition to a list. The conditions can involve comparisons between DataFrame columns, crossed above or below checks, and comparisons with integer or real values. Finally, the function updates the "buy" column of the DataFrame with a value of 1 where all the conditions are satisfied. The updated DataFrame is returned. populate_sell_trend function: This function is similar to the populate_buy_trend function but applies the conditions to the "sell" column of the DataFrame. It iterates over a range determined by the size of the "sell_params" attribute. Inside the loop, it retrieves various parameters related to selling conditions from the "sell_params" dictionary. It then checks the conditions based on the values of these parameters and appends the resulting condition to a list. The conditions can involve comparisons between DataFrame columns, crossed above or below checks, and comparisons with integer or real values. Finally, the function updates the "sell" column of the DataFrame with a value of 1 where all the conditions are satisfied. The updated DataFrame is returned. Overall, the strategy takes a DataFrame with trading data, adds technical analysis indicators, and applies buy and sell conditions based on the specified parameters. It updates the DataFrame with buy and sell signals, which can be used for further analysis and backtesting.
stoploss: -0.229 timeframe: 12h hash(sha256): eda187ff3bbfb59fce95786ecf6f3879452433a3d75c4d706154a570673806fd
Was not able to fetch indicators from Strategyfile.
last change: 2024-04-28 04:36:46