The "GodStraOpt2" strategy is implemented as a class that inherits from the "IStrategy" interface. Here's a description of what the strategy does:
The strategy's "populate_indicators" method takes a DataFrame containing trading data and adds technical analysis indicators to it. The function uses the "add_all_ta_features" method to compute various technical indicators such as moving averages, relative strength index (RSI), and more.
The DataFrame is modified in place, and the updated DataFrame is returned.
The "populate_buy_trend" method determines the conditions for initiating a buy signal based on a set of parameters.
It iterates over a range of buy parameters and checks the specified conditions for each parameter. The conditions are defined by the comparison operators (e.g., >, =, <) and can involve comparisons between indicators, crosses between indicators, or thresholds. If the conditions for a particular parameter are met, a corresponding buy signal is marked in the DataFrame. The DataFrame is then returned. The "populate_sell_trend" method is similar to "populate_buy_trend" but determines the conditions for initiating a sell signal. It iterates over sell parameters and checks the specified conditions using comparison operators and indicator crosses. If the conditions for a sell parameter are met, a sell signal is marked in the DataFrame. The modified DataFrame is returned. In summary, the strategy computes technical indicators in the "populate_indicators" step, determines buy signals based on specified conditions in the "populate_buy_trend" step, and determines sell signals in the "populate_sell_trend" step.
The DataFrame is modified in place, and the updated DataFrame is returned.
The "populate_buy_trend" method determines the conditions for initiating a buy signal based on a set of parameters.
It iterates over a range of buy parameters and checks the specified conditions for each parameter. The conditions are defined by the comparison operators (e.g., >, =, <) and can involve comparisons between indicators, crosses between indicators, or thresholds. If the conditions for a particular parameter are met, a corresponding buy signal is marked in the DataFrame. The DataFrame is then returned. The "populate_sell_trend" method is similar to "populate_buy_trend" but determines the conditions for initiating a sell signal. It iterates over sell parameters and checks the specified conditions using comparison operators and indicator crosses. If the conditions for a sell parameter are met, a sell signal is marked in the DataFrame. The modified DataFrame is returned. In summary, the strategy computes technical indicators in the "populate_indicators" step, determines buy signals based on specified conditions in the "populate_buy_trend" step, and determines sell signals in the "populate_sell_trend" step.
stoploss: -0.229 timeframe: 12h hash(sha256): 55127d7c1e7b99b33cb6ba42deab73b11134f256d9fbae1d3e2dd63d67264967
Was not able to fetch indicators from Strategyfile.
last change: 2024-04-29 02:48:41