The FreqGym strategy is designed to backtest trading strategies using various technical analysis (TA) indicators. It consists of three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. The populate_indicators function takes a raw data DataFrame from the exchange and adds multiple TA indicators to it.
These indicators include plus_dm, plus_di, minus_dm, minus_di, ao (awesome oscillator), uo (ultimate oscillator), ewo (Elliott Wave Oscillator), adx (Average Directional Index) for different time periods, wr (Williams %R) for different time periods, cci (Commodity Channel Index) for different time periods, rsi (Relative Strength Index) for different time periods, fisher_rsi (Fisher Transform of RSI) for different time periods, aroon indicators (aroonup, aroondown, aroonosc), cmo (Chande Momentum Oscillator) for different time periods, and mfi (Money Flow Index) for different time periods.
The function returns the DataFrame with all the added indicators.
The populate_buy_trend function utilizes the populated indicators to determine the buy signal for the given DataFrame. It uses an RL (Reinforcement Learning) model (rl_model_predict) to generate the buy action. The function adds a 'buy' column to the DataFrame, where a value of 1 indicates a buy signal. The populate_sell_trend function is similar to populate_buy_trend but determines the sell signal instead. It also adds a 'sell' column to the DataFrame, with a value of 1 indicating a sell signal. The last part of the code snippet seems to be a separate function or calculation outside of the FreqGym class. It calculates the Williams %R indicator, which measures the current closing price relative to the high and low of the past N days. It returns the Williams %R value multiplied by -100. Overall, the FreqGym strategy incorporates a wide range of TA indicators to populate buy and sell signals for backtesting trading strategies.
These indicators include plus_dm, plus_di, minus_dm, minus_di, ao (awesome oscillator), uo (ultimate oscillator), ewo (Elliott Wave Oscillator), adx (Average Directional Index) for different time periods, wr (Williams %R) for different time periods, cci (Commodity Channel Index) for different time periods, rsi (Relative Strength Index) for different time periods, fisher_rsi (Fisher Transform of RSI) for different time periods, aroon indicators (aroonup, aroondown, aroonosc), cmo (Chande Momentum Oscillator) for different time periods, and mfi (Money Flow Index) for different time periods.
The function returns the DataFrame with all the added indicators.
The populate_buy_trend function utilizes the populated indicators to determine the buy signal for the given DataFrame. It uses an RL (Reinforcement Learning) model (rl_model_predict) to generate the buy action. The function adds a 'buy' column to the DataFrame, where a value of 1 indicates a buy signal. The populate_sell_trend function is similar to populate_buy_trend but determines the sell signal instead. It also adds a 'sell' column to the DataFrame, with a value of 1 indicating a sell signal. The last part of the code snippet seems to be a separate function or calculation outside of the FreqGym class. It calculates the Williams %R indicator, which measures the current closing price relative to the high and low of the past N days. It returns the Williams %R value multiplied by -100. Overall, the FreqGym strategy incorporates a wide range of TA indicators to populate buy and sell signals for backtesting trading strategies.
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last change: 2024-07-27 08:21:48