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The BBRSI strategy is a backtesting strategy implemented in Python using the Freqtrade library. It is designed to generate buy and sell signals based on a combination of technical indicators, namely the RSI (Relative Strength Index) and Bollinger Bands. Here's a breakdown of how the strategy works:
The populate_indicators function is responsible for calculating the required technical indicators.
In this case, it calculates the RSI and Bollinger Bands using the ta.RSI and qtpylib.bollinger_bands functions, respectively.
The minimal_roi dictionary specifies the desired minimum return on investment (ROI) for the strategy at different stages of the trade.
It defines four ROI levels based on the number of trades made. The stoploss variable defines the desired stop-loss level for the strategy, which is set to -0.36828. This means that if the trade goes against the strategy by more than the specified percentage, the position should be closed with a stop-loss order. The ticker_interval variable specifies the desired interval for the ticker data used in the strategy. In this case, it is set to '1h', indicating that the strategy operates on hourly data. The order_types dictionary defines the order types to be used for different types of orders. It specifies that both buy and sell orders should be executed as limit orders, and the stop-loss order should also be a limit order. The order_time_in_force dictionary specifies the time in force for different types of orders. It sets the buy and sell orders to be "good 'til canceled" (gtc), meaning they remain active until filled or manually canceled. The populate_buy_trend function determines the conditions for generating a buy signal. In this case, it checks if the close price is below the lower Bollinger Band with 3 standard deviations and assigns a value of 1 to the 'buy' column in the DataFrame. The populate_sell_trend function determines the conditions for generating a sell signal. Here, it checks if the RSI is above 75 and the close price is above the middle Bollinger Band. If these conditions are met, it assigns a value of 1 to the 'sell' column in the DataFrame. Overall, the strategy aims to identify buying opportunities when the price is relatively low compared to the lower Bollinger Band and selling opportunities when the price is relatively high compared to the middle Bollinger Band and the RSI is above 75. The strategy also incorporates the minimum ROI and stop-loss parameters to manage risk and define exit points for trades.
In this case, it calculates the RSI and Bollinger Bands using the ta.RSI and qtpylib.bollinger_bands functions, respectively.
The minimal_roi dictionary specifies the desired minimum return on investment (ROI) for the strategy at different stages of the trade.
It defines four ROI levels based on the number of trades made. The stoploss variable defines the desired stop-loss level for the strategy, which is set to -0.36828. This means that if the trade goes against the strategy by more than the specified percentage, the position should be closed with a stop-loss order. The ticker_interval variable specifies the desired interval for the ticker data used in the strategy. In this case, it is set to '1h', indicating that the strategy operates on hourly data. The order_types dictionary defines the order types to be used for different types of orders. It specifies that both buy and sell orders should be executed as limit orders, and the stop-loss order should also be a limit order. The order_time_in_force dictionary specifies the time in force for different types of orders. It sets the buy and sell orders to be "good 'til canceled" (gtc), meaning they remain active until filled or manually canceled. The populate_buy_trend function determines the conditions for generating a buy signal. In this case, it checks if the close price is below the lower Bollinger Band with 3 standard deviations and assigns a value of 1 to the 'buy' column in the DataFrame. The populate_sell_trend function determines the conditions for generating a sell signal. Here, it checks if the RSI is above 75 and the close price is above the middle Bollinger Band. If these conditions are met, it assigns a value of 1 to the 'sell' column in the DataFrame. Overall, the strategy aims to identify buying opportunities when the price is relatively low compared to the lower Bollinger Band and selling opportunities when the price is relatively high compared to the middle Bollinger Band and the RSI is above 75. The strategy also incorporates the minimum ROI and stop-loss parameters to manage risk and define exit points for trades.
stoploss: -0.368 timeframe: 1h hash(sha256): acce2a77a10705fb278baad816ac69eba184983fd2629f367a137f6e53662dbe indicators: upper mid lower bb_middleband3 bb_upperband3 bb_middleband rsi close bb_upperband bb_lowerband bb_lowerband3
No similar strategies found. (based on used indicators)
last change: 2022-07-02 19:54:08