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The mabStra strategy is a trading strategy implemented in Python for backtesting purposes. It uses technical indicators and moving averages to generate buy and sell signals. Here's a brief description of how the strategy works:
Buy Parameters:
buy_mojo_ma_timeframe: Timeframe for calculating the moving average of the mojo indicator.
buy_fast_ma_timeframe: Timeframe for calculating the fast moving average.
buy_slow_ma_timeframe: Timeframe for calculating the slow moving average.
buy_div_max: Maximum threshold for the division of mojo moving average by the fast moving average. buy_div_min: Minimum threshold for the division of mojo moving average by the fast moving average. Sell Parameters: sell_mojo_ma_timeframe: Timeframe for calculating the moving average of the mojo indicator. sell_fast_ma_timeframe: Timeframe for calculating the fast moving average. sell_slow_ma_timeframe: Timeframe for calculating the slow moving average. sell_div_max: Maximum threshold for the division of fast moving average by mojo moving average. sell_div_min: Minimum threshold for the division of fast moving average by mojo moving average. Stoploss: The strategy uses a fixed stop-loss value of -0.1 (10% loss). The strategy calculates various moving averages based on the specified timeframes using the Simple Moving Average (SMA) indicator provided by the talib library. It then generates buy signals when certain conditions are met, which include specific ranges for the division of moving averages. For the buy trend, the conditions are: The division of mojo moving average by fast moving average is within the specified range (buy_div_min to buy_div_max). The division of fast moving average by slow moving average is within the specified range (buy_div_min to buy_div_max). For the sell trend, the conditions are: The division of fast moving average by mojo moving average is within the specified range (sell_div_min to sell_div_max). The division of slow moving average by fast moving average is within the specified range (sell_div_min to sell_div_max). The strategy assigns a value of 1 to the "buy" column of the dataframe when the buy conditions are met, and a value of 1 to the "sell" column when the sell conditions are met. Please note that this strategy requires the usage of the Hyperopt optimization tool from the Freqtrade library, as indicated by the important comment in the code. The strategy's parameters can be tuned using Hyperopt to find optimal values for backtesting.
buy_fast_ma_timeframe: Timeframe for calculating the fast moving average.
buy_slow_ma_timeframe: Timeframe for calculating the slow moving average.
buy_div_max: Maximum threshold for the division of mojo moving average by the fast moving average. buy_div_min: Minimum threshold for the division of mojo moving average by the fast moving average. Sell Parameters: sell_mojo_ma_timeframe: Timeframe for calculating the moving average of the mojo indicator. sell_fast_ma_timeframe: Timeframe for calculating the fast moving average. sell_slow_ma_timeframe: Timeframe for calculating the slow moving average. sell_div_max: Maximum threshold for the division of fast moving average by mojo moving average. sell_div_min: Minimum threshold for the division of fast moving average by mojo moving average. Stoploss: The strategy uses a fixed stop-loss value of -0.1 (10% loss). The strategy calculates various moving averages based on the specified timeframes using the Simple Moving Average (SMA) indicator provided by the talib library. It then generates buy signals when certain conditions are met, which include specific ranges for the division of moving averages. For the buy trend, the conditions are: The division of mojo moving average by fast moving average is within the specified range (buy_div_min to buy_div_max). The division of fast moving average by slow moving average is within the specified range (buy_div_min to buy_div_max). For the sell trend, the conditions are: The division of fast moving average by mojo moving average is within the specified range (sell_div_min to sell_div_max). The division of slow moving average by fast moving average is within the specified range (sell_div_min to sell_div_max). The strategy assigns a value of 1 to the "buy" column of the dataframe when the buy conditions are met, and a value of 1 to the "sell" column when the sell conditions are met. Please note that this strategy requires the usage of the Hyperopt optimization tool from the Freqtrade library, as indicated by the important comment in the code. The strategy's parameters can be tuned using Hyperopt to find optimal values for backtesting.
stoploss: -0.1 timeframe: 4h hash(sha256): 34b51f92c4ee6bd21f74247f3ef008bac42f67fe44b1f2af51877b7a0f9c6d63 indicators: buymojoMA buyfastMA buyslowMA sellfastMA sellslowMA sellmojoMA
No similar strategies found. (based on used indicators)
last change: 2023-02-08 10:14:32