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The mabStra strategy, developed by Masoud Azizi (@Mablue), is a trading strategy that can be backtested on a backtesting website. It uses various parameters and indicators to generate buy and sell signals. Here is a breakdown of its main components:
Author: @Mablue (Masoud Azizi)
GitHub: https://github.com/mablue/
To use this strategy, it is important to note that it requires the use of Hyperopt.
The recommended command for running the strategy with Hyperopt is provided.
The strategy utilizes the freqtrade library and imports necessary modules such as IntParameter, DecimalParameter, IStrategy, and DataFrame from pandas.
The strategy defines several parameters for buying and selling: Buy Parameters: buy_mojo_ma_timeframe: Integer parameter representing the time frame for calculating the mojo moving average. buy_fast_ma_timeframe: Integer parameter representing the time frame for calculating the fast moving average. buy_slow_ma_timeframe: Integer parameter representing the time frame for calculating the slow moving average. buy_div_max: Decimal parameter representing the maximum value for the buy divergence. buy_div_min: Decimal parameter representing the minimum value for the buy divergence. Sell Parameters: sell_mojo_ma_timeframe: Integer parameter representing the time frame for calculating the mojo moving average. sell_fast_ma_timeframe: Integer parameter representing the time frame for calculating the fast moving average. sell_slow_ma_timeframe: Integer parameter representing the time frame for calculating the slow moving average. sell_div_max: Decimal parameter representing the maximum value for the sell divergence. sell_div_min: Decimal parameter representing the minimum value for the sell divergence. The strategy also defines the ROI (Return on Investment) table and the stop-loss value. The populate_indicators function is responsible for calculating the simple moving averages (SMA) using the specified time frames for both buying and selling. The populate_buy_trend function generates the buy signals based on specific conditions involving the calculated moving averages and divergence values. The populate_sell_trend function generates the sell signals based on conditions similar to the buy signals. Overall, the strategy aims to generate profitable buy and sell signals by considering the relationships between different moving averages and divergence values.
The recommended command for running the strategy with Hyperopt is provided.
The strategy utilizes the freqtrade library and imports necessary modules such as IntParameter, DecimalParameter, IStrategy, and DataFrame from pandas.
The strategy defines several parameters for buying and selling: Buy Parameters: buy_mojo_ma_timeframe: Integer parameter representing the time frame for calculating the mojo moving average. buy_fast_ma_timeframe: Integer parameter representing the time frame for calculating the fast moving average. buy_slow_ma_timeframe: Integer parameter representing the time frame for calculating the slow moving average. buy_div_max: Decimal parameter representing the maximum value for the buy divergence. buy_div_min: Decimal parameter representing the minimum value for the buy divergence. Sell Parameters: sell_mojo_ma_timeframe: Integer parameter representing the time frame for calculating the mojo moving average. sell_fast_ma_timeframe: Integer parameter representing the time frame for calculating the fast moving average. sell_slow_ma_timeframe: Integer parameter representing the time frame for calculating the slow moving average. sell_div_max: Decimal parameter representing the maximum value for the sell divergence. sell_div_min: Decimal parameter representing the minimum value for the sell divergence. The strategy also defines the ROI (Return on Investment) table and the stop-loss value. The populate_indicators function is responsible for calculating the simple moving averages (SMA) using the specified time frames for both buying and selling. The populate_buy_trend function generates the buy signals based on specific conditions involving the calculated moving averages and divergence values. The populate_sell_trend function generates the sell signals based on conditions similar to the buy signals. Overall, the strategy aims to generate profitable buy and sell signals by considering the relationships between different moving averages and divergence values.
stoploss: -0.128 timeframe: 4h hash(sha256): 75706533652db941417cb8c133b4fb1ad9429ac5627cdc90513b70d708abb106
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-11 15:33:33