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The MabStra strategy, developed by @Mablue (Masoud Azizi), is a trading strategy implemented in Python using the Freqtrade library. The strategy aims to generate profitable trades by identifying buying and selling opportunities based on moving averages and divergence. The strategy uses the following indicators:
Simple Moving Average (SMA): It calculates the average price over a specified number of periods for different moving averages.
buy-mojoMA: SMA calculated over the buy_mojo_ma_timeframe period.
buy-fastMA: SMA calculated over the buy_fast_ma_timeframe period.
buy-slowMA: SMA calculated over the buy_slow_ma_timeframe period. sell-mojoMA: SMA calculated over the sell_mojo_ma_timeframe period. sell-fastMA: SMA calculated over the sell_fast_ma_timeframe period. sell-slowMA: SMA calculated over the sell_slow_ma_timeframe period. The strategy's buy signal is generated when the following conditions are met: The ratio of buy-mojoMA to buy-fastMA is within the range defined by buy_div_min and buy_div_max. The ratio of buy-fastMA to buy-slowMA is within the range defined by buy_div_min and buy_div_max. The strategy's sell signal is generated when the following conditions are met: The ratio of sell-fastMA to sell-mojoMA is within the range defined by sell_div_min and sell_div_max. The ratio of sell-slowMA to sell-fastMA is within the range defined by sell_div_min and sell_div_max. The strategy includes predefined parameters for the buy and sell signals, such as the timeframe for data analysis and specific values for the moving average timeframes and divergence thresholds. These parameters can be adjusted for optimization using the Freqtrade Hyperopt functionality. The strategy also specifies a minimal_roi (return on investment) table, which defines the target returns at different stages of the trade, and a stoploss value to limit potential losses. To run the strategy, it is recommended to use Freqtrade with Hyperopt by executing the provided command. Please note that this description provides a brief overview of the strategy's functionality. For more details and the complete code, you can refer to the GitHub repository provided by the author.
buy-mojoMA: SMA calculated over the buy_mojo_ma_timeframe period.
buy-fastMA: SMA calculated over the buy_fast_ma_timeframe period.
buy-slowMA: SMA calculated over the buy_slow_ma_timeframe period. sell-mojoMA: SMA calculated over the sell_mojo_ma_timeframe period. sell-fastMA: SMA calculated over the sell_fast_ma_timeframe period. sell-slowMA: SMA calculated over the sell_slow_ma_timeframe period. The strategy's buy signal is generated when the following conditions are met: The ratio of buy-mojoMA to buy-fastMA is within the range defined by buy_div_min and buy_div_max. The ratio of buy-fastMA to buy-slowMA is within the range defined by buy_div_min and buy_div_max. The strategy's sell signal is generated when the following conditions are met: The ratio of sell-fastMA to sell-mojoMA is within the range defined by sell_div_min and sell_div_max. The ratio of sell-slowMA to sell-fastMA is within the range defined by sell_div_min and sell_div_max. The strategy includes predefined parameters for the buy and sell signals, such as the timeframe for data analysis and specific values for the moving average timeframes and divergence thresholds. These parameters can be adjusted for optimization using the Freqtrade Hyperopt functionality. The strategy also specifies a minimal_roi (return on investment) table, which defines the target returns at different stages of the trade, and a stoploss value to limit potential losses. To run the strategy, it is recommended to use Freqtrade with Hyperopt by executing the provided command. Please note that this description provides a brief overview of the strategy's functionality. For more details and the complete code, you can refer to the GitHub repository provided by the author.
stoploss: -0.128 timeframe: 4h hash(sha256): 96e9cdead9584832bfdb8fbc46c43a538bed69e40567ace1db0efe899e6440cd indicators: buymojoMA buyfastMA buyslowMA sellfastMA sellslowMA sellmojoMA
No similar strategies found. (based on used indicators)
last change: 2024-04-28 04:50:00