The MultiMa strategy is a trading strategy implemented in Python for backtesting purposes. It utilizes multiple moving averages (MA) to generate buy and sell signals based on their interactions. Here are the key components of the strategy:
The strategy calculates several moving averages based on user-defined parameters for the buy and sell sides.
For the buy side, the strategy calculates moving averages with different time periods and gaps.
The number of moving averages is determined by the buy_ma_count parameter.
The conditions for generating a buy signal involve comparing the values of the moving averages with their respective shifts. If the conditions are met, a buy signal is assigned to the corresponding data points. Similarly, for the sell side, the strategy calculates moving averages with different time periods and gaps. The number of moving averages is determined by the sell_ma_count parameter. The conditions for generating a sell signal involve comparing the values of the moving averages with their respective shifts. If the conditions are met, a sell signal is assigned to the corresponding data points. The strategy also includes parameters for the minimal return on investment (ROI) and stop loss. The timeframe used for analysis is set to "4h". The populate_indicators function is responsible for generating the required indicators, but in this case, it doesn't perform any specific calculations. The populate_buy_trend and populate_sell_trend functions are responsible for generating the buy and sell signals, respectively, based on the defined moving averages and conditions. Overall, the MultiMa strategy aims to capture trading opportunities by analyzing the interactions between multiple moving averages.
For the buy side, the strategy calculates moving averages with different time periods and gaps.
The number of moving averages is determined by the buy_ma_count parameter.
The conditions for generating a buy signal involve comparing the values of the moving averages with their respective shifts. If the conditions are met, a buy signal is assigned to the corresponding data points. Similarly, for the sell side, the strategy calculates moving averages with different time periods and gaps. The number of moving averages is determined by the sell_ma_count parameter. The conditions for generating a sell signal involve comparing the values of the moving averages with their respective shifts. If the conditions are met, a sell signal is assigned to the corresponding data points. The strategy also includes parameters for the minimal return on investment (ROI) and stop loss. The timeframe used for analysis is set to "4h". The populate_indicators function is responsible for generating the required indicators, but in this case, it doesn't perform any specific calculations. The populate_buy_trend and populate_sell_trend functions are responsible for generating the buy and sell signals, respectively, based on the defined moving averages and conditions. Overall, the MultiMa strategy aims to capture trading opportunities by analyzing the interactions between multiple moving averages.
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stoploss: -0.1 timeframe: 5m hash(sha256): 3addd9a053a1c99cb5c6138477cf8e72d547896888c01e2b7563caf2b5e3f222 indicators: f"sellmai1 f"buymai1
No similar strategies found. (based on used indicators)
last change: 2024-07-27 06:08:59