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The MultiMa Strategy V2 is a trading strategy implemented in Python for backtesting purposes. It uses multiple moving averages (MA) to generate buy and sell signals. The strategy calculates various moving averages using the TEMA (Triple Exponential Moving Average) indicator from the TA-Lib library.
The parameters for the number of moving averages and the gap between them can be adjusted.
During the buy signal generation, the strategy checks if the current moving average value is lower than the previous moving average value for each specified count and gap.
If this condition is met, a buy signal is generated. For the sell signal generation, the strategy checks if the current moving average value is higher than the previous moving average value for each specified count and gap. If this condition is met, a sell signal is generated. The strategy also includes parameters for defining the buy and sell hyperspace, minimal return on investment (ROI) values, stop-loss level, and trailing stop options. Overall, the MultiMa Strategy V2 aims to capture trends based on the relationship between multiple moving averages, allowing users to test and optimize their trading strategies on historical data.
The parameters for the number of moving averages and the gap between them can be adjusted.
During the buy signal generation, the strategy checks if the current moving average value is lower than the previous moving average value for each specified count and gap.
If this condition is met, a buy signal is generated. For the sell signal generation, the strategy checks if the current moving average value is higher than the previous moving average value for each specified count and gap. If this condition is met, a sell signal is generated. The strategy also includes parameters for defining the buy and sell hyperspace, minimal return on investment (ROI) values, stop-loss level, and trailing stop options. Overall, the MultiMa Strategy V2 aims to capture trends based on the relationship between multiple moving averages, allowing users to test and optimize their trading strategies on historical data.
stoploss: -0.345 timeframe: 5m hash(sha256): 5933a6cacc906c15340f9ff6530ced00d9ee4c60cd3fedd42895be1dd978c1a7
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last change: 2022-07-02 19:54:08