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The MultiMa Strategy V2 is a trading strategy implemented in Python for backtesting purposes. It is designed to generate buy and sell signals based on multiple moving averages (MA) calculated using the TEMA (Triple Exponential Moving Average) indicator from the TA-Lib library. Here's a breakdown of the important parts of the strategy:
Buy Parameters:
buy_ma_count: The number of consecutive MAs used for generating a buy signal.
buy_ma_gap: The time gap between each consecutive MA used for generating a buy signal.
Sell Parameters: sell_ma_count: The number of consecutive MAs used for generating a sell signal.
sell_ma_gap: The time gap between each consecutive MA used for generating a sell signal. ROI Table: The strategy includes a predefined table (minimal_roi) that maps specific time periods to corresponding return on investment (ROI) values. The table defines different ROI values at different time periods. Stoploss: The strategy incorporates a stoploss value, which is set to -0.345. This indicates that if the trade goes against the expected direction by a certain percentage, the position will be automatically closed to limit losses. Trailing Stop: The strategy does not use a trailing stop feature. Optimal Timeframe: The strategy is designed to work on the 4-hour timeframe. Indicator Calculation: The populate_indicators function calculates the TEMA indicator for various combinations of count and gap values, ranging from 1 to count_max and 1 to gap_max, respectively. These calculated indicators are added to the input dataframe. Buy Signal Generation: The populate_buy_trend function generates buy signals based on the comparison of TEMA values between consecutive MAs. If the TEMA value at a certain key is lower than the TEMA value at the corresponding past key, a buy signal is generated. Sell Signal Generation: The populate_sell_trend function generates sell signals based on the comparison of TEMA values between consecutive MAs. If the TEMA value at a certain key is higher than the TEMA value at the corresponding past key, a sell signal is generated. Overall, the MultiMa Strategy V2 aims to capture potential buy and sell opportunities in the market using a combination of moving averages. The strategy parameters and indicators can be adjusted to optimize performance and adapt to different trading scenarios.
buy_ma_gap: The time gap between each consecutive MA used for generating a buy signal.
Sell Parameters: sell_ma_count: The number of consecutive MAs used for generating a sell signal.
sell_ma_gap: The time gap between each consecutive MA used for generating a sell signal. ROI Table: The strategy includes a predefined table (minimal_roi) that maps specific time periods to corresponding return on investment (ROI) values. The table defines different ROI values at different time periods. Stoploss: The strategy incorporates a stoploss value, which is set to -0.345. This indicates that if the trade goes against the expected direction by a certain percentage, the position will be automatically closed to limit losses. Trailing Stop: The strategy does not use a trailing stop feature. Optimal Timeframe: The strategy is designed to work on the 4-hour timeframe. Indicator Calculation: The populate_indicators function calculates the TEMA indicator for various combinations of count and gap values, ranging from 1 to count_max and 1 to gap_max, respectively. These calculated indicators are added to the input dataframe. Buy Signal Generation: The populate_buy_trend function generates buy signals based on the comparison of TEMA values between consecutive MAs. If the TEMA value at a certain key is lower than the TEMA value at the corresponding past key, a buy signal is generated. Sell Signal Generation: The populate_sell_trend function generates sell signals based on the comparison of TEMA values between consecutive MAs. If the TEMA value at a certain key is higher than the TEMA value at the corresponding past key, a sell signal is generated. Overall, the MultiMa Strategy V2 aims to capture potential buy and sell opportunities in the market using a combination of moving averages. The strategy parameters and indicators can be adjusted to optimize performance and adapt to different trading scenarios.
stoploss: -0.345 timeframe: 5m hash(sha256): 865731f7e9470009a4c2799267b63d3a6b47c47c139347b4fd765911aae739a2 indicators: count*gap key
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last change: 2023-08-17 19:47:40