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The MultiMa Strategy V2 is a trading strategy implemented in Python using the Freqtrade library. It is designed for backtesting and evaluating various trading strategies. Here is a brief description of the important parts of the strategy:
The strategy uses moving averages (MA) as indicators for generating buy and sell signals.
The buy signals are generated based on the specified parameters: "buy_ma_count" and "buy_ma_gap".
The strategy checks if the current MA value is lower than the previous MA value for each specified count and gap.
The sell signals are generated in a similar manner but with different parameters: "sell_ma_count" and "sell_ma_gap". The strategy checks if the current MA value is higher than the previous MA value for each specified count and gap. The strategy uses the TEMA (Triple Exponential Moving Average) indicator from the TA-Lib library to calculate the MA values. The strategy defines a minimal return on investment (ROI) table, specifying different levels of ROI at different time intervals. A stop-loss value of -0.345 is set, indicating that if the loss exceeds this value, the strategy will exit the position. The strategy uses a 4-hour timeframe for its analysis. The strategy defines parameters for hyperparameter optimization, such as the range and default values for the buy and sell MA counts and gaps. The strategy provides methods for populating indicators, buy trends, and sell trends in the trading dataframe. Please note that this description provides a high-level overview of the strategy, and for a more detailed understanding, it would be necessary to review the code implementation and the specific logic behind the indicators and conditions used.
The buy signals are generated based on the specified parameters: "buy_ma_count" and "buy_ma_gap".
The strategy checks if the current MA value is lower than the previous MA value for each specified count and gap.
The sell signals are generated in a similar manner but with different parameters: "sell_ma_count" and "sell_ma_gap". The strategy checks if the current MA value is higher than the previous MA value for each specified count and gap. The strategy uses the TEMA (Triple Exponential Moving Average) indicator from the TA-Lib library to calculate the MA values. The strategy defines a minimal return on investment (ROI) table, specifying different levels of ROI at different time intervals. A stop-loss value of -0.345 is set, indicating that if the loss exceeds this value, the strategy will exit the position. The strategy uses a 4-hour timeframe for its analysis. The strategy defines parameters for hyperparameter optimization, such as the range and default values for the buy and sell MA counts and gaps. The strategy provides methods for populating indicators, buy trends, and sell trends in the trading dataframe. Please note that this description provides a high-level overview of the strategy, and for a more detailed understanding, it would be necessary to review the code implementation and the specific logic behind the indicators and conditions used.
stoploss: -0.345 timeframe: 5m hash(sha256): 513c1188d74771e63ac6e365ab5e5d7b902e3fc286c90edff8ac3fd941e67ad3
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last change: 2022-07-12 09:14:44