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The EasyInEasyOut strategy is a trading strategy implemented in Python for backtesting purposes. Here is a short description of what the strategy does:
The strategy uses the Hull Moving Average (HMA) indicator, implemented using the hull_moving_average function from the freqtrade.vendor.qtpylib.indicators module. It calculates the HMA with a window of 20 periods and stores the values in the hma_20 column of the dataframe.
In the populate_indicators method, additional columns are created in the dataframe to store the previous and current values of the closing price (close_prev and close_curr) and the previous and current values of the HMA (hma_20_prev and hma_20_current).
The populate_buy_trend method populates the buy column of the dataframe with a value of 1 when the following condition is met: the current closing price is greater than the current HMA value (close_curr > hma_20_current), and the previous closing price is lower than the previous HMA value (close_prev < hma_20_prev).
This indicates a potential buying opportunity. The populate_sell_trend method does not make any changes to the dataframe, as it is not used to determine sell signals in this strategy. The strategy defines parameters for buying (buy_params) and selling (sell_params), but these parameters are not specified in the provided code snippet. It also sets a minimal return on investment (ROI) requirement (minimal_roi) and a stop loss value (stoploss). The timeframe for the strategy is set to 1 minute (timeframe = '1m'). Additionally, the strategy is configured to only consider exiting trades when they are profitable (exit_profit_only = True). Overall, the EasyInEasyOut strategy aims to identify buying opportunities when the current closing price crosses above the HMA while the previous closing price was below the HMA. The strategy does not specify any specific conditions for selling positions.
In the populate_indicators method, additional columns are created in the dataframe to store the previous and current values of the closing price (close_prev and close_curr) and the previous and current values of the HMA (hma_20_prev and hma_20_current).
The populate_buy_trend method populates the buy column of the dataframe with a value of 1 when the following condition is met: the current closing price is greater than the current HMA value (close_curr > hma_20_current), and the previous closing price is lower than the previous HMA value (close_prev < hma_20_prev).
This indicates a potential buying opportunity. The populate_sell_trend method does not make any changes to the dataframe, as it is not used to determine sell signals in this strategy. The strategy defines parameters for buying (buy_params) and selling (sell_params), but these parameters are not specified in the provided code snippet. It also sets a minimal return on investment (ROI) requirement (minimal_roi) and a stop loss value (stoploss). The timeframe for the strategy is set to 1 minute (timeframe = '1m'). Additionally, the strategy is configured to only consider exiting trades when they are profitable (exit_profit_only = True). Overall, the EasyInEasyOut strategy aims to identify buying opportunities when the current closing price crosses above the HMA while the previous closing price was below the HMA. The strategy does not specify any specific conditions for selling positions.
stoploss: -1 timeframe: 1m hash(sha256): 07e228cc77effc9e80891d4bd5bd9b6a2f6a70845daa0db91f9e1d3933ee461a indicators: hma_20_prev close_prev close hma_20 close_curr hma_20_current
No similar strategies found. (based on used indicators)
last change: 2023-06-29 18:02:22