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The SMAOffsetBZed strategy is designed to backtest trading strategies. Here is a brief description of what it does:
The populate_indicators function calculates various indicators, such as moving averages and exponential moving averages, using the provided dataframe and metadata. If the run mode is not set to 'hyperopt', the function also calculates the buy and sell offsets based on the selected trigger types and offsets.
The function merges informative data from a 1-hour timeframe with the original dataframe.
Finally, the function calculates the 200-day exponential moving average (ema_200) and returns the updated dataframe.
The populate_buy_trend function is responsible for determining the buy signals based on specific conditions. Here's a breakdown of those conditions: If the run mode is set to 'hyperopt', the function calculates the buy offset using the selected trigger type and offset. It checks various conditions, including: The close price is above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). The 1-hour exponential moving averages (ema_50_1h and ema_100_1h) are in an upward trend. The current price is below the buy offsets. The volume is greater than 0. If all conditions are met, the function sets the 'buy' column of the dataframe to 1 to indicate a buy signal. The populate_sell_trend function determines the sell signals based on the following conditions: If the run mode is set to 'hyperopt', the function calculates the sell offset using the selected trigger type and offset. It checks if the close price is above the sell offset and the volume is greater than 0. If the conditions are met, the function sets the 'sell' column of the dataframe to 1 to indicate a sell signal. Overall, the strategy utilizes moving averages, exponential moving averages, buy offsets, and sell offsets to generate buy and sell signals based on specific conditions.
The function merges informative data from a 1-hour timeframe with the original dataframe.
Finally, the function calculates the 200-day exponential moving average (ema_200) and returns the updated dataframe.
The populate_buy_trend function is responsible for determining the buy signals based on specific conditions. Here's a breakdown of those conditions: If the run mode is set to 'hyperopt', the function calculates the buy offset using the selected trigger type and offset. It checks various conditions, including: The close price is above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). The 1-hour exponential moving averages (ema_50_1h and ema_100_1h) are in an upward trend. The current price is below the buy offsets. The volume is greater than 0. If all conditions are met, the function sets the 'buy' column of the dataframe to 1 to indicate a buy signal. The populate_sell_trend function determines the sell signals based on the following conditions: If the run mode is set to 'hyperopt', the function calculates the sell offset using the selected trigger type and offset. It checks if the close price is above the sell offset and the volume is greater than 0. If the conditions are met, the function sets the 'sell' column of the dataframe to 1 to indicate a sell signal. Overall, the strategy utilizes moving averages, exponential moving averages, buy offsets, and sell offsets to generate buy and sell signals based on specific conditions.
stoploss: -0.23 timeframe: 5m hash(sha256): a2d949c21283a291a13e9fc18d52a43adbe23af3a8f977de505dcd5a380d5668 indicators: volume ema_200 ema_50 ema_100_1h ma_offset_sell close ma_offset_buy runmode open ema_200_1h ema_100 ema_50_1h
No similar strategies found. (based on used indicators)
last change: 2022-07-29 13:13:50