The MultiMACD strategy is a trading strategy that uses multiple moving average convergence divergence (MACD) indicators to determine buy and sell signals. Here's a breakdown of what the strategy does:
The strategy defines a minimal return on investment (ROI) of 0.01, indicating the minimum desired profit for a trade. It sets a stop loss of -0.05, which means that if the trade's value drops by 5%, it will be automatically sold to limit losses.
The strategy operates on a 4-hour timeframe, meaning that it analyzes price data at 4-hour intervals.
The populate_indicators function resamples the input price data to create two additional dataframes: dataframe_short and dataframe_long.
dataframe_short is resampled to twice the timeframe (8 hours in this case), while dataframe_long is resampled to eight times the timeframe (32 hours). MACD (moving average convergence divergence) is calculated for both the short and long dataframes using the ta.macd function from the talib library. The MACD calculation parameters are set with fastperiod=12, slowperiod=26, and signalperiod=7. The two resampled dataframes are merged back into the original dataframe using the resampled_merge function. The populate_buy_trend function identifies buy signals based on the MACD values. It sets the 'buy' column of the dataframe to 1 when the MACD line (dataframe_long['macd']) crosses above the signal line (dataframe_long['macdsignal']). The populate_sell_trend function determines sell signals, although the specific conditions are currently commented out in the code. It seems to be using the RSI (relative strength index) values from the resampled dataframes, but those lines are currently commented out. Overall, the strategy uses MACD crossovers to generate buy signals and potentially other indicators (such as RSI) for sell signals. The specific conditions for sell signals need to be uncommented and implemented for the strategy to be fully functional.
The strategy operates on a 4-hour timeframe, meaning that it analyzes price data at 4-hour intervals.
The populate_indicators function resamples the input price data to create two additional dataframes: dataframe_short and dataframe_long.
dataframe_short is resampled to twice the timeframe (8 hours in this case), while dataframe_long is resampled to eight times the timeframe (32 hours). MACD (moving average convergence divergence) is calculated for both the short and long dataframes using the ta.macd function from the talib library. The MACD calculation parameters are set with fastperiod=12, slowperiod=26, and signalperiod=7. The two resampled dataframes are merged back into the original dataframe using the resampled_merge function. The populate_buy_trend function identifies buy signals based on the MACD values. It sets the 'buy' column of the dataframe to 1 when the MACD line (dataframe_long['macd']) crosses above the signal line (dataframe_long['macdsignal']). The populate_sell_trend function determines sell signals, although the specific conditions are currently commented out in the code. It seems to be using the RSI (relative strength index) values from the resampled dataframes, but those lines are currently commented out. Overall, the strategy uses MACD crossovers to generate buy signals and potentially other indicators (such as RSI) for sell signals. The specific conditions for sell signals need to be uncommented and implemented for the strategy to be fully functional.
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stoploss: -0.05 timeframe: 4h hash(sha256): 60747f3ecb4502fea11e346dedfbeae9312747a691df4b0639877ad39f072381 indicators: macdsignal _long _longmacd _short _shortmacd _shortmacdsignal macd _longmacdsignal
No similar strategies found. (based on used indicators)
last change: 2024-07-27 23:37:22