The AutoArimaTripleV1 strategy is designed to backtest trading strategies by adding various technical analysis (TA) indicators to a given DataFrame. It follows a specific structure and implements three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. The populate_indicators function is responsible for adding TA indicators to the DataFrame.
It checks if there is a stored predicted value for the 5-minute (5m) indicator.
If available, it adds the stored prediction to the DataFrame.
If not, it initializes an ArimaPredictor object and predicts the 5m indicator using the input DataFrame. The predicted value is then added to the DataFrame and stored for future use. The populate_buy_trend function populates the buy signal based on the TA indicators in the DataFrame. The specific conditions for generating the buy signal are not mentioned in the provided code snippet. The populate_sell_trend function populates the sell signal based on the TA indicators in the DataFrame. Similarly, the specific conditions for generating the sell signal are not mentioned. Finally, there is a separate class called ArimaPredictor that is used for predicting the 5m indicator value. However, the code snippet for this class is not provided, so its functionality cannot be described in detail. Overall, the AutoArimaTripleV1 strategy applies TA indicators, generates buy and sell signals based on these indicators, and utilizes an ArimaPredictor for predicting the 5m indicator value. The specific conditions for generating signals and the details of the ArimaPredictor class are not fully explained in the given code snippet.
It checks if there is a stored predicted value for the 5-minute (5m) indicator.
If available, it adds the stored prediction to the DataFrame.
If not, it initializes an ArimaPredictor object and predicts the 5m indicator using the input DataFrame. The predicted value is then added to the DataFrame and stored for future use. The populate_buy_trend function populates the buy signal based on the TA indicators in the DataFrame. The specific conditions for generating the buy signal are not mentioned in the provided code snippet. The populate_sell_trend function populates the sell signal based on the TA indicators in the DataFrame. Similarly, the specific conditions for generating the sell signal are not mentioned. Finally, there is a separate class called ArimaPredictor that is used for predicting the 5m indicator value. However, the code snippet for this class is not provided, so its functionality cannot be described in detail. Overall, the AutoArimaTripleV1 strategy applies TA indicators, generates buy and sell signals based on these indicators, and utilizes an ArimaPredictor for predicting the 5m indicator value. The specific conditions for generating signals and the details of the ArimaPredictor class are not fully explained in the given code snippet.
stoploss: -0.1 timeframe: 5m hash(sha256): 28658be5735d28879c3f4aac0a7b46367308215d2f3291992cb2ac1e831056c9 indicators: high prediction volume mean low step_ahead date open high low close volume
No similar strategies found. (based on used indicators)
last change: 2024-04-29 02:56:29