Cenderawasih_freqai

The Cenderawasih_freqai strategy is a backtesting strategy that aims to generate trading signals based on the provided indicators and entry/exit conditions. Here's a breakdown of what the strategy does: populate_indicators function: This function calculates and populates various indicators for the given dataframe, such as moving averages and correlation values. It iterates over different timeframes and pairs to generate the indicators.

populate_entry_trend function: This function determines the conditions for entering a long trade based on the calculated indicators.

If the conditions are met, it sets the "enter_long" and "enter_tag" columns of the dataframe accordingly.

populate_exit_trend function: This function determines the conditions for exiting a long trade based on the indicators. If the conditions are met, it sets the "exit_long" column of the dataframe to 1. get_interval function: This function returns the timeframe interval used for the strategy, extracted from the configuration. on_buy_signal function: This function is called when a buy signal is generated by the strategy. It handles the actions to be taken, such as updating the prediction and saving the data. Moving Average Weighted (WMA) calculation: This code block defines a weighted moving average (WMA) calculation. It takes a dataframe, WMA length, and field as input, and returns the WMA values for the specified field. Hull Moving Average (HMA) calculation: This code block defines a Hull Moving Average (HMA) calculation. It takes a dataframe, HMA length, and field as input, and returns the HMA values for the specified field. These functions and calculations work together to backtest trading strategies, generate entry/exit signals, and calculate moving average indicators.