The FreqaiExampleStrategy is a trading strategy implemented as a class in Python. Here is a brief description of what the strategy does:
The strategy first populates indicators using the provided dataframe and metadata. It calculates the target and sell ROI (Return on Investment) based on the "s_close_mean" and "s_close_std" columns of the dataframe.
It then populates the entry trend by setting the "enter_long" and "enter_short" columns of the dataframe based on certain conditions.
These conditions include the "do_predict" column being 1 and the current close price being above the target ROI or below the sell ROI.
The exit trend is populated by setting the "exit_long" and "exit_short" columns of the dataframe based on conditions similar to the entry trend but with different thresholds. The strategy returns the timeframe specified in the configuration, minus the last character. There is a method to handle trades, which updates the prediction value based on the trade information and saves it to the pair dictionary or follower dictionary, depending on the mode. It also calculates the ROI decay based on the current time and trade open date. Another method handles trade exits, resetting the prediction value for the trade in the dictionary and saving it. It returns True. There is also a method to check if a trade entry is allowed based on the current rate and the last candle's close price. This is a high-level overview of the strategy. More details can be obtained by examining the specific code implementation.
It then populates the entry trend by setting the "enter_long" and "enter_short" columns of the dataframe based on certain conditions.
These conditions include the "do_predict" column being 1 and the current close price being above the target ROI or below the sell ROI.
The exit trend is populated by setting the "exit_long" and "exit_short" columns of the dataframe based on conditions similar to the entry trend but with different thresholds. The strategy returns the timeframe specified in the configuration, minus the last character. There is a method to handle trades, which updates the prediction value based on the trade information and saves it to the pair dictionary or follower dictionary, depending on the mode. It also calculates the ROI decay based on the current time and trade open date. Another method handles trade exits, resetting the prediction value for the trade in the dictionary and saving it. It returns True. There is also a method to check if a trade entry is allowed based on the current rate and the last candle's close price. This is a high-level overview of the strategy. More details can be obtained by examining the specific code implementation.
stoploss: -0.05 timeframe: 5m hash(sha256): 8fefc75801c52a362a90e14657721db520b1e03b9699d5fed6df5c833cd50497 indicators: sell_roi freqai &s_close_mean target_roi &s_close_std
No similar strategies found. (based on used indicators)
last change: 2024-04-28 15:09:25