The MyCustomStrategy is a trading strategy implemented in Python using the Freqtrade library. Here is a short description of what the strategy does:
populate_indicators function:
Trains a linear regression model called model using training data (X_train and y_train). Uses the trained model to predict prices based on selected features (feature1, feature2, feature3) from the input dataframe.
Adds the price predictions to the dataframe as a new column called 'prediction'.
Calculates the upper and lower bounds of the linear regression channel using the predicted prices and adds them as columns ('upper_channel' and 'lower_channel') to the dataframe.
Returns the modified dataframe. populate_buy_trend function: Sets the buy trend as positive (1) if the close prices are lower than the lower bound of the linear regression channel. Updates the 'buy' column in the dataframe accordingly. Returns the modified dataframe. populate_sell_trend function: Sets the sell trend as positive (1) if the close prices are higher than the upper bound of the linear regression channel. Updates the 'sell' column in the dataframe accordingly. Returns the modified dataframe. Overall, the strategy uses a linear regression model to predict prices based on specific features and creates a regression channel around the predicted prices. It generates buy signals when prices fall below the lower channel and sell signals when prices rise above the upper channel.
Adds the price predictions to the dataframe as a new column called 'prediction'.
Calculates the upper and lower bounds of the linear regression channel using the predicted prices and adds them as columns ('upper_channel' and 'lower_channel') to the dataframe.
Returns the modified dataframe. populate_buy_trend function: Sets the buy trend as positive (1) if the close prices are lower than the lower bound of the linear regression channel. Updates the 'buy' column in the dataframe accordingly. Returns the modified dataframe. populate_sell_trend function: Sets the sell trend as positive (1) if the close prices are higher than the upper bound of the linear regression channel. Updates the 'sell' column in the dataframe accordingly. Returns the modified dataframe. Overall, the strategy uses a linear regression model to predict prices based on specific features and creates a regression channel around the predicted prices. It generates buy signals when prices fall below the lower channel and sell signals when prices rise above the upper channel.
stoploss: 0 timeframe: 5m hash(sha256): 83ec2b9dcdfcbc930a2007c2ca865957d2739182735e0e5bcceb8b38b3e2e12a indicators: feature1 feature2 feature3 prediction feature1 close lower_channel upper_channel
No similar strategies found. (based on used indicators)
last change: 2024-04-28 23:16:41