The MyCustomStrategy is a trading strategy implemented in Python for backtesting purposes. It utilizes a CNN-LSTM model to make price predictions based on input data. Here's a breakdown of its important parts:
populate_indicators: This function takes a DataFrame containing price data as input and uses a pre-trained model (model) to generate price predictions.
The predictions are then added as a new column named 'prediction' to the DataFrame.
The updated DataFrame is returned.
populate_buy_trend: In this function, the buy trend is determined based on a threshold. If the price predictions in the DataFrame are higher than the threshold value of 0.5, the 'buy' column is set to 1, indicating a positive buy trend. The modified DataFrame is returned. populate_sell_trend: Similarly, the sell trend is determined in this function using a threshold value of 0.5. If the price predictions in the DataFrame are lower than the threshold, the 'sell' column is set to 1, indicating a positive sell trend. The modified DataFrame is returned. Overall, this strategy uses a machine learning model to generate price predictions and then defines the buy and sell trends based on these predictions.
The predictions are then added as a new column named 'prediction' to the DataFrame.
The updated DataFrame is returned.
populate_buy_trend: In this function, the buy trend is determined based on a threshold. If the price predictions in the DataFrame are higher than the threshold value of 0.5, the 'buy' column is set to 1, indicating a positive buy trend. The modified DataFrame is returned. populate_sell_trend: Similarly, the sell trend is determined in this function using a threshold value of 0.5. If the price predictions in the DataFrame are lower than the threshold, the 'sell' column is set to 1, indicating a positive sell trend. The modified DataFrame is returned. Overall, this strategy uses a machine learning model to generate price predictions and then defines the buy and sell trends based on these predictions.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: 0 timeframe: 5m hash(sha256): c6e35ab4eb83a5b7f5c3938738239d8e3dec8daa856751ebef065ae70c92fffc indicators: prediction
No similar strategies found. (based on used indicators)
last change: 2024-07-28 21:45:33