Thanks for the code. found it helpful in understanding the complexities of stock price prediction.
line #208 in base.env.market.py
if date_index < self.bound_index:
# Get y, y is not at date index, but plus 1. (Training Set)
instruments_y = scaled_frame.iloc[date_index + 1]['close']
else:
# Get y, y is at date index. (Test Set)
instruments_y = scaled_frame.iloc[date_index + 1]['close']
both the if and else conditions have the same code. should the else statement be
instruments_y = scaled_frame.iloc[date_index]['close']
secondly, i had a question. how do i convert the scaled values back to the normal values after the predict function is called ? I would like to see the predicted price as a non scaled value ?
thanks and appreciate your effort.
Thanks for the code. found it helpful in understanding the complexities of stock price prediction. line #208 in base.env.market.py
both the if and else conditions have the same code. should the else statement be
instruments_y = scaled_frame.iloc[date_index]['close']
secondly, i had a question. how do i convert the scaled values back to the normal values after the predict function is called ? I would like to see the predicted price as a non scaled value ? thanks and appreciate your
effort.