Stock-Trading-Environment
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_next_observation method might be looking into the future
Hello, I'm still getting acquainted with OpenAI Gym, so I'm not entirely sure about this issue, but it's possible that for each step, the _next_observation method is sending the next (future) 5 candles information, instead of the last five candles.
def _next_observation(self):
# Get the stock data points for the last 5 days and scale to between 0-1
frame = np.array([
self.df.loc[self.current_step: self.current_step +
5, 'Open'].values / MAX_SHARE_PRICE,
self.df.loc[self.current_step: self.current_step +
5, 'High'].values / MAX_SHARE_PRICE,
self.df.loc[self.current_step: self.current_step +
5, 'Low'].values / MAX_SHARE_PRICE,
self.df.loc[self.current_step: self.current_step +
5, 'Close'].values / MAX_SHARE_PRICE,
self.df.loc[self.current_step: self.current_step +
5, 'Volume'].values / MAX_NUM_SHARES,
])
This is the first 6 days of the dataframe (AAPL.csv):
Index Date Open High Low Close Volume 0 1998-01-02 13.63 16.25 13.50 16.25 6411700.0 1 1998-01-05 16.50 16.56 15.19 15.88 5820300.0 2 1998-01-06 15.94 20.00 14.75 18.94 16182800.0 3 1998-01-07 18.81 19.00 17.31 17.50 9300200.0 4 1998-01-08 17.44 18.62 16.94 18.19 6910900.0 5 1998-01-09 18.12 19.37 17.50 18.19 7915600.0
Setting the self.current_step
to 0, and printing the frame
array:
[[0.002726, 0.0033, 0.003188, 0.003762, 0.003488, 0.003624],
[0.00325, 0.003312, 0.004, 0.0038, 0.003724, 0.003874],
[0.0027, 0.003038, 0.00295, 0.003462, 0.003388, 0.0035],
[0.00325, 0.003176, 0.003788, 0.0035, 0.003638, 0.003638],
[0.00298568, 0.00271029, 0.0075357, 0.00433074, 0.00321814, 0.00368599]])
If we remove the normalization:
[13.63, 16.5, 15.94, 18.81, 17.44, 18.12] #Open
[16.25, 16.56, 20. , 19. , 18.62, 19.37] #High
[13.5, 15.19, 14.75, 17.31, 16.94, 17.5] #Low
[16.25, 15.88, 18.94, 17.5, 18.19, 18.19] #Close
[ 6411700. , 5820300. , 16182800. , 9300200. , 6910900. , 7915600. ] #Volume
If you check the DF you will see that those are the next 5 future prices/volume for the next steps. Logic applies to all following steps as well, not limited to step 0.