yhilpisch / aiif Goto Github PK

tradingbot.plot_performance(agent)

---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
<ipython-input-13-80d9132e183d> in <module>
----> 1 tradingbot.plot_performance(agent)
      2 # plt.savefig('../../images/ch11/figure_rm_02.png');

D:\trade-projects\aiif\code\tradingbot.py in plot_performance(agent)
    180     plt.figure(figsize=(10, 6))
    181     x = range(1, len(agent.performances) + 1)
--> 182     y = np.polyval(np.polyfit(x, agent.performances, deg=3), x)
    183     plt.plot(x, agent.performances[:], label='training')
    184     plt.plot(x, y, 'r--', label='regression (train)')

<__array_function__ internals> in polyfit(*args, **kwargs)

D:\anaconda3\envs\aiif\lib\site-packages\numpy\lib\polynomial.py in polyfit(x, y, deg, rcond, full, w, cov)
    629     scale = NX.sqrt((lhs*lhs).sum(axis=0))
    630     lhs /= scale
--> 631     c, resids, rank, s = lstsq(lhs, rhs, rcond)
    632     c = (c.T/scale).T  # broadcast scale coefficients
    633 

<__array_function__ internals> in lstsq(*args, **kwargs)

D:\anaconda3\envs\aiif\lib\site-packages\numpy\linalg\linalg.py in lstsq(a, b, rcond)
   2257         # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
   2258         b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2259     x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
   2260     if m == 0:
   2261         x[...] = 0

D:\anaconda3\envs\aiif\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_lstsq(err, flag)
    107 
    108 def _raise_linalgerror_lstsq(err, flag):
--> 109     raise LinAlgError("SVD did not converge in Linear Least Squares")
    110 
    111 def get_linalg_error_extobj(callback):

LinAlgError: SVD did not converge in Linear Least Squares

In [55]:
test = data.loc[split:].copy()

In [55]:
test = data.iloc[split:].copy()

        state = self.env.get_state(bar)
        action = np.argmax(self.model.predict(
                    self._reshape(state.values))[0, 0])
        position = 1 if action == 1 else -1
        if self.position in [0, -1] and position == 1:
            if self.verbose:
                print(50 * '-')
                print(f'{date} | *** GOING LONG ***')
            if self.position == -1:
                self.place_buy_order(bar - 1, units=-self.units)
            self.place_buy_order(bar - 1,
                                 amount=self.current_balance)
            if self.verbose:
                self.print_net_wealth(bar)
            self.position = 1
        elif self.position in [0, 1] and position == -1:
            if self.verbose:
                print(50 * '-')
                print(f'{date} | *** GOING SHORT ***')
            if self.position == 1:
                self.place_sell_order(bar - 1, units=self.units)
            self.place_sell_order(bar - 1,
                                  amount=self.current_balance)
            if self.verbose:
                self.print_net_wealth(bar)
            self.position = -1