Test.py 文件源码

def deep_q_investing():
    profit = 0
    stocks_invested = 0
    stocks_iterated = 0

    y = []
    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())
        state = env.reset()
        while stocks_iterated < stocks_to_iterate:

            state = np.squeeze(np.reshape(state, [80, 80]))

            state = np.stack([state] * 4, axis=2)
            state = np.array([state])
            q_values = estimator.predict(sess, state)[0]
            best_action = np.argmax(q_values)
            action = VALID_ACTIONS[best_action]
            next_state, reward, done, _ = env.step(action)

            if done:
                profit += reward
                stocks_invested  += reward != 0
                y.append(profit/(stocks_invested or 1))
                state = env.reset()
                stocks_iterated += 1
                print ("Stock {}/{} , Profit: {}".format(stocks_iterated, stocks_to_iterate, profit/(stocks_invested or 1)))


            else:
                state = next_state
    x_new = np.linspace(x.min(),x.max(),smoothing)
    y = np.array(y)
    y_smooth = spline(x, y, x_new)
    return     [plt.plot(x_new, y_smooth, linewidth=2, label='Deep Q'),profit / (stocks_invested or 1)]