smarttrader.py 文件源码

def _create_loss(self):
        '''
        Risk estimation loss function. The output is the planed position we should hold to next day. The change rate of
        next day is self.y, so we loss two categories of money: - self.y * self.position is trade loss,
        cost * self.position is constant loss because of tax and like missing profit of buying national debt. Therefore,
        the loss function is formulated as: 100 * (- self.y * self.position + cost * self.position) = -100 * ((self.y - cost) * self.position)
        :return:
        '''
        #with tf.device("/cpu:0"):
        xx = tf.unstack(self.x, self.step, 1)
        lstm_cell = rnn.LSTMCell(self.hidden_size, forget_bias=1.0, initializer=orthogonal_initializer())
        dropout_cell = DropoutWrapper(lstm_cell, input_keep_prob=self.keep_rate, output_keep_prob=self.keep_rate, state_keep_prob=self.keep_rate)
        outputs, states = rnn.static_rnn(dropout_cell, xx, dtype=tf.float32)
        signal = tf.matmul(outputs[-1], self.weights['out']) + self.biases['out']
        scope = "activation_batch_norm"
        norm_signal = self.batch_norm_layer(signal, scope=scope)
        # batch_norm(signal, 0.9, center=True, scale=True, epsilon=0.001, activation_fn=tf.nn.relu6,
        #           is_training=is_training, scope="activation_batch_norm", reuse=False)
        self.position = tf.nn.relu6(norm_signal, name="relu_limit") / 6.
        self.avg_position = tf.reduce_mean(self.position)
        # self.cost = 0.0002
        self.loss = -100. * tf.reduce_mean(tf.multiply((self.y - self.cost), self.position, name="estimated_risk"))