Keras loss functions

volatility.py

# https://github.com/Rachnog/Deep-Trading/blob/master/volatility/volatility.py

epsilon = 1.0e-9
def qlike_loss(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)
    loss = K.log(y_pred) + y_true / y_pred
    return K.mean(loss, axis=-1)

def mse_log(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)
    loss = K.square(K.log(y_true) - K.log(y_pred))
    return K.mean(loss, axis=-1)

def mse_sd(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)
    loss = K.square(y_true - K.sqrt(y_pred))
    return K.mean(loss, axis=-1)    

def hmse(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)
    loss = K.square(y_true / y_pred - 1.)
    return K.mean(loss, axis=-1)    

def stock_loss(y_true, y_pred):
    alpha = 100.
    loss = K.switch(K.less(y_true * y_pred, 0), \
        alpha*y_pred**2 - K.sign(y_true)*y_pred + K.abs(y_true), \
        K.abs(y_true - y_pred)
        )
    return K.mean(loss, axis=-1)