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model.py
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model.py
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import tensorflow as tf
from data import Data
import numpy as np
import logging
logging.getLogger('tensorflow').disabled = True
from statistics import mean, variance
from math import sqrt
import matplotlib.pyplot as plt
class Model:
def __init__(self, data: Data, combined=False, train=True, ticker="SPY"):
self.data = data
self.epochs = 250 # May cause overfitting if too high
self.vix_var, self.vix_mean = self.get_vix_params()
self.ticker = ticker
self.combined = combined
self.model_name = self.get_model_name()
# Standardize each sample --> this makes the most sense
self.training_input, self.testing_input = self.get_standardized_samples()
self.training_input_vol, self.testing_input_vol = self.create_vix_input()
# Get targets to predict change in vol
self.training_output, self.testing_output = self.target_data_delta()
if train:
if combined:
self.model = self.define_combined_lstm_model()
self.train_combined_model()
else:
self.model = self.define_lstm_model()
self.train_model()
self.load_model()
if combined:
self.eval_combined_model()
else:
self.eval_model()
def get_model_name(self):
'''
Finds the model name using the ticker and whether it is the combined model
'''
if self.combined:
filename = self.ticker + '_' + 'combined_lstm_model'
else:
filename = self.ticker + '_' + 'lstm_model'
return filename
def get_vix_params(self):
'''
Finds average and variance of the IV data
:return:
'''
vix_data = self.data.training_vix_data_split
vix_var = variance(vix_data)
vix_mean = mean(vix_data)
return vix_var, vix_mean
def create_vix_input(self):
training_input_vol = self._get_all_vol_input(self.data.training_data)
testing_input_vol = self._get_all_vol_input(self.data.testing_data)
return training_input_vol, testing_input_vol
def _get_all_vol_input(self, vix_list):
'''
Creates the set of initial IV values for the combined model
:param vix_list: list of samples where sample = Tuple([price],[IV])
:return: IV[0] for each sample
'''
input_vol = []
for sample in vix_list:
initial_vol = sample[1][0]
std_sample = self.standardize_vix_sample(initial_vol)
input_vol.append(std_sample)
return input_vol
def standardize_vix_sample(self, vix_val, epsilon=1e-8):
'''
Standardize vix values, assumes mean and var already found
'''
return (vix_val - self.vix_mean) / sqrt(self.vix_var + epsilon)
def unstandardize_vix_sample(self, prediction, epsilon=1e-8):
'''
Convert a standardized output back to an unstandardized output
'''
return prediction * sqrt(self.vix_var + epsilon) + - self.vix_mean
def eval_model(self):
'''
Evalutes the standard LSTM model
Plots predicted IV change and IV
'''
output_arr = []
actual_arr = []
vix_actual = self.data.testing_vix_data_split[0:9]
vix_predicted = self.data.testing_vix_data_split[0:9]
start_ind = 1
end_ind = 10
correct_sign = 0
for ind, sample in enumerate(self.testing_input):
reshaped_sample = sample.reshape(1, 1, 10)
prediction = self.model.predict(reshaped_sample)[0][0]
output_arr.append(prediction[0])
actual_arr.append(self.testing_output[ind])
if np.sign(self.testing_output[ind]) == np.sign(prediction[0]):
correct_sign = correct_sign + 1
vix_actual.append(self.data.testing_vix_data_split[start_ind] + self.testing_output[ind])
vix_predicted.append(self.data.testing_vix_data_split[start_ind] + prediction[0])
start_ind += 1
end_ind += 1
print('Correlation with IV change:', np.corrcoef(output_arr, actual_arr))
print('Correlation with IV:', np.corrcoef(vix_predicted, vix_actual))
print('Sign Percentage:', correct_sign/len(actual_arr))
self.plot_change_in_vol(output_arr, actual_arr, combined=False)
self.plot_vol(vix_predicted, vix_actual, combined=False)
def eval_combined_model(self):
'''
Evalutes the combined LSTM model (with additional non-sequential data)
Plots predicted IV change and IV
'''
output_arr = []
actual_arr = []
vix_actual = self.data.testing_vix_data_split[0:9]
vix_predicted = self.data.testing_vix_data_split[0:9]
start_ind = 1
end_ind = 10
correct_sign = 0
for ind, sample in enumerate(self.testing_input):
reshaped_sample = sample.reshape(1, 1, 10)
vix_input = self.testing_input_vol[ind]
std_vix_input = self.standardize_vix_sample(vix_input)
reshaped_vix_sample = std_vix_input.reshape(1, 1, 1)
prediction = self.model.predict({"vol_input": reshaped_vix_sample, "lstm_input":reshaped_sample})[0][0]
output_arr.append(prediction[0])
actual_arr.append(self.testing_output[ind])
if np.sign(self.testing_output[ind]) == np.sign(prediction[0]):
correct_sign = correct_sign + 1
vix_actual.append(self.data.testing_vix_data_split[start_ind] + self.testing_output[ind])
vix_predicted.append(self.data.testing_vix_data_split[start_ind] + prediction[0])
start_ind += 1
end_ind += 1
print('Correlation with IV change:', np.corrcoef(output_arr, actual_arr))
print('Correlation with IV:', np.corrcoef(vix_predicted, vix_actual))
print('Sign Percentage:', correct_sign / len(actual_arr))
plt.figure()
plt.plot(output_arr, label='Output')
plt.plot(actual_arr, label='Actual')
plt.ylabel('Predicted Change in Volatility', fontsize=18)
plt.title('Predicted vs Actual Change in Volatility, Combined '+ self.ticker, fontsize=18)
plt.legend()
plt.show()
self.plot_change_in_vol(output_arr, actual_arr, combined=True)
self.plot_vol(vix_predicted, vix_actual, combined=True)
def plot_change_in_vol(self, output_arr, actual_arr, combined=False):
'''
Plots predicted IV change vs actual IV change
'''
plt.figure()
plt.plot(output_arr, label='Output')
plt.plot(actual_arr, label='Actual')
plt.ylabel('Predicted Change in Volatility', fontsize=18)
if combined:
plt.title('Predicted vs Actual Change in Volatility, Combined ' + self.ticker, fontsize=18)
else:
plt.title('Predicted vs Actual Change in Volatility ' + self.ticker, fontsize=18)
plt.legend()
plt.show()
def plot_vol(self, vix_predicted, vix_actual, combined=False):
'''
Plots predicted IV vs actual IV
'''
plt.figure()
plt.plot(vix_predicted, label='Output')
plt.plot(vix_actual, label='Actual')
plt.ylabel('Predicted Value of Volatility', fontsize=18)
if combined:
plt.title('Predicted vs Actual Volatility, Combined ' + self.ticker, fontsize=18)
else:
plt.title('Predicted vs Actual Volatility ' + self.ticker, fontsize=18)
plt.legend()
plt.show()
def define_combined_lstm_model(self):
'''
LSTM with additional non-time dependent input
:return:
'''
# Step 1: Define LSTM input
lstm_inputs = tf.keras.Input(shape=(None, 10), name="lstm_input")
lstm_layer = tf.keras.layers.LSTM(10, return_sequences=True, return_state=True, activation='relu')
lstm_outputs, state_h, state_c = lstm_layer(lstm_inputs)
# Step 2: Define Vol input
init_vol_inputs = tf.keras.Input(shape=(None, 1), name="vol_input")
# Concatenate LSTM output with the initial volitility
concat_layer = tf.keras.layers.concatenate([init_vol_inputs, lstm_outputs])
# Output a single numerical value
output_layer = tf.keras.layers.Dense(1)
network_output = output_layer(concat_layer)
lstm_model = tf.keras.Model(inputs=[init_vol_inputs, lstm_inputs], outputs=network_output)
# Compile model
lstm_model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])
print(lstm_model.summary())
return lstm_model
def define_lstm_model(self):
'''
This is set up for a single output value
:return:
'''
# Step 1: Define input
lstm_inputs = tf.keras.Input(shape=(None, 10))
lstm_layer = tf.keras.layers.LSTM(10, return_sequences=True, return_state=True, activation='relu')
lstm_outputs, state_h, state_c = lstm_layer(lstm_inputs)
# Output a single numerical value
output_layer = tf.keras.layers.Dense(1)
network_output = output_layer(lstm_outputs)
lstm_model = tf.keras.Model(inputs=lstm_inputs, outputs=network_output)
# Compile model
lstm_model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])
print(lstm_model.summary())
return lstm_model
def load_model(self):
'''
Loads saved model
:return:
'''
self.model = tf.keras.models.load_model(self.model_name)
print('Loaded model:', self.model_name)
def train_model(self):
'''
Train standard LSTM model
:return:
'''
reshaped_input = np.asarray(self.training_input).reshape(len(self.training_input), 1, 10)
self.model.fit(reshaped_input, np.asarray(self.training_output), epochs=self.epochs, validation_split=0.2, batch_size=5)
self.model.save(self.model_name)
print("Saved:", self.model_name)
def train_combined_model(self):
'''
Trains the LSTM model that takes the initial volatility as an input
:return:
'''
reshaped_lstm_input = np.asarray(self.training_input).reshape(len(self.training_input), 1, 10)
reshaped_vol_input = np.asarray(self.training_input_vol).reshape(len(self.training_input_vol), 1, 1)
self.model.fit({"vol_input": reshaped_vol_input, "lstm_input": reshaped_lstm_input}, np.asarray(self.training_output), epochs=self.epochs, validation_split=0.2, batch_size=5)
self.model.save(self.model_name)
print("Saved:", self.model_name)
def target_data_delta(self):
'''
Defines target output as the change in vol over the given time period
:return:
'''
training_targets = []
for sample in self.data.training_data:
training_targets.append(self._vol_diff(sample))
testing_targets = []
for sample in self.data.testing_data:
testing_targets.append(self._vol_diff(sample))
return training_targets, testing_targets
def target_data_all(self):
'''
Returns training data consisting of the list of 10 IV values
:return:
'''
training_targets = []
for sample in self.data.training_data:
training_targets.append(sample[1])
testing_targets = []
for sample in self.data.testing_data:
testing_targets.append(sample[1])
return training_targets, testing_targets
def get_standardized_samples(self):
'''
Standardizes each sample individually
:return:
'''
training_samples = []
for sample in self.data.training_data:
std_sample = self._standardize_sample(sample[0])
training_samples.append(std_sample)
testing_samples = []
for sample in self.data.testing_data:
std_sample = self._standardize_sample(sample[0])
testing_samples.append(std_sample)
return training_samples, testing_samples
@staticmethod
def _standardize_sample(sample, epsilon=1e-8):
mean_val = mean(sample)
var = variance(sample)
standardized_sample = (sample - mean_val) / sqrt(var + epsilon)
return standardized_sample
@staticmethod
def _vol_diff(sample):
return sample[1][-1] - sample[1][0]