advanced-algorithmic-trading

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delta = 1e-5
trans_cov = delta / (1 - delta) * np.eye(2)
obs_mat = np.vstack(
[prices[etfs[0]], np.ones(prices[etfs[0]].shape)]
).T[:, np.newaxis]
kf = KalmanFilter(
n_dim_obs=1,
n_dim_state=2,
initial_state_mean=np.zeros(2),
initial_state_covariance=np.ones((2, 2)),
transition_matrices=np.eye(2),
observation_matrices=obs_mat,
observation_covariance=1.0,
transition_covariance=trans_cov
)
state_means, state_covs = kf.filter(prices[etfs[1]].values)
return state_means, state_covs
def draw_slope_intercept_changes(prices, state_means):
"""
Plot the slope and intercept changes from the
Kalman Filter calculated values.
"""
pd.DataFrame(
dict(
slope=state_means[:, 0],
intercept=state_means[:, 1]
), index=prices.index
).plot(subplots=True)
plt.show()
if __name__ == "__main__":
# Choose the ETF symbols to work with along with
# start and end dates for the price histories
etfs = [’TLT’, ’IEI’]
start_date = "2010-8-01"
end_date = "2016-08-01"
# Obtain the adjusted closing prices from Yahoo finance
etf_df1 = pdr.get_data_yahoo(etfs[0], start_date, end_date)
etf_df2 = pdr.get_data_yahoo(etfs[1], start_date, end_date)