INNER CODE UNIT · Python

ev

dmarienko/QuantTradingResearch · models/kalman_filter_adjust.py:16

    b, err, ev = kalman_regression_estimator(x_train.values, y_train.values, **p, intercept=False)
    
    err = err[50:]
    ev = ev[50:]
    ll = -0.5*(N * np.log(2*np.pi) + np.log(ev) + err**2/ev).sum()
    if ll > ll_max:
        ll_max = ll
        best = p
best

b, err, evars = kalman_regression_estimator(X[constituents].values, Y.values, **best, intercept=False)
cut_off = slice(15, None)
e_s = pd.Series(err, index=Y.index)[cut_off]
v_s = pd.Series(np.sqrt(evars), index=Y.index)[cut_off]
b_a = pd.DataFrame(b.T, index=Y.index, columns=constituents)[cut_off]
b_a_w = b_a.div(b_a.sum(axis=1), axis=0)

fig(16, 5)

View source record →