Write a code using Kalman filter to predict rise and fall ofstock prices
Solution
import filterpyimport numpy as npfrom filterpy.kalman import KalmanFiltermy_filter = KalmanFilter(dim_x=1, dim_z=1, dim_u=1)numsteps = 80f = my_filterinit_state = 1.f.x = np.array([[init_state]])f.F = np.array([[1]])f.H = np.array([[1]])# covariance matrixstate_noise = 0.02f.P = state_noise# measurement noisemeasure_noise = 0.8f.R = np.array([[measure_noise]]) # state uncertaintyf.Q = np.array([[state_noise]])# control inputscontrols = np.array([0]*1 + [0]*19 + [3]*40 + [0]*20)# get true statestrue_states = np.zeros(numsteps)true_states[0] = init_state true_states += controls# state noisetrue_states += np.random.normal(0, state_noise, numsteps)# measurementsmeasurements = [(s + np.random.normal(0, measure_noise)) for s in true_states]all_obs = []estimates = []num_obs = numstepscovs = []for