val_data['return'] = df.loc[val_index, 'return']
mu = np.mean(est_data['return'])
est_data['return_dm'] = est_data['return'] - mu
val_data['return_dm'] = val_data['return'] - mu
est_data['return_dm2'] = est_data['return_dm']**2
val_data['return_dm2'] = val_data['return_dm']**2
variance = np.var(est_data['return_dm'])
print('average = ', mu)
print('variance = ', variance)
# Initialazing with an instance of the model object
# -------------------------------------------------
hidden_dim = 12
model = rnn(1, hidden_dim, 1, variance=variance, model_type='Jordan')
print('w dimensions =', model.w_dim)
#state, est_data['sigma2'] = model.foward_prop(est_data['return'])
# Minimize loss function
# ----------------------
# Initializing with random weights uniformly on the interval -1/sqrt(k) to -1/sqrt(k)
# where k = previous layers dimensions.
W_H = np.random.uniform(-np.sqrt(1. / 3), np.sqrt(1. / 3), (hidden_dim, 3))
W_O = np.random.uniform(-np.sqrt(1. / hidden_dim), np.sqrt(1. / hidden_dim),
(1, hidden_dim + 1))
w0 = np.hstack((W_H.flatten(), W_O.flatten()))
print('Initial weights =', w0)
#est_dct = json.load(open('Jordan_est_Mon_02-11-2015_06.57.json'))
#w0 = np.array(est_dct['w_opt'])
import pandas as pd
import numpy as np
from RNNnumpy import RNNnumpy as rnn
import json
from pprint import pprint
import scipy.stats as sps
import matplotlib.pyplot as plt
from pprint import pprint
#est_dct = json.load(open('Jordan_est_Mon_02-11-2015_06.57.json')) # Bedste bud kl 8:00
est_dct = json.load(open('Jordan_est_Tue_03-11-2015_18.35.json'))
#pprint(est_dct)
model = rnn(1,est_dct['D-M-K'][1],1,
#w = est_dct['w_opt'],
w = est_dct['w_cross_val'],
#w = est_dct['w_all'][40],
variance=est_dct['variance'],
mu = est_dct['mean'],
model_type=est_dct['model_type'])
print(model)
print(est_dct['variance'])
est_dct_garch = json.load(open('GARCH_est_Sun_01-11-2015_21.06.57.json'))
pprint(est_dct_garch)
# Plot News impact curve
# ----------------------
col_lst = plt.cm.Set1(np.linspace(0, 1, 9))
y = np.arange(-4,4,0.001)
sigma2 = np.array([model.forward_prop([yi]) for yi in y])
# GARCH par
import numpy as np
from RNNnumpy import RNNnumpy as rnn
import json
from pprint import pprint
import scipy.stats as sps
import matplotlib.pyplot as plt
from pprint import pprint
#est_dct = json.load(open('Jordan_est_Mon_02-11-2015_06.57.json')) # Bedste bud kl 8:00
est_dct = json.load(open('Jordan_est_Tue_03-11-2015_18.35.json'))
#pprint(est_dct)
model = rnn(
1,
est_dct['D-M-K'][1],
1,
#w = est_dct['w_opt'],
w=est_dct['w_cross_val'],
#w = est_dct['w_all'][40],
variance=est_dct['variance'],
mu=est_dct['mean'],
model_type=est_dct['model_type'])
print(model)
print(est_dct['variance'])
est_dct_garch = json.load(open('GARCH_est_Sun_01-11-2015_21.06.57.json'))
pprint(est_dct_garch)
# Plot News impact curve
# ----------------------
col_lst = plt.cm.Set1(np.linspace(0, 1, 9))
y = np.arange(-4, 4, 0.001)
sigma2 = np.array([model.forward_prop([yi]) for yi in y])
val_data['return'] = df.loc[val_index,'return']
mu = np.mean(est_data['return'])
est_data['return_dm'] = est_data['return'] - mu
val_data['return_dm'] = val_data['return'] - mu
est_data['return_dm2'] = est_data['return_dm']**2
val_data['return_dm2'] = val_data['return_dm']**2
variance = np.var(est_data['return_dm'])
print('average = ', mu)
print('variance = ', variance)
# Initialazing with an instance of the model object
# -------------------------------------------------
hidden_dim = 12
model = rnn(1,hidden_dim,1,variance=variance,model_type='Jordan')
print('w dimensions =',model.w_dim)
#state, est_data['sigma2'] = model.foward_prop(est_data['return'])
# Minimize loss function
# ----------------------
# Initializing with random weights uniformly on the interval -1/sqrt(k) to -1/sqrt(k)
# where k = previous layers dimensions.
W_H = np.random.uniform(-np.sqrt(1./3),np.sqrt(1./3),(hidden_dim,3))
W_O = np.random.uniform(-np.sqrt(1./hidden_dim),np.sqrt(1./hidden_dim),(1,hidden_dim+1))
w0 = np.hstack((W_H.flatten(),W_O.flatten()))
print('Initial weights =',w0)
#est_dct = json.load(open('Jordan_est_Mon_02-11-2015_06.57.json'))
#w0 = np.array(est_dct['w_opt'])
