def get_socre(symbol):
all_data = hist_prices = myHMM.parseStockPrices(test_start_date,
test_end_date, symbol)
num_correct = 0.0
test_window = 6
N = len(all_data)
num_tests = N // test_window
for n in xrange(1, N - test_window, test_window):
train_data = all_data[-n:-n - test_window:-1, :]
hist_moves = myHMM.calculateDailyMoves(train_data, 1)
hist_O = np.array(
list(map(lambda x: 1 if x > 0 else (0
if x < 0 else 2), hist_moves)))
hist_O = hist_O[::-1]
(a, b, pi_est, alpha_est) = hmm.HMMBaumWelch(hist_O, 2, False, False)
path = hmm.HMMViterbi(a, b, hist_O, pi_est)
prediction_state = np.argmax(a[int(path[-1]), :])
prediction = np.argmax(b[prediction_state, :])
if ((all_data[-n - test_window - 1, 1] -
all_data[-n - test_window, 1]) >= 0 and prediction == 1) or (
(all_data[-n - test_window - 1, 1] -
all_data[-n - test_window, 1]) <= 0
and prediction == 0) or ((all_data[-n - test_window - 1, 1] -
all_data[-n - test_window, 1]) == 0
and prediction == 2):
num_correct += 1.0
if num_tests > 0:
return num_correct, num_tests, (num_correct / num_tests)
else:
return 0, 0, 0
xrange = range
hmm = myHMM.myHMM()
test_start_date = '2017-01-01'
test_end_date = '2017-11-11'
all_data = hist_prices = myHMM.parseStockPrices(test_start_date, test_end_date,
'002415')
num_correct = 0.0
test_window = 6
N = len(all_data)
num_tests = N // test_window
for n in xrange(1, N - test_window, test_window):
train_data = all_data[-n:-n - test_window:-1, :]
hist_moves = myHMM.calculateDailyMoves(train_data, 1)
hist_O = np.array(
list(map(lambda x: 1 if x > 0 else (0 if x < 0 else 2), hist_moves)))
hist_O = hist_O[::-1]
(a, b, pi_est, alpha_est) = hmm.HMMBaumWelch(hist_O, 2, False, False)
path = hmm.HMMViterbi(a, b, hist_O, pi_est)
prediction_state = np.argmax(a[int(path[-1]), :])
prediction = np.argmax(b[prediction_state, :])
if ((all_data[-n - test_window - 1, 1] - all_data[-n - test_window, 1]) > 0
and prediction == 1) or (
(all_data[-n - test_window - 1, 1] -
all_data[-n - test_window, 1]) < 0 and prediction == 0) or (
(all_data[-n - test_window - 1, 1] -
all_data[-n - test_window, 1]) == 0 and prediction == 2):
num_correct += 1.0
print(num_correct / num_tests)