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@meocong
meocong committed Jun 4, 2017
1 parent 1f7c68d commit 014aec6
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Showing 3 changed files with 56 additions and 24 deletions.
50 changes: 33 additions & 17 deletions HMM_predict_by_open_today.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@


class ModelHMM():
def __init__(self, company, day_start, day_end, n_days_previous, n_states, verbose, n_decimals):
def __init__(self, company, day_start, day_end, n_days_previous, n_states, verbose, n_decimals, latex):
self.company = company
self.day_start = day_start
self.day_end = day_end
Expand All @@ -18,6 +18,7 @@ def __init__(self, company, day_start, day_end, n_days_previous, n_states, verbo
self.verbose = verbose
self.print_model = verbose
self.n_decimals = n_decimals
self.latex = latex

def _get_value_by_positions(self, df, start_index, end_index):
X = df.ix[start_index:end_index]
Expand Down Expand Up @@ -90,31 +91,46 @@ def _predict(self, company, day_start, day_end, n_previous, n_cluster):
counting_error = 0

for i in range(n_previous, n_days):
model = GaussianHMM(n_components=n_cluster, covariance_type="diag", n_iter=5, verbose=False,
init_params='mtsc')
model = GaussianHMM(n_components=n_cluster, covariance_type="diag", n_iter=2, verbose=False,
init_params='mstc')
X, dates, close_v, volume_v, high_v, open_v, low_v = self._get_value_by_positions(df, i - n_previous, i)
if (self.verbose == True):
print "Predicting in", i - n_previous + 1, "th/", n_days - n_previous + 1, "days..."

try:
temp_model = model.fit(X)
# try:
temp_model = model.fit(X)

if (self.print_model == True):
np.set_printoptions(precision=self.n_decimals)
if (self.print_model == True):
np.set_printoptions(precision=self.n_decimals)
if (self.latex == False):
print "Transform matrix : "
print np.around(np.array(temp_model.transmat_), decimals=self.n_decimals)
print "Starting probability : "
print np.around(np.array(temp_model.startprob_), decimals=self.n_decimals)
self.print_model = False
else:
print "Transform matrix : "
temp_mat = np.around(np.array(temp_model.transmat_), decimals=self.n_decimals)

print "\hline"
for xxx in temp_mat:
print " & ".join([str(x) for x in xxx]), " \\\\"
print "\hline"

X, dates, close_v, volume_v, high_v, open_v, low_v = self._get_value_by_positions(df, i, i + 1)
hidden_states = temp_model.predict(X)
predicted.append(temp_model.means_[hidden_states[0]][0] * open_v[0] + open_v[0])
except:
counting_error += 1
print(counting_error)
X, dates, close_v, volume_v, high_v, open_v, low_v = self._get_value_by_positions(df, i, i + 1)
predicted.append(open_v[0])
print "Starting probability : "
temp_mat = np.around(np.array(temp_model.startprob_), decimals=self.n_decimals)
print "\hline"
print " & ".join([str(x) for x in temp_mat]), " \\\\"
print "\hline"
self.print_model = False

X, dates, close_v, volume_v, high_v, open_v, low_v = self._get_value_by_positions(df, i, i + 1)
hidden_states = temp_model.predict(X)
predicted.append(temp_model.means_[hidden_states[0]][0] * open_v[0] + open_v[0])
# except:
# counting_error += 1
# print(counting_error)
# X, dates, close_v, volume_v, high_v, open_v, low_v = self._get_value_by_positions(df, i, i + 1)
# predicted.append(open_v[0])

print "Finished predicting", n_days - n_previous + 1, "days in ", time.time() - start_time, " s"
error = self._show_plot(v_dates[n_previous:], v_close_v[n_previous:], predicted, 'Trained data')
Expand All @@ -126,5 +142,5 @@ def _predict(self, company, day_start, day_end, n_previous, n_cluster):
day_start = datetime.datetime(2016, 1, 1)
day_end = pd.datetime.today()

model = ModelHMM(company="AAPL", day_start=day_start, day_end=day_end, n_days_previous=100, n_states=10, verbose=True, n_decimals = 3)
model = ModelHMM(company="AAPL", day_start=day_start, day_end=day_end, n_days_previous=100, n_states=10, verbose=True, n_decimals = 3, latex = True)
model.predict()
30 changes: 23 additions & 7 deletions HMM_predict_continous_days.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@


class ModelHMM():
def __init__(self, company, day_start, day_end, n_days_previous, n_states, n_days_predict, verbose, n_decimals):
def __init__(self, company, day_start, day_end, n_days_previous, n_states, n_days_predict, verbose, n_decimals, latex):
self.company = company
self.day_start = day_start
self.day_end = day_end
Expand All @@ -19,6 +19,7 @@ def __init__(self, company, day_start, day_end, n_days_previous, n_states, n_day
self.verbose = verbose
self.print_model = verbose
self.n_decimals = n_decimals
self.latex = latex

def _get_value_by_positions(self, df, start_index, end_index):
X = df.ix[start_index:end_index]
Expand Down Expand Up @@ -92,10 +93,25 @@ def _predict(self, company, day_start, day_end, n_previous, n_cluster, n_days_pr

if (self.print_model == True):
np.set_printoptions(precision=self.n_decimals)
print "Transform matrix : "
print np.around(np.array(temp_model.transmat_), decimals=self.n_decimals)
print "Starting probability : "
print np.around(np.array(temp_model.startprob_), decimals=self.n_decimals)
if (self.latex == False):
print "Transform matrix : "
print np.around(np.array(temp_model.transmat_), decimals=self.n_decimals)
print "Starting probability : "
print np.around(np.array(temp_model.startprob_), decimals=self.n_decimals)
else:
print "Transform matrix : "
temp_mat = np.around(np.array(temp_model.transmat_), decimals=self.n_decimals)

print "\hline"
for xxx in temp_mat:
print " & ".join([str(x) for x in xxx]), " \\\\"
print "\hline"

print "Starting probability : "
temp_mat = np.around(np.array(temp_model.startprob_), decimals=self.n_decimals)
print "\hline"
print " & ".join([str(x) for x in temp_mat]), " \\\\"
print "\hline"
self.print_model = False

last_close = v_close_v[day]
Expand All @@ -121,6 +137,6 @@ def _predict(self, company, day_start, day_end, n_previous, n_cluster, n_days_pr
day_start = datetime.datetime(2016, 1, 1)
day_end = pd.datetime.today()

model = ModelHMM(company="AAPL", day_start=day_start, day_end=day_end, n_days_previous=100, n_states=20,
n_days_predict=2, verbose=True, n_decimals = 3)
model = ModelHMM(company="AAPL", day_start=day_start, day_end=day_end, n_days_previous=100, n_states=10,
n_days_predict=2, verbose=True, n_decimals = 3, latex = False)
model.predict()
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