# Start with 100 points of 1H data
# iterate (I know!) over the range, getting history data and transform into features
count = 0
for row in hour_data.itertuples(index=True):
#print (getattr(row, "Index"), getattr(row, 'open'))
time = getattr(row,"Index")
#open_price = getattr(row,"open")
# reomoved time here, will it work?
features = all_data.getFeatures()
feature_series = pd.Series(features)
feature_series.name = time
features_df = features_df.append(feature_series)
'''
if (count > 1000):
break
count +=1
'''
print ('features_df shape:', features_df.shape)
print (features_df.head())
print(X_train.shape)
y_train = y_train.values.ravel()
svclassifier = SVC(kernel='rbf')
svclassifier.fit(X_train, y_train)
loop = True
while (loop):
price = all_data.getTimePrice()
print('current price:', price)
print('start time is:', all_data.virtual_time)
current_sample = all_data.getFeatures()
print('current sample:', current_sample.shape)
current_sample = current_sample.reshape(1, -1)
print('after reshape:', current_sample.shape)
y_pred = svclassifier.predict(current_sample)
#y_pred = svclassifier.predict(X_test)
print('current prediction:', y_pred)
if (y_pred[0] == 2):
print('SELL!!!!!!')
