# set trade date
tradeDate = tradeDate.strftime('%Y-%m-%d')
print(tradeDate)
# get first/last row
df_start_date = temp_raw_df.Date[0]
lastRow = temp_raw_df.shape[0]
df_end_date = temp_raw_df.Date[lastRow-1]
feat_df = dSet.set_date_range(temp_raw_df, df_start_date, df_end_date)
# Resolve any NA's for now
feat_df.fillna(method='ffill', inplace=True)
#set beLong level
beLongThreshold = 0.000
feat_df = ct.setTarget(temp_raw_df, "Long", beLongThreshold)
# Adding features with new day
input_dict = sysUtil.get_dict(system_directory, 'input_dict')
feat_df = featureGen.generate_features(feat_df, input_dict)
feat_df = transf.normalizer(feat_df, 'Volume', 50)
col_vals = [k for k,v in feature_dict.items() if v == 'Drop']
to_drop = ['Open','High','Low', 'gainAhead', 'Close', 'Volume', 'AdjClose', 'beLong']
for x in to_drop:
col_vals.append(x)
model_data = dSet.drop_columns(feat_df, col_vals)
# Retrieve model
best_model_name = "SVM"
best_model_segment = "segment-0"
isOosDates = timeUtil.is_oos_data_split(issue, pivotDate, is_oos_ratio,
oos_months, segments)
dataLoadStartDate = isOosDates[0]
is_start_date = isOosDates[1]
oos_start_date = isOosDates[2]
is_months = isOosDates[3]
is_end_date = isOosDates[4]
oos_end_date = isOosDates[5]
#load data
dataSet = dSet.read_issue_data(issue)
dataSet = dSet.set_date_range(dataSet, dataLoadStartDate, pivotDate)
#set beLong level
beLongThreshold = 0.0
dataSet = ct.setTarget(dataSet, "Long", beLongThreshold)
# def stationarity_tests(s_df, signal, issue):
# print("=============================================================")
# adf_test(s_df, signal, issue)
# hurst_setup(s_df[signal][:], issue)
# print("========================================")
for i in range(segments):
modelData = dSet.set_date_range(dataSet, is_start_date, is_end_date)
print("\n\n\nIN SAMPLE")
# Stationarity tests
stationarity_tests(modelData, 'Close', issue)
stationarity_tests(modelData, 'beLong', issue)
#print_beLongs(modelData)
if doPlot:
print(new_data_df.tail(2))
# get first row
df_start_date = new_data_df.Date[0]
# get last row
lastRow = new_data_df.shape[0]
df_end_date = new_data_df.Date[lastRow - 1]
feat_df = dSet.set_date_range(new_data_df, df_start_date, df_end_date)
# Resolve any NA's for now
feat_df.fillna(method='ffill', inplace=True)
#set beLong level
beLongThreshold = 0.000
feat_df = ct.setTarget(new_data_df, "Long", beLongThreshold)
print(feat_df.tail(2))
# Adding features with new day
input_dict = sysUtil.get_dict(system_directory, 'input_dict')
feat_df = featureGen.generate_features(feat_df, input_dict)
feat_df = transf.normalizer(feat_df, 'Volume', 50)
col_vals = [k for k, v in feature_dict.items() if v == 'Drop']
to_drop = [
'Open', 'High', 'Low', 'gainAhead', 'Close', 'Volume', 'AdjClose',
'beLong'
]
for x in to_drop: