def get_bus_dates_from_agg_method_and_contracts_back(**kwargs):
ref_date = kwargs['ref_date']
aggregation_method = kwargs['aggregation_method']
contracts_back = kwargs['contracts_back']
ref_datetime = cu.convert_doubledate_2datetime(ref_date)
if aggregation_method == 12:
cal_date_list = [ref_datetime - relativedelta(years=x) for x in range(1, contracts_back+1)]
elif aggregation_method == 1:
cal_date_list = [ref_datetime - relativedelta(months=x) for x in range(1, contracts_back+1)]
bday_us = CustomBusinessDay(expcalendar=exp.get_calendar_4ticker_head(const.reference_tickerhead_4business_calendar))
if 'shift_bus_days' in kwargs.keys():
shift_bus_days = kwargs['shift_bus_days']
if shift_bus_days >= 0:
bus_date_list = [pd.date_range(x, periods=shift_bus_days+1, freq=bday_us)[shift_bus_days].to_datetime() for x in cal_date_list]
elif shift_bus_days < 0:
bus_date_list = [pd.date_range(start=x-relativedelta(days=(max(m.ceil(-shift_bus_days*7/5)+5, -shift_bus_days+5))), end=x, freq=bday_us)[shift_bus_days-1].to_datetime() for x in cal_date_list]
else:
bus_date_list = [pd.date_range(x, periods=1, freq=bday_us)[0].to_datetime() for x in cal_date_list]
return bus_date_list
def get_bus_dates_from_agg_method_and_contracts_back(**kwargs):
ref_date = kwargs['ref_date']
aggregation_method = kwargs['aggregation_method']
contracts_back = kwargs['contracts_back']
ref_datetime = cu.convert_doubledate_2datetime(ref_date)
if aggregation_method == 12:
cal_date_list = [
ref_datetime - relativedelta(years=x)
for x in range(1, contracts_back + 1)
]
elif aggregation_method == 1:
cal_date_list = [
ref_datetime - relativedelta(months=x)
for x in range(1, contracts_back + 1)
]
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head(
const.reference_tickerhead_4business_calendar))
if 'shift_bus_days' in kwargs.keys():
shift_bus_days = kwargs['shift_bus_days']
if shift_bus_days >= 0:
bus_date_list = [
pd.date_range(x, periods=shift_bus_days + 1,
freq=bday_us)[shift_bus_days].to_pydatetime()
for x in cal_date_list
]
elif shift_bus_days < 0:
bus_date_list = [
pd.date_range(start=x - relativedelta(days=(max(
m.ceil(-shift_bus_days * 7 / 5) + 5, -shift_bus_days +
5))),
end=x,
freq=bday_us)[shift_bus_days -
1].to_pydatetime()
for x in cal_date_list
]
else:
bus_date_list = [
pd.date_range(x, periods=1, freq=bday_us)[0].to_pydatetime()
for x in cal_date_list
]
return bus_date_list
def get_backtesting_dates(**kwargs):
date_to = kwargs['date_to']
years_back = kwargs['years_back']
if 'day_of_week' in kwargs.keys():
day_of_week = kwargs['day_of_week']
else:
day_of_week = 2
date_from = cu.doubledate_shift(date_to, years_back*365)
trading_calendar = exp.get_calendar_4ticker_head('CL')
bday_us = CustomBusinessDay(calendar=trading_calendar)
dts = pd.date_range(start=cu.convert_doubledate_2datetime(date_from),
end=cu.convert_doubledate_2datetime(date_to), freq=bday_us)
dts = dts[dts.dayofweek==day_of_week]
return {'date_time_dates': dts,
'double_dates': [int(x.strftime('%Y%m%d')) for x in dts]}
def get_backtesting_dates(**kwargs):
date_to = kwargs['date_to']
years_back = kwargs['years_back']
if 'day_of_week' in kwargs.keys():
day_of_week = kwargs['day_of_week']
else:
day_of_week = 2
date_from = cu.doubledate_shift(date_to, years_back * 365)
trading_calendar = exp.get_calendar_4ticker_head('CL')
bday_us = CustomBusinessDay(calendar=trading_calendar)
dts = pd.date_range(start=cu.convert_doubledate_2datetime(date_from),
end=cu.convert_doubledate_2datetime(date_to),
freq=bday_us)
dts = dts[dts.dayofweek == day_of_week]
return {
'date_time_dates': dts,
'double_dates': [int(x.strftime('%Y%m%d')) for x in dts]
}
def update_options_price_database_from_cme_files_4ticker(**kwargs):
ticker = kwargs['ticker']
contract_specs_output = cmi.get_contract_specs(ticker)
ticker_head = contract_specs_output['ticker_head']
ticker_month_num = contract_specs_output['ticker_month_num']
ticker_year = contract_specs_output['ticker_year']
if 'settle_date' in kwargs.keys():
settle_date = kwargs['settle_date']
kwargs['report_date'] = settle_date
else:
settle_date = int(time.strftime('%Y%m%d'))
kwargs['settle_date'] = settle_date
kwargs['report_date'] = settle_date
if not exp.is_business_day(double_date=settle_date,
reference_tickerhead=ticker_head):
return
if 'expiration_date' in kwargs.keys():
expiration_date = kwargs['expiration_date']
else:
expiration_date = exp.get_options_expiration(ticker)
expiration_date = expiration_date.date()
settle_datetime = cu.convert_doubledate_2datetime(settle_date)
if 'cal_dte' in kwargs.keys():
cal_dte = kwargs['cal_dte']
else:
cal_dte = (expiration_date - settle_datetime.date()).days
if 'tr_dte' in kwargs.keys():
tr_dte = kwargs['tr_dte']
else:
bday_us = CustomBusinessDay(
calendar=exp.get_calendar_4ticker_head(ticker_head))
dts = pd.date_range(start=settle_datetime,
end=expiration_date,
freq=bday_us)
tr_dte = len(
[x for x in dts if x.to_pydatetime().date() < expiration_date])
data_vendor_id = 2
now = dt.datetime.now()
con = msu.get_my_sql_connection(**kwargs)
process_output = pco.process_cme_options_4ticker(**kwargs)
if process_output['success']:
settle_frame = process_output['settle_frame']
else:
if 'con' not in kwargs.keys():
con.close()
return
column_names = settle_frame.columns.tolist()
option_type_indx = column_names.index('option_type')
strike_indx = column_names.index('strike')
settle_indx = column_names.index('settle')
volume_indx = column_names.index('volume')
interest_indx = column_names.index('interest')
tuples = [
tuple([
data_vendor_id, ticker_head, ticker_month_num, ticker_year, ticker,
x[option_type_indx], x[strike_indx],
settle_datetime.date(), cal_dte, tr_dte, now, now,
None if np.isnan(x[settle_indx]) else x[settle_indx],
None if np.isnan(x[volume_indx]) else x[volume_indx],
None if np.isnan(x[interest_indx]) else x[interest_indx]
]) for x in settle_frame.values
]
column_str = "data_vendor_id, ticker_head, ticker_month, ticker_year, ticker, " \
" option_type, strike, price_date, cal_dte, tr_dte, " \
" created_date,last_updated_date, close_price, volume, open_interest"
insert_str = ("%s, " * len(column_str.split(',')))[:-2]
final_str = "REPLACE INTO daily_option_price (%s) VALUES (%s)" % (
column_str, insert_str)
msu.sql_execute_many_wrapper(final_str=final_str, tuples=tuples, con=con)
if 'con' not in kwargs.keys():
con.close()
def load_price_data_4ticker(load_price_data_input):
ticker = load_price_data_input['ticker']
expiration_date = load_price_data_input['expiration_date']
data_vendor_id = load_price_data_input['data_vendor_id']
symbol_id = load_price_data_input['symbol_id']
quandl_input = {'ticker': ticker}
if 'date_to' in load_price_data_input.keys():
quandl_input['date_to'] = load_price_data_input['date_to']
if 'date_from' in load_price_data_input.keys() and load_price_data_input['date_from'] is not None:
quandl_input['date_from'] = load_price_data_input['date_from']
quandl_out = gdq.get_daily_historic_data_quandl(**quandl_input)
if not quandl_out['success']:
return
price_data = quandl_out['data_out']
if price_data.empty:
print('Empty Results For ' + ticker)
return
contract_specs_output = cmi.get_contract_specs(ticker)
if contract_specs_output['ticker_head'] == 'JY':
price_multiplier = 10
else:
price_multiplier = 1
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head(contract_specs_output['ticker_head']))
dts = pd.date_range(start=price_data.index[0], end=expiration_date, freq=bday_us)
dts = [x.date() for x in dts]
now = datetime.datetime.now()
price_data['price_date'] = pd.Series(price_data.index, index=price_data.index)
column_names = price_data.columns.tolist()
open_indx = column_names.index('Open')
high_indx = column_names.index('High')
low_indx = column_names.index('Low')
settle_indx = column_names.index('Settle')
volume_indx = column_names.index('Volume')
interest_indx = column_names.index('Open Interest')
date_indx = column_names.index('price_date')
tuples = [tuple([data_vendor_id, symbol_id,
contract_specs_output['ticker_head'],
contract_specs_output['ticker_month_num'],
x[date_indx].to_datetime().date(),
(expiration_date-x[date_indx].to_datetime().date()).days,
len([y for y in dts if y > x[date_indx].to_datetime().date()]),
now, now,
None if np.isnan(x[open_indx]) else price_multiplier*x[open_indx],
None if np.isnan(x[high_indx]) else price_multiplier*x[high_indx],
None if np.isnan(x[low_indx]) else price_multiplier*x[low_indx],
None if np.isnan(x[settle_indx]) else price_multiplier*x[settle_indx],
None if np.isnan(x[volume_indx]) else x[volume_indx],
None if np.isnan(x[interest_indx]) else x[interest_indx]]) for x in price_data.values]
column_str = "data_vendor_id, symbol_id, ticker_head, ticker_month, price_date,cal_dte, tr_dte, created_date,last_updated_date, open_price, high_price, low_price, close_price, volume, open_interest"
insert_str = ("%s, " * 15)[:-2]
final_str = "REPLACE INTO daily_price (%s) VALUES (%s)" % (column_str, insert_str)
con = msu.get_my_sql_connection(**load_price_data_input)
msu.sql_execute_many_wrapper(final_str=final_str, tuples=tuples, con=con)
if 'con' not in load_price_data_input.keys():
con.close()
def update_futures_price_database_from_cme_file(**kwargs):
ticker_head_list = cmi.cme_futures_tickerhead_list
import time
con = msu.get_my_sql_connection(**kwargs)
if 'settle_date' in kwargs.keys():
run_date = kwargs['settle_date']
else:
run_date = int(time.strftime('%Y%m%d'))
#run_date = 20160225
data_vendor_id = 2
now = datetime.datetime.now()
run_datetime = cu.convert_doubledate_2datetime(run_date)
for ticker_head in ticker_head_list:
#print(ticker_head)
contract_list = []
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head(ticker_head))
if not exp.is_business_day(double_date=run_date, reference_tickerhead=ticker_head):
continue
cme_output = pcf.process_cme_futures_4tickerhead(ticker_head=ticker_head, report_date=run_date)
settle_frame = cme_output['settle_frame']
for ticker_month in cmi.futures_contract_months[ticker_head]:
ticker_month_num = cmi.letter_month_string.find(ticker_month)+1
max_cal_dte = cmi.get_max_cal_dte(ticker_head=ticker_head, ticker_month=ticker_month_num)
contract_list.extend(cl.get_db_contract_list_filtered(expiration_date_from=run_date,
expiration_date_to=cu.doubledate_shift(run_date, -max_cal_dte),
ticker_head=ticker_head, ticker_month=ticker_month_num, con=con,
instrument='futures'))
contract_frame = pd.DataFrame(contract_list, columns=['symbol_id', 'ticker', 'expiration_date'])
merged_frame = pd.merge(contract_frame,settle_frame, how='inner', on='ticker')
merged_frame.sort('expiration_date', ascending=True, inplace=True)
column_names = merged_frame.columns.tolist()
symbol_id_indx = column_names.index('symbol_id')
ticker_month_indx = column_names.index('ticker_month')
open_indx = column_names.index('open')
high_indx = column_names.index('high')
low_indx = column_names.index('low')
settle_indx = column_names.index('settle')
volume_indx = column_names.index('volume')
interest_indx = column_names.index('interest')
expiration_indx = column_names.index('expiration_date')
dts = pd.date_range(start=run_datetime, end=merged_frame['expiration_date'].iloc[-1], freq=bday_us)
tuples = [tuple([data_vendor_id, x[symbol_id_indx],
ticker_head,
x[ticker_month_indx],
run_datetime.date(),
(x[expiration_indx]-run_datetime.date()).days,
len([y for y in dts if y.to_datetime().date() < x[expiration_indx]]),
now, now,
None if np.isnan(x[open_indx]) else x[open_indx],
None if np.isnan(x[high_indx]) else x[high_indx],
None if np.isnan(x[low_indx]) else x[low_indx],
None if np.isnan(x[settle_indx]) else x[settle_indx],
None if np.isnan(x[volume_indx]) else x[volume_indx],
None if np.isnan(x[interest_indx]) else x[interest_indx]]) for x in merged_frame.values]
column_str = "data_vendor_id, symbol_id, ticker_head, ticker_month, price_date,cal_dte, tr_dte, created_date,last_updated_date, open_price, high_price, low_price, close_price, volume, open_interest"
insert_str = ("%s, " * 15)[:-2]
final_str = "REPLACE INTO daily_price (%s) VALUES (%s)" % (column_str, insert_str)
msu.sql_execute_many_wrapper(final_str=final_str, tuples=tuples, con=con)
if 'con' not in kwargs.keys():
con.close()
def generate_and_update_futures_data_file_4tickerhead(**kwargs):
ticker_head = kwargs['ticker_head']
con = msu.get_my_sql_connection(**kwargs)
if os.path.isfile(presaved_futures_data_folder + '/' + ticker_head +
'.pkl'):
old_data = pd.read_pickle(presaved_futures_data_folder + '/' +
ticker_head + '.pkl')
last_available_date = int(
old_data['settle_date'].max().to_pydatetime().strftime('%Y%m%d'))
date_from = cu.doubledate_shift(last_available_date, 60)
data4_tickerhead = gfp.get_futures_price_4ticker(
ticker_head=ticker_head, date_from=date_from, con=con)
else:
data4_tickerhead = gfp.get_futures_price_4ticker(
ticker_head=ticker_head, con=con)
data4_tickerhead = pd.merge(data4_tickerhead,
dirty_data_points,
on=['settle_date', 'ticker'],
how='left')
data4_tickerhead = data4_tickerhead[data4_tickerhead['discard'] != True]
data4_tickerhead = data4_tickerhead.drop('discard', 1)
data4_tickerhead['close_price'] = [
float(x) if x is not None else float('NaN')
for x in data4_tickerhead['close_price'].values
]
data4_tickerhead['open_price'] = [
float(x) if x is not None else float('NaN')
for x in data4_tickerhead['open_price'].values
]
data4_tickerhead['high_price'] = [
float(x) if x is not None else float('NaN')
for x in data4_tickerhead['high_price'].values
]
data4_tickerhead['low_price'] = [
float(x) if x is not None else float('NaN')
for x in data4_tickerhead['low_price'].values
]
data4_tickerhead['cont_indx'] = 100 * data4_tickerhead[
'ticker_year'] + data4_tickerhead['ticker_month']
unique_cont_indx_list = data4_tickerhead['cont_indx'].unique()
num_contracts = len(unique_cont_indx_list)
unique_cont_indx_list = np.sort(unique_cont_indx_list)
merged_dataframe_list = [None] * num_contracts
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head('CL'))
full_dates = pd.date_range(start=data4_tickerhead['settle_date'].min(),
end=data4_tickerhead['settle_date'].max(),
freq=bday_us)
for i in range(num_contracts):
contract_data = data4_tickerhead[data4_tickerhead['cont_indx'] ==
unique_cont_indx_list[i]]
contract_full_dates = full_dates[
(full_dates >= contract_data['settle_date'].min())
& (full_dates <= contract_data['settle_date'].max())]
full_date_frame = pd.DataFrame(contract_full_dates,
columns=['settle_date'])
merged_dataframe_list[i] = pd.merge(full_date_frame,
contract_data,
on='settle_date',
how='left')
merged_dataframe_list[i]['ticker'] = contract_data['ticker'][
contract_data.index[0]]
merged_dataframe_list[i]['ticker_head'] = contract_data['ticker_head'][
contract_data.index[0]]
merged_dataframe_list[i]['ticker_month'] = contract_data[
'ticker_month'][contract_data.index[0]]
merged_dataframe_list[i]['ticker_year'] = contract_data['ticker_year'][
contract_data.index[0]]
merged_dataframe_list[i]['cont_indx'] = contract_data['cont_indx'][
contract_data.index[0]]
merged_dataframe_list[i][
'change1'] = merged_dataframe_list[i]['close_price'].shift(
-2) - merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change1_instant'] = merged_dataframe_list[i][
'close_price'].shift(-1) - merged_dataframe_list[i]['close_price']
merged_dataframe_list[i]['high1_instant'] = merged_dataframe_list[i][
'high_price'].shift(-1)
merged_dataframe_list[i]['low1_instant'] = merged_dataframe_list[i][
'low_price'].shift(-1)
merged_dataframe_list[i][
'change2'] = merged_dataframe_list[i]['close_price'].shift(
-3) - merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i][
'change5'] = merged_dataframe_list[i]['close_price'].shift(
-6) - merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i][
'change10'] = merged_dataframe_list[i]['close_price'].shift(
-11) - merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i][
'change20'] = merged_dataframe_list[i]['close_price'].shift(
-21) - merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change_5'] = merged_dataframe_list[i][
'close_price'] - merged_dataframe_list[i]['close_price'].shift(5)
merged_dataframe_list[i]['change_1'] = merged_dataframe_list[i][
'close_price'] - merged_dataframe_list[i]['close_price'].shift(1)
data4_tickerhead = pd.concat(merged_dataframe_list)
if os.path.isfile(presaved_futures_data_folder + '/' + ticker_head +
'.pkl'):
data4_tickerhead['past_indx'] = [
1 if np.isfinite(x) else 0
for x in data4_tickerhead['change_5'].values
]
clean_data = data4_tickerhead
clean_data['frame_indx'] = 1
data_columns = old_data.columns
old_data['frame_indx'] = 0
old_data['past_indx'] = [
1 if np.isfinite(x) else 0 for x in old_data['change_5'].values
]
merged_data = pd.concat([old_data, clean_data],
ignore_index=True,
sort=True)
merged_data.sort_values(
['cont_indx', 'settle_date', 'past_indx', 'frame_indx'],
ascending=[True, True, False, False],
inplace=True)
merged_data.drop_duplicates(subset=['settle_date', 'cont_indx'],
keep='first',
inplace=True)
data4_tickerhead = merged_data.drop(['frame_indx', 'past_indx'],
1,
inplace=False)
data4_tickerhead = data4_tickerhead[data_columns]
data4_tickerhead.to_pickle(presaved_futures_data_folder + '/' +
ticker_head + '.pkl')
if 'con' not in kwargs.keys():
con.close()
def update_options_price_database_from_cme_files_4ticker(**kwargs):
ticker = kwargs['ticker']
contract_specs_output = cmi.get_contract_specs(ticker)
ticker_head = contract_specs_output['ticker_head']
ticker_month_num = contract_specs_output['ticker_month_num']
ticker_year = contract_specs_output['ticker_year']
if 'settle_date' in kwargs.keys():
settle_date = kwargs['settle_date']
kwargs['report_date'] = settle_date
else:
settle_date = int(time.strftime('%Y%m%d'))
kwargs['settle_date'] = settle_date
kwargs['report_date'] = settle_date
if not exp.is_business_day(double_date=settle_date, reference_tickerhead=ticker_head):
return
if 'expiration_date' in kwargs.keys():
expiration_date = kwargs['expiration_date']
else:
expiration_date = exp.get_options_expiration(ticker)
expiration_date = expiration_date.date()
settle_datetime = cu.convert_doubledate_2datetime(settle_date)
if 'cal_dte' in kwargs.keys():
cal_dte = kwargs['cal_dte']
else:
cal_dte = (expiration_date-settle_datetime.date()).days
if 'tr_dte' in kwargs.keys():
tr_dte = kwargs['tr_dte']
else:
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head(ticker_head))
dts = pd.date_range(start=settle_datetime, end=expiration_date, freq=bday_us)
tr_dte = len([x for x in dts if x.to_datetime().date() < expiration_date])
data_vendor_id = 2
now = dt.datetime.now()
con = msu.get_my_sql_connection(**kwargs)
process_output = pco.process_cme_options_4ticker(**kwargs)
if process_output['success']:
settle_frame = process_output['settle_frame']
else:
if 'con' not in kwargs.keys():
con.close()
return
column_names = settle_frame.columns.tolist()
option_type_indx = column_names.index('option_type')
strike_indx = column_names.index('strike')
settle_indx = column_names.index('settle')
volume_indx = column_names.index('volume')
interest_indx = column_names.index('interest')
tuples = [tuple([data_vendor_id, ticker_head, ticker_month_num, ticker_year,
ticker, x[option_type_indx],x[strike_indx],settle_datetime.date(),
cal_dte, tr_dte, now, now,
None if np.isnan(x[settle_indx]) else x[settle_indx],
None if np.isnan(x[volume_indx]) else x[volume_indx],
None if np.isnan(x[interest_indx]) else x[interest_indx]]) for x in settle_frame.values]
column_str = "data_vendor_id, ticker_head, ticker_month, ticker_year, ticker, " \
" option_type, strike, price_date, cal_dte, tr_dte, " \
" created_date,last_updated_date, close_price, volume, open_interest"
insert_str = ("%s, " * len(column_str.split(',')))[:-2]
final_str = "REPLACE INTO daily_option_price (%s) VALUES (%s)" % (column_str, insert_str)
msu.sql_execute_many_wrapper(final_str=final_str, tuples=tuples, con=con)
if 'con' not in kwargs.keys():
con.close()
def generate_and_update_futures_data_file_4tickerhead(**kwargs):
ticker_head = kwargs['ticker_head']
con = msu.get_my_sql_connection(**kwargs)
if os.path.isfile(presaved_futures_data_folder + '/' + ticker_head + '.pkl'):
old_data = pd.read_pickle(presaved_futures_data_folder + '/' + ticker_head + '.pkl')
last_available_date = int(old_data['settle_date'].max().to_datetime().strftime('%Y%m%d'))
date_from = cu.doubledate_shift(last_available_date, 60)
data4_tickerhead = gfp.get_futures_price_4ticker(ticker_head=ticker_head, date_from=date_from, con=con)
else:
data4_tickerhead = gfp.get_futures_price_4ticker(ticker_head=ticker_head, con=con)
data4_tickerhead = pd.merge(data4_tickerhead, dirty_data_points, on=['settle_date', 'ticker'],how='left')
data4_tickerhead = data4_tickerhead[data4_tickerhead['discard'] != True]
data4_tickerhead = data4_tickerhead.drop('discard', 1)
data4_tickerhead['close_price'] = [float(x) if x is not None else float('NaN') for x in data4_tickerhead['close_price'].values]
data4_tickerhead['open_price'] = [float(x) if x is not None else float('NaN') for x in data4_tickerhead['open_price'].values]
data4_tickerhead['high_price'] = [float(x) if x is not None else float('NaN') for x in data4_tickerhead['high_price'].values]
data4_tickerhead['low_price'] = [float(x) if x is not None else float('NaN') for x in data4_tickerhead['low_price'].values]
data4_tickerhead['cont_indx'] = 100*data4_tickerhead['ticker_year']+data4_tickerhead['ticker_month']
unique_cont_indx_list = data4_tickerhead['cont_indx'].unique()
num_contracts = len(unique_cont_indx_list)
unique_cont_indx_list = np.sort(unique_cont_indx_list)
merged_dataframe_list = [None]*num_contracts
bday_us = CustomBusinessDay(calendar=exp.get_calendar_4ticker_head('CL'))
full_dates = pd.date_range(start=data4_tickerhead['settle_date'].min(),end=data4_tickerhead['settle_date'].max(), freq=bday_us)
for i in range(num_contracts):
contract_data = data4_tickerhead[data4_tickerhead['cont_indx']==unique_cont_indx_list[i]]
contract_full_dates = full_dates[(full_dates >= contract_data['settle_date'].min()) & (full_dates<=contract_data['settle_date'].max())]
full_date_frame = pd.DataFrame(contract_full_dates, columns=['settle_date'])
merged_dataframe_list[i] = pd.merge(full_date_frame,contract_data,on='settle_date',how='left')
merged_dataframe_list[i]['ticker'] = contract_data['ticker'][contract_data.index[0]]
merged_dataframe_list[i]['ticker_head'] = contract_data['ticker_head'][contract_data.index[0]]
merged_dataframe_list[i]['ticker_month'] = contract_data['ticker_month'][contract_data.index[0]]
merged_dataframe_list[i]['ticker_year'] = contract_data['ticker_year'][contract_data.index[0]]
merged_dataframe_list[i]['cont_indx'] = contract_data['cont_indx'][contract_data.index[0]]
merged_dataframe_list[i]['change1'] = merged_dataframe_list[i]['close_price'].shift(-2)-merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change1_instant'] = merged_dataframe_list[i]['close_price'].shift(-1)-merged_dataframe_list[i]['close_price']
merged_dataframe_list[i]['change2'] = merged_dataframe_list[i]['close_price'].shift(-3)-merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change5'] = merged_dataframe_list[i]['close_price'].shift(-6)-merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change10'] = merged_dataframe_list[i]['close_price'].shift(-11)-merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change20'] = merged_dataframe_list[i]['close_price'].shift(-21)-merged_dataframe_list[i]['close_price'].shift(-1)
merged_dataframe_list[i]['change_5'] = merged_dataframe_list[i]['close_price']-merged_dataframe_list[i]['close_price'].shift(5)
merged_dataframe_list[i]['change_1'] = merged_dataframe_list[i]['close_price']-merged_dataframe_list[i]['close_price'].shift(1)
data4_tickerhead = pd.concat(merged_dataframe_list)
if os.path.isfile(presaved_futures_data_folder + '/' + ticker_head + '.pkl'):
clean_data = data4_tickerhead[np.isfinite(data4_tickerhead['change_5'])]
clean_data['frame_indx'] = 1
old_data['frame_indx'] = 0
merged_data = pd.concat([old_data,clean_data],ignore_index=True)
merged_data.sort(['cont_indx', 'settle_date', 'frame_indx'], ascending=[True, True, False], inplace=True)
merged_data.drop_duplicates(subset=['settle_date', 'cont_indx'], take_last=False, inplace=True)
data4_tickerhead = merged_data.drop('frame_indx', 1, inplace=False)
data4_tickerhead.to_pickle(presaved_futures_data_folder + '/' + ticker_head + '.pkl')
if 'con' not in kwargs.keys():
con.close()
