def cal_greeks_4option_maturity(**kwargs):
option_prices = gop.get_options_price_from_db(**kwargs)
if option_prices.empty:
return pd.DataFrame()
option_prices = option_prices[option_prices['strike'] > 0]
option_prices.reset_index(drop=True, inplace=True)
contract_specs_out = cmi.get_contract_specs(kwargs['ticker'])
exercise_type = cmi.get_option_exercise_type(**contract_specs_out)
underlying_ticker = oput.get_option_underlying(**kwargs)
futures_price_output = gfp.get_futures_price_preloaded(
ticker=underlying_ticker, settle_date=kwargs['settle_date'])
if futures_price_output.empty:
return pd.DataFrame()
underlying_price = futures_price_output['close_price'].iloc[0]
expiration_datetime = exp.get_expiration_from_db(instrument='options',
**kwargs)
expiration_date = int(expiration_datetime.strftime('%Y%m%d'))
interest_rate = grfs.get_simple_rate(
as_of_date=kwargs['settle_date'],
date_to=expiration_date)['rate_output']
option_greeks = [
qom.get_option_greeks(underlying=underlying_price,
option_price=float(
option_prices['close_price'].iloc[x]),
strike=float(option_prices['strike'].iloc[x]),
risk_free_rate=interest_rate,
expiration_date=expiration_date,
calculation_date=kwargs['settle_date'],
option_type=option_prices['option_type'].iloc[x],
exercise_type=exercise_type)
for x in range(len(option_prices.index))
]
greek_frame = pd.DataFrame(option_greeks)
return pd.concat([
greek_frame[['delta', 'gamma', 'implied_vol', 'theta', 'vega']],
option_prices
],
axis=1)
def cal_greeks_4option_maturity(**kwargs):
option_prices = gop.get_options_price_from_db(**kwargs)
if option_prices.empty:
return pd.DataFrame()
contract_specs_out = cmi.get_contract_specs(kwargs['ticker'])
exercise_type = cmi.get_option_exercise_type(**contract_specs_out)
underlying_ticker = oput.get_option_underlying(**kwargs)
futures_price_output = gfp.get_futures_price_preloaded(ticker=underlying_ticker, settle_date=kwargs['settle_date'])
if futures_price_output.empty:
return pd.DataFrame()
underlying_price = futures_price_output['close_price'].iloc[0]
expiration_datetime = exp.get_expiration_from_db(instrument='options', **kwargs)
expiration_date = int(expiration_datetime.strftime('%Y%m%d'))
interest_rate = grfs.get_simple_rate(as_of_date=kwargs['settle_date'], date_to=expiration_date)['rate_output']
#print(kwargs['settle_date'])
#print(expiration_date)
option_greeks = [qom.get_option_greeks(underlying=underlying_price,
option_price=float(option_prices['close_price'].iloc[x]),
strike=float(option_prices['strike'].iloc[x]),
risk_free_rate=interest_rate,
expiration_date=expiration_date,
calculation_date=kwargs['settle_date'],
option_type=option_prices['option_type'].iloc[x],
exercise_type=exercise_type) for x in range(len(option_prices.index))]
greek_frame = pd.DataFrame(option_greeks)
return pd.concat([greek_frame[['delta', 'gamma', 'implied_vol', 'theta', 'vega']], option_prices], axis=1)
def main():
app = algo.Algo()
report_date = exp.doubledate_shift_bus_days()
todays_date = cu.get_doubledate()
con = msu.get_my_sql_connection()
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
filter_out = of.get_vcs_filters(data_frame_input=vcs_pairs,
filter_list=['long2', 'short2'])
vcs_pairs = filter_out['selected_frame']
vcs_pairs = vcs_pairs[vcs_pairs['downside'].notnull()
& vcs_pairs['upside'].notnull()]
# &(vcs_pairs.tickerClass!='Energy')
vcs_pairs = vcs_pairs[(vcs_pairs['trDte1'] >= 50)
& (vcs_pairs.tickerClass != 'Metal') &
(vcs_pairs.tickerClass != 'FX') &
(vcs_pairs.tickerClass != 'Energy')]
vcs_pairs = vcs_pairs[((vcs_pairs['Q'] <= 30) &
(vcs_pairs['fwdVolQ'] >= 30)) |
((vcs_pairs['Q'] >= 70) &
(vcs_pairs['fwdVolQ'] <= 70))]
vcs_pairs.reset_index(drop=True, inplace=True)
vcs_pairs['underlying_ticker1'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker1']
]
vcs_pairs['underlying_ticker2'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker2']
]
vcs_pairs['underlying_tickerhead'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in vcs_pairs['underlying_ticker1']
]
futures_data_dictionary = {
x: gfp.get_futures_price_preloaded(ticker_head=x)
for x in vcs_pairs['underlying_tickerhead'].unique()
}
proxy_output_list1 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker1']
]
vcs_pairs['proxy_ticker1'] = [x['ticker'] for x in proxy_output_list1]
vcs_pairs['add_2_proxy1'] = [x['add_2_proxy'] for x in proxy_output_list1]
proxy_output_list2 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker2']
]
vcs_pairs['proxy_ticker2'] = [x['ticker'] for x in proxy_output_list2]
vcs_pairs['add_2_proxy2'] = [x['add_2_proxy'] for x in proxy_output_list2]
vcs_pairs['expiration_date1'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker1']
]
vcs_pairs['expiration_date2'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker2']
]
vcs_pairs['interest_date1'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date1']
]
vcs_pairs['interest_date2'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date2']
]
vcs_pairs['exercise_type'] = [
cmi.get_option_exercise_type(ticker_head=x)
for x in vcs_pairs['tickerHead']
]
admin_dir = dna.get_directory_name(ext='admin')
risk_file_out = su.read_text_file(file_name=admin_dir +
'/RiskParameter.txt')
vcs_risk_parameter = 5 * 2 * float(risk_file_out[0])
vcs_pairs['long_quantity'] = vcs_risk_parameter / abs(
vcs_pairs['downside'])
vcs_pairs['short_quantity'] = vcs_risk_parameter / vcs_pairs['upside']
vcs_pairs['long_quantity'] = vcs_pairs['long_quantity'].round()
vcs_pairs['short_quantity'] = vcs_pairs['short_quantity'].round()
vcs_pairs['alias'] = [
generate_vcs_alias(vcs_row=vcs_pairs.iloc[x])
for x in range(len(vcs_pairs.index))
]
vcs_pairs['call_mid_price1'] = np.nan
vcs_pairs['put_mid_price1'] = np.nan
vcs_pairs['call_mid_price2'] = np.nan
vcs_pairs['put_mid_price2'] = np.nan
vcs_pairs['call_iv1'] = np.nan
vcs_pairs['put_iv1'] = np.nan
vcs_pairs['call_iv2'] = np.nan
vcs_pairs['put_iv2'] = np.nan
vcs_pairs['underlying_mid_price1'] = np.nan
vcs_pairs['underlying_mid_price2'] = np.nan
vcs_pairs['proxy_mid_price1'] = np.nan
vcs_pairs['proxy_mid_price2'] = np.nan
vcs_pairs['current_strike1'] = np.nan
vcs_pairs['current_strike2'] = np.nan
ta_folder = dn.get_dated_directory_extension(folder_date=todays_date,
ext='ta')
app.vcs_pairs = vcs_pairs
app.con = con
app.futures_data_dictionary = futures_data_dictionary
app.report_date = report_date
app.todays_date = todays_date
app.log = lg.get_logger(file_identifier='vcs', log_level='INFO')
app.trade_file = ta_folder + '/trade_dir.csv'
app.vcs_risk_parameter = vcs_risk_parameter
app.connect(client_id=3)
app.run()
def get_hedge_4strategy(**kwargs):
con = msu.get_my_sql_connection(**kwargs)
current_date = cu.get_doubledate()
settle_price_date = exp.doubledate_shift_bus_days(double_date=current_date, shift_in_days=1)
position_frame = tas.get_net_position_4strategy_alias(alias=kwargs['alias'],as_of_date=current_date,con=con)
intraday_price_frame = gip.get_cme_direct_prices()
intraday_price_frame.rename(columns={'ticker': 'underlying_ticker'},inplace=True)
intraday_price_frame['ticker_head'] = [cmi.get_contract_specs(x)['ticker_head'] for x in intraday_price_frame['underlying_ticker']]
intraday_price_frame['mid_price'] = (intraday_price_frame['bid_price'] + intraday_price_frame['ask_price'])/2
intraday_price_frame['mid_price'] = [tfl.convert_trade_price_from_cme_direct(ticker_head=intraday_price_frame['ticker_head'].iloc[x],
price=intraday_price_frame['mid_price'].iloc[x]) for x in range(len(intraday_price_frame.index))]
options_frame = position_frame[position_frame['instrument'] == 'O']
futures_frame = position_frame[position_frame['instrument'] == 'F']
if options_frame.empty:
futures_frame.rename(columns={'ticker': 'underlying_ticker', 'qty': 'underlying_delta'},inplace=True)
futures_frame = futures_frame[['underlying_ticker', 'underlying_delta']]
net_position = pd.merge(futures_frame, intraday_price_frame, how='left', on='underlying_ticker')
net_position['hedge_price'] = net_position['mid_price']
net_position['hedge'] = -net_position['underlying_delta']
return net_position
imp_vol_list = [gop.get_options_price_from_db(ticker=options_frame['ticker'].iloc[x],
settle_date=settle_price_date,
strike=options_frame['strike_price'].iloc[x],
column_names=['imp_vol'],
con=con)['imp_vol'] for x in range(len(options_frame.index))]
options_frame['imp_vol'] = [imp_vol_list[x][1] if (np.isnan(imp_vol_list[x][0]) and len(imp_vol_list[x]) > 1) else imp_vol_list[x][0]
for x in range(len(options_frame.index))]
options_frame['underlying_ticker'] = [omu.get_option_underlying(ticker=x) for x in options_frame['ticker']]
options_frame = pd.merge(options_frame, intraday_price_frame, how='left', on='underlying_ticker')
options_frame['ticker_head'] = [cmi.get_contract_specs(x)['ticker_head'] for x in options_frame['ticker']]
options_frame['exercise_type'] = [cmi.get_option_exercise_type(ticker_head=x) for x in options_frame['ticker_head']]
options_frame['strike_price'] = options_frame['strike_price'].astype('float64')
options_frame['delta'] = [omu.option_model_wrapper(ticker=options_frame['ticker'].iloc[x],
calculation_date=current_date,
interest_rate_date=settle_price_date,
underlying=options_frame['mid_price'].iloc[x],
strike=options_frame['strike_price'].iloc[x],
implied_vol=options_frame['imp_vol'].iloc[x],
option_type=options_frame['option_type'].iloc[x],
exercise_type=options_frame['exercise_type'].iloc[x],
con=con)['delta'] for x in range(len(options_frame.index))]
options_frame['total_delta'] = options_frame['qty']*options_frame['delta']
grouped = options_frame.groupby('underlying_ticker')
net_position = pd.DataFrame()
net_position['underlying_ticker'] = (grouped['underlying_ticker'].first()).values
net_position['hedge_price'] = (grouped['mid_price'].first()).values
net_position['option_delta'] = (grouped['total_delta'].sum()).values
net_position['option_delta'] = net_position['option_delta'].round(2)
if futures_frame.empty:
net_position['total_delta'] = net_position['option_delta']
else:
futures_frame.rename(columns={'ticker': 'underlying_ticker', 'qty': 'underlying_delta'},inplace=True)
futures_frame = futures_frame[['underlying_ticker', 'underlying_delta']]
isinOptions = futures_frame['underlying_ticker'].isin(net_position['underlying_ticker'])
futures_frame_w_options = futures_frame[isinOptions]
futures_frame_wo_options = futures_frame[~isinOptions]
if futures_frame_w_options.empty:
net_position['underlying_delta'] = 0
net_position['total_delta'] = net_position['option_delta']
else:
net_position = pd.merge(net_position, futures_frame_w_options, how='outer', on='underlying_ticker')
net_position['total_delta'] = net_position['option_delta']+net_position['underlying_delta']
if not futures_frame_wo_options.empty:
net_position_futures = pd.merge(futures_frame_wo_options, intraday_price_frame, how='left', on='underlying_ticker')
net_position_futures['hedge_price'] = net_position_futures['mid_price']
net_position_futures['option_delta'] = 0
net_position_futures['total_delta'] = net_position_futures['underlying_delta']
net_position = pd.concat([net_position,net_position_futures[['underlying_ticker','hedge_price','option_delta','underlying_delta','total_delta']]])
net_position['hedge'] = -net_position['total_delta']
if 'con' not in kwargs.keys():
con.close()
return net_position
def generate_test_cases_from_aligned_option_data(**kwargs):
test_data_dir = dn.get_directory_name(ext='option_model_test_data')
if os.path.isfile(test_data_dir + '/option_model_test_data.pkl'):
data_frame_test = pd.read_pickle(test_data_dir + '/option_model_test_data.pkl')
return data_frame_test
ticker_head_list = cmi.cme_option_tickerhead_list
tr_dte_center_list = [10, 15, 20, 25, 30, 35, 40, 45, 50,
60, 70, 80, 90, 100, 120, 140, 180,
200, 220, 240, 260, 280, 300, 330, 360, 390]
delta_list = [0.1, 0.15, 0.25, 0.35, 0.5, -0.1, -0.15, -0.25, -0.35, -0.5]
data_frame_list = []
aligned_column_names = ['TickerYear','TickerMonth','settleDates','calDTE','rate2OptExp', 'theoValue' , 'impVol', 'atmVol', 'delta',
'strike', 'underlying', 'dollarVega', 'dollarTheta','gamma','dollarGamma', 'optionPnL', 'deltaPnL', 'gammaPnL', 'thetaPnL']
#ticker_head_list = ticker_head_list[3:5]
for i in range(len(ticker_head_list)):
if ticker_head_list[i] in ['ED', 'E0', 'E2', 'E3', 'E4', 'E5']:
model = 'OU'
else:
model = 'BS'
ticker_class = cmi.ticker_class[ticker_head_list[i]]
contract_multiplier = cmi.contract_multiplier[ticker_head_list[i]]
if ticker_class in ['Livestock', 'Ag'] or ticker_head_list[i] == 'NG':
month_specificQ = True
contract_month_list = cmi.get_option_contract_months(ticker_head=ticker_head_list[i])
else:
month_specificQ = False
for j in range(len(tr_dte_center_list)):
for k in range(len(delta_list)):
if month_specificQ:
for contract_month in contract_month_list:
data_frame = gop.load_aligend_options_data_file(ticker_head=ticker_head_list[i],
tr_dte_center=tr_dte_center_list[j],
delta_center=delta_list[k],
contract_month_letter=contract_month,
model=model)
if data_frame.empty:
continue
else:
x1 = round(len(data_frame.index)/4)
x2 = round(2*len(data_frame.index)/4)
x3 = round(3*len(data_frame.index)/4)
data_frame_select = data_frame[aligned_column_names].iloc[[-x3, -x2, -x1, -1]]
data_frame_select['tickerHead'] = ticker_head_list[i]
data_frame_select['contractMultiplier'] = contract_multiplier
data_frame_select['exercise_type'] = cmi.get_option_exercise_type(ticker_head=ticker_head_list[i])
data_frame_list.append(data_frame_select)
else:
data_frame = gop.load_aligend_options_data_file(ticker_head=ticker_head_list[i],
tr_dte_center=tr_dte_center_list[j],
delta_center=delta_list[k],
model=model)
if data_frame.empty:
continue
else:
x1 = round(len(data_frame.index)/4)
x2 = round(2*len(data_frame.index)/4)
x3 = round(3*len(data_frame.index)/4)
data_frame_select = data_frame[aligned_column_names].iloc[[-x3, -x2, -x1, -1]]
data_frame_select['tickerHead'] = ticker_head_list[i]
data_frame_select['contractMultiplier'] = contract_multiplier
data_frame_select['exercise_type'] = cmi.get_option_exercise_type(ticker_head=ticker_head_list[i])
data_frame_list.append(data_frame_select)
data_frame_test = pd.concat(data_frame_list)
data_frame_test['optionPnL'] = data_frame_test['optionPnL']*data_frame_test['contractMultiplier']
data_frame_test['deltaPnL'] = data_frame_test['deltaPnL']*data_frame_test['contractMultiplier']
data_frame_test['ticker'] = [data_frame_test['tickerHead'].iloc[x] +
cmi.letter_month_string[int(data_frame_test['TickerMonth'].iloc[x]-1)] +
str(int(data_frame_test['TickerYear'].iloc[x]))
for x in range(len(data_frame_test.index))]
data_frame_test['option_type'] = 'C'
data_frame_test['option_type'][data_frame_test['delta'] < 0] = 'P'
data_frame_test.to_pickle(test_data_dir + '/option_model_test_data.pkl')
return data_frame_test
def get_hedge_4strategy(**kwargs):
con = msu.get_my_sql_connection(**kwargs)
current_date = cu.get_doubledate()
settle_price_date = exp.doubledate_shift_bus_days(double_date=current_date,
shift_in_days=1)
position_frame = tas.get_net_position_4strategy_alias(
alias=kwargs['alias'], as_of_date=current_date, con=con)
if 'intraday_price_frame' in kwargs.keys():
intraday_price_frame = kwargs['intraday_price_frame']
else:
intraday_price_frame = gip.get_cme_direct_prices()
intraday_price_frame.rename(columns={'ticker': 'underlying_ticker'},
inplace=True)
intraday_price_frame['ticker_head'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in intraday_price_frame['underlying_ticker']
]
intraday_price_frame['mid_price'] = (
intraday_price_frame['bid_price'] +
intraday_price_frame['ask_price']) / 2
intraday_price_frame['mid_price'] = [
tfl.convert_trade_price_from_cme_direct(
ticker_head=intraday_price_frame['ticker_head'].iloc[x],
price=intraday_price_frame['mid_price'].iloc[x])
for x in range(len(intraday_price_frame.index))
]
options_frame = position_frame[position_frame['instrument'] == 'O']
futures_frame = position_frame[position_frame['instrument'] == 'F']
if options_frame.empty:
futures_frame.rename(columns={
'ticker': 'underlying_ticker',
'qty': 'underlying_delta'
},
inplace=True)
futures_frame = futures_frame[[
'underlying_ticker', 'underlying_delta'
]]
net_position = pd.merge(futures_frame,
intraday_price_frame,
how='left',
on='underlying_ticker')
net_position['hedge_price'] = net_position['mid_price']
net_position['hedge'] = -net_position['underlying_delta']
return net_position
imp_vol_list = [
gop.get_options_price_from_db(
ticker=options_frame['ticker'].iloc[x],
settle_date=settle_price_date,
strike=options_frame['strike_price'].iloc[x],
column_names=['imp_vol'],
con=con)['imp_vol'] for x in range(len(options_frame.index))
]
options_frame['imp_vol'] = [
imp_vol_list[x][1] if
(np.isnan(imp_vol_list[x][0])
and len(imp_vol_list[x]) > 1) else imp_vol_list[x][0]
for x in range(len(options_frame.index))
]
options_frame['underlying_ticker'] = [
omu.get_option_underlying(ticker=x) for x in options_frame['ticker']
]
#print(intraday_price_frame)
options_frame = pd.merge(options_frame,
intraday_price_frame,
how='left',
on='underlying_ticker')
options_frame['ticker_head'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in options_frame['ticker']
]
options_frame['exercise_type'] = [
cmi.get_option_exercise_type(ticker_head=x)
for x in options_frame['ticker_head']
]
options_frame['strike_price'] = options_frame['strike_price'].astype(
'float64')
options_frame['delta'] = [
omu.option_model_wrapper(
ticker=options_frame['ticker'].iloc[x],
calculation_date=current_date,
interest_rate_date=settle_price_date,
underlying=options_frame['mid_price'].iloc[x],
strike=options_frame['strike_price'].iloc[x],
implied_vol=options_frame['imp_vol'].iloc[x],
option_type=options_frame['option_type'].iloc[x],
exercise_type=options_frame['exercise_type'].iloc[x],
con=con)['delta'] for x in range(len(options_frame.index))
]
options_frame[
'total_delta'] = options_frame['qty'] * options_frame['delta']
grouped = options_frame.groupby('underlying_ticker')
net_position = pd.DataFrame()
net_position['underlying_ticker'] = (
grouped['underlying_ticker'].first()).values
net_position['hedge_price'] = (grouped['mid_price'].first()).values
net_position['option_delta'] = (grouped['total_delta'].sum()).values
net_position['option_delta'] = net_position['option_delta'].round(2)
if futures_frame.empty:
net_position['total_delta'] = net_position['option_delta']
else:
futures_frame.rename(columns={
'ticker': 'underlying_ticker',
'qty': 'underlying_delta'
},
inplace=True)
futures_frame = futures_frame[[
'underlying_ticker', 'underlying_delta'
]]
isinOptions = futures_frame['underlying_ticker'].isin(
net_position['underlying_ticker'])
futures_frame_w_options = futures_frame[isinOptions]
futures_frame_wo_options = futures_frame[~isinOptions]
if futures_frame_w_options.empty:
net_position['underlying_delta'] = 0
net_position['total_delta'] = net_position['option_delta']
else:
net_position = pd.merge(net_position,
futures_frame_w_options,
how='outer',
on='underlying_ticker')
net_position[['underlying_delta'
]] = net_position[['underlying_delta'
]].fillna(value=0, inplace=False)
net_position['total_delta'] = net_position[
'option_delta'] + net_position['underlying_delta']
if not futures_frame_wo_options.empty:
net_position_futures = pd.merge(futures_frame_wo_options,
intraday_price_frame,
how='left',
on='underlying_ticker')
net_position_futures['hedge_price'] = net_position_futures[
'mid_price']
net_position_futures['option_delta'] = 0
net_position_futures['total_delta'] = net_position_futures[
'underlying_delta']
net_position = pd.concat([
net_position, net_position_futures[[
'underlying_ticker', 'hedge_price', 'option_delta',
'underlying_delta', 'total_delta'
]]
])
net_position['hedge'] = -net_position['total_delta']
if 'con' not in kwargs.keys():
con.close()
return net_position
