def test_option_models(**kwargs):
test_data_dir = dn.get_directory_name(ext='option_model_test_data')
engine_name = kwargs['engine_name']
if os.path.isfile(test_data_dir + '/' + engine_name + '.pkl'):
data_frame_test = pd.read_pickle(test_data_dir + '/' + engine_name + '.pkl')
return data_frame_test
data_frame_test = generate_test_cases_from_aligned_option_data()
data_frame_test = data_frame_test[data_frame_test['strike'].notnull()]
if 'num_cases' in kwargs.keys():
data_frame_test = data_frame_test.iloc[0:kwargs['num_cases']]
model_wrapper_output = []
#return data_frame_test['settleDates'].iloc[no]
for no in range(len(data_frame_test.index)):
#print('true vol: ' + str(data_frame_test['impVol'].iloc[no]))
#print('int rate: ' + str(data_frame_test['rate2OptExp'].iloc[no]))
print(no)
model_wrapper_output.append(omu.option_model_wrapper(ticker=data_frame_test['ticker'].iloc[no],
calculation_date=int(data_frame_test['settleDates'].iloc[no]),
underlying=data_frame_test['underlying'].iloc[no],
strike = data_frame_test['strike'].iloc[no],
option_price=data_frame_test['theoValue'].iloc[no],
exercise_type = data_frame_test['exercise_type'].iloc[no],
option_type=data_frame_test['option_type'].iloc[no],
engine_name=engine_name))
#return model_wrapper_output
data_frame_test['vol_deviation'] = [100*(model_wrapper_output[no]['implied_vol']-data_frame_test['impVol'].iloc[no])/data_frame_test['impVol'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test['dollar_gamma_deviation'] = [100*(model_wrapper_output[no]['dollar_gamma']-data_frame_test['dollarGamma'].iloc[no])/data_frame_test['dollarGamma'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test['gamma_deviation'] = [100*(model_wrapper_output[no]['gamma']-data_frame_test['gamma'].iloc[no])/data_frame_test['gamma'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test['delta_deviation'] = [100*(model_wrapper_output[no]['delta']-data_frame_test['delta'].iloc[no]) for no in range(len(data_frame_test.index))]
data_frame_test['dollar_vega_deviation'] = [100*(model_wrapper_output[no]['dollar_vega']-data_frame_test['dollarVega'].iloc[no])/data_frame_test['dollarVega'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test['dollar_theta_deviation'] = [100*(model_wrapper_output[no]['dollar_theta']-data_frame_test['dollarTheta'].iloc[no])/data_frame_test['dollarTheta'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test['interest_rate_deviation'] = [100*(model_wrapper_output[no]['interest_rate']-data_frame_test['rate2OptExp'].iloc[no]) for no in range(len(data_frame_test.index))]
data_frame_test['cal_dte_deviation'] = [model_wrapper_output[no]['cal_dte']-data_frame_test['calDTE'].iloc[no] for no in range(len(data_frame_test.index))]
data_frame_test.to_pickle(test_data_dir + '/' + engine_name + '.pkl')
return data_frame_test
def generate_csv_file(**kwargs):
output_dir = dn.get_directory_name(ext='test_data')
engine_name = kwargs['engine_name']
data_frame_test = generate_test_cases_from_aligned_option_data()
data_frame_test = data_frame_test[data_frame_test['strike'].notnull()]
if 'num_cases' in kwargs.keys():
data_frame_test = data_frame_test.iloc[0:kwargs['num_cases']]
model_wrapper_output = []
for no in range(len(data_frame_test.index)):
#print('true vol: ' + str(data_frame_test['impVol'].iloc[no]))
#print('int rate: ' + str(data_frame_test['rate2OptExp'].iloc[no]))
print(no)
model_wrapper_output.append(omu.option_model_wrapper(ticker=data_frame_test['ticker'].iloc[no],
calculation_date=int(data_frame_test['settleDates'].iloc[no]),
underlying=data_frame_test['underlying'].iloc[no],
strike = data_frame_test['strike'].iloc[no],
option_price=data_frame_test['theoValue'].iloc[no],
exercise_type = data_frame_test['exercise_type'].iloc[no],
option_type=data_frame_test['option_type'].iloc[no],
engine_name=engine_name))
#return model_wrapper_output
data_frame_test['impVol'] = [model_wrapper_output[no]['implied_vol'] for no in range(len(data_frame_test.index))]
data_frame_test['dollarGamma'] = [model_wrapper_output[no]['dollar_gamma'] for no in range(len(data_frame_test.index))]
data_frame_test['gamma'] = [model_wrapper_output[no]['gamma'] for no in range(len(data_frame_test.index))]
data_frame_test['delta'] = [model_wrapper_output[no]['delta'] for no in range(len(data_frame_test.index))]
data_frame_test['dollarVega'] = [model_wrapper_output[no]['dollar_vega']for no in range(len(data_frame_test.index))]
data_frame_test['dollarTheta'] = [model_wrapper_output[no]['dollar_theta'] for no in range(len(data_frame_test.index))]
data_frame_test['rate2OptExp'] = [model_wrapper_output[no]['interest_rate'] for no in range(len(data_frame_test.index))]
data_frame_test['calDTE'] = [model_wrapper_output[no]['cal_dte'] for no in range(len(data_frame_test.index))]
data_frame_test = data_frame_test[data_frame_test['impVol'].notnull()]
data_frame_test = data_frame_test[~data_frame_test['tickerHead'].isin(['ED','E0','E1','E2','E3','E4','E5'])]
data_frame_test = data_frame_test[['settleDates','ticker','option_type','strike','underlying','theoValue',
'impVol','delta','dollarVega','dollarTheta','dollarGamma','rate2OptExp']]
data_frame_test.reset_index(drop=True, inplace=True)
writer = pd.ExcelWriter(output_dir + '/' + 'option_model_test' + '.xlsx', engine='xlsxwriter')
data_frame_test.to_excel(writer, sheet_name='all')
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 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
