def wrapper(self, *args, **kwargs):
self.elapsed, id, cash, chain = time.time(), True if len(
args) == 4 else False, self.get_cash(), Chain(
args[0]) if not len(args) == 4 else None
option = chain.get_option(
args[1], args[2],
args[3]) if not id else ClientMethods.fetch_by_id(
self.client, args[1])
self.direction = 'debit' if args[
4 if not id else 2] == 'buy' else 'credit'
self.legs = [{
'option': option['url'],
'side': args[4 if not id else 2],
'position_effect': args[5 if not id else 3],
'ratio_quantity': kwargs.get('ratio_quantity', 1)
}]
self.price = option[
'high_fill_rate_buy_price' if args[4 if not id else 2] ==
'buy' else 'high_fill_rate_sell_price'] if not kwargs.get(
'price') else kwargs['price']
self.quantity = kwargs.get(
'quantity', 1) if not kwargs.get('max_quantity') else (
math.floor(cash / self.price) if math.floor(
cash / self.price) <= kwargs['max_quantity'] else 1)
return func(self, *args, **kwargs)
def get_theoretical_mark_of_positions(self, **kwargs):
""" A method to get the theoretical mark price of current positions
Returns
-------
pd.DataFrame
a dataframe of each option and other data including the theoretical mark
"""
positions = self.get_option_positions(ids=True)
data = [
ClientMethods.fetch_by_id(self.client, id)
for id in positions['id']
]
prices = {
sym: ClientMethods.fetch_price(self.client, sym)
for sym in list(pd.DataFrame(data)['chain_symbol'].unique())
}
for option in data:
option['theoretical_mark'] = black_scholes(
prices[option['chain_symbol']], float(option['strike_price']),
dte(option['expiration_date']) / 365, .0094,
float(option['implied_volatility']), option['type'])
res = pd.merge(pd.DataFrame(data), positions, on='instrument')[[
'chain_symbol', 'strike_price', 'type', 'side', 'quantity',
'expiration_date', 'theoretical_mark', 'mark_price', 'volume',
'open_interest'
]]
res['theoretical_value'] = [
float(r['theoretical_mark']) * float(r['quantity'])
for i, r in res.iterrows()
]
return res