| Overall Statistics |
|
Total Trades 420 Average Win 0.00% Average Loss 0.00% Compounding Annual Return -0.130% Drawdown 0.500% Expectancy -0.398 Net Profit -0.391% Sharpe Ratio -1.09 Probabilistic Sharpe Ratio 0.000% Loss Rate 65% Win Rate 35% Profit-Loss Ratio 0.71 Alpha -0.001 Beta 0 Annual Standard Deviation 0.001 Annual Variance 0 Information Ratio -0.709 Tracking Error 0.118 Treynor Ratio -8.203 Total Fees $420.00 |
class LongStraddleAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 1, 1)
self.SetEndDate(2019, 1, 1)
self.SetCash(100000)
self.equity = self.AddEquity("SPY", Resolution.Minute)
self.equity.SetDataNormalizationMode(DataNormalizationMode.Raw)
self.underlyingsymbol = self.equity.Symbol
# use the underlying equity GOOG as the benchmark
self.SetBenchmark(self.underlyingsymbol)
self.Schedule.On(self.DateRules.EveryDay(),self.TimeRules.At(15,30),self.close_pos)
self.call_cost = 0
self.put_cost = 0
def OnData(self,slice):
if not self.Portfolio.Invested:
contracts = self.OptionChainProvider.GetOptionContractList(self.underlyingsymbol, self.Time.date())
if self.Time.hour == 9 and self.Time.minute == 32:
self.TradeOptions(contracts)
self.call_cost = self.Securities[self.call.Value].Price
self.put_cost = self.Securities[self.put.Value].Price
if self.Portfolio.Invested:
#if price of underlying goes beyond the range of (- 2%) it will liquidate
if self.Securities[self.call.Value].Price < 0.98 * self.call_cost:
self.Liquidate(self.call.Value)
self.Debug("call liquidated")
elif self.Securities[self.put.Value].Price < 0.98 * self.put_cost:
self.Liquidate(self.put.Value)
self.Debug("put liquidated")
def TradeOptions(self,contracts):
# run CoarseSelection method and get a list of contracts expire within 0 to 8 days from now on
# and the strike price between rank -1 to rank 1
filtered_contracts = self.CoarseSelection(self.underlyingsymbol, contracts, -1, 1, 0, 8)
if filtered_contracts is None: return
expiry = sorted(filtered_contracts,key = lambda x: x.ID.Date, reverse=True)[0].ID.Date
# filter the call options from the contracts expire on that date
call = [i for i in filtered_contracts if i.ID.Date == expiry and i.ID.OptionRight == 0]
# sorted the contracts according to their ATM strike prices
call_contracts = sorted(call , key = lambda x: abs(x.ID.StrikePrice-self.Securities[self.underlyingsymbol].Price))
if len(call_contracts) <= 0: return
self.call = call_contracts[0]
for i in filtered_contracts:
if i.ID.Date == expiry and i.ID.OptionRight == 1 and i.ID.StrikePrice ==call_contracts[0].ID.StrikePrice:
self.put = i
self.AddOptionContract(self.call, Resolution.Minute)
self.AddOptionContract(self.put, Resolution.Minute)
self.Sell(self.call.Value ,1)
self.Sell(self.put.Value ,1)
def CoarseSelection(self, underlyingsymbol, symbol_list, min_strike_rank, max_strike_rank, min_expiry, max_expiry):
# fitler the contracts based on the expiry range
contract_list = [i for i in symbol_list if min_expiry < (i.ID.Date.date() - self.Time.date()).days < max_expiry]
# find the strike price of ATM option
if len(contract_list) <= 0: return
atm_strike = sorted(contract_list,
key = lambda x: abs(x.ID.StrikePrice - self.Securities[underlyingsymbol].Price))[0].ID.StrikePrice
strike_list = sorted(set([i.ID.StrikePrice for i in contract_list]))
# find the index of ATM strike in the sorted strike list
atm_strike_rank = strike_list.index(atm_strike)
try:
min_strike = strike_list[atm_strike_rank + min_strike_rank]
max_strike = strike_list[atm_strike_rank + max_strike_rank]
except:
min_strike = strike_list[0]
max_strike = strike_list[-1]
# filter the contracts based on the range of the strike price rank
filtered_contracts = [i for i in contract_list if i.ID.StrikePrice >= min_strike and i.ID.StrikePrice <= max_strike]
return filtered_contracts
def close_pos(self):
if self.Portfolio.Invested:
self.Liquidate()