Overall Statistics Total Trades 5 Average Win 4.88% Average Loss 0% Compounding Annual Return 42.818% Drawdown 13.600% Expectancy 0 Net Profit 6.120% Sharpe Ratio 1.245 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha 3.419 Beta -183.185 Annual Standard Deviation 0.269 Annual Variance 0.073 Information Ratio 1.181 Tracking Error 0.27 Treynor Ratio -0.002 Total Fees \$5.00
```import numpy as np
from datetime import timedelta
import pandas as pd

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class CustomSlippageModel:
def GetSlippageApproximation(self, asset, order):
return np.float( asset.Price ) * 0.002

class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2016,1,1)  #Set Start Date
self.SetEndDate(2016,3,1)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash
self.stocks = ["DUST","NUGT"]
self.order_tickets = {}
self.nearest_option = 27
self.sell_with_days_left = 5
self.short_call = True

for stock in self.stocks:
# self.Securities[stock].SetDataNormalizationMode(DataNormalizationMode.Raw)
self.Securities[stock].SetSlippageModel(CustomSlippageModel())
self.order_tickets[ stock ] = []
option.SetFilter(lambda u: u.IncludeWeeklys().Strikes(-1, +1).Expiration(timedelta(self.nearest_option), timedelta(45)))

self.lev = 1.0

self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.stocks[0], 10), Action(self.activate))
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.stocks[0], 6), Action(self.deactivate))

def activate(self):

def deactivate(self):

def OnData(self, data):
'''

'''
nav = np.float(self.Portfolio.TotalPortfolioValue)

'''check current option holdings, do not hold options to expiration'''
option_holding_underlyings = []
for sym in self.Portfolio.Keys:
holding = self.Portfolio[sym]
if holding.HoldStock and holding.Symbol.SecurityType == SecurityType.Option:
if int( ( info['expiry'] - data.Time ).days ) <= self.sell_with_days_left:
self.SetHoldings(sym, 0)
else:
if holding.Quantity > 0:
dir = "long"
else:
dir = "short"

if info['type'] == 1:
option_holding_underlyings.append( [str(holding.Symbol.Underlying), "put", dir] )
else:
option_holding_underlyings.append( [str(holding.Symbol.Underlying), "call", dir] )

self.Log( str( holding.Symbol ) )

options = {}
for stock in self.stocks:
options[stock] = { 'put': None, 'call': None, 'qnty': None }

for i in data.OptionChains:
optionchain = list( i.Value )

if len(optionchain) == 0:
continue

try:
usym = str( optionchain[0].UnderlyingSymbol )
upric = float( optionchain[0].UnderlyingLastPrice )
except:
return

# create a data frame to show the filtered contracts
# type(call 0, put 1)
df = pd.DataFrame([[ x.Right, np.float(x.Strike), x.Expiry, np.float(x.BidPrice), np.float(x.AskPrice), x.Symbol] for x in optionchain ],
index=[ x.Symbol.Value for x in optionchain ],
columns=[ 'type', 'strike', 'expiry', 'ask', 'bid', 'Symbol' ])

df = df.loc[ ( df['expiry'] > data.Time + timedelta(days=self.nearest_option) ) ]

self.Log(usym+" "+str(upric))

puts = df.loc[ df['type'] == 1 ]

calls = df.loc[ df['type'] == 0 ]

if len( calls.index ) < 1 or len( puts.index ) < 1:
continue

options[ usym ]['call'] = calls.sort_values(['expiry','strike'], ascending=[True, False]).iloc[0]
options[ usym ]['put'] = puts.sort_values(['expiry','strike'], ascending=[False, True]).iloc[0]
options[ usym ]['qnty'] = np.round( self.lev * nav * 0.5 / np.float( np.mean([options[ usym ]['call'].strike, options[ usym ]['put'].strike]) ) / 100 )

for stock in self.stocks:
if [stock,'call','short'] not in option_holding_underlyings and self.short_call == True:
c = options[ stock ]['call']
self.Log( "trying to sell %s call contracts" % options[ stock ]['qnty'] )
self.Log( "at strike price %s" % c['strike'] )
self.MarketOrder( c['Symbol'] , -options[ stock ]['qnty'] )