from AlgoLib import *
class ShortInterestEffect(XXX):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
# NOTE: We use only s&p 100 stocks so it's possible to fetch short interest data from quandl.
self.symbols = [
'AAPL','MSFT','AMZN','FB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE',
'CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO',
'COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM',
'CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI',
'COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'
]
for symbol in self.symbols:
data = self.AddEquity(symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
self.AddData(QuandlFINRA_ShortVolume, 'FINRA/FNSQ_' + symbol, Resolution.Daily)
self.recent_month = -1
def OnData(self, data):
if self.recent_month == self.Time.month:
return
self.recent_month = self.Time.month
short_interest = {}
for symbol in self.symbols:
sym = 'FINRA/FNSQ_' + symbol
if sym in data and data[sym] and symbol in data and data[symbol]:
short_vol = data[sym].GetProperty("SHORTVOLUME")
total_vol = data[sym].GetProperty("TOTALVOLUME")
short_interest[symbol] = short_vol / total_vol
long = []
short = []
if len(short_interest) >= 10:
sorted_by_short_interest = sorted(short_interest.items(), key = lambda x: x[1], reverse = True)
decile = int(len(sorted_by_short_interest) / 10)
long = [x[0] for x in sorted_by_short_interest[-decile:]]
short = [x[0] for x in sorted_by_short_interest[:decile]]
# trade execution
stocks_invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
for symbol in stocks_invested:
if symbol not in long + short:
self.Liquidate(symbol)
for symbol in long:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1 / len(long))
for symbol in short:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, -1 / len(short))
class QuandlFINRA_ShortVolume(PythonQuandl):
def __init__(self):
self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
