| Overall Statistics |
|
Total Trades 109 Average Win 0.32% Average Loss -0.02% Compounding Annual Return 57.930% Drawdown 1.500% Expectancy 2.979 Net Profit 3.909% Sharpe Ratio 5.652 Probabilistic Sharpe Ratio 90.435% Loss Rate 73% Win Rate 27% Profit-Loss Ratio 13.85 Alpha 0.436 Beta 0.091 Annual Standard Deviation 0.082 Annual Variance 0.007 Information Ratio 1.18 Tracking Error 0.156 Treynor Ratio 5.052 Total Fees $123.12 Estimated Strategy Capacity $3400000.00 |
from datetime import timedelta, datetime
class SMAPairsTrading(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 3, 1)
self.SetEndDate(2021, 3, 31)
self.SetCash(100000)
pairIndex = int(self.GetParameter("pairIndex"))
symbolPairs = []
symbolPairs.append ( [Symbol.Create("DISH", SecurityType.Equity, Market.USA), Symbol.Create("UNM", SecurityType.Equity, Market.USA)] )
symbolPairs.append ( [Symbol.Create("SBUX", SecurityType.Equity, Market.USA), Symbol.Create("IBM", SecurityType.Equity, Market.USA)] )
symbolPairs.append ( [Symbol.Create("TSLA", SecurityType.Equity, Market.USA), Symbol.Create("SPY", SecurityType.Equity, Market.USA)] )
symbols = symbolPairs[pairIndex]
self.AddUniverseSelection(ManualUniverseSelectionModel(symbols))
self.UniverseSettings.Resolution = Resolution.Hour
self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
self.AddAlpha(PairsTradingAlphaModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
def OnEndOfDay(self, symbol):
self.Log("Taking a position of " + str(self.Portfolio[symbol].Quantity) + " units of symbol " + str(symbol))
class PairsTradingAlphaModel(AlphaModel):
def __init__(self):
self.pair = [ ]
self.spreadMean = SimpleMovingAverage(500)
self.spreadStd = StandardDeviation(500)
self.period = timedelta(hours=2)
def Update(self, algorithm, data):
spread = self.pair[1].Price - self.pair[0].Price
self.spreadMean.Update(algorithm.Time, spread)
self.spreadStd.Update(algorithm.Time, spread)
upperthreshold = self.spreadMean.Current.Value + self.spreadStd.Current.Value
lowerthreshold = self.spreadMean.Current.Value - self.spreadStd.Current.Value
if spread > upperthreshold:
return Insight.Group(
[
Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Up),
Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Down)
])
if spread < lowerthreshold:
return Insight.Group(
[
Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Down),
Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Up)
])
return []
def OnSecuritiesChanged(self, algorithm, changes):
self.pair = [x for x in changes.AddedSecurities]
#1. Call for 500 bars of history data for each symbol in the pair and save to the variable history
history = algorithm.History([x.Symbol for x in self.pair], 500)
#2. Unstack the Pandas data frame to reduce it to the history close price
history = history.close.unstack(level=0)
#3. Iterate through the history tuple and update the mean and standard deviation with historical data
for tuple in history.itertuples():
self.spreadMean.Update(tuple[0], tuple[2]-tuple[1])
self.spreadStd.Update(tuple[0], tuple[2]-tuple[1])