| Overall Statistics |
|
Total Orders 503 Average Win 0.32% Average Loss -0.31% Compounding Annual Return 0.819% Drawdown 2.000% Expectancy 0.023 Start Equity 100000 End Equity 101487.50 Net Profit 1.487% Sharpe Ratio -2.673 Sortino Ratio -2.463 Probabilistic Sharpe Ratio 15.988% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 1.01 Alpha -0.038 Beta -0.01 Annual Standard Deviation 0.015 Annual Variance 0 Information Ratio -0.789 Tracking Error 0.146 Treynor Ratio 3.98 Total Fees $508.42 Estimated Strategy Capacity $190000000.00 Lowest Capacity Asset QQQ RIWIV7K5Z9LX Portfolio Turnover 38.50% |
#region imports
from AlgorithmImports import *
#endregion
from datetime import timedelta, datetime
class SMAPairsTrading(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 6, 1)
self.SetEndDate(2024, 6, 1)
self.SetCash(100000)
symbols = [
Symbol.Create("QQQQ", SecurityType.Equity, Market.USA),
Symbol.Create("SPY", SecurityType.Equity, Market.USA)
]
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.spreadMean1 = SimpleMovingAverage(12)
self.spreadMean2 = SimpleMovingAverage(26)
self.period = timedelta(hours=4)
def Update(self, algorithm, data):
spread = self.pair[1].Price - self.pair[0].Price
self.spreadMean1.Update(algorithm.Time, spread)
self.spreadMean2.Update(algorithm.Time, spread)
sprmean1_t1 = self.spreadMean1[1]
sprmean2_t1 = self.spreadMean2[1]
sprmean1_t2 = self.spreadMean1[2]
sprmean2_t2 = self.spreadMean2[2]
sprmean1_t3 = self.spreadMean1[3]
sprmean2_t3 = self.spreadMean2[3]
indicator_valid = all([ ele is not None for ele in [sprmean1_t1, sprmean2_t1, sprmean1_t2, sprmean2_t2, sprmean1_t3, sprmean2_t3] ])
if indicator_valid:
buy_signal = (sprmean1_t1>sprmean2_t1) and (sprmean1_t2<sprmean2_t2) and (sprmean1_t3<sprmean2_t3)
sell_signal = (sprmean1_t1<sprmean2_t1) and (sprmean1_t2>sprmean2_t2) and (sprmean1_t3>sprmean2_t3)
else:
buy_signal = False
sell_signal = False
if indicator_valid and buy_signal:
return Insight.Group(
[
Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Up),
Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Down)
])
if indicator_valid and sell_signal:
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.spreadMean1.Update(tuple[0], tuple[2]-tuple[1])
self.spreadMean2.Update(tuple[0], tuple[2]-tuple[1])