Hello,

I this is my first algo on quantconnect. I tried to implement an pairs trading algo with automatic pairs selection.

I use predefined models except for my alpha model -> See in first code Snippet.

`import statsmodels.api as sm`

from statsmodels.tsa.stattools import adfuller

import numpy as np

import pandas as pd

from QuantConnect.Algorithm.Framework.Alphas import *

class PairsAlpha(AlphaModel):

def __init__(self,algorithm):

try:

self.securities =[]

self.look_back_window = 30

self.pairs=dict()

self.coint_p_val = 0.05

self.max_trade_pairs = 15

algorithm.Debug("Init Alpha")

self.asset1priceHist=[]

self.asset2priceHist=[]

except:

algorithm.Debug("Init Alpha exep")

def setPairs(self,pairs):

self.pairs = pairs

def OnSecuritiesChanged(self, algorithm, changes):

algorithm.Debug("Onchange Alpha")

for security in changes.AddedSecurities:

if not security in self.securities:

self.securities.append(security)

for security in changes.RemovedSecurities:

if security in self.securities:

self.securities.remove(security)

def Update(self, algorithm, data):

algorithm.Debug("Update Alpha")

pairs = self.pairs

if(len(pairs)==0):

self.find_pairs(algorithm)

pairs = self.pairs

if(len(pairs)==0):

return

pairs_w_weights = []

for pair in pairs:

pairs_w_weights.append([pair[0],pair[1],self.get_weights(self,algorithm,pair,1.25,3.0)])

algorithm.Debug(pairs_w_weights[:5])

Insights=[]

for pairWeight in pairs_w_weights[:15]:

if pairWeight[2]>0:

InsightA=Insight.Price(pairWeight[0], timedelta(minutes = 20), InsightDirection.Up, pairWeight[2], 0.95,None,None,None)

InsightB=Insight.Price(pairWeight[1], timedelta(minutes = 20), InsightDirection.Down, pairWeight[2], 0.95,None,None,None)

Insights.append([InsightA,InsightB])

elif pairWeight[2]<0:

InsightA=Insight.Price(pairWeight[0], timedelta(minutes = 20), InsightDirection.Down, pairWeight[2], 0.95,None,None,None)

InsightB=Insight.Price(pairWeight[1], timedelta(minutes = 20), InsightDirection.Up, pairWeight[2], 0.95,None,None,None)

Insights.append([InsightA,InsightB])

return Insight.Group(Insights)

# Skipping magnitude, confidence and source model and assigning 25% to weighting.

# insight = Insight.Price("IBM", timedelta(minutes = 20), InsightDirection.Up, None, None, None, 0.25)

#higher price stock (stock A) long -> the difference between the pair will grow if get_weights > 0

def get_weights(self,algorithm,pair,realizeProfitThreshold,stopLossThreshold):

algorithm.Log("getWeights regular")

try:

asset1PriceHist=pair.get_asset1hist

asset2PriceHist=pair.get_asset2hist

pricedif=asset1PriceHist.sub(asset2PriceHist)

hist_mean_pricedif=pricedif.mean()

hist_std_pricedif=pricedif.std()

asset1_curPrice= algorithm.History(pair[0], 3, Resolution.Minute).mean()

asset2_curPrice= algorithm.History(pair[1], 3, Resolution.Minute).mean()

curPriceDif=asset1_curPrice-asset2_curPrice

curDeviation=abs(curPriceDif-hist_mean_pricedif)/hist_std_pricedif

asset1_curPrice=asset1_curPrice.mean()

asset2_curPrice=asset2_curPrice.mean()

maxprice=max(asset1_curPrice,asset2_curPrice)

weight=0

if(hist_std_pricedif/maxprice > 0.1):

return 0

if(curDeviation>realizeProfitThreshold and curDeviation<stopLossThreshold):

if curPriceDif > hist_mean_pricedif:

weight=(-1.0*curDeviation)

else:

weight=(1.0*curDeviation)

return weight

except:

algorithm.Log("getWeight")

def find_pairs(self,algorithm):

algorithm.Debug("find_pairs")

try:

algorithm.Log("before assign Symbols")

symbols = list()

for kvp in algorithm.ActiveSecurities:

security = kvp.Value

symbol = security.Symbol

symbols.append(security)

lengthofSymbols = len(symbols)

lengthofSymbols = len(symbols)

if(len(symbols)==0):

return

for i in range(0, len(symbols)):

algorithm.Log("symbol loop start")

asset_i = symbols[i]

algorithm.Log("assign asset symbol")

for j in range(1 + i, len(symbols)):

asset_j = symbols[j]

pair_symbol = (asset_i, asset_j)

invert = (asset_j, asset_i)

if len(pair_symbol)==0:

return

PairPassTest = self.HasPassedTest(algorithm=algorithm,asset1=asset_i,asset2=asset_j,significance_level= self.coint_p_val)

self

if not PairPassTest: #jedes mal neu prÃ¼fen ob noch cointegrated, wenn nicht mehr -> dann raus aus der Liste

if pair_symbol in self.pairs:

self.pairs.pop(pair_symbol)

elif invert in self.pairs:

self.pairs.pop(invert)

continue

if(len(self.pairs)>=self.max_trade_pairs):

continue

pair = Pair(asset_i,asset_j)

pair.set_asset1hist(hist=self.asset1PriceHist)

pair.set_asset2hist(hist=self.asset2PriceHist)

self.pairs[pair_symbol] = pair #eql to pair hist

except Exception as e:

algorithm.Log(str(e))

algorithm.Log("UpdatePairs Exception")

def HasPassedTest(self,algorithm, asset1,asset2,significance_level):

self.asset1PriceHist=algorithm.History(asset1.Symbol, self.look_back_window, Resolution.Daily)

self.asset2PriceHist=algorithm.History(asset2.Symbol, self.look_back_window, Resolution.Daily)

return self.is_cointegrated(algorithm=algorithm,data_x=self.asset1PriceHist,data_y=self.asset2PriceHist,cutoff=significance_level)

'''Check whether the assets pass a pairs trading test

Args:

self: The self instance that experienced the change in securities

asset1: The first asset's symbol in the pair

asset2: The second asset's symbol in the pair

Returns:

True if the statistical test for the pair is successful'''

def is_stationary(self,algorithm,data_x,cutoff):

#Augmented Dickey-Fuller unit root test

p_value = adfuller(data_x)[1]

if p_value < cutoff:

return True

else:

return False

def is_cointegrated(self,algorithm,data_x,data_y,cutoff):

#Check linear reg

algorithm.Debug(cutoff)

X=pd.DataFrame(data_x)

Y=pd.DataFrame(data_y)

if(Y.empty or X.empty):

return False

X=sm.add_constant(X['open'])

Y=pd.Series(Y['open'])

index=0

YPrice=[x for x in range(0,self.look_back_window)]

for data in Y:

YPrice[index]=data

index+=1

XonlyVals=[x for x in range(0,self.look_back_window)]

index=0

for xdata in X['open']:

XonlyVals[index]=xdata

index+=1

xdata

X=sm.add_constant(XonlyVals)

Y=YPrice

Xtype=type(X)

Ytype=type(Y)

Ylen=len(Y)

#Xlen=len(X)

#algorithm.Debug(len(Y))

#X.reindex(Y.index)

#Ylen=len(Y)

#Xlen=len(X)

linear_comb=sm.OLS(Y,X,'drop',True)

fitted=linear_comb.fit()

const,beta=fitted.params

residual=fitted.resid

if (beta < 0):

return False

else:

return self.is_stationary(algorithm=algorithm,data_x=residual,cutoff=cutoff)

class Pair():

def __init__(self,asset1Symbol,asset2Symbol):

self.assetList = [asset1Symbol,asset2Symbol]

self.asset1hist=[]

self.asset2hist=[]

def get_asset1hist(self):

return self.asset1hist

def get_asset2hist(self):

return self.asset2hist

def set_asset1hist(self,hist):

self.asset1hist=hist

def set_asset2hist(self,hist):

self.asset2hist=hist

def __getitem__(self, key):

return self.assetList[key]

For your Interest my algo:

Â

`from Execution.ImmediateExecutionModel import ImmediateExecutionModel`

import time

from PairAlpha import PairsAlpha

from Selection.QC500UniverseSelectionModel import QC500UniverseSelectionModel

class TransdimensionalUncoupledShield(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2019, 1, 5) # Set Start Date

self.SetEndDate(2019,6,25)

self.SetCash(100000) # Set Strategy Cash

self.look_back_window=30

self.coint_p_val=0.05

self.SetUniverseSelection(QC500UniverseSelectionModel())

self.UniverseSettings.Resolution = Resolution.Daily

self.max_trade_pairs = 15

self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel())

self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.02))

self.SetExecution(ImmediateExecutionModel())

self.SetWarmup(timedelta(7))

self.Debug("init algoparts done")

self.RemoveSecurity("MSG")

self.SetAlpha(PairsAlpha(algorithm=self))

def OnData(self, data):

self.Debug("onData")

data

'''OnData event is the primary entry point for your self. Each new data point will be pumped in here.

Arguments:

data: Slice object keyed by symbol containing the stock data

'''

# if not self.Portfolio.Invested:

# self.SetHoldings("SPY", 1)

# sort the data by daily dollar volume and take the top '5'

def CoarseSelectionFunction(self, coarse):

# sort descending by daily dollar volume

sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)

MinPrice = [ x.Symbol for x in sortedByDollarVolume if x.Price > 20 ]

# return the symbol objects of the top entries from our sorted collection

return MinPrice[:100]

and the error message/Stacktrace:

System.TimeoutException: Algorithm took longer than 10 minutes on a single time loop. CurrentTimeStepElapsed: 10.0 minutes

at QuantConnect.Isolator.MonitorTask (System.Threading.Tasks.Task task, System.TimeSpan timeSpan, System.Func`1[TResult] withinCustomLimits, System.Int64 memoryCap, System.Int32 sleepIntervalMillis) [0x002d3] in <78602b4cadea4f3a881b10b839873497>:0

at QuantConnect.Isolator.ExecuteWithTimeLimit (System.TimeSpan timeSpan, System.Func`1[TResult] withinCustomLimits, System.Action codeBlock, System.Int64 memoryCap, System.Int32 sleepIntervalMillis, QuantConnect.Util.WorkerThread workerThread) [0x00092] in <78602b4cadea4f3a881b10b839873497>:0

at QuantConnect.Lean.Engine.Engine.Run (QuantConnect.Packets.AlgorithmNodePacket job, QuantConnect.Lean.Engine.AlgorithmManager manager, System.String assemblyPath, QuantConnect.Util.WorkerThread workerThread) [0x009f0] in Lean.Engine.AlgorithmManager manager, System.String assemblyPath, QuantConnect.Util.WorkerThread workerThread) [0x009f0] in <7aff72eb1cda49b9aff04ff68484be84>:0