by: avi thaker algorithmic trading: an introduction · hurst(gbm): 0.498349157279 hurst(mr):...

AlgorithmicTrading:AnIntroduction

BY:AVI THAKER

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TableofContentsLetsPlayaGame

ETFCreation/Redemption

ETF/IndexArbitrage◦ HFTandFlow◦ DealFlow

StatisticalTesting◦ AugmentedDickey-Fuller(ADF)Test◦ HurstExponent◦ VarianceandTermStructure

NeuralNetworks

PersonalLearnings

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LetsPlayaGame:TradingObjective:MakeasmuchmoneyaspossibleReward:$5AmazonGiftCardperteammemberTeamSize3-4◦ 1Trader– tradesonthefloorwithothertraders◦ 1Runner– Runestheordersmadebythetrader◦ 1+Backend”PrimeBroker”– Clearthetradesmadewiththeteams

Tradingageometricrandomwalk;6rounds45secondseachMustquoteaspread(unitvalues),e.g.30-32Mustaccepttradeifsomeonehits (acceptsyourbid)ortakes (acceptsyourask)Tradesmustbecleared(thebackendandrunnersverifytradewithotherbackend)tocountWillfillallunfilledordersattheendwith2unitspenaltycost

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LetsPlaya Game:TheTicketTEAMID:0001TRADEPRICE:32TRADESIZE:10TRADEDIRECTION:BUY

TRADEDWITH:0003INITIAL:TS

TEAMID:0003TRADEPRICE:32TRADESIZE:10TRADEDIRECTION:SELL

TRADEDWITH:0001INITIAL:MK

GlobalWorkbook(GoogleSheet) PersonalWorkbook

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ETFCreation/RedemptionAuthorizedparticipant(marketmaker,intuitionalinvestor,specialist)borrowsstocksharesandplacestheminatrusttoformETFcreationunits – bundlesofstockunits

TrustprovidessharestotheAP,andsharessoldtopubliconopenmarket

RedeemingETF◦ Sellsharesonopenmarket◦ Formacreationunitandandexchangeforunderlyingsecurity

◦ Taxefficient

CreationUnit LastTrade Bid Ask Size Net PercentageAMD 13.7 13.69 13.7 100 1370 16.31% INTC 35.16 35.16 35.17 100 3516 41.85% AAPL 140.64 140.63 140.64 25 3516 41.85% CreationUnit 84.02 100 8402 100.00%

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ETF/IndexArbitrageThisisthemostcommonstrategyemployedbymostquantfirmsandbanks◦ JaneStreet,AQR,JumpTrading

Youneedtobefast orhaveflow◦ Someofyoumayhavedonethisinthegame

ArbitragehappenswhenETFtradesatadiscountorpremiumtotheNAV◦ Institutional:WhenETFprice>NAV,theAPwillsellsharesitreceivedduringcreationandmakeaspreadbetweenthecostoftheassetsitboughtfortheETFissuerandthesellingpricefromtheETFshares.APcanalsobuytheunderlyingsharesthatcomposetheETFdirectlyatlowerprices,sellETFsharesontheopenmarketatthehigherprice,capturingthespread.

◦ Individuals:WhentheETFissellingatapremium(ordiscount),individualscanbuy(short)theunderlyingsecuritiesinthesameproportionsandshort(orbuy)theETF.Limitedbyliquidityandspread◦ IfinsidethespreadneedtoknowiftheETFgoestosharepriceorsharepricegoestoETFprice

DothisataninternationallevelwithADR’s

Unit LastTrade Bid Ask Size Net PercentageAMD 13.7 13.69 13.7 100 1370 16.31% INTC 35.16 35.16 35.17 100 3516 41.85% AAPL 140.64 140.63 140.64 25 3516 41.85% CreationUnit 84.02 84.01 84.03 100 8402 100.00%

Example PriceCalculatedAsk 84.03CalculatedBid 84.0075ETFBid 84.04ETFAsk 84.05

Whatisthepotentialprofitofthistrade?

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HFTandFlowPureArbitrage◦ Fastestalwayswins

DealFlow◦ Ordersexecutedonbehalfofanotherclient◦ E*TRADE– guaranteed2secondexecutionmarketorder◦ SmartOrders

ExampleCompanies◦ Citadel,marketmakers,Goldman,JPMorgan,etc.

NYSE:GLD127.04-127.05

BuythenSell

LSE:GLD127.05-127.06

SellthenBuy

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DealFlow:TheRussellRebalanceBankwilltradeallofthepositionsonbehalfofFTSERussell(moves~20Billioninafewhours)forasingleclient◦ Buysupinanticipationofthetradeandsellstheirownsharestotheclient

◦ Massivemarketmoves

Legalizedinsidertradingandmarketmanipulationduetosheersizeoforders

GoldmanactuallypaysRussell(andthelike)fortheirorderflow!

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DealFlow:MarketMicrostructureCapturetheSpread

IncreaseLiquidity

Onbothsidesofthemarket

Riskyduringtimesofvolatility

Mustbefastandhaveexcellentqueueposition

Mathisgenerallymorecomplicated

Manipulatemarketwhenincomingmarketordertogetbetterprice

Bids Price Asks100.03 2,1

100.02 3,7,8100.01 5,2,15

100 1,2,5

1,2 99.99

2,5,8 99.98

3,8,1,5,3 99.97

2,3 99.85

Lotsofmachinelearning:thinkBayesianandneuralnetworks,ML

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StatisticalTestingMeanReversion◦ Aprocessthatreferstoatimeseriesthatdisplaysatendencytoreverttoitshistoricalmean

◦ Morespecifically:ifthepriceswithintheseriesmoveawayfromtheirinitialvaluefasterthanthatofGeometricBrownianMotion

◦ Ornstein-Uhlenbeck process(arandomwalkhasnomemory)

Momentum◦ Theexactoppositeofmeanreversion◦ Movementawayfromtheinitialvaluefasterthanthatofrandomwalk

Meanreversionandmomentumgohandinhand,inidentifyingoneyoumayidentifytheother

Willcovertwomethods:AugmentedDickey-Fullertest,andtheHurstExponent

PicturesFrom:http://marcoagd.usuarios.rdc.puc-rio.br/revers.htmlhttp://www.stockcharts.com

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StatisticalTesting:TermsOrenstein-Uhlenbeck SDE

ChangeinpriceseriesinnexttimeperiodisproportionaltothedifferencebetweenthemeanpriceandthecurrentpricewithGaussiannoise

MotivatesAugmentedDickey-Fuller(ADF)Test

𝒅𝒙𝒕 = 𝜽 𝝁 − 𝒙𝒕 𝒅𝒕 + 𝝈𝒅𝑾𝒕𝜃 = 𝑟𝑎𝑡𝑒𝑜𝑓𝑟𝑒𝑣𝑒𝑟𝑠𝑖𝑜𝑛𝑡𝑜𝑚𝑒𝑎𝑛𝜇 = 𝑚𝑒𝑎𝑛𝑣𝑎𝑙𝑢𝑒𝑜𝑓𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝜎 = 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒𝑜𝑓𝑡ℎ𝑒𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑊@ = 𝑊𝑖𝑒𝑛𝑒𝑟𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑜𝑟

𝐵𝑟𝑜𝑤𝑛𝑖𝑎𝑛𝑀𝑜𝑡𝑖𝑜𝑛


AugmentedDickey-Fuller(ADF)TestIdentifypresenceofaunitrootinautoregressivetimeseries

Reliesonthefactthatifapriceserieshasameanreversionthenthenextpricewillbeproportionaltothecurrentprice

LinearModelofOrderpΔ𝑦@ = 𝛼 + 𝛽𝑡 + 𝛾𝑦@JK + 𝛿KΔ𝑦@JK + ⋯+ 𝛿NJKΔ𝑦@JNOK + 𝜖@

𝛼 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝛽 = 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑜𝑓𝑡𝑖𝑚𝑒𝑡𝑟𝑒𝑛𝑑(𝑙𝑜𝑛𝑔𝑡𝑒𝑟𝑚𝑑𝑟𝑖𝑓𝑡)

Δ𝑦@ = 𝑦 𝑡 − 𝑦 𝑡 − 1

Testingnullhypothesis:𝛾 = 0◦ Indicatesthatprocessisarandomwalk(𝛼 = 𝛽 = 0)


AugmentedDickey-Fuller(ADF)TestTeststatistic:sampleproportionality/standarderrorofsampleproportionality

𝐷𝐹Y =𝛾Z

𝑆𝐸(𝛾Z)Negativenumber,andmustbelessthancriticalvaluestobesignificant

Codeadf_test.py

Calculated Test Statistic: -2.1900105031287529 P-Value: 0.2098910250427564# Datapoints: 210610%: -2.56750111766769565%: -2.86291337107029831%: -3.4334588739173006

Cannotrejectnullhypothesis,andunlikelytohavefoundameanrevertingtimeseries


HurstExponentAstochasticprocessisstronglystationaryifitsjoinprobabilitydistributionisinvariantundertranslationsintimeorspace◦ Meanandvarianceofprocessdonotchangeovertimeanddonotfollowatrend

HurstExponenthelpstocharacterizethestationarityofatimeseries◦ Reverting,trending,orneither

Varianceofalogpriceseriestoidentifyrateofdiffusivebehavior𝑉𝑎𝑟 𝜏 = log 𝑡 + 𝜏 − log 𝑡 b

Sincelarge𝜏,varianceisproportionalto𝜏 forGeometricBrownianMotion𝜏~ log 𝑡 + 𝜏 − log 𝑡 b

Ifautocorrelationsexisttherelationshipisnotvalid,butcanbemodifiedtoinclude2HwiththeHurstExponentvalueH

𝜏bd~ log 𝑡 + 𝜏 − log 𝑡 b


HurstExponent:Meaning𝐻 < 0.5meanrevertingprocess

𝐻 == 0.5 GBM𝐻 > 0.5 trendingprocessCharacterizesextent◦ Closerto0moremeanreverting◦ Closerto1moretrending

Trydifferenttimeperiods,differentstocks

Hurst(GBM):0.498349157279Hurst(MR):-6.26637088795e-05Hurst(TR):0.95964231812Hurst(GOOG):0.50788012279


VarianceandTermStructure

Notincludedincode

Plotoflog 𝑉𝑎𝑟 𝜏 𝑣𝑠 log 𝜏 forSPY◦ Slope/2istheHurstexponent◦ Intraday

◦ Returnsofmid-pricesfrom1minuteto2^10minutes◦ H = 0.494 ± 0.003;slightlymeanreverting

◦ Daily◦ Returnsfrom1dayto2^8days◦ H = 0.469 ± 0.007;stronglymeanreverting

MeanreversionstrategiesshouldworkbetterthanintradaystrategiesonSPY

http://epchan.blogspot.com/2016/04/[email protected] 16

VarianceandTermStructure:Gold

Intraday: 𝐻 = 0.505 ± 0.002

Daily: 𝐻 = 0.469 ± 0.007

16-32daysvolatilitiesdriftfromtheregression◦ Thisiswhereweshouldswitchfrommomentumtomeanreversionstrategies

ATrendingExample:USOIntraday 𝐻 = 0.515 ± 0.001Daily 𝐻 = 0.560 ± 0.020

Momentumstrategiesshouldworkwellhere http://epchan.blogspot.com/2016/04/mean-reversion-momentum-and-volatility.html


ExampleStrategiesMomentum◦ Exponentialmovingaverages(MACD◦ Breakouts◦ VolatilitySurges◦ Newsdriven◦ Tendtohavelowwinratesbuthighprofitability

Reversion◦ Bollingerbands◦ Statisticalpairstradingandindextrading◦ Tendtohavehighwinratesandlowprofitability


StrategyDetail:ExponentialMovingAverage:EMA

Infiniteimpulseresponsefilter

LesslagthanSMA

Commonlyusedsignal

𝛼 =2

𝑛 + 1𝐸𝑀𝐴stuuvw@=𝑝K + 1 − 𝛼 𝑝b + 1 − 𝛼 b + ⋯1 + 1 − 𝛼 + 1 − 𝛼 b +…

= 𝐸𝑀𝐴Nuvyz{t| + 𝛼 𝑝stuuvw@ − 𝐸𝑀𝐴Nuvyz{t|


TradingTheEMA

EnterLong:Close>EMA&Prev_Close >EMA_PrevEnterShort:Close<EMA&Prev_Close <EMA_Prev


StrategyDetail:CommonSignalsBollingerBands

VolatilityBands◦ Baseduponstandarddeviation◦ Identifiespointsofreversion

MiddleBand=50-DaySMA

UpperBand=50-DaySMA+50-DaySDofPrice

LowerBand=50-DaySMA- 50-DaySDofPrice


Example:TradingtheBollingerBands


NeuralNetworksThebelowlinkcontainsatutorialinwhichaneuralnetworkisusedtopredicttimeseriesstockdatausingMicrosoft’sdeeplearningplatformCNTKpublishedinconjunctionwithMSR

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb


PersonalLearningsLESSONSFROMFAILURE,SUCCESS,ANDPUREDUMBLUCK


TechnicalIndicators

Technicalindicatorsaremostlyuselessontheirown

Mustidentifysomethingthathappensinthemarket,andusetheindicators(orcomeupwithyourown)torepresentthatsomething

Datavisualizationiscrucial

Simplicityisusuallybetter

RSIRelativeStrengthIndexParabolicSAR– ParabolicStopandReversePriceChannelsVWAP– VolumeWeightedAveragePriceZigZagMACD– MovingAverageConvergenceDivergencePPO– PercentagePriceOscillatorKST- KnowSureThingUltimateOscillatorVortexIndicator…Thelistgoesonforever


Backtesting aStrategy/Risk

Provideevidenceofprofitability◦ Curvefitting/optimizationbias◦ In-samplevsout-of-sample◦ Forwardlookingbias

Risktolerance

KeyStatisticsAveragewins ::0.637USDAverageloss ::-0.438USD#Wins ::214#Losses::210#Neutrals ::3WinRate ::0.501PPC ::0.104USD#Traded ::427.0Ann.Sharpe ::2.335


Backtesting aStrategyDoesthestrategyworkacrossmanyassets?

Howmanyyearsdoesitworkfor?

Doesitescapethebid-askbounce?

RiskTolerance?◦ MaximumDrawdown?

Fees?Tradingfrequency?

InSample:SPY2004-2010OutofSample:AssetsRandomlySelected:ADBEXLNXBBBYCFNEMCADPAFLDETSPLSDGADSALLMETCLPXWYN

Overall:19802016Sharpe:2.12PPC:0.13Wins:12634Losses:10527Trades:23666

Sharpe:1.299PPC:0.338Wins:255Losses:202Trades:463.0

Wouldyoutrademe?


OrderSizing

Generallysizeordersinverselyproportionaltovolatility



NotVolatilitySizedOrders VolatilitySizedOrders


BiasesandPitfallsThesecanbedoneunintentionally

CurveFittingBias◦ Adjusting/addingparametersuntilthestrategylooksattractiveinbacktest

Forwardlookingbias◦ Programlooksatfutureduetobugincode◦ Calculatingoptimalparameters,optimizations◦ Lookingatthedata!

SurvivorshipBias◦ Notincludingfulluniverse(pre2008crash,2007algo tradingblowup)

PsychologicalBias◦ Canyoutoleratea5monthdrawdown?Losehalfyourportfolio◦ Yourbacktests willsuggestpossibleseverity


GeneralTipsThisisnotagetrichquickscheme

Findingalphaishard,donotgetdiscouraged

Drawdownarepainful,becarefulwithleverage

Trustyouralpha(ifyouhavesome),strategiesareusuallysimple

Performance◦ Outofsampleperformanceisgenerally½ofinsampleperformance◦ Livetradingperformanceisgenerally¼ofinsampleperformance◦ Duetocurvefitting,unexpectedslippage,etc.

Makesureyouaccountfortransactionfeesandslippageand ordersizes

Funandexcitingwaytolearnnotonlythemarketsbutalsocomputerscienceandmath

Dataisyourfriend

Buildyourownbacktester/executionenvironment


SystemArchitectureOverview

PythonCompiledC

Multi-Threaded◦ Caninstantiatemultiplestrategies

EventDrivenBacktester◦ Eliminateserrors

Canusethesamestrategyfortradingandbacktesting

Strategy

RSI

BacktesterClient(IB) ClientA UDP ClientB

• Redundantinstances• MultipleinstancescommunicateoverUDPtocheckstate• Masterslave/slavesarchitecture• CanextendtoNinstances

• AWSandPersonalServer


LimitOrderExecution– PlaceOrderBids Price Asks

100.03 2,1

100.02 3,7,8100.01 5,2,15

100 1,2,5

1,2 99.99

2,5,8 99.98

3,8,1,5,3 99.97

2,3 99.85Placelimitorderof2lotsat99.99


LimitOrderExecution– BookMovementBids Price Asks

100.03 2,1

100.02 3,7,8

100.01 5,2,15

100 1,2,5

1,2,5 99.99

2,5,8 99.98

3,8,1,5,3 99.97

2,3 99.85

Fillat99.99,thisbecomesremoved,andpositionadvances.Atradehappens

Anotherorderisplacedbehindyou

Peoplecanceltheirorders


LimitOrderExecution– OrderFillBids Price Asks

100.03 2,1

100.02 3,7,8

100.01 5,2,15

100 1,2,5

2,5 99.99

2 99.98

3,8,1,5,3 99.97

2,3 99.85

Afteranorderisfilledyoumoveupinthequeue,untilyoueitherarefilledorcanceltheorderWearenowfirstinthequeue


Backtesting aStrategyBacktesting withLimitorderExecution◦ Simulatebyplacinglimitorders◦ Needtocheckforfills◦ Complexandrequirestime◦ Doesnotperfectlymodelslippage

Backtesting withCloseexecution◦ Ordersfilledoncloseofbar◦ Subjecttobid/askbounce◦ Mustsubtractslippagenumbers◦ Morethan2ticks?

EventDriven


AppendixDETAILSTHATMIGHTBEINTERESTINGTOREAD


Appendix:FurtherReadingsBestguidetostartingalgo trading(intro/backtester takenfromhere)◦ http://www.quantstart.com/

ExecutionEnvironment/Backtester/Community◦ https://www.quantopian.com/

CheaptradingplatformwithAPI◦ https://www.interactivebrokers.com/ind/en/main.php

◦ Stellardocumentationonhowtodoexecution

TechnicalAnalysisLibraryTA-Lib◦ http://ta-lib.org/◦ https://pypi.python.org/pypi/TA-Lib

Data:◦ Free:YahooFinance,GoogleFinance– errorprone◦ Cheap:PiTrading,Kibot,Tickwrite


Appendix:SharpeRatio𝑆ℎ𝑎𝑟𝑝𝑒 =

𝑟N − 𝑟}𝜎N

𝑟N = 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜𝑟𝑒𝑡𝑢𝑟𝑛𝑟} = 𝑟𝑖𝑠𝑘𝑓𝑟𝑒𝑒𝑟𝑎𝑡𝑒

𝜎N = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛𝑜𝑓𝑟𝑒𝑡𝑢𝑟𝑛Measuresriskadjustedperformance◦ Riskvs.Reward

HigherisusuallybetterRiskfreeratesometimesassumedtobe0Usuallyannualizedandvolatilitytakenasstandarddeviation◦ Monthly:Volatilitysampledmonthly*sqrt(12)◦ Daily:Volatilitysampleddaily*sqrt(252)◦ Minutely:Volatilitysampledminutely*sqrt(390*252)


Appendix:Candlestick/BarDataOpen– priceatstartofbar

High– highestprice

Low– lowestprice

Close– priceatendofbar

Volume– numbertradedduringbar

Canbeonanytimescale:secondstomonthly

http://www.financial-spread-betting.com/course/candle-stick-charting.html


Appendix:OrderSizingAverageTrueRangeScaling

Reducestradesizeduringtimesofvolatility,Increaseduringlowvolatility

IncreasesSharpeRatio

Canadjusttosizeofcontract,and/orcontractprice

𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙 = $1,000

𝑇𝑟𝑎𝑑𝑒𝑆𝑖𝑧𝑒 = 𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙

𝐴𝑇𝑅 10 ∗ 𝑀𝑖𝑛𝑇𝑖𝑐𝑘𝑆𝑖𝑧𝑒($)𝑇𝑟𝑢𝑒𝑅𝑎𝑛𝑔𝑒 = max ℎ𝑖𝑔ℎ − 𝑙𝑜𝑤 , 𝑎𝑏𝑠 ℎ𝑖𝑔ℎ − 𝑐𝑙𝑜𝑠𝑒Nuvy , 𝑎𝑏𝑠 𝑙𝑜𝑤 − 𝑐𝑙𝑜𝑠𝑒Nuvy

𝐴𝑇𝑅@ =𝐴𝑇𝑅@JK 𝑛 − 1 + 𝑇𝑟𝑢𝑒𝑅𝑎𝑛𝑔𝑒@

𝑛


Appendix:PPCProfitPerContract

𝑟�𝑐 ∗ 𝑡|

𝑟� = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑟𝑒𝑡𝑢𝑟𝑛𝑐 = 𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑠𝑡𝑟𝑎𝑑𝑒𝑑

𝑡| = 𝑡𝑖𝑐𝑘𝑠𝑖𝑧𝑒

Ameasureofprofitability,measuredinticks

Ahighlyliquidstockusuallyhasaticksizeofapenny

Ifyourstrategyhasmorethan2ticks,itisconsideredprofitable(canescapethebid/askbounce),iftestingonbardatawithoutlimitorderexecutiononbarcloses◦ Youcansubmitmarketordersandstillmakemoney

◦ Assumesliquidity!!!!!


Appendix:CAPMCapitalAssetPricingModel

𝑟� = 𝑟} + 𝐵� 𝑟� − 𝑟}𝑟} = 𝑅𝑖𝑠𝑘𝐹𝑟𝑒𝑒𝑅𝑎𝑡𝑒𝐵� = 𝐵𝑒𝑡𝑎𝑜𝑓𝑆𝑒𝑐𝑢𝑟𝑖𝑡𝑦

𝑟� = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑𝑀𝑎𝑟𝑘𝑒𝑡𝑅𝑒𝑡𝑢𝑟𝑛𝑟� = 𝐴𝑠𝑠𝑒𝑡𝑅𝑒𝑡𝑢𝑟𝑛

Describestherelationshipbetweenriskandtheexpectedreturn

Investorsneedtobecompensatedfortime(riskfreerate)andrisk(beta)


Appendix:DrawdownThemeasureofthelargestdropfrompeaktobottom(inpercentage)◦ Itisapainindexmeasure

Extremelyimportanttomeasurethedurationofthedrawdown◦ Doyouwanttobelosingmoneyforyears?

𝐷 𝑇 = max@∈(�,�)

{𝑋(𝑡) − 𝑋 𝑇 }

MDD 𝑇 = max@∈(�,�)

[ max@∈(�,Y)

{𝑋 𝑡 − 𝑋(𝜏)}]

Where𝑋 = 𝑋 𝑡 , 𝑡 ≥ 0 isarandomprocess

Simplyputmaximumdrawdownis:◦ (Peakvaluebeforelargestdrop– lowestvaluebeforenewhigh)/Peakvaluebeforedrop


Appendix:UnderwaterCurve

Goodwaytovisualizehowmuchofthetimeyouareinadrawdown

Letsyouevaluatehowmuchpainyoushouldbeabletohandle

http://ctaperformance.com/wntn


Appendix:DistributionofReturns

Generallyahistogramofreturns

Lookatcenter,shape,distribution,spread◦ Wantpositivecenter,andnomajoroutliers

http://ctaperformance.com/wntn


Appendix:StrategyCorrelation

Generallyyouwanttomakesurethatyourstrategiesarenotcorrelatedtoeachother(lookatdailyreturns)◦ Youdonotwanteverythingtohaveabaddayatthesametime◦ Balancedreturnsaregood

UncorrelatedstrategiestendtoyieldhigherSharperatioswhenmixed

Correlatedstrategiestendtoreflectthesamealpha◦ Thesestrategiestendtocompetewitheachother

Negativelycorrelatedstrategiescanbegood◦ Highlynegativelycorrelatedstrategiescanindicateproblemswithyouralpha

ThankyouAaronRosenforyourfeedback


Appendix:TradableAUMNotallstrategiesarecreatedequal

StrategyAmightbeabletotrade$1,000,000withoutincurringlargeslippagebuttrading$100,000,000itmightincurmuchmoreslippageandkillthestrategy◦ Marketmaking– yourabilitytocapturetheinsidebidofferdecreaseswithsize◦ Highfrequencystrategies◦ Somemomentumstrategies

SharperatiosandAUMtradableareusuallyinverselycorrelated◦ Therearesomeexceptions

Notethatthesenumbersareartificial

ThankyouAaronRosenforyourfeedback


by: avi thaker algorithmic trading: an introduction · hurst(gbm): 0.498349157279 hurst(mr):...

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