TableofContentsLetsPlayaGame
ETFCreation/Redemption
ETF/IndexArbitrage◦ HFTandFlow◦ DealFlow
StatisticalTesting◦ AugmentedDickey-Fuller(ADF)Test◦ HurstExponent◦ VarianceandTermStructure
NeuralNetworks
PersonalLearnings
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
LetsPlaya Game:TheTicketTEAMID:0001TRADEPRICE:32TRADESIZE:10TRADEDIRECTION:BUY
TRADEDWITH:0003INITIAL:TS
TEAMID:0003TRADEPRICE:32TRADESIZE:10TRADEDIRECTION:SELL
TRADEDWITH:0001INITIAL:MK
GlobalWorkbook(GoogleSheet) PersonalWorkbook
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%
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?
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
DealFlow:TheRussellRebalanceBankwilltradeallofthepositionsonbehalfofFTSERussell(moves~20Billioninafewhours)forasingleclient◦ Buysupinanticipationofthetradeandsellstheirownsharestotheclient
◦ Massivemarketmoves
Legalizedinsidertradingandmarketmanipulationduetosheersizeoforders
GoldmanactuallypaysRussell(andthelike)fortheirorderflow!
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
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
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
Overall:20012016Sharpe:2.38PPC:0.19Wins:23448Losses:19719Trades:43378
Overall:20012016Sharpe:2.91PPC:0.12Wins:23448Losses:19719Trades:43378
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
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