PortfolioEffectHFT MATLAB Toolbox
Contents
1. UWEKWE UAE UUZE Bas Ha oe ee Ge 4 3 1 Window Length i 2o w ea AA EE Be OR eS ae Doe 4 3 2 Time Scale aa eea doe om ee gagap n Ro Ox Rb be Ra Be eee lee von 4 3 3 Microstructure Noise Modell 4 3 4 Jumps Outliers Model ee 43 5 Density Model esse Os blew ads ha a o A a A e 4 3 0 Eactor Modell ses nao a ee ba ee ee a ee RON A IE oe Ox iesu 28 24 2 Saye ae oe P Rod A XS dd GSA ons pedo o Seele ere ta a 4A Transactional Costs osas o eee E donor PO a SE RH eredi 44 1 Cost Per Share ee eee Be DA a ee a Rcs RC E eek p e ee wa Aaa a Ts Be ead th Gee cop Oe Baek BAe te pS O wk OR MUR Pow ie o E AA ie d Rr a yy UR GC OS og 5 1 Key beat res x om RR oro REESE a OR ee ec RO Rok X 2 a eR RR TTL 5 L 3 Adding Constraints v 222 3 Xen Pa ee SOTERA EN Oe DR 4 ee UR e S eel on 0 1 4 Scalar Constraints se e socie dd oesi a ps KAA AA S oss 5 1 5 Vector Constraints ae 2302 pop ea Rum x dons UA AAA ur ed 10 10 11 11 12 12 12 12 14 1 Toolbox Installation PortfolioEffectHFT toolbox for MATLAB is provided as a zip archive for all types of operating systems and as a self install executable file for Windows 1 1 Zip Archive All OS Download zip archive with a toolbox from PortfolioEffect downloads section or MATLAB Central File Exchange Once downloaded unpack archive into the folder and add the PortfolioEffectHFT folder2. 30m set optimization goal and define constraints optimizer optimization_goal portfolio goal SharpeRatio direction maximize optimization constraint beta optimizer lt 0 1 4 run optimization optimalPortfolio optimization run optimizer plot results util plot2d portfolio beta optimalPortfolio Optimal Beta title Beta util line2d portfolio beta portfolio Original Beta 5 1 5 Vector Constraints Instead of using a single scalar one could specify an vector of constraint values with corresponding timestamps Optimization algorithm would then automatically determine when certain constraint value should be applied based on the current rebalancing time 4 create portfolio and add positions portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio AAPL GO0G 500 600 4 rebalancing every minute static portfolio ignores rebalancing history portfolio_settings portfolio portfolioMetricsMode price resultsSamplingInterval 30m betaVector 1412171500000 0 1 1412262500000 0 5 4 set optimization goal and define constraints optimizer optimization_goal portfolio goal SharpeRatio direction maximize optimizer optimization constraint beta optimizer lt betaVector run optimization optimalPortfol
3. GO0G 300 200 Single Index Model is used portfolio settings portfolio factorModel sim variance sim portfolio variance portfolio 4 Direct model is used portfolio settings portfolio factorModel direct variance direct portfolio variance portfolio util plot2d variance sim sim title Variance factorModel util line2d variance direct direct 12 4 3 7 Drift Term Used to enable drift term expected return when computing probability density approximation and related metrics e g CVaR Omega Ratio etc Defaults to TRUE which implies that distribution is centered around expected return portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 Drift term is enabled portfolio settings portfolio driftTerm true CVaR driftTerm TRUE portfolio CVaR portfolio 0 05 4 Drift term is disabled portfolio settings portfolio driftTerm false CVaR driftTerm FALSE portfolio CVaR portfolio 0 05 util plot2d CVaR driftTerm TRUE sim title CVaR driftTerm util line2d CVaR driftTerm FALSE direct 4 4 Transactional Costs These settings provide a framework for adding variable and fixed transactional costs into return expected return and profit calculations All metrics based on expe
4. 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 HF noise model is enabled portfolio settings portfolio noiseModel true variance noiseModel TRUE portfolio variance portfolio 4 HF noise model is disabled portfolio settings portfolio noiseModel false variance noiseModel FALSE portfolio variance portfolio util plot2d variance noiseModel TRUE true title Variance noiseModel util line2d variance noiseModel FALSE false 4 34 Jumps Outliers Model Used to select jump filtering mode when computing return statistics Available modes are e none price jumps are not filtered anywhere e moments price jumps are filtered only when computing return moments i e for expected return variance skewness kurtosis and derived metrics e all price jumps are filtered from computed returns prices and all return metrics portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 04 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 Price jumps detection is enabled for returns and moments portfolio_settings portfolio jumpsModel all variance_all portfolio_variance portfolio 11 4 Price jumps detection is disabled portfolio settings portfolio jumpsModel none variance none portfolio varianc
5. dd e g 2014 10 01 e t N e g 6 5 is latest trading time minus 5 days e UTC timestamp in milliseconds mills from 1970 01 01 00 00 00 in EST time zone 4 Timestamp in yyyy MM dd HH MM SS format portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 4 Timestamp in yyyy MM dd format portfolio portfolio_create fromTime 2014 10 01 toTime 2014 10 02 4 Timestamp in t N format portfolio portfolio_create fromTime t 5 toTime t 3 2 2 Add Positions Positions are added by calling portfolio addPosition method on a portfolio object with a list of symbols and quantities For positions that were rebalanced or had non default holding periods a time argument could be used to specify rebalancing timestamps 4 Single position without rebalancing portfolio addPosition portfolio G00G 200 Multiple positions without rebalancing portfolio_addPosition portfolio C GO0G 300 200 4 Single position with rebalancing portfolio addPosition portfolio AAPL 300 150 time 2014 10 02 09 30 01 2014 10 02 11 30 01 4 Portfolio Settings 4 1 Portfolio Metrics These settings regulate how portfolio returns and return moments are computed 4 1 1 Portfolio Metrics Mode One of the two modes for collecting portfolio metrics that could be used e portfolio portfolio metrics are comp
6. cted return like Sharpe Ratio VaR with drift term enabled would reflect transactional costsin their computations 4 4 1 Cost Per Share Amount of transaction costs per share Default value is 0 portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio AAPL 300 100 50 150 time 2014 10 01 09 30 00 2014 10 01 13 30 00 2014 10 01 16 00 00 2014 10 02 13 30 00 Transactional costs per share are 0 5 cent portfolio settings portfolio txnCostPerShare 0 05 return bO portfolio return portfolio Transactional costs per share are 0 1 cent portfolio settings portfolio txnCostPerShare 0 001 return i portfolio return portfolio util plot2d return 50 0 05 title Return txnCostPerShare util line2d return 1 70 001 4 4 2 Cost Per Transaction Amount of fixed costs per transaction Defaults to 0 portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio AAPL 300 100 50 150 time 2014 10 01 09 30 00 2014 10 01 13 30 00 2014 10 01 16 00 00 2014 10 02 13 30 00 4 Fixed costs per transaction are 9 dollars portfolio settings portfolio txnCostFixed 19 13 return_19 portfolio_return portfolio 4 Fixed costs per transaction are 1 dollar portfo
7. dReturn portfolio expected return Return portfolio return SharpeRatio portfolio Sharpe Ratio ModifiedSharpeRatio portfolio modified Sharpe Ratio StarrRatio portfolio STARR Ratio ContraintsOnly no optimization is performed This is used for returning an arbitrary portfolio that meets specified set of constraints None no optimization is performed and constraints are not processes Portfolio positions are returned with equal weights 5 1 3 Adding Constraints Optimization constraints cover both metric based and weight based constraints Metric based constraints limit portfolio level metrics to a certain range of values For example zero beta constraint would produce market neutral optimal portfolio Weight based constraints operate on optimal position weights or sum of weights to give control over position concentration risks or short sales assumptions Constraint methods could be chained to produce complex optimization rules Since position quantities are integer numbers and weights are decimals a discretization error is introduced while converting optimal position weights to corresponding quantities By default optimal portfolio starts with a value of the initial portfolio Portfolio value could be fixed to a constant level at every optimization step see corresponding constraint below Higher portfolio value could be used to keep difference between computed optimal weights and effective we
8. ds to set your account API credentials for the PortfolioEffectHF T Toolbox for MAT LAB You will need to do it only once as your credentials are stored between sessions on your local machine to speed up future logons You would need to repeat this procedure if you change your account password or install PortfolioEffect HFT toolbox on another computer util_setCredentials API Username API Password API Key You are now ready to call PortfolioEffect methods 3 Portfolio Construction 3 1 User Data Users may supply their own historical datasets for index and position entries This external data could be one a OHLC bar column element e g 1 second close prices or a vector of actual transaction prices that contains non equidistant data points You might want to pre pend at least N 4 x windowLength data points to the beginning of the interval of interest which would be used for initial calibration of portfolio metrics 3 1 1 Create Portfolio Method portfolio create takes a vector of index prices in the format UTC timestamp price with UTC timestamp expressed in milliseconds from 1970 01 01 00 00 00 EST Time Value 1 1412256601000 99 30 2 1412256602000 99 33 3 1412256603000 99 30 4 1412256604000 99 26 5 1412256605000 99 36 6 1412256606000 99 36 7 1412256607000 99 36 8 1412256608000 99 38 9 1412256609000 99 40 10 1412256610000 99 37 If index symbol is specified it is silentl
9. e portfolio util plot2d variance all all title Variance jumpsModel util line2d variance none none 4 3 5 Density Model Used to select density approximation model of return distribution Available models are e GLD Generalized Lambda Distribution e CORNER_FISHER Corner Fisher approximation e NORMAL Gaussian distribution Defaults to GLD which would fit a very broad range of distribution shapes portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 Using normal density portfolio settings portfolio densityModel NORMAL util plotDensity portfolio pdf portfolio 0 6 1 100 true 4 Using Generalized Lambda density portfolio settings portfolio densityModel GLD util plotDensity portfolio pdf portfolio 0 6 1 100 true 4 3 6 Factor Model Factor model to be used when computing portfolio metrics Available models are e sim portfolio metrics are computed using the Single Index Model e direct portfolio metrics are computed using portfolio value itself experimental Defaults to sim which implies that the Single Index Model is used to compute portfolio metrics portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C
10. ights based on position quantities small Lower portfolio value or higher asset price would normally increase discretization error 4 create portfolio and add positions portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C G0O0G 300 200 portfolio_settings portfolio portfolioMetricsMode price resultsSamplingInterval 30m 4 set optimization goal optimizer optimization_goal portfolio goal SharpeRatio direction maximize 4 add constraints optimization constraint beta optimizer 0 optimization constraint weight optimizer 0 5 C optimization constraint variance optimizer 0 02 16 launch optimization and obtain optimal portfolio optimalPortfolio optimization run optimizer util plot2d portfolio sharpeRatio portfolio Simple Portfolio title Sharpe Ratio util line2d portfolio sharpeRatio optimalPortfolio Optimal Portfolio util plot2d portfolio beta portfolio Simple Portfolio title Beta util line2d portfolio beta optimalPortfolio Optimal Portfolio util plot2d portfolio variance portfolio Simple Portfolio title Variance util line2d portfolio variance optimalPortfolio Optimal Portfolio The following constraint methods are available optimization constraint a
11. in portfolio settings portfolio inputSamplingInterval 5m variance bm portfolio variance portfolio util plot2d variance 30s 30s title Variance inputSamplingInterval util line2d variance 5m 5m 4 3 Model Pipeline 4 3 1 Window Length Specifies rolling window length that should be used for computing portfolio and position metrics When portfolio mode is set to portfolio it is also the length of rebalancing history window to be used Available interval values are e Xs seconds e Xm minutes e Xh hours e Xd trading days 6 5 calendar hours in a trading day e Xw weeks 5 trading days in 1 week e Xmo month 21 trading day in 1 month e Xy years 256 trading days in 1 year e all all observations are used Default value is 1d one trading day portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 1 hour rolling window portfolio_settings portfolio windowLength 1h variance_1h portfolio_variance portfolio 1 week rolling window portfolio settings portfolio windowLength 1d variance id portfolio variance portfolio util plot2d variance 1h 1h title Variance windowLength util line2d variance 1d 1d 4 3 2 Time Scale Interval
12. io optimization run optimizer plot results util plot2d portfolio beta optimalPortfolio Optimal Beta title Beta util line2d portfolio beta portfolio Original Beta 18
13. lio settings For example short windowLength would produce spot versions of portfolio metrics and computed optimal weights would change faster to reflect shortened metric horizon 5 1 2 Optimization Goals Optimization algorithm requires a single maximization minimization goal to be set using optimization goal method that operates on a portfolio see portfolio construction Returned optimizer object could be used to add optional optimization costraints and then passed to the method to launch portfolio optimization portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 portfolio_settings portfolio portfolioMetricsMode price resultsSamplingInterval 30m set optimization goal optimizer optimization_goal portfolio goal Return direction maximize launch optimization and obtain optimal portfolio optimalPortfolio optimization run optimizer util plot2d portfolio return portfolio Simple Portfolio title Portfolio Return util line2d portfolio return optimalPortfolio Optimal Portfolio The following portfolio metrics could currently be used as optimization goals 15 Variance portfolio returns variance VaR portfolio Value at Risk CVaR portfolio Conditional Value at Risk Expected Tail Loss Expecte
14. lio_settings portfolio txnCostFixed 1 return_1 portfolio_return portfolio util_plot2d return_19 19 title Return txnCostFixed util_line2d return_1 1 14 5 Portfolio Optimization 5 1 Optimization Goals amp Constraints A classic problem of constructing a portfolio that meets certain maximization minimization goals and constraints is addressed in our version of a multi start portfolio optimization algorithm At every time step optimization algorithm tries to find position weights that best meet optimization goals and constraints 5 1 1 Key Features A multi start approach is used to compare local optima with each other and select a global optimum Local optima are computed using a modified method of parallel tangents PARTAN e When optimization algorithm is supplied with mutually exclusive constraints it would try to produce result that is equally close in absolute terms to all constraint boundaries For instance constraints x i 6 and x 4 are mutually exclusive so the optimization algorithm would choose x 5 which is a value that has the smallest distance to both constraints e Portfolio metrics change over time but optimization uses only the latest value in the time series Therefore the faster metric series would change the more likely current optimal weights would deviate from the optimal weights at the next time step e Optimization results depend on provided portfo
15. llWeights portfolio weights of all positions optimization constraint weigh portfolio position weights optimization constraint sumOfAbsWeights portfolio s sum of absolute positions weights for selected positions optimization constraint return portfolio return optimization constraint expectedReturn portfolio expected return optimization constraint variance portfolio returns variance optimization constraint beta portfolio beta optimization constraint VaR portfolio Value at Risk optimization constraint C VaR portfolio Conditional Value at Risk Expected Tail Loss optimization constraint modifiedSharpeRatio portfolio modified Sharpe Ratio optimization constraint sharpeRatio portfolio Sharpe Ratio optimization constraint starrRatio portfolio STARR Ratio 5 1 4 Scalar Constraints Scalar constraints are the simplest type of optimization boundaries They require a single constant that is applied over a full time span of portfolio optimization An example below sets portfolio beta constraint to be greater or equal to 0 1 4 create portfolio and add positions portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C G0O0G 300 200 17 4 rebalancing every minute static portfolio ignores rebalancing history portfolio_settings portfolio portfolioMetricsMode price resultsSamplingInterval
16. riance_markowitz markowitz title Variance shortSalesMode util_line2d variance_lintner lintner 4 1 3 Short Sales Mode This setting is used to specify how position weights are computed Available modes are e lintner the sum of absolute weights is equal to 1 Lintner assumption e markowitz the sum of weights must equal to 1 Markowitz assumption Defaults to lintner which implies that the sum of absolute weights is used to normalize investment weights 4 2 Data Sampling These settings regulate how results of portfolio computations are returned Depending on your usage scenario some of them might bring significantly imporvement to speed of your portfolio computations 4 2 1 Results Sampling Interval Interval to be used for sampling computed results before returning them to the caller Available interval values are e Xs seconds e Xm minutes e Xh hours e Xd trading days 6 5 hours in a trading day e Xw weeks 5 trading days in 1 week e Xmo month 21 trading day in 1 month e Xy years 256 trading days in 1 year e none no sampling e last only the very last data point is returned Large sampling interval would produce smaller vector of results and would require less time spent on data transfer Default value of 1s indicates that data is returned for every second during trading hours portfolio po
17. rtain points i e when position quantity were zero When set to TRUE trading strategy metrics are annualized only based on actual holding intervals portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio AAPL 300 0 150 time 2014 10 01 09 30 00 gt 2014 10 01 13 30 00 2014 10 02 13 30 00 1 3 4 enable holdingPeriodsOnly portfolio settings portfolio holdingPeriodsOnly true variance holdingPeriodsOnly TRUE portfolio variance portfolio 4 disable holdingPeriodsOnly portfolio settings portfolio holdingPeriodsOnly false variance holdingPeriodsOnly FALSE portfolio variance portfolio util_plot2d variance_holdingPeriodsOnly_TRUE true title Variance holdingPeriodsOnly util_line2d variance_holdingPeriodsOnly_FALSE false portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 weights are normalized based on a simple sum Markowitz portfolio_settings portfolio shortSalesMode markowitz variance_markowitz portfolio_variance portfolio 4 weights are normalized based on a sum of absolute values Lintner portfolio_settings portfolio shortSalesMode lintner variance_lintner portfolio_variance portfolio util_plot2d va
18. rtfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 01 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 sample results every 30 seconds portfolio settings portfolio resultsSamplingInterval 30s variance 30s portfolio variance portfolio sample results every 5 minutes portfolio settings portfolio resultsSamplingInterval 15m variance ibm portfolio variance portfolio util_plot2d variance_30s 30s title Variance resultsSamplingInterval util line2d variance 15m 15m 4 2 2 Input Sampling Interval Interval to be used as a minimum step for sampling input prices Available interval values are e Xs seconds e Xm minutes e Xh hours e Xd trading days 6 5 hours in a trading day e Xw weeks 5 trading days in 1 week e Xmo month 21 trading day in 1 month e Xy years 256 trading days in 1 year e none no sampling Default value is none which indicates that no sampling is applied portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 sample input prices every 30 seconds portfolio_settings portfolio inputSamplingInterval 30s variance_30s portfolio_variance portfolio 4 sample input prices every 5 m
19. to MATLAB s path using Set Path menu Then call any method of the package in MATLAB editor to continue with the set up TEE EE EE EE HE HE HEHEHEHEHEHE EHE EE ERE EE EHE EHE HE P HERE E E E RR RR Welcome to PortfolioEffectHFT Toolbox Setup will download required binary files 5mb Please wait SUCCESS File downloaded to home appadmin matlab R2015a portfolioeffect quant client 1 0 allinone jar Updating java class path file SUCCESS Java class path updated Setup complete Restart Matlab session now AS Restart MATLAB to complete installation 1 2 Install Wizard Windows Download self install executable for Windows from PortfolioEfect downloads section Follow the installation instructions Once the wizard completes add the PortfolioEffectHFT folder to MATLAB s path using Set Path menu The PortfolioEffectHFT toolbox is now fully configured 2 Account Credentials All portfolio computations are performed on PortfolioEffect cloud servers To obtain a free non professional account you need to follow a quick sign up process on our website www portfolioeffect com registration Please use a valid sign up address it will be used to email your account activation link 2 1 Locate API Credentials Log in to you account and locate your API credentials on the main page API Settings API Username API Password Your account password API Key 2 2 Set API Credentials in MATLAB Run the following comman
20. to be used for scaling return distribution statistics and producing metrics forecasts at different horizons Available interval values are e Xs seconds e Xm minutes e Xh hours e Xd trading days 6 5 hours in a trading day e Xw weeks 5 trading days in 1 week e Xmo month 21 trading day in 1 month e Xy years 256 trading days in 1 year 10 e all actual interval specified during portfolio creation Default value is 1d one trading day portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 1 hour time scale portfolio settings portfolio timeScale ih variance ih portfolio variance portfolio 4 1 week time scale portfolio settings portfolio timeScale 1d variance id portfolio variance portfolio util plot2d variance 1h 1h title Variance timeScale util line2d variance 1d id 4 3 3 Microstructure Noise Model Enables market mirostructure noise model of distribution returns Defaults to TRUE which means that microstructure effects are modeled and resulting HF noise is removed from metric caluclations When FALSE HF microstructure noise is not separated from asset returns which at high trading frequences could yield noise contaminated results portfolio portfolio_create fromTime
21. uted using previous history of position rebalancing Portfolio risk and performance metrics account for the periods with no market exposure i e when no positions are held depending on the holding periods accounting settings see holding periods mode below e price at any given point of time both position and portfolio metrics are computed for a buy and hold strategy This mode is a common for classic portfolio theory and is often used in academic literature for portfolio optimization or when computing price statistics By default mode is set to portfolio portfolio portfolio_create fromTime 2014 10 01 09 30 00 toTime 2014 10 02 16 00 00 portfolio_addPosition portfolio C GO0G 300 200 4 price mode portfolio_settings portfolio portfolioMetricsMode price variance_price portfolio_variance portfolio 4 portfolio mode portfolio_settings portfolio portfolioMetricsMode portfolio variance_portfolio portfolio_variance portfolio util_plot2d variance_price price title Variance portfolioMetricsMode util_line2d variance_portfolio portfolio 4 1 2 Holding Periods Only This setting should only be used when portfolio metrics mode is set to portfolio When holdingPeriodsOnly is set to FALSE trading strategy risk and performance metrics will be annualized to include time intervals when strategy had no market exposure at ce
22. www portfolioeffect com High Frequency Portfolio Analytics User Manual PortfolioEfftectHFT MATLAB Toolbox High Frequency Portfolio Backtesting amp Optimization Andrey Kostin andrey kostin portfolioeffect com Released Under BSD License by Snowfall Systems Inc August 20 2015 Contents 1 Toolbox Installation 1 1 Zip Archive All OS e rmx heh rec OE eee ee RA eS 1 2 Install Wizard Windows wwa waa Wa ee 2 Account Credentials 2 1 Locate API Credentials oco a sawana eo KA Wa aa ea a aiea aeaa a 2 2 Set API CredentialsimnmMATLAB boy Baie a 9e m Bone AAA EAS Oa aed OE ee n a WAA A wok GOR Kok Se ae MON Re Gre ln gg a NR ae E 3 1 2 Add Positions s users hk Seen mo moek aoe aii re Rep ewe WUE edd d le WA 2 23 Servet Datalls 5 bs isa GR A ka eae ee EE a ee A ee ee es c PP 3 2 2 Add Positions lt i 3 6 095 BAVA ae See OE X m eRe LE ee 78 E ORO d Db Sow A 4 Portfolio Settings 4 1 Porttolio Metrics wc muss 4 bee Bok Rok RM xoc XR Bem or moo X E Sexy WO ORO A OUR o 4 1 1 Portfolio Metrics Model 4 1 2 Holding Periods Only commons ok o Re Y a 4 1 3 Short Sales Mode i 44 o woe SE XO e a i RE TU RR SRI UE p ROT 42 Data Sampling Deci sa wok RE ves ea 3 dem 9o d de e m Ee WOO RT e kou dob eR ree ee 4 21 Results Sampling Interval 4 2 2 Input Sampling Interval
23. y ignored data spy importdata data spy mat 4 Create portfolio portfolio portfolio create priceDatalx data spy 3 1 2 Add Positions Positions are added using portfolio addPosition with priceData in the same format as index price data goog importdata data goog mat data aapl importdata data aapl mat 4 Single position without rebalancing portfolio addPosition portfolio G00G 100 priceData data goog 4 Single position with rebalancing portfolio_addPosition portfolio AAPL 300 150 time 1412266600000 1412276600000 priceData data_aap1 3 2 Server Data At PortfolioEffect we are capturing and storing 1 second intraday bar history for a all NASDAQ traded equites This server side dataset spans from January 2013 to the latest trading time minus five minutes It could be used to construct asset portfolios and compute intraday portfolio metrics 3 2 1 Create Portfolio Method portfolio_create creates new asset portfolio or overwrites an existing portfolio object with the same name When using server side data it only requires a time interval that would be treated as a default position holding period unless positions are added with rebalancing Index symbol could be specified as well with a default value of SPY SPDR S amp P 500 ETF Trust Interval boundaries are passed in the following format e yyyy MM dd HH MM SS e g 2014 10 01 09 30 00 e yyyy MM
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