Proposal On Financial Computing Algorithm And Analysis

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International Journal of Computer Applications (0975 – 8887)Volume 125 – No.7, September 2015Proposal on Financial Computing Algorithm andAnalysisRaghav SethiStudentDepartment Of Computer Science Engineering,MPSTME, NMIMS UniversityMumbai, IndiaABSTRACTAlgorithmic Finance is currently a nascent topic in today‟stime and is being researched around by various researchersaround the globe. It involves the use of computer programs tobuy and sell securities based on a pre-determined set of rules.Such algorithms have gained widespread use amonginstitutional investors in major markets around the world. Thispaper discusses the various aspects of a financial algorithmand its prospects in the future. Algorithmic finance, being arelatively new field has plenty of scope to grow. The scope ofthis field of study, which we are inclined towards, is todevelop software, which would help us understand the markettrends. Since it is a computer algorithm, it would save a lot oftime and effort, and also reduce the risk of our investment bya considerable margin. Algorithmic trading may be used inany investment strategy or trading strategy, including marketmaking, inter-market spreading, arbitrage, or pure speculation(including trend following). The investment decision andimplementation may be augmented at any stage withalgorithmic support or may operate completely automatically.Expert Advisors (trading robots) - the MQL5 IDE. It alsoincludes a new object-oriented language, an editor with adebugger and a multicurrency strategy tester. Meta Trader 5Trading Platform has an improved distributed architecture inwhich five servers balance the workload. This allows forscaling the platform and further enhancing its performance.Functionality, flexibility and security of the Meta Trader 5Trading Platform are the key features of this product.2.2 Algo TraderAlgo Trader is an all- in- one proprietary traders, quantitativehedge funds, and investment banks. It can trade Forex,futures, Options, Stocks & Commodities on any market. Itenables development, simulation and execution of multiplecomplex strategies in parallel. It is based on open sourcetechnologies like Hibernate, Spring, Esper, ActiveMQ,AndroMDA, Grails and QuickFIX/J.Algo Trader follows following steps to deliver results to thecustomers :1.Onsite and remote consultation and trainingGeneral Terms2.Automation and migration of existing strategiesTrading, Stocks, Algorithmic trading, Financial instruments,Investment Strategy, Trading Strategy3.Improving and optimizing existing strategiesKeywords4.Prototyping and back testing new strategiesFinancial instruments, Derivatives, Funds, Indices, Statisticalgraphs, small cap, mid cap, High frequency trading, algotrading5.Developing customized functionality1. INTRODUCTIONAlgorithmic finance, being a relatively new field has plenty ofscope to grow. This software can change the way peopletrade. The reason being that it assists the investor at everypossible step and also helps to reduce the risk by a goodmargin. This algorithm also helps the amateur investor toinvest smartly and without the extra hassle of tons ofbackground research. There are already pre existingalgorithms such as the Monte Carlo trading algorithm and thegenetic algorithm. These Algorithms have been reviewed inthe second half of this research paper. The second half of thisreview paper not only discusses the currently existingalgorithms, but also types of architectures proposed whichwould best suit the system. It also includes differentsoftwares, which can be used to implement a new algorithm.2. EXISTINGTECHNIQUES/SOFTWARES2.1 Meta-Trader 5Meta Trader 5 offers a more powerful tool for developers of2.2.1 Unique feature of Algo Trader2.2.1.1 Incremental Innovation:1.It is a cost friendly method2.Updates are made in small amountsFor example: Algo trader comes up with new type of formulasand algorithms so that the customer is shown a more accurateresult of the amount of profit the customer is going to gain.2.2.1.2 Radical Innovation:1.Radical Invention on the other hand explores newtechnology.2.It creates a new market rather than altering anexisting one to fit its needs3.It can be seen as a breakthrough innovation that isan innovation, which changes the market for acertain product completely.2.2.2 Evolution of Algo TraderAfter achieving great success with Microsoft, a renownedgenius software developer retired as a multi-millionaire, then36

International Journal of Computer Applications (0975 – 8887)Volume 125 – No.7, September 2015lost half of his retirement fortune in the stock market crash of2000. That is when he realized a computer program couldprofit from the stock market much faster and better than anyhuman ever could, including the most successful professionaltraders. So, in 2000, he came out of retirement and spent thenext 5 years writing over 5 million lines of code to create theworld's first and only fully automated system for individualinvestors to generate profits in the stock market every day3.Statistical CalculationAmount change in stock price the expected stock price theeffect of the volatility of people randomly buying and sellingthe stock over time has on that expected growth.2.3 Monte Carlo Stimulation MethodMC simulation is a technique that can help the customersestimate the risk and profitability of there trading strategyusing the ATS. It can help to decide if the strategy is robust,what profit / drawdown can be expected from the strategy andif this trade strategy should be used at all. The basis of MonteCarlo method is running the same simulation a number oftimes, each time with small random changes. By simplyreshuffling the trades, the final profit will stay the same, butdrawdown can change a lot. Instead of drawdown 10% itmight end with drawdown 30% just by changing the order ofthe trades.2.3.1 Processes Followed in Monte Carlo1.Flow Diagram3.1.1 Monte Caro vs. Algo TraderMonte Carlo Stimulation Method is better than Algo Trader.The answer lies in statistics, which are the basics of MonteCarlo. The customer can let a program run this reshuffling ahundred times and it will show what is the best, worst andaverage drawdown achieved during these random runs.3.2 Genetic Algorithm: Skipping TradesIn this test some trades will be randomly missed (with givenprobability). In real trading often a trade can be missedbecause of platform or Internet failure, or simply becausetrading is paused for some time.This test will give an idea how the equity curve might looklike if some trades are randomly skipped.1.2.Formula UsedFormula and Graph Used:2Performance StatisticsAfter performing a real time test on the the genetic algorithmfollowing statistics were generated37

International Journal of Computer Applications (0975 – 8887)Volume 125 – No.7, September 2015The software architecture, which we believe would best suitthe algorithm, which we want to implement, would be theCyan Spring Architecture. Cyan Spring ATS can be run witha distributed architecture. Multiple servers can join a cluster toshare the workload. Server can be a single server instance ormultiple ones to form a server cluster. Client (CSTW) canconnect to multiple servers in the same cluster and monitor allstrategies run in the same cluster.Cyan Spring ATS is built on top of the following softwarepackages.They are all free and solid open source software.1.1 Percentage Of Volume (POV) The PoV (Percentage of Volume) algorithmaddresses the problem of VWAP by using the actualtraded volume of the day as benchmark. The idea isto have a constant percentage participation in themarket along the trading period. Apache Active MQ(Can be replaced by any JMS) Quick Fix/J Spring Frame Work Eclipse RCP Xstream SLF4J Apache LOG4J Apache Derby DBIf the quantity that remains to be traded is Q, andthe participation ratio is , the algo algo computesthe volume V traded in the period (t- ΔT, t) andexecutes a quantityq min(Q,V*g )V (t) total volume traded in the market up to time tQ(t) number of shares that remain to be traded. (Q(0) initial quantity)2.2 Proposed StrategiesA trading strategy is composed of two parts Strategy data- these are parameters forstrategies Strategy logic- java codes that are built on topof the software (Cyan Spring ATS) framework.Single-order Strategies 2. PROPOSED SYSTEM2.1 ArchitectureA Single-order strategy, or single-orderstrategy, involves in trading only oneinstrument and one side transaction (buy orsell). It is usually created from an order, e.g.buy 0005.HK 200000 @ 68.2.The strategy is considered completed when the order is fullyfilled. The typical usage, though not limited to, is to avoidmarket impact while trading some large volume of orders.2.3 Strategy testingExecuting all transactions in the database during simulation is38

International Journal of Computer Applications (0975 – 8887)Volume 125 – No.7, September 2015useful for reporting but also incurs some additional processingtime during. For trivial strategies that do not need to performany sort of sophisticated querying based on transaction dataan additional in-process / in-memory exchange simulator isavailable that uses Hash Maps as the underlying storagemechanism.This will allow for significantly faster processing oftransactions during simulation.Formulae Used1.Simulator simulator new Simulator();2.Order order mulator.sendOrder(order);7.Position position gy, security);2.4.3 FeaturesDuring a simulation process the following steps are executedsequentially: The database is reset to its original state via theReset Service . Esper Engines are initialized for theAlgoTrader Server as well as all strategiesmarked as autoActivate. All Esper Modules defined in columninitModules and runModules of the tablestrategy are deployed . A Portfolio Rebalance is executed to distributethe initial CREDIT (of 1'000'000 USD) tostrategies according to their allocation. For every Strategy Service the methodinitSimulation is invoked to execute anyinitialization tasks if necessary . Pause order-the algo engine pauses the autoexecution of order Stop order-the algo engine stops the autoexecution of order Start order - the algo engine resume the autoexecution of order Cancel order - the order is canceled. Algoengine withdraws orders and terminates theauto execution Show or hide filter - show or hide a filter panelwhere you may specify a filtering criteria tonarrow down the parent orders shown in thisview Enter order - entering a new order for algoexecution.2.4.4 Statistical Analysis2.4 Software Building Phase Prototype –2.4.1 Technologies used JavaJDKSE1.6 Eclipse2.4.2 Exchange Simulator2.4.5 CANDLE STICK GRAPHThe proposed system uses the features of candle Stick graph.Candle Stick graph is explained below: In order to create a candlestick chart, a data set isrequired that contains open, high, low and closevalues for each time period that is to be displayed. The hollow or filled portion of the candlestick is39

International Journal of Computer Applications (0975 – 8887)Volume 125 – No.7, September 2015called “the body” (also referred to as “the realbody”). The long thin lines above and below thebody represent the high/low range and are called“shadows” (also referred to as “wicks” and “tails”). The high is marked by the top of the upper shadowand the low by the bottom of the lower shadow. If the stock closes higher than its opening price, ahollow candlestick is drawn with the bottom of thebody representing the opening price and the top ofthe body representing the closing price. If the stock closes lower than its opening price, afilled candlestick is drawn with the top of the bodyrepresenting the opening price and the bottom of thebody representing the closing price.Thus the potential of algorithmic finance has been reviewedand the scope, which it has in the future, has ben determined.The existing algorithms have been analyzed and the flawshave been found out alongside the ways to overcome thosefaults. The potential architectures for our proposed system hasbeen researched upon. The various simulators have also beenlooked into, which will help to implement our algorithm.4. FUTURE SCOPEAlgorithms are widely recognized as one of the fastestmoving bandwagons in the capital markets. Employing rulesbased strategies has enabled buy-side firms to increaseproductivity, lower commission costs and reduceimplementation shortfall. Algorithmic trading cuts downtransaction costs and allows investment managers to takecontrol of their own trading processes. By breaking largeorders into smaller chunks, buy-side institutions are able todis- guise their orders and participate in a stock‟s tradingvolume across an entire day or for a few hours. Moresophisticated algorithms allow buy-side firms to fine-tune thetrading parameters in terms of start time, end time, andaggressiveness. In today‟s hyper-competitive, cost-conscioustrading environment, being the first to innovate can give abroker a significant advantage over the competition both incapturing the order flow of early adopters and building areputation as a thought leader. In the future, a range ofimportant drivers of change, acting alone and in concert, willsubstantially influence the development and uptake ofcomputer-based trading.5. REFERENCES[1] Adaptive arrival place by Robert Almgren and JulianLorenz[2] Monte-Carlo Evaluation of trading systems by TimothyMastersCompared to traditional bar charts, many traders considercandlestick charts more visually appealing and easier tointerpret. Each candlestick provides an easy-to-decipherpicture of price action. Immediately a trader can compare therelationship between the open and close as well as the highand low. The relationship between the open and close isconsidered vital information and forms the essence ofcandlesticks. Hollow candlesticks, where the close is greaterthan the open, indicate buying pressure. Filled candlesticks,where the close is less than the open, indicate selling pressure. Below is the Candle Stick used in the proposedalgorithm for a time period of one hour[3] Algorithmic Trading and Computational Finance byMichael Kearns (Computer and information science,University of Pennsylvania)[4] Online Financial Algorithms by Sandeep Kumar andDeepak Telang (Thapar University, Patiala)[5] Daniel Safarik Algorithms a la Carte. Wall Street &Technology, January 30, 2006.[6] Nenad Yashruti Seeing Is Believing. Head Trader,Freestone Capital Management; Wall Street &Technology; November 21, 2006.[7]John Bates Algorithmic gymnastics – keeping at leastone vault ahead of the rest. Hedge Funds Review, June2006[8] Katherine Heires Algorithms and Clearing Wrapped Upin One Algorithmic Trading. Wall Street & Technology;May 25, 2006[9] Daniel Safarik The „Holy Grail‟: Pre-Trade Analytics.Wall Street & Technology March 01, 2005.[10] I. Domowitz, H. Yegerman, “Measuring and interpretingthe performance of broker algorithms” in ITG Inc.Research Report, August 2005.3. CONCLUSIONIJCATM : www.ijcaonline.org40

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