Manish final report


Published on

trading strategy

Published in: Business, Economy & Finance
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Manish final report

  2. 2. 2
  3. 3. CERTIFICATEThis is to certify that the project report on ―ART OF MAKINGMONEY…ALGORITHMIC TRADING‖ has been prepared out by MR.MANISH KUMAR KESHARI under my supervision and guidance. Theproject report is submitted towards the partial fulfillment of 2011-2012 year,full time Master of Business Administration.MR. RAHUL CHANDRADate: 11-JUNE-2012 3
  4. 4. ACKNOWLEDGEMENTI would like to take this opportunity to thanks all those who contribute to this projectwork and helped me at every step. I express my sincere thanks to Mr. Akash Singh,Noida-62, for his guidance during the course of my training which has helped me toenhance my knowledge in the internal working environment of a company. We wouldalso thank him for giving his valuable time and patience which has made this projectsuccessful.Last but not least, I would like to thank all my friends and faculty members and myinternal guide Mr. Rahul Chandra faculty school of business, Galgotias University,Greater Noida for their valuable suggestions and moral support. 4
  5. 5. MANISH KUMAR KESHARIDECLARATIONI, MANISH KUMAR KESHARI enrollment no 1103102069, student of MBA ofSchool of Business: Galgotias University, Greater Noida , hereby declarethat the project report on ―ART OF MAKING MONEY…ALGORITHMICTRADING‖ at GREATRER NOIDA‖ is an original and authenticated workdone by me. The project was of 45 days duration and was completedbetween 11-JUNE-2012 to 23-JULY-2012.I further declare that it has not been submitted elsewhere by any other personin any of the Institutes for the award of any degree or diploma.MANISH KUMAR KESHARIDate: - 11-JUNE-2012 5
  6. 6. CONTENTS1. Executive Summary 5Part-A 2. Introduction 9 3. Company Profile 10Part-B4 .Introduction of Topic 15 5. Research Methodlogy 916.Discussion/Description 947.Conclusion AndRecommendations 95 8. Bibliography 96 9. Annexure 97 6
  7. 7. EXECUTIVE SUMMARYAlgorithmic TradingAlgorithmic trading is automated trading, i.e. a computer system is completingall work from trading decision to execution. Algorithmic trading has becomepossible with the existence of fully electronic infrastructure in stock tradingsystems from market access, exchange and market data provision. Thefollowing gives an overview of chances and challenges of algorithmic tradingas well as an introduction of several components needed to set up acompetitive trading algorithm.Chances and challenges.There are several advantages in contrast from algorithmic trading to tradingby human beings. Computer systems have in general a much shorterreaction time and reach a very high level of reliability. The decisions reachedby a computer system rely on the underlying strategy with specified rules.This leads to reproducibility of the decisions. Thus, back-testing andimproving the strategy by variation of underlying rules is allowed. Algorithmictrading ensures objectivity in trading decisions and is not exposed tosubjective influences (such as panic, for example). When trading manydifferent securities at the same time, a computer system may substitute manyhuman traders. So the observation and trading securities of a large universebecome possible for companies without dozens of traders. Altogether theseeffects may result in better performance of the investment strategy as well asin lower trading costs. On the other hand, it is challenging to automatize thecomplete process from deriving investment decisions to execution because ofthe need of system stability. The algorithm has to be robust againstnumerous possible errors in services the system is dependent on, such asmarket data provision, connection to market and the exchange itself. Theseare technical issues which can be achieved by spending some effort in theimplementation. Even more complex is the development of an investmentstrategy, i. e. deriving trading decisions, and strategies to realize thesedecisions. This work is focused on the realization and thus the executionstrategy by assuming given investment decisions. It is beyond this work tointroduce in how to derive investment decisions. All necessary informationfor the input of the execution algorithm is assumed to be available. Inputvariables may be the security names, the number of shares, and the tradingdirection. But also assumed available are variables like aggressivity andconstraints, such as market neutrality when trading a portfolio. The mainchallenge for trading algorithms is the realization of low trading costs in 7
  8. 8. preferably all market environments independent from falling or rising marketsas well as high and low liquid securities. Another critical point which has tobe takeninto account is the transparency of the execution strategy for othermarket participants. If a structured execution strategy acts in repeatingprocesses, for example, orders are sent in periodical iterations; othermarket participants may then observe patterns in market data and may takean advantage of the situation.Components of automated trading system.A fully automated trading system is complex with regard to technicalrequirements, but the numerous different research issues which have to beconsidered lead to even more effort and potential for improvement. Anautomated stock trading algorithm has to take many aspects into accountwhich are addressed in this work. Reaching favourable trading costs,numerous cognitions of market microstructure theory have been incorporatedinto such a system. Strategies mentioned in 2. 2. are just simpleformalizations of market attributes. They are seen as an approximation of thestrategy leading to minimal execution costs, but by far do not take allmicrostructure aspects into account. Probably all currently existing systemsdo not contain much more than such an approximation. A suggestion for anautomated trading system can be constructed of three components as it isdenoted, pre-trade analysis component provides a previous estimate oftransaction costs of a given order. Therefore, an econometric model basedon historical trading data is used. The pre-trade analysis can be used tooptimize the expected transaction costs by varying the parameters or eventhe trading strategy. 8
  9. 9. INTRODUCTIONAlgorithmic trading is the act of making trades in a market, based purely oninstructions generated by quantitative algorithms. Each algorithm is assumedto have access to current and historical prices of instruments that can bebought and sold, and can perform any computations it wants based on theseprices. In many cases, an algorithm will be coded in some programminglanguage and will run as an application that places its own orders, but itdoesnt have to do this. For example, a person could put through tradesaccording to the prescription of an algorithm.Algorithmic trading is carried out by hedge funds and proprietary tradinggroups, but can also be performed by an individual with a trading accountwith a broker. All that is needed is a reasonably good computer, a broker (Iuse InteractiveBrokers, but there are many others you could use) and asource of historical data. (I also use Interactive Brokers for this, but they areprimarily a broker rather than a data provider, and you can find better sourcesof historical data, depending on your budget and requirements. ) If you wantto automate your algorithmic trading, that is, make your computer placeorders for you, then you will also need good programming skills and anapplication programming interface (API) from your broker. The API typicallyincludes libraries and documentation that allow you to connect your ownprogram directly to the broker to automate order-placement, retrieve historicaldata, etc.Algorithmic trading is very different from the act of placing trades based on (a)a personal belief that something is over/under-priced, (b) gut-feelingpredictions, (c) a compulsive desire to gamble. Most novice traders beginusing one or more of these styles, and lose substantial sums of money beforestopping. I will refer to trades based on (a), (b) or (c) as discretionarytrades. Some people do have the ability to make money using gut-instinctsto place trades, but these people have normally spent a lot of time tradingand studying the market. Its a very dangerous way to start out a tradingcareer. 9
  10. 10. COMPANY PROFILEHistoryIn 2008 a special quantitative analytic division was created within AppinTechnologies to cater to specialized projects which required advancedalgorithms, data mining and artificial intelligence. This group conducted in-depth research and developed proprietary techniques to analyze data. Thegroup had many projects related to financial time series and quantitativetrading.In 2009 Appin technologies decided to create a spin off called ―ProphecisConsulting and Analytics Pvt ltd‖ with a mandate to create products andservices for financial institutions in capital markets segment. The companymanaged outsourcing contracts for hedge funds in Europe.In 2010 Prophecis generated many proprietary algorithms and techniques totrade on financial markets. In one year the spinoff generated close to 200different robust trading systems. A large Indian conglomerate invited thecompany to manage part of its portfolio with certain guaranteed riskparameters. Till date, Prophecis has maintained the downside risk as per theguidelines while beating similar benchmarks.In 2011, Prophecis started developing an advance1d trading platform whichcould handle the exceptionally advanced and complex algorithms which wereprevalent in quantitative trading domain. The first release was made inMarch.CompanyProphecis is an analytics and consulting firm that provides analytics andadvisory services to proprietary trading houses, banks, hedge funds andfinancial institutions in India, US and Europe. The firm is expert in datamining, machine learning and quantitative analysis. The firm was foundedby IIT, ISB and imperial college alumnus. Our human capital hasamalgamated experience from different sections of financial markets. 10
  11. 11. Prophecis stands for prudence in converging analytical principles withtechnology. We strive to apply sound financial principles using cutting edgein computational technology. Our immense experience with advanced datamining and machine learning coupled with high end computing infrastructuregives us the edge in implementation of analytical solutions. We undertakeresearch in financial markets while keeping abreast with the latestintechnology, hence capable of making previously impractical solutionspossibleServicesAssets ManagementAsset Management offers a range of investment products and services acrossthe risk return spectrum to investors. We emphasize on client requirementswhile designing products which offer the best opportunity for asset growth andwealth enhancement. Our investment products comprises of wide variety ofalgorithmic trading systems. Trading system is a set of specific rules thatdetermine entry and exit points for a set of tradable instruments. These aremore easily implemented by computers because machines can react morerapidly to temporary mispricing and examine prices from several marketssimultaneously. Our mission is to ensure our clients receive the superiorperformance through market cycles by virtue of our deep understanding of theequities markets and our analytical approach to risks and return.AnalyticsThe objective of the Diversification program is to attain maximum returns withdefined risk limitations. To meet these targets, we employs a portfolio ofobjective, technically-based trading systems and a multidimensionaldiversified strategy which allocates capital to different markets, tradingstrategies, and time frames.The selection of component strategies, timeframes and markets follows a rigorous quantitative analysis that considers theliquidity and volatility of markets traded, types of strategies employed, tradeduration, risk of loss, and probability of achieving performance objectives.These factors, along with measures of correlation between the systemcomponents, attempt to ensure synergy at the portfolio level while limiting riskby maintaining diversification across multiple dimensions.The resulting multi-dimensional approach gives us the ability to profit (orsuffer losses) in virtually any environment, be it rising or falling markets,quick or long term moves, or trending versus oscillating markets.We have thoroughly analysed different tradable instruments using statisticaland Analytical data mining tools. This leads to discovery of various hiddenpatterns and various indicators from the historic data that have probablepredictive capability in investment decision. 11
  12. 12. Our market diversification is achieved by trading positions across a widerange of global markets and market groups. These include various stockmarket indices (US large cap, small cap, etc. ), energy futures (crude oil,gas), industrial and precious metals (gold, silver), and various agriculturalproducts (grains, meats and "soft" commodities such as coffee, sugar, etc.).Limitations are placed on each market group, or sector, so that no onesector can risk more than a certain percentage of the entire portfolio.ProductsAlphaBOXAlgorithms have become such a common feature in the trading landscape thatit is unthinkable for a broker not to offer them because that is what clientsdemand. These mathematical models analyze every quote and trade in thestock market, identify liquidity opportunities, and turn the information intointelligent trading decisions. Algorithmic trading, or computer-directedtrading, cuts down transaction costs, and allows investment managers totake control of their own trading processes. It is a style of trading. No matterwhich markets you trade or whether you enter your trades automatically ormanually, AlphaBox can help you execute your trades quickly, accurately andefficiently.Automated Order Entry: - With fully automated trading, AlphaBOXmonitors the markets for you based on your own custom buy and sell rulesand executes your trades faster and more efficiently than humanly possible.Using the speed of direct-access execution, AlphaBOX automatically sendsyour stock, futures orders to the major exchange or ECN youve chosen inyour strategy.AlphaBOX tracks all your strategies‘ open positions in real time andcontinuously monitors the markets based on your trading rules, ensuring thatyou dont miss your exit point, no matter how simple or complex your exitcriteria. You can automate virtually any trading strategy imaginable,including multiple conditional entries and exits, profit targets, protectivestops, trailing stops, partial fills and more.Manual Order Entry: - In addition to its unique automated trading features,AlphaBOX also offers multiple advanced order-entry tools for when youchoose to enter your stock trades manually: 1. Order Bar 2. Trade from Chart AlphaBOX 12
  13. 13. DataRIVER QuoteCANVAS AlgoWRITER AlgoANALYTICS TradeBOT TradeSERVOSolutionIndividualNo single technique of trading works forever and best traders know when toswitch between different trading styles. Our software supports you if you area Scalper/Jobber,Arbitrager, Positional/ Swing Trader, Intraday Trader or amixture of all. You can write your own strategies and see how they wouldhave performed in the past with complete statistical analysis.Traders can alsoavail of our pre-defined adaptable trading models which have been rigorouslytested;we have more than 500 such adjustable systems to choose from.Wealso provide courseware which allows traders to keep up with the latestmethods and techniques in the market and new traders to get started. If youare a new trader, you can go for our starter kit which includes all you need totrade accurately.Small Medium BusinessWe offer a wide variety of products and services to suit the needs of a tradingand broker desk. Starting from, trading strategies, to the execution andmanagement of positions, our solutions make sure that your operations areexecuted with maximum efficiency. We offer brokers a state of art tradingplatform which can be given to the end customer to enhance ease of tradeand streamline all processes. Brokers can also use the platform as achannel to sell products and services to their clients. Our online marketplaceallows clients to buy subscriptions to trading strategies. We also offerlicensing of strategies from us which you can sell to end consumers.Our software development is expert in creating online trading websites andlow latency market data adapters. We help new or small brokers establishtheir IT setup. We also offer complete end-to-end management of tradinginfrastructure. We have specific knowledge in high speed servers andprovide co-location services to trading desks. We also undertake customsoftware development projects at very competitive rates. 13
  14. 14. IndividualWe have a strong data mining and analytics capability which we leveraged toapplications in financial markets. During our research we have developedmany proprietary algorithms to mine data and detect anomalies and trends indata. Our statistical analysis process is exhaustive and is adaptable to awide variety of purposes.Right from Monte Carlo simulations to quantitative trading models, we havethe capability to deliver a diverse spectrum of analytics products and services.Our suite of analysis tools let you do highly complicated event based studiesand backrests. Our portfolio design and simulation tools provide managerswith accurate analytics to make prudent decisions. We also manage fundsand assets of institutional clients with end-to-end portfolio and riskmanagement. Our history shows our commitment towards downside riskmanagement. 14
  15. 15. INTRODUCTION OF TOPICTRADINGTrade is the transfer of ownership of goods and services from one person orentity to another by getting something in exchange from the buyer. Trade issometimes loosely called commerce or financial transaction or barter. Anetwork that allows trade is called a market. The original form of trade wasbarter, the direct exchange of goods and services. Later one side of thebarter were the metals, precious metals (poles, coins), bill, and papermoney. Modern traders instead generally negotiate through a medium ofexchange, such as money. As a result, buying can be separated fromselling, or earning. The invention of money (and later credit, paper moneyand non-physical money) greatly simplified and promoted trade. Tradebetween two traders is called bilateral trade, while trade between more thantwo traders is called multilateral trade.Trade exists for man due to specialization and division of labor, most peopleconcentrate on a small aspect of production, trading for other products.Trade exists between regions because different regions have a comparativeadvantage in the production of some tradable commodity, or becausedifferent regions size allows for the benefits of mass production. As such,trade at market prices between locations benefits both locations.Retail trade consists of the sale of goods or merchandise from a very fixedlocation, such as a department store, boutique or kiosk, or by mail, in smallor individual lots for direct consumption by the purchaser. Wholesale trade isdefined as the sale of goods or merchandise to retailers, to industrial,commercial, institutional, or other professional business users, or to otherwholesalers and related subordinated services. [PrehistoryTrade originated with the start of communication in prehistoric times. Tradingwas the main facility of prehistoric people, who bartered goods and servicesfrom each other before the innovation of the modern day currency. PeterWatson dates the history of long-distance commerce from circa 150, 000years ago. In the Mediterranean region the earliest contact between cultureswere of members of the species Homo sapiens principally using the Danuberiver, at a time beginning 35-30, 000 BC. 15
  16. 16. Day TradingDay trading refers to the practice of speculation in securities, specificallybuying and selling financial instruments within the same trading day, suchthat all positions are usually closed before the market close for the tradingday. Traders who participate in day trading are called active traders or daytraders. Traders, who trade in this capacity with the motive of profit, assumethe capital markets role of speculator. Not widely known, the correctdefinition of an "intra-day" means the move as measured from the previousclose and not just relative to another price traded on the same day. Some ofthe more commonly day-traded financial instruments are stocks, stockoptions, currencies, and a host of futures contracts such as equity indexfutures, interest rate futures, and commodity futures.Day trading used to be an activity exclusive to financial firms and professionalspeculators. Indeed, many day traders are bank or investment firmemployees working as specialists in equity investment and fund management.However, with the advent of electronic trading and margin trading, daytrading has become increasingly popular among at-home traders.CharacteristicsTrade frequencyAlthough collectively called day trading, there are many styles with specificqualities and risks. Scalping is an intra-day speculation technique thatusually has the trader holding a position for a few minutes or even seconds.Shaving is a method which allows the scalping speculator to jump ahead by atenth of a cent, and a full round trip (a buy and a sell order) is oftencompleted in less than one second. Instead of bidding $10.20 per share, thescalper will jump the bid at $10. 201, thus becoming the best bid andtherefore the first in line to be able to purchase the stock. When the best"Offer" is $10.21, the shaver will again jump first in line and sell a tenth of acent cheaper at $10. 209 for a profit of 0.008 of a dollar. The profits add upwhen using 10, 000 share lots each time and the combined earnings fromRebates (read below) for creating liquidity. A day trader is actively searchingfor potential trading setups (that is, any stock or other financial instrumentsthat, in the judgment of the day trader, is in a tension state, ready toaccelerate in price in either direction, that when traded well has a potential fora substantial profit). The number of trades one can make per day is almostunlimited, as are the profits and losses. 16
  17. 17. The price of financial instruments can vary greatly within the same trading day(screen capture from Google Finance).Some day traders focus on very short-term trading within the trading day, inwhich a trade may last just a few minutes. Day traders may buy and sellmany times in a trading day and may receive trading fee discounts from theirbroker for this trading volume. Some daytrader‘s focus only on pricemomentum, others on technical patterns, and still others on an unlimitednumber of strategies they feel can be profitable. Most day traders exitpositions before the market closes to avoid unmanageable risks—negativeprice gaps (differences between the previous days close and the next daysopen bull price) at the open—overnight price movements against the positionheld. Other traders believe they should let the profits run, so it isacceptable to stay with a position after the market closes. Day traderssometimes borrow money to trade. This is called margin trading. Sincemargin interests are typically only charged on overnight balances, the traderpays no fees for the margin benefit, though still running the risk of a Margincall. The margin interest rate is usually based on the Brokers call.Profit and risksBecause of the nature of financial leverage and the rapid returns that arepossible, day trading can be either extremely profitable or extremelyunprofitable, and high-risk profile traders can generate either hugepercentage returns or huge percentage losses. Because of the high profits(and losses) that day trading makes possible, these traders are sometimesportrayed as "bandits" or "gamblers" by other investors. Some individuals,however, make a consistent living from day trading.Nevertheless day trading can be very risky, especially if any of the followingis present while trading: 17
  18. 18. trading a losers game/system rather than a game thats at least winnable, trading with poor discipline (ignoring your own day trading strategy, tactics, rules), inadequate risk capital with the accompanying excess stress of having to "survive", Incompetent money management (I. E. executing trades poorly).The common use of buying on margin (using borrowed funds) amplifies gainsand losses, such that substantial losses or gains can occur in a very shortperiod of time. In addition, brokers usually allow bigger margins for daytraders. Where overnight margins required to hold a stock position arenormally 50% of the stocks value, many brokers allow pattern day traderaccounts to use levels as low as 25% for intraday purchases. This means aday trader with the legal minimum $25, 000 in his or her account can buy$100, 000 (4x leverage) worth of stock during the day, as long as half ofthose positions are exited before the market close. Because of the high riskof margin use, and of other day trading practices, a day trader will often haveto exit a losing position very quickly, in order to prevent a greater,unacceptable loss, or even a disastrous loss, much larger than his or heroriginal investment, or even larger than his or her total assets.Historystocks were traded on the New York Stock Exchange. A trader wouldcontact a stockbroker, who would relay the order to a specialist on the floor ofthe NYSE. These specialists would each make markets in only a handful ofstocks. The specialist would match the purchaser with another brokersseller; write up physical tickets that, once processed, would effectivelytransfer the stock; and relay the information back to both brokers.Brokerage commissions were fixed at 1% of the amount of the trade, i. E. topurchase $10, 000 worth of stock cost the buyer $100 in commissions.(Meaning that to profit trades had to make over 1.010101. . . % to make anyreal gain.)One of the first steps to make day trading of shares potentiallyprofitable was the change in the commission scheme. In 1975, the UnitedStates Securities and Exchange Commission (SEC) made fixed commissionrates illegal, giving rise to discount brokers offering much reducedcommission rates. 18
  19. 19. Financial settlementFinancial settlement periods used to be much longer: Before the early1990s at the London Stock Exchange, for example, stock could be paid forup to 10 working days after it was bought, allowing traders to buy (or sell)shares at the beginning of a settlement period only to sell (or buy) thembefore the end of the period hoping for a rise in price. This activity wasidentical to modern day trading, but for the longer duration of the settlementperiod. But today, to reduce market risk, the settlement period is typicallythree working days. Reducing the settlement period reduces the likelihood ofdefault, but was impossible before the advent of electronic ownershiptransfer.Electronic communication networksThe systems by which stocks are traded have also evolved, the second halfof the twentieth century having seen the advent of electronic communicationnetworks (ECNs). These are essentially large proprietary computer networkson which brokers could list a certain amount of securities to sell at a certainprice (the asking price or "ask") or offer to buy a certain amount of securitiesat a certain price (the "bid"). ECNs and exchanges are usually known totraders by three- or four-letter designators, which identify the ECN orexchange on Level II stock screens. The first of these was Instinet (or "inet"),which was founded in 1969 as a way for major institutions to bypass theincreasingly cumbersome and expensive NYSE, also allowing them to tradeduring hours when the exchanges were closed. Early ECNs such as Instinetwere very unfriendly to small investors, because they tended to give largeinstitutions better prices than were available to the public. This resulted in afragmented and sometimes illiquid market.The next important step in facilitating day trading was the founding in 1971 ofNASDAQ—a virtual stock exchange on which orders were transmittedelectronically. Moving from paper share certificates and written shareregisters to "dematerialized" shares, computerized trading and registrationrequired not only extensive changes to legislation but also the development ofthe necessary technology: online and real time systems rather than batch;electronic communications rather than the postal service, telex or thephysical shipment of computer tapes, and the development of securecryptographic algorithms.These developments heralded the appearance of "market makers": theNASDAQ equivalent of a NYSE specialist. A market maker has an inventory 19
  20. 20. of stocks to buy and sell, and simultaneously offers to buy and sell the samestock. Obviously, it will offer to sell stock at a higher price than the price atwhich it offers to buy. This difference is known as the "spread". The marketmaker is indifferent as to whether the stock goes up or down;it simply tries toconstantly buy for less than it sells. A persistent trend in one direction willresult in a loss for the market maker, but the strategy is overall positive(otherwise they would exit the business). Today there are about 500 firmswho participate as market-makers on ECNs, each generally making a marketin four to forty different stocks. Without any legal obligations, market-makerswere free to offer smaller spreads on ECNs than on the NASDAQ. A smallinvestor might have to pay a $0. 25 spread (e. g. he might have to pay $10.50 to buy a share of stock but could only get $10. 25 for selling it), while aninstitution would only pay a $0.05 spread (buying at $10. 40 and selling at$10.35).Technology bubble (1997–2000)In 1997, the SEC adopted "Order Handling Rules" which required market-makers to publish their best bid and ask on the NASDAQ. Another reformmade during this period was the "Small Order Execution System", or "SOES",which required market makers to buy or sell, immediately, small orders (upto 1000 shares) at the market-makers listed bid or ask. A defect in thesystem gave rise to arbitrage by a small group of traders known as the "SOESbandits", who made fortunes buying and selling small orders to marketmakers.The existing ECNs began to offer their services to small investors. Newbrokerage firms which specialized in serving online traders who wanted totrade on the ECNs emerged. New ECNs also arose, most importantlyArchipelago ("arca") and Island ("isld"). Archipelago eventually became astock exchange and in 2005 was purchased by the NYSE. (At this time, theNYSE has proposed merging Archipelago with itself, although someresistance has arisen from NYSE members. ) Commissions plummeted. Togive an extreme example (trading 1000 shares of Google), an online trader in2005 might have bought $300, 000 of stock at a commission of about $10,compared to the $3, 000 commission the trader would have paid in 1974.Moreover, the trader was able in 2005 to buy the stock almost instantly andgot it at a cheaper price.ECNs are in constant flux. New ones are formed, while existing ones arebought or merged. As of the end of 2006, the most important ECNs to theindividual trader were: Instinet (which bought Island in 2002), Archipelago (although technically it is now an exchange rather than an ECN), 20
  21. 21. the Brass Utility ("brut"), and theSuperDot electronic system now used by the NYSE.The evolution of average NASDAQ share prices between 1994 and 2004This combination of factors has made day trading in stocks and stockderivatives (such as ETFs) possible. The low commission rates allow anindividual or small firm to make a large number of trades during a single day.The liquidity and small spreads provided by ECNs allow an individual to makenear-instantaneous trades and to get favorable pricing. High-volume issuessuch as Intel or Microsoft generally have a spread of only $0. 01, so theprice only needs to move a few pennies for the trader to cover his commissioncosts and show a profit.The ability for individuals to day trade coincided with the extreme bull marketin technological issues from 1997 to early 2000, known as the Dot-combubble. From 1997 to 2000, the NASDAQ rose from 1200 to 5000. Manynaive investors with little market experience made huge profits buying thesestocks in the morning and selling them in the afternoon, at 400% marginrates.Adding to the day-trading frenzy were the enormous profits made by the"SOES bandits" who, unlike the new day traders, were highly-experiencedprofessional traders able to exploit the arbitrage opportunity created bySOES.In March, 2000, this bubble burst, and a large number of less-experiencedday traders began to lose money as fast, or faster, than they had madeduring the buying frenzy. The NASDAQ crashed from 5000 back to 1200;many of the less-experienced traders went broke, although obviously it waspossible to have made a fortune during that time by shorting or playing onvolatility.TechniquesThe following are several basic strategies by which day traders attempt tomake profits. Besides these, some day traders also use contrarian (reverse)strategies (more commonly seen in algorithmic trading) to trade specificallyagainst irrational behavior from day traders using these approaches. 21
  22. 22. Some of these approaches require shorting stocks instead of buying them:the trader borrows stock from his broker and sells the borrowed stock, hopingthat the price will fall and he will be able to purchase the shares at a lowerprice. There are several technical problems with short sales—the broker maynot have shares to lend in a specific issue, some short sales can only bemade if the stock price or bid has just risen (known as an "uptick"), and thebroker can call for the return of its shares at any time. Some of theserestrictions (in particular the uptick rule) dont apply to trades of stocks that areactually shares of an exchange-traded fund (ETF).The Securities and Exchange Commission removed the uptick requirementfor short sales on July 6, 2007.Trend followingTrend following, a strategy used in all trading time-frames, assumes thatfinancial instruments which have been rising steadily will continue to rise, andvice versa with falling. The trend follower buys an instrument which has beenrising, or short sells a falling one, in the expectation that the trend willcontinue.Contrarian investingContrarian investing is a market timing strategy used in all trading time-frames. It assumes that financial instruments which have been rising steadilywill reverse and start to fall, and vice versa with falling. The contrarian traderbuys an instrument which has been falling or short-sells a rising one, in theexpectation that the trend will change.Range tradingRange trading, or range-bound trading, is a trading style in which stocks arewatched that have either been rising off a support price or falling off aresistance price. That is, every time the stock hits a high, it falls back to thelow, and vice versa. Such a stock is said to be "trading in a range", which isthe opposite of trending. The range trader therefore buys the stock at or nearthe low price, and sells (and possibly short sells) at the high. A relatedapproach to range trading is looking for moves outside of an establishedrange, called a breakout (price moves up) or a breakdown (price movesdown), and assume that once the range has been broken prices will continuein that direction for some time.ScalpingScalping was originally referred to as spread trading. Scalping is a tradingstyle where small price gaps created by the bid-ask spread is exploited by thespeculator. It normally involves establishing and liquidating a positionquickly, usually within minutes or even seconds. 22
  23. 23. Scalping highly liquid instruments for off-the-floor day traders involves takingquick profits while minimizing risk (loss exposure). It applies technicalanalysis concepts such as over/under-bought, support and resistance zonesas well as trendline, trading channel to enter the market at key points andtake quick profits from small moves. The basic idea of scalping is to exploitthe inefficiency of the market when volatility increases and the trading rangeexpands.Rebate tradingRebate trading is an equity trading style that uses ECN rebates as a primarysource of profit and revenue. Most ECNs charge commissions to customerswho want to have their orders filled immediately at the best prices available,but the ECNs pay commissions to buyers or sellers who "add liquidity" byplacing limit orders that create "market-making" in a security. Rebate tradersseek to make money from these rebates and will usually maximize theirreturns by trading low priced, high volume stocks. This enables them totrade more shares and contribute more liquidity with a set amount of capital,while limiting the risk that they will not be able to exit a position in the stock.Rebate trading was pioneered at Datek Online and Domestic Securities.Omar Amanat founded Tradescape and the rebate trading group atTradescape helped to contribute to a $280 million buyout from online tradinggiant E*Trade.News playingNews playing is primarily the realm of the day trader. The basic strategy is tobuy a stock which has just announced good news, or short sell on bad news.Such events provide enormous volatility in a stock and therefore the greatestchance for quick profits (or losses). Determining whether news is "good" or"bad" must be determined by the price action of the stock, because themarket reaction may not match the tone of the news itself. The mostcommon cause for this is when rumors or estimates of the event (like thoseissued by market and industry analysts) were already circulated before theofficial release, and prices have already moved in anticipation—the news isalready priced in the stock.Price actionKeeping things simple can also be an effective methodology when it comes totrading. There are groups of traders known as price action traders who are aform of technical traders that rely on technical analysis but do not rely onconventional indicators to point them in the direction of a trade or not. Thesetraders rely on a combination of price movement, chart patterns, volume,and other raw market data to gauge whether or not they should take a trade.This is seen as a "simplistic" and "minimalist" approach to trading but is not byany means easier than any other trading methodology. It requires a soundbackground in understanding how markets work and the core principles within 23
  24. 24. a market, but the good thing about this type of methodology is it will work invirtually any market that exists (stocks, foreign exchange, futures, gold, oil,etc. ).Artificial intelligenceAn estimated one third of stock trades in 2005 in United States weregenerated by automatic algorithms, or high-frequency trading. Theincreased use of algorithms and quantitative techniques has led to morecompetition and smaller profits.Trading equipmentSome day trading strategies (including scalping and arbitrage) requirerelatively sophisticated trading systems and software. This software can cost$45, 000 or more. Since the masses have now entered the day tradingspace, strategies can now be found for as little as $5, 000. Many daytraders use multiple monitors or even multiple computers to execute theirorders. Some use real time filtering software which is programmed to sendstock symbols to a screen which meet specific criteria during the day, suchas displaying stocks that are turning from positive to negative. Some tradersuse community based tools including forums, message boards and chatrooms.BrokerageDay traders do not use discount brokers because they are slower to executetrades, trade against order flow, and charge higher commissions than directaccess brokers, who allow the trader to send their orders directly to theECNs. Direct access trading offers substantial improvements in transactionspeed and will usually result in better trade execution prices (reducing thecosts of trading). Outside the US, day traders will often use CFD or financialspread betting brokers for the same reasons.CommissionCommissions for direct-access brokers are calculated based on volume. Themore shares traded, the cheaper the commission. The average commissionper trade is roughly $5 per round trip (getting in and out of a position). Whilea retail broker might charge $7 or more per trade regardless of the trade size,a typical direct-access broker may charge anywhere from $0. 01 to $0.0002per share traded (from $10 down to $. 20 per 1000 shares), or $0.25 perfutures contract. A scalper can cover such costs with even a minimal gain.As for the calculation method, some use pro-rata to calculate commissionsand charges, where each tier of volumes charges different commissions.Other brokers use a flat rate, where all commissions and charges are basedon which volume threshold one reaches. 24
  25. 25. SpreadThe numerical difference between the bid and ask prices is referred to as thebid-ask spread. Most worldwide markets operate on a bid-ask-based system.The ask prices are immediate execution (market) prices for quick buyers(ask takers) while bid prices are for quick sellers (bid takers). If a trade isexecuted at quoted prices, closing the trade immediately without queuingwould not cause a loss because the bid price is always less than the ask priceat any point in time.The bid-ask spread is two sides of the same coin. The spread can be viewedas trading bonuses or costs according to different parties and differentstrategies. On one hand, traders who do NOT wish to queue their order,instead paying the market price, pay the spreads (costs). On the other hand,traders who wish to queue and wait for execution receive the spreads(bonuses). Some day trading strategies attempt to capture the spread asadditional, or even the only, profits for successful trades.Market dataMarket data is necessary for day traders, rather than using the delayed (byanything from 10 to 60 minutes, per exchange rules) market data that isavailable for free. A real-time data feed requires paying fees to therespective stock exchanges, usually combined with the brokers charges;these fees are usually very low compared to the other costs of trading. Thefees may be waived for promotional purposes or for customers meeting aminimum monthly volume of trades. Even a moderately active day trader canexpect to meet these requirements, making the basic data feed essentially"free".In addition to the raw market data, some traders purchase more advanceddata feeds that include historical data and features such as scanning largenumbers of stocks in the live market for unusual activity. Complicatedanalysis and charting software are other popular additions. These types ofsystems can cost from tens to hundreds of dollars per month to access.Candlestick chartsCandlestick charts are used by traders using technical analysis to determinechart patterns. Once a pattern is recognized in the chart, traders use theinformation to take a position. Some traders consider this method to be apart of price action trading.Regulations and restrictions 25
  26. 26. Day trading is considered a risky trading style, and regulations requirebrokerage firms to ask whether the clients understand the risks of day tradingand whether they have prior trading experience before entering the market.WHAT IS INTRA-DAY TRADING?Intraday TradingIntraday Trading, also known as Day Trading, is the system where you takea position on a stock and release that position before the end of that daystrading session. Thereby making a profit for yourself in that buy-sell or sell-buy exercise. All in one day.You are not concerned about whether the market is going down or up. Youare not concerned with market sentiments. You are not concerned with thefundamental strengths (or the lack of it) of any company. All you need topredict is that the stock price will either rise or fall very sharply in the course ofthe day.When you take up day trading, the rules that may have helped you pick goodstocks or find great money makers over the years, trading normally, will nolonger apply. This is a different game with different rules.All of the methods that are used to identify stocks that are appropriate fornormal delivery-based trading are dependent on either technical analysis,fundamentals or insider information. Technical analysis with charts is a wayof using historical price/volume patterns to predict future behavior.Fundamentals deal with the market strength of a company, involving detailedstudy of balance sheets, branding, positioning, etc.None of these, on its own, hold good for day trading. The day traders choiceof scrips and positions has to work out in a day. Theres no waiting untiltomorrow to see how the charts play out before committing capital. If the daytrader sees an opportunity, he has to go for it. NOW. Or its gone. Thingscan change drastically in minutes. When its time to buy or sell, its time tobuy or sell, and thats all there is to it.Day trading can be a great way to make money all on your own. Its also agreat way to lose a ton of money, all on your own.Not everyone can be a day trader, nor should everyone try it. If the idea ofbeing in charge of your own business and your own trading account isexciting, then day trading might be a good career option for you. 26
  27. 27. FundamentalsWhat are the objectives of the intraday trader? One point objective: to makeprofits. As much as possible.Simple. Whether the market is going up ordown, we are not concerned. Whether there is a recession or not, we dontcare. We want our daily profits. Simple. But to realise this simple objectivewe have to undertake one very difficult step. That is: Pick out a few stocks that can possibly give good profits through Intraday Trading. It is not physically possible to track in real-time all of the 1000+ scrips listed at NSE every day to see which is going up or down sharply. So we need to make a few educated guesses and narrow down our watch-list to 5-to-7 stocks that show promise for the day. The process of finding these stocks is not easy. Because none of the normal methods used in locating stocks for investment work here. Statements like "ABC has gained by 25 points today" is good news to many players in the stock market. But it has no meaning in intraday trading if ABC has opened 24 points higher than yesterdays close and has then risen by only 1 point throughout the day. On the other hand, if ABC has opened at +1, gone down to -5 and then rallied to close at +25, it will be the toast of intraday traders for that day. You can make your profits only if ABC was spotted in advance and entry/exit points were proper. It is here that IntradayTrade dot Net can help, by identifying potential winners in advance. In another scenario, company GHF is in the red as it has lost 50 points. People who have bought shares of GHF have lost out. However, if in this journey of -50, it has gone down to -80 then recovered to +5, finally ending at -50, intraday traders have had a field day. In all the daily reports and comments given by experts GHF will be shunned as a loser and the public will be strongly advised to stay away from GHF. But to intraday traders, its a winner. How do you lay your hands on the likes of ABC and GHF before all this happens? We at IntradayTrade dot Net specialise in giving you the names of such stocks in our daily Suggests. Check our past performance. Same happens when the NIFTY falls. If the NIFTY is rallying strong and moving up fast, all major stocks are also rising. Finding stocks in this situation for intraday trading in LONG is not difficult, as everything is rising. But when the NIFTY is going down, all are going down with it. Finding that exception which has gone up even on those days, or has shown enough up-down range to give intraday profits in LONG, is the real challenge. 27
  28. 28. IntradayTrade dot Net has won these challenges many times and have Suggested stocks that have given profits of at least 1-to-2% even on such bad days in LONG.You can trust IntradayTrade dot Net to overcome this one fundamental task offinding which stocks to track to realise maximum profits through intradaytrading. Irrespective of market conditions.How to go about it?Like any stock trader, to make money through intraday trading at the stockmarket you must have a trading plan, set limits and stick to them. You musttrade based on the data on the screen — not based on emotions like hope,fear, doubt and greed.To put that plan in action you need do some preparation and define anobjective. Thats a basic strategy for any endeavor, whether its running amarathon, changing your car, or taking up day trading.Day traders have to move quickly, so they also have to take decisionsquickly. You must also have patience. Some days there is nothing good tobuy. Other days it seems like every trade can bring you money. Buteverything just turns around as soon as you really put in some money. Bepatient, and take a calculated decision.What if its a bad decision? Well, of course some decisions are going to bebad. Thats the risk of making any kind of an investment, and without risk,there is no return. Anyone playing around in the markets has to accept that.Yes, a lot of day traders lose money, and some lose everything that theystart out with. Many others dont lose all of their trading capital, but theyleave because they just decide that there are better uses of their time andbetter ways to make money.Yes, most day traders fail — about 80 percent in the first year. But so do alarge percentage of people who start new businesses or enter otheroccupations.But two good day trading practices help limit the effects of making a baddecision: 1. The first is the use of stop and limit orders, which automatically close out losing positions. 2. The second is closing out all positions at the end of every day, which lets traders start fresh the next day. 28
  29. 29. Because they close out their positions in the stocks they own at the end of theday, whether winning or losing, some of the risks are limited. There is nohangover. Each day is a new day, and nothing can happen overnight todisturb an existing profit position.Day Trading as a hobby?Day Trading as a hobby is a bad idea. Also, trading without a plan andwithout committing the time and energy to do it right will surely bring losses.Professional traders are betting that there will be plenty of suckers out there,because that creates the losers that allow you to take profits in a zero-summarket.Day Trading part-time?Can you make money day trading part-time? Yes, you can, and some peopledo. To do this, they approach trading as a part-time job, not as a little gameto play when they have nothing else to do. A part-time trader may commit totrading three days a week, or to closing out at noon instead of at the close ofthe market. A successful part-time trader still has a business plan, still setslimits, and still acts like any professional trader would, just for a smaller partof the day or week.TRADING GUIDELINESRemember: You only make money if someone else loses it. If you are notfully committed — you will lose money, and someone else will take it away!Trading is a serious business. You will need (1) a good trading method and(2) good money management policies. You will also need four importantweapons: Confidence, Discipline, Focus and Patience. We will explainthese requirements in detail.Objectives 29
  30. 30. But, before that, lets get some basics right. As an intraday trader, what areyour objectives for the day? To make profits. As much as possible.Whetherthe market is going up or down. Bull or Bear, you want your daily profits.Very Good. Now, let us look a little more closely. In real terms, right at thebeginning, you should be doing these:How much to invest? Start with a fixed investment. How much? Answer: the amount you are ready to lose in the stock market. If you suddenly lose the whole of this amount, your normal life-style should not be disrupted. This amount can be as low as Rs. 5, 000/- to begin with. 15k is a fair amount to start with. If you are new to intraday trading, or you are here to "try your hand" at day-trading, start with 5k. Anything below 5K is not worth it. For this discussion, we will assume you have started with an investment of 15K. This means, with the (minimum) 4-times margins that on-line brokers allow, you can buy stocks worth Rs. 60, 000/- for intraday trading.How much do you earn per day? Now, if you had taken this 15K on interest from the open (unsecured) market, you would be paying about 5%-7% interest per month. That is, 700-1000 per month. In the stock market, you have to earn at least 5 times that amount: 3500-5000 per month. So, set yourself a target: You have to earn Rs. 300/- per day. With an average of 20 working days per month, this means 6000. There is a little margin here to take care of the rainy day, commissions and taxes. 300 is the daily figure. You should now forget about your monthly targets. Simply concentrate on your daily 300.How many stocks to buy? Suppose you have been suggested a scrip whose price is around 600 each. Total purchase price cannot exceed 60K. So, you buy 100 shares. Here weve made a very important statement: once your budget is fixed, you will not get disturbed by the price of the share you are trading today. If price is around 600 each, you buy 100 shares, so that total purchase price does not exceed 60K. If the price is 1000 each you buy 60. If the price is 70 each, you buy 800 shares. The example given here is on going LONG. Same points that are made here also apply if you are going SHORT. If the market is going up, look to go LONG. If the market is falling, look for SHORTING opportunities.How to play? Once the number of shares has been fixed, you will need to calculate how many points increase or decrease will be required to meet your 30
  31. 31. target. On a LONG example, if youve taken 60 of 1000 each you will need an increase of 6 each to meet your daily requirement (60 x 6 = 360). The extra is to take care of brokerage, etc. In this example, youve taken a position on 100 shares. Since your daily target is a profit of 300, you should be looking to sell and square up this trade when price reaches 603 (3 x 100 = 300). Similarly, if you look to buy a scrip worth 95 each, buy 600 shares and look for a profit of about 0. 50p per share. (600 x 0. 5 = 300)When to STOP? If you can make more than the required 300 from your first trade of the day, very good and well played! But do not get carried away. Most importantly, never ever risk away todays income. You MUST take home todays 300 first. Do not try to insulate yourself in advance for a possible bad day tomorrow. Tomorrow will be a new day, with new possibilities, which may be even better than today. Well see about all that tomorrow. Today you take your 300 and go home.Play on. . . You might get another opportunity with another stock later in the same day. What is to be done in this situation? Depends on your position at that point of time, with respect to your total earning in the earlier part of the day. Never look at your monthly figure. Only consider todays position. If you have made 400 earlier, you can take a risk with the extra 100 youve earned. Or, if you have only made 100 in the first trade, look to make another 200 with this opportunity. But, if you have actually made that 400 in the first trade today, it is strongly advised that you call it quits. Keep the extra profit. Dont let someone else take away this money. Take the rest of the day off. Enjoy!If your investment is different from the 15K in this example, all the calculatedfigures will change proportionately. Examples are given for taking LONGpositions. Same will apply in the opposite direction when you go SHORT,daily target remaining the same.Important Note: at this site we have declared our objective as giving youevery day at least 2 Suggests that will give minimum 500 in profits eachinstead of the 300 discussed above. . .Just consider this: on an investment of 15K, you stand to make 4K+ permonth. You double your money in less than 4 months. And it looks prettyeasy! Increase of 3 for a stock of 600 value is not a big deal at all. A rise of0.50p for a stock with value of 95 each is also commonplace. Even in theworst of days.So, where is the catch? Why do people lose money at the stock market? Thecatch is not in the WHY?, or the HOW?, but in the WHERE? There is also aWHEN? 31
  32. 32. Where?Finding the right stock that will rise from 600 to 603, or from 97 to 97. 50 onthat particular day is the challenge. Finding that one amongst the 1000+available at NSE is where most people falter. People put their money at thewrong places only to see losses.Here you can depend on IntradayTrade dot Net. Since the time weve comeonline weve given you names that have fulfilled your requirement everyday.Look at our past results.When?Like weve said at the beginning, Intraday Trading is a serious business.And after you know which stock to invest in, this When? is a vital point inthat serious business. This mainly deals with your entry and exit points.As mentioned earlier, to control these points you will need (1) a good tradingmethod and (2) good money management policies. You will also need fourimportant weapons: Confidence, Discipline, Focus and Patience.Algorithmic TradingAlgorithmic trading, also known as automated trading, algo trading,black-box trading, whitebox trading or robo trading, is the use ofelectronic platforms for entering trading orders with an algorithm deciding onaspects of the order such as the timing, price, or quantity of the order, or inmany cases initiating the order without human intervention. Algorithmictrading is widely used by pension funds, mutual funds, and other buy side(investor driven) institutional traders, to divide large trades into severalsmaller trades to manage market impact, and risk. Sell side traders, suchas market makers and some hedge funds, provide liquidity to the market,generating and executing orders automatically.A special class of algorithmic trading is "high-frequency trading" (HFT), inwhich computers make elaborate decisions to initiate orders based oninformation that is received electronically, before human traders are capableof processing the information they observe. This has resulted in a dramaticchange of the market microstructure, particularly in the way liquidity isprovided. Algorithmic trading may be used in any investment strategy,including market making, inter-market spreading, arbitrage, or purespeculation (including trend following). The investment decision andimplementation may be augmented at any stage with algorithmic support ormay operate completely automatically.A third of all European Union and United States stock trades in 2006 weredriven by automatic programs, or algorithms, according to Boston-basedfinancial services industry research and consulting firm Aite Group. As of 32
  33. 33. 2009, HFT firms account for 73% of all US equity trading volume. In 2006 atthe London Stock Exchange, over 40% of all orders were entered by algotraders, with 60% predicted for 2007. American markets and Europeanmarkets generally have a higher proportion of algo trades than other markets,and estimates for 2008 range as high as an 80% proportion in some markets.Foreign exchange markets also have active algo trading (about 25% of ordersin 2006). Futures and options markets are considered fairly easy tointegrated into algorithmic trading, with about 20% of options volumeexpected to be computer-generated by 2010. Bond markets are movingtoward more access to algorithmic traders. One of the main issues regardingHFT is the difficulty in determining just how profitable it is. A report releasedin August 2009 by the TABB Group, a financial services industry researchfirm, estimated that the 300 securities firms and hedge funds that specializein this type of trading took in roughly US$21 billion in profits in 2008.Algorithmic and HFT have been the subject of much public debate since theU. S. Securities and Exchange Commission and the Commodity FuturesTrading Commission said they contributed to some of the volatility during the2010 Flash Crash, when the Dow Jones Industrial Average suffered itssecond largest intraday point swing ever to that date, though prices quicklyrecovered. (See List of largest daily changes in the Dow Jones IndustrialAverage. ) A July, 2011 report by the International Organization of SecuritiesCommissions (IOSCO), an international body of securities regulators,concluded that while "algorithms and HFT technology have been used bymarket participants to manage their trading and risk, their usage was alsoclearly a contributing factor in the flash crash event of May 6, 2010."StrategiesTrend followingTrend following is an investment strategy that tries to take advantage of long-term, medium-term, and short-term moves that sometimes occur in variousmarkets. The strategy aims to take advantage of a market trend on bothsides, going long (buying) or short (selling) in a market in an attempt to profitfrom the ups and downs of the stock or futures markets. Traders who use thisapproach can use current market price calculation, moving averages andchannel breakouts to determine the general direction of the market and togenerate trade signals. Traders who subscribe to a trend following strategydo not aim to forecast or predict specific price levels; they initiate a tradewhen a trend appears to have started, and exit the trade once the trendappears to have ended.Pair tradingThe pairs trade or pair trading is a market neutral trading strategy enablingtraders to profit from virtually any market conditions: uptrend, downtrend, 33
  34. 34. or sidewise movement. This trading strategy is categorized as a statisticalarbitrage and convergence trading strategy.Delta neutral strategiesIn finance, delta neutral describes a portfolio of related financial securities, inwhich the portfolio value remains unchanged due to small changes in thevalue of the underlying security. Such a portfolio typically contains optionsand their corresponding underlying securities such that positive and negativedelta components offset, resulting in the portfolios value being relativelyinsensitive to changes in the value of the underlying security.ArbitrageIn economics and finance, arbitrage/ˈ the practice of taking advantage of a isprice difference between two or more markets: striking a combination ofmatching deals that capitalize upon the imbalance, the profit being thedifference between the market prices. When used by academics, anarbitrage is a transaction that involves no negative cash flow at anyprobabilistic or temporal state and a positive cash flow in at least one state;in simple terms, it is the possibility of a risk-free profit at zero cost.Conditions for arbitrageArbitrage is possible when one of three conditions is met: 1. The same asset does not trade at the same price on all markets (the "law of one price"). 2. Two assets with identical cash flows do not trade at the same price. 3. An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or, the asset does not have negligible costs of storage; as such, for example, this condition holds for grain but not for securities).Arbitrage is not simply the act of buying a product in one market and selling itin another for a higher price at some later time. The transactions must occursimultaneously to avoid exposure to market risk, or the risk that prices maychange on one market before both transactions are complete. In practicalterms, this is generally only possible with securities and financial productswhich can be traded electronically, and even then, when each leg of thetrade is executed the prices in the market may have moved. Missing one ofthe legs of the trade (and subsequently having to trade it soon after at a worseprice) is called execution risk or more specifically leg risk. 34
  35. 35. In the simplest example, any good sold in one market should sell for thesame price in another. Traders may, for example, find that the price ofwheat is lower in agricultural regions than in cities, purchase the good, andtransport it to another region to sell at a higher price. This type of pricearbitrage is the most common, but this simple example ignores the cost oftransport, storage, risk, and other factors. "True" arbitrage requires thatthere be no market risk involved. Where securities are traded on more thanone exchange, arbitrage occurs by simultaneously buying in one and sellingon the other. See rational pricing, particularly arbitrage mechanics, forfurther discussion.Mean reversionMean reversion is a mathematical methodology sometimes used for stockinvesting, but it can be applied to other processes. In general terms the ideais that both a stocks high and low prices are temporary, and that a stocksprice tends to have an average price over time. Mean reversion involves firstidentifying the trading range for a stock, and then computing the averageprice using analytical techniques as it relates to assets, earnings, etc. Whenthe current market price is less than the average price, the stock isconsidered attractive for purchase, with the expectation that the price willrise. When the current market price is above the average price, the marketprice is expected to fall. In other words, deviations from the average priceare expected to revert to the average.The Standard deviation of the most recent prices (e.g. , the last 20) is oftenused as a buy or sell indicator. Stock reporting services (such as Yahoo!Finance, MS Investor, Morningstar, etc. ), commonly offer movingaverages for periods such as 50 and 100 days. While reporting servicesprovide the averages, identifying the high and low prices for the study periodis still necessary. Mean reversion has the appearance of a more scientificmethod of choosing stock buy and sell points than charting, because precisenumerical values are derived from historical data to identify the buy/sellvalues, rather than trying to interpret price movements using charts (charting,also known as technical analysis).ScalpingScalping (trading) is a method of arbitrage of small price gaps created by thebid-ask spread. Scalpers attempt to act like traditional market makers orspecialists. To make the spread means to buy at the bid price and sell at theask price, to gain the bid/ask difference. This procedure allows for profiteven when the bid and ask do not move at all, as long as there are traderswho are willing to take market prices. It normally involves establishing andliquidating a position quickly, usually within minutes or even seconds. Therole of a scalper is actually the role of market makers or specialists who are tomaintain the liquidity and order flow of a product of a market. A marketmaker is basically a specialized scalper. The volume a market maker trades 35
  36. 36. are many times more than the average individual scalpers. A market makerhas a sophisticated trading system to monitor trading activity. However, amarket maker is bound by strict exchange rules while the individual trader isnot. For instance, NASDAQ requires each market maker to post at least onebid and one ask at some price level, so as to maintain a two-sided market foreach stock represented.Transaction cost reductionMost strategies referred to as algorithmic trading (as well as algorithmicliquidity seeking) fall into the cost-reduction category. Large orders arebroken down into several smaller orders and entered into the market overtime. This basic strategy is called "iceberging". The success of this strategymay be measured by the average purchase price against the volume-weighted average price for the market over that time period. One algorithmdesigned to find hidden orders or icebergs is called "Stealth". Most of thesestrategies were first documented in Optimal Trading Strategies by RobertKissell.Strategies that only pertain to dark poolsRecently, HFT, which comprises a broad set of buy-side as well as marketmaking sell side traders, has become more prominent and controversial.These algorithms or techniques are commonly given names such as "Stealth"(developed by the Deutsche Bank), "Iceberg", "Dagger", "Guerrilla","Sniper", "BASOR" (developed by Quod Financial) and "Sniffer". Yet are attheir core quite simple mathematical constructs.Dark pools are alternativeelectronic stock exchanges where trading takes place anonymously, withmost orders hidden or "iceberged. " Gamers or "sharks" sniff out large ordersby "pinging" small market orders to buy and sell. When several small ordersare filled the sharks may have discovered the presence of a large icebergedorder.―Now it‘s an arms race, ‖ said Andrew Lo, director of the MassachusettsInstitute of Technology‘s Laboratory for Financial Engineering. ―Everyone isbuilding more sophisticated algorithms, and the more competition exists, thesmaller the profits. ‖ One of the unintended adverse effects of algorithmictrading, has been the dramatic increase in the volume of trade allocations andsettlements, as well as the transaction settlement costs associated with them.Since 2004, there have been a number of technological advances andservice providers by individuals like Scott Kurland, who have built solutionsfor aggregating trades executed across algorithms to counter these risingsettlement costs.High-frequency trading 36
  37. 37. In the U.S. , high-frequency trading (HFT) firms represent 2% of theapproximately 20, 000 firms operating today, but account for 73% of all equitytrading volume. As of the first quarter in 2009, total assets undermanagement for hedge funds with HFT strategies were US$141 billion, downabout 21% from their high. The HFT strategy was first made successful byRenaissance Technologies. High-frequency funds started to becomeespecially popular in 2007 and 2008. Many HFT firms are market makersand provide liquidity to the market, which has lowered volatility and helpednarrow Bid-offer spreads making trading and investing cheaper for othermarket participants. HFT has been a subject of intense public focus sincethe U. S. Securities and Exchange Commission and the Commodity FuturesTrading Commission stated that both algorithmic and HFT contributed tovolatility in the May 6, 2010 Flash Crash. Major players in HFT includeGETCO LLC, Jump Trading LLC, Tower Research Capital, Hudson RiverTrading as well as Citadel Investment Group, Goldman Sachs, DE Shaw,RenTech. High-frequency trading is quantitative trading that is characterizedby short portfolio holding periods (see Wilmott (2008), Aldridge (2009)).There are four key categories of HFT strategies: market-making based onorder flow, market-making based on tick data information, event arbitrageand statistical arbitrage. All portfolio-allocation decisions are made bycomputerized quantitative models. The success of HFT strategies is largelydriven by their ability to simultaneously process volumes of information,something ordinary human traders cannot do.Market makingMarket making is a set of HFT strategies that involves placing a limit order tosell (or offer) above the current market price or a buy limit order (or bid) belowthe current price to benefit from the bid-ask spread. Automated Trading Desk,which was bought by Citigroup in July 2007, has been an active marketmaker, accounting for about 6% of total volume on both NASDAQ and theNew York Stock Exchange.Statistical arbitrageAnother set of HFT strategies is classical arbitrage strategy might involveseveral securities such as covered interest rate parity in the foreign exchangemarket which gives a relation between the prices of a domestic bond, a bonddenominated in a foreign currency, the spot price of the currency, and theprice of a forward contract on the currency. If the market prices aresufficiently different from those implied in the model to cover transaction costthen four transactions can be made to guarantee a risk-free profit. HFTallows similar arbitrages using models of greater complexity involving manymore than 4 securities. The TABB Group estimates that annual aggregateprofits of low latency arbitrage strategies currently exceed US$21 billion.A wide range of statistical arbitrage strategies have been developed wherebytrading decisions are made on the basis of deviations from statistically 37
  38. 38. significant relationships. Like market-making strategies, statistical arbitragecan be applied in all asset classes. [31]Event arbitrageA subset of risk, merger, convertible, or distressed securities arbitrage thatcounts on a specific event, such as a contract signing, regulatory approval,judicial decision, etc. , to change the price or rate relationship of two or morefinancial instruments and permit the arbitrageur to earn a profit.Merger arbitrage also called risk arbitrage would be an example of this.Merger arbitrage generally consists of buying the stock of a company that isthe target of a takeover while shorting the stock of the acquiring company.Usually the market price of the target company is less than the price offeredby the acquiring company. The spread between these two prices dependsmainly on the probability and the timing of the takeover being completed aswell as the prevailing level of interest rates. The bet in a merger arbitrage isthat such a spread will eventually be zero, if and when the takeover iscompleted. The risk is that the deal "breaks" and the spread massivelywidens.Low-latency tradingHFT is often confused with low-latency trading that uses computers thatexecute trades within milliseconds, or "with extremely low latency" in thejargon of the trade. Low-latency traders depend on ultra-low latencynetworks. They profit by providing information, such as competing bids andoffers, to their algorithms microseconds faster than their competitors. [5] Therevolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform to benefit from implementing high-frequencystrategies. [5] Strategies are constantly altered to reflect the subtle changes inthe market as well as to combat the threat of the strategy being reverseengineered by competitors. There is also a very strong pressure tocontinuously add features or improvements to a particular algorithm, such asclient specific modifications and various performance enhancing changes(regarding benchmark trading performance, cost reduction for the trading firmor a range of other implementations). This is due to the evolutionary natureof algorithmic trading strategies – they must be able to adapt and tradeintelligently, regardless of market conditions, which involves being flexibleenough to withstand a vast array of market scenarios. As a result, asignificant proportion of net revenue from firms is spent on the R&D of theseautonomous trading systems.Strategy implementationMost of the algorithmic strategies are implemented using modernprogramming languages, although some still implement strategies designedin spreadsheets. Increasingly, the algorithms used by large brokerages andasset managers are written to the FIX Protocols Algorithmic TradingDefinition Language (FIXatdl), which allows firms receiving orders to specify 38
  39. 39. exactly how their electronic orders should be expressed. Orders built usingFIXatdl can then be transmitted from traders systems via the FIX Protocol.Basic models can rely on as little as a linear regression, while more complexgame-theoretic and pattern recognitionor predictive models can also be usedto initiate trading. Neural networks and genetic programming have been usedto create these models.Issues and developmentsAlgorithmic trading has been shown to substantially improve marketliquidityamong other benefits. However, improvements in productivitybrought by algorithmic trading have been opposed by human brokers andtraders facing stiff competition from computers.Concerns―The downside with these systems is their black box-ness, ‖ Mr. Williamssaid. ―Traders have intuitive senses of how the world works. But with thesesystems you pour in a bunch of numbers, and something comes out the otherend, and it‘s not always intuitive or clear why the black box latched ontocertain data or relationships. ‖ ―The Financial Services Authority has been keeping a watchful eye on thedevelopment of black box trading. In its annual report the regulator remarkedon the great benefits of efficiency that new technology is bringing to themarket. But it also pointed out that ‗greater reliance on sophisticatedtechnology and modelling brings with it a greater risk that systems failure canresult in business interruption‘. ‖UK Treasury minister Lord Myners has warned that companies could becomethe "playthings" of speculators because of automatic high-frequency trading.Lord Myners said the process risked destroying the relationship between aninvestor and a company. Other issues include the technical problem oflatency or the delay in getting quotes to traders, security and the possibility ofa complete system breakdown leading to a market crash. "Goldman spendstens of millions of dollars on this stuff. They have more people working intheir technology area than people on the trading desk. . . The nature of themarkets has changed dramatically. " Algorithmic and HFT were shown tohave contributed to volatility during the May 6, 2010 Flash Crash, when theDow Jones Industrial Average plunged about 600 points only to recover thoselosses within minutes. At the time, it was the second largest point swing, 1,010. 14 points, and the biggest one-day point decline, 998. 5 points, on anintraday basis in Dow Jones Industrial Average history.Recent developments 39
  40. 40. Financial market news is now being formatted by firms such as Need ToKnow News, Thomson Reuters, Dow Jones, and Bloomberg, to be read andtraded on via algorithms. "Computers are now being used to generate newsstories about company earnings results or economic statistics as they arereleased. And this almost instantaneous information forms a direct feed intoother computers which trade on the news. " The algorithms do not simplytrade on simple news stories but also interpret more difficult to understandnews. Some firms are also attempting to automatically assign sentiment(deciding if the news is good or bad) to news stories so that automatedtrading can work directly on the news story."Increasingly, people are looking at all forms of news and building their ownindicators around it in a semi-structured way, " as they constantly seek outnew trading advantages said Rob Passarella, global director of strategy atDow Jones Enterprise Media Group. His firm provides both a low latencynews feed and news analytics for traders. Passarella also pointed to newacademic research being conducted on the degree to which frequent Googlesearches on various stocks can serve as trading indicators, the potentialimpact of various phrases and words that may appear in Securities andExchange Commission statements and the latest wave of online communitiesdevoted to stock trading topics."Markets are by their very nature conversations, having grown out of coffeehouses and taverns", he said. So the way conversations get created in adigital society will be used to convert news into trades, as well, Passarellasaid. ―There is a real interest in moving the process of interpreting news fromthe humans to the machines‖ says KirstiSuutari, global business manager ofalgorithmic trading at Reuters. "More of our customers are finding ways touse news content to make money. "An example of the importance of news reporting speed to algorithmic traderswas an advertising campaign by Dow Jones (appearances included pageW15 of the Wall Street Journal, on March 1, 2008) claiming that their servicehad beaten other news services by 2 seconds in reporting an interest rate cutby the Bank of England. In July 2007, Citigroup, which had alreadydeveloped its own trading algorithms, paid $680 million for AutomatedTrading Desk, a 19-year-old firm that trades about 200 million shares a day.Citigroup had previously bought Lava Trading and OnTrade Inc. In late 2010,The UK Government Office for Science initiated a Foresight projectinvestigating the future of computer trading in the financial markets, led byDame Clara Furse, ex-CEO of the London Stock Exchange and inSeptember 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additionalpapers that provide supporting evidence. All of these findings are authoredor co-authored by leading academics and practitioners, and were subjectedto anonymous peer-review. The Foresight project is set to conclude in late2012.In September 2011, RYBN has launched "ADM8", an open sourceTrading Bot prototype, already active on the financial markets. 40
  41. 41. Technical designThe technical designs of such systems are not standardized. Conceptually,the design can be divided into logical units: 1. The data stream unit (the part of the systems that receives data (e. g. quotes, news) from external sources). 2. The decision or strategy unit 3. The execution unit.With the wide use of social networks, some systems implement scanning orscreening technologies to read posts of users extracting human sentimentand influence the trading strategies.EffectsThough its development may have been prompted by decreasing trade sizescaused by decimalization, algorithmic trading has reduced trade sizes further.Jobs once done by human traders are being switched to computers. Thespeeds of computer connections, measured in milliseconds and evenmicroseconds, have become very important. More fully automated marketssuch as NASDAQ, Direct Edge and BATS, in the US, have gained marketsharefrom less automated markets such as the NYSE. Economies of scalein electronic trading have contributed to lowering commissions and tradeprocessing fees, and contributed to international mergers and consolidationof financial exchanges.Competition is developing among exchanges for the fastest processing timesfor completing trades. For example, in June 2007, the London StockExchange launched a new system called TradElect that promises an average10 millisecond turnaround time from placing an order to final confirmation andcan process 3, 000 orders per second. Since then, competitive exchangeshave continued to reduce latency with turnaround times of 3 millisecondsavailable. This is of great importance to high-frequency traders, becausethey have to attempt to pinpoint the consistent and probable performanceranges of given financial instruments. These professionals are often dealingin versions of stock index funds like the E-mini S&Ps, because they seekconsistency and risk-mitigation along with top performance. They must filtermarket data to work into their software programming so that there is thelowest latency and highest liquidity at the time for placing stop-losses and/ortaking profits. With high volatility in these markets, this becomes a complexand potentially nerve-wracking endeavor, where a small mistake can lead toa large loss. Absolute frequency data play into the development of thetraders pre-programmed instructions.Spending on computers and software in the financial industry increased to$26. 4 billion in 2005. 41
  42. 42. Communication standardsAlgorithmic trades require communicating considerably more parameters thantraditional market and limit orders. A trader on one end (the "buy side") mustenable their trading system (often called an "order management system" or"execution management system") to understand a constantly proliferating flowof new algorithmic order types. The R&D and other costs to constructcomplex new algorithmic orders types, along with the executioninfrastructure, and marketing costs to distribute them, are fairly substantial.What was needed was a way that marketers (the "sell side") could expressalgo orders electronically such that buy-side traders could just drop the neworder types into their system and be ready to trade them without constantcoding custom new order entry screens each time.FIX Protocol LTD http: //www. fixprotocol. org is a trade association thatpublishes free, open standards in the securities trading area. The FIXlanguage was originally created by Fidelity Investments, and the associationMembers include virtually all large and many midsized and smaller brokerdealers, money center banks, institutional investors, mutual funds, etc.This institution dominates standard setting in the pretrade and trade areas ofsecurity transactions. In 2006-2007 several members got together andpublished a draft XML standard for expressing algorithmic order types. Thestandard is called FIX Algorithmic Trading Definition Language (FIXatdl). Thefirst version of this standard, 1.0 was not widely adopted due to limitations inthe specification, but the second version, 1. 1 (released in March 2010) isexpected to achieve broad adoption and in the process dramatically reducetime-to-market and costs associated with distributing new algorithms.High-frequency tradingHigh-frequency trading (HFT) is the use of sophisticated technological toolsto trade securities like stocks or options, and is typically characterized byseveral distinguishing features: It is highly quantitative, employing computerized algorithms to analyze incoming market data and implement proprietary trading strategies; An investment position is held only for very brief periods of time - from seconds to hours - and rapidly trades into and out of those positions, sometimes thousands or tens of thousands of times a day; At the end of a trading day there is no net investment position; It is mostly employed by proprietary firms or on proprietary trading desks in larger, diversified firms; It is very sensitive to the processing speed of markets and of their own access to the market; 42
  43. 43. Many high-frequency traders provide liquidity and price discovery to the markets through market-making and arbitrage trading.High-frequency trading removes any value from the trade of securities inexchange for rapid profits; thus many believe the overall effect of high-frequency trading is more comparable to a casino than actual trading.Positions are taken in equities, options, futures, ETFs, currencies, andother financial instruments that can be traded electronically. High-frequencytraders compete on a basis of speed with other high-frequency traders, notlong-term investors (who typically look for opportunities over a period ofweeks, months, or years), and compete for very small, consistent profits.As a result, high-frequency trading has been shown to have a potentialSharpe ratio (measure of reward per unit of risk) thousands of times higherthan the traditional buy-and-hold strategies. Aiming to capture just a fractionof a penny per share or currency unit on every trade, high-frequency tradersmove in and out of such short-term positions several times each day.Fractions of a penny accumulate fast to produce significantly positive resultsat the end of every day. High-frequency trading firms do not employsignificant leverage, do not accumulate positions, and typically liquidate theirentire portfolios on a daily basis.By 2010 high-frequency trading accounted for over 70% of equity trades in theUS and was rapidly growing in popularity in Europe and Asia. Algorithmic andhigh-frequency trading were both found to have contributed to volatility in theMay 6, 2010 Flash Crash, when high-frequency liquidity providers were infact found to have withdrawn from the market. A July, 2011 report by theInternational Organization of Securities Commissions (IOSCO), aninternational body of securities regulators, concluded that while "algorithmsand HFT technology have been used by market participants to manage theirtrading and risk, their usage was also clearly a contributing factor in the flashcrash event of May 6, 2010. "[HistoryHigh-frequency trading has taken place at least since 1999, after the U. S.Securities and Exchange Commission (SEC) authorized electronic exchangesin 1998. At the turn of the 21st century, HFT trades had an execution time ofseveral seconds, whereas by 2010 this had decreased to milli- and evenmicroseconds. Until recently, high-frequency trading was a little-known topicoutside the financial sector, with an article published by the New York Timesin July 2009 being one of the first to bring the subject to the publics attention. 43
  44. 44. Market growthIn the early 2000s, high-frequency trading still accounted for less than 10% ofequity orders, but this proportion was soon to begin rapid growth. Accordingto data from the NYSE, trading volume grew by about 164% between 2005and 2009 for which high-frequency trading might be accounted. As of thefirst quarter in 2009, total assets under management for hedge funds withhigh-frequency trading strategies were $141 billion, down about 21% fromtheir peak before the worst of the crises. The high-frequency strategy wasfirst made successful by Renaissance Technologies. Many high-frequencyfirms are market makers and provide liquidity to the market which has loweredvolatility and helped narrow Bid-offer spreads, making trading and investingcheaper for other market participants. In the United States, high-frequencytrading firms represent 2% of the approximately 20, 000 firms operating today,but account for 73% of all equity orders volume. The largest high-frequencytrading firms in the US include names like Getco LLC, Knight Capital Group,Jump Trading, and Citadel LLC. The Bank of England estimates similarpercentages for the 2010 US market share, also suggesting that in EuropeHFT accounts for about 40% of equity orders volume and for Asia about 5-10%, with potential for rapid growth. By value, HFT was estimated in 2010by consultancy Tabb Group to make up 56% of equity trades in the US and38% in Europe.High-frequency trading strategiesHigh-frequency trading is quantitative trading that is characterized by shortportfolio holding periods (see Wilmott (2008)). All portfolio-allocationdecisions are made by computerized quantitative models. The success ofhigh-frequency trading strategies is largely driven by their ability tosimultaneously process volumes of information, something ordinary humantraders cannot do. Specific algorithms are closely guarded by their ownersand are known as "algos".Most high-frequency trading strategies fall within one of the following tradingstrategies: Market making Ticker tape trading Event arbitrage High-frequency statistical arbitrage 44