SUMMER TRAINING PROJECT REPORT ON

(ART OF MAKING MONEY…ALGORITHMIC TRADING)

FOR THE PARTIAL FULFILLMENTOF THE REQUIREMENT FOR THE AWARD OF MASTER OF BUSINESS ADMINISTRATION

UNDER THE GUIDANCE OF: UNDER THE SUPERVISION OF: PROF. RAHUL CHANDRA MR. AMRIK SINGH

SUBMITTED BY: MANISH KUMAR KESHARI MBA 2011-13

School of Business, Galgotias University

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CERTIFICATE

This is to certify that the project report on “ART OF MAKING MONEY…ALGORITHMIC TRADING” has been prepared out by MR. MANISH KUMAR KESHARI under my supervision and guidance. The project report is submitted towards the partial fulfillment of 2011-2012 year, full time Master of Business Administration.

MR. RAHUL CHANDRA

Date: 11-JUNE-2012

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ACKNOWLEDGEMENT

I would like to take this opportunity to thanks all those who contribute to this project

work 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 to

enhance my knowledge in the internal working environment of a company. We

would also thank him for giving his valuable time and patience which has made this

project successful.

Last but not least, I would like to thank all my friends and faculty members and my

internal guide Mr. Rahul Chandra faculty school of business, Galgotias University,

Greater Noida for their valuable suggestions and moral support.

MANISH KUMAR KESHARI

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DECLARATION

I, MANISH KUMAR KESHARI enrollment no 1103102069, student of MBA

of School of Business: Galgotias University, Greater Noida , hereby

declare that the project report on “ART OF MAKING MONEY…

ALGORITHMIC TRADING” at GREATRER NOIDA” is an original and

authenticated work done by me. The project was of 45 days duration and

was completed between 11-JUNE-2012 to 23-JULY-2012.

I further declare that it has not been submitted elsewhere by any other person

in any of the Institutes for the award of any degree or diploma.

MANISH KUMAR KESHARI Date: - 11-JUNE-2012

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CONTENTS

1. Executive Summary 5

Part-A

2. Introduction 9

3. Company Profile 10

Part-B

4 .Introduction of Topic 15

5. Research Methodlogy 91

6. Discussion/Description 94

7. Conclusion And Recommendations 95

8. Bibliography 96

9. Annexure 97

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EXECUTIVE SUMMARY

Algorithmic Trading

Algorithmic trading is automated trading, i.e. a computer system is completing all work from trading decision to execution. Algorithmic trading has become possible with the existence of fully electronic infrastructure in stock trading systems from market access, exchange and market data provision. The following gives an overview of chances and challenges of algorithmic trading as well as an introduction of several components needed to set up a competitive trading algorithm.

Chances and challenges.

There are several advantages in contrast from algorithmic trading to trading by human beings. Computer systems have in general a much shorter reaction time and reach a very high level of reliability. The decisions reached by a computer system rely on the underlying strategy with specified rules. This leads to reproducibility of the decisions. Thus, back-testing and improving the strategy by variation of underlying rules is allowed. Algorithmic trading ensures objectivity in trading decisions and is not exposed to subjective influences (such as panic, for example). When trading many different securities at the same time, a computer system may substitute many human traders. So the observation and trading securities of a large universe become possible for companies without dozens of traders. Altogether these effects may result in better performance of the investment strategy as well as in lower trading costs. On the other hand, it is challenging to automatize the complete process from deriving investment decisions to execution because of the need of system stability. The algorithm has to be robust against numerous possible errors in services the system is dependent on, such as market data provision, connection to market and the exchange itself. These are technical issues which can be achieved by spending some effort in the implementation. Even more complex is the development of an investment strategy, i. e. deriving trading decisions, and strategies to realize these decisions. This work is focused on the realization and thus the execution strategy by assuming given investment decisions. It is beyond this work to introduce in how to derive investment decisions. All necessary information for the input of the execution algorithm is assumed to be available. Input variables may be the security names, the number of shares, and the trading direction. But also assumed available are variables like aggressivity and constraints, such as market neutrality when trading a portfolio. The main challenge for trading algorithms is the realization of low trading costs in preferably all market environments independent from falling or rising markets as well as high and low liquid

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securities. Another critical point which has to be takeninto account is the transparency of the execution strategy for other market participants. If a structured execution strategy acts in repeating processes, for example, orders are sent in periodical iterations; other market participants may then observe patterns in market data and may take an advantage of the situation.

Components of automated trading system.

A fully automated trading system is complex with regard to technical requirements, but the numerous different research issues which have to be considered lead to even more effort and potential for improvement. An automated stock trading algorithm has to take many aspects into account which are addressed in this work. Reaching favourable trading costs, numerous cognitions of market microstructure theory have been incorporated into such a system. Strategies mentioned in 2. 2. are just simple formalizations of market attributes. They are seen as an approximation of the strategy leading to minimal execution costs, but by far do not take all microstructure aspects into account. Probably all currently existing systems do not contain much more than such an approximation. A suggestion for an automated trading system can be constructed of three components as it is denoted, pre-trade analysis component provides a previous estimate of transaction costs of a given order. Therefore, an econometric model based on historical trading data is used. The pre-trade analysis can be used to optimize the expected transaction costs by varying the parameters or even the trading strategy.

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INTRODUCTION

Algorithmic trading is the act of making trades in a market, based purely on instructions generated by quantitative algorithms. Each algorithm is assumed to have access to current and historical prices of instruments that can be bought and sold, and can perform any computations it wants based on these prices. In many cases, an algorithm will be coded in some programming language and will run as an application that places its own orders, but it doesn't have to do this. For example, a person could put through trades according to the prescription of an algorithm.

Algorithmic trading is carried out by hedge funds and proprietary trading groups, but can also be performed by an individual with a trading account with a broker. All that is needed is a reasonably good computer, a broker (I use InteractiveBrokers, but there are many others you could use) and a source of historical data. (I also use Interactive Brokers for this, but they are primarily a broker rather than a data provider, and you can find better sources of historical data, depending on your budget and requirements. ) If you want to automate your algorithmic trading, that is, make your computer place orders for you, then you will also need good programming skills and an application programming interface (API) from your broker. The API typically includes libraries and documentation that allow you to connect your own program directly to the broker to automate order-placement, retrieve historical data, 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-feeling predictions, (c) a compulsive desire to gamble. Most novice traders begin using one or more of these styles, and lose substantial sums of money before stopping. I will refer to trades based on (a), (b) or (c) as discretionary trades. Some people do have the ability to make money using gut-instincts to place trades, but these people have normally spent a lot of time trading and studying the market. It's a very dangerous way to start out a trading career.

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COMPANY PROFILE

History

In 2008 a special quantitative analytic division was created within Appin Technologies to cater to specialized projects which required advanced algorithms, data mining and artificial intelligence. This group conducted in-depth research and developed proprietary techniques to analyze data. The group had many projects related to financial time series and quantitative trading.

In 2009 Appin technologies decided to create a spin off called “Prophecis Consulting and Analytics Pvt ltd” with a mandate to create products and services for financial institutions in capital markets segment. The company managed outsourcing contracts for hedge funds in Europe.

In 2010 Prophecis generated many proprietary algorithms and techniques to trade on financial markets. In one year the spinoff generated close to 200 different robust trading systems. A large Indian conglomerate invited the company to manage part of its portfolio with certain guaranteed risk parameters. Till date, Prophecis has maintained the downside risk as per the guidelines while beating similar benchmarks.

In 2011, Prophecis started developing an advance1d trading platform which could handle the exceptionally advanced and complex algorithms which were prevalent in quantitative trading domain. The first release was made in March.

Company

Prophecis is an analytics and consulting firm that provides analytics and advisory services to proprietary trading houses, banks, hedge funds and financial institutions in India, US and Europe. The firm is expert in data mining, machine learning and quantitative analysis. The firm was founded by IIT, ISB and imperial college alumnus. Our human capital has amalgamated experience from different sections of financial markets. Prophecis stands for prudence in converging analytical principles with technology. We strive to apply sound financial principles using cutting edge

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in computational technology. Our immense experience with advanced data mining and machine learning coupled with high end computing infrastructure gives us the edge in implementation of analytical solutions. We undertake research in financial markets while keeping abreast with the latest in technology, hence capable of making previously impractical solutions possible

Services

Assets Management

Asset Management offers a range of investment products and services across the risk return spectrum to investors. We emphasize on client requirements while designing products which offer the best opportunity for asset growth and wealth enhancement. Our investment products comprises of wide variety of algorithmic trading systems. Trading system is a set of specific rules that determine entry and exit points for a set of tradable instruments. These are more easily implemented by computers because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously. Our mission is to ensure our clients receive the superior performance through market cycles by virtue of our deep understanding of the equities markets and our analytical approach to risks and return.

Analytics

The objective of the Diversification program is to attain maximum returns with defined risk limitations. To meet these targets, we employs a portfolio of objective, technically-based trading systems and a multidimensional diversified strategy which allocates capital to different markets, trading strategies, and time frames. The selection of component strategies, time frames and markets follows a rigorous quantitative analysis that considers the liquidity and volatility of markets traded, types of strategies employed, trade duration, risk of loss, and probability of achieving performance objectives.

These factors, along with measures of correlation between the system components, attempt to ensure synergy at the portfolio level while limiting risk by maintaining diversification across multiple dimensions.

The resulting multi-dimensional approach gives us the ability to profit (or suffer 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 statistical and Analytical data mining tools. This leads to discovery of various hidden patterns and various indicators from the historic data that have probable predictive capability in investment decision.

Our market diversification is achieved by trading positions across a wide range of global markets and market groups. These include various stock market indices (US large cap, small cap, etc. ), energy futures (crude oil,

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gas), industrial and precious metals (gold, silver), and various agricultural products (grains, meats and "soft" commodities such as coffee, sugar, etc.). Limitations are placed on each market group, or sector, so that no one sector can risk more than a certain percentage of the entire portfolio.

Products

AlphaBOX

Algorithms have become such a common feature in the trading landscape that it is unthinkable for a broker not to offer them because that is what clients demand. These mathematical models analyze every quote and trade in the stock market, identify liquidity opportunities, and turn the information into intelligent trading decisions. Algorithmic trading, or computer-directed trading, cuts down transaction costs, and allows investment managers to take control of their own trading processes. It is a style of trading. No matter which markets you trade or whether you enter your trades automatically or manually, AlphaBox can help you execute your trades quickly, accurately and efficiently.

Automated Order Entry: - With fully automated trading, AlphaBOX monitors the markets for you based on your own custom buy and sell rules and executes your trades faster and more efficiently than humanly possible. Using the speed of direct-access execution, AlphaBOX automatically sends your stock, futures orders to the major exchange or ECN you've chosen in your strategy.

AlphaBOX tracks all your strategies’ open positions in real time and continuously monitors the markets based on your trading rules, ensuring that you don't miss your exit point, no matter how simple or complex your exit criteria. You can automate virtually any trading strategy imaginable, including multiple conditional entries and exits, profit targets, protective stops, 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 you choose to enter your stock trades manually:

1. Order Bar2. Trade from Chart

AlphaBOX DataRIVER QuoteCANVAS AlgoWRITER

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AlgoANALYTICS TradeBOT TradeSERVO

Solution

Individual

No single technique of trading works forever and best traders know when to switch between different trading styles. Our software supports you if you are a Scalper/Jobber, Arbitrager, Positional/ Swing Trader, Intraday Trader or a mixture of all. You can write your own strategies and see how they would have performed in the past with complete statistical analysis. Traders can also avail of our pre-defined adaptable trading models which have been rigorously tested; we have more than 500 such adjustable systems to choose from. We also provide courseware which allows traders to keep up with the latest methods and techniques in the market and new traders to get started. If you are a new trader, you can go for our starter kit which includes all you need to trade accurately.

Small Medium Business

We offer a wide variety of products and services to suit the needs of a trading and broker desk. Starting from, trading strategies, to the execution and management of positions, our solutions make sure that your operations are executed with maximum efficiency. We offer brokers a state of art trading platform which can be given to the end customer to enhance ease of trade and streamline all processes. Brokers can also use the platform as a channel to sell products and services to their clients. Our online marketplace allows clients to buy subscriptions to trading strategies. We also offer licensing of strategies from us which you can sell to end consumers.

Our software development is expert in creating online trading websites and low latency market data adapters. We help new or small brokers establish their IT setup. We also offer complete end-to-end management of trading infrastructure. We have specific knowledge in high speed servers and provide co-location services to trading desks. We also undertake custom software development projects at very competitive rates.

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Individual

We have a strong data mining and analytics capability which we leveraged to applications in financial markets. During our research we have developed many proprietary algorithms to mine data and detect anomalies and trends in data. Our statistical analysis process is exhaustive and is adaptable to a wide variety of purposes.

Right from Monte Carlo simulations to quantitative trading models, we have the capability to deliver a diverse spectrum of analytics products and services.

Our suite of analysis tools let you do highly complicated event based studies and backrests. Our portfolio design and simulation tools provide managers with accurate analytics to make prudent decisions. We also manage funds and assets of institutional clients with end-to-end portfolio and risk management. Our history shows our commitment towards downside risk management.

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INTRODUCTION OF TOPIC

TRADING

Trade is the transfer of ownership of goods and services from one person or entity to another by getting something in exchange from the buyer. Trade is sometimes loosely called commerce or financial transaction or barter. A network that allows trade is called a market. The original form of trade was barter, the direct exchange of goods and services. Later one side of the barter were the metals, precious metals (poles, coins), bill, and paper money. Modern traders instead generally negotiate through a medium of exchange, such as money. As a result, buying can be separated from selling, or earning. The invention of money (and later credit, paper money and non-physical money) greatly simplified and promoted trade. Trade between two traders is called bilateral trade, while trade between more than two traders is called multilateral trade.

Trade exists for man due to specialization and division of labor, most people concentrate on a small aspect of production, trading for other products. Trade exists between regions because different regions have a comparative advantage in the production of some tradable commodity, or because different 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 fixed location, such as a department store, boutique or kiosk, or by mail, in small or individual lots for direct consumption by the purchaser. Wholesale trade is defined as the sale of goods or merchandise to retailers, to industrial, commercial, institutional, or other professional business users, or to other wholesalers and related subordinated services. [

Prehistory

Trade originated with the start of communication in prehistoric times. Trading was the main facility of prehistoric people, who bartered goods and services from each other before the innovation of the modern day currency. Peter Watson dates the history of long-distance commerce from circa 150, 000 years ago. In the Mediterranean region the earliest contact between cultures were of members of the species Homo sapiens principally using the Danube river, at a time beginning 35-30, 000 BC.

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Day Trading

Day trading refers to the practice of speculation in securities, specifically buying and selling financial instruments within the same trading day, such that all positions are usually closed before the market close for the trading day. Traders who participate in day trading are called active traders or day traders. Traders, who trade in this capacity with the motive of profit, assume the capital markets role of speculator. Not widely known, the correct definition of an "intra-day" means the move as measured from the previous close and not just relative to another price traded on the same day. Some of the more commonly day-traded financial instruments are stocks, stock options, currencies, and a host of futures contracts such as equity index futures, interest rate futures, and commodity futures.

Day trading used to be an activity exclusive to financial firms and professional speculators. Indeed, many day traders are bank or investment firm employees working as specialists in equity investment and fund management. However, with the advent of electronic trading and margin trading, day trading has become increasingly popular among at-home traders.

Characteristics

Trade frequency

Although collectively called day trading, there are many styles with specific qualities and risks. Scalping is an intra-day speculation technique that usually 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 a tenth of a cent, and a full round trip (a buy and a sell order) is often completed in less than one second. Instead of bidding $10.20 per share, the scalper will jump the bid at $10. 201, thus becoming the best bid and therefore 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 a cent cheaper at $10. 209 for a profit of 0.008 of a dollar. The profits add up when using 10, 000 share lots each time and the combined earnings from Rebates (read below) for creating liquidity. A day trader is actively searching for potential trading setups (that is, any stock or other financial instruments that, in the judgment of the day trader, is in a tension state, ready to accelerate in price in either direction, that when traded well has a potential for a substantial profit). The number of trades one can make per day is almost unlimited, as are the profits and losses.

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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, in which a trade may last just a few minutes. Day traders may buy and sell many times in a trading day and may receive trading fee discounts from their broker for this trading volume. Some day trader’s focus only on price momentum, others on technical patterns, and still others on an unlimited number of strategies they feel can be profitable. Most day traders exit positions before the market closes to avoid unmanageable risks—negative price gaps (differences between the previous day's close and the next day's open bull price) at the open—overnight price movements against the position held. Other traders believe they should let the profits run, so it is acceptable to stay with a position after the market closes. Day traders sometimes borrow money to trade. This is called margin trading. Since margin interests are typically only charged on overnight balances, the trader pays no fees for the margin benefit, though still running the risk of a Margin call. The margin interest rate is usually based on the Broker's call.

Profit and risks

Because of the nature of financial leverage and the rapid returns that are possible, day trading can be either extremely profitable or extremely unprofitable, and high-risk profile traders can generate either huge percentage returns or huge percentage losses. Because of the high profits (and losses) that day trading makes possible, these traders are sometimes portrayed 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 following is present while trading:

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trading a loser's game/system rather than a game that's 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 gains and losses, such that substantial losses or gains can occur in a very short period of time. In addition, brokers usually allow bigger margins for day traders. Where overnight margins required to hold a stock position are normally 50% of the stock's value, many brokers allow pattern day trader accounts to use levels as low as 25% for intraday purchases. This means a day 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 of those positions are exited before the market close. Because of the high risk of margin use, and of other day trading practices, a day trader will often have to exit a losing position very quickly, in order to prevent a greater, unacceptable loss, or even a disastrous loss, much larger than his or her original investment, or even larger than his or her total assets.

History

stocks were traded on the New York Stock Exchange. A trader would contact a stockbroker, who would relay the order to a specialist on the floor of the NYSE. These specialists would each make markets in only a handful of stocks. The specialist would match the purchaser with another broker's seller; write up physical tickets that, once processed, would effectively transfer the stock; and relay the information back to both brokers. Brokerage commissions were fixed at 1% of the amount of the trade, i. E. to purchase $10, 000 worth of stock cost the buyer $100 in commissions. (Meaning that to profit trades had to make over 1.010101. . . % to make any real gain.) One of the first steps to make day trading of shares potentially profitable was the change in the commission scheme. In 1975, the United States Securities and Exchange Commission (SEC) made fixed commission rates illegal, giving rise to discount brokers offering much reduced commission rates.

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Financial settlement

Financial settlement periods used to be much longer: Before the early 1990s at the London Stock Exchange, for example, stock could be paid for up 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) them before the end of the period hoping for a rise in price. This activity was identical to modern day trading, but for the longer duration of the settlement period. But today, to reduce market risk, the settlement period is typically three working days. Reducing the settlement period reduces the likelihood of default, but was impossible before the advent of electronic ownership transfer.

Electronic communication networks

The systems by which stocks are traded have also evolved, the second half of the twentieth century having seen the advent of electronic communication networks (ECNs). These are essentially large proprietary computer networks on which brokers could list a certain amount of securities to sell at a certain price (the asking price or "ask") or offer to buy a certain amount of securities at a certain price (the "bid"). ECNs and exchanges are usually known to traders by three- or four-letter designators, which identify the ECN or exchange 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 the increasingly cumbersome and expensive NYSE, also allowing them to trade during hours when the exchanges were closed. Early ECNs such as Instinet were very unfriendly to small investors, because they tended to give large institutions better prices than were available to the public. This resulted in a fragmented and sometimes illiquid market.

The next important step in facilitating day trading was the founding in 1971 of NASDAQ—a virtual stock exchange on which orders were transmitted electronically. Moving from paper share certificates and written share registers to "dematerialized" shares, computerized trading and registration required not only extensive changes to legislation but also the development of the necessary technology: online and real time systems rather than batch; electronic communications rather than the postal service, telex or the physical shipment of computer tapes, and the development of secure cryptographic algorithms.

These developments heralded the appearance of "market makers": the NASDAQ equivalent of a NYSE specialist. A market maker has an inventory

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of stocks to buy and sell, and simultaneously offers to buy and sell the same stock. Obviously, it will offer to sell stock at a higher price than the price at which it offers to buy. This difference is known as the "spread". The market maker is indifferent as to whether the stock goes up or down; it simply tries to constantly buy for less than it sells. A persistent trend in one direction will result in a loss for the market maker, but the strategy is overall positive (otherwise they would exit the business). Today there are about 500 firms who participate as market-makers on ECNs, each generally making a market in four to forty different stocks. Without any legal obligations, market-makers were free to offer smaller spreads on ECNs than on the NASDAQ. A small investor 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 an institution 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 reform made during this period was the "Small Order Execution System", or "SOES", which required market makers to buy or sell, immediately, small orders (up to 1000 shares) at the market-makers listed bid or ask. A defect in the system gave rise to arbitrage by a small group of traders known as the "SOES bandits", who made fortunes buying and selling small orders to market makers.

The existing ECNs began to offer their services to small investors. New brokerage firms which specialized in serving online traders who wanted to trade on the ECNs emerged. New ECNs also arose, most importantly Archipelago ("arca") and Island ("isld"). Archipelago eventually became a stock exchange and in 2005 was purchased by the NYSE. (At this time, the NYSE has proposed merging Archipelago with itself, although some resistance has arisen from NYSE members. ) Commissions plummeted. To give an extreme example (trading 1000 shares of Google), an online trader in 2005 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 and got it at a cheaper price.

ECNs are in constant flux. New ones are formed, while existing ones are bought or merged. As of the end of 2006, the most important ECNs to the individual trader were:

Instinet (which bought Island in 2002), Archipelago (although technically it is now an exchange rather than an

ECN),

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the Brass Utility ("brut"), and theSuperDot electronic system now used by the NYSE.

The evolution of average NASDAQ share prices between 1994 and 2004

This combination of factors has made day trading in stocks and stock derivatives (such as ETFs) possible. The low commission rates allow an individual 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 make near-instantaneous trades and to get favorable pricing. High-volume issues such as Intel or Microsoft generally have a spread of only $0. 01, so the price only needs to move a few pennies for the trader to cover his commission costs and show a profit.

The ability for individuals to day trade coincided with the extreme bull market in technological issues from 1997 to early 2000, known as the Dot-com bubble. From 1997 to 2000, the NASDAQ rose from 1200 to 5000. Many naive investors with little market experience made huge profits buying these stocks in the morning and selling them in the afternoon, at 400% margin rates.

Adding to the day-trading frenzy were the enormous profits made by the "SOES bandits" who, unlike the new day traders, were highly-experienced professional traders able to exploit the arbitrage opportunity created by SOES.

In March, 2000, this bubble burst, and a large number of less-experienced day traders began to lose money as fast, or faster, than they had made during the buying frenzy. The NASDAQ crashed from 5000 back to 1200; many of the less-experienced traders went broke, although obviously it was possible to have made a fortune during that time by shorting or playing on volatility.

Techniques

The following are several basic strategies by which day traders attempt to make profits. Besides these, some day traders also use contrarian (reverse) strategies (more commonly seen in algorithmic trading) to trade

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specifically against irrational behavior from day traders using these approaches.

Some of these approaches require shorting stocks instead of buying them: the trader borrows stock from his broker and sells the borrowed stock, hoping that the price will fall and he will be able to purchase the shares at a lower price. There are several technical problems with short sales—the broker may not have shares to lend in a specific issue, some short sales can only be made if the stock price or bid has just risen (known as an "uptick"), and the broker can call for the return of its shares at any time. Some of these restrictions (in particular the uptick rule) don't apply to trades of stocks that are actually shares of an exchange-traded fund (ETF).

The Securities and Exchange Commission removed the uptick requirement for short sales on July 6, 2007.

Trend following

Trend following, a strategy used in all trading time-frames, assumes that financial instruments which have been rising steadily will continue to rise, and vice versa with falling. The trend follower buys an instrument which has been rising, or short sells a falling one, in the expectation that the trend will continue.

Contrarian investing

Contrarian investing is a market timing strategy used in all trading time-frames. It assumes that financial instruments which have been rising steadily will reverse and start to fall, and vice versa with falling. The contrarian trader buys an instrument which has been falling or short-sells a rising one, in the expectation that the trend will change.

Range trading

Range trading, or range-bound trading, is a trading style in which stocks are watched that have either been rising off a support price or falling off a resistance price. That is, every time the stock hits a high, it falls back to the low, and vice versa. Such a stock is said to be "trading in a range", which is the opposite of trending. The range trader therefore buys the stock at or near the low price, and sells (and possibly short sells) at the high. A related approach to range trading is looking for moves outside of an established range, called a breakout (price moves up) or a breakdown (price moves down), and assume that once the range has been broken prices will continue in that direction for some time.

Scalping

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Scalping was originally referred to as spread trading. Scalping is a trading style where small price gaps created by the bid-ask spread is exploited by the speculator. It normally involves establishing and liquidating a position quickly, usually within minutes or even seconds.

Scalping highly liquid instruments for off-the-floor day traders involves taking quick profits while minimizing risk (loss exposure). It applies technical analysis concepts such as over/under-bought, support and resistance zones as well as trendline, trading channel to enter the market at key points and take quick profits from small moves. The basic idea of scalping is to exploit the inefficiency of the market when volatility increases and the trading range expands.

Rebate trading

Rebate trading is an equity trading style that uses ECN rebates as a primary source of profit and revenue. Most ECNs charge commissions to customers who want to have their orders filled immediately at the best prices available, but the ECNs pay commissions to buyers or sellers who "add liquidity" by placing limit orders that create "market-making" in a security. Rebate traders seek to make money from these rebates and will usually maximize their returns by trading low priced, high volume stocks. This enables them to trade 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 at Tradescape helped to contribute to a $280 million buyout from online trading giant E*Trade.

News playing

News playing is primarily the realm of the day trader. The basic strategy is to buy 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 greatest chance for quick profits (or losses). Determining whether news is "good" or "bad" must be determined by the price action of the stock, because the market reaction may not match the tone of the news itself. The most common cause for this is when rumors or estimates of the event (like those issued by market and industry analysts) were already circulated before the official release, and prices have already moved in anticipation—the news is already priced in the stock.

Price action

Keeping things simple can also be an effective methodology when it comes to trading. There are groups of traders known as price action traders who are a form of technical traders that rely on technical analysis but do not rely on conventional indicators to point them in the direction of a trade or not. These

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traders 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 by any means easier than any other trading methodology. It requires a sound background in understanding how markets work and the core principles within a market, but the good thing about this type of methodology is it will work in virtually any market that exists (stocks, foreign exchange, futures, gold, oil, etc. ).

Artificial intelligence

An estimated one third of stock trades in 2005 in United States were generated by automatic algorithms, or high-frequency trading. The increased use of algorithms and quantitative techniques has led to more competition and smaller profits.

Trading equipment

Some day trading strategies (including scalping and arbitrage) require relatively sophisticated trading systems and software. This software can cost $45, 000 or more. Since the masses have now entered the day trading space, strategies can now be found for as little as $5, 000. Many day traders use multiple monitors or even multiple computers to execute their orders. Some use real time filtering software which is programmed to send stock symbols to a screen which meet specific criteria during the day, such as displaying stocks that are turning from positive to negative. Some traders use community based tools including forums, message boards and chat rooms.

Brokerage

Day traders do not use discount brokers because they are slower to execute trades, trade against order flow, and charge higher commissions than direct access brokers, who allow the trader to send their orders directly to the ECNs. Direct access trading offers substantial improvements in transaction speed and will usually result in better trade execution prices (reducing the costs of trading). Outside the US, day traders will often use CFD or financial spread betting brokers for the same reasons.

Commission

Commissions for direct-access brokers are calculated based on volume. The more shares traded, the cheaper the commission. The average commission per trade is roughly $5 per round trip (getting in and out of a position). While a 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 $0.0002 per share traded (from $10 down to $. 20 per 1000 shares), or $0.25 per futures contract. A scalper can cover such costs with even a minimal gain.

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As for the calculation method, some use pro-rata to calculate commissions and charges, where each tier of volumes charges different commissions. Other brokers use a flat rate, where all commissions and charges are based on which volume threshold one reaches.

Spread

The numerical difference between the bid and ask prices is referred to as the bid-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 is executed at quoted prices, closing the trade immediately without queuing would not cause a loss because the bid price is always less than the ask price at any point in time.

The bid-ask spread is two sides of the same coin. The spread can be viewed as trading bonuses or costs according to different parties and different strategies. 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 as additional, or even the only, profits for successful trades.

Market data

Market data is necessary for day traders, rather than using the delayed (by anything from 10 to 60 minutes, per exchange rules) market data that is available for free. A real-time data feed requires paying fees to the respective stock exchanges, usually combined with the broker's charges; these fees are usually very low compared to the other costs of trading. The fees may be waived for promotional purposes or for customers meeting a minimum monthly volume of trades. Even a moderately active day trader can expect to meet these requirements, making the basic data feed essentially "free".

In addition to the raw market data, some traders purchase more advanced data feeds that include historical data and features such as scanning large numbers of stocks in the live market for unusual activity. Complicated analysis and charting software are other popular additions. These types of systems can cost from tens to hundreds of dollars per month to access.

Candlestick charts

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Candlestick charts are used by traders using technical analysis to determine chart patterns. Once a pattern is recognized in the chart, traders use the information to take a position. Some traders consider this method to be a part of price action trading.

Regulations and restrictions

Day trading is considered a risky trading style, and regulations require brokerage firms to ask whether the clients understand the risks of day trading and whether they have prior trading experience before entering the market.

WHAT IS INTRA-DAY TRADING? Intraday Trading

Intraday Trading, also known as Day Trading, is the system where you take a position on a stock and release that position before the end of that day's trading 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. You are not concerned with market sentiments. You are not concerned with the fundamental strengths (or the lack of it) of any company. All you need to predict is that the stock price will either rise or fall very sharply in the course of the day. When you take up day trading, the rules that may have helped you pick good stocks or find great money makers over the years, trading 'normally', will no longer apply. This is a different game with different rules. All of the methods that are used to identify stocks that are appropriate for normal delivery-based trading are dependent on either technical analysis, fundamentals or insider information. Technical analysis with charts is a way of using historical price/volume patterns to predict future behavior. Fundamentals deal with the market strength of a company, involving detailed study of balance sheets, branding, positioning, etc. None of these, on its own, hold good for day trading. The day trader's choice of scrips and positions has to work out in a day. There's no waiting until tomorrow to see how the charts play out before committing capital. If the day trader sees an opportunity, he has to go for it. NOW. Or it's gone. Things can change drastically in minutes. When it's time to buy or sell, it's time to buy or sell, and that's all there is to it.

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Day trading can be a great way to make money all on your own. It's also a great 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 of being in charge of your own business and your own trading account is exciting, then day trading might be a good career option for you.

Fundamentals

What are the objectives of the intraday trader? One point objective: to make profits. As much as possible.Simple. Whether the market is going up or down, we are not concerned. Whether there is a recession or not, we don't care. We want our daily profits. Simple. But to realise this 'simple' objective we 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 yesterday's 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.

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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.

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 of finding which stocks to track to realise maximum profits through intraday trading. Irrespective of market conditions.

How to go about it?

Like any stock trader, to make money through intraday trading at the stock market you must have a trading plan, set limits and stick to them. You must trade 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 an objective. That's a basic strategy for any endeavor, whether it's running a marathon, changing your car, or taking up day trading. Day traders have to move quickly, so they also have to take decisions quickly. You must also have patience. Some days there is nothing good to buy. Other days it seems like every trade can bring you money. But everything just turns around as soon as you really put in some money. Be patient, and take a calculated decision. What if it's a bad decision? Well, of course some decisions are going to be bad. That's 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 they start out with. Many others don't lose all of their trading capital, but they leave because they just decide that there are better uses of their time and better ways to make money. Yes, most day traders fail — about 80 percent in the first year. But so do a large percentage of people who start new businesses or enter other occupations. But two good day trading practices help limit the effects of making a bad decision:

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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.

Because they close out their positions in the stocks they own at the end of the day, whether winning or losing, some of the risks are limited. There is no hangover. Each day is a new day, and nothing can happen overnight to disturb an existing profit position.

Day Trading as a hobby?

Day Trading as a hobby is a bad idea. Also, trading without a plan and without 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-sum market.

Day Trading part-time?

Can you make money day trading part-time? Yes, you can, and some people do. To do this, they approach trading as a part-time job, not as a little game to play when they have nothing else to do. A part-time trader may commit to trading three days a week, or to closing out at noon instead of at the close of the market. A successful part-time trader still has a business plan, still sets limits, and still acts like any professional trader would, just for a smaller part of the day or week.

TRADING GUIDELINES

Remember: You only make money if someone else loses it. If you are not fully 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 important

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weapons: Confidence, Discipline, Focus and Patience. We will explain these requirements in detail.

Objectives

But, before that, lets get some basics right. As an intraday trader, what are your objectives for the day? To make profits. As much as possible.Whether the 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 the beginning, 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 we've 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

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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 target. On a LONG example, if you've 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, you've 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 today's income. You MUST take home today's 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. We'll 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 today's position. If you have made 400 earlier, you can take a risk with the extra 100 you've 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. Don't 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 calculated figures will change proportionately. Examples are given for taking LONG positions. 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 you every day at least 2 'Suggests' that will give minimum 500 in profits each instead of the 300 discussed above. . .

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Just consider this: on an investment of 15K, you stand to make 4K+ per month. You double your money in less than 4 months. And it looks pretty easy! Increase of 3 for a stock of 600 value is not a big deal at all. A rise of 0.50p for a stock with value of 95 each is also commonplace. Even in the worst of days.

So, where is the catch? Why do people lose money at the stock market? The catch is not in the WHY?, or the HOW?, but in the WHERE? There is also a WHEN?

Where?

Finding the right stock that will rise from 600 to 603, or from 97 to 97. 50 on that particular day is the challenge. Finding that one amongst the 1000+ available at NSE is where most people falter. People put their money at the wrong places only to see losses. Here you can depend on IntradayTrade dot Net. Since the time we've come online we've given you names that have fulfilled your requirement everyday. Look at our past results.

When?

Like we've 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 in that serious business. This mainly deals with your entry and exit points. As mentioned earlier, to control these points you will need (1) a good trading method and (2) good money management policies. You will also need four important weapons: Confidence, Discipline, Focus and Patience.

Algorithmic Trading

Algorithmic trading, also known as automated trading, algo trading, black-box trading, whitebox trading or robo trading, is the use of electronic platforms for entering trading orders with an algorithm deciding on aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention. Algorithmic trading is widely used by pension funds, mutual funds, and other buy side (investor driven) institutional traders, to divide large trades into several smaller trades to manage market impact, and risk. Sell side traders, such as 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), in which computers make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. This has resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure

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speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.

A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group. As of 2009, HFT firms account for 73% of all US equity trading volume. In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and European markets 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 orders in 2006). Futures and options markets are considered fairly easy to integrated into algorithmic trading, with about 20% of options volume expected to be computer-generated by 2010. Bond markets are moving toward more access to algorithmic traders. One of the main issues regarding HFT is the difficulty in determining just how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in 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 the U. S. Securities and Exchange Commission and the Commodity Futures Trading Commission said they contributed to some of the volatility during the 2010 Flash Crash, when the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average. ) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."

Strategies

Trend following

Trend following is an investment strategy that tries to take advantage of long-term, medium-term, and short-term moves that sometimes occur in various markets. The strategy aims to take advantage of a market trend on both sides, going long (buying) or short (selling) in a market in an attempt to profit from the ups and downs of the stock or futures markets. Traders who use this approach can use current market price calculation, moving averages and channel breakouts to determine the general direction of the market and to

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generate trade signals. Traders who subscribe to a trend following strategy do not aim to forecast or predict specific price levels; they initiate a trade when a trend appears to have started, and exit the trade once the trend appears to have ended.

Pair trading

The pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sidewise movement. This trading strategy is categorized as a statistical arbitrage and convergence trading strategy.

Delta neutral strategies

In finance, delta neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. Such a portfolio typically contains options and their corresponding underlying securities such that positive and negative delta components offset, resulting in the portfolio's value being relatively insensitive to changes in the value of the underlying security.

Arbitrage

In economics and finance, arbitrage/ˈis the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic 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 arbitrage

Arbitrage 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).

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Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time. The transactions must occur simultaneously to avoid exposure to market risk, or the risk that prices may change on one market before both transactions are complete. In practical terms, this is generally only possible with securities and financial products which can be traded electronically, and even then, when each leg of the trade is executed the prices in the market may have moved. Missing one of the legs of the trade (and subsequently having to trade it soon after at a worse price) is called 'execution risk' or more specifically 'leg risk'.

In the simplest example, any good sold in one market should sell for the same price in another. Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a higher price. This type of price arbitrage is the most common, but this simple example ignores the cost of transport, storage, risk, and other factors. "True" arbitrage requires that there be no market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other. See rational pricing, particularly arbitrage mechanics, for further discussion.

Mean reversion

Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock's high and low prices are temporary, and that a stock's price tends to have an average price over time. Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc. When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.

The Standard deviation of the most recent prices (e.g. , the last 20) is often used as a buy or sell indicator. Stock reporting services (such as Yahoo! Finance, MS Investor, Morningstar, etc. ), commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary. Mean reversion has the appearance of a more scientific method of choosing stock buy and sell points than charting, because precise numerical values are derived from historical data to identify the buy/sell values, rather than trying to interpret price movements using charts (charting, also known as technical analysis).

Scalping

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Scalping (trading) is a method of arbitrage of small price gaps created by the bid-ask spread. Scalpers attempt to act like traditional market makers or specialists. To make the spread means to buy at the bid price and sell at the ask price, to gain the bid/ask difference. This procedure allows for profit even when the bid and ask do not move at all, as long as there are traders who are willing to take market prices. It normally involves establishing and liquidating a position quickly, usually within minutes or even seconds. The role of a scalper is actually the role of market makers or specialists who are to maintain the liquidity and order flow of a product of a market. A market maker is basically a specialized scalper. The volume a market maker trades are many times more than the average individual scalpers. A market maker has a sophisticated trading system to monitor trading activity. However, a market maker is bound by strict exchange rules while the individual trader is not. For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented.

Transaction cost reduction

Most strategies referred to as algorithmic trading (as well as algorithmic liquidity seeking) fall into the cost-reduction category. Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the volume-weighted average price for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Stealth". Most of these strategies were first documented in 'Optimal Trading Strategies' by Robert Kissell.

Strategies that only pertain to dark pools

Recently, HFT, which comprises a broad set of buy-side as well as market making 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 at their core quite simple mathematical constructs.Dark pools are alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or "iceberged. " Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

“Now it’s an arms race, ” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists,

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the smaller the profits. ” One of the unintended adverse effects of algorithmic trading, has been the dramatic increase in the volume of trade allocations and settlements, as well as the transaction settlement costs associated with them. Since 2004, there have been a number of technological advances and service providers by individuals like Scott Kurland, who have built solutions for aggregating trades executed across algorithms to counter these rising settlement costs.

High-frequency trading

In the U.S. , high-frequency trading (HFT) firms represent 2% of the approximately 20, 000 firms operating today, but account for 73% of all equity trading volume. As of the first quarter in 2009, total assets under management for hedge funds with HFT strategies were US$141 billion, down about 21% from their high. The HFT strategy was first made successful by Renaissance Technologies. High-frequency funds started to become especially popular in 2007 and 2008. Many HFT firms are market makers and provide liquidity to the market, which has lowered volatility and helped narrow Bid-offer spreads making trading and investing cheaper for other market participants. HFT has been a subject of intense public focus since the U. S. Securities and Exchange Commission and the Commodity Futures Trading Commission stated that both algorithmic and HFT contributed to volatility in the May 6, 2010 Flash Crash. Major players in HFT include GETCO LLC, Jump Trading LLC, Tower Research Capital, Hudson River Trading as well as Citadel Investment Group, Goldman Sachs, DE Shaw, RenTech. High-frequency trading is quantitative trading that is characterized by short portfolio holding periods (see Wilmott (2008), Aldridge (2009)). There are four key categories of HFT strategies: market-making based on order flow, market-making based on tick data information, event arbitrage and statistical arbitrage. All portfolio-allocation decisions are made by computerized quantitative models. The success of HFT strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.

Market making

Market making is a set of HFT strategies that involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price to benefit from the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange.

Statistical arbitrage

Another set of HFT strategies is classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are

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sufficiently different from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit. HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits of low latency arbitrage strategies currently exceed US$21 billion.

A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes. [31]

Event arbitrage

A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc. , to change the price or rate relationship of two or more financial 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 is the 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 offered by the acquiring company. The spread between these two prices depends mainly on the probability and the timing of the takeover being completed as well as the prevailing level of interest rates. The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed. The risk is that the deal "breaks" and the spread massively widens.

Low-latency trading

HFT is often confused with low-latency trading that uses computers that execute trades within milliseconds, or "with extremely low latency" in the jargon of the trade. Low-latency traders depend on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. [5] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform to benefit from implementing high-frequency strategies. [5] Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies – they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a

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significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.

Strategy implementation

Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets. Increasingly, the algorithms used by large brokerages and asset managers are written to the FIX Protocol's Algorithmic Trading Definition Language (FIXatdl), which allows firms receiving orders to specify exactly how their electronic orders should be expressed. Orders built using FIXatdl can then be transmitted from traders' systems via the FIX Protocol. Basic models can rely on as little as a linear regression, while more complex game-theoretic and pattern recognitionor predictive models can also be used to initiate trading. Neural networks and genetic programming have been used to create these models.

Issues and developments

Algorithmic trading has been shown to substantially improve market liquidityamong other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Concerns

“The downside with these systems is their black box-ness, ” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships. ”

“The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’. ”

UK Treasury minister Lord Myners has warned that companies could become the "playthings" of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company. Other issues include the technical problem of latency or the delay in getting quotes to traders, security and the possibility of a complete system breakdown leading to a market crash. "Goldman

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spends tens of millions of dollars on this stuff. They have more people working in their technology area than people on the trading desk. . . The nature of the markets has changed dramatically. " Algorithmic and HFT were shown to have contributed to volatility during the May 6, 2010 Flash Crash, when the Dow Jones Industrial Average plunged about 600 points only to recover those losses 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 an intraday basis in Dow Jones Industrial Average history.

Recent developments

Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms. "Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news. " The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story.

"Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way, " as they constantly seek out new trading advantages said Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics.

"Markets are by their very nature conversations, having grown out of coffee houses and taverns", he said. So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said. “There is a real interest in moving the process of interpreting news from the humans to the machines” says KirstiSuutari, global business manager of algorithmic trading at Reuters. "More of our customers are finding ways to use news content to make money. "

An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of the Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by 2 seconds in reporting an interest rate cut by the Bank of England. In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day.

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Citigroup had previously bought Lava Trading and OnTrade Inc. In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets, led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. The Foresight project is set to conclude in late 2012. In September 2011, RYBN has launched "ADM8", an open source Trading Bot prototype, already active on the financial markets.

Technical design

The 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 unit3. The execution unit.

With the wide use of social networks, some systems implement scanning or screening technologies to read posts of users extracting human sentiment and influence the trading strategies.

Effects

Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds and even microseconds, have become very important. More fully automated markets such as NASDAQ, Direct Edge and BATS, in the US, have gained market sharefrom less automated markets such as the NYSE. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

Competition is developing among exchanges for the fastest processing times for completing trades. For example, in June 2007, the London Stock Exchange launched a new system called TradElect that promises an average 10 millisecond turnaround time from placing an order to final confirmation and can process 3, 000 orders per second. Since then, competitive exchanges have continued to reduce latency with turnaround times of 3 milliseconds available. This is of great importance to high-frequency traders, because they have to attempt to pinpoint the consistent and probable performance ranges of given financial instruments. These professionals are often dealing

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in versions of stock index funds like the E-mini S&Ps, because they seek consistency and risk-mitigation along with top performance. They must filter market data to work into their software programming so that there is the lowest latency and highest liquidity at the time for placing stop-losses and/or taking profits. With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, where a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions.

Spending on computers and software in the financial industry increased to $26. 4 billion in 2005.

Communication standards

Algorithmic trades require communicating considerably more parameters than traditional market and limit orders. A trader on one end (the "buy side") must enable their trading system (often called an "order management system" or "execution management system") to understand a constantly proliferating flow of new algorithmic order types. The R&D and other costs to construct complex new algorithmic orders types, along with the execution infrastructure, and marketing costs to distribute them, are fairly substantial. What was needed was a way that marketers (the "sell side") could express algo orders electronically such that buy-side traders could just drop the new order types into their system and be ready to trade them without constant coding custom new order entry screens each time.

FIX Protocol LTD http: //www. fixprotocol. org is a trade association that publishes free, open standards in the securities trading area. The FIX language was originally created by Fidelity Investments, and the association Members include virtually all large and many midsized and smaller broker dealers, money center banks, institutional investors, mutual funds, etc. This institution dominates standard setting in the pretrade and trade areas of security transactions. In 2006-2007 several members got together and published a draft XML standard for expressing algorithmic order types. The standard is called FIX Algorithmic Trading Definition Language (FIXatdl). The first version of this standard, 1.0 was not widely adopted due to limitations in the specification, but the second version, 1. 1 (released in March 2010) is expected to achieve broad adoption and in the process dramatically reduce time-to-market and costs associated with distributing new algorithms.

High-frequency trading

High-frequency trading (HFT) is the use of sophisticated technological tools to trade securities like stocks or options, and is typically characterized by several distinguishing features:

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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; 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 in exchange 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, and other financial instruments that can be traded electronically. High-frequency traders compete on a basis of speed with other high-frequency traders, not long-term investors (who typically look for opportunities over a period of weeks, months, or years), and compete for very small, consistent profits. As a result, high-frequency trading has been shown to have a potential Sharpe ratio (measure of reward per unit of risk) thousands of times higher than the traditional buy-and-hold strategies. Aiming to capture just a fraction of a penny per share or currency unit on every trade, high-frequency traders move in and out of such short-term positions several times each day. Fractions of a penny accumulate fast to produce significantly positive results at the end of every day. High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.

By 2010 high-frequency trading accounted for over 70% of equity trades in the US and was rapidly growing in popularity in Europe and Asia. Algorithmic and high-frequency trading were both found to have contributed to volatility in the May 6, 2010 Flash Crash, when high-frequency liquidity providers were in fact found to have withdrawn from the market. A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010. "[

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History

High-frequency trading has taken place at least since 1999, after the U. S. Securities and Exchange Commission (SEC) authorized electronic exchanges in 1998. At the turn of the 21st century, HFT trades had an execution time of several seconds, whereas by 2010 this had decreased to milli- and even microseconds. Until recently, high-frequency trading was a little-known topic outside the financial sector, with an article published by the New York Times in July 2009 being one of the first to bring the subject to the public's attention.

Market growth

In the early 2000s, high-frequency trading still accounted for less than 10% of equity orders, but this proportion was soon to begin rapid growth. According to data from the NYSE, trading volume grew by about 164% between 2005 and 2009 for which high-frequency trading might be accounted. As of the first quarter in 2009, total assets under management for hedge funds with high-frequency trading strategies were $141 billion, down about 21% from their peak before the worst of the crises. The high-frequency strategy was first made successful by Renaissance Technologies. Many high-frequency firms are market makers and provide liquidity to the market which has lowered volatility and helped narrow Bid-offer spreads, making trading and investing cheaper for other market participants. In the United States, high-frequency trading firms represent 2% of the approximately 20, 000 firms operating today, but account for 73% of all equity orders volume. The largest high-frequency trading firms in the US include names like Getco LLC, Knight Capital Group, Jump Trading, and Citadel LLC. The Bank of England estimates similar percentages for the 2010 US market share, also suggesting that in Europe HFT 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 2010 by consultancy Tabb Group to make up 56% of equity trades in the US and 38% in Europe.

High-frequency trading strategies

High-frequency trading is quantitative trading that is characterized by short portfolio holding periods (see Wilmott (2008)). All portfolio-allocation decisions are made by computerized quantitative models. The success of high-frequency trading strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human

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traders cannot do. Specific algorithms are closely guarded by their owners and are known as "algos".

Most high-frequency trading strategies fall within one of the following trading strategies:

Market making Ticker tape trading Event arbitrage High-frequency statistical arbitrage

Market making

Market making is a set of high-frequency trading strategies that involve placing a limit order to sell (or offer) or a buy limit order (or bid) in order to earn the bid-ask spread. By doing so, market makers provide counterpart to incoming market orders. Although the role of market maker was traditionally fulfilled by specialist firms, this class of strategy is now implemented by a large range of investors, thanks to wide adoption of direct market access. As pointed out by empirical studies this renewed competition among liquidity providers causes reduced effective market spreads, and therefore reduced indirect costs for final investors.

Some high-frequency trading firms use market making as their primary trading strategy. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both the NASDAQ and the New York Stock Exchange. Building up market making strategies typically involves precise modeling of the target market microstructure together with stochastic control techniques.

These strategies appear intimately related to the entry of new electronic venues. Academic study of Chi-X's entry into the European equity market reveals that its launch coincided with a large HFT that made markets using both the incumbent market, NYSE-Euronext, and the new market, Chi-X. The study shows that the new market provided ideal conditions for HFT market-making, low fees (i. e. , rebates for quotes that led to execution) and a fast system, yet the HFT was equally active in the incumbent market to offload nonzero positions. New market entry and HFT arrival are further shown to coincide with a significant improvement in liquidity supply.

Ticker tape trading

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Much information happens to be unwittingly embedded in market data, such as quotes and volumes. By observing a flow of quotes, high-frequency trading machines are capable of extracting information that has not yet crossed the news screens. Since all quote and volume information is public, such strategies are fully compliant with all the applicable laws. Filter trading is one of the more primitive high-frequency trading strategies that involves monitoring large amounts of stocks for significant or unusual price changes or volume activity. This includes trading on announcements, news, or other event criteria. Software would then generate a buy or sell order depending on the nature of the event being looked for.

Event arbitrage

Certain recurring events generate predictable short-term responses in a selected set of securities. High-frequency traders take advantage of such predictability to generate short-term profits.

Statistical arbitrage

Another set of high-frequency trading strategies are strategies that exploit predictable temporary deviations from stable statistical relationships among securities. Statistical arbitrage at high frequencies is actively used in all liquid securities, including equities, bonds, futures, foreign exchange, etc. Such strategies may also involve classical arbitrage strategies, such as covered interest rate parity in the foreign exchange market, which gives a relationship between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. High-frequency trading allows similar arbitrages using models of greater complexity involving many more than four securities. The TABB Group estimates that annual aggregate profits of high-frequency arbitrage strategies currently exceed US$21 billion.

Low-latency strategies

A separate, "naïve" class of high-frequency trading strategies relies exclusively on ultra-low latency direct market access technology. In these strategies, computer scientists rely on speed to gain minuscule advantages in arbitraging price discrepancies in some particular security trading simultaneously on disparate markets.

Effects

The effects of algorithmic and high-frequency trading in volatile markets are the subject of ongoing research since regulators claim these practices contributed to volatility in the May 6, 2010 Flash Crash, as discussed later in this section. "The fast-growing practice of high-frequency trading, in which traders place vast flurries of securities trades, is speeding up execution

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times for all investors, making it cheaper to buy or sell and posing no risk to small investors. " -Chicago Board Options Exchange

Members of the financial industry claim high-frequency trading substantially improves market liquidity, narrows Bid-offer spread, lowers volatility and makes trading and investing cheaper for other market participants. An academic study found that, for large-cap stocks and in quiescent markets during periods of "generally rising stock prices", high-frequency trading lowers the cost of trading and increases the informativeness of quotes; however, it found "no significant effects for smaller-cap stocks"and "it remains an open question whether algorithmic trading and algorithmic liquidity supply are equally beneficial in more turbulent or declining markets. . . algorithmic liquidity suppliers may simply turn off their machines when markets spike downward. "

More fully automated markets such as NASDAQ, Direct Edge, and BATS, in the US, have gained market share from less automated markets such as the NYSE. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

The speeds of computer connections, measured in milliseconds or microseconds, have become important. Competition is developing among exchanges for the fastest processing times for completing trades. For example, in 2009 the London Stock Exchange bought a technology firm called MillenniumIT and announced plans to implement its Millennium Exchange platformwhich they claim has an average latency of 126 microseconds. Since then, competitive exchanges have continued to reduce latency, and today, with turnaround times of three milliseconds available, are useful to traders to pinpoint the consistent and probable performance ranges of financial instruments. These professionals are often dealing in versions of stock index funds like the E-mini S&Ps because they seek consistency and risk-mitigation along with top performance. They must filter market data to work into their software programming so that there is the lowest latency and highest liquidity at the time for placing stop-losses and/or taking profits. With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, in which a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions.

Spending on computers and software in the financial industry increased to $26. 4 billion in 2005. The brief but dramatic stock market crash of May 6,

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2010 was originally alleged to have been caused by high-frequency trading. However, CME Group, a large futures exchange, stated that, insofar as stock index futures traded on CME Group were concerned, its investigation had found no support for the notion that high-frequency trading was related to the crash, and actually stated it had a market stabilizing effect. This conclusion is contradicted in a report on the Flash Crash by the U. S. Securities and Exchange Commission and the Commodity Futures Trading Commission, wherein regulators stated that the actions of high-frequency trading firms on May 6, 2010 contributed to volatility during the crash. Despite the original perception that high-frequency traders typically cause no market price impact, and have a stabilizing effect in times of volatility, and some suggestions that they may actually have been a major factor in minimizing and partially reversing the Flash Crash, later reports determined that high-frequency trading had significant price impact and a destabilizing role during the Flash Crash, helping to drive prices down.

After almost five months of investigations, the U. S. Securities and Exchange Commission and the Commodity Futures Trading Commission issued a joint report identifying the cause that set off the sequence of events leading to the Flash Crash. The report found that the cause was a single sale of $4. 1 billion in futures contracts by a mutual fund, identified as Waddell & Reed Financial, in an aggressive attempt to hedge its investment position. The joint report also found that "high-frequency traders quickly magnified the impact of the mutual fund's selling. " The joint report "portrayed a market so fragmented and fragile that a single large trade could send stocks into a sudden spiral, " that a large mutual fund firm "chose to sell a big number of futures contracts using a computer program that essentially ended up wiping out available buyers in the market, " that as a result high-frequency firms "were also aggressively selling the E-mini contracts, " contributing to rapid price declines. The joint report also noted "'HFTs began to quickly buy and then resell contracts to each other — generating a 'hot-potato' volume effect as the same positions were passed rapidly back and forth. '" The combined sales by Waddell and high-frequency firms quickly drove "the E-mini price down 3% in just four minutes. " As prices in the futures market fell, there was a spillover into the equities markets where "the liquidity in the market evaporated because the automated systems used by most firms to keep pace with the market paused" and scaled back their trading or withdrew from the markets altogether. The joint report then noted that "Automatic computerized traders on the stock market shut down as they detected the sharp rise in buying and selling. " As computerized high-frequency traders exited the stock market, the resulting lack of liquidity ". . . caused shares of some prominent companies like Procter & Gamble and Accenture to trade down as low as a penny or as high as $100, 000. " While some firms exited the market, high-frequency firms that remained in the market exacerbated

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price declines because they "'escalated their aggressive selling' during the downdraft. "

Controversy

This article may contain too much repetition or redundant language. Please help improve it by merging similar text or removing repeated statements.

High-frequency trading has been the subject of intense public focus since regulators claimed these practices contributed to volatility on May 6, 2010, popularly known as the 2010 Flash Crash, a United States stock market crash on May 6, 2010 in which the Dow Jones Industrial Average plunged to its largest intraday point loss, but not percentage loss, in history, only to recover much of those losses within minutes. Another area of controversy, related to SEC and CFTC findings in their joint report on the Flash Crash that equity market "market makers and other liquidity providers widened their quote spreads, others reduced offered liquidity, and a significant number withdrew completely from the markets"during the Flash Crash, is whether high-frequency market makers should be subject to regulations that would require them to stay active in volatile markets. As SEC Chairman Mary Schapiro said in a speech on September 22, 2010, ". . . high frequency trading firms have a tremendous capacity to affect the stability and integrity of the equity markets. Currently, however, high frequency trading firms are subject to very little in the way of obligations either to protect that stability by promoting reasonable price continuity in tough times, or to refrain from exacerbating price volatility. " Despite studies reporting positive findings about high-frequency trading, including that high-frequency trading reduces volatility and does not pose a systemic risk, and both lowers transaction costs for retail investors, and at the same time does so without impacting long term investors, high-frequency trading is the subject of increased debate. This debate has been fueled by U. S. Securities and Exchange Commission and Commodity Futures Trading Commission empirical findings that high-frequency trading contributed to volatility in the May 6, 2010 Flash Crash. Politicians, regulators, journalists and market participants have all raised concerns on both sides of the Atlantic. In September 2010, SEC chairperson Mary Schapiro signaled that US authorities were considering the introduction of regulations targeted at HFT, such as a minimum "time in force" rule, to prevent buy orders being canceled very soon after being issued. Criticisms of this proposed law are that currently exchanges allow excess message traffic to queue up at their servers' ports, where it is processed sequentially at a fixed rate and as a result poses no threat to the exchanges. In addition to this, equity options markets produce far more message volume than equity markets and have consistently handled the data without issue. Some HFT systems cancel many of their orders almost immediately after placing them as they don't intend the trades to carry through; the false orders are used as part of a pinging tactic to discover the upper price other traders are willing to pay. Some high-frequency trading firms state that so many orders get canceled because the orders people get

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are not the same ones they send. This happens frequently because of an existing regulation regarding re-priced orders.

Another area of concern relates to flash trading. Flash trading is a form of trading in which certain market participants are allowed to see incoming orders to buy or sell securities very slightly earlier than the general market participants, typically 30 milliseconds, in exchange for a fee. According to some sources, the programs can inspect major orders as they come in and use that information to profit. Currently, the majority of exchanges either do not offer flash trading, or have discontinued it, although the exchange Direct Edge currently does offer it to participants. Direct Edge's response to this is that flash trading reduces market impact, increases average size of executed orders, reduces trading latency, and provides additional liquidity. Direct Edge also allows all of its subscribers to determine whether they want their orders to participate in flash trading or not so brokers have the option to opt out of flash orders on behalf of their clients if they choose to. Due to the fact that market participants can choose to utilize it for additional liquidity or not participate in it at all, Direct Edge believes the controversy is overstated, stating:

"Misconceptions respecting flash technology have, to date, stirred a passionate but ill informed debate. ” Counter Punch, a bi-weekly political newsletter, contends that this creates a two-tiered market in which a certain class of traders can unfairly exploit others, akin to front running. Exchanges claim that the procedure benefits all traders by creating more market liquidity and the opportunity for price improvement.

Direct Edge's response to the "two-tiered market" criticism is as follows:

"First it is difficult to address concerns that may result, particularly when there is no empirical data to support such a result. Furthermore, we do not view technology that instantaneously aggregates passive and aggressive liquidity as creating a two-tier market. Rather, flash technology democratizes access to the non-displayed market and in this regard, removes different "tiers" in market access. Additionally, any subscriber of Direct Edge can be a recipient of flashed orders.

Advanced trading platforms

Advanced computerized trading platforms and market gateways are becoming standard tools of most types of traders, including high-frequency traders. Broker-dealers now compete on routing order flow directly, in the fastest and most efficient manner, to the line handler where it undergoes a strict set of Risk Filters before hitting the execution venue(s). Ultra Low Latency Direct Market Access (ULLDMA) is a hot topic amongst Brokers and Technology vendors such as Goldman Sachs, Credit Suisse, and UBS. Typically,

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ULLDMA systems can currently handle high amounts of volume and boast round-trip order execution speeds (from hitting "transmit order" to receiving an acknowledgment) of 10 milliseconds or less. Such performance is achieved with the use of hardware acceleration or even full-hardware processing of incoming Market data, in association with high-speed communication protocols, such as 10 Gigabit Ethernet or PCI Express. More specifically, some companies provide full-hardware appliances based FPGA to obtain sub-microsecond end-to-end Market data processing

Why Do It?

People are attracted to algorithmic trading for a number of reasons, including the following.

Back-tests can be performed. In other words, one can simulate the algorithm as it would have performed on data in the past. This makes it possible to develop strategies that would have performed well in the past, and then one can hope that they will continue to perform well in the future. This is simply not possible with discretionary trading.

Speed is good. Computers can perform computations and place orders extremely quickly. A trading algorithm running on a decent computer can react to changing situations in milliseconds, while human traders cannot even move their fingers to the keys that quickly, let alone make a decision in that time.

Algorithms have no emotions. Novice traders lose a lot of money because of their emotions (see the many books devoted to the topic of trading psychology). One classic response is to panic and sell something when the price drops rapidly. Sometimes this is the right thing to do and sometimes it's the wrong thing to do. But fear alone should never be a reason to sell. An algorithm will never make this decision because of fear.

Most strategies are easily scalable. If you can make $50000 a year using algorithm trading, it is (theoretically) a simple matter of doubling all your order sizes to make $100000 a year instead. Of course, your losses within the year will be doubled as well, and increasing order sizes beyond a certain point can lead to deterioration in performance due to market impact. But the concept of a "volume control" for your own salary is very attractive. Of course, unlike other jobs, trading earns income that is random, and may even be negative. Good traders manage their risk by choosing their scale carefully. You want

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the probability of losing all of your trading capital in the first month to be very low.

If things are going well with algorithmic trading, one could obtain cumulative profit over time that looks something like the following. In this particular case I am showing a small portion of my own P&L (profits and losses) over successive trades in my personal trading account using my own home-built infrastructure.

Does Algorithmic Trading Work?

Actually the question is ill-posed. What exactly does it mean to "work"? If the question is "Can I make a guaranteed salary of X$ every month by algorithmic trading?", then the answer is no. If the question is "Can I make a random salary every month and make a living, losing money some months?", then the answer is a highly qualified "yes". To make it work, you will need most (if not all) of the following:

strong quantitative and data analysis skills, good programming skills, an ability to think and make decisions in terms of probabilities, a feel for the behaviour of the market, and strong nerves.

If you have these abilities, then you can probably succeed in algorithmic trading development and implementation, after a few years of hard work.

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But that's just my opinion. Any given individual might be faster or slower at this process.

To decide whether or not an algorithmic trading strategy is "working", you can evaluate it using various performance measures.

Where do Algorithmic Trading Strategies Come From?

Analysis! Whenever you see someone describing the performance of their fund/system/algorithm, you'll see the standard qualifier "past performance is not an indicator of future results". Actually this is an over-done legal statement. It's not exactly true, otherwise people would not even bother showing you their past performance. Past performance can be an indicator for future results (unless it has been faked - for a well-known example look at the behaviourofBernard Madoff, but that's another story). It's just a very weak indicator. However, given a choice between no indicator, and a weak indicator, you would probably choose the weak one. This is the basis behind algorithmic trading strategy development. By analyzing past price history, and particularly patterns therein, people aim to find means of predicting future behaviour. Amazingly, this actually works. However, there are many pitfalls and it takes experience to be able to do this without finding false positives. I won't list any secrets here, but will point out a few of the well-known things that you need to be careful of when looking for patterns.

Patterns in the clouds: The single biggest trap is seeing patterns that are not going to repeat themselves in the future. If you have ever looked at a cloud and seen some image, like a face, or a shoe, you have probably wondered how it got there. While some people go for religious or mystical explanations, I just stick with the idea that there are lots of clouds, lots of wind gusts, and every now and then by chance you see some half-recognizable image. If you study price histories for long enough, you will see patterns that just happen to be there by chance.

Unusable patterns: Once you work out how to avoid spotting false positives, you'll probably begin by finding a number of strategies that would make millions if not for transaction costs. Transaction costs eat up a little bit of money every time you make a trade and if you don't account for this, you can get very inaccurate pictures of strategy performance. This effect is more important the higher the frequency of trading.

Structural change in the markets: Patterns do not persist forever. Patterns come about because of the collective behaviour of many market participants. As participants come and go, particularly big players who trade large volume, the patterns themselves change. Other factors can affect these patterns as well. Changes in the rules of the game (for example, a temporary ban on short-selling certain

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securities) can also have a striking effect. As a trader, whether discretionary or algorithmic, you must be prepared for such situations.

Entry Exit Type

• Financial trading uses some phrases that are unique, including the terms that describe whether a trade was entered by buying or selling.

• Basically there are two types of Entry/Exit condition; 1. Entry= Long=Short

2. Exit=sell=buy to cover

Long Entry: • In an uptrend or long position, take profits at or slightly above the

former price high in the current trend. • When an investor goes long on an investment, it means that he or

she has bought a stock believing its price will rise in the future

Short Entry: • In a downtrend or short position, take profits at or slightly below the

former price low in the current trend. • When an investor goes short he/she is anticipating a decrease in

share price. • Short selling is the selling of a stock that the seller doesn't own. • The shares are sold and are credited to your account. • Then you return them to your broker by buying back shares. • If the price drops, you can buy back the stock at the lower price and

make a profit on the difference.

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Technical Analysis Chart Pattern

Headnad and Shoulders

This is one of the most popular and reliable chart patterns in technical analysis.  

Head and shoulders is a reversal chart pattern that when formed, signals that the security is likely to move against the previous trend.

There are two versions of the head and shoulders chart pattern. Head and shoulders top (shown on the left) is a chart pattern that is

formed at the high of an upward movement and signals that the upward trend is about to end.

Head and shoulders bottom, also known as inverse head and shoulders (shown on the right) is the lesser known of the two, but is used to signal a reversal in a downtrend.

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Both of these head and shoulders patterns are similar in that there are four main parts: two shoulders, a head and a neckline.

Also, each individual head and shoulder is comprised of a high and a low.

For e. g. the left shoulder is made up of a high followed by a low. In this pattern, the neckline is a level of support or resistance. Remember that an upward trend is a period of successive rising highs

and rising lows. The head and shoulders chart pattern, therefore, illustrates a

weakening in a trend by showing the deterioration in the successive movements of the highs and lows.

Cup and Handle

A cup and handle chart is a bullish continuation pattern in which the upward trend has paused but will continue in an upward direction once the pattern is confirmed.

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This price pattern forms what looks like a cup, which is preceded by an upward trend.

The handle follows the cup formation and is formed by a generally downward/sideways movement in the security's price.

Once the price movement pushes above the resistance lines formed in the handle, the upward trend can continue.

There is a wide ranging time frame for this type of pattern, with the span ranging from several months to more than a year.

Double Tops and Bottoms

This chart pattern is another well-known pattern that signals a trend reversal - it is considered to be one of the most reliable and is commonly used.

These patterns are formed after a sustained trend and signal to chartists that the trend is about to reverse.

The pattern is created when a price movement tests support or resistance levels twice and is unable to break through.

This pattern is often used to signal intermediate and long-term trend reversals.

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In the case of the double top pattern the price movement has twice tried to move above a certain price level.

After two unsuccessful attempts at pushing the price higher, the trend reverses and the price heads lower.

In the case of a double bottom (shown on the right), the price movement has tried to go lower twice, but has found support each time.

After the second bounce off of the support, the security enters a new trend and heads upward.

Triangle  

Triangles are some of the most well-known chart patterns used in technical analysis.

The three types of triangles, which vary in construct and implication, are the symmetrical triangle,  ascending and descending triangle.

These chart patterns are considered to last anywhere from a couple of weeks to several months

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The symmetrical triangle is a pattern in which two trendlines converge toward each other.

This pattern is neutral in that a breakout to the upside or downside is a confirmation of a trend in that direction.

In an ascending triangle, the upper trendline is flat, while the bottom trendline is upward sloping.

This is generally thought of as a bullish pattern in which chartists look for an upside breakout.

In a descending triangle, the lower trendline is flat and the upper trendline is descending.

Flag and Pennant

These two short-term chart patterns are continuation patterns that are formed when there is a sharp price movement followed by a generally sideways price movement.

This pattern is then completed upon another sharp price movement in the same direction as the move that started the trend.

The patterns are generally thought to last from one to three weeks

As you can see there is little difference between a pennant and a flag.

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The main difference between these price movements can be seen in the middle section of the chart pattern.

In a pennant, the middle section is characterized by converging trendlines, much like what is seen in a symmetrical triangle.

The middle section on the flag pattern, on the other hand, shows a channel pattern, with no convergence between the trendlines.

In both cases, the trend is expected to continue when the price moves above the upper trendline

Wedge

The wedge chart pattern can be either a continuation or reversal pattern.

It is similar to a symmetrical triangle except that the wedge pattern slants in an upward or downward direction

While the symmetrical triangle generally shows a sideways movement. The other difference is that wedges tend to form over longer periods,

usually between three and six months. The fact that wedges are classified as both continuation and reversal

patterns can make reading signals confusing. However, at the most basic level, a falling wedge is bullish and a

rising wedge is bearish. We have a falling wedge in which two trendlines are converging in a

downward direction. If the price was to rise above the upper trendline, it would form

a continuation pattern, while a move below the lower trendline would signal a reversal pattern

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GAPS

A gap in a chart is an empty space between a trading period and the following trading period.

This occurs when there is a large difference in prices between two sequential trading periods.

For e. g. if the trading range in one period is between $25 and $30 and the next trading period opens at $40, there will be a large gap on the chart between these two periods.

Gap price movements can be found on bar charts and candlestick charts but will not be found on point and figure or basic line charts.

Gaps generally show that something of significance has happened in the security, such as a better-than-expected earnings announcement.

There are three main types of gaps,  breakaway, runaway (measuring) and exhaustion.

A breakaway gap forms at the start of a trend, a runaway gap forms during the middle of a trend and an exhaustion gap forms near the end of a trend. (For more insight, read Playing The Gap. )

Triple Tops and Bottom

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Triple tops and triple bottoms are another type of reversal chart pattern in chart analysis.

These are not as prevalent in charts as head and shoulders and double tops and bottoms, but they act in a similar fashion.

These two chart patterns are formed when the price movement tests a level of support or resistance three times and is unable to break through; this signals a reversal of the prior trend.  

Confusion can form with triple tops and bottoms during the formation of the pattern because they can look similar to other chart patterns.

After the first two support/resistance tests are formed in the price movement

The pattern will look like a double top or bottom, which could lead a chartist to enter a reversal position too soon

Indicator

Statistics used to measure current conditions as well as to forecast financial or economic trends. Indicators are used extensively in technical analysis to predict changes in stock trends or price patterns. In fundamental analysis, economic indicators that quantify current economic and industry conditions are used to provide insight into the future profitability potential of public companies.

Technical IndicatorPrice

Price represents the value of an instrument, and can be displayed in MarketmakerTM in a number of ways (typically as a line, bar chart,

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candlestick or mountain). Most users choose Candlestick charts because of the depth of information they provide.

Charts

Line, Step, Scatter, Mountain charts

Line, Step, Scatter and Mountain charts display closing prices in a linear format to make the rising and falling of an instrument easy to detect. In addition, Mountain charts shade the area below this line, emphasising market peaks and troughs.

Bar Charts (Open/High/Low/Close charts)

Bar Charts (or Open/High/Low/Close charts), show four price points for each day on a vertical line. The top and bottom of the line represent the high and low respectively. The small notches on the left and right of the line represent the open and close respectively.

Candle charts

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Candle charts show the same information as a bar chart, but the difference between the open and close is displayed as a solid body rather than as notches. The colour of the body indicates that the close on a certain day was either above or below the market’s opening price (typically green and red respectively).

Heikin-Ashi

Heikin-Ashi charts appear similar to standard candle charts, but use different values for each bar. The Heikin-Ashi technique modifies the open-high-low-close (OHLC) bars of standard candle charts, using Close Open High Low instead:

Studies The most commonly used studies are grouped together at the top of the Studies list, making it easier to tailor charts to your requirement.

Moving Averages

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Moving Average (MA) is one of the most popular technical analysis tools, it is used to determine upwards and downwards trends in the market. An MA smoothes out short-term fluctuations making it easier to identify general trends, and identify potential market turning points.

Moving Averages work best in markets that display a definite trend. Be careful when using Moving Averages in a trendless market, Because the calculation lags behind the current price, and can lead to misleading trend information. This lag is affected by the number of events used to calculate the average, which can vary between 2 or 3 to over 200 events.

MarketmakerTM contains the four main types of Moving Average:

Standard (also known as Simple) Weighted Exponential Triangular

Selecting the correct Moving Average for your needs is a process of trial and error. More than one type of MA can be shown on a chart to make it easier to identify market trends.

Look out for price moves above or below the Moving Average to indicate when you may wish to buy or sell.

SMA - Standard Moving Average

The Standard Moving Average (SMA) calculates an average over a set number of days. For example, to calculate a 10 Day Moving Average, add together the previous 10 closing prices, then divide by 10.

To provide a basic view of a market’s trends compare Simple Moving Averages against an instrument’s price.

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WMA - Weighted Moving Average

Weighted Moving Averages (WMA) place more emphasis on recent price changes than the SMA. Each day’s price is given a weight depending on how recently it occurred.

Bollinger Bands

Bollinger Bands act as a measure of volatility and constitute strong zones of support and resistance when the market is without a clear trend. A trending market is reflected by the bands moving away from the SMA. When the difference between the two envelopes drops, the trend loses its force.

Bollinger Bands are placed at a distance of two standard deviations from an SMA (typically over a period of 20 events). If prices follow a normal bell curve (Gaussian distribution), 95% of the prices must be inside the bands.

Chande Kroll Stop

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The Chande Kroll Stop is a trend following indicator. It identifies the stop loss for a long or short position by using a variation on directional movement.

It is calculated on the average true range of an instrument’s volatility. The stops are placed under (and on) the high (low) of the last “n” bars. The difference is proportional to the average True Range on “N” bars.

You can use it to trade in a number of ways:

Sell when the price crosses below both lines. Buy when the price crosses above both lines. Or you can trade when the two lines cross each other.

As the price moves sideways you will note that the lines begin to flatten out and the price will trade broadly between the two lines. Make sure that when you trade it is always in the direction of the trend.

Donchian Channels

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Donchian Channels examine trading done over a period of proceeding days trading and plot the highest high and lowest low for each day. This is typically done for a period of 20 days (also known as the Four-Week Rule).

Donchian Channels can also be used to determine the volatility of a market. When a price is stable, the channel is narrow when the price fluctuates, the channel widens. Breakouts from the channel signal long and short positions. A Long is established when the price exceeds the highs of the previous 20 days, and a Short is established when the price falls below the lows of the previous 20 days.

Fibo-Gann Retracement

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Fibo-Gann Retracement works on the idea that prices move upwards with longer upswings and downwards with smaller downswings (which is typical in an uptrend). In a sideways market the upswings tend to be equal in length to the downswings.

Any downswings seen in an uptrend will be fraction of the length of the primary up move and vice-versa. By using Fibonacci fractions like 0.382, 0.5 or 0.618, this tool calculates the percentage retracements on zigzags, which can then be used to calculate future pivot valleys or peaks.

IchiMoku

IchiMoku (from Ichimoku Kinko Hyo, literally one glance balanced chart) is a complex charting system which can be used as part of many trading strategies. It contains 5 lines, which each indicate an average or price:

The most distinctive feature of Ichimoku is Kumo (literally meaning Cloud), the area between Senkou Span A and Senkou Span B. This feature is given its name by the appearance of this area when it is shaded.

Using IchiMoku

Kumo indicates support and resistance levels. If the price is above the cloud, the overall trend is bullish; if the price is below the cloud, the overall trend is bearish.

Unlike typical support or resistance indicators, Kumo has depth, which indicates how likely it is for a price to break through the cloud.

Typically, a buy signal is generated when the Tenkan Line crosses the Kijun Line from below. A sell signal is generated when the Tenkan Line crosses the Kijun Line from above.

Keltner Channels

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Keltner Channels show two channel lines drawn a defined distance above and below a central moving average. The centre line is a 10 day SMA of a typical price (that is, the average of each day’s high, low and close prices). The distance between the channel lines and the central line is the SMA of the past 10 days' trading ranges (that is, the range between the high and low price for each day).

Keltner Channels were described by Chester W. Keltner in his book How To Make Money in Commodities, where they were known as the Ten-Day Moving Average Trading Rule.

Linear Regression

Linear Regression Trend line

Linear Regression is a mathematical way of identifying the relationships between independent and dependent variables (in trading, this would be price and period). This is shown by the trend line, which is a straight line which represents the best fit between the data points.

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Linear Regression Channels

Linear Regression Channels are obtained by drawing parallel lines either side of the Linear Regression line. The distance for this line is determined by the type of channel to be created. Linear Regression channels are used to indicate possible price fluctuations. The top line shows resistance and the bottom shows support. Ordinarily, prices will be contained within the channel, and although you may see prices temporarily crossing these lines, any longer periods outside the channel indicate that the current trend may reverse.

Linear Regression Channel 100% The Linear Regression Channel 100% uses parallel lines that are drawn two standard deviations away from the Linear Regression line.

Linear Regression Channel 50%

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The Linear Regression Channel 50% uses parallel lines that are drawn one standard deviation away from the Linear Regression line.

Standard Deviation Channel

The Standard Deviation Channel uses parallel lines drawn a specified number of standard deviations from the Linear Regression line.

Linear Regression Var

Linear Regression Var is a combination of the Linear Regression line and the Linear Regression Channel 100% lines.

Standard Error Channel .

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The Standard Error Channel uses parallel lines drawn a specified number of standard errors from the Linear Regression.

Linear Regression (Moving Linear Regression)

The Linear Regression line (also known as the Moving Linear Regression indicator or Time Series Forecast) plots the path of endpoint values for previous Linear Regression trend lines over a specified period.

Although it looks like an SMA, it is much more reactive to changes in the market. It can also be used to forecast future prices, using the trend of the prices over the analysis period to predict the next period’s price.

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Linear Regression Channel

The Linear Regression channel is similar to a Bollinger Bands study, in that lines are placed around the moving Linear Regression line, at a distance of two standard deviations. An instrument’s price touching the upper or lower lines of a Linear Regression channel can be taken as a signal to buy or sell.

Parabolic SAR (Stop and Reverse)

Parabolic SAR is used to find trends, and works on the assumption that the longer a trend continues, the more likely it is to reverse. The methods used to calculate the SAR points accelerate the curve towards the price each time a new high is reached.

Parabolic SAR Calculations

The parameters typically used by Parabolic SARs are:

Initial acceleration factor :0. 02

Addition factor : 0. 02

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Acceleration factor limit : 0. 2

The amount by which the stop moves up or down is a function of:

Extreme Point (EP) = the most favourable price reached since the trade was initiated. (i. e. The highest high when long or the lowest low when short). Acceleration Factor (AF). The AF value starts at 0. 02 and is increased by 0. 02 each time a new EP for the trade is made until it reaches 0. 2.

Three situations are encountered during a trend period and usually occur in the following order:

Both the values of EP and AF increase and every time a new EP is reached for the trade, AF is increased by 0. 02. As the AF increases, the SAR curve begins to move faster towards the price. The EP value increases and the AF has reached its maximum value of 0. 2. The SAR is then a function of price only. The values of both EP and AF are constant and no new EP (no higher high or lower low) is made for the trade (the AF value is not increased). The trend falters and the result is usually that the SAR curve catches up with the price action.

Using Parabolic SAR

During a trend, SAR direction remains the same. If the parabola is below the price, the trend is bullish; if the parabola is above the price, the trend is bearish.

It is important to note that the SAR moves only in the direction in which the trade has been initiated. If long, the stop will move up every day; if short, the stop will move down (regardless of the direction any price movement).

When a new trade is initiated, the initial SAR is the previous trade's extreme point (EP), allowing time for the trend to materialise. If the trend fails to materialise, then the system is stopped and the position reversed. Prices passing a SAR point indicate that your position should be liquidated.

The Parabolic SAR is of most use whilst a market is trending. During non-trending periods it tends to get whipsawed. One method of reducing this is to use the Parabolic SAR in conjunction with the Directional Movement Indicator.

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Linear Regression Reversal

The linear Regression Reversal shows +1 when price goes up and -1 when price goes down, changing direction when the price is lower (or higher) than the previous price.

This indicator provides long and short signals, +1 is considered a long position and -1 is considered a short position.

Williams %R

Williams %R measures previous close values in relation to a specified price range. It is similar to the Stochastic oscillator, and is used to identify overbought and oversold levels.

Unlike the Stochastic oscillator, the scale is reversed, so a reading below 20% indicates an instrument has been overbought, and above 80% indicates it has been oversold.

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Creating Indicators

We look to develop a unique indicator using two core elements, a pattern and math functions.

Looking at weekly charts of company XYZ's stock, we notice some basic swings between bullishness and bearishness that each last about five days.

As our indicator is to measure price swings, we should be interested in patterns to define the "swing" and a mathematical function, price averages, to define the scope of these swings.

Now we need to define the rules that govern these elements. The patterns are the easiest to define: they are simply bullish and

bearish patterns that alternate every five or so days. To create an average, we take a sample of the duration of upward

trends and a sample of the duration of downward trends. Our end result should be an expected time period for these moves to

occur. To define the scope of the swings, we use a relative high and a

relative low, and we set these at the high and low of the weekly chart. Next, to create a projection of the current incline/decline based on

past inclines/declines, we simply average the total inclines/declines and predict the same measured moves (+/-) occur in the future.

The direction and duration of the move, again, is determined by the pattern.

We take this strategy and test it manually, or use software to plot it and create signals.

We find that it can successfully return 5% per swing (every five days). The Importance Of Correlation. )

Finally, we go live with this concept and trade with real money.

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Stratigies-

Creating Strategies Before creating a strategy Market Analysis is extremely important. You will need access to charts which reflect the time frame to be

traded. Will you trade on a one-minute time frame or a monthly time frame? Then you'll want to focus on what market you will trade: stocks,

options,  futures, forex or commodities? Once you've chosen a time frame and market, decide what type of

trading you would like to do

Look at rises and falls in price and see if you can find anything that precipitated those movements.

Indicators such as time of day,  candlestick patterns, chart patterns, mini-cycles, volume and other patterns should all be looked at.

Once a potential strategy has been found, go back and see if the same thing occurred for other movements on the chart.

Could a profit have been made over the last day, week or month using this method?

Did other stocks that have similar criteria check whether it would have worked there as well.

After you determine a set of rules that would have allowed you to enter the market to make a profit

look to those same examples and see what your risk would have been. Determine what your stops will need to be on future trades in order to

capture profit without being stopped out. Analyze price movement after entry and see where on your charts a

stop should be placed. When you analyze the movements, look for profitable exit points. Where was the ideal exit point and what indicator or method can be

used to capture most of this movement? When looking at exits, use indicators, candlestick patterns, chart

patterns, percentage retracements, trailing stops, Fibonacci levels or other tactics to help capture profits from the opportunities we are seeing.

Strategies fall in and out of favor over different time frames; occasionally changes will need to be made to accommodate the current market and our personal situation.

-Definition :- A set of objective rules designating the conditions that must be met for

trade entries and exits to occur.

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A trading strategy includes specifications for trade entries, including trade filters and triggers, as well as rules for trade exits, money management, timeframes, order types, etc.

A trading strategy, if based on quantifiable specifications, can be analyzed on historical data to project the future performance of the strategy.

A trading strategy outlines the specifications for making trades, and includes rules for trade entries, exits and money management.

When properly researched and executed, a trading strategy can provide a mathematical expectation for the specified rules

And help traders and investors determine if a trading idea is potentially profitable.

Types of stratigies

Trend Following : -

A trading strategy that attempts to capture gains through the analysis of an asset's momentum in a particular direction.

The trend trader enters into a long position when a stock is trending upward (successively higher highs).

Conversely, a short position is taken when the stock is in a down trend (successively lower highs).

This strategy assumes that the present direction of the stock will continue into the future.

It can be used by short-, intermediate- or long-term traders.

Regardless of their chosen time frame, traders will remain in their position

Until they believe the trend has reversed but reversal may occur at different times for each time frame.

Reversal: -

A change in the direction of a price trend. On a price chart, reversals undergo a recognizable change in the price structure.

An uptrend, which is a series of higher highs and higher lows, reverses into a downtrend by changing to a series of lower highs and lower lows.

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A downtrend, which is a series of lower highs and lower lows, reverses into an uptrend by changing to a series of higher highs and higher lows.

A reversal can be a positive or negative change against the prevailing trend.

Technical analysts watch for these patterns because they can indicate the need for a different trading strategy on the same security.  

For e. g. if a technical analyst holds stock ABC and notices a reversal pattern

He/She may want to consider closing his or her existing long position and assuming a short position to capitalize on the potential downward movement of the stock's price.

Contrarian: -

An investment style that goes against prevailing market trends by buying assets that are performing poorly and then selling when they perform well.

A contrarian investor believes that the people who say the market is going up do so

Only when they are fully invested and have no further purchasing power.

At this point, the market is at a peak

On the other hand, when people predict a downturn

They have already sold out, at which point the market can only go up.

Contrarian investing also emphasizes out-of-favor securities with low P/E ratios.

Break Out: -

A price movement through an identified level of support or resistance, which is usually followed by heavy volume and increased volatility.  

Traders will buy the underlying asset when the price breaks above a level of resistance and sell when it breaks below support.

A breakout is the bullish counterpart to a breakdown.

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A breakout is most commonly used to refer to a situation where the price breaks above a level of resistance and heads higher, rather than breaking below a level of support and heading lower.

THE OBJECTIVE OF ALGO TRADING STRATEGIES

Algorithm trading is designed to lower transaction costs, enhance liquidity,

and handle decision making. Certain of the strategies are implemented on

the selling end–investment banks and traditional banks–and the buying end–

hedge funds and mutual funds. The strategies include automated trading,

high frequency trading, market making, and discretionary trading.

1. High Frequency Trading

High-frequency traders jump in and out of the finance markets at an extremely

high rate. Their objective is to catch minuscule fluctuations in the market and

quickly grab profits. Of the total number of equity trading companies who are

operating currently, high-frequency companies account for only two percent.

However, they account for over seventy percent of the equity volume’s total.

2. Discretionary Trading

Discretionary Trading refers to an analyst or portfolio manager who enters

and exits a trading position without using a specific rule or strategy to handle

the decision making process. A discretionary trader uses algorithm trading

software to exit and enter a position in order to obtain the best prices, and an

acceptable average price. For markets such as futures and stocks, the

algorithm functions are referred to as “Volume Weighted Average Price” and

these are beneficial when used in the discretionary trading method.

2. Market Making

Wall Street’s selling side is comprised of dealers, investment bankers and

traditional bankers. These are firms that typically quote a price for agencies

on the buying side, such as hedge funds and mutual funds. When a firm on

the selling side creates a market, an algorithm handles their hedging

strategy. Algo trading software programs then efficiently place orders in the

finance markets, in order to offset the cost of the recently quoted trade.

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3. Automated Trading

Many investors utilize automated trading to handle their investing procedures.

Computer software handles the decision making process and is programmed

to execute the entry, exit, and stop loss procedures. Certain of these

trading methods utilize statistical analysis in order to discover minor changes

in the financial markets, and certain types also use fundamental and

technical analysis to discover major trends in the market.

One can profit from such strategies, as this type of software is capable of

slicing and dicing large trades, and then working to minimize the erosion of

prices through the execution of millions of trades per second. This is not

something of which a human being is capable. Additionally, through high

frequency algorithmic trading, trends can be spotted and strategies changed

within milliseconds, putting the investor light years ahead of the game.

Factor affecting in algorithmic Trading Various Exchanges

A marketplace in which securities, commodities, derivatives and other financial instruments are traded.

The core function of an exchange - such as a stock exchange - is to ensure fair and orderly trading

As well as efficient dissemination of price information for any securities trading on that exchange.

Exchanges give companies, governments and other groups a platform to sell securities to the investing public.

An exchange may be a physical location where traders meet to conduct business or an electronic platform.

In the 19th century, exchanges were opened to trade. 

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Forward contracts on commodities.

Exchange traded forward contracts are called futures contracts.

Big Point Value

Big Point Value is the amount of money you will make if you buy one contract and it goes up by one full point.

In the case of stocks, the Big Point Value is usually 1, in that 1 point of movement represents 1 dollar.

However it may vary. For e. g. the Big Point Value for the S&P Futures is 250, where 1 point of price movement represents 250 dollars.

The Big Point Value for a stock such as JDSU is 1, where 1 point of movement represents 1 dollar.

Leverage

The use of various financial instruments or borrowed capital, such as margin, to increase the potential return of an investment.

The amount of debt used to finance a firm's assets.

A firm with significantly more debt than equity is considered to be highly leveraged.

Leverage is most commonly used in real estate transactions through the use of mortgages to purchase a home.

Standard Deviation

In finance, standard deviation is applied to the annual rate of return of an investment to measure the investment's volatility.

Standard deviation is also known as historical volatility and is used by investors as a gauge for the amount of expected volatility.

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Standard deviation is a statistical measurement that sheds light on historical volatility.

For example,  

A volatile stock will have a high standard deviation while the deviation of a stable blue chip stock will be lower.

Volatility A statistical measure of the dispersion of returns for a given security or

market index.

Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index.

Commonly, the higher the volatility, the riskier the security.

In other words, volatility refers to the amount of uncertainty or risk about the size of changes in a security's value.

A higher volatility means that a security's value can potentially be spread out over a larger range of values.

This means that the price of the security can change dramatically over a short time period in either direction.

A lower volatility means that a security's value does not fluctuate dramatically, but changes in value at a steady pace over a period of time.

Liquidity

The degree to which an asset or security can be bought or sold in the market without affecting the asset's price.

Liquidity is characterized by a high level of trading activity.

Assets that can be easily bought or sold are known as liquid assets.

The ability to convert an asset to cash quickly. Also known as "marketability".

There is no specific liquidity formula; however, liquidity is often calculated by using liquidity ratios.

It is safer to invest in liquid assets than illiquid ones

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Because it is easier for an investor to get his/her money out of the investment.

Examples of assets that are easily converted into cash include blue chip and money market securities

Slippage and Commission

Slippage:

The difference between the expected price of a trade, and the price the trade actually executes at.

Slippage often occurs during periods of higher volatility, when market orders are used

And also when large orders are executed when there may not be enough interest at the desired price level to maintain the expected price of trade.

Commission:

A service charge assessed by a broker or investment advisor in return for providing investment advice and/or handling the purchase or sale of a security.

Most major, full-service brokerages derive most of their profits from charging commissions on client transactions.

Commissions vary widely from brokerage to brokerage.

Capital

A measure of both a company's efficiency and its short-term financial health.

The working capital ratio is calculated as:

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Positive working capital means that the company is able to pay off its short-term liabilities.  

Negative working capital means that a company currently is unable to meet its short-term liabilities with its current assets (cash, accounts receivable and inventory)

Also known as "net working capital", or the "working capital ratio“.

Risk Management

The process of identification, analysis and either acceptance or mitigation of uncertainty in investment decision-making.

Essentially, risk management occurs anytime an investor or fund manager analyzes and attempts to quantify the potential for losses in an investment and then takes the appropriate action (or inaction) given their investment objectives and risk tolerance.

Inadequate risk management can result in severe consequences for companies as well as individuals. For example, the recession that began in 2008 was largely caused by the loose credit risk management of financial firms.

Simply put, risk management is a two-step process - determining what risks exist in an investment and then handling those risks in a way best-suited to your investment objectives.

Risk management occurs everywhere in the financial world.

It occurs when an investor buys low-risk government bonds over more risky corporate debt, when a fund manager hedges their currency exposure with currency derivatives

And when a bank performs a credit check on an individual before issuing them a personal line of credit.

Money Management The process of budgeting, saving, investing, spending or otherwise

in overseeing the cash usage of an individual or group.

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The predominant use of the phrase in financial markets is that of an investment professional making investment decisions for large pools of funds, such as mutual funds or pension plans.

Also referred to as "investment management" and/or "portfolio management"

Creating Functions

Functions are nothing but composition of mathematical formulae's

Mathematical formulae's are written in the form of computer algorithms

These formulae's are used in the following: -

-Indicators

-Strategies

Each of the functions have to be defined at least once so as to use again & again.

Once functions are created or defined then they can be used just by entering the keyword.

While defining the functions data types are also defined

There are three types of Data types namely

-Numeric series (Input of prices)

-Numeric simple(Input of length)

-Numeric reference (Output values

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LIMITATION OF ALGORITHMIC TRADING

We are all familiarized with the over-hyped internet marketing fever that has grown around the world of automated trading, a phenomena that has become very strong since the capital entry barriers were greatly lowered giving the ability to trade this way to people with modest income levels. This has created a great problem – in which most retail traders simply fail with automated trading – mainly due to the very large number of misconceptions and disinformation created by marketers with the goal of selling products without any regard for the long term success or viability of their products. What they don’t realize is that they are not only creating a lot of financial loss through account trading losses but also making sure that most people will never succeed as most of the things they learn are very wrong and a lethal approach to this form of trading.

Within this post I want to share with you some of the most basic aspects of automated trading, particularly regarding what it can and cannot do for you. Most people enter the world of automated trading believing that this is something it is not (an ATM) which in time makes them take a lot of financial loss and frustration as what they see on marketing and what they “want to be true” does not resemble what reality has to offer. So what can automated trading in fact do for you and what does it simply cannot achieve ? Keep reading the next few paragraphs to find out.

Let us start with the blunt truths about the things which automated trading simply will not be able to achieve for you. Automated trading will NOT achieve you hands-free, stress-free income without moving a finger and it will not involve any less work and studying than manual trading. Automated trading is – to say the least – harder than manual trading in the sense that you need to acquire a whole lot of additional knowledge – besides knowledge related to trading – tt be successful (such as programming, statistics, mathematics, etc). Many of you may think that this is simply “not true” as someone else can simply program you a “profitable system” but the truth is that all system eventually fail and all systems need to be continuously monitored to see if they fail their long term statistical characteristics. If you simply lack the knowledge to develop new systems and adequately know how to monitor the performance of your strategies (like for example carrying out Monte Carlo simulations) then you are probably doomed to fail.

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Certainly automated trading will NOT save you from learning how to trade, it is vital for anyone trading a system to know exactly how it works, exactly how reliable its simulations are and exactly what its worst case thresholds are. Failing to understand the market and trading “black boxes” will only lead you to try many different systems you “believe can work” only to find out that they crash your account despite their beautiful -yet very unreliable- simulations. Automated trading is – in every sense – a long path, and it will NOT allow you to “cut corners” in trading. If you are into algorithmic trading because it’s “easy” then you need to think about this more in depth.

Perhaps the biggest problem with inexperienced retail traders and algorithmic trading is the fact that just because execution is relegated to a computer it gives it the illusion of being a quick path to profitable trading but – quite oppositely – automated trading can be a quick path to ruin in a much faster and less productive way than manual trading would since most people lose their money to commercial EA black boxes from which they can learn little or nothing as they have no access to the code and to what actually happened while losing money with a manual account at least gives the user some very valuable experience regarding trading difficulty and psychology.

Another extremely common thing that people think automated trading will do – which it does NOT – is remove emotion from trading. You would think that having an “emotionless” computer executing trades would make your trading rock solid but the truth is that YOU are the one who allows that system to trade. Your emotions will affect you just as badly when you trade in an automated fashion since a 20% draw down is – after all – a 20% draw down. Inexperienced traders – who are not emotionally intelligent – will quickly remove a “losing system” from their accounts and increase risk when a system shows profits (or even before just to have more potential profit), however the truth is that emotions remain there and success in automated trading is as dependent on emotional intelligence as manual trading is However you should not think that just because automated trading is hard it is “not worth doing”. There are many things that automated trading CAN do for you which would be very hard or almost impossible when trading manually. For example I currently trade/manage more than 80 live accounts and it would certainly be impossible to execute the almost 40 different trading system instances I have running on them 24/7 if I did this by hand. Automated trading allows you to increase the number of strategies you trade and reduce your need to stay “glued to the screen” under hours in which you have to sleep. For example some of my strategies trade the European open (at about 2-3 AM my time) so for me trading this in manual way would be disastrous (as I need to sleep!).

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Automated trading also allows you to know in a much better way what you are trading. Trading strategies which are designed with RELIABLE testing in mind can give you accurate simulations of what the past would have been like allowing you to see the ability of your strategy to tackle past market conditions. Simulations also allow you to perform stress tests (like those described on a post I wrote this week) which allow you to see how robust your trading strategy is against things like out-of-sample tests and spread variations.

Algorithmic trading allows you to have a much more diversified trading profile, to spend your hours doing system development and analysis instead of trading – which frees you from having to stay away late at night – and to increase your account management capabilities beyond anything you could do from a manual point of view. So yes, automated trading has many positive things, allowing you to quickly evaluate/discard system ideas, to measure your system’s historical edges and to even develop system in an almost automatic fashion (like what Coatl does).

So long story short, automated trading is a way of trading with many advantages and the possibility to expand your trading possibilities beyond your wildest dreams but it requires very hard work and an in-depth understanding of computers, trading and statistics. Automated trading is NOT an ATM and its not a short-cut to profitable trading, it is just a way of trading which requires time, effort and understanding, the main reason why you do not see a lot of people living from algorithmic trading and certainly no one living from black box commercial systems.

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RESEARCH METHODLOGY

Research Objective

To study the process of Algorithmic Trading in market.

To make a comparative study with Indicator and Strategies.

To analyzerisks and benefits of back testing in market.

To analyze through questionnaire how investors are benefited in Algorithmic trading.

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SCOPE OF STUDY:

The scope of the project is to study and know about Algorithmic Trading and making

strategies in Prophecis By studying the algorithmic Trading and and making strategies, a

clear option of dealing in stock exchange has been understood. This concept of algorithmic

trading has been used for fast interaction of shares of shareholder. By this we can access

anywhere and know the present dealings in shares.

Methodology:

Facts expressed in quantity form can be termed as “data”. Data maybe

classified either as:

i. Primary data

ii. Secondary data

i) Primary Data:

Primary data is the first hand information that a researcher gets from various

sources like respondents, analogous case situations and research

experiments. Primary data is the data that is generated by the researcher for

the specific purpose of research situation at hand.

For this project the primary data will be collected from the personnel.

This data can also be obtained through a questionnaire, based upon which

some statistical techniques are applied.

ii) Secondary Data:

Secondary data is already published data collected for some purpose

other than the one confronting the researcher at a given point of time. The

secondary data can be gathered from various sources like statistics,

libraries, research agencies etc. In this case the secondary information is

to be collected from newspapers like “Economic Times” and news tv like

“cnn” and Internet.

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iii) Sampling Data:

Size- 25

Techique- Questionnaire and personal veiw

Area- Noida.

Limitations of survey:

The project may suffer from the following limitations: -

The required data may not be available due to which it cannot be accurate.

Some of the important information is included because of time constraint.

It was deliberately difficult to collect the data from the clients, as they are

apparently busy.

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DISCUSSION/DESCRIPTION

Challenges

Difficult for shy students or those lacking confidence in subject matter.

Requires proper use of time management.

Requires motivation.

Potential Information overload.

Strengths

Time flexibility in participation.

Promotes collaboration and community

Encourages higher level learning .

Can be a confidence builder once student is accustomed to the procedure.

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CONCLUSION AND RECOMMENDATIONS

Algorithms are widely recognized as one of the fastest moving bandwagons in the capital markets. Employing rules-based strategies has enabled buy-side firms to increase productivity, lower commission costs and reduce implementation shortfall.

Algorithmic trading cuts down transaction costs and allows investment managers to take control of their own trading processes. By breaking large orders into smaller chunks, buy-side institutions are able to disguise their orders and participate in a stock’s trading volume across an entire day or for a few hours. More sophisticated algorithms allow buy-side firms to fine-tune the trading parameters in terms of start time, end time, and aggressiveness. In today’s hyper-competitive, cost-conscious trading environment, being the first to innovate can give a broker a significant advantage over the competition both in capturing the order flow of early adopters and building a reputation as a thought leader.

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BIBLIOGRAPHY

1. www. google. com

2. www. investopidia. com

3. www. finviz. com

4. www. amazon. com/The-Best- Trading -Books/lm/3W227627GVTZX

5. www. spreadco. com/ Financial SpreadBet

Newspaper…

1. Economic Times

2. Times of India

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ANNEXURE

Art of Making Money… Algorithmic Trading

QUESTIONNAIRE

Name: -------------------------------------------

PERSONAL DETAILS

1. Age: -------------------------------------------

2. Qualifications: -------------------------------------------

3. Are you a Member () or a Client () ?

TRADING DETAILS:

4. In which exchanges do you trade in

A .Europe

B .Indian

C .U. S

5. Which indicator do you use most?

A .RSI

B .BOLLINGBAND

C .STOCASTIC

D .PARABOLIC SAR

E .ANY OTHER

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6. Have you traded through any broker(s). Yes () / No ()

If yes, Names:

_______________________________________________

7. Since how many years have you been using algorithmic trading?

______________ Time period

8. On what basis do you select indicator on algorithmic trading?

A .Market basis

B .Trend

C .Data type

D .Any other _______________________________________________

9. From where do you gather information about the algorithmic trading?

A .Centre

B .New paper

C .Website….

D .If any…………….

10. Which would you recommend as the three most favorableindicator?

__________________________________________________________-

III. TRADING STRATEGIES DETAILS

11 .How do you know if the strategies is any good?

A .Back testing

B .Live log

C .if any…

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12. What are the Risks?

A .System risk

B .Capital risk

C .Co-relation risk

. 13. Is strategies are valid approach to algorithmic- trading futures?

A .Yes

B .No

C .May be

14. How do you rate algorithmic trading ?

A .Good

B .Better

C .Best

15. Reasons of using algorithmic trading?

A .Maximum trading

B .Stoploss

C .Maximum profit

D .Low latency

E .speed

F .Any other……

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