trading strategy

1. 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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2. 2

3. 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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4. 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.
4

5. MANISH KUMAR KESHARI
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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6. 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 AndRecommendations 95
8. Bibliography 96
9. Annexure 97
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7. 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
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8. preferably all market environments independent from falling or rising markets
as well as high and low liquid 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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9. 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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10. 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.
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11. Prophecis stands for prudence in converging analytical principles with
technology. We strive to apply sound financial principles using cutting edge
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
intechnology, 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.
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12. 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,
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 Bar
2. Trade from Chart
AlphaBOX
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13. DataRIVER
QuoteCANVAS
AlgoWRITER
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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14. 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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15. 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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16. 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.
16

17. The price of financial instruments can vary greatly within the same trading day
(screen capture from Google Finance).
Some day traders focus on very short-term trading within the trading day, 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 daytrader‘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:
17

18. 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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19. 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
19

20. 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),
20

21. 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 specifically
against irrational behavior from day traders using these approaches.
21

22. 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
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.
22

23. 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
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
23

24. 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.
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.
24

25. 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
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
25

26. 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.
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.
26

27. 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.
Same happens when the NIFTY falls. If the NIFTY is rallying strong
and moving up fast, all major stocks are also rising. Finding stocks in
this situation for intraday trading in LONG is not difficult, as everything
is rising. But when the NIFTY is going down, all are going down with
it. Finding that exception which has gone up even on those days, or
has shown enough up-down range to give intraday profits in LONG, is
the real challenge.
27

28. 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:
1. The first is the use of stop and limit orders, which automatically close
out losing positions.
2. The second is closing out all positions at the end of every day, which
lets traders start fresh the next day.
28

29. 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
weapons: Confidence, Discipline, Focus and Patience. We will explain
these requirements in detail.
Objectives
29

30. 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
up, look to go LONG. If the market is falling, look for SHORTING
opportunities.
How to play?
Once the number of shares has been fixed, you will need to calculate
how many points increase or decrease will be required to meet your
30

31. 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. . .
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?
31

32. 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
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
32

33. 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
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,
33

34. 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/ˈ the practice of taking advantage of a
is
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).
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'.
34

35. 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
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
35

36. 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, 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
36

37. 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
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
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38. 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
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
38

39. 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 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
39

40. 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.
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.
40

41. 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 unit
3. 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
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.
41

42. 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:
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;
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43. Many high-frequency traders provide liquidity and price discovery to the
markets through market-making and arbitrage trading.
High-frequency trading removes any value from the trade of securities 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. "[
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.
43

44. 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
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
44

45. 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
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
45