DECODING

ALGORITHMS — the di�erence between you and the people who actually make money on Wall Street

What Are Algorithms?

Financial Algorithms

The Algorithm Advantage

Incredible Performance

Types of Algorithms

Algorithmic Strategies

Made by Quants

Criticism for Algorithms

Algorithms are the math computers use to make decisions.

Algorithms are used in well-known products, like:

Algorithms are also used in investing. Financial engineers create algorithms to analyze and buy or sell stocks, and other financial instruments automatically. In the past 10 years, algorithmic trading has become a huge factor in trading.

Advantage 1: Process More, and Faster

Advantage 3: Execute Faster

Advantage 4: Manage Risk

Advantage 2: Emotionless

Google Search Amazon Book Recommendation

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ALGORITHM

They process huge data sets

Make predictions based on probability.

of today’s global markets is driven by algorithms. 75% VOLUME

75% volume is executed by algorithms

(55% done by high frequency shops)

25% volume is executed by humans

WALL ST

1 Use advanced math to find patterns

2 3

BUY

Algorithms have changed the global investment game, putting individual investors at a major disadvantage to investment banks and hedge funds. Here are some of the reasons why:

Funds that use algorithmic strategies exclusively have performed exceptionally well historically, beating benchmark indexes. Total returns for the past �ve years:

Algorithms take advantage of computers’ ability to process data. They can compare price and metrics of millions of �nancial instruments faster than individuals.

Emotions can get in the way of sound investing, causing many to buy when prices are going up and sell when they are going down. In contrast, algorithms buy when the market prediction says buy.

Algorithms are operated on computers, which react faster than humans to market changes.With low-latency �ber optic cables* connecting the world’s biggest markets, algorithms can trade near the speed of light, which is signi�cantly faster than your WiFi connection.

Algorithmic strategies can be developed to make calculated decisions balancing risk and reward to maximize pro�ts and minimize losses. Stop-Loss controls can be added to automatically liquidate a position if an algorithm loses more than an de�ned percentage.Risk algorithms are also used to manage correlated instruments in a portfolio, limiting exposure to big losses.

Investment managers may use algorithms to analyze trades, and buy or sell manuallybased on the algorithm’s suggestions, or may allow the algorithm to trade automatically, intervening on occasion. Algorithms that are 100% automatic are called black box because the decision-making process is hidden in code.

An algorithmic investment system is made up of algorithms that ful�ll speci�c functions.

Below are a few types of strategies that are commonly used in algorithmic trading.

Algoritmic trading, speci�cally high frequency strategies have come under criticism in the past few years. According to a study by the British research group Foresight, “While no systematic evidence that HFT reduced stability, the interactions between algorithms may cause instability di�erent from interactions between human.”

Algorithms have been blamed for:

Financial algorithms are written by quants, or quantitative analysts. Quants use computer programming languages to design algorithms, drawing on advanced math techniques, and scienti�c thinking and as well as knowledge of �nancial markets.

While there are several degree programs, many top quant shops hire based on skills in science and math, with no background in �nance required. According to QuantNet, the top �ve Masters programs in 2011 were:

DISCRETIONARYTRADING 100% AUTOMATIC

1,000 Shares

These strategies act like market makers, and make small profits

on differences in a bid/ask spread.

SCALPING

The philosophy behind these strategies is that stocks have an average price over time. They use historical data to compute an average price.

MEAN REVERSION

These strategies depend on speed to catch price

imbalances between different markets.

ARBITRAGE

These strategies follow general trends in a set of data by comparing historical and

current prices, profiting whether prices go up or down

TREND FOLLOWING

This cost-reduction strategy works to conceal large orders by breaking them into smaller orders, executed over time.

ICEBERGING

The strategy of these algorithms is to uncover the

large iceberged orders that have been cut into

smaller orders.

STEALTH

Operating at the millisecond level, these strategies use

arbitrage or scalping techniques at high volumes

and super fast speeds.

HIGH FREQUENCYTRADING

These strategies scan social networks and make trades

influenced by how anxious or positive people feel.

HUMANSENTIMENT

These strategies scan the news and invest based on what is happening in the

world. News wires have been adapted for these algorithms.

NEWSREADING

$ $

WAIT!SELL!NO!!!!

There and back in 55.7 milliseconds

2x faster than the average blink

5,443 km

human intervent ion

BLACK BOX

Predicts future pro�tability of instruments in portfolio

Limits exposure to assetswith high risk and low reward

Determines ideal portfolio holdings based on input of alpha, risk and cost models, and makes trade orders.

Analyzes the trade orders and the liquidity of the market to place orders in the most e�cient way, for the best price

Veri�es that potential pro�ts are greater costs of trades

Industry LeaderRenaissance

Medallion Fund

Industry LeaderSAC Capital

Industry LeaderPDT Advisors

BenchmarkS&P 500 Index

-0.75%

465%

293%371%

Alpha Model Risk Model Transaction Cost Model

Portfolio Construction Model

Execution Model

Algorithms are commonly written in:

C++ Java C#

1 Carnegie Mellon University Computational Finance

2 Princeton University Master in Finance

3 Columbia University Financial Engineering

4 New York University Mathematics in Finance 5 Baruch College Financial Engineering

INSTITUTION DEGREE

SOURCES: Kevin Slavin: How Algorithms Shape Our World-Ted.comhttp://www.ted.com/talks/lang/en/kevin_slavin_how_algorithms_shape_our_world.html

Wikipedia Renaissance Technologies, SAC Capitalhttp://en.wikipedia.org/wiki/Renaissance_technologieshttp://en.wikipedia.org/wiki/SAC_Capital_Advisors

PDThttp://www.thetradenews.com/print.aspx?id=5402

Is the Market Rigged? http://finance.yahoo.com/blogs/daily-ticker/market-rigged-absolutely-says-streettalk-advisors-ceo-174425615.html

Trading at the Speed of Light—Bloomberg Business-week, Matthew Philips http://www.businessweek.com/articles/2012-03-29/trading-at-the-speed-of-light

New York—London Fiber Optic http://www.hiberniaatlantic.com/services.html

Algorithms Take Control of Wall Street—Wired, Felix Salmon and John Stokes http://www.wired.com/magazine/2010/12/ff_ai_flashtrading/

Blink Speedhttp://bionumbers.hms.harvard.edu/bionumber.aspx?s=y&id=100706&ver=0

Wikipedia Algorithmic Tradinghttp://en.wikipedia.org/wiki/Algorithmic_trading#cite_note-54

Foresight Study http://www.bis.gov.uk/foresight/our-work/projects/current-projects/computer-trading/working-paper

Wikipedia Quantitative Analyst http://en.wikipedia.org/wiki/Quantitative_analyst#Algorithmic_trading_quantitative_analyst

Inside the Black Box: The Simple Truth About Quantita-tive Trading—Rishi K Narang

Long-Term Capital Management—Investopediahttp://www.investopedia.com/terms/l/longtermcapital.asp#axzz22KkYpb79

How a Trading Algorithm Went Awry—WSJ.com, Tom Lauricella, Kara Scannell and Jenny Strasburghttp://online.wsj.com/article/SB10001424052748704029304575526390131916792.html

Dow Takes a Harrowing 1,010.14-Point Trip–WSJ.com, Tom Lauricella and Peter A. Mckayhttp://online.wsj.com/article/SB10001424052748704370704575227754131412596.html

BATS IPO Canceled in Share Crash—The Street, Antoine Garahttp://www.thestreet.com/story/11468126/1/bats-global-flash-crashes-on-ipo.html

The Bats Affair: When Machines Humiliate Their Masters—BLoomberg Businessweek, Brian Bremner http://www.businessweek.com/articles/2012-03-23/the-bats-affair-when-machines-humiliate-their-masters

Apple Flash Crash: Stock Halted After Trade Causes 9% Plunge—CNBC, John Melloyhttp://www.cnbc.com/id/46835129/Apple_Flash_Crash_Stock_Halted_After_Trade_Causes_9_Plunge

QuantNethttps://www.quantnet.com/pages/mfe-programs-rankings/

*Banks and funds pay millions of dollars a year to access these networks, but they use algorithms to take advantage of the speed.

NEW YORK LONDON

Source: Inside the Black Box

www.quantconnect.com

Oil price vs

Canadian Dollar

Potential risk Positive correlation

Potential reward high

LONG-TERM CAPITAL MANAGEMENT

—1998—

FLASH CRASH—MAY 6 2010—

BETTER ALTERNATIVE TRADING SYSTEM (BATS) IPO—MARCH 2012—

The quant hedge fund was highly leveraged in the �xed-income market that went bust when Russia defaulted on their bonds. The Federal Government organized a bailout to

a avoid global �nancial crisis.

Waddell & Reed Financial Inc., a mutual fund ran an execution algorithm that set o� a feedback loop among HFT, sparking

the largest intraday dip in the Dow’s history–998.50 points.

At the IPO of the BATS electronic exchanges, a trading glitch drove the stock’s shares from their opening price of $15.25 to 3.8 cents, forcing the company to halt trading and withdraw

their IPO. On the same day, Apple stocks dropped 9% on one share on BATS Global Exchange.