systematic trading

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Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fear index, fixed income, global macro, implied volatility, index fund, interest rate swap, Long Term Capital Management, low interest rates, margin call, Market Wizards by Jack D. Schwager, merger arbitrage, Nick Leeson, paper trading, performance metric, proprietary trading, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Two Sigma, Y Combinator, yield curve

Indeed professional gamblers usually have a better understanding of risk management than many people working in the investment industry. ix Systematic Trading I trade a portfolio of UK equities using the framework I’ve outlined here for semiautomatic traders. Staunch systems trader The staunch systems trader is a true believer in the benefits of fully systematic trading. Unlike the semi-automatic trader and the asset allocating investor, they embrace the use of systematic trading rules to forecast price changes, but within the same common framework for position risk management. Many systems traders think they can find trading rules that give them extra profits, or alpha.

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Robert, who has bachelors and masters degrees in Economics, now systematically trades his own portfolio of futures and equities. Every owner of a physical copy of this version of Systematic Trading can download the eBook for free direct from us at Harriman House, in a format that can be read on any eReader, tablet or smartphone. Simply head to: ebooks.harriman-house.com/systematictrading to get your free eBook now. Systematic Trading A unique new method for designing trading and investing systems Robert Carver HARRIMAN HOUSE LTD 18 College Street Petersfield Hampshire GU31 4AD GREAT BRITAIN Tel: +44 (0)1730 233870 Email: contact@harriman-house.com Website: www.harriman-house.com First published in Great Britain in 2015 Copyright © Robert Carver The right of Robert Carver to be identified as the Author has been asserted in accordance with the Copyright, Designs and Patents Act 1988.

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My website also includes some details on my own automated system and guidance to help you develop your own. xi Contents Prefacevii Systematic trading and investing vii Who should read this book viii The technical stuff x What is coming xi Introduction1 January 2009 1 September 2008 2 Why you should start system trading now 3 It’s dangerous out there 5 Why you should read this book 6 Part One. Theory 9 Part Two. Toolbox 49 Chapter One. The Flawed Human Brain 11 Chapter overview 11 Humans should be great traders, but... 11 Simple trading rules 16 Sticking to the plan 16 Good system design 19 Chapter Two. Systematic Trading Rules 25 Chapter overview 25 What makes a good trading rule 26 When trading rules don’t work 29 Why certain rules are profitable 30 Classifying trading styles 38 Achievable Sharpe ratios 46 Conclusion48 Chapter Three.

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial engineering, financial intermediation, fixed income, global macro, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, Jim Simons, law of one price, Long Term Capital Management, Louis Bachelier, machine readable, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, Performance of Mutual Funds in the Period, pneumatic tube, profit motive, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, tail risk, trade route, transaction costs, value at risk, yield curve, zero-sum game

Nasdaq’s computer-assisted execution system, available to broker-dealers, was rolled out in 1983, with the small-order execution system following in 1984. While computer-based execution has been available on selected exchanges and networks since the mid-1980s, systematic trading did not gain traction until the 1990s. According to Goodhart and O’Hara (1997), the main reasons for the delay in adopting systematic trading were the high costs of computing as well as the low throughput of electronic orders on many exchanges. NASDAQ, for example, introduced its electronic execution capability in 1985, but made it available only for smaller orders of up to 1,000 shares at a time.

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Initially, Globex traded only CME futures on the most liquid currency pairs: Deutsche mark and Japanese yen. Electronic trading was subsequently extended to CME futures on British pounds, Swiss francs, and Australian and Canadian dollars. In 1993, systematic trading was enabled for CME equity futures. By October 2002, electronic trading on the CME reached an average daily volume of 1.2 million contracts, and innovation and expansion of trading technology continued henceforth, causing an explosion in systematic trading in futures along the way. The first fully electronic U.S. options exchange was launched in 2000 by the New York–based International Securities Exchange (ISE). As of mid-2008, seven exchanges offered either fully electronic or a hybrid mix of floor and electronic trading in options.

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Technological progress enabled exchanges to adapt to the new technology-driven culture and offer docking convenient for trading. Computerized trading became known as “systematic trading” after the computer systems that processed run-time data and made and executed buy-and-sell decisions. High-frequency trading developed in the 1990s in response to advances in computer technology and the adoption of the new technology by the exchanges. From the original rudimentary order processing to the current state-of-the-art all-inclusive trading systems, high-frequency trading has evolved into a billion-dollar industry. To ensure optimal execution of systematic trading, algorithms were designed to mimic established execution strategies of traditional traders.

Commodity Trading Advisors: Risk, Performance Analysis, and Selection by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

Asian financial crisis, asset allocation, backtesting, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, financial engineering, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, managed futures, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Performance of Mutual Funds in the Period, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, tail risk, technology bubble, transaction costs, value at risk, zero-sum game

In the next section, we examine how this kinked relationship can be quantified. 15.00% 10.00% 5.00% 0.00% –5.00% –10.00% –20.00% –15.00% –10.00% –5.00% 0.00% 5.00% 10.00% 15.00% S&P 100 Excess Returns Diversified Trading Regression Line FIGURE 9.2 Barclay Diversified Trading Index Systematic Excess Returns 188 RISK AND MANAGED FUTURES INVESTING 0.200 0.150 0.100 0.050 0.000 –0.050 –0.100 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns Systematic Trading Regression Line FIGURE 9.3 Barclay Systematic Trading Index MLMI Excess Returns 0.060 0.040 0.020 0.000 –0.020 –0.040 –0.060 –0.080 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns MLM Index Regression Line FIGURE 9.4 MLM Index Measuring the Long Volatility Strategies of Managed Futures 189 FITTING THE REGRESSION LINE The previous discussion provides a general framework in which to describe empirically the long volatility exposure embedded within CTA trendfollowing strategies.

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Our next step is to provide some Value at Risk analysis. 194 RISK AND MANAGED FUTURES INVESTING Diversified Excess Returns 15.00% 10.00% 5.00% 0.00% –5.00% –10.00% –20.00% –15.00% –10.00% –5.00% 0.00% 5.00% 10.00% 15.00% S&P 100 Excess Returns Diversified Trading Mimicking Portfolio Systematic Excess Returns FIGURE 9.6 Mimicking Portfolio Returns for the Barclay Diversified Trading Index 0.200 0.150 0.100 0.050 0.000 –0.050 –0.100 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns Systematic Trading Mimicking Portfolio FIGURE 9.7 Mimicking Portfolio Returns for the Barclay Systematic Trading Index 195 MLMI Excess Returns Measuring the Long Volatility Strategies of Managed Futures 0.080 0.060 0.040 0.020 0.000 –0.020 –0.040 –0.060 –0.080 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns MLM Index Mimicking Portfolio FIGURE 9.8 Mimicking Portfolio Returns for the MLM Index VALUE AT RISK FOR MANAGED FUTURES The main reason for building mimicking portfolios is to simulate the returns to trend-following strategies for developing risk estimates.

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Fung and Hsieh (1997b) documented that commodity trading advisors apply predominantly trend-following strategies. Measuring the Long Volatility Strategies of Managed Futures 185 In our research we use three Barclay Commodity Trading Advisor indices to capture the trading dynamics of the CTA market: Commodity Trading Index, Diversified Commodity Trading Advisor Index, and Systematic Trading Index. These indices are an equally weighted average of a group of CTAs who identify themselves as belonging to one of the three strategies. There are alternative ways to gain exposure to the futures markets without the use of a CTA. One way is a passive managed futures index, such as the Mount Lucas Management Index (MLMI).

Statistical Arbitrage: Algorithmic Trading Insights and Techniques by Andrew Pole

algorithmic trading, Benoit Mandelbrot, constrained optimization, Dava Sobel, deal flow, financial engineering, George Santayana, Long Term Capital Management, Louis Pasteur, low interest rates, mandelbrot fractal, market clearing, market fundamentalism, merger arbitrage, pattern recognition, price discrimination, profit maximization, proprietary trading, quantitative trading / quantitative finance, risk tolerance, Sharpe ratio, statistical arbitrage, statistical model, stochastic volatility, systematic trading, transaction costs

What are the implications for daily price behavior? Can a process be elucidated under which continuous trading from 9:30 a.m. through 4 p.m. will, ceteris paribus, generate daily price patterns structurally, notably, describably different depending on the size of the individual market price increment? If not, then systematic trading models evaluated on daily closing prices will also Trinity Troubles 159 not exhibit distinguishable outcomes except to the extent that the bid–ask spread (at the close) is somehow captured by a strategy. In fact, simulations of many such models exhibited poor returns over 2003–2004. Either the observable, acknowledged, structural changes to price moves within the day resulting from the change to decimal quotes and penny increments led to change in the structure of end-of-day prices across time, or some factor or factors other than the change to decimalization explain the simulation outcome.

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Either the observable, acknowledged, structural changes to price moves within the day resulting from the change to decimal quotes and penny increments led to change in the structure of end-of-day prices across time, or some factor or factors other than the change to decimalization explain the simulation outcome. If a contrary observation had been made, then a plausible argument from decimalization to systematic trading strategy return could be constructed: If day-to-day trading shows positive return but intraday trading shows no return then price moves in reaction to trades eliminate the opportunity. The evidence to date neither supports nor contradicts such a hypothesis. It is much more likely than not that decimalization was a bit player in the explanation of statistical arbitrage performance decline.

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The reduction in number of opportunities is directly related to volatility, which may very well be reduced in some part by greater competition among a larger number of statistical arbitrage managers. That still leaves the important question: Why is the sum total of return on the identified opportunities reduced to zero? Let us accept that competition in systematic trading of equities has increased. There is no evidence, notwithstanding performance problems, to support concomitant increase of market impact, and consequently no evidence that greater competition is the major cause of the decline of statistical arbitrage performance. Trinity Troubles 9.5 163 INSTITUTIONAL INVESTORS ‘‘Pension funds and mutual funds have become more efficient in their trading.’’

The New Sell and Sell Short: How to Take Profits, Cut Losses, and Benefit From Price Declines by Alexander Elder

Atul Gawande, backtesting, Bear Stearns, Boeing 747, buy and hold, buy low sell high, Checklist Manifesto, double helix, impulse control, low interest rates, paper trading, short selling, systematic trading, The Wealth of Nations by Adam Smith, uptick rule

They have different temperaments, exploit different opportunities, and face different challenges. Most of us gravitate towards one of these trading styles without giving our decision much thought. It is much better to figure out who you are, what you like or dislike and trade accordingly. • Discretionary vs. Systematic Trading A discretionary trader looks at a chart, reads and interprets its signals, then makes a decision to buy or sell short. He monitors his chart and at some point recognizes an exit signal, then places an order to exit from his trade. Analyzing charts and making decisions is an exciting and engaging process for many of us.

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We call a system robust when it continues to perform reasonably well even after market conditions change. Both types of trading have a downside. The trouble with discretionary trading is that it seduces beginners into making impulsive decisions. On the other hand, a beginner attracted to systematic trading often falls into the sin of curve-fitting. He spends time polishing his backward-looking telescope until he has a system that would have worked perfectly in the past—if only the past repeated itself perfectly, which it almost never does. I am attracted to the freedom of discretionary trading.

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Most of us decide on the basis of our temperament. This is not different from deciding where to live, what education to pursue, and whether or whom to marry—we usually decide on the basis of emotion. Paradoxically, at the top end of the performance scale there is a surprising degree of convergence between discretionary and systematic trading. A top-notch systematic trader keeps making what looks to me like discretionary decisions: when to activate System A, when to reduce funding of System B, when to add a new market or drop a market from the list. At the same time, a savvy discretionary trader has a number of firm rules that feel very systematic.

Trend Commandments: Trading for Exceptional Returns by Michael W. Covel

Alan Greenspan, Albert Einstein, Alvin Toffler, behavioural economics, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, disinformation, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, global macro, Jim Simons, Lao Tzu, Long Term Capital Management, managed futures, market bubble, market microstructure, Market Wizards by Jack D. Schwager, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Sharpe ratio, systematic trading, the scientific method, three-martini lunch, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

Trend following trading is reactive. It does not predict market direction. Trend trading demands self-discipline to follow precise rules (no guessing or wild emotions). It involves a certain risk management that uses the current market price, equity level in your account, and current market volatility. We decided that systematic trading was best. Fundamental trading gave me ulcers.2 Trend traders use an initial risk rule to determine their trading size at entry. That means you know exactly how much to buy or sell based on how much money you have. Changes in price may lead to a gradual reduction or increase of your initial trade.

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Michael Lewis, Moneyball: The Art of Winning an Unfair Game. New York: W.W. Norton and Company, 2003. 3. Greg Burns, “Former ‘Turtle’ Turns Caution into an Asset.” Chicago Sun-Times, May 29, 1989, p. 33. Robust 1. Dave Druz interview with Covel, 2011. 2. Covel, Trend Following, p. 271. 3. Ken Tropin speaking on “Systematic Trading Strategies in Managed Futures.” The Greenwich Roundtable, November 20, 2003. 4. Futures Industry Association Review: Interview: Money Managers. See http://www.fiafii.org. Push the Button 1. Television commercial introducing the new Apple McIntosh computer, January 1984. 2. Sharon Schwartzman, “Computers Keep Funds in Mint Condition: A Major Money Manager Combines the Scientific Approach with Human Ingenuity.”

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Sharon Schwartzman, “Computers Keep Funds in Mint Condition: A Major Money Manager Combines the Scientific Approach with Human Ingenuity.” Wall Street Computer Review, Vol. 8, No. 6, March 1991, 13. 252 Tre n d C o m m a n d m e n t s 3. Ibid. 4. George Crapple speaking on “Systematic Trading Strategies in Managed Futures.” The Greenwich Roundtable, November 20, 2003. 5. Chuck Cain blog post, January 9, 2011. See http://www.michaelcovel.com/2011/01/ 09/computers-are-uselesswithout-you/. Wash, Rinse, Repeat 1. Gregory J. Millman, The Chief Executive. January–February 2003. 2. Herb Greeenberg, “Answering the Question—Who Wins From Derivatives Losers.”

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Alvin Toffler, Atul Gawande, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, Carl Icahn, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, Future Shock, game design, global macro, hindsight bias, housing crisis, index fund, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, managed futures, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, Market Wizards by Jack D. Schwager, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, Teledyne, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

You might consider trading a chart with a long enough time scale that transaction costs are a minor factor— something like a daily price chart, going back a year or two.” 375 C He’s barely rated a mention in the nation’s most important newspapers, but pay close attention to what Institutional Investor wrote about him… “Jim Simons [president of Renaissance Technologies and operator of the Medallion Fund] may very well be the best money manager on earth.” Long Island Business News 376 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets Toby Crabel has made a 180-degree turn from discretionary to systematic trading. In the early days, he used discretion to devise the systemgenerated signals and to decide whether or not to take the trade signals. “However, I have now come to the conclusion that systematic trading is more suited to me… It’s only one in 500 or so cases that we do not trade a signal because of execution problems or some other technical reason… Now I am less emotionally involved in the markets and I believe being more objective helps.”

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We’re trading these great systems, and testing, and making sure what we do has worked in the past. And being disciplined, and unemotional, and applying our methods to the futures markets, but limiting our trading to this one group of markets. We need to look at the investment world globally and communicate our expertise of systematic trading.”31 Bruce Terry, president of Weston Capital Investment Services and a disciple of Richard Donchian, dismisses out of hand that trend following is not for stocks: “Originally in the 1950s, technical models came out of studying stocks. Commodity Trading Advisors (CTA) applied these to futures.

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Is our expertise in that, or is our expertise in systematic Chapter 11 • The Game trend following or model development. So maybe we trend follow with Chinese porcelain. Maybe we trend follow with gold and silver, or stock futures, or whatever the client needs. We need to look at the investment world globally and communicate our expertise of systematic trading…People look at systematic and computerized trading with too much skepticism. But a day will come when people will see that systematic trend following is one of the best ways to limit risk and create a portfolio that has some reasonable expectation of making money…I think we’ve miscommunicated to our clients what our expertise really is.”8 In an unpredictable world, trend following is one of the best tools to manage risk and, ultimately, uncertainty.

The Global Minotaur by Yanis Varoufakis, Paul Mason

active measures, Alan Greenspan, AOL-Time Warner, banking crisis, Bear Stearns, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business climate, business cycle, capital controls, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, colonial rule, corporate governance, correlation coefficient, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, declining real wages, deindustrialization, Easter island, endogenous growth, eurozone crisis, financial engineering, financial innovation, first-past-the-post, full employment, Glass-Steagall Act, Great Leap Forward, guns versus butter model, Hyman Minsky, industrial robot, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, labour market flexibility, light touch regulation, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market fundamentalism, Mexican peso crisis / tequila crisis, military-industrial complex, Money creation, money market fund, mortgage debt, Myron Scholes, negative equity, new economy, Nixon triggered the end of the Bretton Woods system, Northern Rock, paper trading, Paul Samuelson, planetary scale, post-oil, price stability, quantitative easing, reserve currency, rising living standards, Ronald Reagan, special economic zone, Steve Jobs, structural adjustment programs, Suez crisis 1956, systematic trading, too big to fail, trickle-down economics, urban renewal, War on Poverty, WikiLeaks, Yom Kippur War

Keynes knew that, at a time of crisis, it would be politically impossible to force the deficit countries to apply the agreed rules. Other deficit countries would follow suit and the system of fixed exchange rates would collapse. Just as it did on 15 August 1971. With these troubled thoughts in mind, Keynes designed and proposed the ICU so as to deal with two potential problems at once: to avert systematic trade imbalances and to endow the commonwealth of capitalist nations with the flexibility necessary to deal with future catastrophic crashes (like that of 1929). The proposal was both simple and audacious: the ICU would grant each member country an overdraft facility, i.e. the right to borrow at zero interest from the international central bank.

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Lionel Robbins, an influential British economist and the pioneer behind the rise of the London School of Economics and Political Science, wrote that, upon hearing Keynes’ proposals, the conference participants were stunned: ‘[I]t would be difficult to exaggerate the electrifying effect on thought throughout the whole relevant apparatus of government…nothing so imaginative and so ambitious had ever been discussed.’ Nevertheless, the intellectual value and technical competence of this well-laid plan was not in tune with America’s priorities.4 The United States, which emerged from the war as the world’s powerhouse, had no interest in restraining its own capacity to run large, systematic trade surpluses with the rest of the world. The New Dealers, however respectful they might have been of John Maynard Keynes, had another plan: a Global Plan, according to which the dollar would effectively become the world currency and the United States would export goods and capital to Europe and Japan in return for direct investment and political patronage – a hegemony based on the direct financing of foreign capitalist centres in return for an American trade surplus with them.5 The rise of the fallen The Global Plan started life as an attempt to kick-start international trade, create markets for US exports, and address the dearth of international investment by private US companies.

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CHAPTER 4 The Global Minotaur The Global Plan’s Achilles heel The Global Plan unravelled because of a major design flaw in its original architecture. John Maynard Keynes had spotted the flaw during the 1944 Bretton Woods conference but was overruled by the Americans. What was it? It was the lack of any automated global surplus recycling mechanism (GSRM) that would keep systematic trade imbalances constantly in check. The American side vetoed Keynes’ proposed mechanism, the International Currency Union, thinking that the US could, and should, manage the global flow of trade and capital itself, without committing to some formal, automated GSRM. The new hegemon, blinded by its newfangled superpower status, failed to recognize the wisdom of Odysseus’s strategy of binding itself voluntarily to some Homeric mast.

Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, book value, Brownian motion, business continuity plan, buy and hold, classic study, compound rate of return, Edward Thorp, Elliott wave, endowment effect, financial engineering, fixed income, general-purpose programming language, index fund, Jim Simons, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk free rate, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, content Web site, epchan.com/subscriptions, programming knowledge, and real-time system implementation to develop and run an algorithmic trading which you’ll have free access to with purchase of business step by step in Quantitative Trading.” this book. —YASER ANWAR, trader As an independent trader, you’re free from the con- “Quantitative systematic trading is a challenging field that has always been shrouded in mystery, straints found in today’s institutional environment— seemingly too difficult to master by all but an elite few. In this honest and practical guide, Dr. Chan and as long as you adhere to the discipline of highlights the essential cornerstones of a successful automated trading operation and shares lessons he quantitative trading, you can achieve significant learned the hard way while offering clear direction to steer readers away from common traps that both returns.

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Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, content Web site, epchan.com/subscriptions, programming knowledge, and real-time system implementation to develop and run an algorithmic trading which you’ll have free access to with purchase of business step by step in Quantitative Trading.” this book. —YASER ANWAR, trader As an independent trader, you’re free from the con- “Quantitative systematic trading is a challenging field that has always been shrouded in mystery, straints found in today’s institutional environment— seemingly too difficult to master by all but an elite few. In this honest and practical guide, Dr. Chan and as long as you adhere to the discipline of highlights the essential cornerstones of a successful automated trading operation and shares lessons he quantitative trading, you can achieve significant learned the hard way while offering clear direction to steer readers away from common traps that both returns.

The Handbook of Personal Wealth Management by Reuvid, Jonathan.

asset allocation, banking crisis, BRICs, business cycle, buy and hold, carbon credits, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, currency risk, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, global macro, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, low interest rates, managed futures, market bubble, merger arbitrage, negative equity, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, proprietary trading, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

Global macro Macro funds may invest in any market, and frequently use leverage and derivatives, futures and swaps to make directional trades in equities, interest rates, currencies and commodities. Macro funds also tend to be very concentrated in their bets. Traders can use fundamental trading strategies where they examine the factors that affect the supply and demand for particular futures and forwards contracts in order to predict future prices as well as technical analysis. Systematic trading (CTAs) These funds attempt to profit from patterns in market moves at different time horizons. Typically short-term CTAs are equipped to benefit from sharp intra-day moves, with longer-term CTAs seeking to generate profits from more established trends. Short-term CTAs have developed sophisticated platforms where the average holding period can range from minutes to just several trading days.

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Much of this positive performance can be attributed to foreign exchange positions, rates and bets on the large rise and subsequent fall of the oil price. Throughout 2008 many discretionary macro managers reduced their risk exposures, believing that financial markets will deteriorate further. Systematic trading Systematic managers use computer-based algorithms to generate buy and sell signals based on trends in the market. During 2008, managers took profits from longer-term themes, such as the rise in commodity prices and allocated more capital to shorter-term trends. Convertible bond arbitrage Poor performance has been the result of credit spreads widening and considerable distressed sell off as investors took flight to quality assets.

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, Bear Stearns, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, deal flow, discounted cash flows, family office, financial engineering, fixed income, global macro, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, managed futures, merger arbitrage, offshore financial centre, proprietary trading, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stock buybacks, stocks for the long run, systematic trading, two and twenty, unpaid internship, value at risk, yield curve, yield management

Merger arbitrage may hedge against market risk by purchasing Standard & Poor’s (S&P) 500 put options or put option spreads. Statistical Arbitrage Stat arb funds focus on the statistical mispricing of one or more assets based on the expected value of those assets. This is a very quantitative and systematic trading strategy that uses advanced software programs. Note: These funds typically hire PhDs, mathematicians, and/or programming experts. Emerging Markets This strategy involves equity or fixed income investing in emerging markets around the world. As emerging markets have matured so too has investing in them.

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c01.indd 10 1/10/08 11:00:57 AM Getting Started 11 Multi-strategy Multi-strategy investing uses various strategies simultaneously to realize short- and long-term gains. Rather than making dramatic shifts between styles, multi-strategy funds are more apt to reallocate managers within their selected strategies based on the performance of the managers. Quantitative Strategies Quantitative funds, which use systematic trading, are highly model-driven and usually rely on detailed software programs to determine when to buy and sell. While most quantitative funds invest in equities, others target fixed-income securities, commodities, currencies, and market indexes. These funds, some of which have billions of dollars in assets, can move the markets in which they invest when an internal buy or sell order is triggered.

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Black Swan, Bob Litterman, bond market vigilante , book value, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, carbon credits, Carmen Reinhart, central bank independence, classic study, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, deal flow, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global macro, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, inverted yield curve, invisible hand, John Bogle, junk bonds, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, pension time bomb, performance metric, Phillips curve, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, savings glut, search costs, selection bias, seminal paper, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stock buybacks, stocks for the long run, survivorship bias, systematic trading, tail risk, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

Some of the best sources I have yet to meet personally, but I am a voracious reader—to which this book’s lengthy reference list attests. There are too many people to thank by name, but I make an exception for Rory Byrne to whom this book is dedicated. For several years Rory was my main partner in developing and implementing systematic trading models and always a sensible sounding board. Sadly, Rory succumbed last year to a persistent tumor at the age of 35. A most emphatic thank-you goes to Laurence Siegel, Knut Kjaer, Matti Ilmanen, and Victor Haghani who carefully read the manuscript (or evolving versions of it) and greatly improved the book.

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They can be mitigated but not fully avoided. 24.1 INTRODUCTION Valuation indicators are effective in providing a long-horizon (multi-year) view on an asset’s prospects. In this chapter, I turn to dynamic trading strategies and forecasting models that have a shorter horizon (one week, month, or quarter). I first describe the generics—model types, assets traded, indicators—and then comment on possible improvements and pitfalls for the systematic trading style. I keep this chapter brief so as to retain some of my proprietary trade secrets, but Chapters 8 through 10 review several publicly known market-timing indicators for equities, duration, and credit, while Chapters 12 through 15 review four popular dynamic trading strategies: equity value, foreign exchange carry, commodity momentum, and volatility selling.

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However, these estimates are subject to measurement errors (even if we ignore likely time variation in these premia, many factor exposures are estimated with noise), and to specification errors (the model may omit important factors, the assumption of linear relations may be faulty, etc.). Combining models While I will not review portfolio construction issues in this book beyond the discussion in Chapter 28, I note that so-called Black–Litterman optimizers are particularly well suited for combining information from systematic trading models. Black–Litterman optimizers enable (i) blending historical experience with anchoring priors (such as perceived market equilibrium returns) and/or with active views; (ii) inputting expected return views on particular trades (instead of on each asset separately); and (iii) incorporating a measure of uncertainty for each view.

Risk Management in Trading by Davis Edwards

Abraham Maslow, asset allocation, asset-backed security, backtesting, Bear Stearns, Black-Scholes formula, Brownian motion, business cycle, computerized trading, correlation coefficient, Credit Default Swap, discrete time, diversified portfolio, financial engineering, fixed income, Glass-Steagall Act, global macro, implied volatility, intangible asset, interest rate swap, iterative process, John Meriwether, junk bonds, London Whale, Long Term Capital Management, low interest rates, margin call, Myron Scholes, Nick Leeson, p-value, paper trading, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, statistical model, stochastic process, systematic trading, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

For trading desks, the decision to invest money into a trading strategy is a core element of the decision to “accept risk” or to “avoid risk.” This is a strategic decision. It defines the risks that will be voluntarily taken on even if, or perhaps especially if, each strategy is highly successful. These techniques are often grouped into a category called strategic risk management. t SYSTEMATIC TRADING Many professional traders follow a systematic approach to trading which they call a trading strategy. The goal of a trading strategy is to remove emotion from investing and rigorously test an idea before placing an investment. There are several phases to developing trading strategies. Each stage is 95 96 RISK MANAGEMENT IN TRADING designed to test the trader’s preconceptions about the market before a trade is executed and quantify how the strategy might have worked under different market conditions.

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See real estate investment trusts reputational risk, 25 results, randomness and, 111 retrospective testing, 188 S sales, 13 scheduling, 13 second derivative, 84–85 securities, 22 settlement risk, 262–263 Sharpe Ratio, 109–110 short selling, regulations about, 16 shortfall, expected, 172–173 shorting, 4 simulation accuracy, 98 skew, 70–72 slippage, 101–105 social activity, trading as, 238 306 speculators, market stability and, 136 spot prices, 21 statistics, 66–67 stochastic processes, 64, 72–75 stocks, 42, 44–45 stop limit orders, 19 stop orders, 18–19 strategic risk, 25 strategies, 6–8 combining, 111–112 comparing, 108–111 strategy testing, 97–101 support and control, 13–14 systematic trading, 95–96 T Taylor Series Expansion, 89–90, 203–204 testing hedge effectiveness, 187–189 strategy, 97–101 tests, regression, 191–194 theta, 202, 226–230 time until expiration, 201 time value of money, 90–92 rho and, 232 time, vega and, 232 timing, 101 trade forensics, 1–2, 10 trade surveillance, 112–118 trading, 12–16 as social activity, 238 requirements for, 16 systematic, 95–96 trading decisions, risk and, 10–11 trading desks, 2–3 risk tolerance and, 111 trading limits, 147–148 trading positions, 20–21 INDEX trading risk, managing, 21–23 transactions, 130 transactions costs, 101–105 transfer, risk, 29, 267 Treasury bills, 49 U UL.

The New Science of Asset Allocation: Risk Management in a Multi-Asset World by Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi

asset allocation, backtesting, Bear Stearns, behavioural economics, Bernie Madoff, Black Swan, book value, business cycle, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, diversification, diversified portfolio, financial engineering, fixed income, global macro, high net worth, implied volatility, index fund, interest rate swap, invisible hand, managed futures, market microstructure, merger arbitrage, moral hazard, Myron Scholes, passive investing, Richard Feynman, Richard Feynman: Challenger O-ring, risk free rate, risk tolerance, risk-adjusted returns, risk/return, search costs, selection bias, Sharpe ratio, short selling, statistical model, stocks for the long run, survivorship bias, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve, zero-sum game

As indicated in Exhibit 7.11, the results show that with the exception of CTAs who trade primarily in equity futures, most CTA managers (market or strategy based) have a low correlation with most traditional stock and bond markets. In Exhibit 7.12, the correlation of various CTA strategies are given. In general most CTAs trade using systematic trading models. As a result, results in Exhibit 7.12 show a high correlation between the CTA systematic index and other market based CTA strategies (financial). However, results in Exhibit 7.12 also show a low correlation between the CTA systematic index and the CTA discretionary index reflecting the differential trading styles.

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CASAM/CISDM Equal Weight Discretionary Index (CISDM Discretionary Index): Trade financial, currency, and commodity futures/options based on a wide variety of trading models including those based on fundamental economic data and/or individual traders’ beliefs. CASAM/CISDM Equal Weight Systematic Index (CISDM Systematic Index): Trade primarily in the context of a predetermined systematic trading model. Most systematic CTAs follow a trend-following program although some trade countertrend. In addition, trend-following CTAs may concentrate on short-, mid-, or long-term trends or a combination thereof. CASAM/CISDM Equal Weight Currency Index (CISDM Currency Index): Trade currency futures/options and forward contracts.

Unknown Market Wizards by Jack D. Schwager

3D printing, algorithmic trading, automated trading system, backtesting, barriers to entry, Black Monday: stock market crash in 1987, Brexit referendum, buy and hold, commodity trading advisor, computerized trading, COVID-19, cryptocurrency, diversification, Donald Trump, eurozone crisis, family office, financial deregulation, fixed income, forward guidance, index fund, Jim Simons, litecoin, Long Term Capital Management, margin call, market bubble, Market Wizards by Jack D. Schwager, Nick Leeson, performance metric, placebo effect, proprietary trading, quantitative easing, Reminiscences of a Stock Operator, risk tolerance, risk-adjusted returns, Sharpe ratio, short squeeze, side project, systematic trading, tail risk, transaction costs

So, I closed my CTA again and went to work for Walter Garrison. Having a full-time quant assigned to me allowed me to quantify a lot of what I was doing. We ran the program mostly systematized, but sometimes I would override a system trade, and sometimes I would take a trade the system wasn’t putting on. Were those non-systematic trades helpful or detrimental? They made money. My trader would argue with me all the time, saying, “Why are we doing this?” And I would tell him, “Because it makes money.” We tracked what happened when I overrode the system. If it didn’t make money, I wouldn’t have continued to do it. What types of trades were you putting on that weren’t part of the system?

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Parker’s school had a Data General Nova computer, which provided his first exposure to programming. His interest in programming was rekindled in college through access to a computer lab and abetted by a free DEC VT-180 PC his mother had arranged for him to receive. This hobby led to a programming career and, eventually, systematic trading. Parker’s trading career could be divided into three distinct phases: an initial 14-year period of consistent profitability; a subsequent three-year period that very nearly drove him to quit trading permanently; and the most recent four-year period when he achieved his best return/risk numbers ever.

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Black-Scholes formula, book value, Brownian motion, business cycle, buy and hold, buy low sell high, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, currency risk, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, global macro, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, junk bonds, late capitalism, law of one price, Long Term Capital Management, low interest rates, managed futures, margin call, market clearing, market design, market friction, Market Wizards by Jack D. Schwager, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, Phillips curve, price discovery process, price stability, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, short squeeze, SoftBank, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, tail risk, technology bubble, time dilation, time value of money, total factor productivity, transaction costs, two and twenty, value at risk, Vanguard fund, yield curve, zero-coupon bond

After a year, AQR convinced me to take a leave of absence from NYU to join them full time starting on July 1, 2007. Moving from Greenwich Village to Greenwich, CT, the first big shock was how dark and quiet it was at night compared to the constant buzz of Manhattan, but a bigger shock was around the corner. My job was to develop new systematic trading strategies as a member of the Global Asset Allocation team, focusing on global equity indices, bonds, commodities, and currencies, and I also had opportunities to contribute to the research going on in the Global Stock Selection and arbitrage teams. However, my start as a full-time practitioner happened to coincide with the beginning of the subprime credit crisis.

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. ___________________ 1 Quantitative traders are close cousins to, but perform different roles than, the “sell-side quants” described in Emanuel Derman’s interesting autobiography My Life as a Quant (2004). Sell-side quants provide analytical tools that are helpful for hedging, risk management, discretionary traders, clients, and other purposes. In contrast, quantitative traders work on the “buy-side” and build models that are used directly as a tool for systematic trading. 2 See Damodaran (2012) for an extensive description of equity valuation and financial statement analysis. 3 To see this result, first note that Then change index on the first book value and make the appropriate adjustments to arrive at which gives the residual income model. This version of the dividend discount model goes back to Preinreich (1938). 4 See Hou, van Dijk, and Zhang (2012) and references therein.

Hedge Fund Market Wizards by Jack D. Schwager

asset-backed security, backtesting, banking crisis, barriers to entry, Bear Stearns, beat the dealer, Bernie Madoff, Black-Scholes formula, book value, British Empire, business cycle, buy and hold, buy the rumour, sell the news, Claude Shannon: information theory, clean tech, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, do what you love, Edward Thorp, family office, financial independence, fixed income, Flash crash, global macro, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, legacy carrier, Long Term Capital Management, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, Michael Milken, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, proprietary trading, quantitative easing, quantitative trading / quantitative finance, Reminiscences of a Stock Operator, Right to Buy, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Savings and loan crisis, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

These types of systems are designed to identify patterns that suggest a greater probability for either higher or lower prices over the near term. Woodriff is among the small minority of CTAs who employ such pattern-recognition approaches, and he does so using his own unique methodology. He is one of the most successful practitioners of systematic trading of any kind. Woodriff grew up on a working farm near Charlottesville, Virginia. Woodriff’s perceptions of work were colored by his childhood experiences. When he was in high school, Woodriff thought it was sad that most people loved Fridays and hated Mondays. “I was going to make sure that wasn’t me,” he says.

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Rather than blindly searching through the data for patterns—an approach whose methodological dangers are widely appreciated within, for example, the natural science and medical research communities—we typically start by formulating a hypothesis based on some sort of structural theory or qualitative understanding of the market, and then test that hypothesis to see whether it is supported by the data. [Woodriff speaking emphatically] I don’t do that. I read all of that just to get to the point that I do what I am not supposed to do, which is a really interesting observation because I am supposed to fail. According to almost everyone, you have to approach systematic trading (and predictive modeling in general) from the framework of “Here is a valid hypothesis that makes sense within the context of the markets.” Instead, I blindly search through the data. It’s nice that people want hypotheses that make sense. But I thought that was very limiting. I want to be able to search the rest of the stuff.

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, book value, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, financial engineering, forensic accounting, Henry Singleton, hindsight bias, intangible asset, Jim Simons, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk free rate, risk-adjusted returns, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, stock buybacks, survivorship bias, systematic trading, Teledyne, The Myth of the Rational Market, time value of money, transaction costs

He is also an assistant professor of finance at Drexel University's Lebow College of Business, where his research focus is on value investing and behavioral finance. Professor Gray teaches graduate-level investment management and a seminar on hedge fund strategies and operations. Dr. Gray's professional and leadership experiences include over 14 years building systematic trading systems, trading special situations, and service as a U.S. Marine Corps intelligence officer (Captain) in Iraq and various posts in Asia. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago Booth School of Business. He graduated magna cum laude with a BS in economics from the Wharton School, University of Pennsylvania.

The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds by Maneet Ahuja, Myron Scholes, Mohamed El-Erian

"World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Alan Greenspan, Asian financial crisis, asset allocation, asset-backed security, backtesting, Bear Stearns, Bernie Madoff, book value, Bretton Woods, business process, call centre, Carl Icahn, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, financial engineering, fixed income, global macro, high net worth, high-speed rail, impact investing, interest rate derivative, Isaac Newton, Jim Simons, junk bonds, Long Term Capital Management, managed futures, Marc Andreessen, Mark Zuckerberg, merger arbitrage, Michael Milken, Myron Scholes, NetJets, oil shock, pattern recognition, Pershing Square Capital Management, Ponzi scheme, proprietary trading, quantitative easing, quantitative trading / quantitative finance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, Savings and loan crisis, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, stock buybacks, systematic bias, systematic trading, tail risk, two and twenty, zero-sum game

There have been times when we’ve had to put a risk factor in an equation, and the math people have been going back and forth over whether to make it 2.3 or 2.4. And I say, come on, just make it three. I just want something that works.” Wong says that the one thing most people don’t understand about systematic trading is the trade-off between profit potential in the long term and the potential for short-term fluctuation and losses. “We are all about the long run,” he says. “It’s why I say, over and over, the trend is your friend.” “If you’re a macro trader and you basically have 20 positions, you better make sure that no more than two or three are wrong.

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backpropagation, backtesting, barriers to entry, behavioural economics, book value, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, data science, deep learning, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial intermediation, Flash crash, Geoffrey Hinton, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, low interest rates, machine readable, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, Performance of Mutual Funds in the Period, popular capitalism, prediction markets, price discovery process, profit motive, proprietary trading, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

The shape of the volatility skew, the volatility spread, the options trading volume, and the options open interest are all useful tools for predicting near-term performance of the underlying stock. 5 Alpha = open interest of call options/open interest of put options. 24 Institutional Research 101: Analyst Reports By Benjamin Ee, Hardik Agarwal, Shubham Goyal, Abhishek Panigrahy, and Anant Pushkar This chapter is a general overview of analyst research reports and stock recommendations that alpha researchers may encounter in financial media sources. We will discuss the best ways to access analyst recommendations, and address the all-important question of how these reports can help inspire systematic trading ideas. Sell-side analysts’ recommendations, ratings, and price-target changes – on companies and entire industries – are featured prominently in finan­cial newspapers, conferences, blogs, and databases, which often cite these reports to explain major stock price movements. Indeed, numerous studies by industry associations and academics have found that analyst research contains valuable information.

Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

algorithmic trading, Amazon Web Services, automated trading system, backtesting, barriers to entry, bitcoin, Brownian motion, cloud computing, coronavirus, cryptocurrency, data science, deep learning, Edward Thorp, fiat currency, global macro, Gordon Gekko, Guido van Rossum, implied volatility, information retrieval, margin call, market microstructure, Myron Scholes, natural language processing, paper trading, passive investing, popular electronics, prediction markets, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Rubik’s Cube, seminal paper, Sharpe ratio, short selling, sorting algorithm, systematic trading, transaction costs, value at risk

In summary, it is rather safe to say that Python plays an important role in algorithmic trading already and seems to have strong momentum to become even more important in the future. It is therefore a good choice for anyone trying to enter the space, be it as an ambitious “retail” trader or as a professional employed by a leading financial institution engaged in systematic trading. Focus and Prerequisites The focus of this book is on Python as a programming language for algorithmic trading. The book assumes that the reader already has some experience with Python and popular Python packages used for data analytics. Good introductory books are, for example, Hilpisch (2018), McKinney (2017), and VanderPlas (2016), which all can be consulted to build a solid foundation in Python for data analysis and finance.

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation by Sebastien Page

Andrei Shleifer, asset allocation, backtesting, Bernie Madoff, bitcoin, Black Swan, Bob Litterman, book value, business cycle, buy and hold, Cal Newport, capital asset pricing model, commodity super cycle, coronavirus, corporate governance, COVID-19, cryptocurrency, currency risk, discounted cash flows, diversification, diversified portfolio, en.wikipedia.org, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, future of work, Future Shock, G4S, global macro, implied volatility, index fund, information asymmetry, iterative process, loss aversion, low interest rates, market friction, mental accounting, merger arbitrage, oil shock, passive investing, prediction markets, publication bias, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, robo advisor, seminal paper, shareholder value, Sharpe ratio, sovereign wealth fund, stochastic process, stochastic volatility, stocks for the long run, systematic bias, systematic trading, tail risk, transaction costs, TSMC, value at risk, yield curve, zero-coupon bond, zero-sum game

In a 2017 paper titled “Macroeconomic Dashboards for Tactical Asset Allocation,” my colleagues David Clewell, Chris Faulkner-MacDonagh, David Giroux, Charles Shriver, and I take the practitioner’s perspective. We show how to build dashboards to integrate macro factors into a broader, discretionary TAA process. Our goal is not to design stand-alone systematic trading strategies based on macro factors. Rather, we show how investors can build macro factor dashboards to introduce discipline into their asset allocation process (in combination with other inputs, such as relative valuations). In Chapter 3, I showed that valuation signals don’t always have very high correlation with forward returns.

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

The increased use of flawed risk models, of hyper-leveraged hedge funds and investment banks, meant the financial system, more than ever, was a castle built on sand (or TNT). It meant more collapses, more blowups, more crashes. Taleb had been making money on crashes and blowups for nearly fifteen years, sometimes by accident. Increasingly he began to toy with the notion of a systematic trading strategy to exploit Wall Street’s hidden, quant-contrived flaws. He’d won his battle with throat cancer. Two years of radiation treatment eliminated the disease. But the brush with fate caused him to reconsider the course of his career. The pressure of trading—or more important, the pressure of avoiding the career-ending risk of blowing up—might have been responsible for his illness, he worried.

Capital Ideas Evolving by Peter L. Bernstein

Albert Einstein, algorithmic trading, Andrei Shleifer, asset allocation, behavioural economics, Black Monday: stock market crash in 1987, Bob Litterman, book value, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, computerized trading, creative destruction, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, diversification, diversified portfolio, endowment effect, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, high net worth, hiring and firing, index fund, invisible hand, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, mental accounting, money market fund, Myron Scholes, paper trading, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, price anchoring, price stability, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, seminal paper, Sharpe ratio, short selling, short squeeze, Silicon Valley, South Sea Bubble, statistical model, survivorship bias, systematic trading, tail risk, technology bubble, The Wealth of Nations by Adam Smith, transaction costs, yield curve, Yogi Berra, zero-sum game

.* Kurz’s Theory of Rational Beliefs is in the spirit of Daniel Kahneman’s observation to me that “The failure in the rational model is . . . in the human brain it requires. Who could design a brain that could perform in the way this model mandates? Every single one of us would have to know and understand everything, completely, and at once.” In a similar vein, Kurz takes the position that investors are rational because they do think about the systematic trade-offs between risk and return just as the theory of efficient markets or the Capital Asset Pricing Model describe. Yet they face an impossible task. The world never stands still, and the information on hand is too complex. We suffer from what economists call “non-stationarity.” If the world were stationary, everybody would get everything right.

Moneyball by Michael Lewis

Cass Sunstein, high batting average, Norman Mailer, old-boy network, placebo effect, RAND corporation, Richard Thaler, systematic trading, the new new thing, the scientific method, upwardly mobile

The headline, along with the mug shots of the players, read: “In a city of so many multicultural faces, Toronto’s baseball team is the whitest in the league. Why?” The baseball writer behind the article, Geoff Baker, had made his own little study. He’d found that there were ten nonwhite players on the average big league twenty-five-man roster and that, after Ricciardi’s wheeling and dealing, the new Jays had only six. The new GM seemed to be systematically trading for lower-priced white guys. How sad, how regrettable, in a city as famous for its diversity as Toronto, that the Blue Jays no longer represented it. “Ricciardi is at a loss to explain the numbers as anything beyond coincidence,” wrote Baker, who was not similarly at a loss. He found an explanation in the way J.

The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management by Alexander Elder

additive manufacturing, Atul Gawande, backtesting, behavioural economics, Benoit Mandelbrot, buy and hold, buy low sell high, Checklist Manifesto, computerized trading, deliberate practice, diversification, Elliott wave, endowment effect, fear index, loss aversion, mandelbrot fractal, margin call, offshore financial centre, paper trading, Ponzi scheme, price stability, psychological pricing, quantitative easing, random walk, Reminiscences of a Stock Operator, risk tolerance, short selling, South Sea Bubble, systematic trading, systems thinking, The Wisdom of Crowds, transaction costs, transfer pricing, traveling salesman, tulip mania, zero-sum game

For example, a friend who is a died-in-the-wool mechanical trader uses three systems in his hedge fund but keeps rebalancing capital allocated to each of them. He shifts millions of dollars from System A to System B or C, and back again. In other words, his discretionary decisions augment his systematic trading. I am a discretionary trader, but follow several strict rules that prohibit me from buying above the upper channel line, shorting below the lower channel line, or putting on trades against the Impulse system (described below). These mechanical rules reduce the number of bad discretionary trades.

How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Bear Stearns, Black-Scholes formula, Bob Litterman, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, business process, butter production in bangladesh, buy and hold, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency risk, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, full employment, George Akerlof, global macro, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, Ivan Sutherland, John Bogle, John von Neumann, junk bonds, linear programming, Loma Prieta earthquake, Long Term Capital Management, machine readable, margin call, market friction, market microstructure, martingale, merger arbitrage, Michael Milken, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Richard Feynman, Richard Stallman, risk free rate, risk-adjusted returns, risk/return, seminal paper, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, subscription business, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

By the end of my second year of research at the fund, I had gone about as far as I could go; but fortunately so had one of the fund’s general partners with whom I happened (not accidentally) to have developed a very good working relationship. He had decided to branch off and create a personal family-and-friends fund that would combine the existing systematic trading strategies we were using with an overlay of fundamental stock and commodity analyses. He had all the capital he needed and asked me to join him in his new venture. Ever the opportunist, I agreed. We crossed the Hudson and setup shop under the auspices of ED&F Man in the World Financial Center, right in the heart of downtown New York City.

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals by David Aronson

Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, book value, butter production in bangladesh, buy and hold, capital asset pricing model, cognitive dissonance, compound rate of return, computerized trading, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, equity risk premium, feminist movement, Great Leap Forward, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, managed futures, mental accounting, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk free rate, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, sugar pill, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

Over the 38 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS full five-and-one-half-year period, my results, to put it generously, were lackluster. Prior to joining Spear, Leeds & Kellogg, I had been a proponent of objective trading methods, so while at Spear, I made efforts to develop a systematic trading program in hopes that it would improve my performance. However, with limited time and development capital, these plans never came to fruition. Thus, I continued to rely on classical barchart analysis, supplemented with several indicators that I interpreted subjectively. However, I was objective in several ways.

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, Alan Greenspan, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bear Stearns, Bernie Madoff, Black-Scholes formula, Bob Litterman, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, Glass-Steagall Act, global macro, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low interest rates, low skilled workers, managed futures, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, National best bid and offer, negative equity, Northern Rock, Occupy movement, oil shock, price stability, proprietary trading, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, tail risk, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

LTCM’s principals were aware of option theory’s strengths and weaknesses and did their best to apply it accordingly.28 LTCM never relied solely on option-pricing models or other financial models. Once the firm’s models spotted deviations, LTCM principals examined them to determine whether there was an underlying economic reason for the discrepancy. Only then did they implement systematic trades. The flaw was more in LTCM’s trade choices than in its hedging tools. Three valid criticisms may stand against the LTCM quants. First, LTCM may have relied too much on models that specified deviations in security prices. Second, many of their bets, such as the short volatility bet, involved positions in illiquid securities.