systematic trading

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pages: 354 words: 26,550

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

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algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, 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, trade route, transaction costs, value at risk, yield curve

DOT was accessible only to NYSE floor specialists, making it useful only for facilitation of the NYSE’s internal operations. 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. Exchanges such as the American Stock Exchange (AMEX) and the NYSE developed hybrid electronic/floor markets that did not fully utilize electronic trading capabilities.

Coincidentally, in 1992 the Chicago Mercantile Exchange (CME) launched its first electronic platform, Globex. 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.

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. To this day, the term “algorithmic trading” usually refers to the systematic execution process—that is, the optimization of buy-and-sell decisions once these buy-and-sell decisions were made by another part of the systematic trading process or by a human portfolio manager.

 

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

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Asian financial crisis, asset allocation, backtesting, 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, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, fixed income, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk-adjusted returns, risk/return, Sharpe ratio, short selling, stochastic process, systematic trading, technology bubble, transaction costs, value at risk

Figures 9.2 through 9.4 demonstrate a similar “kinked” relationship for the Barclay Diversified Trading Index, Systematic Trading Index, and the MLMI. Each figure demonstrates a long put optionlike exposure. 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.

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.

Fung and Hsieh (1997a) found that trend-following styles have a return profile similar to a long option straddle position—a long volatility position. 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). The MLMI applies a mechanical trading rule for following the price trends in several futures markets.

 

pages: 257 words: 13,443

Statistical Arbitrage: Algorithmic Trading Insights and Techniques by Andrew Pole

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algorithmic trading, Benoit Mandelbrot, Chance favours the prepared mind, constrained optimization, Dava Sobel, Long Term Capital Management, Louis Pasteur, mandelbrot fractal, market clearing, market fundamentalism, merger arbitrage, pattern recognition, price discrimination, profit maximization, quantitative trading / quantitative finance, risk tolerance, Sharpe ratio, statistical arbitrage, statistical model, stochastic volatility, systematic trading, transaction costs

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. 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 fact that many bets continue to be identified with a substantial consumer surplus component belies the argument. 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 temporal aspect of the trades is the source of strategy profit; trades at a point in time are the means by which the opportunity is exploited. The foregoing argument demonstrates that competition has not inhibited the ability of managers to exploit identified opportunities. But has competition, or decimalization, or something else altered the temporal structure, the evolution, of prices such that identified patterns ceased to yield a positive return? Did short-term stock price structure change such that systematic trading models were reduced to noise models? If so, can the progenitor forces driving the evolution be identified? Was decimalization or competition influential? Were they active agents, catalysts, or simply coincidental elements? Are they still active factors? If there are other factors, how have they caused structural change? Is the process over? Is there a new stable state—now or yet to be—established, or will the status quo be restored?

 

pages: 394 words: 85,252

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

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Atul Gawande, backtesting, buy low sell high, Checklist Manifesto, double helix, impulse control, paper trading, short selling, systematic trading, The Wealth of Nations by Adam Smith

In his brilliant book Mechanical Trading Systems: Pairing Trader Psychology with Technical Analysis, Richard Weissman draws a clear distinction between three types of traders: trend-followers, mean-reversal (counter-trend) traders, and day-traders. 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. Figure 1.1 Moving Averages Identify Value Daily chart of MW, 26-day and 13-day EMAs The slow EMA (exponential moving average) rarely changes direction; its angle identifies the increase or the decrease of value.

The good ones know that while patterns repeat, they do not repeat perfectly. The most valuable quality of a good system is its robustness. 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. I like to study broad indexes and industry groups and decide whether to trade from the long or short side.

The decision to be a discretionary or a systematic trader is rarely based on cost/benefit analysis. 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. For example, I will never enter a position against the weekly Impulse system, and you couldn’t pay me to buy above the upper channel line or short below the lower channel line on a daily chart.

 

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

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Albert Einstein, Bernie Madoff, Black Swan, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K

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.” 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.

If you do not like the phrase trend following, substitute your term as you keep reading. 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. On the other hand, adverse price movements will lead to an exit. A trend trader’s average profit per trade is significantly higher than the average loss per trade.

 

pages: 467 words: 154,960

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

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Albert Einstein, asset allocation, Atul Gawande, backtesting, Bernie Madoff, Black Swan, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, volatility arbitrage, William of Occam

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.” Managed Account Reports I agree with Seykota’s wisdom, but he is not saying short-term is impossible.

Maybe we trend follow with gold and silver, or stock futures, or whatever the client needs. 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. In the late 1970s and early 1980s, stocks were quiet and futures markets took off. That is how the CTA market started.

Original turtle trader Jerry Parker, for one, thinks trend followers could do better at explaining their skill set: “I think another mistake we made was defining ourselves as managed futures, where we immediately limit our universe. 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.

 

pages: 394 words: 85,734

The Global Minotaur by Yanis Varoufakis, Paul Mason

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banking crisis, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business climate, capital controls, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, colonial rule, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, declining real wages, deindustrialization, eurozone crisis, financial innovation, first-past-the-post, full employment, Hyman Minsky, industrial robot, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, market fundamentalism, Mexican peso crisis / tequila crisis, mortgage debt, new economy, Northern Rock, paper trading, planetary scale, post-oil, price stability, quantitative easing, reserve currency, rising living standards, Ronald Reagan, special economic zone, Steve Jobs, structural adjustment programs, systematic trading, too big to fail, trickle-down economics, urban renewal, War on Poverty, 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. Loans in excess of 50 per cent of a deficit country’s average trade volume (measured in bancors) would also be made, but at the cost of a fixed interest rate.

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.

The simple lesson that the Global Plan can teach us today is that world capitalism’s finest hour came when the policy makers of the strongest political union on the planet decided to play a hegemonic role – a role that involved not only the exercise of military and political might, but also the kind of massive redistribution of surpluses across the globe that the market mechanism is utterly incapable of effecting. 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

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algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, compound rate of return, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, 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.

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.

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asset allocation, banking crisis, BRICs, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, market bubble, merger arbitrage, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, 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.

Table 1.3.1 Performance of hedge fund indices in 2008 Strategy Net of fees year to date returns to 31 Oct 08 (USD) HFRI Fund Weighted Composite Index HFRI Equity Hedge (Total) Index HFRI EH: Equity Market Neutral Index HFRI EH: Quantitative Directional HFRI EH: Short Bias Index HFRI Event-Driven (Total) Index HFRI ED: Merger Arbitrage Index HFRI Macro (Total) Index HFRI Relative Value (Total) Index HFRI RV: Fixed Income–Asset Backed HFRI RV: Fixed Income–Convertible Arbitrage Index HFRI RV: Fixed Income–Corporate Index HFRI RV: Multi–Strategy Index –15.48 –22.49 –3.78 –19.04 21.18 –16.66 –5.37 5.55 –17.11 0.07 –35.06 –18.32 –20.69 ________________________________________________ HEDGE FUND STRATEGIES 33 ឣ Discretionary macro Discretionary macro is one of the few strategies that has posted positive performance in the year to date (end October 2008). 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.

 

pages: 192 words: 75,440

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

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backtesting, barriers to entry, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, family office, fixed income, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, merger arbitrage, offshore financial centre, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, systematic trading, unpaid internship, value at risk, yield curve, yield management

As such, they are typically long the stock of the company being acquired and short the stock of the acquirer. The principal risk is deal risk, should the deal fail to close. 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. Whereas until recently most emerging markets funds were long only, some of these same funds may now incorporate the use of short selling, futures, or other derivative products with which to hedge their investments.

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.

 

pages: 1,088 words: 228,743

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

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Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, New Journalism, oil shock, p-value, passive investing, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, systematic trading, 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

I have benefited from the thinking of fellow students, colleagues, customers, and research peers in academia and business. 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. I am also grateful to Andrew Ang, Cliff Asness, William Bernstein, Francis Breedon, Alistair Byrne, Adrian Eterovic, Kenneth French, Pal Haugerud, Susan Hudson-Wilson, Doug Huggins, Ray Iwanowski, Matthew James, Robert Kosowski, John Liew, Thomas Maloney, Mikko Niskanen, Lasse Heje Pedersen, Jonas Rinné, Rudi Schadt, Matti Suominen, Etienne Varloot, and Gerlof de Vrij, for their helpful comments on parts of my manuscript.

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. What types of models are used?

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. Both inputs from trading model backtests (quality of trading performance, current signal strength) and the user’s subjective assessments of model reliability can be included.

 

pages: 317 words: 106,130

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

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asset allocation, backtesting, Bernie Madoff, Black Swan, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index fund, interest rate swap, invisible hand, market microstructure, merger arbitrage, moral hazard, passive investing, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, statistical model, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve

As a result, CTAs may be separated into a range of various strategy and market focus groupings including currency, financial, diversified CTAs, as 148 EXHIBIT 7.10 THE NEW SCIENCE OF ASSET ALLOCATION Multi-Asset Portfolio Performance (2001–2008) Portfolio A B C D Annualized Returns 1.7% 2.5% 2.2% 2.9% Standard Deviation 7.5% 6.6% 8.8% 7.8% Information Ratio 0.22 0.37 0.25 0.37 Maximum Drawdown −21.0% −17.3% −27.8% −23.6% Correlation with CTA (0.22) (0.14) Portfolio A Equal Weights S&P 500 and BarCap US Aggregate Portfolio B 90% Portfolio A and 10% CTAs 75% Portfolio A and 25% HF/Commodities/Private Portfolio C Equity/Real Estate Portfolio D 90% Portfolio C and 10% CTAs well as systematic and discretionary CTAs. 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. Managed Futures Performance in Down and Up Equity Markets Exhibit 7.13 depicts the performance over various CTA strategies in months in which the S&P 500 had its worst and best performance over the period 2001 to 2008.

Asset weighted and equal weighted indices also exist at the subindex level. 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. CASAM/CISDM Equal Weight Diversified Index (CISDM Diversifed Index): Trade financial futures/options, currency futures/options, and forward contracts, as well as commodity futures/options.

 

pages: 504 words: 139,137

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

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algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, mortgage debt, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond

Most excitingly, my own research was being put into practice. 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. As I began working in July 2007, AQR was actually profiting from some bets against the subprime market but was starting to experience a puzzling behavior of the equity markets.

. ___________________ 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.

 

pages: 263 words: 75,455

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

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Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, Black Swan, 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, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, systematic trading, The Myth of the Rational Market, time value of money, transaction costs

Gray, PhD, is the founder and executive managing member of Empiritrage, LLC, an SEC-Registered Investment Advisor, and Turnkey Analyst, LLC, a firm dedicated to educating and sharing quantitative investment techniques to the general public. 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. Tobias E. Carlisle, LLB, B. Bus(Man), PLEAT, is the founder of Eyquem Investment Management LLC, portfolio manager of the Eyquem Fund LP and its precursor Eyquem Global Value Fund, and the author of the award-winning website greenbackd.com, which covers deep value, contrarian, and activist investment strategies.

 

pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

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asset-backed security, backtesting, banking crisis, barriers to entry, Bernie Madoff, Black-Scholes formula, British Empire, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, James Dyson, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative finance, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

There is a third category of systematic approaches whose signals do not seek to profit from either continuations or reversals of trend. 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. “I really wanted to find a way to make Mondays as exciting as Fridays.” Another childhood experience taught Woodriff a lesson about work incentives.

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. I want to automate that process. If you set the problem up really well with cross validation, then overfitting is a problem that can be overcome.

 

pages: 316 words: 105,384

Moneyball by Michael Lewis

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Cass Sunstein, high batting average, placebo effect, RAND corporation, Richard Thaler, systematic trading, 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. P. Ricciardi ran a baseball team. It was an intriguing line of attack, but with a tactical weakness.

 

pages: 302 words: 86,614

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

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Asian financial crisis, asset allocation, asset-backed security, backtesting, Bernie Madoff, Bretton Woods, business process, call centre, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, fixed income, high net worth, interest rate derivative, Isaac Newton, Long Term Capital Management, Mark Zuckerberg, merger arbitrage, NetJets, oil shock, pattern recognition, Ponzi scheme, quantitative easing, quantitative trading / quantitative finance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, systematic trading

It’s absolutely not what I do. And I know I drive the people who come from a pure math background crazy. 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. But we base our positions on statistical models, and we take hundreds of positions.

 

pages: 464 words: 117,495

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

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additive manufacturing, Atul Gawande, backtesting, Benoit Mandelbrot, buy low sell high, Checklist Manifesto, deliberate practice, diversification, Elliott wave, endowment effect, loss aversion, mandelbrot fractal, margin call, offshore financial centre, paper trading, Ponzi scheme, price stability, psychological pricing, quantitative easing, random walk, risk tolerance, short selling, South Sea Bubble, systematic trading, The Wisdom of Crowds, transaction costs, transfer pricing, traveling salesman, tulip mania

Paradoxically, at the high end of performance, these two approaches begin to converge. Advanced traders combine mechanical and discretionary methods. 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. Much of this book deals with discretionary trading, but you can use the tools described in it for mechanical trading.

 

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

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Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business process, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, discounted cash flows, disintermediation, diversification, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, full employment, George Akerlof, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, John von Neumann, linear programming, Loma Prieta earthquake, Long Term Capital Management, margin call, market friction, market microstructure, martingale, merger arbitrage, Nick Leeson, P = NP, pattern recognition, pensions crisis, performance metric, prediction markets, profit maximization, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Richard Feynman, Richard Stallman, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, 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

In order to succeed in experimental physics, you had to build up a real intuition about what types of results were real, and what was nothing more than data-mining, curve-fitting, or just plain statistical anomaly. 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. As a two-person operation, my first task was simple—recreate, from scratch, everything that the previous 30-person fund had done, but in a way that could be wholly automated and required no additional staff.

 

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

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Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, capital asset pricing model, cognitive dissonance, compound rate of return, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, statistical model, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

In other words, I had not generated a significantly positive alpha.5 Also suggestive was the fact that I gave back all my gains in the two years after the market trend turned down in March 2000. 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. Early in my trading career with Spear, I began keeping a detailed journal. Prior to each trade I would note its TA rationale.

 

pages: 701 words: 199,010

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

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affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, 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, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, labour mobility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Option pricing theory doesn’t work perfectly in the real world, but can help hedge and reduce risks when it’s used properly. 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. When the Russian crisis emerged, traders with illiquid positions paid a very heavy price.