Systematic Trading: A Unique New Method for Designing Trading and Investing Systems
by
Robert Carver
Published 13 Sep 2015
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
Published 1 Dec 2009
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
and
Fabrice Douglas Rouah
Published 23 Sep 2004
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
Published 14 Sep 2007
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
Published 1 Jan 2008
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
Published 14 Jun 2011
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
Published 19 Mar 2007
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
and
Paul Mason
Published 4 Jul 2015
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
Published 17 Nov 2008
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.
Published 30 Oct 2011
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
and
Aaron Finkel
Published 8 Feb 2008
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
Published 4 Apr 2011
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.
The New Science of Asset Allocation: Risk Management in a Multi-Asset World
by
Thomas Schneeweis
,
Garry B. Crowder
and
Hossein Kazemi
Published 8 Mar 2010
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.
Risk Management in Trading
by
Davis Edwards
Published 10 Jul 2014
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.
Unknown Market Wizards: The Best Traders You've Never Heard Of
by
Jack D. Schwager
Published 2 Nov 2020
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
Published 12 Apr 2015
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
Published 24 Apr 2012
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
and
Tobias E. Carlisle
Published 29 Nov 2012
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
and
Mohamed El-Erian
Published 29 May 2012
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
Published 30 Sep 2019
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
Published 8 Dec 2020
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
Published 4 Nov 2020
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
Published 5 Jun 2023
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.
Moneyball
by
Michael Lewis
Published 1 Jan 2003
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.
Capital Ideas Evolving
by
Peter L. Bernstein
Published 3 May 2007
.* 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.
The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management
by
Alexander Elder
Published 28 Sep 2014
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.
The Fund: Ray Dalio, Bridgewater Associates, and the Unraveling of a Wall Street Legend
by
Rob Copeland
Published 7 Nov 2023
They added that the hedge fund “maintains a tight set of controls on how trades are determined, which are regularly audited to be consistent with Bridgewater’s trading protocols, and any deviation requires approval of the Chief Investment Officers.” * Lawyers for Dalio and Bridgewater said that “greater than 98 percent of Bridgewater’s risk budget and returns have been, and remain, driven by systematic trading strategies” and that it “adheres to strict rules around any type of discretionary trading.” They added, “Bridgewater’s trading strategies are systematized and diversified.” A lawyer for Dalio separately said that Bridgewater’s investment team “follows big developments and historical developments to understand cause-effect relationships in the markets.
How I Became a Quant: Insights From 25 of Wall Street's Elite
by
Richard R. Lindsey
and
Barry Schachter
Published 30 Jun 2007
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
Published 1 Nov 2006
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
Published 29 Jul 2012
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.