systematic trading Concepts

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

by Robert Carver · 13 Sep 2015

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

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their predictions. Complexity may work in the odd case but more often than not it reduces validity.” Daniel Kahneman, Thinking, Fast and Slow Preface Systematic trading and investing I am very bad at making financial decisions. Like most people I find it difficult to manage my investments without becoming emotional and

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financial risk on uncertain outcomes. 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

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Three. Fitting Chapter overview The perils of over-fitting Some rules for effective fitting How I choose my rules 51 51 52 65 67 xiii Systematic Trading Chapter Four. Portfolio Allocation Chapter overview Optimising gone bad Saving optimisation from itself Making weights by hand Incorporating Sharpe ratios 69 70 70 75

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261 Appendices273 Appendix A. Resources Further reading Sources of free data Brokers and platforms Automation and coding 275 275 277 278 279 xv Systematic Trading Appendix B. Trading Rules 281 The A and B system: Early profit taker and early loss taker 281 The exponentially weighted moving average crossover (EWMAC

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several relatively large systematic hedge funds, including the fund I used to work for, significantly less than 10% of actively managed global assets is fully systematically traded. But the investment world is changing and now is a better time than ever before to consider trading or investing with systems. Firstly, institutional

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MINDS CAN DO WONDERFUL THINGS, BUT ARE DEEPLY flawed when making financial decisions. In this chapter I explore economic theories about human behaviour, and why systematic trading and investing makes sense. I also show how our irrationality can interfere with the design of systematic strategies. Chapter overview Humans should be great traders

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overconfident. There is a significant amount of academic research showing examples of overconfidence in discretionary finance, for example amongst equity analysts and macroeconomic forecasters. 19 Systematic Trading Firstly ‘narrative fallacy’: our brains are great at seeing patterns where there are none. Whilst developing trading rules there is often a fitting process during

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can classify rules into trading styles, and the important characteristics each style has. Achievable Sharpe ratios What are realistic Sharpe ratios to expect? 25 Systematic Trading What makes a good trading rule Staunch systems trader This section mostly relates to how trading rules are fitted. This is less applicable to asset

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method which is to use an idea which you cannot or will not test on historical data. Strictly speaking, this wouldn’t be systematic trading. 26 Chapter Two. Systematic Trading Rules Advantages of ideas first Advantages of data first If an idea works no further potentially dangerous fitting is required. There will be a

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Not all styles of trading are entirely suitable for this, because they are inherently subjective or because of data limitations. They can 28 Chapter Two. Systematic Trading Rules still however be wrapped up inside a systematic position management framework, as I show in the semi-automatic trader example. Too subjective Many methods

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all assets have symmetrically distributed returns. But in practice assets with the same SR 25. See The Greatest Trade Ever by Gregory Zuckerman. 31 Systematic Trading could make steady losses with occasional large returns (like an insurance buyer), or steady gains with occasional large losses (as a seller of insurance does

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For example derivatives related to mortgage backed assets were liquid and attracted premium prices at the end of 2006; a year later they were 35 Systematic Trading almost untradeable and willing buyers could get significant discounts even on heavily depressed official quotes. When others have to trade Not everyone trades because they

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. Hedge fund manager George Soros bet against the Bank of England and made a billion pounds or so when they capitulated. 36 Chapter Two. Systematic Trading Rules Behavioural effects A classical financial economist would be comfortable with most of the above explanations. But you can also create rules which extract returns

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lower than for negative skew. Often requires leverage to achieve decent absolute returns in normal times; so gets killed in bad times. 40 Chapter Two. Systematic Trading Rules Positive skew Negative skew Examples: Examples: • Trend following strategies. • FX carry • Bets done by buying options, e.g. if you think the stock

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available profits being reduced, the apparent risk falling, and required leverage increasing further. Then the music stops and their negative skew becomes horribly apparent. 45 Systematic Trading Two classic examples are the meltdown of fixed income relative value hedge fund manager Long Term Capital Management in mid-199836 and the Quant Quake

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the results, as Andrew Lo’s paper discusses. The skew of the rules is also important, even when comparing rules with similar skew. 63 Systematic Trading considerable historical data except for the rare cases of variations which are both highly correlated, and also perform very differently. TABLE 6: CAN YOU DISTINGUISH

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from your trading rules have identical expected standard deviation of returns. This is because of the volatility standardisation I spoke about in chapter two, ‘Systematic Trading Rules’. By using this technique you simplify the problem and only need to use expected Sharpe ratios and correlations to work out your weights. Although

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optimal portfolios will be close to equal weights. But with significant differences in Sharpe ratios or correlations similar portfolios will crop up repeatedly, 75 Systematic Trading and the average will reflect that. The averaged weights naturally reflect the amount of uncertainty that the data has. Let’s see the results of

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you want to deal with, based on your knowledge and familiarity with different markets. However there are certain instruments that should be completely avoided for systematic trading. Others have characteristics which make them worse than other alternatives, or would force you to trade them in a particular way. Finally there is

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the individual shares, trading a future, a spread bet, a contract for difference, a passive index fund or an active fund. Which is best? 101 Systematic Trading Chapter overview Necessities The minimum requirements that need to be met before you can trade an instrument. Instrument choice and trading style Characteristics that influence

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It’s also possible to use my framework without any systematic rules at all, as a semi-automatic trader making your own discretionary forecasts. 109 Systematic Trading Chapter overview What makes a good forecast Understand the important properties of the forecasts which trading rules produce. Discretionary trading with stop losses How semi

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forecast values over 20 around 5% of the time, so there is only limited evidence that forecasts of this magnitude are actually correct. 113 Systematic Trading Extremes are often different Normally markets trend with falls followed by further falls, but after very sharp drops subsequent one day rises are more likely

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, as described in chapter thirteen. Asset allocating investors: Use a constant forecast of +10 for all assets. Staunch systematic traders: Use a combination of systematic trading rules like EWMAC and Carry, design your own rule or adapt others. Follow the advice earlier in this chapter, and in chapter three, when selecting

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different instruments (which is my preferred approach), before calculating correlation matrices. 92. The precise formula and spreadsheet method is in appendix D (page 297). 129 Systematic Trading FIGURE 20: COMBINING DIFFERENT FORECASTS REDUCES VARIABILITY. RESCALING FIXES THIS Figure 20 shows this effect for two uncorrelated forecasts A and B, which I’ve

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on most bets with occasional large losses. Columns D and E of table 26 show the appropriate volatility targets for this type of trader. 147 Systematic Trading TABLE 26: WHAT VOLATILITY TARGET SHOULD ASSET ALLOCATING INVESTORS AND SEMIAUTOMATIC TRADERS USE? Recommended percentage volatility target Expected SR (C) Asset allocating investor (D)

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FIGURE 23: MEASURING CRUDE OIL PRICE VOLATILITY USING SIMPLE MOVING AVERAGE (MA) AND EXPONENTIALLY WEIGHTED (EWMA) STANDARD DEVIATIONS WITH VARIOUS LOOKBACKS, IN DAYS 157 Systematic Trading Returning to the example I opened the chapter with, if you use the bottom panel of figure 23 the price volatility of crude oil futures

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your trading capital currency. Equal to instrument currency volatility multiplied by the exchange rate. It will be in units of your account currency. 161 Systematic Trading Volatility scalar Number of position units to hold if you are investing your entire trading capital in one instrument, ignoring forecasts. Equal to daily cash

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move by less than 1% a day in major currencies. You can’t reduce these trades, which in any case are relatively small. 185 Systematic Trading System parameters These are parameters in the trading system which should only change infrequently if at all: Forecast weights, Instrument weights, forecast and instrument diversification

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expected costs of trading a particular rule. Looking solely at the turnover of the forecast is the simplest approach and is usually conservative enough. 193 Systematic Trading determine the right weights, accounting for predictable costs and uncertain profits. By comparing the weights found when pooling results across the portfolio with those from

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trip (buy and sell): Twice the cost to trade one block divided by annualised instrument currency volatility (daily instrument currency volatility multiplied by 16). 203 Systematic Trading Calculating cost of trading rules and trading subsystems Approximate turnover from a back-test To calculate the standardised turnover in round trips (buys and sells

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) per year: (Number of instrument blocks traded per year) ÷ (2 × average absolute number of blocks held) Turnover for systematic trading rules Calculated from back-test or implied from rules of thumb. Rules of thumb for my suggested trading rules explained in chapter seven, ‘Forecasts’, are

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sense, such as relative value or mean reverting strategies, but in my opinion it is better to build the closing rule into a fully systematic trading rule. 213 Systematic Trading This implies you’ll have an annual volatility target of £100,000 × 15% = £15,000 and a daily target of £15,000 ÷ 16 = £

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30% to emerging markets (constrained), leaves 70% to developed markets. Top level grouping Across asset classes 40% in bonds (constrained), leaves 60% in equities. 231 Systematic Trading You can see the final weights in table 41. Using these weights, the correlations and the formula on page 297, I get an instrument diversification

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Sharpe ratios using your estimate of price volatility. Optional: Adjust instrument weights Using Sharpe ratio predictions table 12 (page 86), column B ‘Without certainty’. 233 Systematic Trading Portfolio instrument position Subsystem position multiplied by instrument weight (from table 41 for the example), with Sharpe ratio adjustments if required, and by instrument diversification

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. 153. Appendix A provides pointers to where you can find further help and advice, including the website for this book: www.systematictrading.org 245 Systematic Trading Using the framework Instrument choice: Size, diversification and costs Unless you have many millions of dollars, choosing which futures to trade is mostly a balance

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table shows handcrafted instrument weights for the staunch systems trader example. Numbers in bold have been adjusted because of Euro Stoxx minimum size issues. 253 Systematic Trading Daily process This process can be done using spreadsheets, or entirely automated if desired. Detailed calculations are shown in the trading diary below. Get account

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either be the discretionary forecast of a semi-automatic trader or the fixed forecast of the asset allocating investor; for staunch systems traders a single systematic trading rule or a combined forecast blending different trading rules. See page 110. Forecast diversification multiplier The diversification multiplier required so that the combined forecast for

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same currency as the trader or investor’s trading capital. Equal to instrument currency volatility multiplied by the relevant exchange rate. See page 158. 265 Systematic Trading Instrument weights In my framework the weights that different trading subsystems, each trading a single instrument, have in a portfolio of subsystems. See page 165

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Fascinating and esoteric book by a famous partly systematic, negative skew, hedge fund manager. Part autobiography, part book on the philosophy of trading. Optional reading. Systematic trading rules More Money than God, Sebastian Mallaby, 2011, Penguin A history of hedge funds, but also a very readable guide to different strategies. Compulsory reading

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money to someone else’s software! There are also now ‘social trading’ online platforms that allow you to put your money into other amateurs’ systematic trading strategies. I would strongly advise against this. Automation and coding It is perfectly possible to run non-automated strategies using only simple spreadsheets. There are

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prices are trending down. Since both the EWMAs are measurements of price, the crossover tells you by how much prices have changed recently. 283 Systematic Trading Volatility adjusted EWMA crossover You should divide the raw EWMA crossover by the standard deviation of daily price changes (in price terms, not percentage points

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funding. Net expected return, % Dividend yield minus funding cost. Net expected return in price units Net expected return % points, multiplied by the current price. 285 Systematic Trading Equities, contracts for difference (CFD) Dividend yield, % The expected dividend yield per year during the holding period, or for simplicity just the historic dividend divided

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negative correlations should be floored at zero before the calculation is done, to avoid dangerously inflating the multiplier. 167. ‘T’ is the transposition operator. 297 Systematic Trading Instrument diversification multiplier Given N trading subsystems with a correlation matrix of returns H and instrument weights W summing to 1, the diversification multiplier will

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

by Antti Ilmanen · 4 Apr 2011 · 1,088pp · 228,743 words

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

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(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

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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

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are likely motivated, in part, by information gathered in hindsight. Chapter 7 discusses data mining further and argues that it is an inevitable feature of systematic trading models. The use of nonstationary (highly persistent) time series as predictors and applying long-horizon regressions are especially likely to result in econometric problems and

Hedge Fund Market Wizards

by Jack D. Schwager · 24 Apr 2012 · 272pp · 19,172 words

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

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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

The New Science of Asset Allocation: Risk Management in a Multi-Asset World

by Thomas Schneeweis, Garry B. Crowder and Hossein Kazemi · 8 Mar 2010 · 317pp · 106,130 words

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

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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

Commodity Trading Advisors: Risk, Performance Analysis, and Selection

by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant and Fabrice Douglas Rouah · 23 Sep 2004

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

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the payoff function for a long put option. 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

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.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

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0.0520 0.0402 −0.0485 −0.0175 −0.4923 0.0029 −0.0717 0.0343 Coefficient −0.9365 −1.2127 −2.1703 t-statistic Systematic Trading 0.1203 0.1094 −0.0926 −0.0437 −0.5893 0.0022 −0.0929 0.0155 Coefficient −3.2138 −1.7743 −2.3223 t-statistic

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to be exercised when the returns to the stock market are negative. Similar results are presented in Table 9.1 for diversified trading managed futures, systematic trading, and the passive MLMI index. In each case, b low is economically and statistically significant. In addition, b low always has a negative sign, indicating

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.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

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1,500 1,000 500 0 −0.0135 −0.0033 0.0070 0.0172 0.0274 0.0377 0.0479 Return FIGURE 9.11 Simulated Systematic Trading Return Distribution 0.0581 More 198 RISK AND MANAGED FUTURES INVESTING 8,000 7,000 6,000 Frequency 5,000 4,000 3,000 2

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that operate independently would each execute a trade based on the respective signal. The main advantage of a multisystem approach is diversification of signals. Although systematic trading effectively removes the emotional element from trade execution, the use of a systematic methodology does not imply that there is a human disconnect. On the

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

by David Aronson · 1 Nov 2006

. 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

Capital Ideas Evolving

by Peter L. Bernstein · 3 May 2007

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

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined

by Lasse Heje Pedersen · 12 Apr 2015 · 504pp · 139,137 words

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

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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

Trend Commandments: Trading for Exceptional Returns

by Michael W. Covel · 14 Jun 2011

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

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-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

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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

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

by Irene Aldridge · 1 Dec 2009 · 354pp · 26,550 words

, 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

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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

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: 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

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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

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trading decisions. 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

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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

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—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. Algorithmic trading may determine how to process an order given current market conditions: whether to execute the order

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to take on an even more active role. Special care should be taken, however, to distinguish high-frequency trading from electronic trading, algorithmic trading, and systematic trading. Figure 2.5 illustrates a schematic difference between high-frequency, systematic, and traditional long-term investing styles. Electronic trading refers to the ability to transmit

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investing Execution latency Algorithmic or electronic trading (execution) High-frequency trading Low Short Long Position holding period FIGURE 2.5 High-frequency trading versus algorithmic (systematic) trading and traditional long-term investing. position in a particular security. For example, algorithmic execution may determine that a received order to buy 1,000,000

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. Stealth execution allows large investors to hide their trading intentions from other market participants, thus deflecting the possibilities of order poaching and increasing overall profitability. Systematic trading refers to computer-driven trading positions that may be held a month or a day or a minute and therefore may or may not be

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high-frequency. An example of systematic trading is a computer program that runs daily, weekly, or even monthly; accepts daily closing prices; outputs portfolio allocation matrices; and places buy-and-sell orders

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trading naturally lends itself to trading applications demanding high speed and precision of execution, as well as high-frequency analysis of volumes of tick data. Systematic trading, in turn, has been shown to outperform human-led trading along several key metrics. Aldridge (2009b), for example, shows that systematic funds consistently outperform traditional

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the systematic funds outperform nonsystematic funds in raw returns in times of crisis. That finding can be attributed to the lack of emotion inherent in systematic trading strategies as compared with emotion-driven human traders. CHAPTER 3 Overview of the Business of High-Frequency Trading ccording to the Technology and High-Frequency

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generate trading signals that result in consistently positive outcomes over a large number of trades. In seeking such signals, both human traders and econometricians designing systematic trading platforms are looking to uncover sources of predictability of future price movements in selected securities. Predictability, both in trading and statistics, is the opposite of

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works best when all orders are initiated, sent through, and executed via computer networks, bypassing any human interference. Depending on the design of a particular systematic trading mechanism, even a second’s worth of delay induced by hesitation or distraction on the part of a human trader can substantially reduce the system

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phase addresses system-wide deviations from planned performance, such as troubleshooting newly discovered bugs. SYSTEM IMPLEMENTATION Key Steps in Implementation of High-Frequency Systems Most systematic trading platforms are organized as shown in Figure 16.2. One or several run-time processors contain the core logic of the trading mechanism and perform

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dominant communication method among various broker-dealers, exchanges, and transacting customers. In fact, according to a survey conducted by fixprotocol.org, FIX was used for systematic trading by 75 percent of buy-side firms, 80 percent of sell-side firms, and over 75 percent of exchanges in 2006. FIX is best described

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the Author rene Aldridge is a managing partner and quantitative portfolio manager at ABLE Alpha Trading, LTD, a proprietary trading vehicle specializing in high-frequency systematic trading strategies. She is also a founder of AbleMarkets.com, an online resource making the latest high-frequency research accessible to institutional and retail investors. Prior

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, 192, 195, 279 Suleiman, Basak, 260 Summers, Lawrence, 179 Swap trading: fixed-income markets, 40–42 foreign exchange markets, 43–46 Sycara, K., 279–280 Systematic trading, 15 distinguished from high-frequency trading, 18–19 System testing, automated system implementation, 248–249 INDEX Tail risk, 50 comparative ratios and, 56 risk measurement

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., 253 Zumbach, Gilles, 120–121 Praise for HIGH-FREQUENCY TRADING “A well thought out, practical guide covering all aspects of high-frequency trading and of systematic trading in general. I recommend this book highly.” —Igor Tulchinsky, CEO, WorldQuant, LLC “For traditional fundamental and technical analysts, Irene Aldridge’s book has the effect