automated trading system

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


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

., Interactive Brokers), and have set up a good operating environment (at first, nothing more than a computer, a high-speed Internet connection, and a real-time newsfeed). You are almost ready to execute your trading strategy—after you have implemented an automated trading system (ATS) to generate and transmit your orders to your brokerage for execution. This chapter is about building such an automated trading system and ways to minimize trading costs and divergence with your expected performance based on your backtests. A WHAT AN AUTOMATED TRADING SYSTEM CAN DO FOR YOU An automated trading system will retrieve up-to-date market data from your brokerage or other data vendors, run a trading algorithm to generate orders, and submit those orders to your brokerage for execution. Sometimes, all these steps are fully automated and implemented as one desktop application installed on your computer.

Even if your brokerage’s API provides an order submission function for your use in an Excel Visual Basic macro, its speed is usually too slow if you have to run this program frequently in order to capture the latest data and generate wave after wave of orders. In this case, one must build a fully automated trading system. Building a Fully Automated Trading System A fully automated trading system (see Figure 5.2) can run the trading algorithm in a loop again and again, constantly scanning the latest prices and generating new waves of orders throughout the trading day. The submission of orders through an API to your brokerage account is automatic, so you would not need to load the trades to a basket trader or spread trader, or even manually run a macro on Real-time data feed Your proprietary desktop C++ program Desktop API from your brokerage Your brokerage account FIGURE 5.2 Fully Automated Trading System P1: JYS c05 JWBK321-Chan September 24, 2008 Execution Systems 13:55 Printer: Yet to come 85 your Excel spreadsheet.

See Seasonal trading strategies Capacity, 27, 158 Capital availability, effect on choices, 15 Capital allocation, optimal, 95–103 Capital IQ, 136 Chicago Mercantile Exchange (CME), 16 Clarifi, 35 CNBC Plus, 76 Cointegrating augmented Dickey-Fuller test, 128 Cointegration, 126–133 forming a good cointegrating pair of stocks, 128–130 Compustat, 136 Contagion, financial, 104–105 Correlation, 131 Covariance matrix, 97, 37, 37 D Dark-pool liquidity, 71, 73, 88 Data mining, 121 Databases, historical, 37 Data-snooping bias, 25–27, 52–60, 91 out-of-sample testing, 53–55 sample size, 53 sensitivity analysis, 60 and underperformance of live trading, 91 Decimalization of stock prices, 91, 120 Printer: Yet to come INDEX Deleveraging, 152 Despair, 110 Disasters, physical or natural, 108 Discovery (Alphacet), 35, 36, 55, 85, 122–126 charting application, 125 Dollar-neutral portfolio, 43–44 Dow Jones, 36, 75 Drawdown, 20, 21–22, 43, 95 maximum, 21 calculating, 48–50 maximum duration, 21 calculating, 48–50, 37 Dynamic data exchange (DDE) link, 80, 81–82, 83, 84, 85 E ECHOtrade, 70 Econometrics toolbox, 168 The Economist, 10 Elite Trader, 10, 74 Elliott wave theory, 116 E-mini S&P 500 future, 16 Endowment effect, 108–109 Equity curve, 20 Excel, 3, 21, 32, 51, 163 dynamic data exchange (DDE) link to, 80, 81–82, 83, 84, 85 using in automated trading systems, 80, 81, 83, 84, 85 using to avoid look-ahead bias, 51 using to calculate maximum drawdown and maximum drawdown duration, 48 using to calculate Sharpe ratio for long-only strategies, 45–46, 47 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 Printer: Yet to come 177 Index Execution systems, 79–94 automated trading system, advantages of, 79–87 fully automated trading system, building a, 84–87 semiautomated trading system, building a, 81–84 paper trading, testing your system by, 89–90 performance, divergence from expectations, 90–92 transaction costs, minimizing, 87–88 Exit strategy, 140–143 F Factor exposure, 134 Factor models, 133–139 principal component analysis as an example of, 136–139 Factor return, 134 FactSet, 35, 36 Fama-French Three-Factor model, 134–135, 153 Financial web sites and blogs, 10 G, 37 GARCH toolbox, 168 Gasoline futures, seasonal trade in, 148–151 Gaussian probability distributions, 96, 105 derivation of Kelly formula in, 112–113 Generalized autoregressive conditional heteroskedasticity (GARCH) model, 120 Genesis Securities, 70, 73, 82 Global Alpha fund (Goldman Sachs), 104 Greed, 110–111 H “Half-Kelly” betting, 98, 105–106 High-frequency trading strategies, 151–153 transaction costs, importance of in testing, 152 High-leverage versus high-beta portfolio, 153–154 High watermark, 21, 48 Historical databases errors in, 117 finding and using, 36–43 high and low data, use of, 42–43 split and dividend-adjusted data, 36–40 survivorship bias, 40–42, 37, 81 Hulbert, Mark (New York Times), 10 I Information ratio.


pages: 443 words: 51,804

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu


algorithmic trading, asset allocation, automated trading system, backtesting, Black-Scholes formula, Brownian motion, business process, continuous integration, corporate governance, discrete time, distributed generation, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, principal–agent problem, random walk, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

The capacity to improve the forecast of earnings surprises and abnormal return using a mixture of well-known economic indicators with social network variables also enriches the debate between the modern finance theory and behavioral finance to show how behavioral patterns can be recognized with a rigorous method of analysis and forecast. 3.4.2 ALGORITHMIC TRADING The transformation of the major stock exchanges into electronic financial markets has encouraged the development of automated trading systems in order to process large amounts of information and make instantaneous investment decisions. Automated trading systems have a long tradition on classical artificial intelligence approaches such as expert systems, fuzzy logic, neural networks, and genetic algorithms. Trippi and Turban (1990, 1993), Trippi and Lee (2000), Deboeck (1994), and Chorafas (1994) have reviewed these early systems. Goonatilake and Treleaven (1995) survey an application of the above methods to automated trading and several other business problems such as credit risk, direct marketing, fraud detection, and price forecasting. Automated trading systems include a backtest or simulation module. In this respect, agent-based models could be useful to explore new ideas without risking any money.11 The Santa Fe stock market model (Arthur et al., l997; LeBarone et al., l998; LeBaron, 2001) has inspired many other agent-based financial market models such as Ehrentreich (2002)’s, which is based on the Grossman and Stiglitz (1980) model.

LeBaron (1998) applied bootstrapping to capture arbitrage opportunities in the foreign exchange market and then used a neural network where its network architecture was determined through an evolutionary process. Finally, Towers and Burgess (2000) used principal components to capture arbitrage opportunities. Creamer and Freund (2007, 2010a) follow the tradition of the papers in this section that use machine learning algorithms to find profitable trading strategies and also build completely automated trading systems. The authors use very well-known technical indicators such as moving averages or Bollinger bands. Therefore, the capacity to anticipate unexpected market movements is reduced because many other traders are expected to be trying to profit from the same indicators. However, the authors reduce this effect because the algorithms try to discover new trading rules using Logitboost instead of following the trading rules suggested by each indicator.

Additionally, transaction costs play a central role to raise performance. Instead of an automatic rebalance of the 66 CHAPTER 3 Using Boosting for Financial Analysis and Trading portfolio, the results of the PLAT competition indicate that if the CRP strategy is implemented only with limit orders, its results improve because of the rebates. Creamer and Freund (2010a) propose a multistock automated trading system. The system is designed to trade stocks, and relies on a layered structure consisting of ADT, which is implemented with Logitboost, as the machine learning algorithm; an on-line learning utility; and a risk management overlay.12 The system generates its own trading rules and weighs the suggestion of the different ADTs or experts to propose a trading position. Finally, the risk management layer can validate the trading signal when it exceeds a specified nonzero threshold and limit the use of a trading strategy when it is not profitable.


pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango


algorithmic trading, automated trading system, backtesting, commodity trading advisor, Credit Default Swap, Elliott wave, fixed income, Long Term Capital Management, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs

As I wrote in one of my blog posts recently, I don’t intend on being Dick Van Dyke, the guy who trips over the ottoman every time. I plan on being the guy that walks around the ottoman next time. CHAPTER 13 Charles German While his career began more than twenty years ago on the floor of the Chicago Board of Trade, Charles German explains that his adoption of a trend-following strategy in 2005 was what ultimately led him to reach new heights in trading. He now uses an automated trading system that combs the markets for setups, places entry and stop orders, and executes with little or no human interaction required. While fully automated trading may be a dream for many traders, it was only through thousands of hours of meticulous backtesting and some costly, difficult lessons learned along the way that German made it a reality. By requiring that his strategy work in all markets and market conditions, he eliminates the need to chase trades or the flavor-of-the-day setup or strategy.

Menaker: That’s a really good question, and I’ve personally gone back and forth on that myself over the years—paying attention to the news or not paying attention to the news—and I think people can succeed going both ways. What I will tell you, though, is that there are hedge funds that are scraping social media, like Twitter and even Facebook, and they are scraping CNBC and Bloomberg for keywords and sentiment. Then they will apply that to an automated trading system. That’s happening more and more. I was in Japan in June for a hedge fund conference, and I met a Tokyo University professor there who’s a financial engineer. He showed me how he and some other guys were starting up a hedge fund using this model that I just explained, and they thought they were the first to do this. And I said, “Actually, no, there are six or seven funds in the United States that I know of that have already been doing that in the last three or four years, and there are probably more than those six or seven.

I Index A Average directional index (ADX) Average true range (ATR) B Baiynd, Anne-Marie course offered daily and weekly time frames double-top action Elliott wave Fibonacci future market longer-term investment market internal mathematician moving average multiple time frames new trader paper trading paper trading account real money trading recruiting role retail trading Simple moving average (SMA) stochastic momentum indicator SUCCESS Magazine seminar, technical trading support and resistance swing trade technical analysis technical indicator technical trader trading day Berger, Serge Advance/Decline (A/D) line Apple Bloomberg terminal currency futures daily chart day-in and day-out economic data in equities equities, equity options, and futures extreme candles favorable intraday financial analyst full gap gap trades half gap liquidity injections longer-term positions macro view mean-reversion trade mind off the markets momentum oscillator opening gap positions reversal candle S&P 500 E-mini futures seasonal factors slow stochastics swing positions swing trade technical analysis technical tools time frame trade duration trade futures trading environment trading methodology trading opportunity trend reversal US equity indices watch list Booker, Rob Brandt, Peter algorithmic trading bid/offer spreads candlestick charts chart trader classical charting definition patterns principles closing price charts commodities floor trader cookie cutter approach corn spread corn trader currency futures Diary of a Professional Commodity Trader electronic trading entry point futures trader head-and-shoulders patterns high bar charts intraday charts longer-term time frames longer-term trades long-term charts margin-to-capital ratio margin-to-equity ratio market signaling niche—money management pit trading positions risk management Russell trade scale out short-term charts standard stop-loss swing trader Technical Analysis of Stock Trends trend line volume/open interest profile weekly chart C, D California Institute of Technology Carter, John best trade big volume Bollinger Bands breakeven career day trader dot-com crash down payment financial analyst first trading day five-year learning curve fundamental factors garden-variety trade great traders overnight guaranteed income industry trends Keltner Channels loan documentation Mastering the Trade money build up money management OEX trade options professional trader resistance point retail traders shorter-term trading six-figure income smaller traders success measurement swing trades teaching technical analysis technical approach tech stocks The Disciplined Trader the squeeze time frames time spent trading industry trading lifestyle trading philosophy trading place trading slumps volatile markets Certified Risk Manager (CRM) Chicago Board of Trade Chicago Mercantile Exchange (CME) City Slickers movie Commodity Trading Advisor (CTA) Currency Strength Index E E-mini S&P futures (ES) European Central Bank (ECB) Exchange-traded funds (ETFs) Exponential moving average (EMA) F Floyd, Gordon & Partners (FGP) Foster, Alex arbitrary profit targets assignment bear market best indicator buy-and-hold approach client vs. own account contract size economic reports ideal trade long-term trend Monsanto and JPMorgan Chase moving averages news following open positions position size price point profitable trades profit targets S&P 500 shorter-term crossover shorter-term moving averages technical indicators time frame trading options trend follower Williams %R G GAIN Capital Asset Management Gartman, Dennis German, Charles ATR automated trading system backtesting daily chart future market green trade independent trader market portfolio mentors money management moving average price action risk management rule-based approach scaling out screen-based trading software stand-alone type of program system rule trend following definition strategies tools Gordon, Todd Aspen Trading Group Australian Dollar average winner and average loser bank research Blue Chips movie chat room CNBC correlation analysis currency markets currency trade day trading decision making Elliott Wave analysis count methodology E-Trade account Fast Money Floyd, Gordon & Partners (FGP), senior technical strategist Forex trading GAIN Capital Asset Management, senior trader global market analysis hedge fund initial amount, full time trading investment banks leverage magic methodology market information market makers market volatility Money in Motion moving averages NASDAQ new trader NYSE stocks NYSE trading strategy personal account research reports research time S&P 500 futures schooling share size shortcuts short-term momentum trading short-term traders SOES bandit sports analogies stock-picking service stop loss Strategy of the Day technical analysis technical charts trader quality trading jobs trading style trend lines H, I, J Hemminger, Patrick agricultural futures trading agricultural pairs trading Brent curve calendar spread commodities core position crude curve economic releases E-Mini S&P vs.


pages: 270 words: 75,803

Wall Street Meat by Andy Kessler


accounting loophole / creative accounting, Andy Kessler, automated trading system, banking crisis, George Gilder, index fund, Jeff Bezos, market bubble, Menlo Park,, rolodex, Sand Hill Road, Silicon Valley, Small Order Execution System, Steve Jobs, technology bubble, Y2K

Underneath the surface, however, changes in how Wall Street was paying for analysts would change the game in subtle ways. Some of it had to do with those over-the-counter traders not answering their phones. Wall Street got sued for not answering phones and the SEC insisted the Street put in a system known as Small Order Execution System, SOES. This automated execution of small orders would lead to day traders and would eventually lead to automated trading systems known as ECNs. These ECN trading systems would represent over half of overthe-counter trades by 2001, with commissions a hundredth of what they had been in 1987. It changed the way Wall Street gets paid. Commissions were toast. Banking fees would replace commissions, and eventually kill research in the process. · · · For the rest of the year, everyone was in shock. Jim Carroll, the #1 oil services analyst, came into my office, and he told Jack Grubman and me that he had just gone into Margo Alexander’s office and told her that she could cut his pay, but to please not fire him.

I’m not so sure. 246 Index Activision, 165 Alexander, Margo, 12, 22, 27–28, 38, 60, 81, 85, 86 offsite analyst meetings, 51, 77–78 Alexander, Pam, 184, 218 Alexander Ogilvy, 184 “All American Research Analyst poll,” 25, 48 Alliance Capital, 224 Alliance Management, 43, 47 allocating capital, 90 Ally, Steve, 30, 67, 141 alternate pay phone companies, 37–38, 174–75, 181, 186–87 AMD, 45, 144 American Electronics Association, 62 American Superconductor, 137 America Online, 105, 156, 178, 218 Amerindo, 168 analyst(s), 8, 234–40 banking, 107–8 basics of, 24–26 boutique, 109 conference calls and, 37–38 after crash of October 1987, 72 “dialing for dollars” and, 47 industry immersion and, 27 institutional clients and, 23 investment bankers and, 241–42 press coverage and, 48 ranking, by Institutional Investor, 25, 46–48 reputation and, 231, 237–41, 244 small cap, 148 types of, 32 visits to accounts by, 47 written reports and, 47–48 Apple Computer, 16–17, 158 Armstrong, Michael, 216 Arrowwood, 51, 77 Ashton-Tate, 82–83 athletes, on Wall Street, 67–68 Index Atlantic Crossing, 213 AT&T, 7, 33, 59, 212, 216–17 automated trading systems, 72 Avid, 162–63 ax in a stock, 35, 112 ax syndrome, 209–18 bankers, technology, 137 banking analysts, 107–8 banking fees, 90 Barlage, Jim, 17 Barnes and Noble Booksellers, 174 Be, Inc., 206 Beard, Anson, 89, 109 Bell Labs, 7 Berens, Rod, 84–87 Berkowitz, Jeff, 202–3 Bezos, Jeff, 174 Biggs, Barton, 24–25, 92, 123–24, 125, 126, 127, 129, 145, 152–53 Blodget, Henry, 181–85, 214–16, 218, 225–26, 231 Blum, Scott, 207 Boesky, Ivan, 56 Bogle, John, 172 bonus pool, 90 Boston Company, 103 Boucher, David, 141–42, 243 boutique analyst, 109 Boutros, George, 170 Brady, Bill, 139–40, 157, 170, 223 Bright Lights, Big City (McInerney), 39–40 248, 177–78 Brooke, Paul, 129, 143 bulge bracket firms, 108, 221 bull market(s), 50–69 takeovers, buyouts and, 53 Burroughs Corporation, 56 Business Week, 216, 180, 207–8 “buying it off the box,” 196–97 buy-side firms, 25 Callahan, Dennis, 73, 150, 215 Cantor Fitzgerald, 67 capital, allocating, 90 Carroll, Jim, 72 Carroll, Paul, 67 Case, Steve, 156 Cashin, Art, 42 C-Cube Microsystem, 165 CDMA, 204–5 Chinese Wall, 94, 216, 221 chip industry, 26–27 CIBC Oppenheimer, 182 Cirrus Logic, 93 Cisco, 102, 105, 126 Citigroup, 216 Citron, Jeff, 197–98, 199 Clark, Jim, 156, 165–67 Clark, Mayree, 226 CMGI, 168 CNBC, 64, 181 Colonna, Jerry, 203–4 commissions, 72, 90 compensation, on Wall Street, 90 Index Compuserve, 36 conference calls, 37 conferences, 119 Contel, 39 convertible bonds, 91 Cordial, Steve, 126 Cornell, Robert (Bob), 5–16, 20, 26–30, 72, 79, 85, 155, 167 Cowan, Ollie, 31, 53 Cramer, Jim, 182, 183, 200, 202–3 crash of October 1987, 71 creative accounting, 219 CS First Boston, 1, 179–80, 190, 223 Cuban, Mark, 178 Cuhney, Adam, 79–80 Curley, Jack, 129–30, 140, 156 Cushman, Jay, 129–30, 132 Dale, Peter, 85, 92, 96, 108, 110 Data Resource Inc.


pages: 354 words: 26,550

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


algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business 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

MODEL DEVELOPMENT LIFE CYCLE High-frequency trading systems, by their nature, require rapid hesitationfree decision making and execution. Properly programmed computer systems typically outperform human traders in these “mission-critical” trading tasks, particularly under treacherous market conditions—see Aldridge (2009), for example. As a result, computer trading systems are rapidly replacing traditional human traders on trading desks around the world. The development of a fully automated trading system follows a path similar to that of the standard software development process. The typical life cycle of a development process is illustrated in Figure 16.1. A sound development process normally consists of the following five phases: 1. Planning 2. Analysis 3. Design 4. Implementation 5. Maintenance Planning Analysis Maintenance Design Implementation FIGURE 16.1 Typical development cycle of a trading system.

., 133 Anonymous orders, 69–70 Apergis, Nicholas, 88 Arca Options, 9 ARCH specification, 88 Asset allocation, portfolio optimization, 213–217 Asymmetric correlation, portfolio optimization, 208–209 Asymmetric information, measures of, 146–148 Augmented Dickey Fuller (ADF) test, 98 Autocorrelation, distribution of returns and, 94–96 Automated liquidity provision, 4 Automated Trading Desk, LLC (ATD), 12 Automated trading systems, implementation, 233–249 model development life cycle, 234–236 pitfalls, 243–246 steps, 236–243 testing, 246–249 Autoregression-based tests, 86 Autoregressive (AR) estimation models, 98–99 Autoregressive analysis, event arbitrage, 167–168 Autoregressive moving average (ARMA) models, 98, 101, 106 Avellaneda, Marco, 138–139 Average annual return, 49–51 327 328 Bachelier, Louis, 80 Back-testing, 28, 219–231 of automated systems, 233 directional forecasts, 220, 222–231 point forecasts, 220–222 risk measurement and, 255, 268 Bae, Kee-Hong, 67, 68 Bagehot, W., 151 Bailey, W., 183 Balduzzi, P., 182 Bangia, A., 263 Bank for International Settlements (BIS), 43–44 BIS Triennial Surveys, 44 Bannister, G.J., 183 Barclay, M.J., 277 Basel Committee on Banking Supervision, 251, 253, 265 Bayesian approach, estimation errors, 209–211 Bayesian error-correction framework, portfolio optimization, 213–214 Bayesian learning, 152–155 Becker, Kent G., 183 Benchmarking, 57–58 post-trade performance analysis, 296–298 Berber, A., 142 Bernanke, Ben S., 180 Bertsimas, D., 274 Bervas, Arnaud, 38, 263, 264 Best, M.J., 209 Bhaduri, R., 270 Biais, Bruno, 12, 67, 160, 163 Bid-ask bounce, tick data and, 120–121 Bid-ask spread: interest rate futures, 40–41 inventory trading, 133, 134–139 limit orders, 67–68 market microstructure trading, information models, 146–147, 149–157 post-trade analysis of, 288 tick data and, 118–120 Bigan, I., 183 Bisiere, Christophe, 12 INDEX BIS Triennial Surveys, 44 Black, Fisher, 193, 212 Bloomfield, R., 133 Bollerslev T., 106, 176–178 Bollinger Bands, 185 Bond markets, 40–42 Boscaljon, Brian L., 174 Boston Options Exchange (BOX), 9 Bowman, R., 174 Boyd, John H., 180 Bredin, Don, 184 Brennan, M.J., 147, 192, 195 Brock, W.A., 13 Broker commissions, post-trade analysis of, 285, 287 Broker-dealers, 10–13, 25 Brooks, C., 55 Brown, Stephen J., 59 Burke, G., 56 Burke ratio, 53t, 56 Business cycle, of high-frequency trading business, 26–27 Caglio, C., 142 Calmar ratio, 53t, 56 Cancel orders, 70 Cao, C., 131, 139, 142 Capital asset pricing model (CAPM), market-neutral arbitrage, 192–195 Capitalization, of high-frequency trading business, 34–35 Capital markets, twentieth-century structure of, 10–13 Capital turnover, 21 Carpenter, J., 253 Carry rate, avoiding overnight, 2, 16, 21–22 Cash interest rates, 40 Caudill, M., 113 Causal modeling, for risk measurement, 254 Chaboud, Alain P., 191 Chakravarty, Sugato, 158–159, 277 Challe, Edouard, 189 Chan, K., 67 Chan, L.K.C., 180, 289, 295 Index Chen, J., 208–209 Chicago Board Options Exchange (CBOE), 9 Chicago Mercantile Exchange (CME), 9, 198 Choi, B.S., 98 Chordia, T., 192, 195, 279 Chriss, N., 274, 275, 295 Chung, K., 67–68 Citadel, 13 Clearing, broker-dealers and, 25 CME Group, 41 Cohen, K., 130 Co-integration, 101–102 Co-integration-based tests, 89 Coleman, M., 89 Collateralized debt obligations (CDOs), 263 Commercial clients, 10 Commodities.


pages: 510 words: 120,048

Who Owns the Future? by Jaron Lanier


3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, book scanning, Burning Man, call centre, carbon footprint, cloud computing, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism,, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, invisible hand, Jacquard loom, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Mark Zuckerberg, meta analysis, meta-analysis, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Peter Thiel, place-making, Plutocrats, plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Richard Feynman, Ronald Reagan, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks

Starting in the 1980s, but really blossoming in the 1990s, finance got networked, and schemes were for the first time able to exceed the pre-digital limitations of human deception. The networking of finance occurred independently and in advance of the rise of the familiar Internet. There were different technical protocols over different infrastructure, though similar principles applied. Some of the early, dimly remembered steps toward digitally networked finance included: 1987’s Black Monday (a market anomaly caused by automated trading systems), Long-Term Capital, and Enron. I will not recount these stories here, but those readers who are not familiar with them would do well to read up on these rehearsals of our current global troubles. In all these cases there was a high-tech network scheme at play that seemed to concentrate wealth while at the same time causing volatility and trauma for ordinary people, particularly taxpayers who often ended up paying for a bailout.

Brian, 169n artificial hearts, 157–58 artificial intelligence (AI), 23, 61, 94, 95, 114, 116, 136, 138n, 147, 155, 157, 178, 191, 192–93, 325, 330, 354, 359n artificial memory, 35 art market, 108 Art of the Long View, The (Schwartz), 214 ashrams, 213 assets, 31, 60 “As We May Think” (Bush), 221n asymmetry, 54–55, 61–66, 118, 188, 203, 246–48, 285–88, 291–92, 310 Athens, 22–25 atomic bomb, 127 “attractor nightmare,” 48 auctions, 170, 286 aulos, 23n austerity, 96, 115, 125, 151, 152, 204, 208 authenticity, 128–32, 137 authors, 62n automata, 11, 12, 17, 23, 42, 55, 85–86, 90–92, 97–100, 111, 129, 135–36, 155, 157, 162, 260, 261, 269, 296n, 342, 359–60 automated services, 62, 63, 64, 147–48 automated trading systems, 74–78, 115 automation, 7, 85, 123–24, 192, 234, 259, 261, 343 automobiles, 43, 86, 90–92, 98, 118–19, 125n, 302, 311, 314, 343, 367 avatar cameras, 265 avatars, 89n, 265, 283–85 baby boomers, 97–100, 339, 346 bailouts, financial, 45, 52, 60, 74–75, 82 Baird-Murray, Kathleen, 200n “Ballad of John Henry, The,” 134–35 bandwidth, 171–72 banking, 32–33, 42, 43, 69, 76–78, 151–52, 251, 269n, 289, 345–46 bankruptcy, 2, 89, 251 bargains, 64–65, 95–96 Barlow, John Perry, 353 Barnes & Noble, 62n, 182 barter system, 20, 57 Battlestar Galactica, 137, 138n “beach fantasy,” 12–13, 18, 236–37, 331, 366–67 Beatles, 211, 212, 213 behavior models, 32, 121, 131, 173–74, 286–87 behavior modification, 173–74 Belarus, 136 belief systems, 139–40 Bell, Gordon, 313 bell curve distribution, 39, 39–45, 204, 208, 262, 291–93 Bell Labs, 94 Bentham, Jeremy, 308n Berners-Lee, Tim, 230 Bezos, Jeff, 352 big business, 265–67, 297–98 big data, 107–40, 150, 151–52, 155, 179, 189, 191–92, 202–4, 265–66, 297–98, 305, 346, 366, 367 big money, 202–4, 265–67 billboards, 170, 267, 310 billing, 171–72, 184–85 Bing, 181–82 biodiversity, 146–47 biological realism, 253–54 biotechnology, 11–13, 17, 18, 109–10, 162, 330–31 Bitcoin, 34n BitTorrent, 223 blackmail, 61, 172–73, 207, 273, 314, 316, 322 Black Monday, 74 blogs, 118n, 120, 225, 245, 259, 349, 350 books, 1–2, 62, 63, 65, 113, 182, 192, 193, 246–47, 277–78, 281, 347, 352–60 bots, 62, 63, 64, 147–48 brain function, 195–96, 260, 328 brain scans, 111–12, 218, 367 Brand, Stewart, 214 brand advertising, 267 Brandeis, Louis, 25, 208 Brazil, 54 Brooks, David, 326 Burma, 200n Burning Man, 132 Bush, George H.


pages: 222 words: 54,506

One Click: Jeff Bezos and the Rise of by Richard L. Brandt


Amazon Web Services, automated trading system, big-box store, call centre, cloud computing, Dynabook, Elon Musk, inventory management, Jeff Bezos, Kevin Kelly, new economy, science of happiness, search inside the book, Silicon Valley, Silicon Valley startup, skunkworks, software patent, Steve Jobs, Stewart Brand, Tony Hsieh, Whole Earth Catalog, Y2K

He sent out his résumé to headhunters. But this time he wanted a real technology company, not another Wall Street firm. One of the headhunters called him and said, “I know you said you would kill me if I even proposed the finance thing, but there’s this opportunity that’s actually a very unusual financial company.” That company was D. E. Shaw. It was founded in 1988 by David Shaw to create a newfangled computer-automated trading system for Wall Street. Shaw was a computer science professor at Columbia University who was lured to Wall Street to help computerize stock trading systems, and then started his own company. At the time, computer software was already being used to track small differences in stock prices around the world, allowing arbitrageurs to buy the stock at one price and immediately sell it for a profit at the higher price.


pages: 280 words: 73,420

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague


algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, credit crunch, Credit Default Swap, financial innovation, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

The market was so tilted in their favor that in March 2011, Credit Suisse saw a big business opportunity in launching a new kind of trading platform—an Electronic Communication Network (ECN) that had rules favoring institutional investors and disadvantaging high-frequency traders. The SEC was so out of touch with the markets, it actually believed that the consolidated tape was the best source of information for the best prices in a listed security, precluding the need for investors to subscribe to data feeds from each of the 10 exchanges and 70-plus automated trading systems (ATS).4 In fact, right under the SEC’s nose, a duel market had developed: a high-end market for the moneyed traders and a low-end, less efficient market for retail investors. The SEC never noticed until Saluzzi and Arnuk got its attention in 2009. Regulators also failed to grasp a more fundamental fact. Because the high-frequency traders were executing related trades simultaneously on all the stock venues and all the commodity venues, the equities and the commodities markets in effect had been unified.


pages: 218 words: 63,471

How We Got Here: A Slightly Irreverent History of Technology and Markets by Andy Kessler


Albert Einstein, Andy Kessler, automated trading system, bank run, Big bang: deregulation of the City of London, Bretton Woods, British Empire, buttonwood tree, Claude Shannon: information theory, Corn Laws, Edward Lloyd's coffeehouse, fiat currency, floating exchange rates, Fractional reserve banking, full employment, Grace Hopper, invention of the steam engine, invention of the telephone, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, James Hargreaves, James Watt: steam engine, John von Neumann, joint-stock company, joint-stock limited liability company, Joseph-Marie Jacquard, Maui Hawaii, Menlo Park, Metcalfe's law, packet switching, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, railway mania, RAND corporation, Silicon Valley, Small Order Execution System, South Sea Bubble, spice trade, spinning jenny, Steve Jobs, supply-chain management, supply-chain management software, trade route, transatlantic slave trade, transatlantic slave trade, tulip mania, Turing machine, Turing test, William Shockley: the traitorous eight

These new fund managers take risks, with assurance that the companies they invest in provide accurate information, have liquid shares, trade cheaply and quickly and exist free of stock manipulation. These funds rarely own Russian gas refiners, as they fail all of the above assurances. But funds do own weird companies that make components for optical wave division multiplexing or some new biopharma company or the latest in supply chain management software, but they require automated trading systems to stay quick of foot. The New York Stock Exchange is still a people intensive exchange. Its specialist system was created in 1871. And we are still stuck with the NYSE monopoly on listed shares. Lots of reasons are offered, such as centralized pools of liquidity or orderly markets, etc. But also to blame are some subtle regulatory technicalities that the NYSE hides behind. One technicality is a network called the Intermarket Trading System, which was also set up in 1975, ostensibly to execute trades between exchanges.


pages: 224 words: 13,238

Electronic and Algorithmic Trading Technology: The Complete Guide by Kendall Kim


algorithmic trading, automated trading system, backtesting, corporate governance, Credit Default Swap, diversification,, family office, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, natural language processing, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, short selling, statistical arbitrage, Steven Levy, transaction costs, yield curve

If the intention is to increase staff and trading volume, as well as venture into different asset classes, a scalable in-house software solution may be the answer. 13.6 Conclusion The sell side will continue to undertake the difficult task of maintaining strong relationships with the buy side, which will allow them to grasp a foothold on market share. Major broker-dealers will enhance their market data infrastructure in order to translate large quantities of real-time data demanded by algorithmic and other automated trading systems for best execution. This will eliminate as much latency as possible. Direct market access companies, OMSs, and ECN aggregators will continue to be acquired by broker-dealers. Individual investors will put further pressure on their brokers and mutual fund managers for more transparency and to better understand management and operation fees. ECN aggregation is a natural progression and will continue to pressure the competition for desk space for the buy-side trader.


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby


Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, Bretton Woods, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, Kenneth Rogoff, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, moral hazard, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, technology bubble, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs

It was one of the first in a long line of automated trading systems spawned by the hedge-fund industry.26 Weymar was initially skeptical of Vannerson’s project.27 His trend-following concept seemed disarmingly simple: Buy things that have just gone up on the theory that they will continue to go up; short things that have just gone down on the theory that they will continue to go down. Even though Vannerson’s program took a step beyond that—it tried to distinguish upticks that might signify a lasting trend from upticks that signified nothing—Weymar still doubted that anyone could make serious money from something apparently so trivial. But by the summer of 1971, Weymar had reversed himself. The humiliation of the corn episode was one reason: The great virtue of an automated trading system was that risk controls had to be programmed into the computer from the start, and there was no danger of overconfident traders exceeding their allowed limits.


pages: 318 words: 87,570

Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio by Sal Arnuk, Joseph Saluzzi


algorithmic trading, automated trading system, Bernie Madoff, buttonwood tree, corporate governance, cuban missile crisis, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, Mark Zuckerberg, market fragmentation, Ponzi scheme, price discovery process, price mechanism, price stability, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, transaction costs, two-sided market

The consolidation of the regionals culminated in one major exchange, the New York Stock Exchange (NYSE); a handful of regionals, such as Boston, Philly and P-coast; and an over-the-counter (OTC) market, NASDAQ, for small growth issues. NASDAQ NASDAQ began trading as the world’s first electronic stock market in 1971. NASDAQ was an acronym for National Association of Securities Dealers Automated Quotation system and was member-owned by the National Association of Securities Dealers (NASD). What started as a bulletin board quotation system grew into a full stock market, as the NASDAQ added volume reporting and automated trade systems. Due to less stringent listing requirements, small startups would raise money via initial public offerings (IPOs) on NASDAQ as opposed to the NYSE. It is hard to believe, but Intel Corp. started out as a $6.8 million IPO and Microsoft as a $60 million IPO. Through NASDAQ, broker dealers competed with each other by providing two-sided quotes in each stock listed. Actual trades would predominantly be agreed to over the telephone.


pages: 250 words: 87,722

Flash Boys: A Wall Street Revolt by Michael Lewis


automated trading system, bash_history, Berlin Wall, Bernie Madoff, collateralized debt obligation, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, latency arbitrage, pattern recognition, risk tolerance, Sergey Aleynikov, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, too big to fail, trade route, transaction costs, Vanguard fund

A fourth investor was told, by three different banks, that they didn’t want to connect to IEX because they didn’t want to pay the $300-a-month connection fee. Of all the banks that dragged their feet after their customers asked them to send their stock market orders to IEX, Goldman Sachs had offered the best excuse: They were afraid to tell their computer system to do anything it hadn’t done before. In August 2013, the Goldman automated trading system generated a bunch of crazy and embarrassing trades that lost Goldman hundreds of millions of dollars (until the public exchanges agreed, amazingly, to cancel them). Goldman wanted to avoid giving new instructions to its trading machines until it figured out why they had ceased to follow the old ones. There was something about the way Goldman had treated Brad when he visited their offices—listening to what he had to say, bouncing him up the chain of command rather than out the door—that led him to believe their excuse.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson


algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, cloud computing, collapse of Lehman Brothers, Donald Trump, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition,, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!

The gloves were coming off. WHILE Maschler’s Datek traders were good at SOES, they had little edge over skilled competitors such as Houtkin. That quickly changed after Josh Levine began tinkering with Datek’s SOES trading system. Levine was still working at Russo and freelancing, but he started to poke around Datek’s operation at 50 Broad Street soon after he’d heard they’d been using an automated trading system. While he didn’t know the ins and outs of SOES at first, he quickly caught on—and liked what he saw. The greedy fat-cat insiders were getting their lunches eaten by Datek’s hit-and-run traders. It was a thing of beauty, a pristine example of how technology could shift the ground beneath the entrenched elite and transfer the power to their smarter, faster rivals. Datek’s grizzled traders at first didn’t know what to make of the slight, baby-faced programmer named Josh.


pages: 374 words: 114,600

The Quants by Scott Patterson


Albert Einstein, asset allocation, automated trading system, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Nash: game theory, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, NetJets, new economy, offshore financial centre, Paul Lévy, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Tuttle’s physics background gave him the tools to understand many of the complex trades PDT executed. But since he didn’t have any computer programming skills, it limited his ability to design and implement models. Instead, he became PDT’s “human trader.” At the time, there were still certain markets, such as stock index futures, that weren’t fully automatic. Trades spat out by PDT’s models had to be called in over the telephone to other desks at Morgan. That was Tuttle’s job. The automated trading system didn’t always go smoothly. Once PDT mistakenly sold roughly $80 million worth of stock in about fifteen minutes due to a bug in the system. Another time Reed, who was running the Japanese stock system at the time, asked another trader to cover for him. “Just hit Y every time it signals a trade,” he said. He failed to mention the need to also hit enter. None of the trades went through properly.


pages: 701 words: 199,010

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


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

In 2006, the NYSE became an electronic exchange, saying goodbye to the traditional floor brokers and specialists who ran around on those old wooden floors, screaming prices as they went.2 Stock purchases and sales can be completed through a variety of networks: a national exchange such as the NYSE, an electronic communication network (ECN), a large broker-dealer, or a dark pool. Most trading—64%—takes place on the exchanges. Broker-dealers account for another 18%, about 10% occurs through ECNs, and the remaining 8% occurs in dark pools.3 The exchanges consist of highly automated trading systems that respond to stock orders in less than one millisecond. ECNs are alternative trading systems that offer services that are very similar to those offered by the exchanges. They try to match buyers and sellers at the best bid and offer prices. All the trades that occur on the exchanges and the ECNs must be posted on the consolidated tape, including price and quantity traded.4 The consolidated tape is a real-time history of every trade in the U.S. markets.