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

., 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.

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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.

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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 CSIdata.com, 37 CRSP.com, 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 DTN.com, 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 GainCapital.com, 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 HQuotes.com, 37, 81 Hulbert, Mark (New York Times), 10 I Information ratio.

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

active measures, Andrei Shleifer, AOL-Time Warner, asset allocation, automated trading system, barriers to entry, Bernie Madoff, Bob Litterman, book value, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, equity risk premium, fault tolerance, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, information security, interest rate swap, invention of the telegraph, job automation, junk bonds, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, National best bid and offer, Nick Leeson, open economy, passive investing, pattern recognition, payment for order flow, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, proprietary trading, race to the bottom, random walk, Reminiscences of a Stock Operator, rent-seeking, risk free rate, risk tolerance, risk-adjusted returns, search costs, selection bias, shareholder value, short selling, short squeeze, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, vertical integration, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game

Electronic trading systems are cheaper to operate because they do not require floor brokers and sales brokers. 27.8 SUMMARY Floor-based trading systems and automated trading systems have different strengths and weaknesses. Consequently, they appeal to different clienteles. It is unlikely that one market structure will dominate all trading. Table 27-1 summarizes strengths and weaknesses of floor-based and automated trading systems. Fully automated systems are very fast and generally cheap to use and operate. These characteristics ensure that active markets and markets that serve small traders will use automated trading systems extensively. In the U.S. equities markets, Bernard L. Madoff Investment Securities, Knight Capital Markets, and other dealers who offer automated execution systems provide excellent service to high volumes of small traders.

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• The proponents of CISC and RISC microprocessors compete with each other in the marketplace. How does their competition differ from the competition among markets for order flow? 27 Floor Versus Automated Trading Systems Advances in communications and computing technologies now allow exchanges to completely automate their trading systems. Many exchanges have done so, and many brokers, ECNs, and dealers have created automated trading systems. Despite these developments, many of the most liquid exchanges in the world still employ floor-based trading systems. The New York Stock Exchange, the Chicago Board of Trade, the Chicago Mercantile Exchange, the New York Mercantile Exchange, and almost all U.S. options exchanges primarily use floor-based trading systems.

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The most successful electronic competitors of the NYSE have been third market dealers, like Bernard L. Madoff Investment Securities and Knight Capital Markets. Their automated trading systems provide very quick service primarily to retail traders represented by discount brokers. * * * ▶ The Bangladeshi Stock Exchange and the New York Stock Exchange In 1999, the Bangladeshi Stock Exchange replaced its trading floor with an automated trading system. At the same time, the New York Stock Exchange considered where to build a new trading floor. The continued commitment of the New York Stock Exchange to its trading floor may be its most important decision at the turn of the millennium.

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, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fear index, financial engineering, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, machine readable, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, power law, principal–agent problem, random walk, risk free rate, 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.

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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.

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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.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

Fellow professors were polite enough not to share their criticism and skepticism, at least within earshot. But Berlekamp knew what they were thinking. “Colleagues avoided or evaded commenting,” he says. Simons didn’t care about the doubters; the gains reinforced his conviction that an automated trading system could beat the market. “There’s a real opportunity here,” he told Berlekamp, his enthusiasm growing. Medallion scored a gain of 55.9 percent in 1990, a dramatic improvement on its 4 percent loss the previous year. The profits were especially impressive because they were over and above the hefty fees charged by the fund, which amounted to 5 percent* of all assets managed and 20 percent of all gains generated by the fund.

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Eventually, Brown and Mercer developed an elaborate stock-trading system that featured a half million lines of code, compared to tens of thousands of lines in Frey’s old system. The new system incorporated all necessary restrictions and requirements; in many ways, it was just the kind of automated trading system Simons had dreamed of years earlier. Because the Nova fund’s stock trades were now less sensitive to the market’s fluctuations, it began holding on to shares a bit longer, two days or so, on average. Crucially, Brown and Mercer retained the prediction model Frey had developed from his Morgan Stanley experience.

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RenTec has built a machine to model this interconnectedness, track its behavior over time, and bet on when prices seem out of whack according to these models.” Outsiders didn’t quite get it, but the real key was the firm’s engineering—how it put all those factors and forces together in an automated trading system. The firm bought a certain number of stocks with positive signals, often a combination of more granular individual signals, and shorted, or bet against, stocks with negative signals, moves determined by thousands of lines of source code. “There is no individual bet we make that we can explain by saying we think one stock is going to go up or another down,” a senior staffer says.

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, buy and hold, commodity trading advisor, Credit Default Swap, Elliott wave, financial engineering, fixed income, global macro, Long Term Capital Management, managed futures, Market Wizards by Jack D. Schwager, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game

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.

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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.

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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 SimplerOptions.com 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 TradeTheMarkets.com 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) FOREX.com, 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.

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets by Donald MacKenzie

algorithmic trading, automated trading system, banking crisis, barriers to entry, bitcoin, blockchain, Bonfire of the Vanities, Bretton Woods, Cambridge Analytica, centralized clearinghouse, Claude Shannon: information theory, coronavirus, COVID-19, cryptocurrency, disintermediation, diversification, en.wikipedia.org, Ethereum, ethereum blockchain, family office, financial intermediation, fixed income, Flash crash, Google Earth, Hacker Ethic, Hibernia Atlantic: Project Express, interest rate derivative, interest rate swap, inventory management, Jim Simons, level 1 cache, light touch regulation, linked data, lockdown, low earth orbit, machine readable, market design, market microstructure, Martin Wolf, proprietary trading, Renaissance Technologies, Satoshi Nakamoto, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, The Great Moderation, transaction costs, UUNET, zero-sum game

To human eyes, trading on Island appeared instantaneous. FIGURE 1.1. 50 Broad Street. Author’s photograph. Just as consequential as Island’s speed was that machines started to trade on it. There had been previous efforts to automate trading, but often they had not gone smoothly. It could be difficult for an automated trading system to interact seamlessly with exchanges’ systems, which in the 1980s and 1990s were usually designed on the assumption that traders were human beings, not machines. Indeed, those who ran exchanges’ early electronic trading systems often protected their human users from “unfair” automated competition by prohibiting the direct connection of computers to them.

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Whitcomb, who continued to teach at Rutgers and live in New York, designed the relatively simple mathematical model described in the next four paragraphs, faxing instructions and formulas to Charleston to be turned into code by Hawkes’s programmers. Around the equation that was the core of the model, Whitcomb designed and the programmers coded the components of what was eventually to become a full automated-trading system. It consisted of a module to process incoming market data; a pricing module that implemented Whitcomb’s equation; a module that tracked the system’s accumulated trading position in each stock and adjusted its trading strategy accordingly; a decision module that calculated how best to trade based on the existing trading position and the pricing module’s predictions; a module that dispatched the resultant orders and if necessary canceled existing orders; a module that calculated in real time the profits or losses being made; and so on—eventually requiring in total some 80,000 lines of code (interviewee BT).

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One, apparently the more common, is simply human error (sometimes called “fat finger” error) in entering into execution algorithms the necessary parameters of price and, especially, total size of order. Rarer, but more relevant to this book, is the second cause: serious technical malfunctions of an HFT or other automated trading system. In chapter 3, we saw how a problem of this kind—the result of a mistake in a single character in a program—caused a financial loss to Automated Trading Desk that could have been catastrophic had the human trader involved not noticed it quickly and switched off the automated system involved.

Wall Street Meat by Andy Kessler

accounting loophole / creative accounting, Alan Greenspan, Andy Kessler, automated trading system, banking crisis, Bob Noyce, George Gilder, index fund, Jeff Bezos, John Bogle, junk bonds, market bubble, Mary Meeker, Menlo Park, Michael Milken, Pepto Bismol, pets.com, Robert Metcalfe, rolodex, Salesforce, Sand Hill Road, Silicon Valley, Small Order Execution System, Steve Jobs, technology bubble, undersea cable, Y2K

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.

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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 Amazon.com, 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 Broadcast.com, 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 Buy.com, 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.

Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, behavioural economics, buy and hold, buy low sell high, cryptocurrency, data science, deep learning, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, OpenAI, p-value, paper trading, performance metric, prediction markets, proprietary trading, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber. Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California.

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They are extremely fast at reacting to market data, and they don't get distracted or make mistakes (unless they were programmed incorrectly, which is a software bug and not a drawback of computers themselves). They don't have emotions, so don't deviate from what they are programmed to do. All of these advantages make computerized automated trading systems extremely profitable when done right, which is where algorithmic trading starts. Evolution of algorithmic trading – from rule-based to AI Let's take a simple example of a trend-following strategy and see how that has evolved from a manual approach all the way to a fully automated algorithmic trading strategy.

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

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

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.

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., 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.

Who Owns the Future? by Jaron Lanier

3D printing, 4chan, Abraham Maslow, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, Black Monday: stock market crash in 1987, book scanning, book value, Burning Man, call centre, carbon credits, carbon footprint, cloud computing, commoditize, company town, computer age, Computer Lib, crowdsourcing, data science, David Brooks, David Graeber, delayed gratification, digital capitalism, digital Maoism, digital rights, Douglas Engelbart, en.wikipedia.org, Everything should be made as simple as possible, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, Garrett Hardin, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, information asymmetry, invisible hand, Ivan Sutherland, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, machine translation, Marc Andreessen, Mark Zuckerberg, meta-analysis, Metcalfe’s law, moral hazard, mutually assured destruction, Neal Stephenson, Network effects, new economy, Norbert Wiener, obamacare, off-the-grid, packet switching, Panopticon Jeremy Bentham, Peter Thiel, place-making, plutocrats, Ponzi scheme, post-oil, pre–internet, Project Xanadu, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, synthetic biology, tech billionaire, technological determinism, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, Tragedy of the Commons, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks, zero-sum game

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.

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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.

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

Amazon Web Services, automated trading system, big-box store, call centre, cloud computing, deal flow, drop ship, Dynabook, Elon Musk, Free Software Foundation, inventory management, Jeff Bezos, Kevin Kelly, Kickstarter, Larry Ellison, Marc Andreessen, new economy, Pershing Square Capital Management, science of happiness, search inside the book, Silicon Valley, Silicon Valley startup, skunkworks, software patent, Steve Jobs, Stewart Brand, Tony Hsieh, two-pizza team, Whole Earth Catalog, Y2K

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.

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

Like many high-frequency trading firms, their business was lucrative. On the morning of July 31, 2012, Knight had $365 million in assets. Within 45 minutes, they would be bankrupt. At 9:30 a.m. Eastern Time on July 31, U.S. markets opened and Knight deployed a new automated trading system. Instantly, it was apparent that something was wrong. One of the functions of the automated trading system was to break up large orders into smaller ones, which then would be executed individually. Knight’s trading system wasn’t registering that these smaller trades were actually completed, however, so it kept tasking them again. This created an endless loop of trades.

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

But this cautionary note apparently hasn’t made it to the decision makers of corporate America, because they still seem to be placing excessive trust in the wisdom of AI, often with consequences much more severe than accidentally advertising some offensive T-shirts or coming up with a disgusting cocktail recipe. A trading firm called Knight Capital, for example, lost $440 million in a forty-five-minute span on August 1, 2012, after an improperly installed automated trading system rapidly bought and sold millions of shares, pushing their prices up and creating massive losses when they had to be resold. The losses nearly put Knight Capital out of business, and the firm had to receive hundreds of millions of dollars in emergency financing in order to stay afloat. Or take Watson, the IBM-owned AI that famously defeated a Jeopardy!

The Asylum: The Renegades Who Hijacked the World's Oil Market by Leah McGrath Goodman

Alan Greenspan, anti-communist, Asian financial crisis, automated trading system, banking crisis, barriers to entry, Bear Stearns, Bernie Madoff, Carl Icahn, computerized trading, corporate governance, corporate raider, credit crunch, Credit Default Swap, East Village, energy security, Etonian, family office, Flash crash, global reserve currency, greed is good, High speed trading, light touch regulation, market fundamentalism, Oscar Wyatt, peak oil, Peter Thiel, pre–internet, price mechanism, profit motive, proprietary trading, regulatory arbitrage, reserve currency, rolodex, Ronald Reagan, side project, Silicon Valley, upwardly mobile, zero-sum game

There also was a David-and-Goliath effect: the scrawnier traders with the intellectual know-how, though perhaps not the athletic prowess that had once dominated the pits, were finally able to match wits against the brawnier traders, without fear of physical retribution. On Wall Street, the move to the screen was inciting a technological arms race among the banks and hedge funds, which began sinking millions of dollars into state-of-the-art automated trading systems that could move at near-lightning speeds, outpacing the floor traders, many of whom still traded with no more than paper and pencil. The New York floor traders, whose brutality had once crushed the Marine Corps officers, were now getting a dose of their own medicine on their home turf—and they did not like it.

…

Once on the screen, the energy traders in arcades around the world became so intent on hitting their financial targets, they lost sight of the potential human repercussions. The screens had desensitized them to what was happening on the ground. Any harm done to energy consumers in the crossfire of their trading games was blithely categorized as collateral damage. Banks and hedge funds, whose automated trading systems bought and sold millions upon millions of energy contracts in just milliseconds, followed the same credo. Computer programmers were being hailed as the new gunslingers of Wall Street, fetching breathtaking salaries. Veteran energy traders began reporting that some of the computerized trading systems were letting banks and funds trade on the screen in ways that would have been illegal in the pits.

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

algorithmic trading, automated trading system, backtesting, Bear Stearns, business logic, commoditize, computerized trading, corporate governance, Credit Default Swap, diversification, en.wikipedia.org, family office, financial engineering, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, money market fund, natural language processing, proprietary trading, 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.

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

Albert Einstein, Andy Kessler, animal electricity, automated trading system, bank run, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Bletchley Park, Bob Noyce, Bretton Woods, British Empire, buttonwood tree, Charles Babbage, Claude Shannon: information theory, Corn Laws, cotton gin, Dennis Ritchie, Douglas Engelbart, Edward Lloyd's coffeehouse, Fairchild Semiconductor, fiat currency, fixed income, floating exchange rates, flying shuttle, Fractional reserve banking, full employment, GPS: selective availability, Grace Hopper, invention of the steam engine, invention of the telephone, invisible hand, Isaac Newton, Jacquard loom, James Hargreaves, James Watt: steam engine, John von Neumann, joint-stock company, joint-stock limited liability company, Joseph-Marie Jacquard, Ken Thompson, Kickstarter, Leonard Kleinrock, Marc Andreessen, Mary Meeker, Maui Hawaii, Menlo Park, Metcalfe's law, Metcalfe’s law, military-industrial complex, Mitch Kapor, Multics, packet switching, pneumatic tube, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, proprietary trading, railway mania, RAND corporation, Robert Metcalfe, Silicon Valley, Small Order Execution System, South Sea Bubble, spice trade, spinning jenny, Steve Jobs, Suez canal 1869, supply-chain management, supply-chain management software, systems thinking, three-martini lunch, trade route, transatlantic slave trade, tulip mania, Turing machine, Turing test, undersea cable, UUNET, Wayback Machine, 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.

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

Alan Greenspan, Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Bear Stearns, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, book value, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, deal flow, do well by doing good, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, Jim Simons, John Bogle, John Meriwether, junk bonds, Kenneth Rogoff, Kickstarter, Long Term Capital Management, low interest rates, machine translation, margin call, market bubble, market clearing, market fundamentalism, Market Wizards by Jack D. Schwager, Mary Meeker, merger arbitrage, Michael Milken, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, operational security, pattern recognition, Paul Samuelson, pre–internet, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Savings and loan crisis, Sharpe ratio, short selling, short squeeze, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, tail risk, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs, two and twenty, uptick rule

Before leaving Nabisco, Vannerson had spent a year working on the Dunn & Hargitt data, analyzing daily prices for fifteen commodities; and by the time Commodities Corporation opened its doors in March 1970, he had satisfied himself that price trends really did exist, no matter what academics might assert to the contrary.25 Moreover, Vannerson had devised a computer program that could trade on that finding. He called his brainchild the Technical Computer System, or TCS. 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.

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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. But the TCS had proved itself to be superior at calling the market too. Weymar’s cocoa model, which had worked so well at Nabisco, had misjudged the direction of the market expensively during Commodities Corporation’s first year.

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

Alan Greenspan, algorithmic trading, automated trading system, Bear Stearns, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Bretton Woods, buttonwood tree, buy and hold, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, junk bonds, locking in a profit, Long Term Capital Management, machine readable, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, Nixon triggered the end of the Bretton Woods system, pattern recognition, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, uptick rule, 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.

Flash Boys: A Wall Street Revolt by Michael Lewis

automated trading system, bash_history, Berlin Wall, Bernie Madoff, collateralized debt obligation, computerized markets, drone strike, Dutch auction, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, information security, latency arbitrage, National best bid and offer, pattern recognition, payment for order flow, Pershing Square Capital Management, proprietary trading, risk tolerance, Rubik’s Cube, Sergey Aleynikov, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, the new new thing, 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.

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

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

How do I start the search?” Even experienced quants working with many alphas can miss key components required to build a robust, diversified portfolio. For instance, one of the most difficult aspects of alpha portfolio construction is the need to optimize the level of diversification of the portfolio. In automated trading systems, decisions on diversification make up a major area of human intervention. It’s not easy to visualize the many pieces of the portfolio, which contain hundreds or thousands of alphas, and their interactions. TAP emerged from those concerns. When new quants start conducting alpha research, many begin by searching the internet for articles about developing alphas.

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, buy and hold, commoditize, computerized trading, corporate governance, cuban missile crisis, financial engineering, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, machine readable, Mark Zuckerberg, market fragmentation, National best bid and offer, payment for order flow, Ponzi scheme, price discovery process, price mechanism, price stability, proprietary trading, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, stocks for the long run, stocks for the long term, transaction costs, two-sided market, uptick rule, zero-sum game

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.

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

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

Whenever it seems appropriate, references are given that point the reader to sources that address issues left open during the exposition. All in all, this book is written for readers who have some experience with both Python and (algorithmic) trading. For such a reader, the book is a practical guide to the creation of automated trading systems using Python and additional packages. This book uses a number of Python programming approaches (for example, object oriented programming) and packages (for example, scikit-learn) that cannot be explained in detail. The focus is on applying these approaches and packages to different steps in an algorithmic trading process.

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

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

www.ivolatility.com Source of option prices. www.oanda.com/currency/historical-rates Source of historic FX rates. stats.oecd.org Official source of macroeconomic data. mba.tuck.dartmouth.edu/pages/faculty/ken. french/data_library.html Academic source of equity value data. Brokers and platforms If you are not running a fully automated trading system, I hesitate to recommend a specific broker. A key selection criteria is price. Estimate the expected annual total fee from how often you’re trading, in what size, and with what total account value. Then use this for comparison purposes. Equally important is whether you can trade the products you want and with leverage if needed.

The Permanent Portfolio by Craig Rowland, J. M. Lawson

Alan Greenspan, Andrei Shleifer, asset allocation, automated trading system, backtesting, bank run, banking crisis, Bear Stearns, Bernie Madoff, buy and hold, capital controls, correlation does not imply causation, Credit Default Swap, currency risk, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, high net worth, High speed trading, index fund, inflation targeting, junk bonds, low interest rates, margin call, market bubble, money market fund, new economy, passive investing, Ponzi scheme, prediction markets, risk tolerance, stocks for the long run, survivorship bias, technology bubble, transaction costs, Vanguard fund

A problem in the trading systems of one firm can cause a cascade effect in the world markets. In spring of 2010 the U.S. stock markets experienced a “Flash Crash” during which the Dow Jones stock index sunk by 1,000 points within five minutes before quickly recovering. During this time, automated trading systems piled on sell orders, making the problem escalate quickly before finally coming back under control (some investors who had set stop losses in their accounts took large losses as they were automatically traded out of positions that recovered almost immediately and other bad trades were later backed out and canceled).

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

Alan Greenspan, algorithmic trading, automated trading system, banking crisis, bash_history, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, financial engineering, fixed income, Flash crash, Ford Model T, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, information security, Jim Simons, Joseph Schumpeter, junk bonds, latency arbitrage, Long Term Capital Management, machine readable, Mark Zuckerberg, market design, market microstructure, Michael Milken, military-industrial complex, pattern recognition, payment for order flow, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, seminal paper, 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, three-martini lunch, Tragedy of the Commons, transaction costs, uptick rule, Watson beat the top human players on Jeopardy!, zero-sum game

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.

Fortunes of Change: The Rise of the Liberal Rich and the Remaking of America by David Callahan

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, affirmative action, Albert Einstein, American Legislative Exchange Council, An Inconvenient Truth, automated trading system, benefit corporation, Bernie Sanders, Big Tech, Bonfire of the Vanities, book value, carbon credits, carbon footprint, carbon tax, Carl Icahn, carried interest, clean water, corporate social responsibility, David Brooks, demographic transition, desegregation, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Thorp, financial deregulation, financial engineering, financial independence, global village, Gordon Gekko, greed is good, Herbert Marcuse, high net worth, income inequality, Irwin Jacobs: Qualcomm, Jeff Bezos, John Bogle, John Markoff, Kickstarter, knowledge economy, knowledge worker, Larry Ellison, Marc Andreessen, Mark Zuckerberg, market fundamentalism, medical malpractice, mega-rich, Mitch Kapor, Naomi Klein, NetJets, new economy, offshore financial centre, Peter Thiel, plutocrats, power law, profit maximization, quantitative trading / quantitative finance, Ralph Nader, Renaissance Technologies, Richard Florida, Robert Bork, rolodex, Ronald Reagan, school vouchers, short selling, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, stem cell, Steve Ballmer, Steve Jobs, systematic bias, systems thinking, unpaid internship, Upton Sinclair, Vanguard fund, War on Poverty, working poor, World Values Survey

Back in the fabled 1980s, the flood of MBAs onto Wall Street was just beginning, and many top earners made do with BAs. Things changed as c01.indd 24 5/11/10 6:17:19 AM educated, rich, and liberal 25 finance became more complicated—as “quants” found new ways to make money using statistical techniques and algorithms, as automated trading systems came online, and as new financial products for managing risk or structuring debt, such as derivatives, came into being. Top universities spat out PhDs in fields such as mathematical finance and computational finance. Newly created master’s programs sprang up in financial engineering. MBA programs bolstered their quantitative offerings.

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, 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

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.

Unknown Market Wizards by Jack D. Schwager

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

In essence, the fundamentalist seeks to uncover trading opportunities by identifying potential transitions to significantly more ample or tighter supply-demand balances. For financial futures, fundamental inputs will include items such as central bank policy, inflation statistics, employment data, etc. Technical and fundamental analysis are not mutually exclusive approaches. Many traders use both in the decision-making process or as components of automated trading systems. Delivery Shorts who maintain their positions in deliverable future contracts after the last trading day are obligated to deliver the given commodity or financial instrument against the contract. Similarly, longs who maintain their positions after the last trading day must accept delivery.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn

A Pattern Language, Airbnb, algorithmic trading, automated trading system, business intelligence, business logic, business process, combinatorial explosion, computer vision, continuous integration, COVID-19, data science, deep learning, DevOps, discrete time, en.wikipedia.org, Hacker News, industrial research laboratory, iterative process, Kubernetes, machine translation, microservices, mobile money, natural language processing, Netflix Prize, optical character recognition, pattern recognition, performance metric, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, speech recognition, statistical model, the payments system, web application

However, such a model will miss all the information held in the destinations that the consolidator or travel agent was interested in at various times—this will affect things like how airports and hotels are embedded. One way to retain that information while not affecting the pricing decision is to use a neutral class for these transactions. Reframing with neutral class Suppose we are training an automated trading system that makes trades based on whether it expects a security to go up or down in price. Because of stock market volatility and the speed with which new information is reflected in stock prices, trying to trade on small predicted ups and downs is likely to lead to high trading costs and poor profits over time.

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

In time, fewer trades will be executed by screaming stockbrokers on trading floors, and more by intelligent trading algorithms that respond to market events at lightning speed. There used to be 600 US cash equities traders at Goldman Sachs’ headquarters in New York. Now there are just two. One-third of the bank’s staff are computer engineers.6 The introduction of automated trading systems has made financial regulation a nightmare for human officials. The algorithms are often too fast, too complex, and too adaptable to be subject to human oversight. OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Force 103 Increasingly, the most practical method of enforcing the law is through the use of other algorithms programmed to detect and prohibit errant behaviour, or at least flag it for human attention.7 One of the digital systems being used to keep tabs on financial behaviour is a version of IBM’s Watson.8 The idea of digital law enforcement may seem weird but it doesn’t really disrupt our notion of what the law is.

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

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

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.