latency arbitrage

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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, buy and hold, commoditize, computerized trading, 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, stocks for the long run, stocks for the long term, transaction costs, two-sided market, zero-sum game

Dick highlights that investors are not allowed to use sub-penny prices to enter orders, such as $25.9999, yet brokerage and HFT firms can. The damage to investors is not only missed trading opportunities due to an uneven playing field, but harm to the integrity of public markets and quoting. What incentive is there for investors to display their quotes when those quotes can be used, in turn, to disadvantage them? How Latency Arbitrage in Dark Pools Hurts Investors Another way HFTs hurt investors in dark pools is through latency arbitrage. Latency arbitrage arises from the two speeds in the markets. On one hand, HFT firms, who colocate at the exchanges and buy their private data feeds, can construct the National Best Bid and Offer (NBBO) of any stock milliseconds before the investing public sees stock prices via the Securities Information Processor feed (SIP). On the other hand, most dark pools use the slower SIP to calculate reference prices (the prices that stocks will trade in their dark pool at any moment in time, such as the NBBO midpoint).

Gates has apparently tested the arbitrage numerous times, across a plethora of dark pools, with consistent results. Goldman Sachs, which operates Sigma X, one of the largest dark pools by volume, has recently undertaken the services of Redline Inc. and its InRush Ticker Plant product to speed up Sigma X’s NBBO calculation.11 To us, this is an acknowledgment of the harm latency arbitrage causes investors. In a white paper we published in December 2009, “Latency Arbitrage: The Real Power Behind Predatory High Frequency Trading,” we raised three serious questions about market integrity surrounding Latency Arbitrage:12 1. The primary response from HFTs or market centers is typically “a penny or two should not matter to long-term investors; this is much ado about nothing,” to paraphrase the CEO of a major ATS who was addressing a financial industry conference in New York City in early November.

Finance website, http://finance.yahoo.com/news/pf_article_109725.html. 11. A-Team Group, “Q&A: Redline’s Mark Skalabrin on Goldman, and Why Cell Is Better” (Sept. 25, 2011), Low-Latency website, http://low-latency.com/article/qa-redlines-mark-skalabrin-goldman-and-why-cell-better. 12. Sal Arnuk and Joseph Saluzzi, “Latency Arbitrage: The Real Power Behind Predatory High Frequency Trading” (Dec. 4, 2009), Themis Trading website, http://www.themistrading.com/article_files/0000/0519/THEMIS_TRADING_White_Paper_--_Latency_Arbitrage_--_December_4__2009.pdf. 13. Mary Schapiro, “Testimony on U.S. Equity Market Structure by the U.S. Securities and Exchange Commission” (Dec. 8, 2010), Securities and Exchange Commission website, http://www.sec.gov/news/testimony/2010/ts120810mls.htm. 9. Dude, Where’s My Order? It was summer 1999, and the NASDAQ was rocking.


High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

Such HFT algorithms exploit a microstructural opportunity in a way similar to that in which large 8 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 9 — #29 i i THE VOLUME CLOCK: INSIGHTS INTO THE HIGH-FREQUENCY PARADIGM speculators exploit a macroeconomic inconsistency. Rather than possessing exogenous information yet to be incorporated in the market price, they know that their endogenous actions are likely to trigger a microstructure mechanism, with a foreseeable outcome. Their advent has transformed liquidity provision into a tactical game. We now list a few examples that are discussed in the literature. • Quote stuffers: these engage in “latency arbitrage”. The strat- egy involves overwhelming an exchange with messages, with the sole intention of slowing down competing algorithms, which are forced to parse messages that only the originators know can be ignored (NANEX 2010). • Quote danglers: this strategy sends quotes forcing a squeezed trader to chase a price against their interests. O’Hara (2010) presents evidence of their disruptive activities. • Liquidity squeezers: when a distressed large investor is forced to unwind their position, the squeezers trade in the same direction, draining as much liquidity as possible.

URL: http://www.sec.gov/ news/speech/2013/spch021913ebw.htm. 230 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 231 — #251 i i Index (page numbers in italic type relate to tables or figures) A B algorithmic execution: and leaking of information, 159–83, 160, 162, 163, 165, 167, 168, 169, 171, 172, 174, 176–7, 178–9; see also AlphaMax; BadMax BadMax approach and data sample, 166–8, 168 and BadMax and gross and net alpha, 168–70 and clustering analysis, 170–4, 171, 172, 174 definition, 160–4 and GSET, 174–5, 176–7 and high alpha of large clusters, 170–4 and trading algorithms, 70–1 algorithms: generations of, 23–7 predatory, 8 tactical liquidity provision 10–11 trading: and algorithmic decision-making, 71–2 and algorithmic execution, 70–1 evolution of, 22–8 generations, 23–7; see also trading algorithms, evolution of and indicator zoology, 27–8 and transaction cost, 28–31 AlphaMax, 160–6 passim see also BadMax; information leakage alternative limit order book, 80–6 agent-based model, 83–5, 86 results and conclusion, 85 and spread/price–time priority, 82–3 BadMax 159–83 passim, 169, 178–9, 180–2 and data sample, 166–8 and gross and net alpha, 168–70 profitability grid, 180–2 see also algorithmic execution: and leaking information; AlphaMax; information leakage Black Wednesday, 8 C clustering analysis, and high alpha of large clusters, 170–4 CME, Nasdaq’s joint project with, xvi cointegration, 44, 53–9 Consolidated Audit Tape (CAT), 216 construction of trading signals, 31–8 and order book imbalance, 36–8 and timescales and weights, 31–3, 33 and trade sign autocorrelations, 34–6 cumulative distribution function, 130–1 D dark pools, smart order routing in, 115–22 E equity markets: execution strategies in, 21–41, 25, 29, 30, 33, 35, 37, 38, 40 and fair value and order protection, 38–41, 40 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 232 — #252 i i HIGH-FREQUENCY TRADING and trading signals, construction of, 31–8; see also trading signals and types of client or market agent, 22 European Exchange Rate Mechanism (ERM), sterling joins, 8 execution shortfall, and information leakage, 164–6, 165; see also information leakage execution strategies: in equity markets, 21–41, 25, 29, 30, 33, 35, 37, 38, 40 and fair value and order protection, 38–41, 40 and trading signals, construction of, 31–8; see also trading signals in fixed-income markets, 43–62, 47, 48, 49, 50, 51, 52, 54, 55, 57, 58, 61 and cointegration, 44, 53–9 and information events, 44, 46–53 and pro rata matching, 44, 59–62 and fixed-income products, 44–6 experimental evaluation, 133–40 F fair value and order protection, 38–41, 40 fixed-income markets: execution strategies in, 43–62, 47, 48, 49, 50, 51, 52, 54, 55, 57, 58, 61 and cointegration, 44, 53–9 and information events, 44, 46–53 and pro rata matching, 44, 59–62 and short-term interest rates, 45–6 and Treasury futures, 46 fixed-income products, 44–6 and short-term interest rates, 45–6 and Treasury futures, 46, 47, 48, 51, 52, 55 see also fixed-income markets flash crash, 2, 77–8, 207, 209–10, 210, 218 see also market stress foreign-exchange markets: and the currency market, 65–73 trading algorithms, 69–72 and trading frequencies, 65–73, 72, 73 venues, 66–9 high-frequency trading in, 65–88, 66, 72, 73, 86 academic literature, 74–80 and alternative limit order book, 80–6; see also main entry Foresight Project, 215, 217, 224 futures markets: microstructural volatility in, 125–41, 133, 134, 136, 137, 138–9 experimental evaluation, 133–40 HDF5 file format, 127 maximum intermediate return, 131–2 parallelisation, 132–3 test data, 126–7 and volume-synchronised probability of informed trading, 128–31 G Goldman Sachs Electronic Trading (GSET), 159, 160, 161, 163, 166–80 passim, 167, 168, 169, 174–5 H HDF5 file format, 127 high-frequency trading (HFT): and “cheetah traders”, 1, 13 232 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 233 — #253 i i INDEX and event-based time paradigm, 15 in FX markets 65–88, 66, 72, 73, 86; see also foreign-exchange markets, 74–80 and alternative limit order book, 80–6; see also main entry and the currency market, 65–73 and trading frequencies, 65–73, 72, 73 legislative changes enable, 2 machine learning for, 91–123, 100, 101, 103, 104, 107, 108–9, 111, 117, 121 and high-frequency data, 94–6 and optimised execution in dark pools via censored exploration, 93 and optimised trade execution via reinforcement learning, 92 and predicting price movement from order book state, 92–3 and price movement from order book state, predicting, 104–15 and reinforcement learning for optimised trade execution, 96–104 and smart order routing in dark pools, 115–22 in market stress, 76–80 central bank interventions, 79–80 flash crash (2010), 77–8 yen appreciation (2007), 77 yen appreciation (2011), 78–9 markets’ operation and dynamic interaction changed by, xv and matching engine, 3, 4 and more than speed, 7–12 new paradigm in, 2–4 paradigm of, insights into, 1–17, 7, 10–11, 14 regulatory challenge of, 207–9, 210, 212, 214 good and bad news concerning, 208–14 and greater surveillance and coordination, proposals for, 215–18 and market rules, proposals to change, 218–25 and proposals to curtail HFT, 225–8 solutions, 214–28 statistics to monitor, developing, 15 and time, meaning of, 5–7 and volatility, heightening of, 12 see also low-frequency trading I implementation shortfall: approach to, illustrated, 192–203 daily estimation, 195–9 intra-day estimation, 199–203 shortfall calculations, 193–5 discussed, 186–9 with transitory price effects, 185–206, 196, 197, 198, 199, 200, 202, 203 implementation details, 204–5 and observed and efficient prices and pricing errors, 189–92 indicator zoology, 27–8 information events, 44, 46–53 and event microscope, 50–3 information leakage: and algorithmic execution, 159–83, 176–7, 178–9 BadMax approach and data sample, 166–8, 168 233 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 234 — #254 i i HIGH-FREQUENCY TRADING and BadMax and gross and net alpha, 168–70 and clustering analysis, 170–4, 171, 172, 174 and GSET, 174–5, 176–7 and high alpha of large clusters, 170–4 defining, 160–4 and execution shortfall, 164–6, 165 see also AlphaMax; BadMax L large clusters, high alpha of, 170–4 Large Hadron Collider, 125–41 latency arbitrage, 9 leakage of information: and algorithmic execution, 159–83, 176–7, 178–9 BadMax approach and data sample, 166–8, 168 and BadMax and gross and net alpha, 168–70 and clustering analysis, 170–4, 171, 172, 174 and GSET, 174–5, 176–7 and high alpha of large clusters, 170–4 defining, 160–4 and execution shortfall, 164–6, 165 see also AlphaMax; BadMax liquidity squeezers, 9 liquidity and toxicity contagion, 143–56, 144, 145, 147, 148, 151, 153, 154 empirical analysis, 151–5 order-flow toxicity contagion model, 146–51 low-frequency trading: choices needed for survival of, 15 and event-based time paradigm, 15 joining the herd, 15 and monitoring of HFT activity, 15 and order-flow toxicity, monitoring, 16 and seasonal effects, avoiding, 16 and smart brokers, 16 see also high-frequency trading M machine learning: for high-frequency trading (HFT) and market microstructure, 91–123, 100, 101, 103, 104, 107, 108–9, 111, 117, 121 and high-frequency data, 94–6 and optimised execution in dark pools via censored exploration, 93 and optimised trade execution via reinforcement learning, 92 and predicting price movement from order book state, 92–3 and price movement from order book state, predicting, 104–15 and reinforcement learning for optimised trade execution, 96–104 and smart order routing in dark pools, 115–22 Market Information Data Analytics System (MIDAS), 215–16 market microstructure: machine learning for, 91–123, 100, 101, 103, 104, 107, 108–9, 111, 117, 121 and high-frequency data, 94–6 and optimised execution in dark pools via censored exploration, 93 and optimised trade execution via reinforcement learning, 92 234 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 235 — #255 i i INDEX and predicting price movement from order book state, 92–3 and price movement from order book state, predicting, 104–15 and reinforcement learning for optimised trade execution, 96–104 and smart order routing in dark pools, 115–22 market stress: and central bank interventions, 79–80 and flash crash (2010), 77–8; see also flash crash and yen appreciation (2007), 77 and yen appreciation (2011), 78–9 Markets in Financial Instruments Directive (MiFID), 2, 21, 143, 216 microstructural volatility: in futures markets, 125–41, 133, 134, 136, 137, 138–9 experimental evaluation, 133–40 HDF5 file format, 127 maximum intermediate return, 131–2 parallelisation, 132–3 test data, 126–7 and volume-synchronised probability of informed trading, 128–31 MIDAS, see Market Information Data Analytics System N Nasdaq, CME’s joint project with, xvi O optimised trade execution, reinforcement learning for, 96–104 order book imbalance, 36–8 order-flow toxicity contagion model, 146–51 see also liquidity and toxicity contagion order protection and fair value, 38–41, 40 P pack hunters, 9 parallelisation, 132–3 price movement from order book state, predicting, 104–15 pro rata matching, 44, 59–62 probability of informed trading (PIN), 7 Project Hiberni, xvi Q quote danglers, 9 quote stuffers, 9 R regulation and high-frequency markets, 81, 207–9, 210, 212, 214 good and bad news concerning, 208–14 solutions, 214–28 and greater surveillance and coordination, proposals for, 215–18 and market rules, proposals to change, 218–25 and proposals to curtail HFT, 225–8 Regulation National Market System (Reg NMS), 2, 21, 143, 219 Regulation SCI, 216 reinforcement learning for optimised trade execution, 96–104 Rothschild, Nathan Mayer, 1 S smart order routing in dark pools, 115–22 spread/price–time priority, 82–3 235 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 236 — #256 i i HIGH-FREQUENCY TRADING T time, meaning of, and high-frequency trading, 5–7, 7 Tobin tax, 17, 81, 87 Tradeworx, 215 trading algorithms, 69–72 and algorithmic decision-making, 71–2 and algorithmic execution, 70–1 evolution of, 22–8 generations, 23–7 and indicator zoology, 27–8 see also algorithms trading frequencies, in currency market, 65–73, 72, 73; see also foreign-exchange markets trading signals: construction of, 31–8 and order book imbalance, 36–8 and timescales and weights, 31–3, 33 and trade sign autocorrelations, 34–6 transaction cost, and algorithms, 28–31 transitory price effects: approach to, illustrated, 192–203 daily estimation, 195–9 implementation shortfall calculations, 193–5 intra-day estimation, 199–203 and information shortfall, 185–206, 196, 197, 198, 199, 200, 202, 203 discussed, 186–9 implementation details, 204–5 and observed and efficient prices and pricing errors, 189–92 Treasury futures, 46, 47, 48, 51, 52, 55 V volume clock, 1–17, 7 and time, meaning of, 5–7 volume-synchronised probability of informed trading, 128–31 bars, 128 buckets, 129–30 cumulative distribution function, 130–1 volume classification, 128–9 W Walter, Elisse, 216 Waterloo, Battle of, 1 Y yen appreciation: 2007, 77 2011, 78–9 see also market stress 236 i i i i


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, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, 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, pets.com, 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!, zero-sum game

CUMMINGS had more tricks up his sleeve. Around 2004, he began to develop a strategy to make money trading in dark pools, a rising force in the early 2000s. While institutional traders were running from the lit markets such as Nasdaq into dark pools, in order to get away from the new breed of high-speed traders, like Tradebot, the speed traders devised methods to swim in the dark as well. Known as “latency arbitrage,” the strategy involved gaming the difference between the price of a stock in a dark pool and its price in the lit markets. Tradebot was effectively exploiting the “latency” of the system, a measurement of the time it takes for information to move from place to place in a closed system, such as a market. Behind the difference: dark pools that priced stocks based on an electronic feed called the Securities Information Processor, or SIP.

While the firm was named after a delicate flower—a trillium is a lily with three leaves—it stayed faithful to its hard-charging push-the-envelope legacy. LEVINE wasn’t quite ready to stop trying to save Wall Street from itself, however. An inventor at heart, he was still determined to change the world through technology. His next project concerned the computer system that disseminated trade data around the market, the Securities Information Processor, or SIP (the same SIP that high-speed firms such as Tradebot exploited for their latency arbitrage strategies). In the early 2000s, the SIP feed was notoriously slow—giving quick-draw firms opportunities to arbitrage a stock trading at slightly different prices on different pools. Levine submitted a proposal to build what he called the Big J SIP to a committee of market experts that was considering remaking the feed in 2002. Levine proposed to do the work for $1. The next lowest bid was on the order of tens of millions.


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, computerized markets, drone strike, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, latency arbitrage, pattern recognition, 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

The activity peaked roughly 350 microseconds after an investor’s order triggered the feeding frenzy, or the time it took for HFT to send its orders from the stock exchange on which the investor had touched down to all of the others. “Your eye will never pick up what is really happening,” said Brad. “You don’t see shit. Even if you’re a fucking cyborg you don’t see it. But if there was no value to reacting, why would anyone react at all?” The arrival of the prey awakened the predator, who deployed his strategies—rebate arbitrage, latency arbitrage, slow market arbitrage. Brad didn’t need to dwell on these; he’d already walked each of the investors through his earlier discoveries. It was his new findings that he wanted them to focus on.†† On IEX’s opening day—when it had traded just half a million shares—the flow of orders through its computers had been too rapid for the human eye to make sense of it. Brad had spent the first week or so glued to his terminal, trying to see whatever he could see.


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, 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, p-value, paper trading, performance metric, prediction markets, 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

This has been assisted by a combination of the evolution of both low-level and high-level programming languages, such as C, C++, and Java, along with improvements in compilers that can produce highly-optimized code; both have significantly improved the scalability and speed of what trading systems and trading strategies can be deployed to live trading markets. A lot of market participants also now have access to microwave networks that can transmit data between locations much faster than physical fiber connections can, leading to latency-arbitrage opportunities. Time and time again, participants who have maintained their technological edge and kept up with the technological advancements made by their competition have been the ones to survive. Large algorithmic/HFT trading firms with superior technologies have even cornered the market on some trades and made it impossible for others to compete with them. To summarize the main point of this section, algorithmic trading firms must continuously evolve their use of technology for their trading business to stay competitive.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

These authors find that small stocks respond differently than large stocks to these events. They conclude that measuring these magnitudes is relevant to model the dynamics of the bid-ask spread. Easley et al. [2012] also argue that large quote cancellation rates may be indicative of low liquidity, as participants are publishing quotes that do not intend to get filled. They discuss four categories of predatory algorithms: Quote stuffers: They engage in “latency arbitrage.” Their strategy involves overwhelming an exchange with messages, with the sole intention of slowing down competing algorithms, which are forced to parse messages that only the originators know can be ignored. Quote danglers: This strategy sends quotes that force a squeezed trader to chase a price against her interests. O'Hara [2011] presents evidence of their disruptive activities. Liquidity squeezers: When a distressed large investor is forced to unwind her position, predatory algorithms trade in the same direction, draining as much liquidity as possible.