algorithmic trading


  • For example, for a highly liquid stock, matching a certain percentage of the overall orders of stock (called volume inline algorithms) is usually a good strategy, but for
    a highly illiquid stock, algorithms try to match every order that has a favorable price (called liquidity-seeking algorithms).

  • If the market prices are different enough from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit.

  • The volume a market maker trades is many times more than the average individual scalper and would make use of more sophisticated trading systems and technology.

  • Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time.

  • [14] This increased market liquidity led to institutional traders splitting up orders according to computer algorithms so they could execute orders at a better average price.

  • The long and short transactions should ideally occur simultaneously to minimize the exposure to market risk, or the risk that prices may change on one market before both transactions
    are complete.

  • Although there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, specialized order types, co-location, very short-term investment
    horizons, and high cancellation rates for orders.

  • Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings,

  • Market making[edit] Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price on
    a regular and continuous basis to capture the bid-ask spread.

  • [65] There are four key categories of HFT strategies: market-making based on order flow, market-making based on tick data information, event arbitrage and statistical arbitrage.

  • [12] In the U.S., decimalization changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share in 2001, and may have encouraged algorithmic trading
    as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers’ trading advantage, thus increasing market liquidity.

  • At about the same time, portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer
    model based on the Black–Scholes option pricing model.

  • A July 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while “algorithms and
    HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010.

  • [12] This changed the way firms traded with rules such as the Trade Through Rule, which mandates that market orders must be posted and executed electronically at the best
    available price, thus preventing brokerages from profiting from the price differences when matching buy and sell orders.

  • Both strategies, often simply lumped together as “program trading”, were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987
    stock market crash.

  • [27] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform to benefit from implementing high-frequency strategies.

  • When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state;
    in simple terms, it is the possibility of a risk-free profit at zero cost.

  • In theory, the long-short nature of the strategy should make it work regardless of the stock market direction.

  • For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented.

  • This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants.

  • [29] Futures markets are considered fairly easy to integrate into algorithmic trading,[30] with about 20% of options volume expected to be computer-generated by 2010.

  • With the rise of fully electronic markets came the introduction of program trading, which is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks
    valued at over US$1 million total.

  • Case studies Profitability projections by the TABB Group, a financial services industry research firm, for the US equities HFT industry were US$1.3 billion before expenses
    for 2014,[21] significantly down on the maximum of US$21 billion that the 300 securities firms and hedge funds that then specialized in this type of trading took in profits in 2008,[22] which the authors had then called “relatively small”
    and “surprisingly modest” when compared to the market’s overall trading volume.

  • The spread between these two prices depends mainly on the probability and the timing of the takeover being completed, as well as the prevailing level of interest rates.

  • Conditions for arbitrage[edit] Further information: Rational pricing § Arbitrage mechanics Arbitrage is possible when one of three conditions is met: • The same asset does
    not trade at the same price on all markets (the “law of one price” is temporarily violated).

  • In practical terms, this is generally only possible with securities and financial products which can be traded electronically, and even then, when first leg(s) of the trade
    is executed, the prices in the other legs may have worsened, locking in a guaranteed loss.

  • In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years the firm as a whole was profitable on 1,277 out of 1,278 trading days,[23] losing
    money just one day, demonstrating the benefits of trading millions of times, across a diverse set of instruments every trading day.

  • Increasingly, the algorithms used by large brokerages and asset managers are written to the FIX Protocol’s Algorithmic Trading Definition Language (FIXatdl), which allows
    firms receiving orders to specify exactly how their electronic orders should be expressed.

  • • An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or, the asset does not have negligible costs
    of storage; as such, for example, this condition holds for grain but not for securities).

  • Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.

  • Furthermore, the use of particle swarm optimization can achieve a bid-offer discount without increasing the risk profile of a trading agent.

  • Unlike in the case of classic arbitrage, in case of pairs trading, the law of one price cannot guarantee convergence of prices.

  • Arbitrage[edit] In economics and finance, arbitrage /ˈɑːrbɪtrɑːʒ/ is the practice of taking advantage of a price difference between two or more markets: striking a combination
    of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices.

  • Event arbitrage[edit] A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval,
    judicial decision, etc., to change the price or rate relationship of two or more financial instruments and permit the arbitrageur to earn a profit.

  • [11] History Early developments[edit] Computerization of the order flow in financial markets began in the early 1970s, when the New York Stock Exchange introduced the “designated
    order turnaround” system (DOT).

  • The standard deviation of the most recent prices (e.g., the last 20) is often used as a buy or sell indicator.

  • In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g.

  • [66] Statistical arbitrage[edit] Another set of HFT strategies in classical arbitrage strategy might involve several securities such as covered interest rate parity in the
    foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency.

  • Robert Greifeld, NASDAQ CEO, April 2011[15] A further encouragement for the adoption of algorithmic trading in the financial markets came in 2001 when a team of IBM researchers
    published a paper[16] at the International Joint Conference on Artificial Intelligence where they showed that in experimental laboratory versions of the electronic auctions used in the financial markets, two algorithmic strategies (IBM’s own
    MGD, and Hewlett-Packard’s ZIP) could consistently out-perform human traders.

  • [2][3] A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.

  • [4] It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast
    for human traders to react to.

  • Usually the market price of the target company is less than the price offered by the acquiring company.

  • [46] Pairs trading[edit] Pairs trading or pair trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative
    value of close substitutes.

  • [5][6] Examples of strategies used in algorithmic trading include systematic trading, market making, inter-market spreading, arbitrage, or pure speculation, such as trend

  • While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.

  • Transaction cost reduction[edit] Most strategies referred to as algorithmic trading (as well as algorithmic liquidity-seeking) fall into the cost-reduction category.

  • [55] Dark pools are alternative trading systems that are private in nature—and thus do not interact with public order flow—and seek instead to provide undisplayed liquidity
    to large blocks of securities.

  • The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed.

  • [62] Many HFT firms are market makers and provide liquidity to the market, which has lowered volatility and helped narrow bid–offer spreads making trading and investing cheaper
    for other market participants.

  • At times, the execution price is also compared with the price of the instrument at the time of placing the order.

  • [44][45][46] Strategies Systematic Trading[edit] Use of computer models to define trade goals, risk controls and rules that can execute trade orders in a methodical way.

  • Joel Hasbrouck and Gideon Saar (2013) measure latency based on three components: the time it takes for (1) information to reach the trader, (2) the trader’s algorithms to
    analyze the information, and (3) the generated action to reach the exchange and get implemented.

  • This procedure allows for profit for so long as price moves are less than this spread and normally involves establishing and liquidating a position quickly, usually within
    minutes or less.

  • However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

  • Systematic trading includes both high frequency trading (HFT, sometimes called algorithmic trading) and slower types of investment such as systematic trend following.

  • Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a
    higher price.

  • As long as there is some difference in the market value and riskiness of the two legs, capital would have to be put up in order to carry the long-short arbitrage position.

  • The basic idea is to break down a large order into small orders and place them in the market over time.

  • The success of computerized strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.

  • [32] Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission
    said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010 Flash Crash.

  • [50] Delta-neutral strategies[edit] In finance, delta-neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to
    small changes in the value of the underlying security.

  • [68] The rapidly placed and canceled orders cause market data feeds that ordinary investors rely on to delay price quotes while the stuffing is occurring.

  • In general terms the idea is that both a stock’s high and low prices are temporary, and that a stock’s price tends to have an average price over time.

  • [13] Refinement and growth[edit] The financial landscape was changed again with the emergence of electronic communication networks (ECNs) in the 1990s, which allowed for trading
    of stock and currencies outside of traditional exchanges.

  • [citation needed] Issues and developments Algorithmic trading has been shown to substantially improve market liquidity[76] among other benefits.

  • [a] In the simplest example, any good sold in one market should sell for the same price in another.

  • [18] In their paper, the IBM team wrote that the financial impact of their results showing MGD and ZIP outperforming human traders “…might be measured in billions of dollars
    annually”; the IBM paper generated international media coverage.

  • Like market-making strategies, statistical arbitrage can be applied in all asset classes.


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Photo credit:’]