Algorithmic trading

In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention. Algorithmic Trading is widely used by pension funds, mutual funds, and other buy side (investor driven) institutional traders, to divide large trades into several smaller trades in order to manage market impact, and risk.[1][2] Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically. In this "high frequency trading" (HFT) computers make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information.

Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically ("on auto-pilot").

A third of all EU and US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group.[3] As of 2009, high frequency trading firms account for 73% of all US equity trading volume.[4]

In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and equity markets generally have a higher proportion of algo trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algo trading (about 25% of orders in 2006).[5] Futures and options markets are considered to be fairly easily integrated into algorithmic trading,[6] with about 20% of options volume expected to be computer generated by 2010.[7] Bond markets are moving toward more access to algorithmic traders.[8]

One of the main issues regarding high frequency trading is the difficulty in determining just how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in rapid fire algorithmic trading took in roughly $21 billion in profits in 2008[9].

History

Computerization of the order flow in financial markets began in the early 1970s with some landmarks being the introduction of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT) which routed orders electronically to the proper trading post to be executed manually, and the "opening automated reporting system" (OARS) which aided the specialist in determining the market clearing opening price (SOR; Smart Order Routing).

Program trading is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over $1 million total. In practice this means that all program trades are entered with the aid of a computer. In the 1980s program trading became widely used in trading between the S&P500 equity and futures markets.

In stock index arbitrage a trader buys (or sells) a stock index futures contract such as the S&P 500 futures and sells (or buys) a portfolio of up to 500 stocks (can be a much smaller representative subset) at the NYSE matched against the futures trade. The program trade at the NYSE would be pre-programmed into a computer to enter the order automatically into the NYSE’s electronic order routing system at a time when the futures price and the stock index were far enough apart to make a profit.

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.

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. Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.[10]

Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., decimalization, which changed the minimum tick size from 1/16th of a dollar ($0.0625) to $0.01 per share, 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.

This increased market liquidity led to institutional traders splitting up orders according to computer algorithms in order to execute their orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time weighted (i.e unweighted) average price TWAP or more usually by the volume weighted average price VWAP.

As more electronic markets opened, other algorithmic trading strategies were introduced. These strategies are more easily implemented by computers because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously. For example Stealth (developed by Deutsche Bank), Sniper and Guerilla (developed by Credit Suisse[11]), arbitrage, statistical arbitrage, trend following, and mean reversion.

This type of trading is what is driving the new demand for Low Latency Proximity Hosting and Global Exchange Connectivity. It is imperative to understand what is latency when putting together a strategy for electronic trading. Latency refers to the 'delay' between the transmission of information from a source and the reception of the information at a destination. Latency has as a lower bound the speed of light; this corresponds to a few microseconds per kilometer of optical fibre. Any signal regenerating or routing equipment will introduce greater latency than this speed-of-light baseline.

Strategies

Many different algorithms have been developed to implement different trading strategies. Much early algo trading was developed for the buy side in order to reduce transactions costs. Recently, high frequency trading, which is generally a type of market making by sell side traders, has become more prominent and controversial.[12] These algorithms or techniques are commonly given names such as "Stealth", "Iceberg", "Dagger", "Guerrilla", "Sniper" and "Sniffer".[13]

[edit] Transaction cost reduction
Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Stealth".

[edit] Market making and high frequency trading
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 in order to benefit from the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange.[14]

High frequency traders use computers that execute trades within milliseconds, or "with extremely low latency" in the jargon of the trade. In the U.S., high-frequency trading firms represent 2.0% of the approximately 20,000 firms operating today, but account for 73.0% of all equity trading volume.[15] As of the first quarter in 2009, total assets under management for hedge funds with high frequency trading strategies were $141 billion, down about 21% from their high.[16] The high frequency strategy was first made successful by Renaissance Technologies.[17] High frequency funds started to become especially popular in 2007 and 2008.[16] Many high frequency firms say they are market makers and that the liquidity they add to the market has lowered volatility and helped narrow spreads, but unlike traditional market makers, such as specialists on the New York Stock Exchange, they have few or no regulatory requirements.[18]

These funds are highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors.[4] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform in order to benefit from implementing high frequency strategies.[4] Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies - they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.[4]

[edit] Arbitrage
A classical arbitrage strategy might involve three or four 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. If the market prices are sufficiently different from those implied in the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Algorithmic trading allows similar arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits of low latency arbitrage strategies currently exceed US$21 billion.[4]

[edit] More complicated strategies
A "benchmarking" algorithm is used by traders attempting to mimic an index's return.

Any type of algo trading which depends on the programming skills of other algo traders is called "gaming". Dark pools are alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or "iceberged."[19] Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

Any sort of pattern recognition or predictive model can be used to initiate algo trading. Neural networks and genetic programming have been used to create these models.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”[20]

The arms race has allegedly included stealing computer code. UBS has sued three of its former traders and Jefferies & Company for stealing algorithmic trading programs.[21]