Electronic trading has advanced dramatically in terms of capabilities, volume, coverage of asset classes, and geographies since networks started routing prices to computer terminals in the 1960s.

Equity markets have led this trend worldwide. The 1997 order-handling rules by the SEC introduced competition to exchanges through electronic communication networks (ECN). ECNs are automated Alternative Trading Systems (ATS) that match buy-and-sell orders at specified prices, primarily for equities and currencies and are registered as broker-dealers. It allows significant brokerages and individual traders in different geographic locations to trade directly without intermediaries, both on exchanges and after hours. Dark pools are another type of ATS that allow investors to place orders and trade without publicly revealing their information, as in the order book maintained by an exchange. Dark pools have grown since a 2007 SEC ruling, are often housed within large banks, and are subject to SEC regulation.

With the rise of electronic trading, algorithms for cost-effective execution have developed rapidly and adoption has spread quickly from the sell side to the buy side and across asset classes. Automated trading emerged around 2000 as a sell-side tool aimed at cost-effective trade execution that spread orders over time to limit the market impact. These tools spread to the buy side and became increasingly sophisticated by taking into account, for example, transaction costs and liquidity, as well as short-term price and volume forecasts.

Direct Market Access (DMA) gives a trader greater control over execution by allowing it to send orders directly to the exchange using the infrastructure and market participant identification of a broker who is a member of an exchange. Sponsored access removes pre-trade risk controls by the brokers and forms the basis for high-frequency trading (HFT).

HFT refers to automated trades in financial instruments that are executed with extremely low latency in the microsecond range and where participants hold positions for very short periods. The goal is to detect and exploit inefficiencies in the market microstructure, the institutional infrastructure of trading venues. HFT has grown substantially over the past ten years and is estimated to make up roughly 55% of trading volume in US equity markets and about 40% in European equity markets. HFT has also grown in futures markets to roughly 80% of foreign-exchange futures volumes and two-thirds of both interest rate and Treasury 10 year futures volumes (FAS 2016).

HFT strategies aim to earn small profits per trade using passive or aggressive strategies. Passive strategies include arbitrage trading to profit from very small price differentials for the same asset, or its derivatives, traded on different venues. Aggressive strategies include order anticipation or momentum ignition. Order anticipation, also known as liquidity detection, involves algorithms that submit small exploratory orders to detect hidden liquidity from large institutional investors and trade ahead of a large order to benefit from subsequent price movements. Momentum ignition implies an algorithm executing and canceling a series of orders to spoof other HFT algorithms into buying (or selling) more aggressively and benefit from the resulting price changes.

Regulators have expressed concern over the potential link between certain aggressive HFT strategies and increased market fragility and volatility, such as that experienced during the May 2010 Flash Crash, the October 2014 Treasury Market volatility, and the sudden crash by over 1,000 points of the Dow Jones Industrial Average on August 24, 2015. At the same time, market liquidity has increased with trading volumes due to the presence of HFT, which has lowered overall transaction costs.

The combination of reduced trading volumes amid lower volatility and rising costs of the technology and access to both data and trading venues has led to financial pressure. Aggregate HFT revenues from US stocks have been estimated to drop beneath $1 billion for the first time since 2008, down from $7.9 billion in 2009.

This trend has led to industry consolidation with various acquisitions by, for example, the largest listed proprietary trading firm Virtu Financial, and shared infrastructure investments, such as the new Go West ultra-low latency route between Chicago and Tokyo. Simultaneously, startups such as Alpha Trading Lab make HFT trading infrastructure and data available to democratize HFT by crowdsourcing algorithms in return for a share of the profits.