Analysis of the Four Major High-Frequency Trading Strategies

High-frequency trading is a computer-based trading strategy that aims to use technological advantages to conduct multiple buying and selling transactions within a short period. It typically uses microseconds (1 second equals 1 million microseconds) as the unit of time for strategy development, seeking a time advantage and thereby obtaining greater profits in the market. High-frequency trading is usually carried out by powerful computer programs, with trading times often under ten milliseconds. Compared with investors using relatively less advanced technology, high-frequency trading firms can place orders more quickly and obtain better trading opportunities in the market. Therefore, high-frequency trading institutions typically deploy their “server farms” very close to exchanges to reduce the time required for trading instructions to travel through fiber-optic cables. Although high-frequency trading is characterized by its “high frequency,” its trading mechanisms vary widely, resulting in different impacts on the market.

Based on existing strategies, high-frequency trading can generally be divided into the following four categories:

The first category is the market-making trading strategy, which earns profits from the spread between buying and selling prices. High-frequency traders can respond quickly to market price changes and earn stable profits through precise pricing.

The second category is the order-splitting strategy, which divides a large order into multiple small orders to avoid having an excessive impact on the market. This type of strategy can help high-frequency traders diversify risk and avoid causing instantaneous market fluctuations

The third category is the quantitative trading strategy, which relies on algorithmic models to make buying and selling decisions. High-frequency traders use large amounts of historical data for modeling, train machine learning models, and conduct rapid buying and selling operations based on the signals produced by the models.

The fourth category is the event-driven trading strategy, which uses information such as news events to respond rapidly to the market. High-frequency traders monitor news, social media, announcements, and other information sources, and conduct rapid market transactions based on the information. These strategies are based on various algorithms and technologies, aiming to identify opportunities in the market instantly and conduct buying and selling transactions before market prices change in order to obtain the greatest possible returns. High-frequency trading has become an important force in financial markets, having a significant impact on market stability and liquidity. However, because high-frequency trading is extremely fast, it is difficult to regulate through traditional methods. Therefore, regulatory authorities need to adopt new methods and technologies to regulate high-frequency trading and ensure market fairness and stability.

1. Market-Making Trading Strategy

International financial markets generally use a market-maker system. Unlike auction trading, market makers are large banks that act as intermediaries, earning profits from the spread between buying and selling securities while providing liquidity to the market. In recent years, the “passive market-making strategy” in high-frequency trading strategies has gradually become popular, originating from a special trading mechanism in the United States. U.S. securities exchanges provide certain trading-fee rebates to brokers that provide liquidity in order to attract more orders. These traders provide liquidity to the market by placing two-sided orders and waiting for execution, allowing other traders with trading needs to trade at lower costs and improving the competitiveness of exchanges. Therefore, electronic exchanges provide rebates to such liquidity providers to encourage them to participate in trading through order placement. In this situation, many small institutions and individual investors can also provide liquidity to the market and serve as de facto market makers. However, market makers also face various risks during trading. First, asset price fluctuations can create inventory risk. Market makers typically hold substantial amounts of capital and trade in large volumes, so inventory risk has a more significant impact on them. Second, the Poisson distribution of buy and sell orders creates trading risk. Traditional market makers primarily use two methods to mitigate these risks. One method is to improve the pricing mechanism, incorporating risk into asset prices in order to transfer the risk. Academia has developed a series of models to address this pricing issue, such as inventory models and information models. The other method is to use hedging strategies, among which the delta-neutral strategy is the most common. Delta is the ratio of the change in the price of a derivative security to the change in the price of the underlying asset. A state in which delta equals zero is called delta-neutral. If high-frequency hedging is used, the delta-neutral strategy becomes a high-frequency trading model. It must sell the asset when the price of the underlying asset begins to fall and buy it when the price begins to rise, so it is essentially a trend-trading method. Based on foreign experience, market-maker trading is the mainstream high-frequency trading strategy. In China, markets such as government bonds and interest-rate swaps have introduced market-maker systems. If this system is implemented in more markets, high-frequency trading will also be used more widely.

2. Order-Splitting Strategy

Institutional investors typically need to conduct large transactions. However, substantial buying and selling often triggers rapid market price fluctuations, thereby increasing transaction costs. The order-splitting strategy emerged to address this problem. This strategy uses various algorithms to divide large orders into multiple small orders, thereby reducing the impact of large orders on the market and lowering transaction costs. The algorithms used in order-splitting strategies can be divided into three generations. First-generation algorithms mainly consider how to reduce market impact, using methods such as time-weighted average price (TWAP), volume-weighted average price (VWAP), and percentage of volume (POV). Among them, the TWAP algorithm divides a large order into small orders at a certain trading frequency within a specified period; the VWAP algorithm divides orders according to the distribution of historical trading volume; and the POV algorithm mixes small orders into the order flow at a fixed ratio to reduce market impact. However, this regular order-splitting method can easily be detected by other traders, who may follow the activity, thereby increasing transaction costs. To address this problem, second-generation order-splitting strategies introduced certain anti-detection techniques. For example, the Iceberg strategy uses random splitting, while the Minimal Impact strategy uses alternative trading systems as the primary trading channel and completes only a small portion of transactions in public trading systems to prevent the disclosure of trading intentions. Third-generation order-splitting strategies hold that if order splitting and avoiding detection are emphasized one-sidedly, there is a risk of failing to complete the trading plan on time, which may instead increase transaction costs. Therefore, third-generation order-splitting strategies emphasize using periods with high trading volume to complete position plans. At the same time, to adapt to rapidly changing market conditions, Kissell, Freyre-Sanders, and Carrie proposed the Adaptive Shortfall strategy, which determines how to execute a position plan based on current price movements. In addition, some brokers have proposed the MC (Market Close) strategy, which completes trading instructions during the latter half of the trading day. Although research and application of these strategies in China are not yet sufficiently mature, as the market-maker system is introduced and institutional investors grow stronger, demand for order splitting in large transactions is expected to increase. In summary, the continuous development and improvement of order-splitting strategies provide institutional investors with more flexible and efficient ways to trade, helping them achieve better investment returns in highly competitive markets.

3. Quantitative Trading Strategy

Quantitative trading strategies in financial markets are playing an increasingly important role in investment decisions. Unlike traditional fundamental analysis and technical analysis, quantitative trading strategies emphasize using quantitative analysis methods from mathematics and statistics to make investment decisions, with the goal of achieving more stable returns in financial markets.

Quantitative trading strategies for individual assets include event arbitrage, order-book trading, and technical analysis. Event arbitrage is a trading strategy based on market reactions before and after a specific event occurs. By predicting the impact of an event on the market in advance, investors can trade based on the short-term news effect. Order-book trading is a strategy that conducts trades based on information such as order flow and trading volume. This strategy assumes that price series and trading volume contain information that has not yet been disclosed, and that this information can be analyzed and used for trading. Technical analysis, meanwhile, uses historical price trends and charts to predict price fluctuations. Among the various technical analysis methods, trend-following trading strategies are the most effective, with moving averages and channel breakouts being the primary methods for implementing trend following. Research shows that emerging stock markets, futures, and foreign exchange markets are the markets most suitable for technical analysis.

Portfolio trading strategies include arbitrage trading and pairs trading. Arbitrage trading earns profits by capturing the price difference between two financial assets with exactly the same underlying. In the U.S. market, the same underlying may simultaneously have multiple financial products, such as options and futures, while each asset may also be listed and traded simultaneously on several exchanges. This market ecosystem provides considerable room for arbitrage trading to operate. Pairs trading assumes that the prices of related underlyings are correlated. Therefore, when the price of one asset rises while the other falls, traders can go long the declining asset and short the rising asset. Statistical arbitrage, which developed from pairs trading, differs from pairs trading in that it does not determine asset correlations based on fundamentals or market characteristics. Instead, it often focuses on statistical correlations among portfolios containing hundreds of assets.

Overall, quantitative trading strategies refer to strategies that use quantitative analysis methods to make investment decisions, and they come in many forms. These strategies all require quantitative analysis methods such as mathematical models and statistical analysis to achieve investment returns by capturing information such as market price fluctuations and event impacts.

4. Event-Driven Trading Strategy

The event-driven trading strategy in high-frequency trading is a trading strategy that uses information asymmetry. It primarily trades on the instantaneous impact that specific events in the market have on asset prices. These events may include corporate restructuring, acquisitions, equity issuance, dividends, the release of macroeconomic indicators, and political events. Event-driven trading strategies generally require rapidly obtaining and analyzing event information and quickly executing trades in the market to obtain profits.

The core of event-driven trading strategies is anticipating and analyzing events. This requires researching and analyzing the impact of various events in the market, the probability of their occurrence, and the extent of their impact. Before an event occurs, traders need to obtain information about it through various channels, such as news, company announcements, and social media, and then conduct analysis and build models to determine the corresponding trading strategy.

In event-driven trading, traders typically need to trade multiple related assets to capture the impact of an event on the broader market. In addition, traders need to execute trades quickly and accurately, typically by using high-frequency trading technology.

Event-driven trading strategies require continuous monitoring and analysis of market conditions and event developments, with timely adjustments to trading strategies and risk controls to ensure successful and profitable trading. This type of trading strategy requires professional technical and analytical skills, as well as a deep understanding of and insight into the market.

5. Other

In addition to the four main trading strategies, high-frequency trading also includes strategies that exploit information advantages, manipulate price movements, and even undermine trading fairness. These mainly include structural strategies and directional strategies.

Structural strategies exploit loopholes arising from unfairness in trading systems to generate profits. For example, some traders use co-location services to obtain price and order data in advance, enabling them to place orders ahead of others and gain trading advantages and profits.

Directional strategies mainly include front-running and trend-ignition strategies. The front-running strategy, also known as “predatory algorithmic trading,” uses technical means to identify potential large buy or sell orders and issue orders in advance, closing positions for a profit when prices rise or fall.

Trend-ignition strategies use pre-established positions to lure other traders into trading, triggering rapid price movements and generating profits. Specific methods include placing a large number of orders to induce other traders to follow market trends and executing large trades to trigger stop-loss orders present in the market. While carrying out market manipulation, traders may also disseminate false information and engage in other activities that further distort market prices.

It should be noted that although these strategies can generate substantial profits in a short period, they may also undermine the fairness and transparency of trading markets and even cause problems such as market volatility and crashes. Therefore, regulatory authorities need to closely monitor the use of these high-frequency trading strategies and guard against the risks they may pose.