Developing High-Frequency Trading Systems

By Sebastien Donadio , Sourav Ghosh , Romain Rossier
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    Chapter 1: Fundamentals of a High-Frequency Trading System
About this book

The world of trading markets is complex, but it can be made easier with technology. Sure, you know how to code, but where do you start? What programming language do you use? How do you solve the problem of latency? This book answers all these questions. It will help you navigate the world of algorithmic trading and show you how to build a high-frequency trading (HFT) system from complex technological components, supported by accurate data.

Starting off with an introduction to HFT, exchanges, and the critical components of a trading system, this book quickly moves on to the nitty-gritty of optimizing hardware and your operating system for low-latency trading, such as bypassing the kernel, memory allocation, and the danger of context switching. Monitoring your system’s performance is vital, so you’ll also focus on logging and statistics. As you move beyond the traditional HFT programming languages, such as C++ and Java, you’ll learn how to use Python to achieve high levels of performance. And what book on trading is complete without diving into cryptocurrency? This guide delivers on that front as well, teaching how to perform high-frequency crypto trading with confidence.

By the end of this trading book, you’ll be ready to take on the markets with HFT systems.

Publication date:
June 2022


Chapter 1: Fundamentals of a High-Frequency Trading System

Welcome to Developing High-Frequency Trading Systems!

High-Frequency Trading (HFT) is a form of automated trading. For the last twenty years, HFT has gained recognition in the media and in society. A book called Flash Boys: A Wall Street Revolt, written by Michael Lewis in 2014, topped the sales on the New York Times Best Seller list for three weeks. It relates to an investigation into the HFT industry and its impact on the trading world. Scholars, the financial world, and the non-financial world are fascinated by this form of trading. Meanwhile, this new era of trading has created a lot of fear while giving more and more control to machines.

The goal of this book is to review what HFT is and how to build such a system from a technical perspective. HFT is a multi-disciplinary matter involving thorough knowledge of computer architecture, operating systems, networking, and programming. By the end of this book, you will understand how to build a trading system from scratch by using the most advanced technical choices for optimizing speed and scalability. We chose to divide this book into three main parts.

In the first part, we'll go through how HFT tactics function and what kind of trading we may expect from HFT. Then we will go over the functions of an HFT system. We will conclude this part with a description of how a trading exchange works.

In the second part of this book, we will explain the theory of operating systems and hardware and the required knowledge to optimize a trading system, taking into account the hardware and operating system features.

The final part will explain in detail how to use C++, Java, Python, and FPGA to create an HFT system. We will also extend this knowledge to crypto trading, and we will review how to build a trading system in the cloud.

In this chapter, we will talk about how we got into HFT. We will review what kind of trading strategies work for HFT. We will explain in detail what makes HFT so different from regular trading.

Our objective in this chapter is to cover the following topics:

  • History of HFT
  • What HFT is
  • Who the participants are
  • What trading strategies work in HFT

History of HFT

Let's discuss the history of exchanges and financial markets prior to 1930.

When we talk about HFT, it is difficult to give a precise date for when it started. We need to come back to the primitive times when trade arose from human contact. Before the invention of modern-day cash, ancient people relied heavily on trading to trade products and services with one another in a gift economy. Long-distance trade extends back to almost 150,000 years ago, according to Peter Watson. Year after year, with more people, more goods, and more money, trading became one of the major activities of humankind. It is obvious that making money implies more business. One of the parameters was speed. If you make more transactions, you will make more money. Many stories describe the ambition of traders to get technologies such as better transportation to make deals more quickly or to get news more quickly to take advantage of other folks who do not have access to these new technical means.

We did not have to wait for too long before seeing cases of unfair trade involving those who have technical advantages over others. In 1790, a Georgia representative spoke to the US House of Representatives to expose high-speed traders. Indeed, Congress was debating the Secretary of the Treasury Alexander Hamilton's proposal that the US government absorb the previous debts accrued by the states during the Revolution (Funding Act of 1790). Traders who had learned the decision immediately bought or rented rapid boats. Their goal was to front-run messengers and buy the old debts since the passage of the Act would increase the market value. During the twentieth century, the idea of speed trading or HFT appeared.

The post-1930s era

Trading is the exchange of items for other items. It can be financial products, services, cash, digital assets, and more. One of the goals of trading is to make a profit from these transactions. The number of transactions will be correlated with the quantity of money generated by the exchange of assets. When we manage to increase the ratio between the number of transactions and the time, we can increase the profitability over time. Therefore, being capable of increasing the number of transactions is critical. Trading actors understood very quickly that they needed to shorten the trading time and started gathering in some specific places. They used to place their orders in these locations, which we call today the trading exchange (or trading floor). Major events participated in the expansion of fast-speed automated trading:

  • 1969: Instinet was one of the first automated system infrastructures. It speeded up the adhesion of high-speed transactions.
  • 1971: The National Association of Securities Dealers Automated Quotations (NASDAQ) was created in 1971 with electronic transactions.

It was the world's first electronic stock market. Initially, it only used to send quotations.

  • 1996: Island ECN was the pioneering electronic communication network for US equities trading, while Archipelago facilitated electronic trading on the US trading exchange by creating Archipelago Exchange (ArcaEx).
  • 2000s: 10% of transactions are HFT transactions.

The financial sector gathered more and more technologists in the early 2000s. By getting this technological intake, the sector started evolving sharply. Automation, throughput, performance, and latency became words that were well known by trading firms. The HFT transactions reached more than 10% of the market. By 2009, 2% of trading firms accounted for 75% of the equity volume. Nowadays, only a few firms remain in HFT, such as Virtu, Jump, Citadel, IMC, and Tower.

The modern era

The post-1930s era focused on transparency and regulation in the equities markets (and the pit in commodities markets). The modern era gives prominence to electronic trading and improves transparency. In 2000, the US Securities and Exchange Commission (SEC) proposed the Central Limit Order Book (CLOB). The CLOB is a transparent system matching orders between participants. Many more exchanges (such as Island and Arca) came to the trading scene. The number of trading firms, hedge funds, and electronic players kept increasing. They created their own technology stack to trade more quickly and stay competitive. After 10 years, only a few trading firms managed to remain competitive, becoming the 2% of the trading firms accountable for 75% of all equity volume.

The savoir faire for competing in HFT requires heavy investment: money, people, and time. It is a marriage of low-level system expertise and quants, as well as smart money (investors are more and more technology savvy). Engineers capable of creating performant code for designing ultra-low latency systems are very expensive. Only a few engineers had these skills. The performance for such a system required specialized hardware. Routers, servers, and network devices are also expensive. Therefore, the experience and the barrier of entry will prevent a lot of new incomers and will limit the competition. On top of the five firms we talked about previously, there are boutique shops that trade HFT strategies using an edge they found either in the market structure or some technical fact that other firms are not exploiting. The giant HFT firms are the companies responsible for moving most of the equity volume. Nowadays, HFT is estimated to account for at least 50% of the US equity (shares) trading volume. The market share of HFT has declined, as has profitability, since the peak year (2009).

After 2015, the growth of digital currencies cleared the way for new opportunities for high-frequency traders. Today, we can see an extensive growth of HFT strategies working with well-known crypto exchanges such as Coinbase, Binance, and hundreds of other crypto exchanges.

This modern era has anchored technology and automated trading for good. Trading models are data driven and model driven. The market data business definitely became a major part of trading. Exchanges and trading firms started making money by generating or collecting market data, the raw material of any algorithm trader.


Why have HFT?

HFT aims at getting many transactions per second. In this way, companies can react more quickly to a changing market. They can take advantage of more opportunities than they would have without this speed. Additionally, large institutions benefit from HFT by gaining a tiny but considerable advantage in exchange for delivering massive volumes of liquidity to markets. They place millions of orders that their systems are capable of placing. They help the market and, as a result, are able to boost earnings in their profitable trades and receive better spreads. Since the return is very low, they must complete many trades to benefit. On top of this revenue, they will gain rebates or discounted transaction fees, which are given by trading venues to make their markets more attractive to HFT firms.


What makes HFT so different from regular trading?

HFT trading should have the shortest feasible data latency (time delays) and the highest level of automation possible. HFT relates to algorithmic trading and automated trading. As a result, participants choose to trade in markets that have a high level of automation and integration in their trading platforms. Firms utilize computers programmed with precise algorithms to find trading opportunities and execute orders in algorithmic trading. To increase the speed of transactions, high-frequency traders use automated trading and fast connections (and cancellations or modifications). This is possible because of the technology that trading firms have in place but also because of the exchange technologies. The following exchanges have invested hundreds of millions of dollars in HFT technologies:

  • NASDAQ, New York City, is the first electronic stock exchange in the world. All of its equities are traded over a computerized network. It revolutionized the financial markets in 1971 by removing the requirement for a physical trading floor and in-person trading. It is the world's second-biggest stock exchange by market capitalization. Half of NASDAQ's composite offering was made up of technology firms. With less than 20% of the overall composite, the consumer sector came in second, followed by healthcare.
  • New York Stock Exchange (NYSE), New York City, is the world's largest exchange for the equity market. In 2013, Intercontinental Exchange, Inc. (ICE) bought NYSE.
  • London Stock Exchange (LSE), London, UK, is the largest stock exchange in Europe and the principal stock exchange in the United Kingdom mainly with regard to trading in company stocks and bonds. It was created about 300 years ago.
  • The Tokyo Stock Exchange (TSE), is Japan's largest stock exchange, with its headquarters in Tokyo. It was founded in 1878. The exchange has more than 3,500 listed businesses. The TSE, which is operated by the Japan Exchange Group, is home to the world's largest and most well-known Japanese corporations, including Toyota, Honda, and Mitsubishi.
  • The Chicago Mercantile Market (CME), sometimes known as the Chicago Merc, is a regulated futures and options exchange in Chicago, Illinois. Agriculture, energy, stock indices, foreign exchange, interest rates, metals, real estate, and weather are among the industries in which the CME trades futures and, in most cases, options.
  • Direct Edge, Jersey City. Its market share rapidly rose to tenth in the US stock market, and it typically transacted more than two billion shares daily. Better Alternative Trading System (BATS) Global Markets was a US-based exchange that traded a variety of assets, including stocks, options, and foreign exchange. CBOE Holdings purchased it in 2017 after it was created in 2005. BATS Global Market was one of the largest US exchanges prior to being bought, and it was well known for its services to broker-dealers, retail, and institutional investors.
  • The CBOE Options Exchange, which was founded in 1973, is the world's largest options exchange, with contracts centered on individual stocks, indexes, and interest rates.

All the preceding exchanges are controlled on several levels:

  • Trading limitations
  • Trading system transparency (information shared among market participants on the specificities of the architecture, as well as the way of handling orders)
  • The type of accepted financial instruments
  • Constraints by security issuers

For most regulated exchanges, the order size is an issue. Large trades have an important effect on the market (they can create market impact). Traders use Alternate Trading Systems (ATS), which have much less regulation in comparison to traditional exchanges (they don't have to be transparent). Dark pools are the most common sorts of ATS. The USA presently has around 30 dark pools, which represent a quarter of the US consolidated trading volume.

Dark pools are beneficial to HFTs because they can handle the speed and the level of automation demands while having reduced fees. This is not the case for any other type of trading, which makes HFT different from regular trading. In the following section, let's learn more about dark pools.

Effect of dark pools

For financial security, buy and sell orders are not displayed in dark pools (price and volume). Dark pools, in other words, are both opaque and anonymous since the order book is not advertised. Because it is not possible to see the size of the orders in this type of trading exchange, investors who place huge orders do not impact markets. Since the other participants do not see the size of the orders, the dark pools execute these large orders at a fixed price. It reduces the negative slippage given by trading exchanges.

Dark pools are obliged to notify deals once they have occurred, notwithstanding the lack of pre-trade transparency.

HFTs and dark pools have a complicated interaction. Dark pools rose in popularity partially as a result of investors seeking protection from HFTs' fraudulent activities on public exchanges, and HFTs finding it impossible to know the large orders in dark pools through pinging. Dark pools introduced a lack of transparency in the markets that allowed ill-equipped players (that is, on the sell side) to keep up with business practices that didn't match the state of the art at the time. And, of course, Haim Bodek wrote two books (The Problem of HFT and The Market Structure Crisis) about finding unordinary order types in dark pools.

On the other hand, a few dark pools encourage HFT traders to trade on their exchange. HFT strategies increase liquidity and the likelihood of having orders filled. Dark pools help HFTs to meet their speed and automation demands while still having reduced expenses. HFTs are responsible for the decrease in order sizes in dark pools. The dark pools have been hit by pinging trading strategies locating hidden large orders.

As a result, if these HFT tactics are present, the benefits of dark pools may be harmed. For example, in 2014, the Attorney General of New York filed a lawsuit against Barclays for its dark pool operations, alleging that it misrepresented the volume of Barclays's activity in dark pools. In 2016, Barclays paid a $35 million fine to the SEC and $70 million to the State of New York.

Dark pools can apply certain constraints to prevent HFTs from engaging in predatory behavior. The goal is to reduce pinging trading strategies. In 2017, Petrescu and Wedow imposed a minimum order size to minimize this type of strategy.

We could spend more time discussing the pros and cons of the impact of HFTs on dark pools, but we end up saying that the advantages of having more liquidity and faster execution are beneficial enough to have some dark pools being in favor of HFTs. It is fair for investors as long as they have a thorough understanding of how trading venues work so they can make educated judgments.

We have talked about the location of the major trading exchanges. Now we will introduce the HFT participants in the next section.


Who trades HFT?

The answer could be summarized in one word: everyone. From the buy side to the sell side, ECNs, and even the inter-dealer and inter-broker-dealer markets, they all use HFT. HFT is dominated by proprietary trading businesses and covers a wide range of products, including stocks, derivatives, index funds, Exchange-Traded Funds (ETFs), currencies, and fixed-income instruments. Proprietary trading businesses accounted for half of the current HFT players, multi-service broker-dealer proprietary trading desks accounted for less than half, and hedge funds accounted for the rest. Proprietary trading businesses such as KCG Holdings (created by the merger of Getco and Knight Capital) and the trading desks of major banks are among the major players in the field. There are some new types of venues (such as Dealerweb's OTR Exchange and IEX) that are looking to provide venues where dealers on the sell side feel safe to execute trades and HFTs are providing liquidity.

It is worth saying that HFTs have become major players in the market. They are also capturing retail flow. Citadel is controlling a large part of the retail flow.


What do I need to start an HFT?

Participants in HFTs must have the following:

  • Fast computers: HFT focuses on single-core throughput in most cases, and parallelism is not used by the strategies necessarily.
  • Exchange proximity: While some countries restrict the use of shared places to have trading systems and exchange, in the US, we use co-location. This is a place where all the HFTs participants have their production servers. They will pay to have their computers co-located with an exchange's computer servers in the same data centers in order to decrease latency and shorten the time it takes to complete a deal – even by microseconds. The cables linking trading systems from all market participants with the server are the same length to guarantee that nobody has an advantage over another market participant. The SEC has issued a wide request for feedback on co-location fees, as well as other concerns impacting the equity market structure. To ensure fairness among market participants, it is important that co-location fees are reasonably priced. The SEC invites the co-locations to report their fees.
  • Low latency: In HFT, latency is the time it takes for data to reach a trader's computer, for the trader to make an order in response to the data, and for the order to be accepted by an exchange. The order may enter the market alongside many other orders issued by other traders at the most profitable time. There is a danger of competing against a large number of other people in this circumstance. The order may not be as profitable as it may have been in this scenario. High-frequency traders are able to make orders at unfathomably quick speeds because of technology advertised as low-latency or ultra-low-latency. It is important to use gear designed to reduce the latency of shuffling data from one place to another.
  • Computer algorithms, which are at the heart of AT and HFT, and real-time data feeds, which could damage earnings.

In the previous sections, we learned where high-frequency traders make business. We also talked about the technological prerequisites to trade faster. Let's now focus on what HFT is in depth.

What are HFT strategies?

HFT strategies are a subset of algorithmic trading strategies. They are executed in the order of the microseconds (and sometimes nanoseconds). The strategies must be aware of this time limitation to be efficient HFT strategies. They deploy cutting-edge technology advancements to obtain information faster than the competition. The main game of this type of strategy is the tick-to-trade, which is the response time to send an order responding to incoming market data. As we will explain in the next chapter, it is important to host trading strategies on cutting-edge machines, and they must also run in a co-location.

We will be defining the domain of applications and some vocab to have when talking about HFT strategies.

Asset classes

HFT strategies can be applied to any asset classes, such as stocks, futures, bonds, options, and FX. We also have cryptocurrencies being traded using HFT strategies, even if the definition of speed is different (because of the settlement time).


The desire of players to interact with regard to a certain asset is known as liquidity.

We define depth as the number of price levels for a given asset. We will say that a book is deep when there are many levels (layers) for a given asset. We will define a book as big or broad if the volume per layer is high. If a book is deep or large, we will define a liquidity of a given asset liquid. The consequence of this statement is that it will be easier for a trader to buy or sell this asset whenever they want to. As a result, trading exchanges with a lot of liquidities are wanted by traders. Crypto trading exchanges have difficulty finding liquidities at the moment.

Tick-by-tick data and data distribution

HFT generates orders every microsecond. Since there are a lot of participants, it is likely to have huge amounts of data. Storage of this data will be key when we study HFT data to create models for trading strategies.

Thousands of ticks (security price changes from one order to another) are generated per trading day on liquid marketplaces, which make up high-frequency data. This material is randomly spaced in time by its very nature. HFT data exhibits fat tail distributions. That means that the trading strategies need to take into account that we can have big losses.

They distribution of the market data can be grouped into two categories:

  • Volatility clustering: Large changes follow large changes whether in terms of signs or numbers, while minor changes follow smaller changes.
  • Long-range dependency (long memory) refers to the pace at which statistical dependence between two sites decays as the time interval or spatial distance between them increases.

Liquidity rebates

To support the provision of stock liquidity, most exchanges have used a maker-taker model. In this arrangement, investors and traders who place limit orders often earn a modest rebate from the exchange when their orders are executed since they are considered to have contributed to stock liquidity, or makers.

Those who place market orders, on the other hand, are considered takers of liquidity and are charged a small fee by the exchange. While the rebates are normally fractions of a penny per share, over the millions of shares exchanged daily by high-frequency traders, they may add up to large amounts. Many HFT businesses use trading techniques that are geared to take advantage of as many liquidity rebates as feasible.

Matching engine

The software program that forms the heart of an exchange's trading system and matches buy and sell orders on a continuous basis, a service traditionally done by trading floor professionals, called the matching engine is critical for guaranteeing the efficient operation of an exchange since it matches buyers and sellers for all stocks. The matching engine is housed on the exchange's computers, and it is the main reason why HFT businesses strive to be as near to the exchange servers as possible. We will learn about it in Chapter 3, Understanding the Trading Exchange Dynamics.

Market making

Before going into details on what market making is, we need to explain the difference between market takers and market makers.

Market taker/maker

Figure 1.1 represents the limit order book on an exchange. When a trading strategy places an order close to the top of the book (the layer representing the best price for bid and for ask), we say that this order is an aggressive order. It means that this order is likely to be matched with another order. If the order is executed, it means that liquidity has been removed from the market; it is a market taker. We will say that a trader crosses the spread when they place a buy order at the price of the ask on the top of the book. If the order is less aggressive (or passive), this order will not remove liquidities from the market; it is a market maker.

Figure 1.1 – Order book – passive/aggressive order

Figure 1.1 – Order book – passive/aggressive order

Let's look at the market-making strategies.

Market-making strategies

A trading corporation can provide market-making as a service on an exchange. Over time, a market maker assists in the matching of buyers and sellers. Rather than purchasing or selling securities based on their underlying assets, market makers maintain a continual offer to buy and sell securities and profit from the spread, which is the difference between the two offers.

To reduce the risk of keeping stocks for extended periods of time, every purchase should be matched with a sale and every sell should be matched with a buy. If a stock is trading at $100, a market maker can keep a buy offer at $99.50 and a sell offer at $100.50. If they are successful in finding both a buyer and a seller, it allows those who want to sell right now to do so even if no one else wants to purchase, and vice versa.

Market makers, in other words, supply liquidity—they make trading simpler. For the most traded stocks, this technique is not important; however, for smaller firms (less traded than the big ones), it can be critical to increase the trading volume to facilitate trading. Market making is one approach that many HFT businesses use. They out-compete everyone else by changing their quotations quickly and reducing the spread even further: they're willing to make less money each time since their market-making business can readily grow to massive quantities. However, an HFT firm's technology can be used for other purposes, such as arbitrage (making money on minor discrepancies between linked securities) or execution (breaking up huge institutions' trades to minimize market effect). I won't go into much more detail because the point is that HFT is capable of more than simply market-making. The only thing that matters is speed.

Market making can be done by the analysis of the order flow:

  • A large volume of buy and sell can drive the market price of buying and selling on the basis of momentum.
  • The flow of liquids (how big are the buy and sell orders: small, medium, or big).
  • Exhaustion of momentum (when the order flow is drying off it may signal a price reversal).

Market-making is the most widely used trading strategy for high-frequency traders. We will talk about the other HFT strategies in the next sections.


Scalping is a trading method that focuses on benefitting from tiny price movements and reselling for a quick profit. Scalping is a phrase used in day trading to describe a technique that focuses on generating large volumes from tiny profits. Scalping necessitates a tight exit plan since a single major loss might wipe out all of the modest wins the trader has worked so hard to achieve. For this technique to work, you'll need the necessary tools, such as a live feed, a direct-access broker, and the endurance to conduct a lot of trades.

The concept behind scalping is that most stocks will finish the first stage of a trend. But it's unclear where things will go from there. Some stocks stop rising after that early stage, while others continue to rise. The goal is to benefit from as many minor transactions as possible. The let your gains run mentality, on the other hand, aims to maximize good trading results by expanding the size of winning deals. By increasing the number of winners while compromising on the magnitude of the gains, this technique accomplishes outcomes. It's very uncommon for a trader with a long time period to produce good profits while winning just 50% of their transactions, or even less – the difference is that the wins are far larger than the losses.

Statistical arbitrage

The Efficient Market Hypothesis (EMH) claims financial markets are informationally efficient, which means that the prices of traded assets are accurate, and at any one moment represent all known information. Based on this hypothesis, the market should not fluctuate if there is not any fundamental news. However, this is not the case, and we can explain that with liquidity.

Throughout the day, many huge institutional trades have little to do with information and everything to do with liquidity. Investors who believe they are overexposed will aggressively hedge or sell their positions, impacting the price. Liquidity seekers are frequently ready to pay a premium to exit their positions, resulting in a profit for liquidity providers. Although this capacity to benefit from knowledge appears to violate efficient market theory, statistical arbitrage is based on it.

Statistical arbitrage seeks to profit from the correlation of price and liquidity by gaining from the perceived mispricing of assets based on the assets' anticipated value given by a statistical model.

Short-term price discrepancies in the same security sold on separate venues, or short-term price differences in related securities, are used in statistical arbitrage, often known as stat arb. Statistical arbitrage is based on the assumption that price differences in securities markets exist but go away quickly. Because the time period during which a price difference occurs might be as short as a fraction of a second, algorithmic trading is well suited to statistical arbitrage.

When trading the same security in several venues, for example, an algorithm tracks all of the locations where the security is exchanged. When a price difference arises, the algorithm buys in the lower market and sells in the higher market, resulting in a profit. Because the window of opportunity for such differences is small (less than 1 millisecond), algorithmic trading is ideally suited to this form of trade.

Statistical arbitrage becomes more challenging when investing in linked securities. An index and a single stock within that index, or a single stock and other stocks in the same sector, are examples of related securities. In linked securities, a statistical arbitrage approach entails gathering a large amount of historical data and estimating the usual connection between the two markets. The algorithm makes a buy or a sell whenever there is a variation from the norm.

Latency arbitrage

Modern equities markets are complicated, requiring highly technical systems to manage vast volumes of data. Because of its intricacy, data is invariably processed at varying speeds. Latency arbitrage takes advantage of market players' differing speeds. Latency arbitrage aims to take advantage of high-frequency traders' greater speed by leveraging high-speed fiber optics, superior bandwidth, co-located servers, and direct-price feeds from exchanges, among other things, to place trades ahead of other market players.

The hypothesis behind latency arbitrage is that in the US, the aggregated feed that determines the National Best Bid and Offer (NBBO) of all US stock exchanges is slower than the direct data feeds from stock exchanges available to high-frequency traders. An HFT program's algorithm can read transaction data more quickly than many other market players, seeing prices a fraction of a second ahead of the Securities Information Processor (SIP) feed, which is the consolidated US stock exchange price feed, thanks to its superior speed. This essentially provides information to the HFT software before it reaches the official market (the SIP feed), allowing high-frequency traders to observe where prices are heading ahead of other market players.

Impact of news

Information is at the heart of all trading, and it is used to make financial decisions. The utilization of news data by algorithmic trading systems to generate trading choices is referred to as information-driven strategies.

Algorithms have been developed to read and analyze news reports from major news organizations, as well as social media. Any news that has the potential to alter market prices causes the algorithm to purchase or sell.

High-frequency traders have gotten so accustomed to using information-driven methods that certain news agencies now package their press releases in a way that makes it simple for computers to analyze them. They employ predetermined keywords to characterize a favorable or bad occurrence, for example, so that an algorithm can act on keywords in a news release. Prior to their planned publication, news providers also place news reports on servers in crucial geographical regions (such as major financial centers). This reduces the amount of time it takes for data to move from one location to another. For this sort of service, news service providers charge an additional fee.

As seen by the hacking of the Associated Press Twitter feed, the use of social media for information-driven initiatives is growing. In 2013, a hacker tweeted that a bomb had gone off in the White House, injuring the president, causing an instantaneous plunge in equities markets throughout the world as algorithms analyzed the bad news from a trustworthy source and began selling in the market.

Next, let's learn about the momentum ignition trading technique.

Momentum ignition

You have the chance to trade financially if an order you send into the market may cause a price change and you know it can. The goal of momentum-ignition trading techniques is to achieve this. The objective is to get other algorithms and traders to start trading in a stock, causing a price change. In essence, a momentum ignition approach attempts to deceive other market players into believing that a large price movement is going to occur, causing them to trade. As a result, the price movement becomes a self-fulfilling prophecy: traders believe a price movement will occur, and their activities cause one to occur.

Sending enormous volumes of orders into the order book and then canceling them is a momentum-ignition approach. This creates the illusion of a huge shift in volume in the stock, which might prompt other traders to place orders, resulting in the start of a short-term price trend. Before attempting to ignite the market movement, the momentum ignition approach includes executing the real targeted trading position. This means that a deal is completed initially that does not significantly influence the market. This permits a trader using the momentum-ignition approach to enter the market before the price movement is initiated. The momentum ignition is set after the deal is completed by submitting a flurry of orders and canceling them in the hopes that other traders will follow suit and move the price.

The trader using the momentum-ignition technique then quits their initial position at a profit as the price begins to move.

Momentum-ignition methods require the use of specific order types, and traders may only utilize algorithms that can send and cancel huge numbers of orders in a short amount of time to execute them.

Rebate strategies

Market order traders must pay a fee to the exchange, whereas the limit order is reimbursed with rebates when they add liquidities. As a result, traders, particularly those engaged in HFT, submit limit orders to build markets, which in turn generates liquidity on the exchange. It is undoubtedly appealing to traders who place a large number of limit orders due to the pricing scheme's lower risk for the limit order.

There is also a charge structure called trader-maker pricing that is the polar opposite of market-taker pricing. In certain markets, it entails giving rebates to market order traders and collecting fees from limit order traders.


Pinging is a strategy for learning about huge orders in trading exchanges and dark pools by placing tiny marketable orders (typically for 100 shares).

To lessen the market effect of large orders, buy-side businesses utilize this trading technique to split large orders into many small orders. This algorithm feeds these orders slowly into the exchange. In order to detect the presence of such large orders, HFT companies arrange bids and offers in 100-share lots for each listed stock.

These ping trades will alert HFT participants to the existence of a large order placed by the buy-side. HFTs will use this information to ensure risk-free profit from the buy-side.

Some significant market participants have compared pinging to baiting because its main objective is to entice institutions with huge orders to expose their hand.

Illegal activities

The SEC, the Federal Bureau of Investigation (FBI), and other regulators have launched crackdowns on alleged HFT violations in recent years. The following sections are examples of possible offenses.


Placing an order based on information that has not been publicly released is called front-running. This technique has been outlawed by SEC and the Financial Industry Regulatory Authority (FINRA). Some have used the term front-running to describe a technique in which HFT firms utilize algorithmic trading technology to identify a large number of new orders for a given instrument. Before the large number of orders comes to the market, we place orders to benefit from this incoming large quantity. HFT corporations can earn almost instantly after purchasing assets by selling them to the original investors. Even if this way of trading is legal, regulators are concerned and may need to control this behavior moving forward.


Spoofing is not a legal trading strategy. It consists of a spoofing strategy sending orders that are not intended to be executed, just to have the other market participants react to these orders. They will probably send orders to get to this price level. Meanwhile, the initial orders are canceled and the spoofer takes advantage of the other orders remaining in the market.

The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 specifically targeted the practice, and even before that, FINRA regulations barred orders whose goal is to mislead the market. The first criminal spoofing case disclosed by legislators in 2014 related to a Chicago trader accused of faking futures markets.


Layering is the same as spoofing except that the orders are placed at different price levels to give the appearance that there is a lot of interest in a certain security. The outcome of this strategy is the same as with regular spoofing. Because of the rapid advancement of technology, massive market manipulation may take place in fractions of a second. Layering, like generic spoofing, is typically illegal and forbidden under FINRA rules.

Even if these strategies are now outlawed, we need to keep in mind that some exchanges are less or not regulated. We will see in Chapter 11, High Frequency FPGA and Crypto, about cryptocurrencies that these strategies can still work.



In this chapter, we reviewed the origins of HFT. We went through what makes HFT so special in comparison to regular trading. We also layered the different types of strategies that any HFT trading system will be able to support. We talked about the history of trading systems. Our goal in this chapter was to give you a good understanding of what HFT is and what trading strategies we can use.

In the next chapter, we will talk about the main functionalities of a trading system. We will describe how to build a trading system.

About the Authors
  • Sebastien Donadio

    Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Socit Gnrale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber.

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  • Sourav Ghosh

    Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California. His areas of interest include Computer Architecture, FinTech, Probability Theory and Stochastic Processes, Statistical Learning and Inference Methods, and Natural Language Processing.

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  • Romain Rossier

    Romain Rossier brings 19+ years of experience mostly as a Software Architect in the financial industry, specializing in low latency, high performance java software design and development. He is currently the Chief Architect for the HCTech FX Proprietary Trading engine. He also built and led the software development team at HCTech, where he oversaw and developed the HFT platform architecture for FX, Futures and Fixed Income. Prior to HCTech, Romain was Director of the Currenex lab where he led the team responsible for the development of the Currenex Intelligent Pricing System. Romain holds a Master of Science in Communication Systems from the Swiss Federal Institute of Technology in Lausanne.

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Developing High-Frequency Trading Systems
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