High Frequency Trading Systems Research Articles

Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection

High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.

Graph-based stock correlation and prediction for high-frequency trading systems

In this paper, we have implemented a high-frequency quantitative system that can obtain stable returns for the Chinese A-share market, which has been running for more than 3 months (from March 27, 2020 to June 30, 2020) with the expected results. A number of rules and barriers exist in the Chinese A-share market such as trading restrictions and high fees, as well as scarce and expensive hedging tools. It is difficult to achieve stable absolute returns in such a market. Stock correlation analysis and price prediction play an important role to achieve any profitable trading. The portfolio management and subsequent trading decisions highly depend on the results of stock correlation analysis and price prediction. However, it is nontrivial to analyze and predict any stocks, being time-varying and affected by unlimited factors in a given market. Traditional methods only take some certain factors into consideration but ignore others that may be changed dynamically. In this paper, we propose a novel machine learning model named Graph Attention Long Short-Term Memory (GALSTM) to learn the correlations between stocks and predict their future prices automatically. First, a multi-Hawkes Process is used to initial a correlation graph between stocks. This procedure provides a good training start as the multi-Hawkes Processes will be studied on the most saint feature fluctuations with any correlations being statistically significant. Then an attention-based LSTM is built to learn the weighting matrix underlying the dynamic graph. In addition, we also build matching data process plus portfolio management modules to form a complete system. The proposed GALSTM enables us to expand the scope of stock selection under the premise of controlling risks with limited hedging tools in the A-share market, thereby effectively increasing high-frequency excess returns. We then construct a long and short positions combination, select long positions in the A shares of the entire market, and use stock index futures to short. With GALSTM model, the products managed by our fully automatic quantitative trading system achieved an absolute annual return rate of 44.71% and the standard deviation of daily returns is only 0.42% in three months of operation. Only 1 week loss in 13 weeks of running time.

MD-MBPLS: A novel explanatory model in computational social science

The newly emerged field of computational social science has witnessed unprecedented opportunities in the past decade with the explosion of data collection. However, these data are of different types, usually consisting of constant attributes (scalar data), temporal dynamics at the individual level (functional data) and group summaries at the collective level (compositional data). Consequently, existing models are substantially challenged by difficulties encountered in data aggregation. Moreover, previous studies excessively focus on the predictive power rather than interpretative power of models, while the latter is genuinely more crucial for developing profound theories and disclosing in-depth mechanisms in social science. This paper proposes a novel model named mixed data multi-block partial least squares (MD-MBPLS) to overcome these difficulties. Four real-world datasets are considered to evaluate the model. These datasets arise from large-scale collections of smart-card sensors, high-frequency trading systems and online social media, representing trending topics of student campus behaviour, stock market volatility, fake news circulation and personality in tweeting. The empirical results congruously reveal the prominent advantages of the model with respect to data fusion and explanatory power. Specifically, interesting patterns are reliably revealed which supplement new insights and help decision makers to obtain guidelines for interventions, such as student academic performance enhancement or precautions against fake news propagation. Our model will enhance the interpretative ability of computational social science studies by fully leveraging mixed types of data.

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability.

Modelling of stock market security price Dynamics Using market microstructure Data

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.

Hedging and non-hedging trading strategies on commodities using the d-Backtest PS method. Optimized trading system hedging

Modern trading systems are mechanic, run automatically on computers inside trading platforms and decide their position against the market through optimized parameters and algorithmic strategies. These systems now, in most cases, comprise high frequency traders, especially in the Forex market.In this research, a piece of software of an automatic high frequency trading system was developed, based on the technical indicator PIVOT (price level breakthrough). The system made transactions on hourly closing prices with weekly parameters optimization period, using the d-Backtest PS method.Through the search and checking of the results, two findings for optimization of trading strategy were found. These findings with the order they were examined and are presented in this paper are as follows: (1) the simultaneous use of “long and short” positions, with different parameters in a hedging account, acts as a hedging strategy, minimizing losses, in relation to a “long or short” in a non-hedging account for the same time period and (2) there is weak correlation of past backtesting periods between the same systems, if they are configured for “long and short” trades, or for just “long” or for just “short”.

Automatic High-Frequency Trading: An Application to Emerging Chilean Stock Market

This research seeks to design, implement, and test a fully automatic high-frequency trading system that operates on the Chilean stock market, so that it is able to generate positive net returns over time. A system that implements high-frequency trading (HFT) is presented through advanced computer tools as an NP-Complete type problem in which it is necessary to optimize the profitability of stock purchase and sale operations. The research performs individual tests of the algorithms implemented, reviewing the theoretical net return (profitability) that can be applied on the last day, month, and semester of real market data. Finally, the research determines which of the variants of the implemented system performs best, using the net returns as a basis for comparison. The use of particle swarm optimization as an optimization algorithm is shown to be an effective solution since it is able to optimize a set of disparate variables but is bounded to a specific domain, resulting in substantial improvement in the final solution.

The market impact of high-frequency trading systems and potential regulation

As transaction costs of trades have decreased, there has been a large growth of high-frequency trading in markets around the world. Although there are benefits that high-frequency trading brings to both the market and individual companies, there are negative and controversial aspects as well. This paper addresses high-fequency trading strategies, systems, and controversies, and then analyzes current and potential laws and regulations and their impacts on the marketplace.

Towards Developing High Frequency Trading Systems

This paper introduces an approach to developing high frequency trading systems. Further, it demonstrates the techniques described using high frequency FX data as a case study. The foreign exchange (FX) spot markets are well suited to high frequency speculative trading. They are highly liquid, leveraged, and trade 24 hours a day, 5 days a week. The framework describes the processes to follow for determining the applicability and robustness of known or suspected stylised facts to speculative trading, and, it also formalizes the technique of dimension-reduction in high frequency financial datasets. Confirmation of stylized facts and dimension reduction are key first steps in the subsequent development of specific trading rules.

Trading System Capability

For firms involved in high-frequency trading, of primary concern is whether or not a particular trading system will generate sufficient profits to cover its considerable research and development, fixed and variable costs. Since these costs are allocated per accounting period, firms view market returns from a bottom line profitability perspective. The current performance metrics of finance do not provide sufficient information to assess investments in high-frequency trading systems in this context. We develop and test a capability measure that captures the performance of high-frequency trading systems in this light.

A Practical Real Options Approach to Valuing High Frequency Trading System R&D Projects

A Practical Real Options Approach to Valuing High Frequency Trading System R&D Projects

A Practical Real Options Approach to Valuing High Frequency Trading System R&D Projects

In the age of automation, trading and market making is about estimating the fair price of automated trading system research and development projects. This requires a new methodology to arrive at such a fair price. A real options framework is a natural choice. In this paper we review a methodology for automated trading system R&D and present a practical real option model for valuing such projects so as to enable rapid strategy cycling.

Financial Markets Monitoring and Surveillance: A Quote Stuffing Case Study

Financial markets are very important components of the world economy whose structure has changed substantially over the last thirty years primarily due to the use of information and communication technologies. Market monitoring and surveillance is an important process to the financial markets in order to ensure that market rules and policies are met as well as to detect any act or attempt of fraudulent manipulation. Recently, a new kind of trading has emerged, High Frequency Trading, which allows traders to place and execute orders within milliseconds via computerized programs. It is still unclear whether existing financial market systems are capable of carrying out effective monitoring and detecting inconsistencies in trades at such high speeds. A strategy that has been made possible in high frequency trading is when very large orders are submitted and withdrawn within milliseconds in an apparent attempt to flood and confuse the market and the competitors, known as “quote stuffing.” This paper presents a high frequency trading analysis of a particular trading scenario and discusses how quote stuffing can affect the function of trading systems. In order to do so, a financial market simulator is developed to model high frequency trading systems and it is used to analyze and demonstrate how quote stuffing can increase the gap of best bid and ask prices between financial markets, contrary to the evidence that high frequency trading has helped to align prices across financial markets. This has some very important implications as traders could profit by artificially creating latencies in trading data feeds and taking advantage of the induced price differences between financial markets. The paper identifies a number of indicators and visualization techniques that demonstrate how efficient control mechanisms could be implemented by a financial market in order to safeguard the behavior of its trading system under a quote stuffing scenario. Moreover, suggestions are put forward on how a real-time engine could be used to detect potential high frequency trading manipulative behaviors.

Developing High-Frequency Foreign Exchange Trading Systems

The foreign exchange (FX) spot markets are well suited to high frequency trading. They are highly liquid, allow leverage, and trade 24 hours a day, 5 days a week. This paper documents and tests the stylized facts known about high-frequency FX markets. It then postulates a high frequency trading system on the basis of these stylized facts. Benchmarking confirms the robustness of the approach, demonstrating the role algorithmic trading has to play in higher frequency trading environments.

Fast learning and predicting of stock returns with virtual generalized random access memory weightless neural networks

SUMMARYWe employ virtual generalized random access memory weightless neural networks, VG‐RAM WNN, for predicting future stock returns. We evaluated our VG‐RAM WNN stock predictor architecture in predicting future weekly returns of the Brazilian stock market and obtained the same error levels and properties of baseline autoregressive neural network predictors; however, our VG‐RAM WNN predictor runs 5000 times faster than autoregressive neural network predictors. This allowed us to employ VG‐RAM WNN predictors to build a high frequency trading system able to achieve a monthly return of approximately 35% in the Brazilian stock market. Copyright © 2011 John Wiley & Sons, Ltd.

Fighting the Machines: New Risks in a Transparent Financial System

Fighting the Machines: New Risks in a Transparent Financial System Grant Long (bio) Transparency became the ultimate buzzword among policymakers and bankers debating the causes of the turmoil that beset international financial markets from August 2007 well into 2009. Many argued that without the tangled, opaque web of liabilities formed by credit default swaps, collateralized debt obligations, special purpose vehicles, and various other financial derivative, securitization, and funding schemes, the financial crisis of 2007 and 2008 would never have occurred. Advocates of this point have a strong case; with a clearer picture of banks’ liabilities, the seizing of credit markets would not have been nearly as pronounced and might have been averted. The regulatory reforms passed by the U.S. Congress in the spring of 2010 put a high priority on increased transparency in financial markets, and much of the international dialogue has also focused on the need for increased transparency to enable greater market efficiency. To promote transparency, reforms have favored normally traded financial instruments over esoteric, customized securities, and promoted the establishment of entirely new central clearing systems for the previously fragmented financial derivatives market. Yet just as Congress was debating the final details of landmark financial reform legislation, the markets demonstrated the hazards of focusing exclusively on financial transparency. On 6 May 2010, in mere minutes, major financial indices dropped by as much as 10 percent. In the ensuing frenzy, analysts and journalists cited concerns over everything from the health of European banks to tensions between North and South Korea in an attempt to explain the startling decline. Over time, investigations revealed that the brief episode of panic that roiled the market that afternoon originated not in rational human concerns, but rather in mechanical error. Four months after the crash, the exact cause of the breakdown remained elusive. The difficulty in pinpointing the exact source of the crash highlights the dangers of excessive reliance on mechanical trading. This computer driven trading has become the favored engine to generate returns for many large hedge funds, and depends on simplified securities and transparent financial exchanges through which computers can analyze and submit trades in a rapid manner. [End Page 33] Though financial reform has in many ways addressed the problems of the recent financial crisis, by focusing on transparency, encouraging the standardization of financial instruments, and mandating large expansions in exchanges for financial derivatives, policymakers have increased the potential for a computer-sparked financial panic. In the same manner that experts lauded the financial innovations that drove derivative and securitization markets until 2007, bankers and economists have traditionally cheered the innovations behind the rise of computer trading. A 2009 examination by a group of Federal Reserve Board economists found that despite alarms that many had raised about the potential for mechanical trading to wreak havoc on markets, empirical evidence suggested that certain computer driven trading platforms actually reduced volatility in foreign exchange markets.1 Their analysis found that human traders, not computers, were responsible for large trades that tended to move market prices, and that in times of high market volatility, computer driven trading platforms tended to provide buyers for panicked investors. These findings echo those of other recent studies, which also cited evidence that certain computer driven trading systems tended to reduce volatility and increase efficiency in the marketplace.2 The greater market transparency advocated by policymakers in the wake of the financial crisis has the potential to facilitate these benefits of computer driven, high frequency trading systems. Yet questions remain about the possibility for computer trading to spark large-scale systematic panics in the market. The conventional wisdom—that computer driven trading contributes to the day-to-day stability of financial markets—may be comforting, but it does not fully account for the potential for isolated events to set off a systemic crisis. Empirical research finds that large-scale correlations in computer trading systems are unlikely, but this does not alone render large movements in the market impossible. Events which have a small probability of occurring at any given time have a much higher probability of occurring over longer time horizons, a lesson in probability that many traders and risk managers painfully learned in 2007 and 2008...