Evolution of Algorithmic Trading Research: A Systematic Literature Review and Identification of Future Research Directions

Tuhin Mukherjee; Anirban Sarkar

doi:10.69968/ijisem.2026v5i3260-276

Authors

Tuhin Mukherjee Associate Professor, Department of Business Administration, University of Kalyani
Anirban Sarkar Research Scholar, Department of Business Administration, University of Kalyani

DOI:

https://doi.org/10.69968/ijisem.2026v5i3260-276

Keywords:

Algorithmic Trading, Automated Trading Systems, High-Frequency Trading, Quantitative Finance

Abstract

Algorithmic trading has transformed modern financial markets through the integration of advanced computational techniques, quantitative models, and automated execution systems. The rapid growth of electronic trading platforms, increasing market data availability, and significant advancements in artificial intelligence (AI), machine learning (ML), deep learning (DL), and high-frequency trading (HFT) have stimulated extensive academic and industry research over the past two decades. Despite this expanding body of literature, existing studies remain fragmented across diverse research themes, methodologies, financial markets, and asset classes, creating a need for a comprehensive synthesis of current knowledge and identification of future research opportunities.

This paper presents a comprehensive literature review of algorithmic trading research published with a special emphasize between 2000 and 2026. The review systematically examines the evolution of algorithmic trading, major algorithmic trading strategies, theoretical foundations, data sources, research methodologies, and performance evaluation metrics employed in prior studies. The review further categorizes existing research into key thematic areas, including trend-following strategies, statistical arbitrage, market making, portfolio optimization, high-frequency trading, sentiment-based trading, and cryptocurrency algorithmic trading.

The analysis identifies several significant research gaps that continue to limit the practical implementation and academic advancement of algorithmic trading. Based on these findings, the paper proposes a comprehensive future research agenda emphasizing hybrid AI-driven trading frameworks. By synthesizing existing knowledge and identifying critical research gaps, this review provides a valuable reference for researchers, practitioners, financial institutions, and policymakers seeking to advance the theory and practice of algorithmic trading.

References

[1] H. Markowitz, “Portfolio selection,” J. Finance, vol. 7, no. 1, pp. 77–91, 1952., doi:

[2] W. F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,” J. Finance, vol. 19, no. 3, pp. 425–442, 1964., doi:

[3] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” J. Finance, vol. 25, no. 2, pp. 383–417, 1970., doi:

[4] F. Black and R. Litterman, “Global portfolio optimization,” Financ. Anal. J., vol. 48, no. 5, pp. 28–43, 1992., doi:

[5] N. Jegadeesh and S. Titman, “Returns to buying winners and selling losers: Implications for stock market efficiency,” J. Finance, vol. 48, no. 1, pp. 65–91, 1993., doi:

[6] M. O’Hara, Market microstructure theory. Blackwell Publishers, 1995.

[7] J. Y. Campbell, A. W. Lo, and A. C. MacKinlay, The econometrics of financial markets. Princeton University Press, 1997. 10.1515/9781400830213

[8] D. Bertsimas and A. W. Lo, “Optimal control of execution costs,” J. Financ. Mark., vol. 1, no. 1, pp. 1–50, 1998., doi:

[9] A. Madhavan, “Market microstructure: A survey,” J. Financ. Mark., vol. 3, no. 3, pp. 205–258, 2000., doi:

[10] A. Shleifer, Inefficient markets: An introduction to behavioral finance. Oxford University Press, 2000. 10.1093/0198292279.001.0001

[11] A. Abraham, B. Nath, and P. K. Mahanti, (2001). Hybrid intelligent systems for stock market analysis. In Proceedings of the International Conference on Computational Science (pp. 337–345). Springer. 10.1007/3-540-45718-6_38

[12] R. Almgren and N. Chriss, “Optimal execution of portfolio transactions,” J. Risk, vol. 3, no. 2, pp. 5–39, 2001., doi:

[13] J. Moody and M. Saffell, “Learning to trade via direct reinforcement,” IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 875–889, 2001., doi:

[14] R. Almgren, “Optimal execution with nonlinear impact functions and trading-enhanced risk,” Appl. Math. Finance, vol. 10, no. 1, pp. 1–18, 2003., doi:

[15] A. W. Lo, “The adaptive markets hypothesis: Market efficiency from an evolutionary perspective,” J. Portfol. Manage., vol. 30, no. 5, pp. 15–29, 2004., doi:

[16] G. Vidyamurthy, Pairs trading: Quantitative methods and analysis. John Wiley & Sons, 2004.

[17] B. Biais, L. Glosten, and C. Spatt, “Market microstructure: A survey of microfoundations, empirical results, and policy implications,” J. Financ. Mark., vol. 8, no. 2, pp. 217–264, 2005., doi:

[18] A. Meucci, Risk and asset allocation. Springer, 2005. 10.1007/978-3-540-27904-4

[19] C. M. Bishop, Pattern recognition and machine learning. Springer, 2006.

[20] E. Gatev, W. N. Goetzmann, and K. G. Rouwenhorst, “Pairs trading: Performance of a relative-value arbitrage rule,” Rev. Financ. Stud., vol. 19, no. 3, pp. 797–827, 2006., doi:

[21] Y. Nevmyvaka, Y. Feng, and M. Kearns, (2006). Reinforcement learning for optimized trade execution. Proceedings of the 23rd International Conference on Machine Learning (pp. 673–680). ACM. https://doi.org/ 10.1145/1143844.1143929

[22] F. J. Fabozzi, P. N. Kolm, D. A. Pachamanova, and S. M. Focardi, Robust portfolio optimization and management. John Wiley & Sons, 2007.

[23] A. Pole, Statistical arbitrage: Algorithmic trading insights and techniques. John Wiley & Sons, 2007.

[24] R. E. Steuer, Y. Qi, and M. Hirschberger, “Multiple objectives in portfolio selection,” Journal of Financial Decision Making, vol. 3, no. 1, pp. 11–26, 2007.

[25] M. Avellaneda and S. Stoikov, “High-frequency trading in a limit order book,” Quant. Finance, vol. 8, no. 3, pp. 217–224, 2008., doi:

[26] P. N. Kolm, S. M. Focardi, and F. J. Fabozzi, “Incorporating trading strategies in the Black–Litterman framework,” in Handbook of finance, F. J. Fabozzi, Ed.John Wiley & Sons, 2008, .

[27] R. O. Michaud and R. O. Michaud, Efficient asset management: A practical guide to stock portfolio optimization and asset allocation, 2nd ed., Oxford University Press, 2008. 10.1093/oso/9780195331912.001.0001

[28] E. P. Chan, Quantitative trading: How to build your own algorithmic trading business. John Wiley & Sons, 2009.

[29] M. W. Covel, (2009). Trend following: Learn to make millions in up or down markets (Updated ed.). FT Press.

[30] C.-H. Lin, S.-Y. Yang, and Y.-T. Wang, “A hybrid stock trading system for intelligent technical analysis-based equivolume charting,” Neurocomputing, vol. 72, no. 16–18, pp. 3517–3528, 2009., doi:

[31] M. Avellaneda and J.-H. Lee, “Statistical arbitrage in the U.S. equities market,” Quant. Finance, vol. 10, no. 7, pp. 761–782, 2010., doi:

[32] W. K. Bertram, “Analytic solutions for optimal statistical arbitrage trading,” Physica A, vol. 389, no. 11, pp. 2234–2243, 2010., doi: 10.1016/j.physa.2010.01.045

33. Johnson, B. (2010). Algorithmic trading and DMA: An introduction to direct access trading strategies. 4Myeloma Press.

[34] J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011., doi:

[35] P. Gomber, B. Arndt, M. Lutat, and T. Uhle, (2011). High-frequency trading. Goethe University Frankfurt, Working Paper. https://doi.org/

[36] T. Hendershott, C. M. Jones, and A. J. Menkveld, “Does algorithmic trading improve liquidity?” J. Finance, vol. 66, no. 1, pp. 1–33, 2011., doi:

[37] D. Kahneman, Thinking, fast and slow. Farrar, Straus and Giroux, 2011.

[38] T. Loughran and B. McDonald, “When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks,” J. Finance, vol. 66, no. 1, pp. 35–65, 2011., doi:

[39] W. Shen, X. Guo, C. Wu, and D. Wu, “Forecasting stock indices using radial basis function neural networks optimized by artificial intelligence techniques,” Neurocomputing, vol. 74, no. 17, pp. 3446–3454, 2011., doi:

[40] X. Zhang, H. Fuehres, and P. A. Gloor, “Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”,” Procedia Soc. Behav. Sci., vol. 26, pp. 55–62, 2011., doi:

[41] F. Bertoluzzo and M. Corazza, “Testing different reinforcement learning configurations for financial trading: Introduction and applications,” Procedia Econ. Finance, vol. 3, pp. 68–77, 2012., doi: 10.1016/S2212-5671(12)00122-0

[42] E. A. Leshik and J. Cralle, An introduction to algorithmic trading: Basic to advanced strategies. John Wiley & Sons, 2012. 10.1002/9781119206033

[43] T. J. Moskowitz, Y. H. Ooi, and L. H. Pedersen, “Time series momentum,” J. Financ. Econ., vol. 104, no. 2, pp. 228–250, 2012., doi:

[44] J. Wang, J. Wang, Z. Zhang, and S. Guo, “Trend following algorithms in automated derivatives market trading,” Expert Syst. Appl., vol. 39, no. 13, pp. 11378–11390, 2012., doi:

[45] G. Ye, Ed. High-frequency trading models. John Wiley & Sons, 2012, .

[46] I. Aldridge, High-frequency trading: A practical guide to algorithmic strategies and trading systems, 2nd ed., John Wiley & Sons, 2013.

[47] C. S. Asness, T. J. Moskowitz, and L. H. Pedersen, “Value and momentum everywhere,” J. Finance, vol. 68, no. 3, pp. 929–985, 2013., doi:

[48] E. P. Chan, Algorithmic trading: Winning strategies and their rationale. John Wiley & Sons, 2013. 10.1002/9781118676998

[49] J. Gatheral and A. Schied, (2013). Dynamical models of market impact and algorithms for order execution. In J.-P. Fouque & J. A. Langsam (Eds.), Handbook on systemic risk (pp. 579–599). Cambridge University Press. 10.1017/CBO9781139151184.030

[50] A. J. Menkveld, “High-frequency trading and the new market makers,” J. Financ. Mark., vol. 16, no. 4, pp. 712–740, 2013., doi:

[51] R. K. Narang, Inside the black box: A simple guide to quantitative and high-frequency trading, 2nd ed., John Wiley & Sons, 2013. 10.1002/9781118662717

[52] A. Obizhaeva and J. Wang, “Optimal trading strategy and supply/demand dynamics,” J. Financ. Mark., vol. 16, no. 1, pp. 1–32, 2013., doi:

[53] P. Treleaven, M. Galas, and V. Lalchand, “Algorithmic trading review,” Commun. ACM, vol. 56, no. 11, pp. 76–85, 2013., doi:

[54] R. Kissell, The science of algorithmic trading and portfolio management. Academic Press, 2014.

[55] Y. Lempérière, C. Deremble, P. Seager, M. Potters, and J.-P. Bouchaud, “Two centuries of trend following,” J. Invest. Strateg., vol. 3, no. 3, pp. 41–61, 2014. 10.21314/JOIS.2014.043

[56] P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala, “Good debt or bad debt: Detecting semantic orientations in economic texts,” J. Assoc. Inf. Sci. Technol., vol. 65, no. 4, pp. 782–796, 2014., doi:

[57] Á. Cartea, S. Jaimungal, and J. Penalva, Algorithmic and high-frequency trading. Cambridge University Press, 2015.

[58] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, 2015., doi:

[59] K. H. Chung and A. J. Lee, “High-frequency trading: Review of the literature and regulatory initiatives around the world,” Asia-Pac. J. Financ. Stud., vol. 45, no. 1, pp. 7–33, 2016., doi:

[60] Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep direct reinforcement learning for financial signal representation and trading,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 3, pp. 653–664, Mar. 2017., doi:

[61] J. B. Heaton, N. G. Polson, and J. H. Witte, “Deep learning in finance,” Annu. Rev. Financ. Econ., vol. 11, pp. 1–23, 2017., doi:

[62] B. Hurst, Y. H. Ooi, and L. H. Pedersen, “A century of evidence on trend-following investing,” J. Portfol. Manage., vol. 44, no. 1, pp. 15–29, 2017., doi:

[63] Z. Jiang, D. Xu, and J. Liang, (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv. https://arxiv.org/abs/1706.10059

[64] C. Krauss, “Statistical arbitrage pairs trading strategies: Review and outlook,” J. Econ. Surv., vol. 31, no. 2, pp. 513–545, 2017., doi:

[65] C. Krauss, X. A. Do, and N. Huck, “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 689–702, 2017., doi:

[66] S. C. Nayak, B. B. Misra, and H. S. Behera, “An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms,” Appl. Soft Comput., vol. 55, pp. 127–140, 2017., doi:

[67] R. T. F. Nazário, J. L. Silva, V. A. Sobreiro, and H. Kimura, “A literature review of technical analysis on stock markets,” Q. Rev. Econ. Finance, vol. 66, pp. 115–126, 2017., doi:

[68] D. M. Q. Nelson, A. C. M. Pereira, and R. A. de Oliveira, (2017). Stock market’s price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN), 1419–1426. https://doi.org/

[69] A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, (2017). Forecasting stock prices from the limit order book using convolutional neural networks. In 2017 IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 7–12). IEEE. https://doi.org/

[70] J. Yeo and G. Papanicolaou, “Risk control of mean-reversion time in statistical arbitrage,” Risk and Decision Analysis, vol. 6, no. 4, pp. 263–290, 2017., doi:

[71] L. Alessandretti, A. ElBahrawy, L. M. Aiello, and A. Baronchelli, “Anticipating cryptocurrency prices using machine learning,” Complexity, vol. 2018, no. 1, p. 8983590, 2018., doi:

[72] T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, 2018., doi:

[73] P. Gomber and K. Zimmermann, “Algorithmic trading in practice,” in The Oxford handbook of computational economics and finance, S.-H. Chen, M. Kaboudan, and Y.-R. Du, Eds.Oxford University Press, 2018, .

[74] Y. Li, (2018). Deep reinforcement learning: An overview. arXiv. https://arxiv.org/abs/1701.07274

[75] M. M. López de Prado, Advances in financial machine learning. John Wiley & Sons, 2018.

[76] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction, 2nd ed., MIT Press, 2018.

[77] C.-F. Tsai, Y.-F. Hsu, and D. C. Yen, “A hybrid financial trading support system using multi-category classifiers and random forest,” Appl. Soft Comput., vol. 67, pp. 337–349, 2018., doi:

[78] Z. Zhao and D. P. Palomar, “Mean-reverting portfolio design for statistical arbitrage,” IEEE Trans. Signal Process., vol. 66, no. 14, pp. 3623–3638, 2018., doi:

[79] D. Araci, (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv. https://arxiv.org/abs/1908.10063

[80] W. Chen, H. Xu, L. Jia, and Y. Gao, “Machine learning model for Bitcoin exchange rate prediction using economic and technical determinants,” Int. J. Forecast., vol. 35, no. 4, pp. 1390–1401, 2019., doi:

[81] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186. https://doi.org/

[82] J. Sirignano and R. Cont, “Universal features of price formation in financial markets: Perspectives from deep learning,” Quant. Finance, vol. 19, no. 9, pp. 1449–1459, 2019., doi:

[83] P. Yu, J. S. Lee, H. Kwon, and S. Kim, “A deep reinforcement learning framework for algorithmic trading using market sentiment,” IEEE Access, vol. 7,153320–153333, 2019., doi:

[84] S. Gu, B. Kelly, and D. Xiu, “Empirical asset pricing via machine learning,” Rev. Financ. Stud., vol. 33, no. 5, pp. 2223–2273, 2020., doi:

[85] S. Jansen, Machine learning for algorithmic trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd ed., Packt Publishing, 2020.

[86] X. Liu, Z. Li, H. Sprekeler, and M. Veloso, “Adaptive trading with deep reinforcement learning,” Proc. Conf. AAAI Artif. Intell., vol. 34, no. 2, pp. 1407–1414, 2020. 10.1609/aaai.v34i02.5587

[87] O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with deep learning: A systematic literature review (2005–2019),” Appl. Soft Comput., vol. 90, p. 106181, 2020., doi:

[88] Y. Yang, Y. Qin, W. Lei, S. Liu, and J. Wang, “News sentiment analysis for stock market prediction: A deep learning approach,” Expert Syst. Appl., vol. 159, p. 113600, 2020., doi:

[89] Y. Zhang, C. Aggarwal, and G. J. Qi, (2020). Stock price prediction via discovering multi-frequency trading patterns. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2141–2149). https://doi.org/

[90] Y. Zhang, C. Chen, and Y. Tao, “Deep learning and algorithmic trading for cryptocurrency markets,” IEEE Access, vol. 8,208699–208709, 2020., doi:

[91] Z. Zhang, S. Zohren, and S. Roberts, “Deep learning for portfolio optimization,” J. Financ. Data Sci., vol. 2, no. 4, pp. 8–20, 2020., doi: 10.3905/jfds.2020.1.042

[92] S. J. Russell and P. Norvig, Artificial intelligence: A modern approach, 4th ed., Pearson, 2021.

[93] T. Théate and D. Ernst, “An application of deep reinforcement learning to algorithmic trading,” Expert Syst. Appl., vol. 173, p. 114632, 2021., doi:

[94] F. Fang, C. Ventre, M. Basios, L. Kanthan, L. Li, D. Martinez-Regoband, et al., “Cryptocurrency trading: A comprehensive survey,” Financ. Innov., vol. 8, no. 1, p. 13, 2022., doi:

[95] L. K. Felizardo, P. Brandimarte, E. Del Moral Hernandez, A. H. Reali Costa, E. Y. Matsumoto, F. C. L. Paiva, et al., “Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market,” Expert Syst. Appl., vol. 202, p. 117259, 2022., doi:

[96] I. Ruiz Roque da Silva, E. H. Junior, and P. P. Balbi, “Cryptocurrencies trading algorithms: A review,” J. Forecast., vol. 41, no. 8, pp. 1661–1668, 2022., doi:

[97] S. Sorsen and J. Schulz, (2022). Algorithmic trading and cryptocurrency: A literature review and key findings. Proceedings of the Midwest Association for Information Systems (MWAIS). https://aisel.aisnet.org/mwais2022/5

[98] A. F. Clenow, Following the trend: Diversified managed futures trading, 2nd ed., John Wiley & Sons, 2023. 10.1002/9781394320516