Journal articles: 'Algorithmic Trading'

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Nuti, Giuseppe, Mahnoosh Mirghaemi, Philip Treleaven, and Chaiyakorn Yingsaeree. "Algorithmic Trading." Computer 44, no. 11 (November 2011): 61–69. http://dx.doi.org/10.1109/mc.2011.31.

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Wang, Yongfeng, and Guofeng Yan. "Survey on the application of deep learning in algorithmic trading." Data Science in Finance and Economics 1, no. 4 (2021): 345–61. http://dx.doi.org/10.3934/dsfe.2021019.

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<abstract> <p>Algorithmic trading is one of the most concerned directions in financial applications. Compared with traditional trading strategies, algorithmic trading applications perform forecasting and arbitrage with higher efficiency and more stable performance. Numerous studies on algorithmic trading models using deep learning have been conducted to perform trading forecasting and analysis. In this article, we firstly summarize several deep learning methods that have shown good performance in algorithmic trading applications, and briefly introduce some applications of deep learning in algorithmic trading. We then try to provide the latest snapshot application for algorithmic trading based on deep learning technology, and show the different implementations of the developed algorithmic trading model. Finally, some possible research issues are suggested in the future. The prime objectives of this paper are to provide a comprehensive research progress of deep learning applications in algorithmic trading, and benefit for subsequent research of computer program trading systems.</p> </abstract>

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Mathur, Medha, Satyam Mhadalekar, Sahil Mhatre, and Vanita Mane. "Algorithmic Trading Bot." ITM Web of Conferences 40 (2021): 03041. http://dx.doi.org/10.1051/itmconf/20214003041.

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Algorithmic trading uses algorithms that follow a trend and defined set of instructions to perform a trade. The trade can generate revenue at an inhuman and enhanced speed and frequency. The characterized sets of trading guidelines that are passed on to the program are reliant upon timing, value, amount, or any mathematical model. Aside from profitable openings for the trader, algo-trading renders the market more liquid and trading more precise by precluding the effect of human feelings on trading. Our project aims to further this revolution in the markets of tomorrow by providing an effective and efficient solution to overcome the drawbacks faced due to manual trading by building an Algorithmic Trading Bot which will automatically trade user strategies alongside its own algorithms for day-to-day trading based on different market conditions and user approach ,and throughout the course of the day invest and trade with continuous modifications to ensure the best trade turnover for the day while reducing the transaction cost, hence enabling huge profits for concerned users be it Organizations or individuals.

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V’yugin, Vladimir V., and Vladimir G. Trunov. "Universal algorithmic trading." Journal of Investment Strategies 2, no. 1 (December 2012): 63–88. http://dx.doi.org/10.21314/jois.2012.014.

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Treleaven, Philip, Michal Galas, and Vidhi Lalchand. "Algorithmic trading review." Communications of the ACM 56, no. 11 (November 2013): 76–85. http://dx.doi.org/10.1145/2500117.

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Martins Pereira, Clara. "Unregulated Algorithmic Trading: Testing the Boundaries of the European Union Algorithmic Trading Regime." Journal of Financial Regulation 6, no. 2 (August 5, 2020): 270–305. http://dx.doi.org/10.1093/jfr/fjaa008.

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Abstract Trading in modern equity markets has come to be dominated by machines and algorithms. However, there is significant concern over the impact of algorithmic trading on market quality and a number of jurisdictions have moved to address the risks associated with this new type of trading. The European Union has been no exception to this trend. This article argues that while the European Union algorithmic trading regime is often perceived as a tough response to the challenges inherent in machine trading, it has one crucial shortcoming: it does not regulate the simpler, basic execution algorithms used in automated order routers. Yet the same risk generally associated with algorithmic trading activity also arises, in particular, from the use of these basic execution algorithms—as was made evident by the trading glitch that led to the fall of United States securities trader Knight Capital in 2012. Indeed, such risk could even be amplified by the lack of sophistication of these simpler execution algorithms. It is thus proposed that the European Union should amend the objective scope of its algorithmic trading regime by expanding the definition of algorithmic trading under the Markets in Financial Instruments Directive (MiFID II) to include all execution algorithms, regardless of their complexity.

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Tucci, Gabriel, and M. Vega. "Optimal trading trajectories for algorithmic trading." Journal of Investment Strategies 5, no. 2 (March 2016): 57–74. http://dx.doi.org/10.21314/jois.2016.065.

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Patil, Mr Mihir Rajan. "Algorithmic Trading & High Frequency Trading." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 1640–42. http://dx.doi.org/10.22214/ijraset.2019.6275.

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Lee, Joseph, and Lukas Schu. "Regulation of Algorithmic Trading: Frameworks or Human Supervision and Direct Market Interventions." European Business Law Review 33, Issue 2 (April 1, 2022): 193–226. http://dx.doi.org/10.54648/eulr2022006.

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This paper identifies the regulatory gaps that currently exist in algorithmic trading and provides a framework for machine learning regulation in finance. It compares the regulation of algorithmic trading in the capital markets by both human supervision and direct market intervention in the UK, the EU and the US to identify techniques they have in common, as well as local differences. Section II sets out what algorithmic trading is, how it is defined, which of its functions have a positive effect and which are negative for risk and impact. Section III examines how trading risks can be managed by human supervision. Section IV looks at how direct market intervention can mitigate the risks of algorithmic trading, focusing on the circuit breaker requirement. Finally, the liability of the parties involved (traders, firms, and trading venues) are examined and the possible enforcement actions that regulators may take are set out. Algorithms, high frequency trading, machine learning, financial regulation, MIFIDII, ESMA, FCA, SEC, circuit breaker, systemic risk

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Palmer, Max. "Algorithmic Trading: A Primer." Journal of Trading 4, no. 3 (June 30, 2009): 30–35. http://dx.doi.org/10.3905/jot.2009.4.3.030.

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S, Prince Nathan. "Profitable Algorithmic Trading Strategy." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 2424–33. http://dx.doi.org/10.22214/ijraset.2021.39101.

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Abstract: Cryptocurrency has drastically increased its growth in recent years and Bitcoin (BTC) is a very popular type of currency among all the other types of cryptocurrencies which is been used in most of the sectors nowadays for trading, transactions, bookings, etc. In this paper, we aim to predict the change in bitcoin prices by using machine learning techniques on data from Investing.com. We interpret the output and accuracy rate using various machine learning models. To see whether to buy or sell the bitcoin we created exploratory data analysis from a year of data set and predict the next 5 days change using machine learning models like logistic Regression, Logistic Regression with PCA (Principal Component Analysis), and Neural network. Keywords: Data Science, Machine Learning, Regression, PCA, Neural Network, Data Analysis

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Jain, Archana, Chinmay Jain, and Christine X. Jiang. "Algorithmic Trading and Fragmentation." Journal of Trading 12, no. 4 (September 30, 2017): 18–28. http://dx.doi.org/10.3905/jot.2017.12.4.018.

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CARTEA, ÁLVARO, SEBASTIAN JAIMUNGAL, and DAMIR KINZEBULATOV. "ALGORITHMIC TRADING WITH LEARNING." International Journal of Theoretical and Applied Finance 19, no. 04 (May 25, 2016): 1650028. http://dx.doi.org/10.1142/s021902491650028x.

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We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of their predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates their predictions to tweak their strategy. Compared to a trader who cannot learn from market dynamics or from a view of the market, the algorithmic trader’s profits are higher and more certain. Even though the trader executes a strategy based on a directional view, the sources of profits are both from making the spread as well as capital appreciation of inventories. Higher volatility of prices considerably impairs the trader’s ability to learn from price innovations, but this adverse effect can be circumvented by learning from a collection of assets that comove. Finally, we provide a proof of convergence of the numerical scheme to the viscosity solution of the dynamic programming equations which uses new results for systems of PDEs.

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Bogoev, Dimitar, and Arzé Karam. "Detection of algorithmic trading." Physica A: Statistical Mechanics and its Applications 484 (October 2017): 168–81. http://dx.doi.org/10.1016/j.physa.2017.04.157.

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Min, Bo Hee, and Christian Borch. "Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets." Social Studies of Science 52, no. 2 (October 6, 2021): 277–302. http://dx.doi.org/10.1177/03063127211048515.

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This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic ‘flash crashes’. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow’s normal accident theory. We suggest that the implementation of ideas from research into high-reliability organizations offers a way for trading firms to curb some of the technological risk associated with algorithmic trading. Paradoxically, however, certain systemic conditions in markets can allow individual firms’ high-reliability practices to exacerbate market instability, rather than reduce it. We therefore conclude that in order to make automated markets more stable (and curb the impact of failures), it is important to both widely implement reliability-enhancing practices in trading firms and address the systemic risks that follow from the tight coupling and complex interactions of markets.

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Batiuk, B. V. "Problems and prospects of algorithmic trade in financial markets." Entrepreneur’s Guide 13, no. 2 (May 1, 2020): 9–16. http://dx.doi.org/10.24182/2073-9885-2020-13-2-9-16.

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The use of algorithms in trading (algorithmic trading) is the trend of recent decades, which has largely changed the market. As part of the research, the fundamentals of algorithmic trading, it’s possible application during exchange trading, were examined in detail. An assessment was also made of the accessibility of obtaining exchange robots for the corporate sector, the benefits of use and optimal conditions for use. Moreover, the impact of exchange trading on the global economy is valuable and a forecast is made for the further development of trading robots.

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Teodorovic, Natasa. "Liquidity, price impact and trade informativeness: Evidence from the London stock exchange." Ekonomski anali 56, no. 188 (2011): 91–123. http://dx.doi.org/10.2298/eka1188091t.

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The rapid development of electronic trading has significantly changed stock exchange markets. Electronic systems providing trading processes have defined a new stock market environment. Such a new environment requires trading process redefinition (generally defined as algorithmic trading), as well as redefinition of well known microstructure hypotheses. This paper conducts standard Hasbrouck?s (1991a, 1991b) market microstructure time series analysis to examine adverse selection and information asymmetry issues on diverse liquidity leveled stocks listed on the London Stock Exchange, which is a market with a significant algorithmic trading share. Based on the results obtained from the considered sample, this paper suggests that the contribution of unexpected trade in the volatility of the efficient price is larger for intensively traded stocks, arguing that Hasbrouck?s (1991a, 1991b) model recognizes algorithmic trading as an unexpected trade, i.e. as a trade caused by superior information.

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Hatch, Brian C., Shane A. Johnson, Qin Emma Wang, and Jun Zhang. "Algorithmic trading and firm value." Journal of Banking & Finance 125 (April 2021): 106090. http://dx.doi.org/10.1016/j.jbankfin.2021.106090.

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Domowitz, Ian, and Henry Yegerman. "The Cost of Algorithmic Trading." Journal of Trading 1, no. 1 (December 31, 2005): 33–42. http://dx.doi.org/10.3905/jot.2006.609174.

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Flatley, Robert. "Algorithmic Trading in Turbulent Markets." Journal of Trading 3, no. 4 (September 30, 2008): 7–13. http://dx.doi.org/10.3905/jot.2008.3.4.7.

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Hanif, Ayub, and Robert Elliott Smith. "Algorithmic, Electronic, and Automated Trading." Journal of Trading 7, no. 4 (September 30, 2012): 78–86. http://dx.doi.org/10.3905/jot.2012.7.4.078.

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22

Sharma, Rohan. "Algorithmic Trading Stock Price Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1853–59. http://dx.doi.org/10.22214/ijraset.2022.45595.

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Abstract: Stock market price prediction is a difficult undertaking that generally requires a lot of human-computer interaction. Traditional batch processing methods cannot be used effectively for stock market analysis due to the linked nature of stock prices. This project presents an online learning technique that employs a recurrent neural network of some sort (RNN) called Long Short-Term Memory (LSTM), which uses stochastic gradient descent to update the weights for individual data points. When compared to existing stock price prediction systems, this will yield more accurate results. With varying sizes of data, the network is trained and evaluated for accuracy, and the results are tallied. A comparison with respect to accuracy is then performed against an Artificial Neural Network. Traditional approaches to securities market analysis and stock value prediction embrace basic analysis, that appearance at a stock's past performance and therefore the general believability of the corporate itself, and applied mathematics analysis, that is exclusively involved with computation and distinguishing patterns available value variation.

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Cartea, Álvaro, Ryan Donnelly, and Sebastian Jaimungal. "Algorithmic Trading with Model Uncertainty." SIAM Journal on Financial Mathematics 8, no. 1 (January 2017): 635–71. http://dx.doi.org/10.1137/16m106282x.

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Rosenbaum, Mathieu. "Algorithmic and High-Frequency Trading." Quantitative Finance 18, no. 1 (October 27, 2017): 7–8. http://dx.doi.org/10.1080/14697688.2017.1380983.

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Emory, Claire. "Does Algorithmic Trading Improve Liquidity?" CFA Digest 41, no. 2 (May 2011): 39–41. http://dx.doi.org/10.2469/dig.v41.n2.36.

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HENDERSHOTT, TERRENCE, CHARLES M. JONES, and ALBERT J. MENKVELD. "Does Algorithmic Trading Improve Liquidity?" Journal of Finance 66, no. 1 (January 6, 2011): 1–33. http://dx.doi.org/10.1111/j.1540-6261.2010.01624.x.

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Bhatia, Randeep, Julia Chuzhoy, Ari Freund, and Joseph (Seffi) Naor. "Algorithmic aspects of bandwidth trading." ACM Transactions on Algorithms 3, no. 1 (February 2007): 1–19. http://dx.doi.org/10.1145/1186810.1186820.

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Lopez de Prado, Marcos. "Algorithmic and High Frequency Trading." Quantitative Finance 16, no. 8 (March 14, 2016): 1175–76. http://dx.doi.org/10.1080/14697688.2016.1143619.

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Broussard, John Paul, and Andrei Nikiforov. "Intraday periodicity in algorithmic trading." Journal of International Financial Markets, Institutions and Money 30 (May 2014): 196–204. http://dx.doi.org/10.1016/j.intfin.2014.03.001.

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Ahrabian, Alireza, Clive Cheong Took, and Danilo P. Mandic. "Algorithmic Trading Using Phase Synchronization." IEEE Journal of Selected Topics in Signal Processing 6, no. 4 (August 2012): 399–404. http://dx.doi.org/10.1109/jstsp.2011.2173900.

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Zhou, Hao, Petko S. Kalev, and Alex Frino. "Algorithmic trading in turbulent markets." Pacific-Basin Finance Journal 62 (September 2020): 101358. http://dx.doi.org/10.1016/j.pacfin.2020.101358.

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Kondratieva, T., L. Prianishnikova, and I. Razveeva. "Machine learning for algorithmic trading." E3S Web of Conferences 224 (2020): 01019. http://dx.doi.org/10.1051/e3sconf/202022401019.

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The purpose of the study is to confirm the feasibility of using machine learning methods to predict the behavior of the foreign exchange market. The article examines the theoretical and practical aspects of the implementation of artificial neural networks in the process of Internet trading. We studied the features of constructing automated trading advisors that perform trading operations based on the forecast of neural networks in combination with indicator signals. As a result, a hybrid system has been built that has a high-precision forecast and allows you to make a profit with the correct selection of parameters.

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Rahate, Shubham R. "Design of Optimized Trading Strategies with Web Assembly." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4997–5001. http://dx.doi.org/10.22214/ijraset.2021.36049.

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Web Assembly is growing and also the most widely studied area which interests many developers when it comes to performance and speed to make web development fast as ever. When it comes to speed and performance algorithms can perform faster computations. Algorithmic trading executes trade at a faster speed. It can buy and sell stocks within a fraction of milliseconds. However, selecting the right tools and technologies is extremely important in algorithmic trading. There are trading strategies which we can use to optimize our trade and increase the return gained on buying and selling stocks. But, choosing an efficient programming language is substantially important. A programming language with a low latency can leverage the trade. Most commonly used languages for algorithmic trading are C/C++, Java, C#, Python. Speed and performance are an essential factor in algorithmic trading. The main purpose of introducing web Assembly in trading as discussed above is speed and performance. Web Assembly is a low-level binary instruction which can execute any program on the web and it can deliver native like performance on the internet. Using Web Assembly, we can compile any code written in languages like C/C++, C#, Java, and python to wasm (Web Assembly executable file) and run on the browser. Web Assembly was developed by W3C, Mozilla Corporation, and Google.

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Syamala, Sudhakara Reddy, and Kavita Wadhwa. "Trading performance and market efficiency: Evidence from algorithmic trading." Research in International Business and Finance 54 (December 2020): 101283. http://dx.doi.org/10.1016/j.ribaf.2020.101283.

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Yan, Ru Zhen, Ping Li, and Yong Zeng. "Optimal Algorithmic Trading Strategy with the Price Appreciation Cost." Applied Mechanics and Materials 631-632 (September 2014): 62–65. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.62.

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Financial markets has witnessed an explosion of algorithmic trading strategy which can help traders especially involved in high-frequency trading efficiently reduce invisible transaction cost. The VWAP strategy usually used by traders can only decrease the cost of price impact by breaking block order into small pieces. However, the behavior of such order splitting may result in inevitable opportunity cost as well as price appreciation. This paper establishes a new algorithmic trading strategy to minimize total transaction costs including price impact, opportunity cost and price appreciation. The results show that the total transaction cost of this optimal trading strategy is lower than VWAP strategy.

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Jóźwicki, Rafał, Paweł Trippner, and Karolina Kłos. "Algorithmic Trading and Efficiency of the Stock Market in Poland." Finanse i Prawo Finansowe 2, no. 30 (June 30, 2021): 75–85. http://dx.doi.org/10.18778/2391-6478.2.30.05.

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The aim of the article is to investigate the impact of algorithmic trading on the returns obtained in the context of market efficiency theory. The research hypothesis is that algorithmic trading can contribute to a better rate of return than when using passive investment strategies. Technological progress can be observed in many different aspects of our lives, including investing in capital markets where we can see changes resulting from the spread of new technologies. The methodology used in this paper consists in confronting a sample trading system based on classical technical analysis tools with a control strategy consisting in buying securities at the beginning of the test period and holding them until the end of this period. The results obtained confirm the validity of the theory of information efficiency of the capital market, as the active investment strategy based on algorithmic trading did not yield better results than the control strategy.

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Yan, Siyuan, Xiaoxu Ling, Tian Cao, Shengqi Hu, Rong Xiong, Hongbo Ye, and Ruihan Zhang. "Algorithmic Trading and Challenges on Retail Investors in Emerging Markets." Journal of Economics, Finance and Accounting Studies 4, no. 3 (August 30, 2022): 36–41. http://dx.doi.org/10.32996/jefas.2022.4.3.4.

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Mixed views on automated trading in the extant literature lead to ongoing debates on algorithmic trading (AT) and high-frequency trading (HFT). This study elaborates on the rising ethical issues and regulatory challenges of algorithmic trading and high-frequency trading in emerging markets. While developed capital markets are dominated by institutional investors, emerging markets consist of a large proportion of retail investors who may suffer from aggravated liquidity asymmetry and stock price turbulence due to HFT and AT. Furthermore, we review current regulations of HFT in the U.S. and European markets and provide a framework of regulatory enforcements on AT and HFT for investor protection in emerging markets. This study cautions policymakers in emerging markets that legal and regulatory monitoring of AT and HFT activities is especially necessary.

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Cohen, Gil, and Mahmoud Qadan. "The Complexity of Cryptocurrencies Algorithmic Trading." Mathematics 10, no. 12 (June 12, 2022): 2037. http://dx.doi.org/10.3390/math10122037.

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In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.

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King, Michael, and Dagfinn Rime. "Algorithmic Trading and FX Market Liquidity." CFA Institute Magazine 22, no. 3 (May 2011): 15–17. http://dx.doi.org/10.2469/cfm.v22.n3.5.

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Prix, Johannes, Otto Loistl, and Michael Huetl. "Algorithmic Trading Patterns in Xetra Orders." European Journal of Finance 13, no. 8 (December 2007): 717–39. http://dx.doi.org/10.1080/13518470701705538.

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Loke, Alexander. "Mistakes in Algorithmic Trading of Cryptocurrencies." Modern Law Review 83, no. 6 (August 11, 2020): 1343–53. http://dx.doi.org/10.1111/1468-2230.12574.

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CARTEA, ÁLVARO, and SEBASTIAN JAIMUNGAL. "ALGORITHMIC TRADING OF CO-INTEGRATED ASSETS." International Journal of Theoretical and Applied Finance 19, no. 06 (September 2016): 1650038. http://dx.doi.org/10.1142/s0219024916500382.

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We assume that the drift in the returns of asset prices consists of an idiosyncratic component and a common component given by a co-integration factor. We analyze the optimal investment strategy for an agent who maximizes expected utility of wealth by dynamically trading in these assets. The optimal solution is constructed explicitly in closed-form and is shown to be affine in the co-integration factor. We calibrate the model to three assets traded on the Nasdaq exchange (Google, Facebook, and Amazon) and employ simulations to showcase the strategy’s performance.

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Bendtsen, Marcus, and Jose M. Peña. "Gated Bayesian networks for algorithmic trading." International Journal of Approximate Reasoning 69 (February 2016): 58–80. http://dx.doi.org/10.1016/j.ijar.2015.11.002.

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Wang, Feng, Keren Dong, and Xiaotie Deng. "Algorithmic trading system: design and applications." Frontiers of Computer Science in China 3, no. 2 (May 16, 2009): 235–46. http://dx.doi.org/10.1007/s11704-009-0030-6.

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Weller, Brian M. "Does Algorithmic Trading Reduce Information Acquisition?" Review of Financial Studies 31, no. 6 (December 7, 2017): 2184–226. http://dx.doi.org/10.1093/rfs/hhx137.

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Kawakatsu, Hiroyuki. "Direct multiperiod forecasting for algorithmic trading." Journal of Forecasting 37, no. 1 (September 15, 2017): 83–101. http://dx.doi.org/10.1002/for.2488.

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SALAKHUTDINOV, Georgy Vladimirovich. "ALGORITHMIC TRADING IN THE STOCK MARKET." Актуальные исследования, no. 51-2 (2022): 74–76. http://dx.doi.org/10.51635/27131513_2022_51_2_74.

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Mukerji, Purba, Christine Chung, Timothy Walsh, and Bo Xiong. "The Impact of Algorithmic Trading in a Simulated Asset Market." Journal of Risk and Financial Management 12, no. 2 (April 20, 2019): 68. http://dx.doi.org/10.3390/jrfm12020068.

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In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.

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Păuna, Cristian. "Reliable Signals Based on Fisher Transform for Algorithmic Trading." Timisoara Journal of Economics and Business 11, no. 1 (June 1, 2018): 87–102. http://dx.doi.org/10.2478/tjeb-2018-0006.

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Abstract Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades is set up automatically today by computers using algorithmic trading procedures. For this particular field, special aspects must be met in order to automate the trading process. This paper presents one of these mathematical models used in automated trading systems, a method based on the Fisher transform. A general form of this method will be presented, the functional parameters and the way to optimize them in order to reduce the risk. It will be also suggested a method to build reliable trading signals with the Fisher function in order to be automated. Three different trading signal types will be explained together with the significance of the functional parameters in the price field. A code sample will be included in this paper to prove the simplicity of this method. Real results obtained with the Fisher trading signals will be also presented, compared and analyzed in order to show how this method can be implemented in algorithmic trading.

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Varma, Penumatcha Bharath, Dr Jaypal Medida, Neeraj Kasheety, Hanumanula Sravya, and Chinthapalli Amarnath Reddy. "Algo-Trading using Statistical Learning and Optimizing Sharpe Ratio and Drawdown." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (November 30, 2021): 95–100. http://dx.doi.org/10.35940/ijrte.d6585.1110421.

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Abstract:

Modernization in computers and Machine Learning have created new opportunities for improving the methods involved in trading, Changes have been noticed parallelly at the level of investment decisions, and at the faster executions of trades via algorithms. Nowadays 90% of the trades are placed by algorithms, to execute a transaction, algorithms that follow a trend and construct a set of instructions are used in algorithmic trading. It executes the trades more precisely by precluding the effect of human feelings on trading. It all started way back in the 20th century and nowadays it’s becoming more and more competitive, with more big players entering the market every day. Our research aims to advance the market revolution by developing an Algorithmic Trading approach that will automatically trade user strategies alongside its own algorithms for intraday trading based on different market conditions and user approach, and throughout the day invest and trade with continuous modifications to ensure the best returns for day traders and investors.

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Journal articles on the topic 'Algorithmic Trading'

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