Tutorial on Automated Trading using API
Pages 305 - 307
Abstract
Automation has played a significant role in many domains, and stock market trading is not an exception. Even retail traders can automate his/her trading strategy using API. A computer program doing trading is known as Algorithmic Trading. It eliminates inefficiency due to human emotions. Trading logic that decides when to buy and sell a stock is generally term as a trading strategy. The availability of large trading data has made it possible to automate the generation of dynamic trading strategies. Speed and accuracy are very vital aspects of profitable stock market trading. Algorithmic trading is far superior to manual trading in terms of speed and accuracy. In this tutorial, we demonstrate the automation of predefined trading strategies using Python API. We will also demonstrate the generation of trading strategies using Reinforcement learning, Deep learning, and various other domains in computer science. Validating the performance of any predefined trading strategy on historical data plays a significant role in its live performance and it is known as backtesting. Backtesting is the key feature of Algorithmic Trading. We will demonstrate backtesting of the trading strategies using a computer program on the Indian and American stock market data.
References
[1]
Jagdish Chakole and Manish Kurhekar. 2020. Trend following deep Q-Learning strategy for stock trading. Expert Systems 37, 4 (2020), e12514.
[2]
Jagdish Bhagwan Chakole, Mugdha S Kolhe, Grishma D Mahapurush, Anushka Yadav, and Manish P Kurhekar. 2021. A Q-learning agent for automated trading in equity stock markets. Expert Systems with Applications 163 (2021), 113761.
[3]
Michael Covel. 2004. Trend following: how great traders make millions in up or down markets. FT Press.
[4]
Terrence Hendershott, Charles M Jones, and Albert J Menkveld. 2011. Does algorithmic trading improve liquidity?The Journal of Finance 66, 1 (2011), 1–33.
[5]
Yong Hu, Bin Feng, Xiangzhou Zhang, EWT Ngai, and Mei Liu. 2015. Stock trading rule discovery with an evolutionary trend following model. Expert Systems with Applications 42, 1 (2015), 212–222.
[6]
Yong Hu, Kang Liu, Xiangzhou Zhang, Lijun Su, EWT Ngai, and Mei Liu. 2015. Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing 36 (2015), 534–551.
[7]
Jessica James 2003. Simple trend-following strategies in currency trading. Quantitative Finance 3, 4 (2003), C75–C77.
[8]
Michael Kearns and Luis Ortiz. 2003. The Penn-Lehman automated trading project. IEEE Intelligent systems 18, 6 (2003), 22–31.
[9]
John Moody and Matthew Saffell. 2001. Learning to trade via direct reinforcement. IEEE transactions on neural Networks 12, 4 (2001), 875–889.
[10]
Philip Treleaven, Michal Galas, and Vidhi Lalchand. 2013. Algorithmic trading review. Commun. ACM 56, 11 (2013), 76–85.
[11]
Yang Wang, Dong Wang, Shiyue Zhang, Yang Feng, Shiyao Li, and Qiang Zhou. 2016. Deep Q-trading. (2016).
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Published In
January 2023
357 pages
ISBN:9781450397971
DOI:10.1145/3570991
Copyright © 2023 ACM.
© 2023 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
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Published: 04 January 2023
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CODS-COMAD 2023
CODS-COMAD 2023: 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)
January 4 - 7, 2023
Mumbai, India
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