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).
Recommendations
Using a Robot Trader for Automatic Trading
ICEMIS '18: Proceedings of the Fourth International Conference on Engineering & MIS 2018The main goal of this paper is the study of the robot trader approaches in order to use them in automatic trading, and we propose some technical indicators and modules to uses it in a robot trader for an automate trading; we can signalize trading in two ...
Fuzzy modeling of stock trading with fuzzy candlesticks
A methodology to model trading rules for candlestick patterns using fuzzy logic.The fuzzy trading system adapts three well-known candlestick patterns to fuzzy rules.The fuzzy system performance is tested in two portfolios from different stock ...
Effects of High Frequency Trading on the Malaysian Stock Market
ICoMS '19: Proceedings of the 2019 2nd International Conference on Mathematics and StatisticsThe high-frequency trading (HFT) has changed the world perspective of how the market behaves. HFT is a good system that provides liquidity to markets whereby it increases the market trend and improves its overall financial growth. On the other hand, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
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.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 January 2023
Check for updates
Author Tags
Qualifiers
- Tutorial
- Research
- Refereed limited
Conference
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
Acceptance Rates
Overall Acceptance Rate 197 of 680 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 95Total Downloads
- Downloads (Last 12 months)33
- Downloads (Last 6 weeks)2
Reflects downloads up to 27 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format