Design and Implementation of a Multi-Strategy Algorithmic Trading Bot

Shahzeb khan; Zawar Ahmed Khan; Saif Ur Rehman; Arbab Masood Ahmad

Authors

Shahzeb khan Department of Computer Systems Engineering University of Engineering & Technology, Peshawar
Zawar Ahmed Khan Department of Computer Systems Engineering University of Engineering & Technology, Peshawar
Saif Ur Rehman Department of Computer Systems Engineering University of Engineering & Technology, Peshawar
Arbab Masood Ahmad Department of Computer Systems Engineering University of Engineering & Technology, Peshawar

Keywords:

Algorithmic Trading, Machine Learning, RSI, Moving Average, Risk Management, Strategies

Abstract

The financial markets require speed and accuracy, and thus, the quick take-up of algorithmic trading systems has ensued. This study presents a hybrid trading bot based on machine learning algorithms and technical indicators such as Moving Average (MA) and Relative Strength Index (RSI). The integration of Random Forest significantly improved signal accuracy and reduced false positives. Back testing over 1 year showed a win rate of 73.2% and a return on investment (ROI) of 42.5%, confirming the effectiveness of the hybrid model. The bot is designed to analyze the market in real-time, and it makes trades autonomously, regulates risk, and adjusts to volatile markets.

References

M. S. M. Bhuiyan et al., “Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies,” Array, vol. 26, p. 100390, Jul. 2025, doi: 10.1016/J.ARRAY.2025.100390.

A. Mittur, R. Kenish, K. Vijayshankar, A. Gottumukkala, and J. S. Nayak, “MultiStrategy Algorithmic Trader,” Smart Innov. Syst. Technol., vol. 316, pp. 371–380, 2023, doi: 10.1007/978-981-19-5403-0_32.

V. Nigam, P. Kumar, C. Lahoti, P. Gupta, and K. Naveen, “Implementation of Algorithm on Intraday Trading Bot for Trading Foreign Exchange Markets Using Python,” SSRN Electron. J., May 2023, doi: 10.2139/SSRN.4492355.

D. M. M. Ali N. Akansu, Sanjeev R. Kulkarni, “Financial Signal Processing and Machine Learning,” Wiley-IEEE Press, vol. 320, 2016, [Online]. Available: https://www.wiley.com/en-ie/Financial+Signal+Processing+and+Machine+Learning-p-9781118745670

N. Y. Wei Wang, “A Machine Learning Framework for Algorithmic Trading with Virtual Bids in Electricity Markets,” IEEE Xplore, 2019, [Online]. Available: https://intra.ece.ucr.edu/~nyu/papers/2019-VB-ML-Trading

M. Khairy, T. M. Mahmoud, A. Omar, and T. Abd El-Hafeez, “Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection,” Lang. Resour. Eval., vol. 58, no. 2, pp. 695–712, Jun. 2024, doi: 10.1007/S10579-023-09683-Y.

John J. Murphy, “Technical Analysis of the Financial Markets,” NEW YORK Inst. Financ., 1999, [Online]. Available: https://preterhuman.net/docs/John_J._Murphy_-_Technical_Analysis_of_the_Financial_Markets_(1999)

M. J. Pring, “Technical Analysis Explained,” IFC Mark., 2002, [Online]. Available: https://www.ifcmarkets.com/pdf_files/Technical_Analysis_explained.pdf

J. W. Wilder, “New concepts in technical trading systems,” Internet Arch., 1978, [Online]. Available: https://archive.org/details/newconceptsintec00wild

A. Thakkar and K. Chaudhari, “A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 2133–2164, Jun. 2021, doi: 10.1007/S11831-020-09448-8.

G. Cohen, “Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies,” Mathematics, vol. 10, no. 18, Sep. 2022, doi: 10.3390/MATH10183302.

A. Golub, J. B. Glattfelder, and R. B. Olsen, “The Alpha Engine: Designing an Automated Trading Algorithm,” High-Performance Comput. Financ., pp. 49–76, Mar. 2019, doi: 10.1201/9781315372006-3.

L. Lotti, “Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange,” Philos. Technol., vol. 31, no. 1, pp. 43–58, Mar. 2018, doi: 10.1007/S13347-016-0249-8.

A. Brooks, “Reading Price Charts Bar by Bar,” Wiley, 2009, [Online]. Available: https://books.google.com/books?id=84VTUGQuRlcC&printsec=copyright#v=onepage&q&f=false

R. Pothumsetty, “APPLICATION OF ARTIFICIAL INTELLIGENCE IN ALGORITHMIC TRADING,” Int. J. Eng. Appl. Sci. Technol., vol. 04, no. 12, pp. 140–149, May 2020, doi: 10.33564/IJEAST.2020.V04I12.019.

J. E. Granville, “New key to stock market profits,” Internet Arch., 1963, [Online]. Available: https://archive.org/details/newkeytostockmar0000gran

W. Zhang, X. Gong, C. Wang, and X. Ye, “Predicting stock market volatility based on textual sentiment: A nonlinear analysis,” J. Forecast., vol. 40, no. 8, pp. 1479–1500, Dec. 2021, doi: 10.1002/FOR.2777.

S. F. Ahmed et al., “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artif. Intell. Rev., vol. 56, no. 11, pp. 13521–13617, Nov. 2023, doi: 10.1007/S10462-023-10466-8.