# A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics

**Authors:** Luyao Zhang, Tianyu Wu, Saad Lahrichi, Carlos-Gustavo Salas-Flores, Jiayi Li  
**arXiv:** [2206.14932](https://arxiv.org/abs/2206.14932)  
**Date:** 2022-06-29  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2206.14932v1/

## Key points

- Authors address the growing significance of algorithmic trading in finance and cryptoeconomics due to advancements in AI
- Highlight the disconnect between research and practical applications in algorithmic trading
- Propose a versatile pipeline for designing, programming, and evaluating algorithmic trading strategies for stock and crypto assets
- Demonstrate effectiveness by applying the pipeline to four conventional algorithms: moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage
- Emphasize importance of object-oriented programming in Python3 for efficient implementation
- Research accepted at The First International Symposium on Recent Advances of Blockchain Evolution: Architecture, Intelligence, Incentive,and Applications
- Provides valuable insights into integrating AI-driven algorithmic trading in finance and cryptoeconomics
- Offers open-source software for future academic research and applications

## Summary

In their paper titled "A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics," authors Luyao Zhang, Tianyu Wu, Saad Lahrichi, Carlos-Gustavo Salas-Flores, and Jiayi Li address the growing significance of algorithmic trading in the financial sector due to recent advancements in Artificial Intelligence (AI). They highlight the existing disconnect between research and practical applications in this field and propose a versatile pipeline for designing, programming, and evaluating algorithmic trading strategies for both stock and crypto assets. The authors demonstrate the effectiveness of their data science pipeline by applying it to four conventional algorithms: moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Through a systematic approach, they showcase how these algorithms can be programmed, evaluated, and compared to optimize trading strategies. Additionally, the study emphasizes the importance of object-oriented programming in Python3 as a means to implement these algorithms efficiently. Accepted at The First International Symposium on Recent Advances of Blockchain Evolution: Architecture, Intelligence, Incentive,and Applications," this research provides valuable insights into the integration of AI-driven algorithmic trading in finance and cryptoeconomics. By offering open-source software for future academic research and applications,the authors contribute to advancing knowledge in this rapidly evolving field.