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

TL;DR

This paper introduces a comprehensive data science pipeline for designing, implementing, and evaluating algorithmic trading strategies across finance and cryptoeconomics, facilitating systematic comparison and open-source development.

Contribution

It presents a general pipeline for algorithmic trading, demonstrating its application to four algorithms and providing an open-source Python implementation for research and practical use.

Findings

Systematic framework for trading strategy development

Application to four conventional algorithms

Open-source Python implementation

Abstract

Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating the algorithmic trading of stock and crypto assets. Moreover, we demonstrate how our data science pipeline works with respect to four conventional algorithms: the moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage algorithms. Our study offers a systematic way to program, evaluate, and compare different trading strategies. Furthermore, we implement our algorithms through object-oriented programming in Python3, which serves as open-source software for future academic research and applications.