A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics
Proposes a data science pipeline for algorithmic trading in finance and cryptoeconomics, using Python and open-source software.
What it examines
This paper proposes a data science pipeline for algorithmic trading in finance and cryptoeconomics, demonstrating its application with four conventional algorithms. It aims to provide a systematic way to program, evaluate, and compare different trading strategies using open-source Python software.
What it concludes
The research demonstrates a versatile data science pipeline for algorithmic trading, applicable to both stock and crypto assets. Potential applications include designing new trading algorithms and enhancing existing ones. Future research could explore advanced reinforcement learning algorithms and sentiment analysis.
Evidence objects
The research demonstrates a versatile data science pipeline for algorithmic trading, applicable to both stock and crypto assets. Potential applications include designing new trading algorithms and enhancing existing ones. Future research could explore advanced reinforcement learning algorithms and sentiment analysis.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 respe… ▽ More 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. △ Less
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