Skip to content
All library documents

A Comparative Pipeline for Evaluating Stock and Crypto Trading Strategies

Article arXiv papers · Author: Luyao Zhang et al.
Read the original

Summary

The document presents a general workflow for designing, implementing, and assessing algorithmic trading strategies across stock and crypto markets. It uses four examples: a moving average crossover, a volume-weighted execution approach, sentiment-based trading, and statistical arbitrage. The aim is to make strategy development and comparison more systematic, with an object-oriented implementation intended to support later research and practical use.

The abstract describes the pipeline and its example strategies, but provides no performance results, datasets, evaluation metrics, or implementation details. It therefore indicates how the framework is organized without establishing which approach works best or whether any strategy is profitable. The discussion also gives no specific treatment of transaction costs, market regimes, or risk controls, so readers would need further evidence before drawing conclusions about real-world suitability.

Key ideas

  • A shared workflow can organize the design and assessment of strategies across stock and crypto markets.
  • The study illustrates its process with four methods spanning technical signals, execution, sentiment, and statistical relationships.
  • A consistent evaluation structure can make comparisons among trading approaches more systematic.
  • The abstract does not report empirical performance or enough methodological detail to judge practical effectiveness.

Tags

Multi-assetBacktestingTechnical indicatorsSentimentArbitrage
Full text
# A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.