 How Sentiment Indicators Improve Algorithmic Trading PerformanceRaúl Gómez-MartÃnez, MarÃa Luisa Medrano-GarcÃa, David López-López and Jose Torres-Pruñonosa SAGE Open, 2025, vol. 15, issue 3, 21582440251369559 Abstract: This study explores the hypothesis that sentiment indicators can enhance the performance of algorithmic trading strategies. Specifically, we investigate the impact of incorporating investor sentiment metrics, such as the CNN Fear & Greed Index and cryptocurrency sentiment, on predictive accuracy and profitability. To test this hypothesis, two trading strategies are compared in the Nasdaq Mini futures market. The first strategy employs traditional technical indicators and machine learning models, whereas sentiment-based indicators are incorporated to the second one to enhance it. Backtests are conducted over the period from May 16, 2022 to December 20, 2024, to evaluate the effectiveness of sentiment signals. The results demonstrate that the sentiment-augmented strategy improves risk-adjusted returns, reduces volatility, and enhances profitability compared to the baseline model. This study provides evidence that sentiment indicators can be a valuable addition to algorithmic trading systems, offering a more stable and risk-managed approach, even though they may not always maximise net profit. Keywords: algorithmic trading; sentiment indicators; technical indicators; CNN fear & greed index; cryptocurrency sentiment; machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251369559 DOI: 10.1177/21582440251369559 Access Statistics for this article More articles in SAGE Open |
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