Machine Learning for Algorithmic Trading: A Comprehensive Review

Thanks to the advancement in computing technologies, we’re seeing more widespread use of machine learning, especially deep learning, in the financial services sector. It’s no longer just a theoretical tool; it's showing up in everything from credit risk models to algorithmic trading strategies.

Reference [1] provides a comprehensive review of deep learning techniques used in the financial sector, with a focus on algorithmic trading. It offers a structured analysis of deep learning’s applications across various areas of trading, aiming to identify key trends, challenges, and emerging opportunities by critically evaluating existing research.

The paper provides detailed insights into methodologies applied in different sub-areas of trading such as,

• Stock price prediction
• Market volatility prediction
• Portfolio optimization
• Sentiment analysis for trading
• Risk management
• Anomaly detection and fraud detection
• Supply chain forecasting

Specifically, in volatility forecasting, it highlights,

Recent studies have emphasized the significance of incorporating multiple data streams, including macroeconomic indicators, sentiment analysis, and high-frequency trading data, to enhance the predictive accuracy of volatility models [129,130]. The findings suggest that hybrid models outperform single-model approaches, but data noise and overfitting remain challenges. As shown in Table 8, a variety of models have been applied to different datasets, each with specific contributions and limitations.

Overall, the authors concluded,

This review has highlighted the transformative potential of deep learning in algorithmic trading, where models such as LSTM, CNN, and Reinforcement Learning have shown substantial improvements in predicting financial markets and optimizing trading strategies. However, significant challenges remain, particularly related to data quality, overfitting, and the interpretability of complex DL models. Financial markets are noisy, volatile, and influenced by a multitude of factors, making it difficult for models to generalize well. Additionally, the black-box nature of DL models raises concerns for traders and regulators who require transparency in decision-making. Emerging trends such as attention mechanisms, transformer architectures, and hybrid models offer promising solutions to these challenges, alongside integrating alternative data sources like social media sentiment and news. Future research must focus on improving model robustness, developing explainable AI techniques, and addressing computational efficiency to unlock the full potential of DL in real-world trading environments. By overcoming these hurdles, DL can significantly enhance the accuracy and effectiveness of algorithmic trading, providing traders with more powerful tools for navigating complex financial markets.

In short, deep learning is useful but still has its limitations.

In our experience, being able to leverage advances in computing is definitely an edge, but domain knowledge remains essential.

Let us know what you think in the comments below or in the discussion forum.

References

[1] MD Shahriar Mahmud Bhuiyan, MD AL Rafi, Gourab Nicholas Rodrigues, MD Nazmul Hossain Mir, Adit Ishraq, M.F. Mridha, Jungpil Shin, Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies, Array, Volume 26, 2025, 100390,

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