Algorithmic Trading Strategy Development and Optimisation
arXiv:2603.15848v1 Announce Type: new Abstract: The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being taken. The results show that the enhanced strategy significantly outperforms the baseline model in terms of total return, Sharpe ratio, and drawdown amongst other factors. The findings helped demonstrate the relevance and effectiveness of combining technical indicators, sentiment analysis, and computational optimisation in algorithmic trading systems.
arXiv:2603.15848v1 Announce Type: new Abstract: The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being taken. The results show that the enhanced strategy significantly outperforms the baseline model in terms of total return, Sharpe ratio, and drawdown amongst other factors. The findings helped demonstrate the relevance and effectiveness of combining technical indicators, sentiment analysis, and computational optimisation in algorithmic trading systems.
Executive Summary
This article presents a novel algorithmic trading strategy that integrates technical indicators, sentiment analysis, and computational optimisation. Utilising historical S&P 500 market data and earnings call sentiment analysis, the proposed strategy significantly outperforms a baseline model in terms of total return, Sharpe ratio, and drawdown. The findings highlight the relevance and effectiveness of combining technical indicators and sentiment analysis in algorithmic trading systems. The study demonstrates the potential of leveraging FinBERT-based sentiment analysis and computational optimisation techniques to improve trading performance. The results have significant implications for both practitioners and policymakers in the field of algorithmic trading.
Key Points
- ▸ Development and optimisation of an enhanced algorithmic trading strategy using historical S&P 500 market data and earnings call sentiment analysis.
- ▸ Integration of technical indicators, including moving averages, momentum, volatility, and FinBERT-based sentiment analysis.
- ▸ Significant outperformance of the enhanced strategy compared to a baseline model in terms of total return, Sharpe ratio, and drawdown.
Merits
Strength in methodology
The use of a robust methodology, including historical market data and sentiment analysis, lends credibility to the findings and provides a solid foundation for further research.
Effective integration of technical indicators
The combination of multiple technical indicators and sentiment analysis demonstrates the potential for improved trading performance and highlights the importance of a holistic approach to algorithmic trading.
Significant improvement in trading performance
The outperformance of the enhanced strategy compared to the baseline model has significant implications for practitioners and policymakers in the field of algorithmic trading.
Demerits
Limited generalizability
The study's reliance on historical S&P 500 market data may limit the generalizability of the findings to other markets or asset classes.
Potential overfitting
The use of computational optimisation techniques may lead to overfitting, particularly if the model is not sufficiently robust or if the optimisation process is not carefully controlled.
Lack of transparency in model development
The article could benefit from greater transparency regarding the development and implementation of the algorithmic trading strategy, including details on model selection, hyperparameter tuning, and performance evaluation.
Expert Commentary
The article presents a novel and compelling approach to algorithmic trading strategy development and optimisation. The use of FinBERT-based sentiment analysis and computational optimisation techniques demonstrates the potential for improved trading performance and highlights the importance of a holistic approach to algorithmic trading. However, the study's limitations, including the potential for overfitting and the lack of transparency in model development, should be carefully considered. The findings have significant implications for both practitioners and policymakers in the field of algorithmic trading and warrant further investigation and refinement.
Recommendations
- ✓ Future studies should focus on developing more robust and generalisable algorithmic trading strategies that can be applied across multiple markets and asset classes.
- ✓ The use of natural language processing techniques, such as FinBERT-based sentiment analysis, should be further explored and developed for applications in finance, including algorithmic trading and risk management.