Algorithmic Trading Strategy Development and Optimisation
This work addresses the problem of enhancing trading returns for financial practitioners, but it is incremental as it combines existing methods like technical indicators and sentiment analysis.
The authors tackled the problem of improving algorithmic trading performance by integrating technical indicators and FinBERT-based sentiment analysis with historical S&P 500 data, resulting in a strategy that significantly outperformed a baseline in total return, Sharpe ratio, and drawdown.
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.