Algorithmic Trading Strategy Development and Optimisation

TL;DR

This paper develops an enhanced algorithmic trading strategy that combines technical indicators, sentiment analysis, and optimization, demonstrating significant performance improvements over baseline models using historical market data.

Contribution

It introduces a novel integration of sentiment analysis with technical indicators and optimization techniques for improved trading strategy performance.

Findings

Enhanced strategy outperforms baseline in total return

Improved Sharpe ratio and reduced drawdown

Validates combining sentiment analysis with technical indicators

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.