Winning Strategies in Statistical Arbitrage

Bonsai’s Quantitative Finance team had an exciting opportunity to participate in IMC’s Prosperity-2, a highly competitive, 15-day long trading simulation. This event provided a unique platform for our team to apply quantitative analysis in a fast-paced, simulated marketplace, where real-time decision-making was key to maximizing returns. The competition was not just about making profits; it was about honing our ability to develop and execute advanced trading strategies using data-driven insights. Throughout the competition, we focused on statistical arbitrage, a strategy that involves identifying pairs of assets whose prices move together but temporarily diverge. By using various forms of statistical analysis, our team was able to identify the ideal pairs for trading. We employed comprehensive backtesting techniques to evaluate the effectiveness of our strategies across historical data, assessing performance metrics such as realized profit and loss, Sharpe ratio, and drawdowns. These analyses provided us with crucial insights into how the strategies performed under different market conditions. In addition to statistical arbitrage, we implemented machine learning techniques to enhance our decision-making. A key innovation in this project was the development of a Jupyter notebook to identify optimal pair-candidates using k-means clustering, a method for grouping similar data points. To refine our entry and exit points, we utilized a Long Short-Term Memory (LSTM) model, a type of recurrent neural network that is particularly effective for forecasting time-series data. This allowed us to predict price movements and optimize our trading positions, giving us a competitive edge. Our participation in the Prosperity-2 competition was a rewarding experience that expanded our understanding of quantitative finance, statistical analysis, and machine learning in trading. The strategies we developed and the lessons we learned during this competition will inform future projects as we continue to explore the intersection of finance and data science.