In this project, I designed and implemented a sophisticated analytical framework to evaluate the performance of various trading strategies based on historical transaction data. The core objective was to identify the most profitable strategies by applying a series of detailed, data-driven metrics.
Key Features of the Project:
Strategy Analysis: Developed a method to filter and evaluate trading strategies by their activation signals ('y' values). This involved assessing each strategy's performance based on wins, losses, and overall profitability.
Concurrent Transactions: Implemented a function to calculate the maximum number of concurrent transactions, providing insights into the trading intensity and risk exposure at any given time.
Metrics Calculation: Calculated comprehensive metrics for each strategy, including overall profit, win/loss ratio, and profitability indices, which helped in determining the effectiveness and efficiency of each trading approach.
Result Output: Automated the generation of output files that ranked strategies based on their profitability, providing clear and actionable insights for potential investment decisions.