08. Computing Performance
AI For Trading C1 L1 A07 Computing Performance V3
Understanding Investment Metrics for Machine Learning
When developing trading algorithms, it's crucial to thoroughly analyze return data using a set of financial metrics. Here's a simple breakdown:
Average Returns: Determines the baseline performance of investments over time. If a machine learning algorithm doesn't significantly outperform this, it may not be valuable.
Standard Deviation: Measures the volatility or risk tied to the returns. Lower risk for the same return indicates a better strategy.
Sharpe Ratio: This metric normalizes returns by comparing them to the risk involved. A higher Sharpe Ratio suggests that the strategy is achieving returns efficiently relative to risk.
Compound Annual Growth Rate (CAGR): Shows the yearly growth rate over a period, accounting for reinvested earnings. It helps compare the performance of different investment strategies.
Applying these metrics allows for better investment decision-making by understanding the risk-return balance, optimizing the value added by machine learning models.
SOLUTION:
- We computed the performance of our investment universe as a benchmark to measure the relative performance of individual investments or strategies, helping us determine whether they are outperforming or underperforming the broader market