Enhancing Trading Strategies with AI - Comparing CatBoost and XGBoost
Introduction
The advent of AI in trading has dramatically transformed the landscape of financial market analysis and execution. AI-driven strategies enable traders to process vast amounts of data, identify patterns, and execute trades with a level of precision and speed that was previously unattainable. By leveraging machine learning algorithms, traders can now develop adaptive models that adjust to market conditions in real-time, providing a competitive edge in the fast-paced world of trading.
Comparing XGBoost and CatBoost
In our previous article, we discussed the use of CatBoost in the FreqaiHybridStrategy. In this section, we compare CatBoost and XGBoost based on various metrics important for financial market modeling.
Performance Metrics
| Metric | XGBoost | CatBoost |
|---|---|---|
| Training Time | Fast | Slow |
| Inference Time | Fast | Moderate |
| CPU Utilization | Moderate | High |
| RAM Consumption | Moderate | High |
| Profitability | High (7% profit) | Moderate (2% profit) |
| Accuracy | High | High |
| Ease of Use | Moderate | High |
Experiment Details
To compare XGBoost and CatBoost, Emergent Methods ran a 3-week long experiment using FreqAI, focusing on the performance of both models in predicting financial market data. The experiment used live data from cryptocurrency markets and evaluated both training and inference times, CPU and RAM usage, and the accuracy and profitability of predictions.
The experiment was conducted by Emergent Methods, who designed and executed the benchmark to evaluate the models' performance in a live, chaotic environment.
The key results showed that while XGBoost was significantly faster and more profitable, CatBoost provided high accuracy but at a higher computational cost. Detailed results and metrics from the experiment can be found in the original article.
Conclusion
By integrating AI with traditional technical analysis, the FreqaiExampleHybridStrategy provides a robust framework for making data-driven trading decisions. This hybrid approach leverages the strengths of both AI and human-designed indicators, enhancing the overall performance of the trading strategy.
For more detailed information on the comparison and the experiment, refer to the full article on Medium.