Decision trees with complex conditions for trading
Design and compare decision trees with multiple-variable conditions for interpretable prediction on trading or fraud data.
Stream graphs for fraud detection
Stream graphs applied to fraud detection.
Enhancing Trading Strategy Robustness through Anomaly Detection
This project explores the use of anomaly detection techniques, such as One-Class SVM, to identify the conditions under which algorithmic trading strategies are likely to succeed, ensuring their applicability and extending the approach to portfolio-wide strategy optimization.
Graph-Based and Time-Series Hybrid Modeling for Fraud Detection
This project develops a hybrid fraud detection model by combining graph-based learning and time-series analysis to capture complex transaction relationships, aiming to enhance fraud prediction accuracy while minimizing false positives in large-scale payment datasets.
Hopular applied to Fraud Detection
Hopular applied to Fraud Detection.
Inverse Reinforcement Learning for portfolio allocation
Inverse Reinforcement Learning for portfolio allocation.
Explainability applied to fraud detection
Constructing probabilistic rule list.
Financial time-series modeling
Comparing hybrid neural networks and traditional models for financial time series.