Raphaël Minato
Transformer-based robust predictive models for financial temporal series with concept drift.
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.
JDSE 2022
The Junior Conference on DataScience and Engeneering 2022 (JDSE) took place on september 15-16, on the Polytechnique campus (Palaiseau).
Hugo Thimonier has the opportunity to present “TracInAD: Measuring Influence for Anomaly Detection” to the audience.
Marc Velay won the best poster contest with his poster about “Robustness Analysis of Deep RL for Portfolio Selection”.
Inverse Reinforcement Learning for portfolio allocation
Inverse Reinforcement Learning for portfolio allocation.
Financial time-series modeling
Comparing hybrid neural networks and traditional models for financial time series.
Reinforcement learning trading strategy analysis
Explaining the decisions of reinforcement learning agents for portfolio management and comparing them with technical trading indicators.
Temporal Point Processes
Machine learning applied to Hawkes processes.