I am lecturing a module on Reinforcement Learning

QFL excels in automated quantitative trading strategies for stocks, futures, and options, leveraging wavelet analysis and Machine Learning for powerful forecasting models.Crafted systematic trading strategies for cash equity, futures, ETFs, and vanilla options, equipping traders with tools to quantify arbitrage opportunities and assess relative value.Pioneered innovative pricing models that address real trading challenges in option pricing, market making, volatility trading, and risk management.Developed advanced AI and ML-driven quantitative strategies to predict returns distributions, paired with a specialized backtesting engine for robust strategy testing.Created a ML-enhanced backtesting engine designed for synthetic scenarios, return distributions, and probabilistic risk/return profiling.Applied cutting-edge research to market forecasting, trading strategy design, risk assessment, and portfolio optimization for optimal client outcomes.http://quantfin.eu/

At Blu Analytics, a tech-driven asset management firm, I lead a forward-thinking team focused on Natural Language Processing (NLP) to predict significant market shifts from news data, transforming these insights into valuable trading signals. We leverage Reinforcement Learning (RL) to design quantitative strategies and drive our risk management system, positioning ourselves at the cutting edge of machine learning to automate event-driven trading while enhancing risk management.My role at Blu Analytics encompasses creating advanced financial strategies, tapping into AI and ML to develop a fully automated, event-driven trading platform, and refining a sophisticated backtesting engine for robust strategy testing.Key Innovations and Contributions:Pioneering Pricing Models: Developing models aimed at real-world trading issues across multiple finance domains: -Option Pricing: Specializing in market making, volatility trading, and risk management for accurate option pricing. -Quantitative Strategy Development: Utilizing AI and ML to forecast future returns distributions, which form the basis for strong, data-driven trading strategies. -Backtesting Engine: Creating a backtesting engine that employs ML to simulate synthetic scenarios and compute risk/reward profiles through probabilistic analysis, adding a data-driven edge to strategy testing.

Support the sales process by crafting tailored machine learning proposals and strategies for prospective clients, aligning technical capabilities with client needs.Drive digital transformation across the business, from program execution planning to full-scale operational implementation. This includes building an execution roadmap, establishing a governance model, and translating business goals into actionable data and AI requirements. Oversee project execution to ensure AI solutions are seamlessly integrated into business processes, promoting standardization, operationalization, and scalability. Develop innovative AI solutions for new clients, enhancing the business’s reach and impact with advanced data-driven capabilities.

Teach quantitative volatility trading.

Led the pricing and risk management library, integrating stochastic and local volatility models for exotic equity products, with automated dynamic volatility surfaces.Developed systematic trading strategies, managed the vanilla options trading platform, and validated Murex for enhanced functionality.Collaborated closely with Model Validation and Market Risk teams to review models, assess risk, and set trading limits for a more resilient trading framework.

Directed the global pricing and risk management library, integrating stochastic interest and FX rate models with local volatility techniques to optimize portfolio valuation and hedging strategies.Collaborated with Model Validation and Market Risk teams to rigorously review models, assess risk, and establish trading limits for comprehensive risk oversight.