The Premier Event for Quantitative Investment Thought Leaders

14 - 15 October 2025 | Convene 22 Bishopsgate, London

Nicole Konigstein

Chief AI Officer, Head of AI & Quant Research quantmade

Nicole is a Data Scientist & Quantitative Researcher currently working as Chief AI Officer & Head of Quantitative Research at quantmate. Alongside her roles in this organization, she also serves as an AI consultant, leading workshops and guiding companies from AI concept to deployment. As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a frequent speaker at AI and Quantitative Finance conferences and events. She is the author of Mathematics for Machine Learning with NLP and Python and Transformers in Action with Manning Publications, and the author of the (forthcoming) book Transformers: The Definitive Guide by O’Reilly Media.

Day 1: 14 October

2:45 PM Trade smarter – Building Agents with reinforcement learning

This hands-on workshop delves into the theory and practical implementation of reinforcement learning (RL) in quantitative finance. Participants will gain insights into building reliable RL models for trading and portfolio management, explore real-world applications, and examine case studies from hedge funds and quant labs.

 

Session Breakdown (80 Minutes):

1. Introduction to Reinforcement Learning in Finance (10 minutes)

  • Overview of RL concepts: agents, environments, states, actions, rewards.
  • Differentiating RL from traditional machine learning approaches.
  • Relevance of RL in trading, portfolio management, and market making.

 

2. Building Reliable RL Models for Trading (20 minutes)

  • Selecting appropriate RL algorithms (e.g., Q-learning, Deep Q-Networks, Policy Gradients).
  • Designing state and action spaces tailored to financial markets.
  • Defining reward functions that align with trading objectives.
  • Addressing challenges like overfitting, non-stationarity, and exploration-exploitation trade-offs.

 

3. Real-World Applications (20 minutes)

  • Examination of RL implementations in hedge funds and quant labs.
  • Strategies for mitigating risks in live trading systems.
  • Lessons learned from deploying RL agents in real market conditions.

 

4. Applications in Asset Allocation, Trade Execution, and Market Making (15 minutes)

  • Utilizing RL for dynamic asset allocation strategies.
  • Enhancing trade execution through RL-driven decision-making.
  • Implementing RL in market-making scenarios to optimize bid-ask spreads and inventory management.

 

5. Interactive Q&A and Discussion (15 minutes)

Open floor for participant questions.


  • Discussion on emerging trends and future directions in RL for finance.
  • Sharing resources for further learning and exploration.

 

Key Takeaways:

  • A solid understanding of how reinforcement learning can be applied to various aspects of quantitative finance.
  • Practical knowledge of building and deploying RL models for trading and portfolio management.
  • Insights into real-world challenges and solutions from industry case studies.
  • Awareness of the potential and limitations of RL in live trading environments.

 


 

Check out the incredible speaker line-up to see who will be joining Nicole.

Download The Latest Agenda