• Is AI’s impact a natural extension of existing quantitative methods, or a fundamental disruption? Explore AI’s capabilities through discussions on:
• What recent advancements in AI and machine learning have significantly improved alpha signal extraction, and how are these integrated into systematic strategies?
• How are reinforcement learning, predictive modeling, and execution algorithms driving measurable improvements in portfolio execution efficiency?
• What distinguishes incremental model enhancements from disruptive AI techniques in the context of strategy evolution and market adaptability?
• How are adaptive AI frameworks—such as agent-based models and meta-learning systems—shaping the future of autonomous trading in complex, dynamic environments?

• Explore the field of reinforcement learning (RL) for financial applications on the buy side.
• Discover RL algorithms for market making and hedging.
• Explore use cases for options trading and discuss real world constraints such as latency.

Machine learning has become increasingly central to both forecasting and portfolio construction. But how do you separate hype from real, actionable value?

• What ML models are most effective for return and risk forecasting?
• How are firms integrating AI and ML signals into quant investment strategies?
• When does ML add value over traditional quant techniques?

Traditional risk models often fall short in capturing the complex dynamics across multiple asset classes, regions, and liquidity profiles. Gain insights into:

• The development and implementation of functional, adaptable, data-driven risk models
• Cross-asset volatility, as well as fact and correlation modelling.
• Practical challenges in stress testing and scenario analysis.

Panel Questions:

• How can we develop and implement functional, adaptable, data-driven risk models that effectively capture the complexities across multiple asset classes, regions, and liquidity profiles?
• In the context of cross-asset volatility, what are the best practices for factor and correlation modeling to ensure accurate risk assessment and management?
• What are the practical challenges faced in conducting stress testing and scenario analysis, and how can these be addressed to improve risk model robustness?
• Given the dynamic nature of financial markets, how can risk models be designed to adapt to changing conditions and provide reliable insights during periods of market stress?

In this session, the story will be shared behind developing and deploying the first fully AI-managed ETF on the New York Stock Exchange—where every trade, rebalance, and decision was autonomously driven by AI. From ingesting every market tick across US large-cap equities to constructing and rebalancing a highly active, cost-sensitive portfolio, the presentation offers a rare, transparent look at how AI can fully automate public investment vehicles.

Understand the technical architecture required to automate ETF management using AI, from ingesting high-frequency market data to portfolio rebalancing and execution.

Explore real-world challenges in data quality, cost optimisation, and regulatory compliance when deploying an AI-driven strategy to a public exchange.

Gain insights into lessons learned from the launch, management, and sale of a fully autonomous ETF—highlighting what it takes to bridge quant innovation and public market accountability.

Machine learning has become increasingly central to both forecasting and portfolio construction. But how do you separate hype from real, actionable value?

• What ML models are most effective for return and risk forecasting?
• How are firms integrating AI and ML signals into quant investment strategies?
• When does ML add value over traditional quant techniques?

This hands-on workshop guides quant analysts through the complete workflow of utilizing KDB and q, the high-performance time-series database and programming language widely adopted in financial institutions. Participants will learn the fundamentals of KDB and q for handling real-time market data at scale, build and test end-to-end workflows for analysis, signal generation, and execution, and gain practical experience in writing, optimizing, and deploying production-ready code in a quant environment.

Session Objectives:

• Understand the core concepts and syntax of KDB and q for efficient data handling.
• Develop and test complete workflows for quantitative analysis and trading strategies.
• Acquire practical skills in optimizing and deploying code suitable for production environments in finance.

Session Breakdown (90 Minutes):

1. Introduction to KDB and q (15 minutes)

• Overview of KDB and q: architecture, features, and advantages in financial data processing.
• Discussion on the role of KDB/q in high-frequency trading and real-time analytics.

2. Hands-On Coding: Data Ingestion and Processing (25 minutes)

• Demonstration of ingesting real-time market data into KDB.
• Writing q scripts for data parsing, cleaning, and transformation.
• Best practices for handling large-scale time-series data.

3. Workflow Development: Analysis to Execution (25 minutes)

• Building analytical models and signal generation using q.
• Integrating analysis with execution systems.
• Testing and validating workflows in a simulated environment.

4. Optimization and Deployment (20 minutes)

• Techniques for optimizing q code for performance and scalability.
• Deploying workflows into production environments.
• Monitoring and maintaining deployed applications.

5. Q&A and Discussion (5 minutes)

• Open floor for participant questions
• Discussion on advanced topics and real-world applications.

Key Takeaways:

• Proficiency in using KDB and q for real-time data processing and analysis.
• Ability to develop end-to-end workflows from data ingestion to execution.
• Knowledge of best practices for optimizing and deploying quant applications in production settings.

Deep learning is no longer just experimental—it’s powering real-time decisions in algorithmic trading and execution. Discover how to:

• Use deep learning to model market microstructure and execution strategies
• Apply neural networks to forecast short-term price movements and order flow
• Understand the real-world limitations of latency, interpretability, and robustness

Large Language Models and NLP’s have moved from labs to trading desks—shaping the way we trade:

1.    Can you give real-world applications of LLMs in quant finance—from sentiment analysis to automated document parsing?

2.    How are you using NLP to accelerate research, generate alpha, and improve operational efficiency?

3.    How have you grasped the strengths, limitations, and responsible deployment of LLMs in production environments?

Time series models are at the core of forecasting in quantitative finance—powering everything from trading signals to risk management strategies:

• Explore key models and deep learning approaches for financial forecasting
• Discover how time series forecasts help you make data-driven decisions on timing, allocation, and risk

Audience Takeaways:

• Discover real examples of how alternative data and NLP are used to generate tradable signals
• Understand the ML methods and quant analytics driving next-generation forecasting models
• Learn how to translate innovative research into practical, scalable investment tools

As algo wheels reshape how buy-side firms select brokers, precision execution is no longer a competitive edge—it’s a baseline requirement. In this session, we introduce “Lenses”, a powerful new framework for dynamically evaluating and enhancing algorithmic trading performance. Going beyond generic benchmarks like VWAP and arrival price, Lenses allow quant teams to zoom in on execution quality through tailored, granular metrics.

With real-world examples, we explore how Lenses help improve routing logic, refine order placement tolerances, and adapt taking logic in real time—including how to clean liquidity, manage spread crossing, and mitigate fading effects. The result: smarter execution, optimized broker selection, and a more dominant position in the competitive landscape of algo wheels.

Overview objectives:

• Leverage Lenses to fine-tune execution strategy, improving routing logic and market placement for optimal fill quality and timing tolerance.
• Enhance taking logic by cleaning liquidity, managing spread crossing intelligently, and reducing the fading effect to protect alpha.
• Position your algorithms to win in broker evaluation frameworks, by aligning to client-specific benchmarks with adaptive, data-driven strategies.

As the lines between public and private markets blur, the demands on portfolio construction models are rapidly evolving. In this keynote, Quant of the Year 2024, Dr. Artur Sepp, Global Head of Investment Services Quant Group at LGT Private Banking, challenges the conventional frameworks that underpin strategic and tactical asset allocation frameworks.

Drawing from pioneering work at the intersection of advanced mathematics and real-world portfolio engineering, Artur reveals how next-generation optimization techniques are enabling fully integrated, cross-asset portfolios—spanning bonds, equities, hedge funds, and private equity. This is not the old-school mean-variance paradigm—it's a reimagining of what quant can achieve in a world where liquidity, transparency, and return dynamics vary dramatically across asset classes.

Key takeaways:

• A first-hand look at the quant frameworks powering multi-asset portfolios across public and private markets.
• Insights into mathematical innovation beyond mean-variance—including clustering and sparse methods for estimation of covariance matrix, tactical signals, and adaptive optimisation.
• A roadmap for building future-fit investment architectures that respond to today’s structural shifts in markets and capital allocation.

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.

Where and how models are deployed can impact everything from execution speed to compliance. In this session:

• Compare performance, scalability, and latency trade-offs between cloud and on-prem deployments
• Understand regulatory, data security, and compliance considerations
• Explore hybrid architectures for balancing speed, control, and flexibility

Discover modelling and investment strategies using alt data for better decision-making and improved investment performance

• Learn how to integrate alternative data into existing model pipelines without breaking stability
• Explore methods that transform large loads of messy data into predictive, tradable signals
• Avoid false positives when working with unconventional inputs

• What are the most impactful advancements in multi-factor risk modelling, and how are they reshaping portfolio construction in high-volatility environments?
• In what ways are AI, machine learning and quantum computing techniques enhancing or challenging traditional portfolio optimization?
• How can quants effectively integrate signal uncertainty and regime shifts into dynamic risk models and portfolio rebalancing strategies?

As digital assets mature beyond the hype cycle, a growing number of institutional investors are exploring systematic strategies to manage volatility and capture alpha in an inherently fragmented, fast-moving market.

Panel Questions:

1.    Can you elaborate on how you are developing the end-to-end architecture of a long/short quant crypto strategy—using fundamental and systematic analysis and CTA models and trading them systematically with quant models?

2.    What are the key considerations when integrating regime-aware modelling into crypto trading systems, and how can systematic approaches be designed to remain adaptive in the face of structural market shifts and extreme volatility?

3.    From your perspective as a specialist in options and crypto market making, how are you using volatility surfaces and systematic derivatives strategies to capture alpha and manage risk in an increasingly complex and illiquid digital asset landscape?

This masterclass will explore the theory and practical implementation of RL for quant finance applications.

· Learn how to build reliable RL models for trading and portfolio management.

· Explore case studies from hedge funds and quant labs and understand how they mitigate them in live trading systems.

· Discover real-world applications in asset allocation, trade execution and market making.