The IQPC brochure places TradeTech FX 2026 in Amsterdam on September 15–17, 2026, with a buy-side-only innovation day on September 15 and main conference days on September 16–17. The official event page also lists Mövenpick Hotel Amsterdam City Centre and IJ Venues. The program is relevant to the conference-tracking work because it combines institutional FX execution, systematic research, portfolio-management workflows, and explicit generative-AI sessions.
The brochure does not disclose any participating firm’s internal AI system. Its value is as a dated public speaker and agenda surface: it gives the research pipeline names to verify, session themes to ingest, and a route into European buy-side FX practitioners.
AI and quant sessions
AI Teach-In: PM and quant workflows
The brochure describes a small-group AI Teach-In titled “AI for Macro Insight: How to accelerate research intake and sharpen PM decision support.” It lists macro filtering, research summarization, sentiment extraction, scenario preparation, data limits, governance, model risk, and integrating AI without compromising investment discipline. The brochure lists Markus Auer, Senior Portfolio Manager at Erste Asset Management, and Kamaldeep Singh, Senior Investment Manager at PGGM, as hosts. The brochure says the Teach-In is pre-booking required and limited to 15 participants.
This is evidence of conference programming around AI-assisted investment workflow design. It is not evidence that Erste has deployed the described tools or that the session will disclose a named model.
AcademiaTalk: AI in financial markets
The September 15 buy-side day lists Nir Vulkan, Professor of Business Economics at Oxford’s Saïd Business School, for “AI in financial markets: Where is the edge—and who has it—humans or machines?” The description connects algorithmic trading, market design, machine-driven decision-making, agentic systems, and the current LLM cycle.
The session is a useful academic-practitioner bridge for the conference map. A program listing does not establish Vulkan’s views beyond the published session description or indicate that a specific manager adopts any position discussed.
Systematic / Quant PM Working Group
The closed-door working-group agenda describes systematic FX strategies, signal refinement, model robustness, signal stability, production adaptation, and exploring LLMs for research and scenario work. It lists Markus Ebner, Head of Multi Asset—Executive Director at Quoniam Asset Management, Denisa Cumova, Head of Portfolio Management & Quantitative Strategies at La Francaise Systematic Asset Management, and Richard Preschern, CIO of RoboSig FX Strategy, as hosts.
This is a high-value discovery lead because it names the exact boundary between LLM-assisted research and systematic production workflow. The session is closed-door and the brochure does not disclose participant comments or live systems.
PM, Quant and Macro Strategy Working Group
The brochure also lists a closed-door working group on “Generative AI in the investment process.” Its description covers reviewing internal discussions, comparing research and macro views, challenging bias, supporting portfolio strategies, adoption blockers, and practical limits. Timothee Consigny, CTO of H2O Asset Management, is listed as host.
This is evidence of a practitioner agenda and a named technology leader, not a deployment claim.
Named hedge-fund and quant-manager signals
The brochure’s trading speaker snapshot names Akshay Padmanabha, Portfolio Manager at Balyasny Asset Management; Sothiro Pin, Quantitative Trader at Man Group; and Julian Gronau, Quantitative Trading Director—FX at Qube Research & Technologies. The investment speaker snapshot names Harel Jacobson, Associate Portfolio Manager at Capstone Investment Advisors. The AI Teach-In also lists Kamaldeep Singh, Senior Investment Manager at PGGM.
The trade-automation panel additionally lists Julian Gronau among speakers discussing auto-execution, thresholds, monitoring, human intervention, and workflow controls. These are personnel and event-program signals, not evidence of AI ownership or model deployment at any of the named firms.
The brochure’s main-conference program also lists an AcademiaTalk with Dave Cliff, Professor of Computer Science at the University of Bristol, titled “Beyond the headlines: What can market practitioners learn from academic research?” Its description explicitly mentions generative AI, agentic systems, automation, early agent-based systems, and the current LLM cycle.
Why this belongs in the conference tracker
TradeTech FX should be tracked as a finance-specific public conference surface, not as a generic AI event. Its value comes from the overlap of:
- named hedge-fund and quant-manager personnel;
- FX trading, systematic strategy, and execution infrastructure;
- AI Teach-Ins with a constrained participant format;
- closed-door working groups on LLM-assisted research and generative AI in the investment process;
- academic sessions that connect market design and algorithmic trading to agentic AI and LLM claims.
The event overlaps in Amsterdam with AGNTCon + MCPCon Europe on September 17–18, 2026. That overlap creates a possible event-bundling route, but the two programs serve different evidence lanes: TradeTech FX is buy-side FX and investment workflow; AGNTCon/MCPCon is agent infrastructure and protocols.
Source and boundaries
Source files: research/06-industry-verticals/raw/tradetech-fx-brochure-2026.pdf and research/06-industry-verticals/raw/tradetech-fx-brochure-2026.chandra.txt.
Primary source: TradeTech FX 2026 brochure, retrieved August 13, 2026. The official event page is used only to cross-check the public event dates and venue. The preserved local PDF, OCR cache, checksum, and raw source ledger are linked from the repository’s pillar-06 raw directory.
Evidence Boundaries
Organizer attendance counts and sponsor claims are retained as organizer claims. Speaker titles are retained as brochure-listed titles and should be rechecked against firm biographies or public profiles before personnel promotion. No claim here establishes current employment, conference attendance, AI system deployment, model ownership, investment authority, or performance.