Quantitative Research & Engineering • Generative AI integration for Capital markets.
AlgoAlps is a quantitative research and engineering firm specializing in AI-driven automation across quantitative research, trading, risk, and reporting - built to integrate cleanly into institutional environments.
What we do
We address your needs with our quantitative and generative AI expertise.
End to end quantitative systems
Research-to-production workflows, backtesting, execution and analytics - engineered to integrate with existing teams, controls, and market infrastructure.
Automation that sticks
LLM-based workflows and durable pipelines to eliminate repetitive reconciliation, reporting, and data prep. Built with testing, monitoring, and auditability in mind.
Cost and complexity reduction
Front-to-back system rationalization, architecture reviews, and realistic plans to consolidate platforms, reduce spend, and improve operational resilience.
Capabilities
Production-ready delivery across research, trading, risk, and enterprise AI.
Quantitative R&D
- Vanilla & structured pricing: FX / Equity / Rates models (e.g., Monte Carlo, PDE engines; xVA).
- Stat arb and portfolio construction: low‑frequency & high‑frequency workflows (offline training, fast online execution).
- Backtesting engines: EOD and tick-based, with lags, slippage simulation, and overfitting detection.
Alternative data & pipelines
- High-throughput pipelines (e.g., processing up to 1M news articles/day) and nonstandard data sources.
- Data sourcing, cleaning, feature engineering, and signal validation.
- Robust ETL with observability, lineage, and automated QA checks.
Trading & risk infrastructure
- Low-latency trading engines and connectivity (FIX, REST, broker & market APIs).
- Pricing infrastructure: voice/e‑trading platforms, FO risk; integration with downstream systems.
- Risk: PFE, VaR, ES, stress; finance constraints such as FRTB and ISDA‑SIMM workflows.
Generative AI for markets
- Automated reporting and daily portfolio summaries.
- Text and image processing for research and operations.
- Enterprise deployment with governance (open & closed source LLMs; on‑prem or cloud).
Engineering stack
Python, C++, C#, and modern AI frameworks (e.g., Torch/NumPy/SciPy/Statsmodels) with SQL/Bash/CUDA as needed. Multi-cloud and hybrid deployments across Linux/Windows environments.
Engagement model
We deliver what you want, not what we already have.
Client-centric delivery
We work with your experts to understand architecture, competitive advantage, and regulatory constraints—then design the simplest solution that will scale.
Flexible commercial model
Fixed daily rate: €490–€1,450/day, or fixed‑price contracts depending on scope and milestones. Quotes are discussed and finalized with the client.
Proven outcomes
Experience merging global teams and systems, rationalizing front‑to‑back stacks, and deploying production platforms in institutional settings.
Founders
Institutional leadership and hands-on engineering—built for real-world constraints.
Niels van Vliet
- 20+ years in quantitative finance and development.
- Managed 100+ staff across quant, e‑trading, and risk teams; oversaw ~£30m annual budget.
- Deep experience in institutional trading systems, risk, and front‑to‑back integration.
Nariman Khaledian
- Award-winning researcher; quant researcher/developer.
- Expertise in systematic trading strategy development and integration of generative AI in finance.
- Alternative data pipelines and research-to-production implementation.
Contact
Tell us what you want to automate. We’ll propose a realistic plan and deliver a production-grade implementation.