Artemis Quantitative is a boutique financial analytics firm. We build automated data infrastructure and quantitative models for mid-market companies and emerging investment managers.
I founded Artemis Quantitative to give mid-market companies and emerging investment managers access to the same analytical infrastructure that large firms take for granted, built faster, at a lower cost, and with direct access to the person actually doing the work.
My background spans quantitative finance, financial data engineering, and systematic trading. I build in Python and Excel, and I've applied these tools across both corporate finance environments and investment management contexts, from automating manual reporting pipelines for finance teams to building VaR models and backtesting frameworks for fund managers.
I also run my own algorithmic trading portfolio, which means the risk frameworks I build for clients are frameworks I use and test against real capital. That informs how I think about model design, robustness, and what actually matters in live environments.
A beautiful dashboard that nobody uses is a failure. Every engagement is measured by whether your team has better information, makes faster decisions, and has more time to think. Not by the sophistication of what was built.
Every system we build is designed for your team to own and operate independently. Full documentation, full training, full transfer. We don't create dependency. We eliminate it.
Two focused service lines done exceptionally well. No generic solutions, no templated approaches. Every engagement is scoped to your specific problem and built to your specific environment.
When a large consultancy takes on a project like this, you're paying for a partner who sells, a manager who oversees, and a junior analyst who builds. The person who understood your problem in the sales meeting is not the person building your solution.
Every email, call, and deliverable goes through the founder. No handoffs, no account managers, no translation layer between what you need and what gets built.
You're not paying for office space, partnership overhead, or brand premiums. You're paying for the work, which means significantly better value for equivalent or superior output.
No internal approvals, no staffing delays, no onboarding a team to your problem. Work starts quickly, decisions get made immediately, and timelines are real.
No proprietary black boxes. Every system is built on tools you already own or can access, so your team can understand, maintain, and extend it.
The financial industry's universal language. We turn it into an enterprise-grade analytics engine without replacing it.
Direct ERP connections, automated data cleaning, and pipeline logic. All native to Excel, zero manual intervention.
pandas, numpy, scipy, and matplotlib for quantitative modeling, statistical analysis, and custom analytics pipelines.
Direct database connectivity to extract raw data at the source, eliminating file downloads and manual exports permanently.
Relational modeling inside Excel, connecting multiple data sources into a single, queryable analytical foundation.
Custom Python-based backtesting and simulation environments for systematic strategy development and validation.
Let's have a 30-minute conversation about what you're working with. No pitch, no commitment. Just a direct look at whether we're the right fit for what you need.