QuantSilico
A structured research and deployment system for systematic trading: one place to define ideas, test them properly, keep evidence, promote what survives and monitor what happens once it runs.
- Python
- Research and validation tooling
- Risk and governance controls
- Deployment pipelines
- Monitoring and telemetry
What it is
QuantSilico is the main system I’m building right now. It started from the infrastructure behind my Model to Market competition entry, but the goal is broader than a competition strategy stack.
I’m building it as a structured research and deployment system: one place to define ideas, test them properly, keep evidence, promote the ones that survive, and monitor what happens once they run. The aim is to make it harder to fool myself with a good-looking backtest, and easier to keep a record of what was tried, what failed, what passed, and why.
The public competition archive shows the first version of that thinking. The current product build is private while I rework it into a larger platform. There is no public traction, launch date or user base to report yet.
Target product architecture
This is the current direction for the platform, not a statement that every layer below is complete.
QuantSilico is organised as connected product surfaces rather than a single sequence. An agent and automation layer handles research tasks, evidence collection, comparison and report generation, and it assists research without bypassing governance. The core research surfaces are Studio (strategy specifications, universes, reusable components, configuration), Discovery (candidate search, comparisons, research prompts, agent-assisted investigation and idea critique) and Validator (leakage checks, cost assumptions, walk-forward tests, sensitivity, stability and promotion evidence). The Validator gates promotion into a governance layer of approval gates, risk limits, audit trail, access boundaries and deployment state. Only after approval do the execution surfaces run: Deploy handles approved configuration bundles, paper and live adapters, promotion controls and execution handoff, and Cockpit shows portfolio state, risk, telemetry, alerts, intervention and post-run review. A Ledger acts as a memory backbone beneath the whole system, recording datasets, versions, experiments, configurations, metrics, results, decisions and approvals; every surface writes evidence to it, and Cockpit telemetry feeds back as evidence. This is the intended product direction, not a claim that each surface is complete.
Why it’s private
The current product repository is private while the platform is being rebuilt. I’d rather ship a version I trust than narrate an in-progress rebuild in public. When there is something verifiable to show beyond the competition archive — a live research surface, a public write-up with real numbers — it will go here first.
Related
QuantSilico’s public predecessor is the Model to Market competition archive, which placed 19th out of 440 entrants with a +5.94% simulated return.