Quantitative Systems

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.

Solo developer and operatorPrivate build — active developmentPRIVATE
Private build · active development

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.

Where things stand

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.

Target product architectureProduct direction, not a completion claim. Agents assist research; they do not bypass governance.
Agent / automation layer
Research tasks
Evidence collection
Comparison
Report generation
Studio
Strategy specifications
Universes
Reusable research components
Configuration
Discovery
Candidate search
Comparisons
Research prompts
Agent-assisted investigation
Idea critique
Validator
Leakage checks
Cost assumptions
Walk-forward tests
Sensitivity
Stability
Promotion evidence
Governance
Approval gates
Risk limits
Audit trail
Access boundaries
Deployment state
Deploy
Approved configuration bundles
Paper / live adapters
Promotion controls
Execution handoff
Cockpit
Portfolio state
Risk
Telemetry
Alerts
Intervention
Post-run review
Ledger — memory backbone
Datasets
Versions
Experiments
Configurations
Metrics
Results
Decisions
Approvals
Research flowAgent assistanceApproval / promotionEvidence to LedgerTelemetry feedback

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.

QuantSilico’s public predecessor is the Model to Market competition archive, which placed 19th out of 440 entrants with a +5.94% simulated return.