Quant engine architecture
Q-Bet
A modular Python quant engine for matched betting, sports arbitrage, strategy simulation and controlled execution planning.
A cloud-ready quant betting portfolio
The intended v1 is a web application with a complete matched-betting engine, simulation mode, capital orchestration, collectors or API adapters, reports and controlled execution. The useful angle is not a betting promise, but a system that turns manual opportunity work into modeled, testable workflows.
Domain models first, execution later
The repo is currently in the core domain stage: typed models for events, markets, offers, opportunities, strategy results and execution plans are implemented and ready for review. Matched-betting calculations, orchestrator logic, collectors, UI and execution adapters come after the domain layer is stable.
Math, risk and software boundaries
Q-Bet should communicate modeling discipline: expected value, ROI, liquidity, risk, capital lock-up and approval boundaries need to become explicit software objects before any real execution is even considered.
Engine progression
From odds data to controlled execution
The page should show the future system without overselling the current implementation. The active work is the left side of the flow; the v1 target is the full chain.
Roadmap
Current stage to v1 target
Stage 1
Core Pydantic domain models and tests for the objects the engines will rely on.
Base Engine
Matched-betting calculations, free-bet strategies, dutching, stake optimization and arbitrage checks.
Capital Layer
An orchestrator that routes capital by EV, ROI, risk, liquidity and lock-up across strategy engines.
Cloud v1
Web UI, collectors, API adapters, simulation controls, performance reports and approved execution flows.
Portfolio angle
What this page should prove
- Q-Bet is early, so the page must clearly separate implemented domain layer from future v1 target.
- The engine flow should emphasize calculations, constraints and approval boundaries, not hype around automation.
- Later portfolio assets should show formulas, example opportunity objects and simulation output.