KOL research
@RohOnChain
An institutional-architecture KOL example. The value is not in one opinion, but in the way the full prediction-market desk—roles, signals, models, and execution layers—is exposed as a coherent system.
📌 Research value
This page is valuable because it shows how a real desk is structured: research, execution, risk, strategy, and DevOps all reinforce each other.
Why this matters: it teaches not just strategy, but why a strategy can exist in the first place when backed by infrastructure and organizational structure.
🏗️ Trading desk architecture
1. Research layer
Find markets with real model advantage and build dependency graphs.
- Bayesian updating and calibration analysis
- Market identification and structure mapping
P(H|E) = [P(E|H) × P(H)] / P(E)
2. Execution layer
Infrastructure, routing, reconciliation, and latency control.
- Order-book ingestion and venue adapters
- Typical stack: Docker / Kubernetes / Prometheus / Grafana
3. Risk layer
Portfolio exposure, VaR, and informed-flow defense.
- Drawdown thresholds and Monte Carlo scenarios
- Auto-withdraw quotes when VPIN is too high
VPIN = |V_buy − V_sell| / (V_buy + V_sell)
4. Strategy layer
Coordinates architecture, risk settings, and production deployment rules.
5. DevOps layer
Node operation, cloud resources, secrets, and system stability.
📡 Signal stack
- Microstructure: depth, spread, aggression, imbalance
- Macro/events: polls, economic indicators, Reuters / Bloomberg
- On-chain: Polygon node, mempool visibility, whale activity
- Cross-venue: Polymarket, Betfair, Smarkets, sports books
Main lesson: institutional edge comes from seeing more layers at once and organizing them into one operating system.
🎯 How to use this page
- Use it as a desk blueprint, not a personal-opinion page
- Study role separation, data input layers, and how risk interacts with execution
- Downscale the institutional system into your own lighter workflow