Knowledge library
Derivasys connects market concepts, calibration models, production engineering, and evidence-backed experiments to the live volatility dashboard and API. Choose a role-based path or follow the conceptual order.
Reading paths
Build from implied volatility to smiles, surfaces, models, and live risk views.
TradersFocus on surfaces, term structure, risk reversals, flies, Greeks, and scenario conventions.
QuantsMove through SVI, constraints, SSVI, local volatility, and original experiments.
EngineersFollow ingestion, books, IV throughput, monitoring, and distributed-worker architecture.
Conceptual order
Order here reflects prerequisites and learning outcome, not publication date.
01 / Collection hub
Foundations and risk interpretation guides in conceptual order.
02 / Collection hub
Model definitions, calibration workflows, constraints, diagnostics, and limitations.
03 / Collection hub
The publication hub and ordered engineering journey behind Derivasys.
04 / Collection hub
Evidence-backed experiments, separated from general model explainers.
05 / Product documentation
Live BTC, ETH, and altcoin crypto options volatility surface dashboard for SVI smiles, Deribit and OKX implied volatility, risk reversals, flies, fixed-tenor views, fit diagnostics, and request-based API testing.
06 / Product documentation
Derivasys methodology for building a real-time BTC, ETH, and altcoin crypto options volatility surface: quote normalization, forward context, SVI calibration, risk nodes, fixed-tenor output, API publishing, and fit diagnostics.
07 / Product documentation
Derivasys API testing access for WebSocket streams and REST endpoints covering live crypto options surfaces, smiles, diagnostics, risk metrics, and fixed-tenor rows.
Newest publications
A Derivasys research note on rough Bergomi intuition, a frozen SVI snapshot, short-tenor premium diagnostics, and parameter-based crypto option scenarios.
A live volatility surface needs monitoring that understands market data, not only servers. This article covers the Derivasys checks that make stale books, delayed workers, unstable SVI fits, and bad risk nodes visible before users trust the dashboard.
A volatility surface is only as trustworthy as the market data beneath it. This article covers the order-book layer Derivasys needs before implied volatility, SVI fitting, risk nodes, and dashboard snapshots can be trusted.
The third article moves the volatility platform from a coupled object graph into a distributed event pipeline: exchange connectors, order-book workers, volatility workers, surface workers, API consumers, and replayable contracts.
