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
Monitor live Bitcoin and Ethereum options volatility surfaces with fitted SVI smiles, term structure, RR25, BF25, venue quote provenance, and calibration diagnostics.
06 / Product documentation
Derivasys methodology for live Bitcoin and Ethereum options volatility surfaces: quote normalization, forward context, SVI calibration, risk nodes, fixed tenors, and fit diagnostics.
07 / Product documentation
Request Derivasys beta WebSocket evaluation for the live Bitcoin and Ethereum surfaces; REST behavior remains testing and is not presented as an open stable endpoint contract.
Engineering series
Series position is maintained separately from publication date.
Series 01
Understand the first end-to-end architecture from live venue data to a rendered surface.
Series 02
Turn venue messages into normalized, sequenced, replayable quote state for IV calculation.
Series 03
Reduce IV inversion and recalculation bottlenecks without publishing stale surface state.
Series 04
Design market-aware freshness, fit-health, lag, and alerting controls for a live surface.
Series 05
Plan replayable topics, independently scalable workers, and a safe native-worker migration.
Series 06
Separate a CPU-bound Python market-data path into a verified, threaded Rust engine without rewriting the fitting and product layers.
Newest publications
Two Python cores were pinned at 100%, Kafka and Kubernetes had cost roughly $1,000 in a week, and asyncio could not create more CPU. Article IV covers the Rust engine, warm-started Newton solver, Unix-socket protocol, LLM-assisted parity testing, and the route from BTC into ETH and SOL.
Tests where a rough-volatility backbone fits a frozen Derivasys surface, where the short end fails, and which overlay is usable for 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.
