Founder, quantitative developer and author behind Derivasys. Sean builds crypto-options data and analytics systems, then writes the market reports, research and engineering notes that explain their output.
Derivasys role
Founder & Quant Developer
Professional role
Quant Dev at JERA Global Markets
Based in
London, United Kingdom
Coverage
Options volatility, market structure and quantitative systems
Professional profile
Derivatives experience, quantitative engineering and accountable publishing.
Sean is the founder and quantitative developer of Derivasys, where he designed the real-time cryptocurrency-options platform, its arbitrage-constrained SVI surface models, market-data infrastructure, APIs and analytical publishing workflow.
He has worked across derivatives and quantitative technology since 2007 and is currently a Quant Dev at JERA Global Markets. His earlier roles at Verition Fund Management, Millennium, Portman Square Capital, BGC Partners and Compagnie Financière Tradition covered portfolio and risk analytics, pricing, trading systems, cloud platforms and production data engineering.
Areas covered
Markets, models and the systems behind them.
Crypto optionsBTC, ETH, SOL and altcoin volatility surfaces.
Volatility analyticsATM IV, skew, butterflies, term structure and fitted smiles.
Quantitative engineeringMarket-data pipelines, calibration, APIs and production monitoring.
Market reportingMeasured surface changes, catalyst checks and cross-asset context.
Bitcoin one-week ATM IV fell 6.73 volatility points to 36.61% over the 24-hour observation window, a 95.2 percentile move in Derivasys history. Bitcoin’s 24-hour spot-index return was -0.87%, while one-week ATM IV remained 2.01 volatility points above seven-day realised volatility of 34.60%. The reset was concentrated at the front end, where ATM IV averaged 36.12%.
Ether one-week ATM IV fell 6.05 volatility points to 53.07% over the 24-hour observation window, an 86.2 percentile move in available Derivasys history. Ether’s 24-hour spot-index return was -1.07%, while one-week ATM IV stood 2.31 volatility points above seven-day realised volatility of 50.76%. Front-end ATM IV averaged 52.16%, showing the compression was concentrated in shorter maturities.
Solana one-week ATM IV fell 2.14 volatility points to 54.33% over the 24-hour observation window. Solana one-week RR25 rose 0.29 volatility points to 1.11%, leaving call volatility richer than put volatility at that tenor. Derivasys data show front-end ATM IV averaged 54.22%; realised-volatility comparisons were unavailable at the cutoff.
Major-coin one-week ATM IV fell across Bitcoin, Ether and Solana at the 16:30 UTC cutoff. Bitcoin one-week ATM IV fell 6.73 volatility points to 36.61%, Ether one-week ATM IV fell 6.05 volatility points to 53.07%, and Solana one-week ATM IV fell 2.14 volatility points to 54.33%. The common decline nevertheless widened relative volatility levels between Solana and Bitcoin to 17.72 volatility points.
Altcoin one-week ATM IV split sharply at the 16:30 UTC cutoff: XRP one-week ATM IV rose 5.34 volatility points to 66.98%, while AVAX one-week ATM IV fell 3.63 volatility points to 58.81%. The resulting 8.97-point spread in daily ATM IV changes marks a clear cross-sectional divergence in Derivasys data, rather than a uniform repricing across the complex.
Bitcoin one-month RR25 fell 1.71 volatility points to -2.10%, leaving put volatility richer than call volatility at the 21:01 UTC cutoff. The shift was large relative to its available history, while Bitcoin one-week ATM IV rose 1.20 volatility points to 39.43%. Bitcoin’s 24-hour spot-index return was -3.91%, and Bitcoin one-week ATM IV stood 4.40 volatility points above seven-day realised volatility. Derivasys data show a more defensive relative-volatility profile even as front-end implied volatility rose only modestly.
Ether one-week RR25 fell 4.48 volatility points to -1.59%, shifting the front-end smile from call-rich to put-rich relative volatility at the cutoff. Ether one-week ATM IV simultaneously fell 2.08 volatility points to 55.04%, so the move was a skew repricing rather than a broad rise in implied volatility. Ether’s 24-hour spot-index return was -6.17%, while Ether one-week ATM IV remained 4.33 volatility points above seven-day realised volatility. Derivasys data also show richer two-week convexity.
Solana’s near-expiry options smile rotated lower over the observation window, alongside a 4.81-volatility-point fall in Solana one-week RR25 to -0.20%. Solana one-week ATM IV fell 1.94 volatility points to 57.41%, and front-end ATM IV averaged 56.03%, down 1.27 volatility points. The combination indicates lower overall front-end implied volatility with a marked move toward relative put-side volatility. Realised-volatility comparisons were unavailable because the required minute-series completeness threshold was not met.
Monitor live XRP, Hyperliquid HYPE, Avalanche AVAX and Tron TRX options volatility surfaces with SVI smiles, ATM term structure, RR25, BF25 and fit diagnostics.
Monitor the live Solana options volatility surface across strikes and expiries with SOL SVI smiles, ATM term structure, RR25, BF25, venue quotes, and fit diagnostics.
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.
Monitor the live Bitcoin options volatility surface across strikes and expiries with BTC SVI smiles, ATM term structure, RR25, BF25, venue quotes, and fit diagnostics.
Monitor the live Ethereum options volatility surface across strikes and expiries with ETH SVI smiles, ATM term structure, RR25, BF25, venue quotes, and fit diagnostics.
An evergreen rough-volatility guide covering roughness intuition, the Hurst parameter, rough Bergomi, implied-surface relationships, and model limitations.
Analysis starts with observable data and reproducible methods. Surface observations are tied to stored report data, assumptions and comparison windows are stated, and market stories remain context unless the evidence supports a stronger relationship.
