QuantStats
github.comTurn portfolio return histories into metrics, charts and shareable reports
Turn portfolio return histories into metrics, charts and shareable reports
AI that explains metric shifts and turns them into decisions
Calculate returns, portfolio statistics and allocation weights in Python
Capture application errors with context and correlate them with performance and deployment changes.
Find content opportunities and assess SEO changes using connected search data.
Performance reviews with separate visibility controls and configurable employee groups
Inspect a verified site's Bing search performance, crawl issues and supported AI citation activity.
Employee evaluations with separate reviewer groups and recurring schedules
Collect WordPress visitor activity in your own database and extend it with Pro reports.
Monitor application, database, server, and web-transaction health from one managed console.
Measure a page load under chosen browser, device, connection, and location conditions, then inspect its request timeline.
Follow Australian and U.S. investments with watchlists and performance history
Trace server-side requests to find slow methods, database queries, and external service calls.
Create and run browser, mobile, API, performance, and accessibility checks with a hosted test platform.
Audit individual web pages and turn failed checks into concrete optimization leads.
Check service availability from several regions and keep customers updated with branded status pages.
Classic and performance cars with motoring heritage
Calculate portfolio returns from a complete transaction history
Performance reviews with related feedback and manager context
Performance reviews connected to goals, calibration and development plans
Investigate database health and query bottlenecks across a mixed database estate from one console.
Track site speed and Core Web Vitals with lab tests, visitor data, and uptime alerts.
Stores and queries continuous application profiles to reveal resource use down to code-level details.
Monitor production ML and LLM systems for drift, quality, fairness, and agent behavior.