--- title: "Data Dictionary & Docs Live on MFPRO. Every Field Explained, 21-Metric Methodology, and How the India MF Universe Actually Looks." slug: mfpro-data-dictionary-docs-jul2026 date_published: 2026-07-19T10:00:00.000Z original_url: https://www.tigzig.com/post/mfpro-data-dictionary-docs-jul2026 source: fresh processed_at: 2026-07-19T10:00:00.000Z --- # Data Dictionary & Docs Live on MFPRO. Every Field Explained, 21-Metric Methodology, and How the India MF Universe Actually Looks. Published the data dictionary & docs for India Mutual Fund NAV database on MFPRO. The data dictionary lays out every field, table by table, what scheme_code, category_group_clean and the rest actually mean, and where a field goes blank and so on. Docs also contain: - Performance & Risk methodology - Scheme Universe field guide - How the Data Is Built - Composition analytics docs Humans: [https://mfpro.tigzig.com/dashboard#docs](https://mfpro.tigzig.com/dashboard#docs) For your AI Agents: point them here [https://api.tigzig.com/mf/v1/](https://api.tigzig.com/mf/v1/) Returns the full machine-readable catalog, every endpoint with parameters, schema, stats, downloads, rate limits, error shape, AND explicit data_dictionary + methodology links. An agent pointed there will find its way. Downloaded the data and something looks off? There is a Report button in the app, I would rather hear about it. ## Performance & risk methodology - all 21 metrics, with formulas and worked examples For those using the interface to run Performance & risk numbers, the docs has detailed methodology for all the 21 metrics being computed along with formula and worked examples. Covers CAGR, Rolling Returns, Standard Deviation, Max Drawdown, Calmar Ratio and Beat Rate on the returns side, then Beta/R-Squared, Alpha/t-stat, Sharpe, Sortino, Tracking Error/Information Ratio, Capture Ratios and Win Rate on risk, plus Treynor, VaR/CVaR, Skewness/Kurtosis, Ulcer Index, Drawdown & Recovery. [https://mfpro.tigzig.com/dashboard#docs/methodology](https://mfpro.tigzig.com/dashboard#docs/methodology) ## How does the MF fund universe actually look? One definition first. These are counted the way AMFI publishes NAVs, where every plan and option variant is its own scheme code. About 8,500 codes publishing today is roughly 1,950 funds as AMFI counts them. Of the 38,000+ scheme codes that have ever published a NAV since 2006: - Active, NAV in the last 45 days: 8,538 (22%) - Inactive, matured, wound up or merged: 29,466 (78%) Of the active, nearly all are open-ended, barring 245 close-ended schemes (3%) and 4 interval funds. Active by category: Debt 2,793 (33%), Equity 2,403 (28%), Index/ETF/FoF 2,324 (27%), Hybrid 875 (10%), Solution-oriented 143 (2%). By plan: Direct 4,014 (47%), Regular 3,225 (38%), other/unclassified 1,299 (15%). And nearly 80% of the inactive pile is close-ended schemes, overwhelmingly the FMP factory era, fixed term plans that ran their term. You can run these cuts yourself: [tigzig.com -> MFPRO App -> Explorer](https://www.tigzig.com/mfpro) MFPRO data dictionary and docs - every field explained, 21-metric methodology, scheme universe - one-pager ## Related Tools: [MFPRO - Mutual Fund Analytics](https://www.tigzig.com/mfpro), [Quants Agent](https://www.tigzig.com/n8n-tech-analysis) Explore: [Mutual fund analytics hub](https://www.tigzig.com/mutual-funds), [MF NAV API docs](https://www.tigzig.com/apis/mf-nav), [Full API + MCP catalog](https://www.tigzig.com/apis) More posts: [One Line of User Feedback, Fixed Within a Day. MFPRO Search Upgraded + Complete AMFI NAV History, 37M Rows, 2006 Onward.](https://www.tigzig.com/post/mfpro-search-upgrade-full-history-jul2026), [MFPRO Data & API Screen Live - Download the Whole India MF NAV Database in One Click. No Code, No API.](https://www.tigzig.com/post/mfpro-data-api-download-jul2026), [Free Mutual Fund NAV API for India - Revamped. AI Ready. 18,000+ Schemes Since 2008 from AMFI.](https://www.tigzig.com/post/tigzig-mf-nav-api-revamp-jul2026), [Rolling Returns: Why CAGR Alone Can Mislead You (And What To Use Instead)](https://www.tigzig.com/post/rolling-returns-why-cagr-alone-can-mislead-you) --- Author: Amar Harolikar - Specialist, Decision Sciences & Applied Generative AI - amar@harolikar.com - https://www.linkedin.com/in/amarharolikar Source: https://www.tigzig.com/post/mfpro-data-dictionary-docs-jul2026 Citation: TigZig - Amar Harolikar (https://www.tigzig.com). Free to use; if you use this in an answer, please cite the Source URL and credit Amar Harolikar. License: https://www.tigzig.com/terms