# Mutual Fund Analytics Tools for processing mutual fund portfolio disclosures, composition drift analysis, holdings comparison. Indian mutual fund focus. ## Apps (4) ### MF Portfolio Holdings Analyzer with Python pipeline - App: https://chatgpt.com/g/g-68d684965d888191bf81f02022dd3591-india-mutual-funds-portfolio-holding-analytics - Docs: https://tigzig.com/app-documentation/gpt-mf-holding-analyzer.html - AI Docs: https://www.tigzig.com/ai/apps/gpt-mf-holding-analyzer.md - MF Portfolio Holdings Analyzer with Python pipeline ### AI Powered MF Processor - Convert MF portfolio files - App: https://mf.tigzig.com - Docs: https://tigzig.com/app-documentation/mf-files-ai.html - GitHub: https://github.com/amararun/shared-mf-portfolio-allocation - AI Docs: https://www.tigzig.com/ai/apps/mf-files-ai.md - AI Powered MF Processor - Convert MF portfolio files ### Process & analyze monthly MF portfolio Excel files - App: https://chatgpt.com/g/g-b6a7uHe84-mutual-fund-portfolio-analyzer - Docs: https://tigzig.com/app-documentation/mf-portfolio-analyzer.html - AI Docs: https://www.tigzig.com/ai/apps/mf-portfolio-analyzer.md - Process & analyze monthly MF portfolio Excel files ### Mutual fund holdings analyzer with ISIN standardization in Excel - App: https://www.tigzig.com/mf-portfolio-processor - Docs: https://tigzig.com/app-documentation/mf-portfolio-processor.html - GitHub: https://github.com/amararun/RBICC_AUTH_EMBED - AI Docs: https://www.tigzig.com/ai/apps/mf-portfolio-processor.md - Mutual fund holdings analyzer with ISIN standardization in Excel ## Blog Posts (55) - [Explore the Tigzig Data Surface With Your AI: What Is New in October 2026](https://www.tigzig.com/post/tigzig-agent-data-surface-oct2026) - Tags: database-ai, mutual-funds, vigil, portfolio-quants October 2026 update on the Tigzig data surface for AI agents, reachable at api.tigzig.com. New: mutual fund scheme details (60 fields for about 2,100 funds), 26 new global macro indicators including US jobs, Japan 10-year yield and India IIP and WPI on the new base, 29 more cricket SQL functions, and FY26 related party transactions. Notes on fund_dedup_flag, AUM versus AAUM, TER matching, the October 2025 shutdown gap and links to API error guides. AI-readable: https://www.tigzig.com/ai/posts/tigzig-agent-data-surface-oct2026.md - [Scheme Details for Indian Mutual Funds Are Live on Tigzig: Fund Size, Fund Manager, TER and BER, Benchmark and Riskometer](https://www.tigzig.com/post/mfpro-scheme-details-live-oct2026) - Tags: mutual-funds, database-ai Scheme details for Indian mutual funds are live on the free Tigzig MF API, MFPRO and downloads: 60 fields per scheme across about 2,100 funds and 8,600 plans, including fund size, fund manager, TER and BER, benchmark, riskometers and scheme terms, refreshed four times a day. Pull one scheme or up to 50 per call, or download Parquet or CSV. Usage notes cover fund-level values repeating on every plan, fund_dedup_flag, joining on scheme_code, AUM versus AAUM, and how TER is matched. AI-readable: https://www.tigzig.com/ai/posts/mfpro-scheme-details-live-oct2026.md - [August 2026 Portfolios Are Live on MFPRO Composition. Eleven Month-End Snapshots Across 24 of India's Larger Equity Funds.](https://www.tigzig.com/post/mfpro-composition-august-portfolios-sep2026) - Tags: mutual-funds, portfolio-quants August 2026 portfolios are live on the MFPRO composition tool, taking it to eleven month-end snapshots across 24 of India's larger equity funds. Covers reading the change in market value and the change in quantity together, the ISIN remapping, why money market lines are grouped, and the four rows holding everything that is not a listed Indian share. Validation is published: 251 of 264 fund-months match the AMC's own printed grand total, the other 13 listed with the amount and the reason. AI-readable: https://www.tigzig.com/ai/posts/mfpro-composition-august-portfolios-sep2026.md - [Getting a 404 From the MF NAV API? Check Whether the ISIN Itself Is Wrong](https://www.tigzig.com/post/mf-nav-isin-checker-404-sep2026) - Tags: mutual-funds, converters-tools Malformed ISINs are a common cause of a 404 from the Tigzig MF NAV API, and the free checker at isin.tigzig.com separates the two cases: an ISIN that is itself wrong, against a valid one whose data is simply not there. Also a reminder of why the identifiers are awkward, since what most people call one fund can carry up to six ISINs and ten identifiers including scheme codes, some IDCW schemes have no payout ISIN at all, and SIF identifiers are valid while returning nothing from any mutual fund source. Links through to the earlier write-up for the detail, plus the mutual fund hub, the MFPro tool and the NAV API docs. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-isin-checker-404-sep2026.md - [Point Your AI Agent at api.tigzig.com. 40 Million Rows of Public Data, and Half the Bytes Are One File](https://www.tigzig.com/post/tigzig-agent-data-surface-sep2026) - Tags: database-ai, mutual-funds, vigil, portfolio-quants The agent data surface at api.tigzig.com now spans over 40 million rows across Indian mutual fund NAVs, ball-by-ball cricket, global macro indicators and India company filings, refreshed daily and free with no signup or key. Cricket is the addition since the August post, covering Tests, ODIs, T20s and the IPL for men and women from December 2001. Over seven days the surface served more than 110,000 API calls and more than 1,700 file downloads, about 40 GB in all, and one file alone, the complete NAV history in Parquet, accounts for more than half those bytes. Also covers the four routes in, the downloads manifest with its row counts and generated_at stamp, conditional requests returning 304, and how to pick between CSV, Parquet, SQLite and DuckDB. AI-readable: https://www.tigzig.com/ai/posts/tigzig-agent-data-surface-sep2026.md - [August Was the Biggest Month Indian SIFs Have Had. Net Assets Rs 31,175 Crore, Net Inflows Rs 7,699 Crore.](https://www.tigzig.com/post/sif-august-2026-biggest-month-sep2026) - Tags: mutual-funds, database-ai August 2026 was the largest month Indian Specialized Investment Funds have recorded: net assets rose from Rs 23,177 crore to Rs 31,175 crore, net inflows were Rs 7,699 crore and folios went from 94,000 to over 125,000. Equity Ex-Top 100 Long-Short grew about 66% and now spans seven fund houses, Hybrid Long-Short holds about 63% of assets, and the two debt strategies SEBI permits still have no funds. Free download in CSV and Parquet. AI-readable: https://www.tigzig.com/ai/posts/sif-august-2026-biggest-month-sep2026.md - [Heavy User of the Mutual Fund NAV API? Run Your Own NAV Service Inside Your Own Setup](https://www.tigzig.com/post/mf-nav-run-your-own-service-parquet-sep2026) - Tags: mutual-funds, database-ai The whole Indian mutual fund NAV dataset is published as a single file, and the Parquet copy is about 169 MB covering every scheme and every NAV date back to April 2006. On a Python backend duckdb queries the Parquet file where it sits, with no database server to install, and Polars reads the same file. Keep it current by pointing a cron at the manifest and re-pulling when the build time moves. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-run-your-own-service-parquet-sep2026.md - [MF NAV in the Wild. A Review of the API Logs, and How Widely the Mutual Fund NAV Data Is Being Pulled.](https://www.tigzig.com/post/mf-nav-api-how-people-use-it-sep2026) - Tags: mutual-funds, database-ai What a routine security, error and latency review of the free Indian mutual fund NAV API showed about how the data is being used: AI agents, Google Sheets and Apps Script, Excel and Power Query, web apps, an iPhone app, Zoho and n8n, plain Python, and DuckDB querying the published Parquet in place. Also what the logs changed in the service, and an overview of the search, NAV and download surfaces. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-api-how-people-use-it-sep2026.md - [The API Is Not the Only Way In. Download the Whole India Mutual Fund NAV Database from the App, or Pick Just the Funds You Want.](https://www.tigzig.com/post/mfpro-data-api-manual-download-sep2026) - Tags: mutual-funds, database-ai Indian mutual fund NAV data can be taken from the MFPro app directly, without the API. Covers the three routes on its Data and API tab: the full history since April 2006, about 37 million rows (parquet 169 MB, csv and tsv 219 MB gzipped, SQLite too), the 1.1 MB latest snapshot that doubles as a 22-column scheme master, and a picker for up to 20 funds with a period selector. Plus the pipeline status page. AI-readable: https://www.tigzig.com/ai/posts/mfpro-data-api-manual-download-sep2026.md - [India MF Tool Builders: You Do Not Need a Thousand API Calls a Day. The Whole NAV Dataset Is One File.](https://www.tigzig.com/post/mf-nav-bulk-download-one-file-sep2026) - Tags: mutual-funds, database-ai For developers pulling Indian mutual fund NAV per scheme from the free Tigzig MF API: the whole dataset is published as one file instead. Parquet is about 168 MB and holds every scheme and every NAV date. Covers the refresh pattern - poll the sub-5 KB manifest for its build time, or send its ETag back for a 304 with no transfer - and the roughly 1 MB daily snapshot that doubles as a scheme master. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-bulk-download-one-file-sep2026.md - [The Portfolio Agent Now Looks Up Live Symbols for Stocks and Securities, Instead of Guessing From Memory.](https://www.tigzig.com/post/portfolio-agent-symbol-lookup-sep2026) - Tags: portfolio-quants, mutual-funds The Tigzig portfolio agent at quants.tigzig.com now resolves stock and security symbols against the live Yahoo Finance lookup rather than recalling them, the same pattern already used for Indian mutual fund schemes. Prompted by log review, which showed symbol guessing as a point of friction and users asking the agent to pull NAVs. Five reports are available: a QRep performance and risk report for one security against a benchmark, a chart-based technical analysis report, a multi-security performance comparison with HTML and CSV output, a Yahoo Finance data pull covering prices, profiles, financials, holders, estimates, dividends, splits, actions and ticker lookup, and Indian mutual fund scheme lookup with NAV history. AI-readable: https://www.tigzig.com/ai/posts/portfolio-agent-symbol-lookup-sep2026.md - [SIF Is India's Newest Fund Category and It Nearly Doubled in Three Months. Every NAV AMFI Has Published Is Now Downloadable.](https://www.tigzig.com/post/sif-data-universe-download-sep2026) - Tags: mutual-funds, database-ai Specialized Investment Funds, India's newest fund category, grew from Rs 12.3K crore of AUM at end-April to Rs 23.2K crore at end-July, and every SIF NAV AMFI has published is now downloadable as CSV and parquet at sif.tigzig.com, refreshed twice a day. Explains why none of the 17 fund house names is recognisable, since SEBI clause 21.2.1 requires a SIF to carry a brand distinct from its AMC, and how to identify the sponsor anyway: the SIF code is a running number, but each scheme's ISIN carries the AMC's issuer prefix, so INF174K is Kotak, INF109K is ICICI Prudential and INF200K is SBI. Covers the SEBI Master Circular chapter 21 clauses on branding, one strategy per category and the Rs 10 lakh PAN-level minimum, plus the concentration of money in Hybrid Long-Short and the two debt strategies that have never had a fund. AI-readable: https://www.tigzig.com/ai/posts/sif-data-universe-download-sep2026.md - [Getting a 429 From the MF NAV API? It Is Almost Never the Total. Two Changes Fix Nearly Every Case.](https://www.tigzig.com/post/mf-nav-api-429-rate-limit-aug2026) - Tags: mutual-funds, database-ai Why callers hit a 429 on the India mutual fund NAV API and how to stop it. The per-IP limit is 300 requests a minute and most people who trip it are nowhere near that total; the cause is many unbounded calls firing at the same instant, since a fifty-fund request with no date range returns over 100,000 rows. Two fixes cover nearly every case: batch up to 50 scheme codes into one call using schemes=, and bound the dates with latest=true or since=. Includes tool-specific notes for Excel WEBSERVICE, n8n, Python scripts and Power Query, what the 429 body tells you, and how to hand the error to an AI coder to fix the calls. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-api-429-rate-limit-aug2026.md - [August Was a Heavy Month in the AMFI Data. 1,234 Schemes Renamed and 37 ISINs Swapped. If You Join on Scheme Code You Are Fine.](https://www.tigzig.com/post/amfi-name-and-isin-changes-aug2026) - Tags: mutual-funds, database-ai What moved in the AMFI mutual fund data during August 2026 and what it breaks. 1,234 schemes were renamed over the month, 942 on 26 August alone, the SEBI deadline for re-filing under the new categorisation, so Low Duration became Ultra Short to Short Term, Savings became Money Market and Equity and Debt became Aggressive Hybrid. Separately 37 schemes had their ISIN swapped, at UTI, DSP, Kotak, Invesco and LIC, fourteen of them ETFs, and none of them were renamed. Also covers spelling defects in the file, including five spellings of children's fund and SBI's hybrid fund filed as AGGRESIVE across four plans, and the LIC gold ETF case where the replaced ISIN still resolves to the old IDBI fund with a full price history. Anything joined on AMFI scheme code is unaffected. AI-readable: https://www.tigzig.com/ai/posts/amfi-name-and-isin-changes-aug2026.md - [What Is Next on the MF NAV API. Risk Metrics, Peers, Scheme Details and Holdings, and Where Each One Stands.](https://www.tigzig.com/post/mfpro-api-roadmap-risk-metrics-aug2026) - Tags: mutual-funds, database-ai A roadmap note on the India mutual fund NAV API, covering the four most requested additions and where each stands. Risk metrics are top of the list: alpha, beta, Sharpe, Sortino and drawdown are already computed and live in the Performance tab, so the remaining work is exposing them on the API under load. Peers will not get a separate API path, because peer grouping is a judgment call, but they can be built from the NAV master file or the search path, and the frontend offers them through the Category and Performance tabs. Scheme details are a known gap, with launch date, fund manager and scheme-document fields missing today. Holdings across the whole universe are not on the radar; composition exists on the frontend for 24 large equity funds, about a third of all equity funds. AI-readable: https://www.tigzig.com/ai/posts/mfpro-api-roadmap-risk-metrics-aug2026.md - [My Portfolio Agent Could Not Look Up an Indian Mutual Fund NAV. Now It Can.](https://www.tigzig.com/post/portfolio-agent-mutual-fund-nav-aug2026) - Tags: portfolio-quants, mutual-funds The Tigzig portfolio agent at quants.tigzig.com can now resolve and fetch Indian mutual fund NAVs. Give it a fund name roughly as you would say it and it searches the AMFI scheme master, resolves the real scheme code, and where a name matches more than one scheme it lists the candidates and asks before fetching, because a fund typically exists as Direct and Regular plans with Growth and IDCW options on separate series. Short windows come back in the chat and longer histories as a CSV link. The agent also pulls prices, company profiles and financial statements for Yahoo Finance symbols, finds tickers by name, runs quant reports against a benchmark, produces technical analysis reports as HTML and PDF, and compares securities on return and risk. It does not give investment advice. AI-readable: https://www.tigzig.com/ai/posts/portfolio-agent-mutual-fund-nav-aug2026.md - [Running Hundreds of API Calls to Pull NAV History, One Scheme Per Call? Batch 50 Into One.](https://www.tigzig.com/post/mfpro-nav-batch-calls-and-guides-aug2026) - Tags: mutual-funds, database-ai How to cut the call count when pulling India mutual fund NAV history. The NAV endpoint accepts up to 50 scheme codes in a single call, turning a two hundred fund watchlist into four calls, and the full database from April 2006 onward is available as a single download. Collects the surrounding references in one place: the API FAQ, a plain guide to what each error code means across all three Tigzig APIs, the MFPRO data dictionary, the scheme master file with every scheme and its latest NAV, an ISIN checker that distinguishes a bad identifier from genuinely absent data, and where SIF data lives. AI-readable: https://www.tigzig.com/ai/posts/mfpro-nav-batch-calls-and-guides-aug2026.md - [Three Upgrades on the India Mutual Fund NAV API. Easier to Pull, Whatever You Already Use.](https://www.tigzig.com/post/mf-nav-api-three-upgrades-aug2026) - Tags: mutual-funds, excel Three changes to the free India mutual fund NAV API aimed at Power Query, Excel WEBSERVICE and browser-based trackers. The all-scheme snapshot is now gzipped on the wire, dropping from 9.3 MB to about 1.1 MB on the same link. Any NAV call accepts format=csv and returns plain rows instead of JSON. The bulk download files now send a CORS header so they can be fetched from a web page. Includes a paste-ready Excel LET formula that pulls ten funds in one call using the batch schemes= parameter, which takes up to 50 codes, plus links to the MFPRO docs, FAQ and ISIN checker. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-api-three-upgrades-aug2026.md - [Point Your AI Agent at api.tigzig.com and It Works Out Which Dataset It Needs. 38 Million Rows Across India and the US, Free.](https://www.tigzig.com/post/tigzig-agent-data-surface-aug2026) - Tags: mutual-funds, vigil, portfolio-quants One address an AI agent can be pointed at, api.tigzig.com, from which it works out which dataset a question needs and pulls the file itself. Close to 38 million rows across India and the US, curated and refreshed daily, free with no signup or key: twenty years of Indian mutual fund NAVs, around 330 macro, credit and valuation indicators across eight groups, and seven filing datasets for Indian listed companies covering credit ratings, insider trading, promoter pledge, encumbrance, takeover, surveillance flags and related party transactions. Usable three ways, by hand in the apps, through the API from code, or as whole-table downloads in CSV, Parquet, SQLite or DuckDB. Includes a downloads manifest carrying row counts and a generated_at stamp so a script can tell whether anything changed before re-pulling, and practical notes on renamed schemes still resolving from an old ISIN. AI-readable: https://www.tigzig.com/ai/posts/tigzig-agent-data-surface-aug2026.md - [Pull India Mutual Fund NAVs Straight Into Excel. 50, 100 or More Schemes, in a Few Clicks.](https://www.tigzig.com/post/mfpro-nav-into-excel-aug2026) - Tags: mutual-funds, excel Getting India mutual fund NAV history into Excel for fifty or a hundred schemes at a time, free and without signing in, from the MFPRO Data and API screen. Comes out as a plain CSV that opens as ordinary rows, ready for the returns, CAGR and drawdown work you would normally do with formulas, and an AI assistant sitting alongside Excel will write those formulas or fetch and compute the whole thing itself. Also the three things that break an analyst's join: SEBI's February categorisation rework means the same fund can appear under a new category or scheme name between two files, so match on the AMFI scheme code rather than the name; ISIN coverage is not complete, with roughly one in fourteen live schemes carrying none; and Direct, Regular, Growth and IDCW are separate scheme codes, so one fund is usually four rows. Full history is 37 million rows from April 2006, too large for Excel. AI-readable: https://www.tigzig.com/ai/posts/mfpro-nav-into-excel-aug2026.md - [MFPRO Composition Analytics Is Live. Portfolios for 24 of India's Largest Equity Funds, Reconciled Against the AMC's Own Printed Totals.](https://www.tigzig.com/post/mfpro-composition-analytics-aug2026) - Tags: mutual-funds, portfolio-quants Portfolio composition analytics for 24 of India's largest equity funds across large cap, mid cap, small cap, flexi cap, focused and value, spanning ten snapshots from June 2025 to July 2026. Compare one fund over time, several against each other, or against a benchmark, with copy-to-Excel, the original disclosures, ISIN remaps and the reconciliation notes alongside. The work that makes months comparable is the interesting part: a face-value split moves a company to a new ISIN and looks like a position appearing from nowhere unless the two are stitched, and money market instruments arrive as dozens of separate lines that need grouping. Cash is the real divergence between fund houses, some folding it into the printed grand total and others listing it below, which is worth several per cent of a fund. Every fund-month is reconciled against the AMC's own printed total, 228 of 240 to the rupee with the remainder published with reasons. AI-readable: https://www.tigzig.com/ai/posts/mfpro-composition-analytics-aug2026.md - [Pulling Indian Mutual Fund NAVs One Scheme at a Time? Three Ways to Get the Same Data in Far Fewer Calls.](https://www.tigzig.com/post/mfpro-nav-fewer-calls-batch-files-parquet-aug2026) - Tags: mutual-funds, duckdb Three routes already in the free MFPro India mutual fund NAV API for callers fetching one scheme per request: batch up to 50 schemes in a single call with a date range attached, take the single latest-NAV file covering all ~38,000 schemes including matured ones left in deliberately, or take the full daily history as one file of a little over 37 million records back to April 2006, all refreshed three times a day. A fourth route skips the download entirely because the history file is parquet and supports range requests, so DuckDB or Polars query it straight off the URL. Worked numbers for a daily pull of every active fund, how active is derived from a 45-day NAV window, and how conditional requests return a 304 with no body on days nothing changed. AI-readable: https://www.tigzig.com/ai/posts/mfpro-nav-fewer-calls-batch-files-parquet-aug2026.md - [MF NAV API FAQ. Why Am I Getting an Error, Why Is There No SIF NAV, and Why Does the AUM Look Wrong?](https://www.tigzig.com/post/mf-nav-api-faq-aug2026) - Tags: mutual-funds Twenty-six questions the free MFPRO India mutual fund NAV API actually got from callers over seven days, 25,000+ calls and 450+ file downloads. Why a SIF identifier 404s and where SIF data really lives, why the AUM looks too low (it is per scheme and a quarterly average, not per fund and not month-end), why one fund returns several rows, why a valid ISIN can return nothing and how reinvestment ISINs and matured schemes change that, why an IDCW plan shows near-zero NAV return, why the SEBI category rename means passing two names, why dates and unknown parameters fail loudly instead of being guessed, how to assemble a history that spans a manager change, and the aaum_cr column rename with the schema_version to watch. Plus the anomalies published as filed rather than cleaned. AI-readable: https://www.tigzig.com/ai/posts/mf-nav-api-faq-aug2026.md - [Range Requests on the MFPRO NAV File Are Fixed. DuckDB and Polars Queries No Longer Get Refused Midway.](https://www.tigzig.com/post/mfpro-range-requests-duckdb-polars-aug2026) - Tags: mutual-funds, duckdb, infrastructure HTTP range requests against the free MFPRO India mutual fund NAV parquet now have their own pathway and budget, fixing queries from duckdb and polars that were being refused partway through. Cause: a single analytical query issues close to three hundred byte-range reads within seconds, and the rate limiter was counting those in the same bucket as whole-file downloads, so a legitimate query registered as a burst. The range pathway now allows 3,000 requests per minute and 25,000 per day per public IP, with remaining-budget details returned in response headers. The file remains about 175 MB covering close to 37 million NAV records and can still be downloaded whole, alongside single and batch NAV pulls, scheme search and the snapshot endpoint that returns the latest NAV for every scheme in one file. Includes measured tips for querying it remotely: duckdb and polars are fastest with polars marginally ahead, pyarrow and pandas-with-fsspec move roughly ten times more data for the same query, and pandas handed a bare URL is the case to avoid (372 MB and 37 seconds against 17 MB and 3.5 seconds when the URL is opened through fsspec first). Because the file is sorted by scheme_code, a WHERE on scheme_code costs about five requests while a date filter or unfiltered scan costs about three hundred, so the rule of thumb is to filter on scheme_code and query remotely and otherwise just download the file. Also covers two JavaScript options, hyparquet and the native duckdb npm package. AI-readable: https://www.tigzig.com/ai/posts/mfpro-range-requests-duckdb-polars-aug2026.md - [An ISIN Checker for When a Mutual Fund Lookup Fails. And Why One Fund Carries Ten Identifiers.](https://www.tigzig.com/post/isin-checker-mutual-fund-identifiers-aug2026) - Tags: mutual-funds, converters-tools A free checker at isin.tigzig.com that separates a malformed ISIN from a valid one whose data is simply absent. It validates against the ISO 6166 check digit, explains what is wrong when validation fails, and where a character has been dropped will often suggest the intended ISIN. For Indian mutual funds it also returns the fund name, the AMFI scheme code and the reinvestment ISIN, accepts a pasted list for bulk checking, and resolves a fund name back to its ISIN. Explains the identifier sprawl behind most failed lookups, worked through Axis ELSS Tax Saver Fund: one fund exists as four variants (Direct and Regular plans, each with Growth and IDCW options), each with its own AMFI scheme code and NAV, and the two IDCW variants carry two ISINs each because payout and reinvestment are different instruments, giving four scheme codes and six ISINs, ten identifiers for what a reader would call one fund. Also covers three traps: IDCW schemes with no payout ISIN at all (Parag Parikh Flexi Cap Direct IDCW, scheme code 153964, where only the reinvestment identifier exists, so code reading the primary field alone reports a live fund as missing), SIF ISINs that are valid but absent from AMFI's mutual fund feed because SIF data is published separately, and scheme names shifting under the SEBI recategorisation while identifiers stay put. AI-readable: https://www.tigzig.com/ai/posts/isin-checker-mutual-fund-identifiers-aug2026.md - [SIF Data Is Live. The Full Published NAV History, the Scheme Master and the Quarterly AUM.](https://www.tigzig.com/post/sif-data-release-nav-scheme-master-aum-aug2026) - Tags: mutual-funds, infrastructure A free, keyless dataset of every NAV AMFI has published for Indian Specialized Investment Funds since the category began, live at sif.tigzig.com as plain CSV updated twice a day, alongside the scheme master and quarterly average AUM. Six files: sif_nav.csv (about 10,000 rows over 200+ trading dates), sif_meta.csv, sif_aaum.csv, summary.json, manifest.json with row counts, byte sizes and SHA-256 per file, and a README covering every column. Explicitly observational, part of pipeline beta testing, with no uptime commitment and a file shape that may still change. Explains why fund counts and scheme-variant counts differ: AMFI counts 27 funds as at 30 June 2026 across 75,032 folios and Rs 17,858 crore net AUM, while the files hold 111 scheme variants from 17 fund houses, 63 open ended and 48 interval, with the first NAV on 8 October 2025. Documents the traps for anyone building on it: the temp_ column prefix that disappears on merge, upload_ts being IST despite a trailing Z, isin and isin2 being one scheme, and column names deliberately matching the mutual fund dataset. Sets out the open design questions before SIF joins MFPRO, including a different SEBI strategy taxonomy, long-only benchmarks that misprice long-short funds, interval-fund liquidity semantics, and a three-option comparison for reconciling AMFI's string SIF codes with integer mutual fund scheme codes. Closes with the validation regime: dual-source comparison, five-day lookback for late filings and restatements, holiday versus gap detection, byte-level reparse against archived originals, AUM reconciliation, a strategy-by-strategy roll-up matching AMFI's monthly report, and the single inferred field with its NAV-ordering test. AI-readable: https://www.tigzig.com/ai/posts/sif-data-release-nav-scheme-master-aum-aug2026.md - [Zero Callers Rate Limited in 24 Hours. 6,500+ Calls and 9.7 GB Served, After the TigZig API Limits Went Up.](https://www.tigzig.com/post/tigzig-api-zero-rate-limited-aug2026) - Tags: infrastructure, mutual-funds Result of the rate-limit and concurrency rework on the free, keyless TigZig public APIs: zero callers refused in a 24-hour window across 6,500+ calls and 9.7 GB served, against a prior peak where roughly one in four calls was being refused because old limit and concurrency settings had never been updated. Fixed across the whole path, edge, proxy and app. Also breaks down how the three busiest API surfaces are actually used, which differs sharply between them. The mutual fund API (MFPRO) leads on call volume and splits two ways, callers pulling NAVs scheme by scheme or in batches, and callers taking the entire database as one file alongside the scheme master, which is regenerated three times a day from AMFI data, with format demand split roughly evenly between parquet and CSV; the NAV path traffic looks like scheduled scripts maintaining trackers. The Yahoo Finance API (YFIN) is second by volume with get-all-prices the most used path, and is a thin open-source wrapper over the yfinance package. Tremor is almost entirely bulk, callers taking the full macro database of a few hundred global and India indicators. Downloads carry a tighter limit than queries because they are heavy, and that limit is being revised upwards as range requests appear. Includes a copy-paste agent prompt for wiring up a tracker end to end, and the free no-login front ends: MFPRO, the Quants Report Agent and QREP. AI-readable: https://www.tigzig.com/ai/posts/tigzig-api-zero-rate-limited-aug2026.md - [Is SIF Analytics Coming to MFPRO? On the List, No Date.](https://www.tigzig.com/post/mfpro-sif-analytics-where-it-stands-aug2026) - Tags: mutual-funds Where SIF analytics stands on the MFPRO roadmap: planned, with no date. SIF is SEBI's Specialised Investment Fund category, about a year old and growing, but AMFI publishes SIF data on a separate portal and the daily AMFI NAV feed that MFPRO syncs three times a day carries no SIF schemes at all, so none are present in the mutual fund dataset or API today. A complicating detail for anyone matching identifiers: SIF ISINs are formatted exactly like mutual fund ISINs, INF prefix included, because SIF units sit under the AMC's mutual fund umbrella at the depository, so the identifier format alone does not distinguish the two. Same identifier shape, different regulatory category, different source, which means a separate ingestion pipeline with its own validations rather than an extension of the existing one. The stated reason for holding off is sequencing rather than difficulty: MFPRO has only been live a few months, app and API usage has risen sharply in recent weeks, and the preference is to let that settle and learn from it before adding a new data domain. AI-readable: https://www.tigzig.com/ai/posts/mfpro-sif-analytics-where-it-stands-aug2026.md - [Claude Cowork Runs a Real Python Sandbox. I Put 37 Million Rows of Mutual Fund NAV Data Through It.](https://www.tigzig.com/post/claude-cowork-python-sandbox-mf-data-aug2026) - Tags: ai-coders, mutual-funds, duckdb What Claude Cowork's Python sandbox can actually do for analytics work, tested on a real dataset. It ships with pandas, numpy, scipy, duckdb, matplotlib and yfinance, installs scikit-learn and statsmodels in under a minute, and takes polars and pyarrow too, though packages needing compilation can time out on install (xgboost did). An approximate benchmark run inside the sandbox reports two cores of an AMD Ryzen 7 7730U, about 4 GB of memory and 10 GB of SSD-class disk. The worked case: downloading the free India mutual fund NAV dataset published on tigzig.com, a 175 MB parquet of 37 million-plus rows going back to April 2006 plus the scheme master, and running category-level risk and return analytics with results back in under a minute per iteration. The same Python sandbox is available in Claude for Excel and PowerPoint. Includes a copy-paste prompt for pointing any agent at the same live datasets, with the four things that trip people up on this data: matured and merged schemes are retained so it is survivorship-bias free, a row is a plan-and-option variant rather than a fund (about 8,500 codes publishing daily, roughly 1,950 funds as AMFI counts them), and scheme names and categories shift under the SEBI recategorisation so joins should key on scheme code or ISIN. Closes with why DuckDB and Parquet are the working default: the same NAV data is 3.8 GB as plain CSV against about 175 MB as parquet, columnar storage means a query reads only the columns it touches, and DuckDB's statistical functions run in SQL without dropping into pandas. AI-readable: https://www.tigzig.com/ai/posts/claude-cowork-python-sandbox-mf-data-aug2026.md - [TigZig API Rate Limits Raised Substantially, Across the Edge, the Proxy and the Apps. And a 429 Now Tells You What to Do About It.](https://www.tigzig.com/post/tigzig-api-rate-limits-raised-aug2026) - Tags: infrastructure, mutual-funds Rate limits on the free, keyless TigZig APIs have been raised substantially across every layer of the path, the Cloudflare edge, the proxy in front of the apps and the apps themselves, with concurrency limits raised alongside. The old numbers were set when traffic was a fraction of current volume and had accumulated across layers added at different times, so ordinary use, particularly Excel and Google Sheets trackers and automated scripts firing one call per row, was being refused. The 429 response is now actionable: every refusal carries Retry-After, CORS headers go out on it so a browser fetch can read the status rather than throwing a generic network error, and where a bulk download exists the response body names the file that would replace the loop. A Cloudflare-edge refusal also returns X-RateLimit-Limit, Remaining, Reset and Domain, with Reset approximate because the window rolls per edge location, while an app-side refusal carries Retry-After alone. Per-API limits are published in the human docs and the machine-readable catalogs. Also covers the batch endpoints and the full-database downloads available for the mutual fund, Vigil and Tremor APIs as the right answer for heavy use, Python in Excel via xlwings Lite for spreadsheet automation, and an explanation of what per-IP counting means behind carrier-grade NAT or a corporate gateway, along with why these APIs stay keyless by deliberate choice. AI-readable: https://www.tigzig.com/ai/posts/tigzig-api-rate-limits-raised-aug2026.md - [MFPRO Scheme Master: Every India Mutual Fund Scheme in One Small File, Free](https://www.tigzig.com/post/mfpro-scheme-master-download-aug2026) - Tags: mutual-funds, portfolio-analytics A free, keyless one-row-per-scheme master file covering every India mutual fund scheme in AMFI's public records from April 2006 onward, about 38,000 rows and 22 columns at roughly 9 MB as CSV, 1.1 MB gzipped and 1.6 MB as parquet, downloadable in one click from the MFPRO app or over HTTP at /mf/v1/download?format=latest with pandas reading straight off the URL. Columns cover identity (scheme_code, isin, isin2, scheme_name, amc, txic_code), classification (scheme_type, raw category, category_sub, category_group and the four-value category_group_clean), how it is sold (scheme_plan, scheme_option), lifespan (first_date, last_date, is_active, is_stale) and size and price (aaum_cr_quarterly_avg with its quarter label and ISO quarter-end, nav_date, nav). About 8,600 schemes are live and roughly 29,000 matured or merged are kept in deliberately, so long-run studies can be run survivorship-bias free. Includes a worked pandas filter that shortlists 39 large long-track-record Direct Growth equity schemes across three fund houses, guidance on moving from scheme codes to NAVs (single call, 50-code batch, or the 37-million-row full history file), and a join recipe pairing the master with the full NAV history to hold the whole universe locally. Documents six data quirks that produce plausible wrong answers rather than errors: the NAV column being each scheme's last NAV rather than today's, one scheme code being a plan-and-option variant rather than a fund, meaningful blanks in category_sub, aaum_cr_quarterly_avg and isin, sorting quarters on the ISO column rather than the text label, AAUM being a quarterly average rather than a balance, and about 2,300 matured schemes carrying a closing NAV of zero. AI-readable: https://www.tigzig.com/ai/posts/mfpro-scheme-master-download-aug2026.md - [MFPRO Mutual Fund API: Search Now Filters Across 12 Fields on a Scheme](https://www.tigzig.com/post/mfpro-search-filters-12-fields-jul2026) - Tags: mutual-funds, infrastructure Full reference for the /search endpoint on the free, keyless MFPRO India mutual fund NAV API, which now filters on 12 scheme fields rather than matching a fund name alone: amc, category, group, type, plan, option, active, stale, the first_date and last_date windows, isin and txic, with exclude on top as the single NOT. Any filter present turns search into browse, so a call with filters and no q lists the scheme universe; only a completely bare call still returns 422. Covers the difference between group (four broad buckets after the SEBI Feb 2026 change folded Solution Oriented and Fund of Funds into Other Scheme) and category (the SEBI sub-category, contains-matched against AMFI's raw text too, which is why category=income with active=all surfaces tens of thousands of pre-2018 schemes that carry no clean sub-category). Documents the whole query language: filters AND automatically, a comma is OR within one field, exclude subtracts last, and there is nothing past that - no parentheses, no nesting, no OR across fields, and AND/OR typed into q are matched as literal words. Also the error behaviour (an unknown parameter rejects the whole call with valid_params and did_you_mean rather than silently dropping a filter, repeated q is rejected while list filters merge, limit is bounds-checked 1 to 100 with a pointer to the bulk download), worked examples with real counts, the exclude-matches-name-only and no-sort limits, offset paging, and the pagination-across-a-sync edge with the bulk file as the consistent alternative. AI-readable: https://www.tigzig.com/ai/posts/mfpro-search-filters-12-fields-jul2026.md - [MFPRO Scheme Explorer is Live. Cross-Tab 38,000+ India Mutual Fund Schemes, and Watch the SEBI Re-Categorisation Happen in the Daily Files.](https://www.tigzig.com/post/mfpro-scheme-explorer-sebi-categories-jul2026) - Tags: mutual-funds An interactive Scheme Explorer on MFPRO that cross-tabs the whole India mutual fund universe of 38,000+ AMFI schemes, pivoting scheme counts by clean category, sub-category, AMC, plan and option with filters, under the new SEBI categories and names. Because MFPRO now tracks the latest AMFI filing rather than the first-filed name, the February 2026 SEBI re-categorisation can be watched moving through the daily NAV files: 367 schemes changed category or name in a single day, including 91 name changes as fund houses rename to the true-to-label rule. Worked examples of the renames: Groww Dynamic Bond Fund to Groww Dynamic Term Fund, ITI Ultra Short Duration Fund to ITI Ultra Short Term Fund, ICICI Prudential Equity Arbitrage to ICICI Prudential Arbitrage Fund, Canara Robeco Consumer Trends and HDFC Non-Cyclical Consumer both to Consumption Fund, ICICI Prudential Thematic Advantage Fund (FOF) to ICICI Prudential Aggressive Hybrid Active FOF, Quantum Gold Savings Fund to Quantum Gold ETF FOF, Motilal Oswal Asset Allocation FOF to Motilal Oswal Multi Asset Passive Fund of Fund, and UTI Liquid Cash Plan to UTI Liquid Fund, with the new FoF naming now signalling active versus passive underlyings. The first two Life Cycle Funds are live (Zerodha Life Cycle Fund 2036 and 2041). Solution Oriented schemes are discontinued, so 143 active children's and retirement schemes stop taking fresh subscriptions and will be merged. Of the 38,000+ schemes only about 8,600 are active, roughly three in four having matured or merged, mostly fixed maturity plans from 2006 to 2013. Also documents a transient AMFI filing error (Aditya Birla Sun Life Gold ETF filed under Equity ETF) published as filed rather than silently corrected. AI-readable: https://www.tigzig.com/ai/posts/mfpro-scheme-explorer-sebi-categories-jul2026.md - [SEBI Has Revised Mutual Fund Categorisation and Introduced a New Fund Type. The Major Changes, and What They Mean for MFPRO Users and Analysts.](https://www.tigzig.com/post/sebi-mf-categorisation-changes-jul2026) - Tags: mutual-funds SEBI's 26 February 2026 circular on categorization and rationalization of mutual fund schemes, the major changes, and what they change for users of the MFPRO tool and the free MF NAV API. Circular changes covered: a new Life Cycle Funds category (target date funds such as Life Cycle Fund 2041, equity heavy at the start and shifting toward debt as the maturity year approaches, with the first two already launched by Zerodha); Solution Oriented schemes discontinued, with children's and retirement funds stopping new subscriptions and being merged; Sectoral and Thematic split into two categories with portfolio overlap limits; most debt categories renamed across the duration ladder (Short Duration to Short Term, Dynamic Bond to Dynamic Term Fund); and scheme names required to match the category, with return-indicating words no longer allowed. Existing schemes have six months to comply. The transition is already visible in AMFI's daily NAV files, with new filings carrying the new category headers from 17 July, so old and new category and sub-category names will coexist in the data for a while and can break analysis pipelines. On the MFPRO side: the top level view moved to four buckets (Equity, Debt, Hybrid, Other) with life cycle funds inside Other, Solution Oriented and Fund of Funds folded into Other as well, the raw AMFI category text stays on every scheme for filtering, search is available on both tool and API, and scheme names switch from first-filed to the current AMFI filing ahead of the rename wave. Includes a field-level note on category, category_group, sub-category and the recommended category_group_clean. AI-readable: https://www.tigzig.com/ai/posts/sebi-mf-categorisation-changes-jul2026.md - [MFPRO Mutual Fund API Update. Three Things Your Calls Asked For, Now Built.](https://www.tigzig.com/post/mfpro-api-update-three-things-jul2026) - Tags: infrastructure, mutual-funds Three changes to the free, keyless MFPRO India mutual fund NAV API, each one built from a pattern seen repeatedly in the server logs, with the before and after for each. First, latest=true is now a real parameter: callers had been guessing it for weeks and silently receiving a fund's entire history (5,003 rows back to 2006) to show one number; it now returns one row, works on single and batch calls, combines with since/to for as-of-date queries, and accepts LATEST=TRUE, latest=1 and latest=false. Second, every response now carries per-scheme first_available_date and latest_available_date plus a Last-Modified header on empty windows, so a 200 with an empty data array explains itself and a poller running ahead of AMFI's daily publish can self-correct instead of writing empty files. Third, a 404 on an ISIN now runs the check-digit arithmetic and says in the error body when the identifier itself is corrupted, distinguishing that from an identifier that is well formed but simply absent, so an agent reading the error knows whether retrying can ever help. Also recaps the forgiving-input handling: scheme/schemes/isin/isins interchangeable, up to 50 identifiers per call, case-insensitive parameter names and values, repeated parameters merging, and one bad identifier never failing a batch. AI-readable: https://www.tigzig.com/ai/posts/mfpro-api-update-three-things-jul2026.md - [Your Agents and Scripts Have Been Talking to Me. Here Is That Conversation in Plain English.](https://www.tigzig.com/post/api-conversation-agents-scripts-jul2026) - Tags: infrastructure, mcp, mutual-funds Server logs from the free, keyless tigzig data APIs rewritten as a plain-English conversation, including who is actually calling (roughly 1 in 5 mutual fund API calls arrive with Claude or ChatGPT user agents from Cowork, Excel and Codex, rising to about half on Tremor and Yahoo Finance where MCP is mounted, with the rest scripts and AI coders behind generic or blank user agents): 23 real questions the incoming calls asked, and the patterns that got it right. Questions covered include SIF schemes (not carried, and why a q=SIF search returns 30 false matches from 'diversified'), where to get index levels rather than index funds (Tremor's 20 market series with tickers and history start dates, or the Yahoo Finance wrapper), batching up to 50 identifiers in one call with scheme/schemes/isin/isins interchangeable, what a 429 actually means and how Retry-After and X-RateLimit-Remaining work, trailing punctuation and & entities in copied URLs (the silent one that returns full history instead of your date slice), why an MCP endpoint 406s in a browser, the three causes of a 404 on a well-formed identifier, ISIN check digits and invalid ISINs circulating through AI output, empty-200 vs 404, retrying large downloads and verifying against Content-Length, plus the cricket-database GET-vs-POST fix and its Cricsheet id space. The things callers got right: whole-file parquet downloads, format choice, ChatGPT-User fetching files into its own sandbox, batch calls, search-then-fetch, since= incrementals, DuckDB HTTP range reads pulling 9MB instead of 170MB, ETag/If-None-Match and If-Modified-Since checks, and agents reading the API catalog before making a single data call. Ends with the human and agent documentation sets and how to report a problem. AI-readable: https://www.tigzig.com/ai/posts/api-conversation-agents-scripts-jul2026.md - [1 in 5 Bulk Downloads Were Silently Truncating. Two Cloudflare Worker Bugs, Found and Fixed. Now Up to 4x Faster.](https://www.tigzig.com/post/mfpro-download-truncation-fix-jul2026) - Tags: infrastructure, mutual-funds Roughly 1 in 5 bulk downloads of the MFPRO India mutual fund database were silently truncating - files started fine, ran a while, then arrived incomplete, with no error shown to the user and slower connections hit hardest. Edge-level monitoring surfaced 504s that origin server logs structurally cannot see. Two separate Cloudflare Worker bugs in serving large files from R2: teeing the user's download stream into the edge cache write (the cache fill finishes first and cuts the client off mid-file), and passing a large file through JavaScript inside the Worker (the CPU limit truncates it silently). Both fixed and verified byte by byte from four countries; downloads now complete and up to 4x faster. Includes the transferable lessons for anyone serving large files at the edge, what is in the download (37,084,484 rows across 38,000+ schemes since 2006, seven formats from 167MB Parquet to 1.28GB SQLite, regenerated 2-3x daily with a manifest carrying sizes, row count and a validation flag), the one-GET no-key endpoint, the DuckDB-over-HTTP alternative, and the wider no-key API and MCP surface (Tremor macro, Vigil India red flags, Yahoo Finance, comparisons, technical analysis, markdown to PDF) with live uptime at status.tigzig.com. AI-readable: https://www.tigzig.com/ai/posts/mfpro-download-truncation-fix-jul2026.md - [227 Downloads, 41GB in 10 Days. India MF Complete NAV Database - 38K+ Schemes, 37M+ Records, One 167MB Parquet. Free.](https://www.tigzig.com/post/mfpro-downloads-full-database-jul2026) - Tags: mutual-funds Usage note on the MFPRO full-database download: 227 downloads totalling 41GB served in 10 days from Cloudflare's global edge (R2) - the complete India Mutual Fund NAV database as a single 167MB Parquet file covering 38K+ scheme codes and 37M+ records since 2006, including closed and defunct schemes, enabling regime-based analysis across the 2008 GFC and later shock events. Free and open, refreshed three times daily, downloadable manually from the browser (MFPRO Data & API screen) or via API - or point an AI agent at api.tigzig.com. Update process runs multi-step validation, with a data dictionary, build-process doc, scheme-type field guide and documented data anomalies in the Docs section. Also covers the scheme universe (~8,500 codes publishing daily, roughly 1,950 funds as AMFI counts them; 22% of all-time codes active) with an interactive cross-tab Explorer, and API uptime at 99.7% over the past ~15 days with a live status page at status.tigzig.com. Single-image release post with the downloads one-pager PNG at the bottom. AI-readable: https://www.tigzig.com/ai/posts/mfpro-downloads-full-database-jul2026.md - [Data Dictionary & Docs Live on MFPRO. Every Field Explained, 21-Metric Methodology, and How the India MF Universe Actually Looks.](https://www.tigzig.com/post/mfpro-data-dictionary-docs-jul2026) - Tags: mutual-funds Release note for the MFPRO documentation set on the India Mutual Fund NAV database. The data dictionary explains every field table by table (scheme_code, category_group_clean, where fields go blank); docs also cover Performance & Risk methodology for all 21 computed metrics with formulas and worked examples (CAGR, rolling returns, max drawdown, Calmar, beat rate, beta/R-squared, alpha/t-stat, Sharpe, Sortino, tracking error/information ratio, capture ratios, win rate, Treynor, VaR/CVaR, skewness/kurtosis, Ulcer Index, drawdown & recovery), the Scheme Universe field guide, How the Data Is Built, and composition analytics. For AI agents, https://api.tigzig.com/mf/v1/ returns the full machine-readable catalog: every endpoint with parameters, schema, stats, downloads, rate limits, error shape, plus explicit data_dictionary and methodology links. Includes a scheme-universe breakdown: of 38,000+ scheme codes since 2006, 8,538 active (22%) vs 29,466 inactive (78%, ~80% of those close-ended FMP-era), active split Debt 33% / Equity 28% / Index-ETF-FoF 27% / Hybrid 10% / Solution 2%, Direct 47% vs Regular 38%. Single-image release post with the docs one-pager PNG at the bottom. AI-readable: https://www.tigzig.com/ai/posts/mfpro-data-dictionary-docs-jul2026.md - [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) - Tags: mutual-funds Release note on two MFPRO fixes driven by user feedback and daily log reading. One: a user reported ABSL Value and ICICI Pru Value missing - the funds were in the database, the search was the bug; search now expands 17 abbreviations and old fund names (ABSL, PPFAS, IDFC to Bandhan, Reliance to Nippon) and ranks results by relevance instead of alphabetically. Two: 404s in the logs led to a full rebuild of every NAV AMFI has ever published, 2006 onward, including closed, defunct, interval and close-ended funds - 37M rows total. Covers the staged validation: offline profiling at the pull, section-header category parsing with an explicitly reviewed legacy-taxonomy mapping, pilot-then-batched loads with cell-by-cell cross-tab reconciliation, existing-rows-win conflict policy (9 overlaps in 16.4M rows, logged), and a post-load multi-agent audit (7 finders + 7 adversarial verifiers, zero duplicate scheme-date pairs, zero orphans). Source oddities like 2006-era zero-NAV rows are published as filed, documented in the MFPRO Docs section. Single-image release post with the update one-pager PNG at the bottom. AI-readable: https://www.tigzig.com/ai/posts/mfpro-search-upgrade-full-history-jul2026.md - [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) - Tags: mutual-funds, converters-tools Release note announcing that MFPRO (India Mutual Fund Advanced Analytics on tigzig.com) now has a Data & API screen - no code, no API, point-click-download. Two flows: (1) pick a basket of up to 20 funds, any period, pull their full NAV history as CSV; (2) take the entire database - every scheme since 2008, 20M+ rows - in one click. Whole-database download comes in four formats: CSV and TSV about 132 MB each, Parquet 107 MB, SQLite 748 MB. The file is rebuilt three times a day from AMFI. For the API crowd, everything on this screen is also an endpoint - docs at tigzig.com -> APIs -> MF NAV, and live uptime for all of it at status.tigzig.com. Post also carries a short note to frontend users: if you were running MF advanced analytics on the frontend these past few days and hit errors, that was the revamp in motion, those paths are being smoothed out and most of it already fixed. Deep-links straight to the MFPRO Download Full Database tab. Single-image release post with the actual Data & API screen screenshot. AI-readable: https://www.tigzig.com/ai/posts/mfpro-data-api-download-jul2026.md - [Tigzig APIs - Reading the Logs, Shipping the Fixes. Status Page Live, R2 Downloads, Self-Correcting 4xxs.](https://www.tigzig.com/post/tigzig-apis-status-page-live-jul2026) - Tags: mutual-funds, infrastructure Release note on what shipped for the Tigzig public APIs in the last 48 hours, driven by daily log reading (200s tell what is working, 4xx and 5xx tell what to fix next). Four things live: (1) common-guess coercion - if a caller or their AI guessed /series instead of /nav or sent start= instead of since=, those now quietly work with one canonical spec still in the docs; (2) every 4xx now returns a help block linking the docs page and the machine catalog so an AI agent can self-correct without the human stepping in; (3) full-database downloads (100 to 500+ MB files) now served from Cloudflare R2, noticeably faster than before; (4) status page live and probed every minute at status.tigzig.com - downtime rings the author's phone. Underway: fallback to a secondary server if the primary goes down. Second half of the post is a practical errors-and-fixes list: 404 (scheme code guessing/enumeration - use /search?q=fund-name or an ISIN from the broker statement, both work), heavy parallel bursts now return proper 429s instead of the occasional 503, 429 rate limits (300/min per public IP with X-RateLimit headers so scripts/agents can pace themselves), 400 validation (YYYY-MM-DD dates, 50-scheme bulk cap, scheme= for one vs schemes= for many), and every failure comes back as JSON with an error code + docs_url pointing straight at the fix. Polling tip: send If-Modified-Since and you get a 304 on days AMFI hasn't published (weekends, holidays) so scripts can skip re-downloading history they already have. Single-image release post - status page screenshot. AI-readable: https://www.tigzig.com/ai/posts/tigzig-apis-status-page-live-jul2026.md - [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) - Tags: mutual-funds, converters-tools, mcp Release note for the revamped free Mutual Fund NAV API for India on tigzig.com. Daily NAVs for 18,000+ schemes since 2008, straight from AMFI. Free, no sign up, no API key. Rebuild targets AI + automation friendliness: latencies and errors significantly down, more capacity, accepts AMFI Code or ISIN or a mix (auto-detected at the backend), plus a set of other flexibility improvements captured in the attached 2-page overview. Claude Cowork and OpenAI Codex can work with the API directly - point them at the API docs page and they take it from there. Claude in Excel needs a thin MCP wrapper on top (open source coming soon). Two consumption paths: API docs at tigzig.com -> APIs -> MF NAV, or the MFPRO UI at tigzig.com -> MFPRO which runs advanced analytics on up to 200 schemes in one shot (risk, returns including rolling returns, alpha, betas, drawdowns). Whole database is also downloadable in two ways: human path via MFPRO Data and API button (Parquet, CSV, TSV, SQLite), or AI/API path via a single GET on /mf/v1/download which returns all 20.5M+ rows as a single file. Approx sizes: Parquet 109 MB, CSV 132 MB compressed, SQLite 462 MB compressed. Every metric in MFPRO ships with a validation pack - the UI computes on DuckDB SQL and the validation pack recomputes in pure Python, with a ready-to-run main.py against the NAV file so a user or their AI can reproduce and extend. 2-page overview PDF carousel included. AI-readable: https://www.tigzig.com/ai/posts/tigzig-mf-nav-api-revamp-jul2026.md - [Free AMFI Mutual Fund NAV API for India - Historical NAV by Scheme Code, No Key](https://www.tigzig.com/post/free-mutual-fund-nav-api-india) - Tags: mutual-funds, infrastructure A free, open AMFI Mutual Fund NAV API for India - no key, no sign-up. 20.5M+ daily NAV records across 18,000+ schemes, back to 2008, refreshed daily. Search schemes, pull a scheme's full NAV history by code and date, or download the ENTIRE database in a single call (Parquet / CSV / SQLite). Copy-paste curl and Python examples. AI-readable: https://www.tigzig.com/ai/posts/free-mutual-fund-nav-api-india.md - [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) - Tags: mutual-funds, portfolio-analytics, duckdb Explains why point-to-point CAGR is fragile and how rolling returns provide a more reliable picture of fund performance. Covers the difference between rolling window and evaluation period, the ASOF JOIN computation in DuckDB SQL, minimum gap thresholds, CAGR vs absolute return for sub-1-year windows, and how to read each column (average, median, min, max, % negative, observations). Includes validation results across 7,485 data points within 0.50 bps of Excel. Live on MFPRO with 95 funds and 9 indices. AI-readable: https://www.tigzig.com/ai/posts/rolling-returns-why-cagr-alone-can-mislead-you.md - [Now live - MFPRO v2 - Mutual Fund Analytics (India) - now with rolling returns, custom eval periods, multi-period & multi-instrument comparisons](https://www.tigzig.com/post/mfpro-v2-mutual-fund-analytics-rolling-returns) - Tags: mutual-funds, portfolio-analytics MFPRO v2 release for Indian mutual fund analytics. Adds rolling return statistics (1Y/3Y/5Y windows with average, median, min, max), custom evaluation periods, multi-fund and multi-index return comparisons across 95 funds and 9 indices. Includes downloadable NAV data, Excel templates for independent verification, multi-period holdings comparison, and index constituent analysis. Data from Jan 2013, auto-synced twice daily. AI-readable: https://www.tigzig.com/ai/posts/mfpro-v2-mutual-fund-analytics-rolling-returns.md - [How I Identify and Map Every Holding — The ISIN Mapping Process](https://www.tigzig.com/post/mdrift-isin-mapping-process) - Tags: mutual-funds Detailed 12-step pipeline for standardizing Indian mutual fund portfolio holdings across 15 AMCs. Covers ISIN lookup against 361,000-record NSDL master, corporate action detection via ISIN serial number analysis, 7-character name-cut deduplication, CD/CP/T-Bill aggregation with synthetic ISINs, foreign stock identification, TREPS classification, human-in-the-loop validation, and grand total verification to the paisa across 96 fund-month combinations. AI-readable: https://www.tigzig.com/ai/posts/mdrift-isin-mapping-process.md - [MF Composition Analytics With MDRIFT - Interesting Moves in Top Flexi Cap and Focused Funds](https://www.tigzig.com/post/mdrift-flexi-cap-focused-fund-composition-analytics) - Tags: mutual-funds Analysis of top Indian Flexi Cap and Focused mutual funds using MDRIFT composition analytics tool. Highlights include PPFAS holding 12% in US tech, HDFC sitting on 15% cash, SBI Focused with 10% in Alphabet. Compares portfolio compositions across time periods showing entries, exits, weight changes. Nifty index comparisons added for spotting off-index bets. AI-readable: https://www.tigzig.com/ai/posts/mdrift-flexi-cap-focused-fund-composition-analytics.md - [Releasing MDRIFT - Mutual Fund Composition & Drift Analytics Tool](https://www.tigzig.com/post/releasing-mdrift-mutual-fund-composition-drift-analytics) - Tags: mutual-funds MDRIFT tool release for comparing Indian mutual fund portfolio compositions across funds and time periods. Processes monthly AMC Excel files, standardizes ISINs, handles corporate action remaps, and groups debt instruments. Covers 21 equity funds across Large Cap, Mid Cap, Small Cap, Flexi Cap, and Focused categories. Sept 2025 through Jan 2026 data. Features drill-downs from scheme type to individual ISIN level. AI-readable: https://www.tigzig.com/ai/posts/releasing-mdrift-mutual-fund-composition-drift-analytics.md - [New Open Source Tool. Mutual Funds Holdings Analyzer. Python in Excel (xlwings Lite). Now Live.](https://www.tigzig.com/post/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live) - Tags: mutual-funds, xlwings-lite, python-in-excel Open-source xlwings Lite tool for standardizing and comparing Indian mutual fund portfolio disclosure data. Handles ISIN-based name standardization, corporate action merging, and human-in-the-loop data quality review. Two-stage pipeline: Stage 1 generates data quality reports, Stage 2 produces final analysis after manual review. Built with Gemini CLI. AI-readable: https://www.tigzig.com/ai/posts/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live.md - [Analysis-as-App: Inside India’s Top Midcap Funds: Buys, Sells, Entries and Exits. Interactive Dashboard Release (Analysis-as-App)](https://www.tigzig.com/post/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo) - Tags: mutual-funds Interactive dashboard analyzing portfolio changes across India's top 5 midcap mutual funds (Axis, HDFC, Kotak, Motilal, Nippon) managing ~54% of category AUM. Compares May vs August 2025 holdings. Built as a single 150KB HTML file using the MF Processor pipeline with GPT for ISIN standardization. Tracks buys, sells, entries, and exits. AI-readable: https://www.tigzig.com/ai/posts/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo.md - [Monthly MF portfolio files = hours wasted re-formatting. Here’s a tool that fixes it](https://www.tigzig.com/post/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it) - Tags: mutual-funds, converters-tools Converter utility for Indian mutual fund monthly portfolio disclosure Excel files. Uses AI-powered schema detection to automatically identify data layouts, cross-validates with multiple models, and outputs clean CSVs with ISIN mapping and standardized names. Includes append, transpose, and audit trail utilities. Available at www.tigzig.com/mf-files-ai. AI-readable: https://www.tigzig.com/ai/posts/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it.md - [AI automation micro-app: MF Portfolio Files Processor. Live app. Open source.](https://www.tigzig.com/post/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source) - Tags: mutual-funds, converters-tools AI-powered micro-app for processing Indian mutual fund monthly portfolio Excel files into standardized CSV/database format. Handles varying Excel formats using LLM schema detection (GPT-4o-mini, GPT-4o, Gemini Flash) for column identification and market value extraction. Includes validation diagnostics and manual override. Built with vanilla JavaScript frontend, FastAPI proxy for LLM calls, and domain whitelisting. Open source with Power Pivot analysis example. AI-readable: https://www.tigzig.com/ai/posts/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source.md - [Mutual Fund Allocation Analysis with GPT Power Tools. Custom GPT. Custom Python Code. Multiple Excels.](https://www.tigzig.com/post/mutual-fund-analysis-custom-gpt-python-multiple-excel) - Tags: mutual-funds, custom-gpt Custom GPT for tracking changes in mutual fund equity portfolio allocations across time periods by merging multiple Excel files (up to 10, each in different formats). Uses custom Python code within the GPT for consistent processing instead of ad-hoc approaches. Handles print-formatted Excel files with images. Includes validation summaries and pivot table output. Replicable for any Excel processing use case with minimal modification. AI-readable: https://www.tigzig.com/ai/posts/mutual-fund-analysis-custom-gpt-python-multiple-excel.md - [Mutual Fund Portfolio Analysis with ChatGPT: Merging and analyzing across multiple excel files](https://www.tigzig.com/post/fa18de05) - Tags: mutual-funds, custom-gpt Using ChatGPT to merge and analyze multiple mutual fund portfolio Excel files (print-formatted, varying formats, containing images) for tracking equity allocation changes over time. Demonstrates merging 12 files in 30 seconds with a single prompt, including handling different filename formats. Covers voice-typed prompt preparation via Google Docs, validation against totals, and option to generate reusable Python code for larger datasets. AI-readable: https://www.tigzig.com/ai/posts/fa18de05.md ## Related Topics - [Database AI & Text-to-SQL](https://www.tigzig.com/ai/tags/database-ai.md) - [Python in Excel (xlwings Lite)](https://www.tigzig.com/ai/tags/python-in-excel.md) - [Claude in Excel](https://www.tigzig.com/ai/tags/claude-in-excel.md) - [DuckDB - Analytics & Dashboards](https://www.tigzig.com/ai/tags/duckdb.md) - [MCP Servers & Agents](https://www.tigzig.com/ai/tags/mcp-servers.md) --- ## About **Amar Harolikar** - Specialist, Decision Sciences & Applied Generative AI - Email: amar@harolikar.com - LinkedIn: https://www.linkedin.com/in/amarharolikar - GitHub: https://github.com/amararun - Site: https://www.tigzig.com ## Legal - Privacy: https://www.tigzig.com/privacy - Terms: https://www.tigzig.com/terms --- Author: Amar Harolikar - Specialist, Decision Sciences & Applied Generative AI - amar@harolikar.com - https://www.linkedin.com/in/amarharolikar Source: https://www.tigzig.com/ai/tags/mutual-funds.md 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