Every NAV that AMFI has published for Indian Specialized Investment Funds since the category began, together with the scheme master and quarterly average AUM. Free to download, no sign-up. Refreshed twice every day, at about 05:05 and 11:45 IST, covering onward.
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This is observational. SIF is a new category, and this collection is currently part of the beta testing of our data pipeline. We are deliberately running it on its own for a while so that the issues can surface and be worked through, before it is integrated into the mutual fund pipeline that powers MFPro. So it is published as it stands, with no uptime commitment, and the shape of the files may still change.
It is a third-party copy of AMFI data, and it is not affiliated with or endorsed by AMFI. If you are building something on it that you rely on, please keep your own copy.
Funds and scheme variants are different counts, and both are shown above. A single fund that offers a Direct and a Regular plan, each with a Growth and an IDCW option, is one fund and four scheme variants. AMFI assigns a separate code to each variant and publishes a separate NAV for each, so the data has one row per variant. If you want to know how many SIFs exist, the fund count is the one to quote.
SEBI created Specialized Investment Funds as a category sitting between mutual funds and portfolio management services. The first NAV in this data is -, so the whole history is still short, and every day of it is being written as we go.
AMFI's main mutual fund feed does not carry SIF schemes at all. A SIF identifier returns nothing from any of the usual mutual fund sources, which is why a separate collection had to exist, and why there is otherwise no straightforward way to get a clean SIF time series.
Everything here is at the scheme level, which is the level AMFI files at. A single fund that offers a Direct and a Regular plan, each with a Growth and an IDCW option, appears as four separate schemes, each with its own code, its own NAV and its own AUM. If you want the number for the fund as a whole, add its variants up.
Twice daily from AMFI's own published sources. Every response is archived exactly as it was received, before anything reads it, so that a change to our parsing is a re-run over what we already hold rather than another request to AMFI, and the original bytes stay available if we ever need to go back to them.
Each run also compares two independent AMFI surfaces against each other for the same date, and re-reads the last five days, so that a value AMFI has restated is detected rather than quietly overwritten. The quarterly AUM is reconciled against AMFI's own published fund, brand and industry totals every time it is refreshed.
Number of schemes reporting a NAV in each month. The rise here is the category being young and new funds arriving, not data going missing.
In Rs crore, summed from AMFI's scheme-wise filing.
SEBI defines seven SIF strategies. These are the ones that have launches so far.
By average AUM in the latest quarter AMFI has published. These are individual schemes rather than whole funds, so the same fund can appear more than once through its Direct and Regular variants.
| Code | Fund house | Scheme | AAUM (Rs cr) |
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Everything in this table is measured on the live data at each run rather than asserted, and it is the same view we use to decide whether the collection is behaving. When something unusual turns up it goes into the list below and stays under review, rather than being quietly corrected.
This data is published exactly as it was filed. Nothing has been cleaned, smoothed or corrected. Some of what AMFI has filed is genuinely strange, and rather than quietly fixing it or leaving you to run into it yourself, it is set out here. Each one was confirmed against the archived source bytes before it was written down.
Every item here is known, reviewed and being tracked. They are listed because they are facts about the source that you should know before using the data, not because they are unresolved surprises. Where something turns out to be an error on our side rather than AMFI's, it gets fixed and drops off this list.
Not a one-off download. The whole set is rebuilt at about 05:05 and 11:45 IST, so a link you save today keeps returning current data. The quarterly AUM file updates weekly, since AMFI publishes it once a quarter.
AMCs typically file their NAVs with AMFI through the evening and into the following morning, and sometimes later than that. We refresh more than once a day so these files pick up the most recent filing rather than a single snapshot.
Every file comes two ways. The CSV opens directly in Excel. The Parquet holds the same rows in the same order from the same query, but it is around a twentieth of the size and it carries types, so a date arrives as a date. Take the CSV to look at it, the Parquet for anything that will query it.
Check generated_at in the manifest for the exact time of the copy you
are holding.
Stable paths, no key, no parameters and no rate limit to think about. You can fetch them directly.
curl -O https://sif.tigzig.com/sif_nav.csv
curl -O https://sif.tigzig.com/sif_meta.csv
curl -O https://sif.tigzig.com/sif_aaum.csv
curl -O https://sif.tigzig.com/sif_nav.parquet # the same rows, much smaller
curl -O https://sif.tigzig.com/sif_meta.parquet
curl -O https://sif.tigzig.com/sif_aaum.parquet
curl -s https://sif.tigzig.com/manifest.json # row counts, sizes, SHA-256, generated_at
curl -s https://sif.tigzig.com/summary.json # precomputed counts and series
curl -s https://sif.tigzig.com/README.txt # columns, units and conventionsFor freshness, read generated_at in manifest.json rather than
inferring it from anything else. Each file carries its SHA-256 there as well, so you can
check that what you downloaded is what we published.
-- DuckDB, straight off the parquet - no download step, types already correct
SELECT * FROM 'https://sif.tigzig.com/sif_nav.parquet';
-- or the CSV
SELECT * FROM read_csv_auto('https://sif.tigzig.com/sif_nav.csv');
# pandas
import pandas as pd
nav = pd.read_parquet('https://sif.tigzig.com/sif_nav.parquet')
meta = pd.read_parquet('https://sif.tigzig.com/sif_meta.parquet')
# or the CSV, where the date column needs parsing yourself
nav = pd.read_csv('https://sif.tigzig.com/sif_nav.csv', parse_dates=['date'])One thing to know about plan_basis in the scheme master. Several fund
houses name only the Direct variant of a scheme and leave its Regular twin unlabelled.
Where that happens we have worked the plan out from the scheme's siblings, and
plan_basis records which values came from AMFI and which are ours.
Filter on plan_basis = 'stated' if you want only what was actually
filed.
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