I'm Sumit, the builder behind WealthTicker a screener for Indian mutual funds built on public AMFI NAV data instead of fund house star ratings.

It came out of a personal annoyance: nearly every "top funds" list in India is either an ad or a rating nobody can reproduce. So I compute everything from raw NAV history CAGR, rolling returns, volatility, Sharpe, max drawdown across ~14,000 schemes, let you overlay up to four funds side by side, and backtest an SIP or lumpsum against what actually happened. Free, no distributor tie-up, so nothing is nudging you toward a particular fund.

What I'd genuinely like picked apart:

1. First 30 seconds is it obvious what to do on the landing page, or do you bounce?

2. The metrics are Sharpe / drawdown / rolling returns the right defaults, or too finance-y for a regular investor? What would you surface instead?

3. Backtesting does " 10k/month since 2015 would have become X" actually change how you'd pick a fund, or is it a toy?

4. Trust for something people make money decisions on, what would make you believe an independent site over the incumbents?

5. The one missing thing that would make this part of your routine rather than a one-time visit.

Blunt answers welcome I'd rather hear it here than watch people leave quietly.

wealthticker.in

Most people pick a mutual fund the same way: open an app, sort by "1-year returns," look at the star rating, tap invest. It feels like research. It isn't it's a leaderboard of whatever happened to work recently.

A screener is the fix. It's just a filter over the entire fund universe that lets you ask a specific question instead of accepting a default ranking. India has ~14,000 schemes. Nobody reads 14,000 factsheets. A screener is how you go from 14,000 to a shortlist of 8 you can actually study.

What a screener actually does for you

1. It replaces "best fund" with "best fit." There is no best fund there's a fund that matches your horizon, your risk tolerance, and what you already own. A screener lets you encode those constraints as filters: category, minimum track record, expense ratio ceiling, drawdown limit. What survives is a candidate set, not an answer.

2. It forces you past returns into risk. Returns are one number; the path to that number is the part you have to live through. Two funds can both show 18% CAGR while one fell 42% in 2020 and the other fell 26%. A screener surfaces volatility, Sharpe ratio, and maximum drawdown alongside CAGR, so you're comparing the ride, not just the destination.

3. It makes cost visible. Expense ratio compounds against you every year, silently. Sorting a category by expense ratio is a five-second filter that can be worth several lakh over 20 years and it's the only variable in this entire exercise you can predict with certainty.

4. It kills survivorship and recency bias. "Top performing funds of 2024" is a list assembled with hindsight. Screening on rolling returns across multiple periods, or on consistency of quartile ranking, asks a much better question: has this fund been decent repeatedly, or lucky once?

5. It exposes overlap. Four "different" flexi-cap funds often hold the same 12 large caps. You feel diversified and aren't. Comparing holdings side by side takes minutes and prevents you from paying four expense ratios for one portfolio.

6. It lets you test, not assume. Backtesting an SIP against actual historical NAV data answers "what would this have felt like" far better than a marketing CAGR. Not a prediction a reality check on your own expectations.

Most Indian investors start fund research at a ratings page 5-star, "top performer," Gold/Silver. But the rating is someone else's judgment call baked into a single symbol, and the methodology behind it is rarely something you can inspect or reproduce.

The alternative is doing it yourself from raw data, which is where it gets tedious. AMFI publishes NAV history for every scheme for free, so rolling returns, drawdowns, volatility, and SIP outcomes are all computable but almost nobody wants to maintain a spreadsheet across 14,000 schemes to get there.

I built WealthTicker (wealthticker.in) partly out of that frustration: everything on it is computed from public AMFI NAV data, so you can see the inputs instead of a verdict. But I'm more curious how other people solve this than in talking about my own tool.

So for those of you who've moved past ratings:

I built WealthTicker because every Indian mutual fund site I used was really a distribution funnel. The "top rated" list was whatever paid best, and the numbers came from a factsheet PDF nobody recomputed.

So I made the boring version: take AMFI's public NAV history for all 14,000+ schemes and compute everything myself.

CAGR, volatility, Sharpe, Sortino, alpha, beta, up/down capture, rolling returns, drawdown episodes all derived from the raw price series, refreshed continuously. If a number is on the page, there's a /methodology entry telling you exactly how it was calculated.

What you can actually do with it: