Systematic investing · SEBI-registered

Where data engineers your portfolio.

Three factor-based investment strategies across Indian equity and ETFs — built, tested, and run every month by a SEBI-registered practitioner who invests in them personally. No black-box. No fund-manager guesswork. Just process.

  • Transparent picks
  • Flat monthly fee
  • SEBI-registered
Portfolio Performance Live data
LIVE this cycle
vs benchmark · model portfolio since the last rebalance
39.6%
SmallMicro · backtest CAGR, after tax
vs
13.41%
Benchmark · backtest, after tax
MindForge SmallMicro 500 Nifty Smallcap 250
+26.15% annual alpha · backtest, after tax 2021–2026 · Backtest, compounded at the CAGR
SEBI Registered Research Analyst
3 Quant strategies
5Y Backtested track record
Broker-verified

Two strategies. Two accounts. Two records we don’t write.

Each paid strategy is traded in its own dedicated Zerodha account, executing that model and nothing else. Zerodha publishes each account’s P&L on its own verified page. The figures below are ours; the record behind the link is not.

LargeMidcap 250 Account 1 Live · model this cycle · vs benchmark Backtest · 2021–2026 · after tax 29.8%CAGR vs 12.14%benchmark +17.66% alpha View the verified P&L
SmallMicro 500 Account 2 Live · model this cycle · vs benchmark Backtest · 2021–2026 · after tax 39.6%CAGR vs 13.41%benchmark +26.15% alpha View the verified P&L

Three different things, deliberately kept apart. The green figure is the model portfolio’s move since the last rebalance — not audited client returns and not the account’s P&L. The CAGR pair is a backtest: simulated, not live trading. Only the linked page is the broker’s own record — open a card to see it. Past performance is not indicative of future returns. MultiAsset is free and is not traded in a dedicated account.

Built on 50 years of academic research.

Factor investing is not a trend — it is peer-reviewed finance, starting with the earliest models of how markets reward risk.

1964
CAPM — The first model
Sharpe, Lintner & Treynor showed that the only risk earning a return is market beta — undiversifiable systematic risk. This became the universal benchmark for separating genuine skill from factor exposure.
1992
Beyond CAPM — the multi-factor era
Nobel laureate Eugene Fama and Kenneth French proved CAPM was incomplete. Additional return factors were shown to systematically explain stock returns that market beta could not. The multi-factor era of academic finance began.
1997
The factor zoo expands
Over the following decades, researchers including Jegadeesh, Titman and Carhart documented further factor premia in equity markets, each surviving rigorous out-of-sample testing. Multi-factor models became the dominant framework for explaining cross-sectional returns.
2026
MindForge — Applied to India
We apply academically validated factors to Indian listed equities and ETFs. Every strategy is built on factors that are persistent, pervasive, robust, investable and intuitive.
Persistent
Holds across decades
Pervasive
Holds across markets
Robust
Not one formulation
Investable
Works after costs
Intuitive
Logical explanation

From a whole universe to what you actually hold.

Both equity strategies run the same disciplined pipeline each month — no overrides, no gut calls. Here's the path from index to portfolio. (MultiAsset rotates eight ETFs by score instead — there is no universe to rank.)

01
The universe
Every eligible name in the index — a broad, rules-defined starting field.
Up to 500 stocks
02
Multi-factor score
Each stock scored on peer-reviewed factors — momentum, trend, volatility & liquidity.
Peer-reviewed
03
Ranked by conviction
Sorted highest-first, so only the strongest signals make the cut.
Strongest First
04
Sector-capped
No more than two names per industry — diversification is structural, not promised.
≤ 2 Per Industry
05
Your portfolio
The top names, held equal-weight until next month's rebalance.
10–15, equal-weight
Up to 500 candidates Scored, Ranked & Capped into Equal Weighted Holdings Rebalanced every month, Published in full.

Use the whole toolkit before you decide anything.

We would rather you audited the process than took our word for it — so the research infrastructure is open. No countdown, no feature locked behind a plan, no card. Three of the four below do not even ask who you are.

What we don't ask for No card No trial clock No usage cap No feature held back

One for every risk appetite.

From capital-efficient multi-asset rotation to concentrated small-cap alpha — subscribe to one or all three.

Before the prices — the number they compete with

What a 2% AUM fee costs on a ₹15,00,000 portfolio over 10 years

₹5,52,592
taken in fees
Charged every year on everything you hold, whether or not anything is done for you.
₹7,33,085
missing from your final corpus
Larger than the fees themselves, because money taken in year 2 cannot compound in years 3 to 10.

Both totals over the same ten years, drawn to the same scale.

Illustration, not a forecast: both cases assume the same gross return, compounded yearly, and the only difference modelled is how the fee is charged. Actual expense ratios, exit loads and taxes vary. Run it on your own numbers →

Small & Microcap
MindForge SmallMicro 500
15 stocks · Smallcap 250 + Microcap 250 · Monthly rebalance
39.6%
2021–2026 backtest CAGR · after tax
13.41%
Nifty Smallcap 250 · Benchmark CAGR · after tax
Live · this cycle
vs benchmark
2021–2026 backtest CAGR vs its benchmark · gap a year
Max fall -22.5%
Deepest backtest drawdown over that window · Sharpe 1.52
Strategy Benchmark

The highest-conviction strategy — selects 15 stocks from 500 small and micro-cap names. A proprietary multi-factor model tuned for the segment's thin-liquidity dynamics. 5-year backtest, after 20% short-term capital-gains tax, shows +26.15% alpha over the Nifty Smallcap 250 benchmark.

How it works
Multi-factor scoring Equal-weight portfolio 2-per-industry cap Liquidity-aware
Free Multi-Asset
MindForge MultiAsset
8 assets · Equity, gold, bonds & global ETFs · Monthly rebalance
15.9%
2023–2026 Backtest CAGR · after tax
9.50%
Nifty 50 · Benchmark CAGR · after tax
Live · this cycle
vs benchmark
2023–2026 backtest CAGR vs its benchmark · gap a year
Max fall -11.6%
Deepest backtest drawdown over that window · Sharpe 0.99
Strategy Benchmark

Rotates systematically across Nifty 50, Next 50, Midcap 150, Smallcap 250, Gold, Silver, Bharat Bond and NASDAQ ETFs using a multi-factor scoring model with a long-term trend overlay. After 20% short-term capital-gains tax, +6.40% annual alpha over the Nifty 50 Index.

How it works
Multi-factor scoring Risk-adjusted ranking Long-term trend overlay
Broker-verified Each paid strategy trades in its own Zerodha account, and Zerodha publishes that account’s P&L itself: LargeMidcap 250 SmallMicro 500

Ten minutes on the 1st. Then nothing.

Every strategy rebalances once a month. The model does the re-scoring; you place the resulting orders in your own broker, in your own time. Here is the whole of it.

A different kind of money manager.

Academic foundations

Every signal in our multi-factor models is grounded in peer-reviewed finance, not a hot tip.

50+ years of research

Built for India

NSE-listed instruments, Indian liquidity dynamics, INR-priced. We trade what you can trade — no offshore wrappers.

100% India-listed

Total transparency

Every monthly rebalance is published in full — exact tickers, exact weights. No black-box claims. You see what the model sees.

0 hidden trades

Honest pricing

A percentage fee takes 2–2.5% of everything you own, every year — ₹7,33,085 out of a ten-year outcome on ₹15,00,000. Ours is flat: about 1.2% a year, and falling as you grow.

See what it costs you
~1.2% fee drag vs 2–2.5% MF

Hands-off operation

Once a month, you receive your rebalance. Place the orders in your broker (or skip a month). No phone calls, no meetings.

See a month, day by day
~10 min per month

SEBI registered

Research published under a SEBI-registered Research Analyst. Compliant by design — your subscription is safe and regulated.

Disclosures & Investor Charter
RA registered

Built by practitioners, not theorists.

MindForge Capital was founded by people who build and run these models themselves.

Sagar Shekhawath
Sagar Shekhawath
Founder & Chief Investment Strategist
Primary architect of MindForge Capital's quantitative strategies, with 8+ years of retail investing and trading experience spanning multiple market cycles. Holds an MBA in Finance with advanced certifications from IIT Kanpur and IIM Kozhikode. Brings 5 years of professional experience leading delivery of critical AI/ML applications for global enterprise clients. SEBI Registered Research Analyst.
Shagun Singh
Shagun Singh
Brand & Content Strategy
Leads MindForge Capital's brand identity, content strategy, and investor communications. With an MBA in Marketing and 2 years of hands-on experience building brand presence for a growing startup, Shagun brings a sharp eye for authentic storytelling — translating complex quantitative strategies into clear, compelling narratives for investors at every stage of their journey.
Ankith Maganti
Ankith Maganti
Principal Data Analyst
Drives the data infrastructure and analytical backbone behind MindForge Capital's strategy performance tracking. With 5 years of professional experience building product dashboards and data pipelines for leading FinTech clients, Ankith ensures our models are grounded in clean, reliable data — and that performance is measured with the precision institutional investors expect.

Everything people ask before subscribing.

What is MindForge Capital?

MindForge Capital is a research platform founded by Sagar Shekhawath, a SEBI-Registered Research Analyst. It publishes systematic, factor-based model portfolios for Indian markets. Three strategies are maintained — MindForge MultiAsset (an 8-asset ETF rotation), MindForge LargeMidcap 250 (10 stocks) and MindForge SmallMicro 500 (15 stocks). Every portfolio is rebuilt by a quantitative model on a fixed schedule and delivered to members as a plain list of holdings and weights. There is no discretionary stock-picking and no tips.

How much does a subscription cost?

MindForge MultiAsset is free for everyone. MindForge LargeMidcap 250 is ₹999 per month and MindForge SmallMicro 500 is ₹1,499 per month. Quarterly, half-yearly and annual billing carry 5%, 10% and 20% discounts respectively, and new members currently get their first month free. You can subscribe to one strategy or to all three. The Fee Calculator compares a flat subscription against a percentage-of-AUM fee at your own portfolio size.

Is MindForge Capital SEBI registered?

MindForge Capital itself is not a SEBI-registered entity — it is a research platform. The SEBI registration is held by Sagar Shekhawath, the founder, who is a SEBI-Registered Research Analyst. All research published on MindForge Capital is published under his registration. We are not a portfolio manager, investment adviser, broker or fiduciary: we publish research, and you make and place every investment decision yourself. The registration details, the Investor Charter, the grievance-redressal escalation matrix and our complaints data are published in full on the Disclosures page.

How often are the portfolios rebalanced?

All three strategies rebalance monthly. On each rebalance the model re-scores its universe and publishes the new holding list to your dashboard — every ticker, its weight and its recommended price. Between rebalances we review all three portfolios twice a month; a review changes nothing you hold and asks nothing of you. You place the resulting orders in your own broker account, in your own time — MindForge never touches your money or executes a trade on your behalf.

Do I need a demat account, and who places the trades?

You need your own demat and trading account with any Indian broker. MindForge Capital delivers the month's holdings and weights to your member dashboard, sized to whatever capital you enter; you place those orders yourself. We never take custody of your funds, never have access to your broker account, and never execute or place an order for you.

What returns do the strategies show?

Each strategy page publishes its backtest CAGR — after 20% short-term capital-gains tax — beside the same-period after-tax CAGR of its own benchmark index, plus the real live return for the current cycle where one exists. Backtested figures are simulated on historical data — they are not real trading results, they carry survivorship and other methodology limitations that are documented in full on the Disclosures page, and past performance does not indicate future results. Investing in equities, and particularly in small and microcap stocks, can result in permanent loss of capital.

Is the live P&L on Zerodha real, and why are there two links?

MindForge LargeMidcap 250 and MindForge SmallMicro 500 are each traded in their own dedicated Zerodha account, executing that one strategy and nothing else. Zerodha publishes a verified P&L page per account, generated by the broker from the account’s own order and holdings history — so there is one link per strategy, and each one evidences only its own book. These are real orders in real accounts, which is a different thing from the backtests shown elsewhere on this site: a backtest is simulated on historical data, a verified P&L is what actually happened. The two links sit side by side at the top of this page and on each strategy page. Returns in a personal account will differ from yours — entry timing, capital and whole-share rounding all move the result — and past performance does not indicate future results.

Are the Stock Scanner and Integrity Score free?

Both are free and need no account. The Stock Scanner filters 2,000+ NSE companies on 40+ fundamentals — valuation, profitability, growth and leverage — with a full equity report for every stock. The Integrity Score grades every listed NSE company 0–100 on Quality (profitability and balance-sheet health) and Value (how cheaply it is priced). The FII/DII activity tracker and the monthly Factor Report are free as well.

How much capital do I need to start?

There is no minimum — the dashboard sizes every position to the capital you enter, so the model works at any amount. Each strategy card does show a recommended investment (₹1,00,000 for MultiAsset, ₹10,00,000 for LargeMidcap 250 and ₹15,00,000 for SmallMicro 500), which is the level at which a book of eight to fifteen positions can be held in whole shares without the rounding materially distorting the model's intended weights. Below that the strategy still runs; the tracking error against the model simply widens.

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