Kalpi Alternatives for Deep Fundamental Research in India

Kalpi is an India-focused platform for rule-based investing, best known for a no-code Basket Builder that lets you define portfolio rules, backtest them, and then take the resulting basket live through a connected broker. If your work sits earlier in the chain, in the fundamental research that decides what belongs in a rule at all, and you need point-in-time history, filing-level figures and a citable report, Altys Labs is an India-first alternative built for that. This piece explains what Kalpi does well, what the two products genuinely share, and where the jobs diverge.

What Kalpi is and who it serves

Kalpi’s own framing is “build, backtest, and invest”, and the product is organised around exactly that sequence. The Basket Builder is a four-step, no-code flow for turning rules into a portfolio, sitting alongside Static Baskets, Template Baskets, and workspaces for “My Strategies” and “Live Strategies”. Because it connects to a broker, the last step is not a spreadsheet export. The basket becomes something you actually hold.

Around that core sits a broad toolkit. Portfolio Analysis covers imported stock portfolios and mutual funds, with risk, diagnostics and factor-exposure views, and a Universal Upload for bringing holdings in. Stock Research provides search and analytics over fundamental and technical data. The research tools list is genuinely wide: indices, sector analysis, relative rotation graphs, an IPO tracker, market events, insider trades, bulk and block deals, FII and DII flow analysis, seasonality, market breadth and a portfolio backtester.

Public reporting in 2026 described a seed round led by Rainmatter Capital, and the company also runs a separate institutional product, KalpiQuant, aimed at PMS firms, AIFs, RIAs, brokers and family offices with a pre-computed factor library, a backtesting engine and portfolio optimisation. So the audience question is not simply retail versus institutional. Treat the details here as accurate to the companies’ public materials at the time of writing and check their sites for anything current.

Where Kalpi is genuinely strong

Three things stand out, and none of them are small.

• Rules to live portfolio in one place. Most tools stop at a screen or a chart. Going from a defined rule, through a backtest, to an actual holding through a broker removes the gap where good intentions usually die. That is a real product achievement and it is the reason the whole thing exists.
• A serious performance-metrics vocabulary. The documented metric set runs from CAGR, win rate and profit factor through max drawdown, volatility, Sharpe, Sortino, Calmar, Treynor and information ratio, on to portfolio P/E and P/B, correlation matrices, rolling returns, scenario and sensitivity analysis. Publishing that glossary openly is a mark of a team that wants users to understand what they are looking at rather than just admire an equity curve. If you want the same vocabulary explained end to end, our portfolio metrics hub covers it, along with pieces on the Sharpe ratio and maximum drawdown.
• Breadth of market context. Relative rotation graphs, breadth, seasonality, insider trades and flow analysis are the kind of surrounding context that a rule-based investor actually uses to sanity-check what a strategy is doing.

For a self-directed or prosumer investor who wants to stop reacting to headlines and start running a disciplined, repeatable process, that is a strong package on its own terms.

What the two products genuinely share

It would be dishonest to draw the line in the wrong place, so here is the overlap stated plainly. Both platforms backtest. Both look at factor exposure. Both cover FII and DII flow data and sector analysis. Both analyse mutual funds as well as stocks. If you sat the two side by side, several screens would answer similar questions, and a reader choosing between them on those features alone would be splitting hairs.

The difference is not in the feature list. It is in what each product treats as the centre of the work.

Where a research-first desk asks a different question

None of the following is a flaw in Kalpi. They are simply different jobs, and a product built around construction and execution is right not to optimise for them.

Fundamentals underneath the rule. A rule-based basket can be expressed almost entirely in price, return and factor terms. A research desk usually needs the layer below: what management guided to on the last call, how the shareholding pattern moved, what a segment’s margin did across a capex cycle, whether a one-off gain flattered the number. That is filing-level work, and it is a different data problem from portfolio analytics.

Point-in-time history under the backtest. Price history is naturally point-in-time. Fundamental history is not. Companies restate, reclassify, and file late, so today’s tidy ten-year table is often not what anyone could have seen at the time. Testing a fundamental rule against restated numbers is how lookahead bias enters quietly, which is why point-in-time data matters and why restatements break models. Any platform that tests fundamental rules faces this, ours included.

Traceability to a filing. When a number goes into an investment committee memo, someone will ask where it came from. “The platform said so” is not an answer that survives a second meeting. Tracing a figure to the document, the line and the date it was reported is a workflow requirement for a regulated or fiduciary desk.

Research output rather than a portfolio. The deliverable at the end of a professional research process is usually a written, citable view that a committee can approve and revisit, not a basket to invest in. That is the shape described in our piece on the institutional equity research workflow.

How Altys approaches the same problem

Altys Labs is an equity research and fundamental analysis platform for Indian stocks (NSE and BSE) and Indian mutual funds, built for professional users: PMS firms, AIFs, family offices and MFDs. It is currently invite-only, in private preview. Stated as focus, not as any claim of superiority:

• India-first and India-deep. Company filings, earnings-call transcripts, management guidance, shareholding patterns, macro series, FII and DII flows, factor scores and mutual-fund data in one place.
• Point-in-time by design. Data is kept as it stood on each past date, so a backtest of a fundamental rule sees what was knowable then.
• Source-linked. Figures trace back to the source document, line and date.
• Calculated, not guessed. Numbers are computed from filings, and forecasts come from explicit statistical methods rather than a language model estimating a growth rate.
• Tools on top. Screening, modelling, forecasting, backtesting and report generation.

Altys is not a broker, not a tip service, and not a SEBI-registered research analyst or adviser. There is no execution. If your workflow ends with placing an order, that is a real gap, and Kalpi’s broker connection fills it in a way Altys does not attempt to.

Which fits whom

	Kalpi	Altys Labs
Centre of gravity	Building, backtesting and running rule-based baskets	Research infrastructure for professional desks
Typical user	Self-directed and prosumer investors, plus institutional users via KalpiQuant	PMS firms, AIFs, family offices, MFDs
Execution	Connects to a broker so a basket can go live	Not a broker, no execution
Backtesting	Portfolio backtester with a deep metrics glossary	Backtesting on point-in-time data
Fundamental depth	Fundamental and technical data in stock research	Filings, concalls, guidance, shareholding
Typical output	A basket you can invest in	A source-linked report a committee can cite
Access	Public product, check the site for current plans	Invite-only private preview

The honest summary

If you want to define rules, see how they behaved, and put money behind them without leaving the platform, Kalpi is a well-built product doing precisely that, and the surrounding market toolkit is broader than most. If your constraint is the research underneath, meaning filing-level fundamentals, point-in-time history, figures you can defend line by line, and an output that reads as a report rather than a portfolio, that is a different tool and Altys is built for it.

Plenty of desks would reasonably use something from both categories. Knowing which question you are actually stuck on is most of the decision.

Related reading

• Portfolio metrics explained: the hub for every risk, return and backtest metric referenced here.
• How to backtest a stock strategy in India: what a credible backtest actually requires.
• What is rule-based investing: rules versus discretion, and what rules really buy you.
• Quant research tools for PMS and AIF: what an institutional desk needs beyond a retail toolkit.
• Why point-in-time data matters: the reason restated history quietly flatters a backtest.

This article is educational. Altys Labs is not a registered research analyst or investment adviser, and nothing here is investment advice or a recommendation to buy, sell, or hold any security.