What is a rule-based investing platform?

It is software that lets you define a portfolio by explicit rules rather than by discretionary picks: a universe, a set of filters or factor scores, a weighting scheme and a rebalancing schedule. Most platforms then let you test those rules against history, and several connect to a broker so the resulting portfolio can be held and rebalanced in a real account.

How do Kalpi and smallcase differ?

They sit at different points in the same category. Kalpi is built around you constructing the rules yourself through a no-code basket builder, backtesting them and taking the result live through a connected broker. smallcase offers readymade model portfolios of stocks, ETFs and mutual funds created and managed by SEBI-registered experts, which you subscribe to and hold in your own broker account. One is mainly a build tool, the other is mainly a subscribe tool, and both execute through a broker.

Which axes matter most when choosing one?

Five: how deep the underlying data goes beyond price, whether fundamental history is stored point-in-time, how realistically the backtest models costs, liquidity and universe membership, whether execution is included, and who the product is designed for. A platform that scores well on execution may not be aiming at data depth at all, and that is a design choice rather than a flaw.

Do any of these platforms remove the need for judgement?

No. Rules make a process repeatable and testable, they do not make it correct. Someone still chooses the universe, the factors, the thresholds and the rebalancing frequency, and those choices carry as much judgement as a discretionary decision. A backtest describes the past under a set of assumptions; it is evidence, not a forecast.

Rule-Based Investing Platforms Compared: The Axes That Actually Matter

Rule-based investing platforms all promise a similar loop, define rules, test them against history, then run them, but they differ sharply in what sits underneath that loop. The five axes that separate them in practice are data depth, point-in-time history, backtest realism, whether execution is included, and who the product is built for. This piece compares the category on those axes and places the main shapes fairly, including Kalpi and smallcase, so you can work out which one matches your job rather than which one has the longest feature list.

If the term itself is new, start with what is rule-based investing, which covers rules versus discretion and what a rule actually buys you.

The shapes in the category

The Indian market has settled into roughly five product shapes. They overlap, and several products span more than one.

Curated basket platforms. You subscribe to a portfolio someone else built and maintains. smallcase is the best known example, describing itself as offering readymade model portfolios of stocks, ETFs and mutual funds managed by SEBI-registered experts, held in your own demat account through a connected broker, with rebalance updates pushed to you. The rules exist, but the publisher owns them. Your decision is which portfolio to follow and when to act on a rebalance.

No-code rule builders with execution. You define the rules yourself. Kalpi sits here, built around a basket builder for constructing rule-based portfolios without writing code, alongside static and template baskets, a portfolio backtester, portfolio analysis for imported stock and mutual fund holdings including factor exposure, and a broad market research toolkit covering indices, sector analysis, relative rotation graphs, IPO and market events, insider trades, bulk and block deals, FII and DII flow analysis, seasonality and market breadth. Its documentation also carries a detailed performance-metrics glossary. Because it connects to a broker, a basket you build can be taken live, so construction and execution sit in one place.

Broker-linked strategy automation. Tools attached to a brokerage that let you specify entry and exit conditions, usually technical, backtest them over price history and automate the orders. The centre of gravity here is trading rather than portfolio construction, and the holding periods are typically shorter.

Code-first and DIY. Python with pandas and an open-source backtesting library, plus whatever data you can license or scrape. Maximum control, no ceiling on what you can express, and complete responsibility for data quality, survivorship handling and cost modelling. Many quant teams start here and stay here for parts of their work.

Research infrastructure with backtesting attached. Platforms whose primary product is the data and the analysis, where backtesting exists so a systematic idea can be validated against the same fundamentals an analyst reads. This is where Altys Labs sits, and it is a different centre of gravity, not a better one.

Axis one: data depth

The first question to ask any platform is what its rules can actually reference.

Price and return data is universal. Above that, availability thins quickly: index membership and corporate actions, then reported financials, then ratios and factor scores computed from them, then the material behind the numbers, filings, earnings-call transcripts, management guidance and its revisions, shareholding patterns and promoter pledge, and segment level detail.

Platforms aimed at construction and execution tend to be strongest at the price, return and factor-exposure end, which is the correct choice for their job. A rule like “top decile twelve-month momentum, rebalanced quarterly” needs price history and a clean universe, not a concall transcript. Platforms aimed at research go deeper on the fundamental end because their users have to defend a holding in prose, not just express it as a filter. Neither depth is wasted; they answer different questions. Decide which question you are asking before comparing feature lists.

Axis two: point-in-time history

This is the axis most buyers skip and most later regret.

Price history is straightforward once adjusted for splits and bonuses. Fundamentals are not, because reported financials change after the fact. Companies restate, reclassify segments, change accounting policy and absorb acquisitions. Results also arrive with a lag, so a March quarter is not knowable in April.

A database that stores only the current version of the past will hand your backtest FY22 revenue as understood today, not as it was reported when you would have acted. That is lookahead bias, and it inflates results quietly rather than obviously. The related trap is universe construction: a test run on today’s index constituents contains only the companies that survived, so delistings and index exits never drag on the result.

Ask directly whether fundamental history is versioned with the date each figure became knowable, and whether the universe is reconstructed as of each test date. Many platforms handle price point-in-time correctly and fundamentals loosely, which is fine for price-based rules and misleading for fundamental ones. Our walkthrough of how to backtest a stock strategy in India covers how to structure a test so this is not left to chance.

Axis three: backtest realism

Two platforms can run the same rule over the same period and report different results, entirely because of assumptions. The ones worth checking:

Costs. Brokerage, STT, exchange fees, stamp duty and GST all bite, and the drag scales with turnover. A quarterly rebalance of a thirty-stock basket is a lot of trading over ten years. See transaction costs in backtests for the arithmetic.
Slippage and impact. The price you model is a close or an open. The price you get depends on how much you are moving relative to what trades. Some platforms let you set a slippage assumption; some assume none.
Liquidity. A small-cap rule can look excellent on paper and be unfillable at size. Realistic tests cap participation as a share of traded volume.
Rebalance timing. Whether a signal computed from a closing price is executed at that same close or at the next open changes results, and only one of those is achievable.
Corporate actions and dividends. Whether returns are total return or price only, and whether the price series is properly adjusted.

None of this is exotic. It is simply the difference between a number you can act on and a number that flatters the idea. When comparing platforms, run the same simple rule on each and read the assumptions rather than the headline compound return.

Axis four: execution

This is the cleanest split in the category, and it is binary.

Platforms in the basket and builder shapes connect to a broker. That connection is genuinely valuable: it closes the gap between a tested idea and a held position, and it handles the tedious part, translating a rebalance into orders. smallcase and Kalpi both work this way, with your holdings sitting in your own broker account.

Research platforms generally do not execute, and that is deliberate rather than a missing feature. Altys Labs is not a broker and does not place orders. If your bottleneck is getting from a good idea to a live position without manual work, an execution-connected platform is the right tool and a research platform will not solve it. If your bottleneck is deciding whether the idea is sound and being able to show your working afterwards, execution is not the constraint.

Axis five: audience

The last axis is the one that quietly determines fit.

Curated basket platforms are built for investors who want a managed, rules-based portfolio without constructing it. Builders like Kalpi are built for self-directed and prosumer investors who want to define and own the rules, with strong price, return and factor analytics behind them. Broker-linked automation serves active traders. Code-first serves quant developers who want no ceiling.

Research infrastructure is built for professional desks: PMS firms, AIFs, family offices and MFDs, where the output is a citable, source-linked piece of research that an investment committee or a client can review, and where a figure has to trace back to a filing, line and date. That audience needs multi-user workflow, versioning and exportable records at least as much as it needs another metric.

An honest read

Most of these platforms are good at what they set out to do, and the useful comparison is not which is best but which job you are hiring for.

If you want a rules-based portfolio built and run for you, the curated basket route is the shortest path. If you want to define your own rules, test them and invest in the result through a broker, a no-code builder is designed exactly for that; Kalpi is a well-built product in that shape. If you need depth on the fundamentals underneath the rules, point-in-time history behind the backtest, and research output you can hand to a committee, that is research infrastructure, and it is where Altys focuses. Altys is currently invite-only and in private preview, and it is not a broker, not a tip service and not a SEBI-registered research analyst or adviser.

Plenty of desks use two of these together, a builder or basket platform for construction and execution alongside a research layer for conviction and record-keeping. That is a reasonable answer, not a failure to choose.

Portfolio metrics explained: the hub for the risk and return statistics every one of these platforms reports.
What is rule-based investing: rules versus discretion, and what rules actually buy you.
How to backtest a stock strategy in India: an end-to-end walkthrough of a credible test.
Transaction costs in backtests: the drag that separates paper results from real ones.
Kalpi alternative in India: a closer look at where a build-and-invest platform and a research platform diverge.

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.