Welcome to NiveshBuddy

What NiveshBuddy is

NiveshBuddy is a no-code platform for Indian retail investors who want to research and follow systematic equity strategies — investing by a set of rules you have written down and tested, rather than by whatever argument sounds most convincing this week.

In practice that means something quite specific. You take an investment idea, state it as explicit rules, check how those rules would have behaved on historical data, study the risk as carefully as the returns, and then follow the strategy forward in a virtual portfolio before any real money is involved.

The idea behind it is short enough to remember: become your own fund manager — with your own rules, tested before you rely on them.

Who it's for, and when it fits

You will probably recognise yourself in at least one of these:

The common thread is that you are willing to be shown you were wrong. A backtest is only useful if you are prepared to believe an unflattering one.

And who it does not fit: if what you want is to be told what to buy on Monday morning, this is the wrong product, and nothing in it is designed to give you that.

How the workflow works

From a hunch to a decision that stays yours

1. Investment ideaA hunch, stated plainly enough to test
2. Build strategyTurn the idea into explicit rules
3. BacktestEvaluate the rules on historical data
4. Review and refineRead the risk alongside the return; compare, customize, or explore variations
5. SimulationFollow it forward with virtual money
6. Decide independentlyYour broker, your call, your responsibility

Following one idea through every stage is the clearest way to see what each is for. Meet Meera — a hypothetical first-time user, used here purely to illustrate the journey. They arrive with the unstructured hunch above: stocks in a steady uptrend seem to keep trending.

Learn

A hunch is not yet a strategy, and a backtest you cannot read is not yet evidence. Meera starts with the concepts needed to interpret what they are about to build — what a drawdown is, what rebalancing does, what a performance metric is actually measuring. That is what this Learn Portal is for.

Build

In the Strategy Builder, the hunch becomes rules: an investment universe to draw from, eligibility filters for market cap and liquidity, a price-based condition expressing "steady uptrend", ranking logic to order the eligible stocks, a number of holdings, and a rebalance frequency. A Quick Strategy offers a guided starting point; the Custom Strategy Builder exposes the rules individually. Either way, the output is the same kind of thing — a strategy explicit enough to be tested.

Backtest

The strategy is evaluated against historical end-of-day data, which in NiveshBuddy runs from 2021 and is adjusted for corporate actions. At each scheduled rebalance the engine applies the eligibility and liquidity rules, ranks what qualifies, constructs the portfolio, and records what happened next. Meera now has an equity curve, a trade log, and a page of metrics instead of a hunch.

Review

This is the stage that earns its keep. Meera's CAGR looks encouraging — and read on its own, it means very little. Next to it sits a maximum drawdown deeper than they expected, a longest-drawdown figure measured in months, and a trade count high enough to matter once real-world costs enter the picture.

That combination is information, not failure. Meera makes one considered change to the rules with Customize and re-tests; both runs stay in the strategy's Backtest History, so the two can be compared side by side. What they deliberately avoid is the tempting loop: tweaking settings repeatedly until the historical CAGR looks its best. A strategy tuned until it fits the past perfectly has usually just been fitted to the past.

On plans that include it, Experiments can test many variations of a strategy in one go and rank them — useful for seeing whether nearby settings behave similarly, and a fast way to overfit if the first-ranked result is treated as the answer. It is an optional step, not a required one.

Simulate

Satisfied that they understand the trade-offs, Meera starts a Simulation — a paper portfolio run with virtual money, following the strategy forward through current market conditions.

One thing is worth knowing up front, because it is easy to assume otherwise. Trades do not execute the instant a signal appears. Before each rebalance they arrive as Scheduled Trades, and the only decision Meera has to make is whether to reject any of them. Every trade not rejected runs automatically at that day's closing price — nothing happens intraday. If Meera later buys or sells at their own broker at a different price or quantity, they record what actually happened in the simulation afterwards, so its record matches theirs.

Decide independently

NiveshBuddy places no trades. If Meera decides the strategy is worth acting on, they do so through their own brokerage account, at their own discretion, and take responsibility for that decision. The simulation was the rehearsal; the decision was never the platform's to make.

What NiveshBuddy does not do

Boundaries are easier to trust when they are stated directly rather than buried in a disclaimer:

There are limits worth carrying with you, too: end-of-day models do not represent intraday movement or every possible execution outcome, and transaction costs, liquidity, market impact, taxes, and broker-specific execution can all make real-world results differ from a test.

Getting the most out of it

Key takeaways