Welcome to NiveshBuddy
What NiveshBuddy is, who it's for, and the path you'll follow here — from an investment idea, through a backtest and a virtual portfolio, to a decision that stays yours.
Every guide on NiveshBuddy Learn, in reading order. Filter by category, level, or topic to narrow it down.
32 articles
What NiveshBuddy is, who it's for, and the path you'll follow here — from an investment idea, through a backtest and a virtual portfolio, to a decision that stays yours.
The ideas that have to make sense before any strategy does — what you actually own when you buy a share, how compounding turns time into your most valuable input, and where investing parts ways with trading and speculation.
Predefined rules can reduce FOMO, panic selling, overconfidence and inconsistency — not by promising better returns, but by making your decisions repeatable, reviewable, and settled before the pressure arrives.
Why a return figure means nothing on its own — how to read volatility, drawdown and risk-adjusted measures together, and how to judge whether a strategy's worst stretch is one you could actually live through.
The eight errors that cost beginners most — reacting to headlines, chasing what has already run, overtrading, restarting after a bad month, thin diversification, vague rules and unrealistic expectations — and the countermeasure for each.
Seven decisions to make on paper before you open any builder — what the money is for, how wide a pool, how many holdings, how long, how much fall you can sit through, how reactive, and how often you review.
What the indicators behind a NiveshBuddy strategy actually measure — moving averages, rate of change, Super Trend and the 52-week high filter — where volume fits, and why each condition you add costs more than it looks.
Momentum, trend-following, breakout and long-horizon approaches are not settings you switch on — they are patterns that emerge from the controls you choose. What each is built from, how responsive each is, where each tends to struggle, and why none of them wins.
Why a strong historical return is not evidence, how curve fitting produces strategies that only ever worked on paper, and the design choices that make a rule set likely to survive conditions it was not built on.
Every exit rule answers the same question: what evidence would show the original idea was wrong? A stop loss answers it with price. What each of the two kinds measures, how an end-of-day model actually executes them, and the specific things neither can promise.
A bonus issue can halve a share price overnight without anyone losing a rupee. What corporate actions are, why they corrupt historical price data if left alone, and how NiveshBuddy handles them — adjusted data in a backtest, restated holdings or a clean exit in a simulation.
A backtest hands you about sixteen numbers, and they answer four different questions — how much a strategy made, how painful the journey was, what the returns cost in risk, and how the strategy actually behaved. Reading them together is the skill.
Two builders sit on one screen, and they are not two products. Both feed the same five-stage engine — universe, eligibility, ranking, selection, rebalance — and every setting in either one attaches to exactly one of those stages.
The Quick Strategy asks three questions and sets about a dozen things from your answers. This is what each of the three dials actually moves underneath — and why Strategy Speed is not a second risk setting.
Five steps, and the decision each one is really asking. What a segment index changes, what Liquidity actually measures, why Exit Rank is set wider than your holdings, and why Exit Delay is counted in periods rather than days.
A backtest replays your rules against history, one rebalance at a time. This is how it decides when to trade, how the results page is laid out, and why a single CAGR can describe two years that felt nothing alike.
Comparing strategies starts with a question most people skip: are these two results even measuring the same thing? Then what to do when the metrics disagree — which they usually will.
An Experiment backtests many variations of one strategy in a single run and ranks them. Used well, it tells you whether a result survives a nudge. Used badly, it hands you the configuration that best fitted the past and calls it first.
A simulation runs your strategy forward through the live market with virtual money, so you can watch it work before you decide anything with real money. What it is, how to start one, what happens in its first days, and how credits, pausing and stopping work.
Before each rebalance, a simulation lists the trades its strategy has decided on. They run on their own at the day's close; your only decision is whether to reject any. What they are, when they appear, and where to see them all at once.
If you follow a simulation at your broker, your real fills will rarely match it exactly. Five tools let you record what you actually did, without placing orders or changing the strategy, so the simulation stays a faithful record of your portfolio.
A simulation's page is the running record of a virtual portfolio: what it's worth, what it holds, every trade and every rupee in and out. What each card and tab shows, which figures sound alike but aren't, and why a simulation carries no Score.
Every figure on a results page is a model output, not a measurement. What the model assumes about data, execution and costs — and how those assumptions change the way you should read a result.
NiveshBuddy gives you evidence about a strategy; your habits decide whether you read it honestly. The do's, the don'ts, and a short checklist for building, backtesting and simulating without fooling yourself.
NiveshBuddy uses two kinds of credit: Strategy credits for Custom backtests and Simulation credits for each trading day a simulation runs. What spends each one, what's free, when they refill, what happens at zero, and how your plan's data window shapes a backtest.
How buying a plan works, from checkout to the bank or UPI approval, and how to manage it afterwards: upgrading, changing to a lower plan, auto-renew, receipts and refunds.
NiveshBuddy can tell you when a backtest is ready, a simulation needs attention or a payment goes through, by email, by push, or both. Which switches you control, how to turn on push on each kind of device, and how to put NiveshBuddy on your phone's Home Screen.
Add NiveshBuddy to Claude or ChatGPT so you can ask, in plain language, about your own strategies, simulations and scheduled trades. How to connect each one, how to check and remove a connection, and why the answers are the assistant's, not advice.
A case study in turning "long-term" into concrete rules — from a Quick Strategy starting point to a Custom version — and then reading two very different backtests honestly, risk first, with one exceptional year in plain view.
A case study in changing one rule for one reason, comparing the result fairly — and then checking whether the improvement survives a period it was not found on. It mostly didn't, and that is the useful part.
Three hypothetical demo strategies — momentum, trend-following and breakout — built on the same universe and tested over the same five years, then read year by year. None of them leads every row, and one exceptional year flatters all three.
One fictional investor takes a broad idea all the way to a running simulation: turning it into rules, reading the backtest, starting a simulation, handling its first scheduled trades, recording a real fill, and then deciding for herself what to do with real money.