# Choosing a stock screener: what to look for

> The five capabilities that separate stock screeners — historical replay, event conditions, delivery data, data depth, and an API — and how to evaluate any screener against them.

Canonical: https://patternsradar.com/blog/chartink-alternative-with-backtesting

Published 2026-08-15. Related scans: https://patternsradar.com/screener/52-week-high-breakout.md, https://patternsradar.com/screener/volume-shockers.md, https://patternsradar.com/screener/delivery-surge.md

Chartink earned its place: it made NSE screening free, fast, and shareable, and its library of public scans taught a generation of Indian traders what a screener even is. If you are reading this, you have probably also met its ceiling — and the most common reason traders go looking elsewhere is that a scan's history is a list of names, and what they want is a **result**.

Whatever you evaluate — including [PatternsRadar](https://patternsradar.com/index.md), which this site is, so weigh the source accordingly — these are the five capabilities that actually separate screeners.

## 1. Can it tell you what happened after the match?

Every screener answers *"who matches today?"* Several will also replay a scan across past sessions and list what matched — Chartink has done that since 2019, nine years deep on daily bars, for free. Far fewer answer the question that decides whether the scan is worth running: *"when this matched in the past, what happened next?"*

Watch for the difference, because both get called backtesting. A trigger history hands you symbols and dates; an outcome measurement attaches prices to them — hit rate, average return, over a stated holding period. Without the second, scan-building is folklore: you run "RSI below 30" because a book said so, not because you have seen how its matches did on NSE stocks. With it, every scan becomes a testable claim you keep or discard on evidence. On every scan page here — the [52-week high breakout](https://patternsradar.com/screener/52-week-high-breakout.md), say — the hit-rate panel reports what matches did over the next 1, 5 and 20 days. It is the single feature we would not give up, and the first thing to demand from any screener you evaluate.

## 2. Can it express conditions about *time*?

Form-based screeners handle snapshots well: RSI below 30, price above the 200-DMA. The conditions that actually describe setups are about **sequence and persistence**, and most tools cannot say them at all:

```
rsi(14) crossed above 30 within 3 bars
close has been above ema(21) for 10 bars
volume rising for 3 bars
close is highest in 52w
```

When a screener lacks event operators like these, users fake them with towering stacks of offset comparisons — that is what most complex Chartink scans are. A [readable query language](https://patternsradar.com/docs/sift.md) is not cosmetic; it determines which questions you can ask, and whether you can still understand your own scan six months later.

## 3. Does it treat delivery percentage as a first-class field?

Delivery data — what fraction of traded volume was actually taken home — is unique to Indian exchanges and is the closest public thing to seeing conviction directly. Most screeners bury it as a column. It belongs in scan logic, combinable and backtestable like any other field: [delivery surge with a price move](https://patternsradar.com/screener/delivery-surge.md) is a different and better signal than either input alone. The [delivery scan family](https://patternsradar.com/scans/delivery.md) shows the range of what it can express.

## 4. How deep and how honest is the data?

Two questions expose most tools. **How far back does daily history go** — enough to test a scan across regimes, or just a recent window? And **is it split-adjusted properly** — because one unadjusted bonus issue in the lookback quietly poisons every moving average and every backtest that touches it. (Here: 30 years, split-adjusted, refreshed after each close.)

## 5. Can you get results out programmatically?

If a screener is part of your process, its results end up needing to reach your process — a script, a sheet, an alert pipeline, an AI agent. An [API](https://patternsradar.com/docs/api.md) turns a screener from a website into an ingredient; the MCP endpoint here exists so agent tools can run scans directly.

## The honest comparison

Chartink remains stronger on four things: real-time intraday scanning, a decade of community scans, universal familiarity, and a trigger history that reaches nine years back against the one year replayed here. If your process is built on intraday triggers, it may still be the right tool — our data is end-of-day by design.

If your horizon is daily bars and your standard is *"show me what the matches did"*, that is the gap this product was built to close. The [feature-by-feature comparison](https://patternsradar.com/compare/chartink-alternative.md) is maintained separately; the shorter version is: run [any scan here](https://patternsradar.com/scans.md), open its hit-rate panel, and decide whether you can go back to screening without one.