How foreign (FII) and domestic (DII) institutions have shifted their holdings across every sector, quarter by quarter. Each cell is the sector's weighted holding that quarter; colour shows the change from the previous quarter — green accumulation, orange reduction. Read a row left-to-right for a sector's trajectory; read a column to see where the money moved that quarter.
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Backtest
Test how the valuation signals would have played out historically — pick a sector, a period and a sell rule, and see every trade the model would have taken along with its return versus buy-and-hold.
Backtest Configuration
Sector
Backtest Period
→
Signal Cooldown
Minimum gap between consecutive signal dates
Buy Trigger
90%
Fires at 90% of weighted ref (±1%)
90%
Fires at 90% of weighted ref (±1%)
Sell Trigger
Hold for a fixed duration after buy, then sell
100%
Sell on first close at or above 100% of weighted ref
Computing…
Projections
Estimates where a sector's index could trade if its valuation multiple reverts to its weighted historical median and earnings (or book value) recover to their historical max. The headline figure is that base case; the chart and matrix flex each lever around it — multiple at 90 / 100 / 110% of median, fundamental at 90 / 100 / 110% of max.
⚠ Educational use only — not investment advice. These are mechanical what-if estimates, not forecasts. Real prices depend on growth, interest rates, sentiment and other factors not modelled here. Past medians and peaks may never repeat. Do your own research.
Projection Inputs
Sector
Valuation Metric
Projected Price · Median × Max
—
Upside vs Current
—
Projection Paths
Scenario Matrix · Projected Price
Projections PLUS
Project where any sector's index could trade if its valuation multiple reverts to its weighted historical median and earnings or book value recover to their peak — with a nine-scenario fan chart and matrix around that base case.
Timeline PLUS
One metric across every sector, measured as growth relative to Nifty 50. The benchmark is the flat 0 line; sectors sit above (outperforming) or below (lagging) it.
Baseline
→
Loading sector history…
Each line is a sector's growth since the window start minus Nifty 50's growth over the same window, so Nifty 50 is the flat 0 line. Newer sectors rebase from their own first in-window date. Hover for a per-sector readout; click a legend chip to toggle a line. Chips grey out when the metric doesn't exist for that sector (e.g. P/B for IT).
Timeline PLUS
Compare every sector's P/E, P/B, EPS or Price as growth relative to Nifty 50, across any date range. Spot which sectors are leading or lagging the index over your chosen window.
Videos
Walkthroughs and method explainers — how the valuation bands are built, what each signal actually claims, and how to read a sector before acting on it. Open to everyone who has supported SectorScope with a donation.
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Videos DONORS
Admin
Operator console — users, revenue, alerts, digest and data-pipeline health. Visible only to allow-listed accounts; every API call is re-verified server-side.
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Data freshness — today's append status
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Weekly digest
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Donations
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Videos — donor-gated library
Paste a JSON array. youtubeId accepts a bare ID or any YouTube URL. Saving replaces the whole list.
Users
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Live alerts
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Help
How this works — a friendly walkthrough, no jargon assumed. We'll start with what each number on a card means, then build up to how the signals are calculated and how to use the Backtest page to sanity-check today's call against history. Takes about ten minutes start to finish.
SectorScope answers one question: which Indian sectors are cheap or expensive right now, compared to how they've traded over the past several years? It doesn't pick stocks. It looks at whole sectors — IT, Banks, Pharma, Auto, and so on — and tells you whether each one is trading at an unusual discount or premium to its own history.
Every sector gets a coloured card. Green means cheap vs history; red means expensive vs history. Cards are sorted with the cheapest on top, so the most interesting names always rise to the front.
A typical workflow
1
Scan the Dashboard. Green cards at the top are sectors trading below their historical anchor. Red cards at the bottom are stretched. Don't act on the colour alone — the next two steps tell you whether the cheapness is genuine or a trap.
2
Open the Detail page. Tap anywhere on a card (except the chart, which opens the expanded chart instead) to open the detail page. The detail page shows an AI-written summary of the situation, a snapshot of the current numbers, how today compares to the 3-year, 5-year, and 10-year medians side by side, and bar charts of the sector's earnings history. This is where you check whether earnings are healthy (good cheap) or collapsing (bad cheap).
3
Check the Backtest page. For any sector that still looks interesting, go to Backtest → pick the sector → run it. You'll see every past time the sector was at this valuation level and what happened to prices over the next 6 months and 1 year. If the historical hit rate is poor, today's signal is probably a value trap.
One thing to keep in mind
Cheap can get cheaper. A "Very Cheap" signal tells you the sector is unusually depressed compared to its own history. It does not predict when the bounce will come. These signals are designed for patient investors with a 6 to 12 month horizon, not for next-week timing.
2 · Valuation basics
If you're already comfortable with P/E, P/B, percentiles and medians, skip to section 3. Otherwise — five minutes here will make everything that follows much easier to read.
What's a sector index?
The Indian stock market is divided into sectors by the kind of business each company runs — software companies form the IT sector, banks form the Banking sector, drug companies form the Pharma sector, and so on. NSE (the National Stock Exchange) publishes a single number for each sector that tracks the combined market value of the largest companies in it. That number is the sector index. When you read "Nifty Bank is up 1.2% today," it means the average bank in that index moved up 1.2%.
SectorScope tracks multiple sector indices: IT, Bank, PSU Bank, Auto, FMCG, Pharma, Realty, Metal, Energy, Infra, Fin Services, Defence, Healthcare. Plus the broad Nifty 50, used as a benchmark inside the Backtest engine.
P/E — Price to Earnings
If a stock trades at ₹100 and the underlying business earns ₹5 per share each year, the P/E is 100 ÷ 5 = 20. Read it as "investors are paying ₹20 for every ₹1 of yearly profit." Lower P/E generally means cheaper; higher P/E generally means richer (often because the market expects growth).
Sector P/E works the same way, but uses the sector's combined earnings instead of a single company's. The same sector can have a wildly different P/E at different points in history — Nifty IT was at P/E 18 in 2020 and over 35 in 2021. SectorScope's job is to tell you where today's P/E sits inside that historical range.
Why P/E isn't the whole story for every sector
Banks earn from lending — and reported earnings get distorted by provisioning cycles (when banks set aside money for expected bad loans). In a stressed quarter, a bank's earnings can collapse temporarily even if the underlying business is fine, which makes P/E look misleadingly high. For Bank and PSU Bank, we use P/B instead.
P/B — Price to Book
Book value is what the company would be worth on paper if you summed up all its assets and subtracted all its debts. P/B compares today's stock price to that book value. A P/B of 1 means you're paying exactly what the company is "worth on paper." A P/B of 3 means you're paying three times that.
P/B is the right lens for banks because their book value moves smoothly even when earnings are noisy. It's also the primary lens for Realty, and a useful second lens for asset-heavy cyclicals like Energy, Metals, and Infra — where we use both P/E and P/B and look for both to agree before calling a sector cheap.
EPS — Earnings Per Share
EPS is just the "E" in P/E, looked at on its own. It's how much profit each share is generating — for a sector, it's the combined earnings divided by the index unit. Tracking EPS over time tells you whether the underlying earnings story is healthy, growing, or breaking down. A sector that looks "cheap" because price has fallen is very different from one that looks "cheap" because earnings have collapsed faster than price.
Median & percentile — the "vs history" comparison
Throughout the app you'll see numbers like "P/E is 18% below the 5-year median" or "ranked at the 12th percentile." These are two different ways of comparing today to the past — both useful, both need a one-line definition.
Median
The middle value of a sorted list. If you take all of Nifty Bank's daily P/B values over the past 5 years, sort them, and pick the one in the exact middle, that's the 5-year median P/B. It's the "typical" valuation over that window. We compare today's value to this number — if today is well below the median, the sector is unusually cheap.
Percentile
A ranking from 0 to 100. If today's P/E is at the 12th percentile of the last 3 years, it means today's P/E is lower than 88% of the daily values from the last 3 years — only 12% of the time was P/E this cheap. The lower the number, the rarer the cheapness.
Why we use both
Median tells you how far below the typical value you are. Percentile tells you how often the sector has been this cheap before. They usually agree — a sector that's well below median is also low on percentile rank. When they disagree, we surface a small warning capsule so you don't take the cheap label at face value.
"Weighted reference" — putting it together
Rather than picking one history window and sticking with it, we compute three medians — the 3-year, 5-year, and 10-year — and blend them: 30% weight on the 3-year, 60% on the 5-year, 10% on the 10-year. The result is what we call the weighted reference, and it's the single number we compare today against to assign a band (Very Cheap, Cheap, Neutral, Expensive, Very Expensive).
Why this blend? The 5-year median is the workhorse — long enough to absorb a couple of cycles, short enough to still be relevant. The 3-year medium adds responsiveness so a sector that has structurally re-rated higher (or lower) gets credit for its more recent regime. The 10-year is a small safety anchor that pulls in deep history so we don't lose context entirely.
Newer sectors — alternate blend
A few sector indices launched relatively recently — Defence (Jan 2022), Healthcare (Nov 2020). For any sector with less than five years of history, the standard blend doesn't work because the 5-year and 10-year medians don't have enough data behind them. We switch to a simpler alternate: 40% all-time + 60% × 3-year. Cards from sectors with less than 10 years of history get a small "N yrs history" badge so you can see at a glance that the reference is computed on a shorter base than the established sectors. Hover the badge for details.
If you only remember one thing
Every signal in this tool is built on the same comparison: today's value vs the sector's own past values. We never compare Nifty IT to Nifty Bank, or to S&P 500. Each sector is judged against its own history. That's what makes the bands meaningful — a P/E of 20 is rich for one sector and cheap for another.
3 · The dashboard
The dashboard is the home screen — one card per sector, all visible at once. This section walks through everything that's on a card, plus the order they appear in.
Sort order — why the green ones are on top
Cards are sorted by how cheap each sector is right now relative to its own history. The cheapest sit on top, the richest at the bottom. Specifically, the sort key is each sector's current valuation expressed as a percentage of its weighted reference (the blended median we covered in section 2).
For sectors where we track both P/E and P/B, we use whichever metric is showing as more expensive. The reasoning: we only want to flag a sector as cheap when both lenses agree. If P/E says cheap but P/B says expensive, that's a yellow flag worth surfacing rather than burying.
Concrete example
If Nifty IT is at 78% of its weighted reference (very cheap) and Nifty Auto is at 92% (slightly cheap), IT lands above Auto on the dashboard. If Nifty Energy (a dual-metric sector) is at 75% on P/E but 110% on P/B, we use the 110% — so it sorts as if it were Neutral, not Very Cheap. The colour and band on the card reflect the same logic.
Anatomy of a card
Each card packs a lot into a small footprint. Here's an annotated example of Nifty Auto — every element explained.
Card front (numbers at a glance)
1 sector name
Auto
2 band · 3 signals
Cheap
P18 · 82% of ref
4 caution capsule (when shown)
⚠ Often Low — Possible Derating
5 index price · change vs previous close
25,905+0.83%
6 metric tiles
P / E
24.5
P18 · 82% of ref
EPS
312
87% of peak
7 sparkline (3yr P/E + EPS)
🔔 Set Alert
1
Sector name. Which Nifty sectoral index this card represents. The coloured top border and card background tint both match the band verdict.
2
Band verdict. The headline signal — Very Cheap / Cheap / Neutral / Expensive / Very Exp. Driven by how today's P/E (or P/B) compares to its weighted historical median. See section 3.
3
Two signals: P18 · 82% of ref.P18 = 18th percentile of its own last 3 years (cheap end). 82% of ref = current P/E is 18% below its historical anchor. The second number drives the band. See section 3 for why.
4
Caution capsule (only when signals disagree). "Often Low — Possible Derating" means: yes, the sector is 18% below its anchor — but it has also spent a lot of the last 3 years at this level. Less contrarian than it looks, and may reflect a structural derating. If there's no capsule, both signals agree.
5
Index price + change. Current index value, with a small green/red badge showing the percentage change vs the previous trading day's close. During market hours this updates live; after-hours it shows the day's full move. Green for positive, red for negative.
6
Metric tiles. Left tile (tinted with band color): the current P/E or P/B. Right tile: current EPS and its % of all-time peak. The full year-on-year EPS history lives on the detail page — see section 4.
7
Sparkline. Three years of daily history at a glance — the primary metric (P/E or P/B) as a coloured line, plus an EPS line for sectors where we track both. The y-axis auto-scales to the actual range, so the line always uses the full vertical space. Tap any sparkline to open an expanded chart with date-range pills, additional metrics, and historical-median overlays. The expanded chart is a Plus feature — see section 8.
Three things you can do with a card
Beyond just reading the numbers, the card has three interactive elements:
↗ Tap the card
Tap anywhere on a card to open a full-screen detail page with the AI-written summary, the current snapshot, historical medians, EPS growth charts, and live constituent stocks. This is the deeper dive — section 5 walks through every element on it.
🔔 Set Alert
Tap the Set Alert button at the bottom of any card to flip the card and configure an email alert for that sector — for example, "ping me when Nifty Bank P/B drops to 85% of its historical median," or "tell me if Defence P/E climbs above 130% of median." Alerts are a Plus feature. Full walkthrough in section 8.
⤢ Tap the sparkline
Tap directly on the chart inside any card to open an expanded version — full-screen, with selectable metrics (P/E, P/B, EPS, Price, Dividend Yield), date-range pills, drag-to-measure, and toggleable historical-median overlays. Plus only. Full walkthrough in section 8.
Which metric does each sector use?
The tool picks the right metric automatically — you don't need to change anything.
P/E only
IT, Auto, FMCG, Pharma, Defence, Healthcare — stable, earnings-driven businesses where P/E is the primary lens.
P/B only
Bank, PSU Bank, Fin Services, Realty — for financial and asset-heavy property indices, earnings are distorted by provisioning and revaluation cycles, so book value is the more reliable anchor.
Both P/E + P/B
Infra, Energy, Metals — asset-heavy cyclicals where you want both an earnings lens and an asset-value lens.
4 · How signals work
Every coloured band, percentile rank, and pill on a dashboard card comes from one of three signals: the band (how far below or above the historical anchor), the percentile cross-check (how often the sector has been at this level), and the EPS pill (whether the underlying earnings story supports the cheapness). This section walks through each, with concrete numbers.
A quick refresher: the weighted reference we'll keep referring to is a blend of three rolling medians (30% · 3-year, 60% · 5-year, 10% · 10-year). Newer sectors with less than 5 years of history use a different blend (40% all-time + 60% × 3-year) and get a small "N yrs history" badge on the card. If that's already feeling fuzzy, jump back to section 2 for the full explanation.
The band — your first read
The card colour is determined by a single number: current value ÷ weighted reference. Call this the ratio. Here's how each ratio maps to a verdict:
Very Cheap≤ 80%
Cheap80–90%
Neutral90–105%
Expensive105–120%
Very Exp.> 120%
The Neutral band is slightly wider on the right (up to 105%) rather than symmetric around 100%. This is because sectors naturally drift a little above their own long-run median over time as the underlying businesses scale — being 5% above your median is not yet a warning.
Worked example — how the band is calculated
Nifty Auto's P/E today is 24.5. Its historical medians over different windows are:
Weighted Reference · Nifty Auto (example)
3-yr median
× 30%
26.2 → 7.86
5-yr median
× 60%
25.1 → 15.06
10-yr median
× 10%
22.8 → 2.28
Weighted reference = 7.86 + 15.06 + 2.28= 25.2
Ratio = 24.5 ÷ 25.2= 97% → Neutral
If the same sector had a P/E of 20.7 instead (say, after a sharp correction), the ratio would be 20.7 ÷ 25.2 = 82% → Cheap. That's the number you see on the card as 82% of ref.
Why 60% weight on the 5-year median?
The 5-year window covers roughly one full business cycle in India — close enough to reflect today's regime, long enough to include both peaks and troughs. The 10-year leg gets only 10% weight so that sectors that have permanently re-rated (like IT after 2020, or PSU Banks after 2022) aren't forever compared against a baseline that no longer applies.
The two signals on each card
Each card shows two valuation signals side-by-side, like P18 · 82% of ref. They measure different things and can sometimes disagree.
% of ref (primary)
What it asksHow far is today from the anchor?
Example82% of ref
Meaning18% below the blended median
Drives band?Yes — this sets the color
Measures magnitude — by how much is it cheap?
Percentile rank (secondary)
What it asksHow rare is this level, last 3 years?
ExampleP18
MeaningOnly 18% of last-3yr readings were lower
Drives band?No — info only
Measures frequency — how often does it get this cheap?
When the two signals disagree — the caution capsule
Sometimes a sector looks cheap vs its anchor (% of ref says Cheap) but is not unusual at all in its recent 3-year distribution (percentile says P45 — it's been here half the time). That's a much weaker signal than a sector that is both below its anchor and historically unusual.
Clean signal — no capsule
% of ref82% → Cheap
PercentileP14 → also Cheap
✓ Both signals agree. Sector is genuinely depressed — it's both below its anchor AND near a 3-year low.
Conflicted — capsule appears
% of ref83% → Cheap
PercentileP44 → Neutral
⚠ "Often Low — Possible Derating" — it IS below the anchor, but the sector has spent nearly half the last 3 years at this level. Less contrarian than the Cheap label suggests, and may signal a structural derating.
The EPS pill — the third signal
The band tells you whether the sector is cheap on multiples. The percentile tells you whether that cheapness is unusual. But neither tells you why it's cheap — and a sector trading at a discount because its earnings are collapsing is very different from one trading at a discount because price has dropped while earnings hold steady.
That's where the EPS pill comes in. It's a small badge that appears on the EPS tile of the dashboard card, and it fires only when EPS is currently low compared to the sector's own all-time peak — meaning the cheapness on multiples might be a genuine opportunity (price has fallen but earnings will recover) or might be a value trap (earnings keep falling and price hasn't caught down yet). The pill tells you which.
▲ LOW · RISING
Green pill — the strongest cheap-sector signal. Fires when (a) EPS is currently "low" — median EPS across the last three quarters is at or below 80% of the all-time peak, AND latest EPS is at or below 90% of peak — AND (b) the two most recent quarter-on-quarter growth readings are both positive. Meaning: earnings are at a depressed base AND actively recovering. Pair this with a Cheap or Very Cheap valuation band and you have a high-conviction setup.
▼ LOW · FALLING
Red pill — explicit warning. Same "low" gate as above, but both QoQ readings are negative — earnings are low and still falling. The cheapness on multiples is being driven by collapsing earnings; the sector will keep looking cheap as long as earnings keep dropping. Do not act on a cheap valuation card with this pill showing.
◆ LOW · MIXED
Amber pill — wait and see. Gate passes (EPS is low), but the two QoQ readings disagree — one positive, one negative. No clear direction yet. Wait for the next quarterly data point before drawing a conclusion.
(no pill)
Neutral. Either EPS isn't "low" versus its peak (the gate fails — earnings are healthy or near peak), or there's insufficient quarterly history to compute the signal. Absence of a pill means use the valuation band on its own — it's not a warning.
When does the pill not appear?
On defensive sectors like FMCG, Pharma, and IT, EPS rarely falls far below its peak — these businesses earn through cycles. So the "low" gate fails most of the time, and the pill stays absent. That's not a bug; it just means the EPS signal isn't applicable. For these sectors, the valuation band alone carries the weight.
Three scenarios — putting the signals together
A sector can look cheap for very different reasons. The combination of valuation band, % of peak EPS, and the EPS pill tells you which scenario you're looking at:
Best setup
Valuation bandVery Cheap
% of peak EPS68%
YoY growth−6%
EPS pill▲ LOW · RISING
Early-cycle turnaroundEarnings are well below peak and the last two quarterly-growth readings have flipped positive. YoY is still red because the year-ago base was higher — but the damage has stopped and recovery is underway. Sector is also cheap on multiples. Highest-conviction setup. Classic Metals / PSU Bank early-cycle entry.
Watch out
Valuation bandCheap
% of peak EPS94%
YoY growth+12%
EPS pill— (gate fails, EPS near peak)
Confirmed recovery, less upsideEarnings are near all-time peak and growing. Sector is cheap on multiples — but because earnings are already strong, the "low base to recover from" catalyst is gone. Still a valid setup, just more symmetrical. Absence of a pill here means things are fine, not bad.
Value trap
Valuation bandVery Cheap
% of peak EPS41%
YoY growth−28%
EPS pill▼ LOW · FALLING
Falling knife — do not catchEarnings are low and still dropping. P/E looks low because E is contracting faster than P. The sector will keep looking cheap as long as E keeps falling. The red pill is an explicit warning: do not act on the valuation signal until the pill flips to MIXED or RISING, or YoY stabilises.
If you only remember one combination
Cheap or Very Cheap band + ▲ LOW · RISING pill + YoY still negative. This is the early-cycle inflection: valuation is in the bottom tier, EPS is depressed vs its own peak, two consecutive quarters of positive growth confirm a turn, and YoY hasn't caught up yet so the rear-view comparison still looks ugly. Works best on cyclicals — Metals, Auto, PSU Bank, Realty. On defensive sectors, the pill rarely fires, and the valuation band alone is the primary signal.
5 · The detail page
When you tap a card, a full-screen overlay opens with everything that didn't fit on the card. There's an AI-written summary at the top (Plus only — covered in section 8), then six information sections: a snapshot of the current numbers, a side-by-side view of how today compares to historical medians at three time horizons, a pair of bar charts showing the sector's earnings trajectory, a live table of the index's constituent stocks (Plus only), a quarterly shareholding-pattern table — FII, DII, promoter and others per constituent (Plus only) — and finally a scrollable quarterly history table.
This is where you go after the dashboard tells you a sector is interesting and you want to confirm whether the cheapness is genuine or a trap.
① Current Snapshot
Four stat cards showing today's key numbers at a glance, each colored by its own band verdict:
P / E
24.5
Cheap
P / B
3.12
Neutral
EPS
312
+14.2% YoY
Max EPS (all-time)
358
87% of peak
P/E and P/B show their individual band labels — useful when a dual-metric sector has one expensive and one cheap, which the card-level verdict averages over. The EPS YoY % shown here is the more granular cousin of the EPS pill on the card — useful for seeing the precise rate of growth or contraction.
② Historical Medians
One block per metric. Each row is a time window — the bar length shows its median relative to the others, and the right column shows current ÷ that median as a multiplier.
P/E Medians Weighted ref: 25.2
3-Year
26.2
0.93×
5-Year
25.1
0.97×
10-Year
22.8
1.07×
Current
24.5
1.00×
Read the multiplier column as: below 1.00× = today is cheaper than that window's median. Above 1.00× = today is more expensive. The 10yr showing 1.07× in the example means today is slightly above the 10yr average — but below the 3yr and 5yr, which is what matters most for the signal.
③ EPS Growth
Two bar charts showing how the sector's earnings have evolved — one at annual resolution, one at quarterly resolution. Green bars = positive growth, red bars = contraction. Use these to sanity-check the valuation signal: a cheap sector with accelerating EPS is far more interesting than a cheap sector with collapsing EPS.
EPS YoY Growth · fiscal year · * partial
EPS QoQ Growth · last 4 quarters
Two chart types · two different questions
Annual YoY
Each complete bar = TTM EPS at end of that fiscal year (March 31) vs end of the prior FY, stepping back up to 10 fiscal years. The right-most bar (marked with an asterisk and shown at reduced opacity) is the in-progress current FY — it compares today's TTM EPS to the close of the last complete FY, so it represents "growth so far this year." Answers "is this sector's earnings trajectory fundamentally up or down across cycles, and how is the current FY tracking against that?"
Quarterly QoQ
Each bar = that quarter's average EPS vs the prior quarter. Answers "is earnings momentum right now accelerating, flat, or turning negative?" A run of green QoQ bars after a red YoY is a classic "earnings turning" setup.
Y-axis auto-scales to the biggest absolute value in the chart — so a quiet sector with ±8% moves shows the same visual weight as a volatile one swinging ±40%. Compare shape and sign, not bar heights across two sectors. Each bar also has its exact percentage printed above (or below, for negatives).
USD growth charts — IT and Pharma only. On the IT and Pharma sector detail pages, two additional charts appear beneath the INR ones — USD EPS YoY Growth and USD EPS QoQ Growth. Same shape and same green/red palette as the INR charts, but the underlying EPS series is converted to USD: each EPS data point uses the USD/INR rate that was in effect on its own historical date, not today's rate, and the % growth is calculated on that derived USD series. Reading the two YoY charts side-by-side tells you the cumulative currency effect: when INR growth substantially exceeds USD growth in a given year, the rupee depreciated; when they roughly match, the rupee was steady. For sectors like IT (~90% USD-billed revenue) and Pharma (~30–50% from US generics), the USD chart is arguably the cleaner read of underlying business performance — it strips out the rupee-tailwind that's flattered INR earnings growth across the last decade. Years where FX coverage isn't sufficient on both endpoints render as empty slots in the USD chart so x-axis years stay aligned with the INR chart above.
④ Index Constituents PLUS
A live table of every stock in the sector's NSE index — sourced directly from NSE's official index-tracker feed and refreshed every 60 seconds while the overlay is open. Useful for confirming whether a sector-level move is broad-based or driven by a few large names.
↑ 7 up↓ 3 down· ₹3,820 Cr traded
As of 3:30 PM
Symbol
Price
% Chg
Day Range
Value (₹Cr)
COFORGE
1,342
+4.67%
376
TCS
2,283
+0.86%
432
WIPRO
191.75
−0.42%
192
What's in each column
Breadth strip
Sits above the table. Shows up / down / flat counts (green / red / grey) plus total traded value across the index in ₹Cr. A quick read on whether a sector move is broad or narrow: 9 up · 1 down is participation, 3 up · 7 down with the index green means a few heavyweights are carrying the index.
Symbol & Price
The NSE ticker and last traded price. Click "Symbol" in the header to sort A→Z.
Chg & % Chg
Absolute and percentage change vs previous close. The % cell wears a coloured chip — green for up, red for down. Click "% Chg" to sort top-gainers / top-losers. The absolute Chg column is useful for high-priced stocks where small percentage moves still mean ₹50–100 swings.
Day Range
A tiny tick on a horizontal bar showing where the current price sits between today's low (left edge) and today's high (right edge). Tick near the right = stock holding near intraday high (often breakout-prone); tick near the left = near intraday low. Sortable too — handy for spotting names at the top of their day's range across the whole sector.
Volume & Value
Total shares traded today (compact units — K, L, Cr) and total traded value in ₹Cr. Sorting by Value brings the day's most-traded names to the top — typically the heaviest-weighted index components.
The table refreshes automatically every 60 seconds while the detail page is open. If you switch tabs and return, it refreshes immediately on focus. Closing the detail page stops the polling. Data is sourced from NSE's official index-tracker API and is cached at the edge for ~60s during market hours, so the entire workflow is cheap even with frequent re-opens.
⑤ Shareholding Pattern PLUS
Every quarter, listed companies file their shareholding pattern with the exchange — exactly how much of the company is held by foreign institutions, domestic institutions and promoters. This section pulls those filings for every constituent of the sector's index and lays out the last 8 quarters.
Four tabs, one metric each:
FII — foreign institutional investors (FPIs), as filed. DII — domestic institutions: mutual funds, insurance companies, banks, pension funds, as filed. Promoter — promoter & promoter group. Near-constant by nature; a step change marks a real event (stake sale, QIP, offer-for-sale). Others — derived, not filed: 100 − promoter − FII − DII. Roughly retail investors plus employee trusts and depository receipts.
Each row is one stock: its index weight, a trend sparkline, then the exact filed percentage for each quarter. Cells are coloured by their quarter-on-quarter move — green means the holding rose from the previous quarter, orange means it fell. The bottom Sector · weighted row rolls all constituents into one sector-level number per quarter.
Promoter pledge badge — a ⚠ 34% badge appears next to a stock's name when promoters have pledged 30% or more of the company's total shares as loan collateral. Pledging means the promoters have borrowed against their own stock: if the share price falls far enough, lenders can force-sell the pledged shares, which pushes the price down further — a self-reinforcing risk that has burned investors in past cycles. High pledge levels alongside falling institutional holdings are a combination worth treating with extra caution. Hover any stock's name to see its exact pledge percentage, including sub-30% levels that don't earn the badge.
How the weights work
Each stock's weight starts from its derived free-float market cap: total shares (from the filing itself) × last price × (1 − promoter holding). Weights are then capped per the NSE index rulebook, the same rules NSE applies to the indices themselves:
• Bank & Fin Services — top 3 stocks capped at 19% / 14% / 10%; everyone else stays below the third.
• Energy — no stock above 10%.
• Infra & Defence — no stock above 20%.
• All other sectors — no stock above 33%, top 3 combined ≤62%.
The float excludes promoters and identifiable strategic holdings — banks holding stakes in other companies (rescue situations like SBI in Yes Bank), employee trusts, ADR/GDR shares and government-promoted entities — mirroring what NSE's investability factors exclude. Still close to, but not exactly, official weights: NSE additionally excludes strategic anchor investors that file as ordinary institutions (private-equity stakes, for instance), which no filing data can distinguish, and it re-caps only at quarterly rebalances while these weights refresh daily. Per-stock figures carry no approximation — they're exactly as filed.
Companies file within ~21 days of quarter end, on different dates — so for a few weeks each quarter the newest column fills in gradually. A Sector·weighted cell appears only once stocks covering at least 80% of sector weight have filed that quarter; until then it shows a dash rather than a misleading partial average. Data updates automatically as filings land.
⑥ Quarterly Averages · Last 5 Years
A scrollable table showing quarterly average Price, P/E, P/B, and EPS going back 5 years. Most recent quarter is at the top. Two cells in each column are highlighted:
Quarter
Price
P/E
P/B
EPS
Q4 FY25
22,840
24.5
3.12
312
Q3 FY25
24,100
26.8
3.35
299
Q2 FY25
25,600
28.1
3.58
287
Q1 FY25
23,900
27.4
3.41
275
Q4 FY24
21,200
27.0
3.22
265
…
…
…
…
…
Q4 FY20
11,400
18.2
2.20
198
Green= best quarter in that column
Red= worst quarter in that column
"Best" and "worst" are inverted for valuation metrics: the lowest P/E and P/B quarter is green (cheapest = good), the highest is red. For Price and EPS it's the other way: the highest price and highest EPS quarter are green. Only the single best and worst quarter across the whole 5-year history gets highlighted — all others are neutral.
Best use of the Detail overlay
The overlay is most useful for dual sectors (Infra, Energy, Metals) where P/E and P/B can disagree. The card front shows a blended verdict — the overlay's Snapshot section shows the two metrics separately, so you can see if the cheapness is driven by one metric or both. The quarterly table is useful for spotting seasonal patterns — e.g. Auto tends to see PE compression every Q2 ahead of new model launches, which can look like a cheap signal but is cyclically normal.
Close the overlay with the ✕ button in the top-right corner, or press Escape on a keyboard.
6 · Backtest
The dashboard tells you which sectors look cheap right now. The Backtest page tells you whether cheap-looking sectors have actually rewarded buyers in the past. It's the historical sanity check before you act on today's signal.
The setup: pick a sector, pick a valuation threshold (e.g. "82% of reference — what the dashboard shows today"), and the engine walks through every day in history and finds matching dates. For each match, it shows you what the price did over the next 6 months and 1 year. If the historical hit rate at this threshold is good, today's signal is more credible. If it's bad, today's signal is probably a value trap — back away.
The key connection to the dashboard
The threshold slider on the Backtest page maps directly to the % of ref badge on a dashboard card. If a card shows 82% of ref today, set the threshold to 82% on the Backtest page and ask: "the last time this sector was at 82% of its reference, what happened over the next year?" — that's your historical precedent for today's signal.
How signals fire — visual
Backtest timeline — when signals fire and what happens next (illustrative)
Each green circle is a signal date — a date when the ratio crossed your threshold. The numbers show what actually happened to the sector's price over the next 12 months from that date. The backtest table shows all of these with full detail.
Setting up a backtest — step by step
1
Pick your sector. Data loads automatically. The P/E and P/B checkboxes pre-tick based on what's appropriate for that sector — but you can change them freely.
2
Set the date range. Defaults to full history. Narrow it to test a specific period, e.g. 2018–today to exclude the pre-GST era for FMCG, or post-2022 for PSU Banks after their re-rating.
3
Set the threshold. This is the key input. A threshold of 80% means "fire a signal on every date when the ratio was at 80% of its reference." Start with whatever % of ref the dashboard shows today — that's the most relevant comparison.
4
Pick a cooldown. The minimum gap between consecutive signal dates. Defaults to 6 months. Without a cooldown, a single multi-week dip below threshold would fire dozens of near-duplicate signals and inflate your sample with correlated trades. See the "Choosing a cooldown" card below for when to change it.
5
Choose a sell trigger. Two options. Time-based holds for a fixed duration (3 / 6 / 9 / 12 / 18 / 24 months) and sells at the close on that date — simple and matches a buy-and-hold investor's plan. Valuation-based sells when the same metric that triggered the buy recovers to your chosen % of weighted ref (default 100% = back to historical median). Each trade then runs as long as it takes for that recovery to happen — the Duration column shows actual hold times, which is the point of the mode.
6
Hit Run. The results show every past signal date, the valuation at that date, and the annualised return from buy to whichever sell trigger you picked.
Choosing a cooldown
Cooldown trades sample size against sample independence. A sector can spend weeks or months hovering near your threshold — treating every one of those days as a separate "signal" isn't really 30 independent bets, it's one bet measured 30 times.
Long (3–12 months) · default for investing. Each signal is a genuinely distinct episode of cheapness. The hit rate and average CAGR you see are close to what a real buyer holding for 6–12 months would have experienced. This is the right setting for the "should I buy this sector today?" question.
Medium (2–8 weeks) · balanced. Useful when the backtest window is short and a 6-month cooldown leaves you with only 2–3 signals. You trade some independence for enough data points to draw any conclusion at all.
Short (1–14 days) · diagnostic only. Shows you every single day the ratio touched your threshold. Hit rates from this setting overstate reliability because consecutive daily signals share almost identical forward returns. Use it to inspect when the sector was cheap, not to judge whether cheap-signals work.
Rule of thumb: your cooldown should be at least as long as your intended holding period. If you plan to hold a sector for 6 months after buying, a cooldown shorter than that will count overlapping trades as separate signals.
Reading the results table
Date
The exact calendar date the signal fired — shown in full (day · month · year).
Price at Buy
The index level of the sector on the signal date. Useful as a reference point when comparing to current levels.
Value at Buy
The P/E or P/B ratio on the signal date. For dual-metric sectors this shows both PE and PB side by side.
Weighted Ref
The weighted historical reference on that date — computed from data available up to that point only (no look-ahead). Confirms what the signal was comparing against.
EPS %
EPS as a % of the sector's max-ever EPS at that signal date. Low values (below 50%) flag potential value traps — the sector was cheap because earnings had collapsed. High values (above 85%) mean the cheapness was price-driven, not earnings-driven — usually higher quality signals.
Duration (valuation-mode only)
Months from buy to sell. Closed trades reached the recovery threshold within the data window; trades labelled (open) never crossed the threshold — their CAGR is extrapolated from the latest available price over the elapsed time, so treat them as estimates, not realised outcomes.
Outcome
Four sub-columns reflecting your chosen sell trigger: Price at Sell (the index level when the trade closed), Absolute Return (total price change, not annualised), CAGR (annualised return), and vs Nifty 50 (sector CAGR minus Nifty 50 CAGR over the same period — positive means the sector beat the index). The vs Nifty 50 column requires SectorScope Plus.
Signals count
How many matching dates the engine found. Very low counts (1–3) should be treated as anecdote, not statistics.
Avg CAGR
Annualised return averaged across all closed signal dates. The vs Nifty 50 column lets you see whether these returns were genuinely alpha or just riding the broader market.
Avg Absolute Return
Total price-change return averaged across all closed signal dates — not annualised. Especially useful in valuation-based mode, where hold times scatter widely and CAGR alone can flatter short trades. A 5% absolute return over 3 months annualises to ~22% CAGR, the same headline number as a 50% absolute return over 24 months — very different bets that this stat lets you see at a glance.
Hit rate
% of closed signals that were in profit. Hit rate above 70% is strong. Below 50% means more losers than winners — inspect the EPS % column to see if value traps are responsible.
Avg Hold (valuation-mode only)
Average months held across closed trades. A high open-trade count alongside this stat means the recovery threshold is rarely reached within available data — consider a lower target.
Row shading — what the colors mean
🟢
Green rowCAGR above +15%. A strong outcome.
🟡
Yellow rowPositive return but not exceptional — a win but modest. Still a hit.
🔴
Red rowCAGR was negative. Check the EPS % column — low values here usually explain the loss.
Quick-reference: what to look for
Interpreting your backtest result
Strong setup: Hit rate above 70%, average CAGR clearly positive, most rows green. Proceed with conviction.
Noisy setup: High average return but low hit rate — a few huge green rows masking several red ones. A small number of outlier wins. Fragile; wait for an additional catalyst.
Broken setup: Mostly red rows, poor hit rate, low average return. Either the threshold is wrong for this sector, or the signals are clustering around earnings collapses. Check the EPS % column.
Too few signals: 1–3 hits is not a pattern — it's a handful of data points. Don't draw strong conclusions. Widen the threshold slightly or extend the date range.
Valuation mode with many open trades: Your recovery target is rarely reached within available data. Either the sector tends not to mean-revert as cleanly as you assumed, or your target % is too aggressive. Try 95% before insisting on 100%.
How to use the EPS % column to filter
Uncheck the red rows where EPS % was very low (below 50%), then re-check the summary stats. If the hit rate and average return improve significantly when you exclude low-EPS signals, that tells you: this sector's cheap-valuation signals only work when earnings are healthy. That's important context for acting on today's signal — check the current EPS tile before committing.
7 · Projections PLUS
Backtest looks backwards — what cheap signals were worth historically. Projections looks forwards: given where a sector trades today, what price would it reach if two things normalised? It's the Projections tab in the top nav, and it's a Plus feature.
The core idea — two levers
A sector's index price is just its valuation multiple times its per-share fundamental: Price = P/E × EPS (or P/B × Book Value for the bank-style sectors). Projections nudges each lever back to a sensible anchor and multiplies them out:
1. The multiple reverts to its weighted median. The same blended 3yr/5yr/10yr reference the dashboard uses — i.e. "what this sector normally trades at." If it's expensive today, this pulls the multiple down; if it's cheap, it pulls it up.
2. The fundamental recovers to its historical max. The highest EPS (or book value) the sector has ever printed — a "full earnings recovery" assumption.
Base case · Nifty IT (illustrative)
Projected Price = weighted-median P/E × max EPS
= 25.0 × 2,000 = ₹50,000 vs current ₹57,600 → about −13% (the multiple is stretched today, so the "fair" price sits below)
What you'll see on the page
1
Pick a sector & metric. Choose a sector from the dropdown. A small P/E · P/B toggle defaults to the right lens automatically — P/B for the bank-style sectors (Bank, PSU Bank, Fin Services, Realty), P/E for everyone else — but you can switch it.
2
Four stat tiles. Current multiple, weighted-median multiple, current EPS/book value, and max EPS/book value — the four inputs that feed the projection.
3
Headline projected price. The base case (median × max) and its upside or downside versus today's price. Green means the fair-value estimate sits above current; red means below.
4
Projection-path chart. The sector's actual price over the last year as a solid line, then nine dotted paths fanning out from today to each scenario target. The projected part of the x-axis is deliberately left open — we model where price could land, not when, because the time to reach any target is unknowable.
5
Scenario matrix. The same nine outcomes as a grid, with the base case highlighted in the centre and the upside/downside printed under each price.
The nine scenarios
Rather than betting everything on one set of assumptions, the chart and matrix flex each lever one notch either side of its anchor. The multiple is tested at 90% / 100% / 110% of its median, and the fundamental at 90% / 100% / 110% of its max — three × three = nine prices. The centre cell (median × max) is the headline base case; the corners are the conservative (90% × 90%) and optimistic (110% × 110%) ends.
Read this before you act on it
These are mechanical what-if estimates, not forecasts. A median multiple and a past-peak fundamental may never recur, and nothing here says when — or whether — a target is reached. Treat it as a structured way to size the gap between today's price and a "things normalise" price, not as a target or a recommendation. For educational use only; do your own research.
8 · SectorScope Plus
Plus unlocks nine features layered on top of the free dashboard: the Timeline tab, the Smart Money tab, the Projections tab, expanded charts, AI sector summaries, live index constituents, email alerts, the weekly digest email, and vs-Nifty-50 alpha in backtests. This section walks through each one in the order you're likely to encounter it, with the concrete UI elements you'll see.
Currently free for signed-in users
SectorScope Plus is in soft launch. Until 31 October 2026, all nine features are free for any signed-in user — no payment, no subscription required. Just sign in with Google from the top-right corner of any page. After 31 October, Plus moves to a paid tier; existing users will get advance notice and a grandfathered offer.
Expanded chart — full historical view
Tap any sector's sparkline (the small chart on a card) to open the full-screen expanded chart for that sector. This is the most useful Plus feature for getting a feel for where a sector sits in its historical context — far more than the 3-year sparkline can show. The expanded view has four sets of controls:
Metric checkboxes
Pick any combination of P/E, P/B, EPS, Price, Dividend Yield to overlay on the chart. P/E and P/B are on the left axis; EPS, Price, and Dividend Yield each get their own right-side scale when selected. The defaults match the sector's primary metrics — so Banks open with P/B+EPS already ticked, IT opens with P/E+EPS, dual-metric sectors open with both P/E and P/B.
EPS (USD) — IT and Pharma only
An additional metric toggle that appears only on IT and Pharma. When enabled, it draws the EPS line in USD terms — every historical EPS data point is converted at the USD/INR rate that was in effect on its own date, not at today's rate. So a 2010 EPS uses 2010's ~₹46/USD; a 2025 EPS uses 2025's ~₹83/USD. The shape of the resulting line is the underlying USD-denominated earnings trajectory net of currency effects — substantially flatter than the INR series for these sectors, because rupee depreciation has accounted for a meaningful chunk of the headline INR growth. Off by default. For sectors with mostly domestic INR revenue (Bank, FMCG, Realty, etc.) this toggle isn't shown — converting their INR earnings to USD would just impose FX-shaped noise on a series whose underlying drivers have nothing to do with the rupee.
Date-range pills
Quick presets sit above the chart: 1W · 1M · 3M · 6M · 1Y · 3Y · 5Y · 10Y · All. Tap any pill to snap the chart to that window. Behind the scenes, the data goes back to whatever the underlying NSE history starts at for each sector — typically 2010 or earlier.
Drag-to-measure
Click and drag horizontally across any portion of the chart to measure how much each displayed metric changed between the two endpoints. A panel appears with the % change for each visible line — useful for quantifying event windows like "how much did Pharma's P/E re-rate from the COVID lows to mid-2021" or "how much did IT's EPS grow over the FY24-25 cycle". Direction-agnostic — drag left-to-right or right-to-left, the result is the same. Click anywhere outside the panel (or tap the ✕) to dismiss. Double-click to reset to the default 3Y window. Use the range pills or date pickers above the chart to actually zoom.
Median, Bands & Zones toggles
Three toggles below the chart control historical-median overlays. Median draws the rolling weighted-reference curve at every historical date — the same blended 3yr/5yr/10yr median the dashboard uses, but plotted continuously through time so you can see how it evolved. Bands draws all three component medians (3yr, 5yr, 10yr) as separate dashed curves with a small legend, useful when you want to see how the windows differ. Zones shades the chart background into five colored regions — Very Cheap / Cheap / Neutral / Expensive / Very Expensive — using the exact same thresholds the dashboard applies to today's reading (≤80% / 80–90% / 90–105% / 105–120% / >120% of weighted ref). Median and Bands are alternatives (pick one or neither), while Zones is independent and can combine with either — most useful as Median + Zones, where you see the rolling reference line traveling through its own colored zones.
Why the median is a curve, not a flat line
A static "5-year median" line would be misleading on a 10-year chart — the median changes as the window slides forward. The expanded chart computes the median at every historical date using only data available up to that point and plots the result as a moving curve. Looking at the curve in 2018 shows you what the reference value would have looked like to a 2018 viewer — no look-ahead. This is the same logic the backtest uses to evaluate signals at each historical date.
Timeline — every sector vs Nifty 50
The Timeline tab (top nav, between Dashboard and Backtest) is the cross-sector counterpart to the expanded chart. Where the expanded chart shows many metrics for one sector, Timeline shows one metric for every sector at once — plotted as growth relative to Nifty 50. It answers a different question from the dashboard: not "is this sector cheap versus its own history," but "which sectors have been leading or lagging the broad market over this window."
Every line is read against the same baseline: each sector's percentage change since the start of the window, minus Nifty 50's percentage change over that same window. Nifty 50 is therefore the flat 0 line. A line above 0 means the sector's chosen metric grew faster than Nifty 50's since the window opened; below 0 means it lagged. The vertical axis is always in percentage points. That's the default; a Baseline control above the chart offers two alternatives — Gold and None — described under the controls below.
Metric selector
Pick one of P/E, P/B, EPS, Price; the whole chart re-plots that single metric across every sector. Each sector chip stays selectable for every metric — Timeline isn't limited to a sector's "primary" metric the way the dashboard cards are, so you can view P/B for a sector normally shown on P/E and vice-versa. Where the underlying NSE history doesn't carry the chosen metric for a sector, that sector simply draws no line until you switch to a metric it does have; its chip never greys out.
Baseline
Choose what the flat 0 line represents. Nifty 50 (default) subtracts the benchmark's growth from every sector, so the chart reads as pure out- or under-performance versus the index. Gold subtracts the growth of NSE gold spot (₹ per 10g) instead — every line becomes that sector's performance in gold terms, answering "did equities actually beat holding gold over this window?" Gold is a Price-metric-only baseline: comparing P/E, P/B, or EPS growth against a metal's price is meaningless, so the Gold button greys out on those metrics and the baseline falls back to Nifty 50 if it was active. When Gold is not the baseline, it's available as an ordinary toggleable amber line via its legend chip (off by default) — useful for eyeballing gold's own trajectory alongside the sectors. None removes the subtraction entirely — each line is its own rebased growth since the window start, and Nifty 50 appears as a normal toggleable line — useful when you want the raw trajectories rather than the spread against a benchmark.
Date range
The same quick presets as the expanded chart — 1W · 1M · 3M · 1Y · 3Y · 5Y · All — plus From/To pickers for a custom window. Editing a date deactivates the presets. Because every line is rebased to the start of whatever window you pick, the range is the reference point: a sector can lead over five years yet lag over the last three months, and Timeline shows both honestly depending on the window.
Legend & toggles
All sectors are drawn by default, each in its dashboard colour, with Nifty 50 marked as the baseline. Click any chip to hide or show that line — handy for isolating two or three sectors against the benchmark. ALL and NONE reset the selection in one tap.
Hover readout
Move the cursor (or drag a finger) across the chart to drop a vertical guide. A panel lists every visible sector's value at that date, sorted from most outperforming to most lagging — green above the line, red below — a quick leaderboard for any point in time.
Why growth, not absolute levels
Plotting raw values across sectors would mislead. Absolute Price levels differ purely by index construction — Nifty Bank sits near 52,000 while Nifty 50 is near 24,000, which says nothing about performance — and absolute EPS is the same story. Even P/E and P/B aren't directly comparable sector to sector, since each trades in its own range. Rebasing everything to growth against Nifty 50 strips out those structural differences and leaves a clean read of relative performance. Newer indices (Defence, Healthcare) rebase from their own first date inside the window rather than faking a value back to the window's edge, so their lines start partway across — expected, not a gap in the data.
Smart Money — where institutions are moving PLUS
The Smart Money tab (top nav, between Timeline and Backtest) is the cross-sector view of the same shareholding data that powers each sector's Shareholding Pattern section. Where that per-sector view answers "who owns this sector," Smart Money answers "which way is institutional money rotating across all sectors at once." It's a heatmap: every sector is a row, every recent quarter a column, and each cell is that sector's weighted institutional holding for the quarter — coloured green where the holding rose from the prior quarter and orange where it fell. Read a row left-to-right for one sector's trajectory; read a column top-to-bottom to see where money moved that quarter. Below the heatmap, two stock activity tables name the individual stocks behind the moves — covered in detail below.
FII / DII toggle
Switch between foreign institutional investors (FII) and domestic institutions (DII — mutual funds, insurance, banks, pensions). The two often move in opposite directions — foreigners selling into domestic buying, or vice versa — so the toggle is where the interesting disagreements show up.
Ranking (# column)
Sectors are ranked by the latest quarter-on-quarter change in holding — biggest inflow at the top, biggest outflow at the bottom — so the rotation story reads top-to-bottom. The number is that rank; the small arrow beside it shows how the rank moved versus the previous quarter (▲ climbing the accumulation table, ▼ sliding, blue dash unchanged). A sector that jumps several ranks is one institutions turned toward sharply that quarter.
Nifty 50 benchmark
Nifty 50 is included as the market reference (highlighted row, "market" tag), ranked in place like any sector rather than pinned. It's the baseline the sectors move around: FIIs trimming the market by 0.5pp while cutting Banks by 3pp means Banks were sold harder than the market, not just sold. Because Nifty 50 overlaps the sectors heavily, treat it as a reference line, not an independent row to sum against.
Coverage & weighting
Each cell uses the same NSE-rulebook-capped, strategic-adjusted free-float weighting as the per-sector view, and a cell appears only once constituents covering at least 80% of sector weight have filed that quarter — so the newest column fills in gradually through filing season rather than showing a misleading partial average.
Stock activity tables
Below the heatmap, two tables answer "which stocks, exactly": ▲ Increased and ▼ Decreased allocation, listing every constituent whose holding moved by at least 0.25 percentage points in the latest filed quarter versus its previous filing. They have their own FII/DII toggle, independent of the heatmap's — so the heatmap can show FII rotation while the tables show DII names. A sort control orders both tables by High → Low (biggest move first), Low → High, or Latest filed (most recently filed first, with the filing date shown next to each move). Each row shows the change in percentage points plus the actual before → after holding level. Unlike the heatmap cells, these figures are unweighted — exactly as filed, no index-weight math — and a stock that hasn't filed for the latest quarter yet is simply excluded rather than shown with a stale number, so the tables grow through filing season just like the newest heatmap column. A move above the threshold on FII only appears under the FII tab only.
What Smart Money is and isn't
It's a factual record of what institutions did — holdings reported to the exchange each quarter, nothing predictive. Rising FII holding at a cheap valuation and rising FII holding at an expensive one are very different situations; Smart Money tells you the flow, the sector cards tell you the valuation, and reading them together is the point. It does not interpret, forecast, or recommend, and quarterly holdings say nothing about intra-quarter timing.
AI sector summary — plain-language read
At the top of every sector's detail page (tap any card to open it) you'll see a "Generate AI Summary" button. Tap it to get a 4-5 sentence plain-English read on the sector's current situation, written by Claude. The summary covers four things: the current valuation band and what it means historically, the EPS trajectory (rising, falling, flat, near peak), any divergence between P/E and P/B for dual-metric sectors, and a one-line note on whether history backs the current signal (drawn from the same backtest engine described in section 6). On IT and Pharma — where USD revenue dominates — the summary may also briefly flag the currency-effect angle, comparing INR earnings growth to USD-denominated growth, but only when the two diverge meaningfully (≳3 percentage points). When INR and USD growth are roughly aligned, the currency note is omitted to keep the summary concise.
What the AI does and doesn't do
Does: Translate the on-screen numbers into a paragraph. Identifies the band, the EPS pill, the divergence (if any), and whether the historical hit rate at this threshold has been favourable.
Doesn't: Access news, macro headlines, central bank policy, recent earnings releases, or anything outside the data shown on screen. The AI is a structured reader of the dashboard, not a research assistant.
Doesn't recommend: No "buy this" or "sell that" calls. The summary is descriptive — it tells you what the data says, not what to do about it.
Daily limit: 30 summaries per account per day, resets at midnight IST. The cap exists because each summary call costs a small amount on the backend; if you hit it, the button disables until midnight.
Index constituents — live prices for every stock
In the sector detail overlay (tap any card to open it), an Index Constituents block shows every stock in that sector's NSE index with live price, intraday change, day-range position, traded volume, and traded value. The data refreshes every 60 seconds while the overlay is open and again the instant you switch back to the tab — so coming back from another window always shows fresh numbers.
At the top of the block sits a breadth strip: a quick read of how many constituents are up vs down vs flat. Useful for distinguishing broad-based sector moves (9 up · 1 down) from narrow ones driven by one or two heavyweights (3 up · 7 down with the index still green). Every column is sortable — click "% Chg" descending for top gainers, click "Volume" to find the day's most-traded names, click "Day Range" to find stocks near intraday highs vs lows. See section 5 for the full column breakdown with a screenshot.
Why this is useful alongside the dashboard signal
The dashboard tells you whether a sector is cheap; the constituents table tells you what's actually moving inside it today. A sector that's "Cheap" on the band and has 8 of 10 stocks green is showing genuine participation. A sector that's "Cheap" but has 7 of 10 red while the index is up — because one mega-cap is rallying — is a much more fragile setup, and worth a second look at the per-stock breakdown before acting.
Email alerts — get notified at your level
Tap the 🔔 Set Alert button at the bottom of any card to flip the card and configure an alert. The form has three sections, top to bottom:
Metric — P/E or P/B (dual-metric sectors only)
For single-metric sectors (IT, Bank, Realty, etc.), the right metric is selected silently — no toggle is shown. For dual-metric sectors (Energy, Infra, Metals) a P/E / P/B toggle appears. Each alert watches exactly one metric. If you want to track both metrics on the same sector, set two separate alerts (still capped at 2 per sector).
Trigger when — At or Below / At or Above
Choose the direction of the cross. At or Below fires when the metric drops to your threshold or lower — the classic "buy when cheap" setup. At or Above fires when the metric rises to your threshold or higher — useful for trimming exposure when a sector gets stretched, or for catching a confirmed rerating breakout.
Threshold slider
Drag to your target, expressed as 50%–150% of the weighted reference. With direction At or Below and slider at 85%: "alert me when this metric drops to 85% of its weighted historical median." Switch direction and the same 85% means "alert me when it climbs to 85%" — though for an above-trigger you'd usually set a value above 100%. The slider starts at the sector's current % of ref, so you're anchored at where things are right now.
Delivery
The system checks live NSE data every 15 minutes during market hours (9:15am–3:30pm IST, Mon–Fri) and emails the address you signed in with. Alerts are one-time — once an alert fires, it's removed automatically and you'll need to set a new one if you want continued notifications. You can have up to 2 active alerts per sector. Manage or delete existing alerts from the same flipped-card view — saved alerts appear at the top of the form.
Weekly digest — Sunday evening email
Where alerts watch one threshold you chose, the weekly digest is the standing summary: one email every Sunday at 8pm IST recapping the week across the whole dashboard, so Monday starts with the lay of the land already in your inbox. Each edition covers six things, in order:
Best & worst sectors
The week's top and bottom three sectors by price move, each with its current valuation band — so a big up-move in an already-expensive sector reads differently from the same move in a cheap one.
Macro strip
Week-over-week readings for Gold (NSE spot, ₹/10g), USD/INR, the RBI repo rate, and the 10-year G-Sec yield — the four backdrop numbers that frame everything else.
Valuation band changes
Any sector that crossed a band boundary during the week — Cheap to Neutral, Expensive to Very Expensive, and so on. These are the week's genuine signal changes, the same transitions the dashboard colours would have shown you day by day.
Earnings growth
Implied EPS, week over week — which sectors' underlying earnings moved, separate from what their prices did.
Book-value growth
Implied book value, week over week — the same read for the balance-sheet metric, most meaningful for the P/B-driven sectors.
Smart-money filings
Every company that filed its quarterly shareholding pattern during the week, with the biggest institutional moves called out — up to six stocks where FII+DII combined added and six where they trimmed, each showing its FII and DII change in percentage points (0.25pp minimum, exactly as filed). During filing season this is where the week's fresh institutional signal lands first; the full list is always on the Smart Money tab.
Who gets it, and how to stop
The digest goes to every signed-in user, alert subscriber, and donor — on by default, no setup needed. To switch it off (or back on), use the 📧 Weekly digest toggle at the top of the My Alerts panel, or tap the one-click unsubscribe link in the footer of any digest email — both control the same preference. If the data pipeline ever falls behind, the digest is skipped for that week rather than sent with stale numbers.
Alpha vs Nifty 50 — in the backtest results table
In the Backtest results table (covered in section 6), each forward-return column has a sub-column labelled vs Nifty 50. This shows the sector's return minus the Nifty 50's return over the same forward window — the genuine alpha generated, separate from whatever the broader market did.
Why this matters: a sector earning +22% CAGR over 12 months sounds strong, but if Nifty 50 returned +20% over the same period, only +2% was sector-specific. The vs-Nifty column does this comparison automatically for every signal row. A robust backtest setup shows not just a high hit rate and positive CAGR, but consistently positive vs-Nifty values too — confirming the sector was beating the market, not just rising with it.
How to enable Plus
Sign in with Google from the top-right corner. Plus features unlock automatically for all signed-in users until 31 October 2026. After that, Plus becomes a paid tier — sign-in alone won't unlock it.
9 · Limits & honest caveats
SectorScope is a decision-sharpening tool, not a forecasting engine. Before you act on what it shows, here's what to keep in mind:
Valuation is not timing. A sector in the "Very Cheap" band can stay cheap — or get cheaper — for months before reversing. These signals are for patient capital with a 6 to 12 month horizon, not for predicting next week's move.
Index composition changes over time. Nifty sectoral indices reconstitute periodically. The 10-year P/E series for Nifty IT in 2025 includes different companies than it did in 2015. Very long histories blend old and new compositions — treat them as approximate, not precise.
No macro overlay. The model knows nothing about RBI rate cycles, the rupee, global commodity prices, or credit cycles. A "Cheap" PSU Bank signal during a credit crisis is a different risk than the same signal in a stable macro environment. Add your own macro read.
EPS can be distorted. The EPS series uses NSE's reported trailing-twelve-month earnings methodology. One-off asset sales, accounting changes, or a single large provisioning quarter can make EPS look unusually high or low for a quarter or two. If the EPS bar charts on the detail page look surprising, cross-check with the actual quarterly results.
Structural regime changes. The model puts only 10% weight on the 10-year median, so it adapts gradually to re-ratings. But if a sector undergoes a genuinely fundamental structural change — like PSU Banks after the IBC clean-up, or IT after the 2020 work-from-home re-rating — the new regime won't fully dominate the reference for another few years. During a transition, treat the signal as directional rather than precise.
Backtest results are conditional, not predictive. A 75% hit rate over the last 10 years tells you what worked historically. It doesn't guarantee the next signal will work. Markets shift; sample sizes are small; tomorrow may not look like yesterday. Use the backtest as a sanity-check, not a guarantee.
How to use this tool well
Treat SectorScope as one structured input among several. The signals carry the most weight when all of these line up:
The valuation band is Cheap or Very Cheap.
The EPS pill is showing ▲ LOW · RISING (or absent for sectors where EPS rarely drops far below peak).
The backtest shows a strong hit rate and positive vs-Nifty alpha at this threshold.
Your own fundamental view of the sector and the macro backdrop doesn't contradict the signal.
▼ LOW · FALLING is a hard veto. Even if everything else looks favourable, a falling-knife pill means earnings are dragging price down and you're trying to catch a sector mid-collapse. Wait for the pill to flip before acting.
India Equity · NSE sector indices · Valuations based on trailing TTM earnings & book value.
10 · Get in touch
Found a bug, have feedback, or want a feature added? We'd love to hear from you. Drop a note — every email gets read.