Capital Market — Indices

Category: capital_market

Sub-section: indices

NSE portal path: All Reports → Capital Market → Indices

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All Indices Daily Close Values

Single-file snapshot of EOD values for all 147+ NSE indices — OHLC, P/E, P/B, Div Yield, Volume.

df = nse.get("capital_market", "indices", "ind_close_all", "2026-05-22")

# Filter for Nifty 50
df[df["Index Name"] == "Nifty 50"]

nse-data get capital_market indices ind_close_all 2026-05-22

File: ind_close_all_{DDMMYYYY}.csv
~147 rows × 13 cols
Columns: Index Name, Index Date, Open Index Value, High Index Value, Low Index Value, Closing Index Value, Points Change, Change(%), Volume, Turnover (Rs. Cr.), P/E, P/B, Div Yield

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Index Top Movers

Top 10 securities by weight/movement for Nifty 50.

df = nse.get("capital_market", "indices", "top_movers", "2026-05-22")

nse-data get capital_market indices top_movers 2026-05-22

File: top10nifty50_{DDMMYY}.csv
Columns: SYMBOL, SECURITY, WEIGHTAGE(%)

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Historical Index Data & Total Return Index (TRI)

Historical OHLC and TRI come from niftyindices.com — a separate source.

⚠️ Cloudflare note: niftyindices.com is Cloudflare-protected. Works from residential IPs. May be blocked from AWS Lambda (AWS IP ranges are blocklisted by Cloudflare). Test it — if it works from your Lambda region, great. If not, use derive_tri() or the S3-handoff pattern.

Install cloudscraper

pip install nse-archives[cloudflare]   # adds cloudscraper
# or: pip install cloudscraper

Historical Price Index (OHLC)

from nsedata import nse

df = nse.get_historical_index("NIFTY 50", "01-Jan-2026", "31-Mar-2026")
df = nse.get_historical_index("nifty50", "01-Jan-2026", "31-Mar-2026")  # shorthand also works

nse-data get-index "NIFTY 50" --from "01-Jan-2026" --to "31-Mar-2026"

Returns: Index Name, Date, Open, High, Low, Close
Date format: dd-Mon-yyyy (different from other datasets)

Total Return Index (TRI)

TRI = price movement + dividends reinvested. Essential for AMC benchmarking.

df = nse.get_tri("NIFTY 50", "01-Jan-2026", "31-Mar-2026")
df = nse.get_tri("niftybank", "01-Apr-2026", "30-Apr-2026")  # shorthand

Returns: Index Name, Date, Total Returns Index, Net Total Return Index

Column	Description
Total Returns Index	Gross TRI — dividends reinvested pre-tax
Net Total Return Index	Net TRI — dividends reinvested at ~15% tax

Available Index Shorthands

nse.list_index_names()  # shows all shorthands and full names

Common: nifty50, nifty100, niftybank, niftyit, niftyauto, niftypharma, niftyfmcg, niftynext50

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Deriving TRI from Price Data (Lambda-compatible)

If niftyindices.com is blocked from Lambda, derive an approximate TRI from ind_close_all data using the formula:

TRI(t) = TRI(t-1) × (Price(t) + Div(t)) / Price(t-1)

# Step 1: get closing index values (works from Lambda)
df = nse.get("capital_market", "indices", "ind_close_all", "2026-05-22")
nifty = df[df["Index Name"] == "Nifty 50"].rename(
    columns={"Index Date": "Date", "Closing Index Value": "Close"})

# Step 2: derive TRI (approximate — uses 1.5% annual yield estimate)
tri_df = nse.derive_tri(nifty)
print(tri_df[["Date", "Close", "Derived_TRI"]])

# Step 3: with actual dividends (more accurate)
# Get corporate actions from PR bundle
bc_df = nse.get("capital_market", "equities_sme", "bhavcopy_pr", "2026-05-22")
# ... extract index constituent dividends and pass to derive_tri(dividends=...)

Note: The derived TRI is approximate. The official TRI uses exact constituent-level dividend data and historical weights. For accurate TRI, use nse.get_tri() directly.