dalal

Unified Python API for Indian stock exchanges (NSE + BSE).

No API keys. No third-party exchange wrappers. Just requests under the hood.

Install

pip install dalal

Quick Start

import dalal

dalal.quote("RELIANCE")                          # NSE (default)
dalal.quote("500325", exchange="BSE")            # BSE
dalal.fundamentals("500325")                     # 3-period financials
dalal.actions("RELIANCE")                        # dividends, splits, bonuses
dalal.history("RELIANCE", "2025-01-01", "2025-12-31")

Need explicit session control? Use the Dalal class directly with a context manager.

Features

Method	NSE	BSE	Default	Description
quote()	Yes	Yes	NSE	Live price, OHLC, 52-week range
history()	Yes	-	NSE	Historical OHLCV (auto-chunks 100-day windows)
actions()	Yes	Yes	NSE	Corporate actions — dividends, splits, bonuses
fundamentals()	-	Yes	BSE	3-period results — Revenue, PAT, EPS, OPM%, NPM%
meta()	-	Yes	BSE	PE, ROE, EPS, P/B ratios
index()	Yes	-	NSE	Index constituents with live prices
holidays()	Yes	-	NSE	Trading/clearing holidays
bulk_deals()	Yes	-	NSE	Historical bulk deal data
block_deals()	Yes	-	NSE	Intraday block deals
shareholding()	Yes	-	NSE	Promoter/public/FII holding pattern
gainers()	Yes	Yes	NSE	Top gainers by index
losers()	Yes	Yes	NSE	Top losers by index
announcements()	Yes	Yes	NSE	Corporate announcements
status()	Yes	Yes	NSE	Market open/closed status
lookup()	Yes	Yes	NSE	Symbol search / autocomplete
result_calendar()	-	Yes	BSE	Upcoming earnings dates
advances()	Yes	-	NSE	Advance/decline ratio

Usage

Quotes

import dalal

nse = dalal.quote("RELIANCE")
# {'symbol': 'RELIANCE', 'ltp': 1365.1, 'open': 1360.0, 'high': 1370.0,
#  'low': 1340.0, 'prev_close': 1350.0, 'year_high': 1600.0, ...}

bse = dalal.quote("500325", exchange="BSE")
# {'scripcode': '500325', 'ltp': 1365.1, 'open': 1340.0, ...}

Historical Data

import dalal

data = dalal.history("RELIANCE", "2025-01-01", "2025-06-30")
# [{'date': '2025-01-02', 'open': 1300.0, 'high': 1320.0,
#   'low': 1290.0, 'close': 1310.0, 'volume': 5000000.0}, ...]

# Works with pandas
import pandas as pd
df = pd.DataFrame(data)

Fundamentals (BSE)

import dalal

f = dalal.fundamentals("500325")
# {'scripcode': '500325', 'currency_unit': 'in Cr.',
#  'periods': [
#    {'period': 'Dec-25', 'revenue': 125741.0, 'net_profit': 9396.0,
#     'eps': 6.94, 'opm_pct': 14.56, 'npm_pct': 7.47},
#    {'period': 'Sep-25', ...},
#    {'period': 'FY24-25', ...}
#  ]}

m = dalal.meta("500325")
# {'eps': 35.21, 'pe': 38.77, 'roe': 9.09, 'pb': 3.52, ...}

Corporate Actions

import dalal

actions = dalal.actions("RELIANCE")
# [{'symbol': 'RELIANCE', 'subject': 'Dividend - Rs 5.5 Per Share',
#   'ex_date': '2025-08-14', ...},
#  {'symbol': 'RELIANCE', 'subject': 'Bonus 1:1',
#   'ex_date': '2024-10-28', ...}]

Index Constituents

import dalal

nifty = dalal.index("NIFTY 50")
# {'name': 'NIFTY 50', 'advance': 30, 'decline': 20,
#  'constituents': [
#    {'symbol': 'RELIANCE', 'ltp': 1365.0, 'pct_change': 1.12, ...},
#    ...
#  ]}

Error Handling

import dalal
from dalal import SymbolNotFound, RateLimited, NetworkError

try:
    dalal.quote("INVALID")
except SymbolNotFound:
    print("Symbol not found")
except RateLimited:
    print("Too many requests — slow down")
except NetworkError:
    print("Connection issue")

All exceptions inherit from DalalError:

DalalError
├── ExchangeError
│   ├── AuthError
│   ├── SymbolNotFound
│   └── DataNotAvailable
├── NetworkError
│   ├── RateLimited
│   └── ExchangeDown
└── ValidationError

Rate Limiting

Built-in rate limiting prevents IP bans:

• NSE: 3 requests/second
• BSE: 8 requests/second

Override if needed:

d = Dalal(nse_rate=2, bse_rate=5)

How It Works

dalal talks directly to the same APIs that nseindia.com and bseindia.com use internally. No API keys needed — it handles browser impersonation and cookie management automatically.

• NSE: Cookie-primed session (auto-refreshes on 401)
• BSE: Simple session with browser-like headers
• Sessions are lazy: NSE session only created on first NSE call, same for BSE

Typing

dalal includes a py.typed marker for typed Python consumers.

Project Docs

• AGENTS.md — AI/human contributor playbook
• CONTRIBUTING.md — contribution workflow
• SECURITY.md — security reporting policy
• CODE_OF_CONDUCT.md — community rules
• CHANGELOG.md — release history
• DISCLAIMER.md — legal and data-source disclaimer
• llms.txt and llms-full.txt — LLM-oriented project map

Requirements

• Python 3.11+
• requests >= 2.31

License

MIT

Author

Aman (@onlyoneaman)