bdshare

bdshare is a Python library for fetching live and historical market data from the Dhaka Stock Exchange (DSE). It handles scraping, retries, caching, and rate limiting so you can focus on your analysis.

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Table of Contents

• Installation
• Quick Start
• Core Concepts
• Usage Guide
  • Live Trading Data
  • Historical Data
  • Market & Index Data
  • News & Announcements
  • Saving to CSV
• OOP Client (BDShare)
• Error Handling
• API Reference
• Examples
• Contributing
• Roadmap
• Disclaimer

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Installation

Requirements: Python 3.7+

pip install bdshare

Install from source (latest development version):

pip install -U git+https://github.com/rochi88/bdshare.git

Dependencies installed automatically: pandas, requests, beautifulsoup4, lxml

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Quick Start

from bdshare import get_current_trade_data, get_hist_data

# Live prices for all instruments
df = get_current_trade_data()
print(df.head())

# Historical data for a specific symbol
df = get_hist_data('2024-01-01', '2024-01-31', 'GP')
print(df.head())

Or use the object-oriented client:

from bdshare import BDShare

with BDShare() as bd:
    print(bd.get_market_summary())
    print(bd.get_current_trades('ACI'))

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Core Concepts

Concept	Details
Retries	All network calls retry up to 3 times with exponential back-off
Fallback URL	Every request has a primary and an alternate DSE endpoint
Caching	The BDShare client caches responses automatically (configurable TTL)
Rate limiting	Built-in sliding-window limiter (5 calls/second) prevents being blocked
Errors	All failures raise BDShareError — never silent

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Usage Guide

Live Trading Data

from bdshare import get_current_trade_data, get_dsex_data, get_current_trading_code

# All instruments — returns columns: symbol, ltp, high, low, close, ycp, change, trade, value, volume
df = get_current_trade_data()

# Single instrument (case-insensitive)
df = get_current_trade_data('GP')

# DSEX index entries
df = get_dsex_data()

# Just the list of tradeable symbols
codes = get_current_trading_code()
print(codes['symbol'].tolist())

Historical Data

from bdshare import get_hist_data, get_basic_hist_data, get_close_price_data
import datetime as dt

start = '2024-01-01'
end   = '2024-03-31'

# Full historical data (ltp, open, high, low, close, volume, trade, value…)
# Indexed by date, sorted newest-first
df = get_hist_data(start, end, 'ACI')

# Simplified OHLCV — sorted oldest-first, ready for TA libraries
df = get_basic_hist_data(start, end, 'ACI')

# Set date as index explicitly
df = get_basic_hist_data(start, end, 'ACI', index='date')

# Rolling 2-year window
end   = dt.date.today()
start = end - dt.timedelta(days=2 * 365)
df    = get_basic_hist_data(str(start), str(end), 'GP')

# Close prices only
df = get_close_price_data(start, end, 'ACI')

Column order note: get_basic_hist_data intentionally returns OHLCV in standard order (open, high, low, close, volume) to be compatible with libraries like ta, pandas-ta, and backtrader.

Market & Index Data

from bdshare import (
    get_market_info,
    get_market_info_more_data,
    get_market_depth_data,
    get_latest_pe,
    get_sector_performance,
    get_top_gainers_losers,
    get_company_info,
)

# Last 30 days of market summary (DSEX, DSES, DS30, DGEN, volumes, market cap)
df = get_market_info()

# Historical market summary between two dates
df = get_market_info_more_data('2024-01-01', '2024-03-31')

# Order book (buy/sell depth) for a symbol
df = get_market_depth_data('ACI')

# P/E ratios for all listed companies
df = get_latest_pe()

# Sector-wise performance
df = get_sector_performance()

# Top 10 gainers and losers (adjust limit as needed)
df = get_top_gainers_losers(limit=10)

# Detailed company profile
tables = get_company_info('GP')

News & Announcements

from bdshare import get_news, get_agm_news, get_all_news

# Unified dispatcher — news_type: 'all' | 'agm' | 'corporate' | 'psn'
df = get_news(news_type='all')
df = get_news(news_type='agm')
df = get_news(news_type='corporate', code='GP')
df = get_news(news_type='psn', code='ACI')   # price-sensitive news

# Direct function calls
df = get_agm_news()                          # AGM / dividend declarations
df = get_all_news(code='BEXIMCO')            # All news for one symbol
df = get_all_news('2024-01-01', '2024-03-31', 'GP')  # Filtered by date + symbol

Saving to CSV

from bdshare import get_basic_hist_data, Store
import datetime as dt

end   = dt.date.today()
start = end - dt.timedelta(days=365)

df = get_basic_hist_data(str(start), str(end), 'GP')
Store(df).save()   # saves to current directory as a CSV

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OOP Client (BDShare)

The BDShare class wraps all functions with automatic caching and rate limiting.

from bdshare import BDShare

bd = BDShare(cache_enabled=True)   # cache_enabled=True is the default

Context manager (auto-cleans cache and session)

with BDShare() as bd:
    data = bd.get_current_trades('GP')

Market methods

bd.get_market_summary()                    # DSEX/DSES/DS30 indices + stats  (1-min TTL)
bd.get_company_profile('ACI')             # Company profile                  (1-hr TTL)
bd.get_latest_pe_ratios()                 # All P/E ratios                   (1-hr TTL)
bd.get_top_movers(limit=10)               # Top gainers/losers               (5-min TTL)
bd.get_sector_performance()               # Sector breakdown                 (5-min TTL)

Trading methods

bd.get_current_trades()                   # All live prices                  (30-sec TTL)
bd.get_current_trades('GP')               # Single symbol
bd.get_dsex_index()                       # DSEX index entries               (1-min TTL)
bd.get_trading_codes()                    # All tradeable symbols            (24-hr TTL)
bd.get_historical_data('GP', '2024-01-01', '2024-03-31')    # OHLCV history

News methods

bd.get_news(news_type='all')              # All news                         (5-min TTL)
bd.get_news(news_type='corporate', code='GP')
bd.get_news(news_type='psn')             # Price-sensitive news

Utility methods

bd.clear_cache()                          # Flush all cached data
bd.configure(proxy_url='http://proxy:8080')
print(bd.version)                         # Package version string

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Error Handling

All failures raise BDShareError. Never catch bare Exception — you'll miss bugs.

from bdshare import BDShare, BDShareError

bd = BDShare()

try:
    df = bd.get_historical_data('INVALID', '2024-01-01', '2024-01-31')
except BDShareError as e:
    print(f"DSE error: {e}")
    # safe fallback logic here

Common causes of BDShareError:

• Symbol not found in the response table
• DSE site returned a non-200 status after all retries
• Table structure changed on the DSE page (report as a bug)
• Network timeout

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API Reference

Trading Functions

Function	Parameters	Returns	Description
get_current_trade_data(symbol?)	symbol: str	DataFrame	Live prices (all or one symbol)
get_dsex_data(symbol?)	symbol: str	DataFrame	DSEX index entries
get_current_trading_code()	—	DataFrame	All tradeable symbols
get_hist_data(start, end, code?)	str, str, str	DataFrame	Full historical OHLCV
get_basic_hist_data(start, end, code?, index?)	str, str, str, str	DataFrame	Simplified OHLCV (TA-ready)
get_close_price_data(start, end, code?)	str, str, str	DataFrame	Close + prior close
get_last_trade_price_data()	—	DataFrame	Last trade from DSE text file

Market Functions

Function	Parameters	Returns	Description
get_market_info()	—	DataFrame	30-day market summary
get_market_info_more_data(start, end)	str, str	DataFrame	Historical market summary
get_market_depth_data(symbol)	str	DataFrame	Order book (buy/sell depth)
get_latest_pe()	—	DataFrame	P/E ratios for all companies
get_company_info(symbol)	str	list[DataFrame]	Detailed company tables
get_sector_performance()	—	DataFrame	Sector-wise performance
get_top_gainers_losers(limit?)	int (default 10)	DataFrame	Top movers

News Functions

Function	Parameters	Returns	Description
get_news(news_type?, code?)	str, str	DataFrame	Unified news dispatcher
get_agm_news()	—	DataFrame	AGM / dividend declarations
get_all_news(start?, end?, code?)	str, str, str	DataFrame	All DSE news
get_corporate_announcements(code?)	str	DataFrame	Corporate actions
get_price_sensitive_news(code?)	str	DataFrame	Price-sensitive news

get_news news_type values

Value	Equivalent direct function
'all'	get_all_news()
'agm'	get_agm_news()
'corporate'	get_corporate_announcements()
'psn'	get_price_sensitive_news()

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Examples

Stock performance summary

import datetime as dt
from bdshare import BDShare, BDShareError

def summarize(symbol: str, days: int = 30) -> dict:
    end   = dt.date.today()
    start = end - dt.timedelta(days=days)

    with BDShare() as bd:
        try:
            df = bd.get_historical_data(symbol, str(start), str(end))
        except BDShareError as e:
            print(f"Could not fetch data: {e}")
            return {}

    return {
        'symbol':       symbol,
        'current':      df['close'].iloc[0],
        'period_high':  df['high'].max(),
        'period_low':   df['low'].min(),
        'avg_volume':   df['volume'].mean(),
        'change_pct':   (df['close'].iloc[0] - df['close'].iloc[-1])
                        / df['close'].iloc[-1] * 100,
    }

result = summarize('GP', days=30)
print(f"{result['symbol']}: {result['change_pct']:.2f}% over 30 days")

Simple portfolio tracker

from bdshare import BDShare, BDShareError

PORTFOLIO = {
    'GP':      {'qty': 100, 'cost': 450.50},
    'ACI':     {'qty':  50, 'cost': 225.75},
    'BEXIMCO': {'qty': 200, 'cost': 125.25},
}

with BDShare() as bd:
    total_cost = total_value = 0

    for symbol, pos in PORTFOLIO.items():
        try:
            row = bd.get_current_trades(symbol).iloc[0]
            price        = row['ltp']
            market_value = pos['qty'] * price
            cost         = pos['qty'] * pos['cost']
            pnl          = market_value - cost

            print(f"{symbol:10s}  price={price:8.2f}  P&L={pnl:+10.2f}")
            total_cost  += cost
            total_value += market_value

        except BDShareError as e:
            print(f"{symbol}: fetch error — {e}")

    print(f"\nPortfolio P&L: {total_value - total_cost:+.2f} "
          f"({(total_value/total_cost - 1)*100:+.2f}%)")

Fetch and screen top gainers above 5 %

from bdshare import get_top_gainers_losers

df = get_top_gainers_losers(limit=20)
big_movers = df[df['change'] > 5]
print(big_movers[['symbol', 'ltp', 'change']])

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Contributing

Contributions are welcome! To get started:

git clone https://github.com/rochi88/bdshare.git
cd bdshare
pip install -e ".[dev]"
pytest

Please open an issue before submitting a pull request for significant changes. See CONTRIBUTING.md for the full guide.

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Roadmap

• Chittagong Stock Exchange (CSE) support
• WebSocket streaming for real-time ticks
• Built-in technical indicators (ta integration)
• Portfolio management helpers
• Docker demo examples
• Shared session with exponential back-off
• lxml-based fast parsing
• BDShareError for clean error handling
• Unified get_news() dispatcher
• Rate limiter and response caching

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Support

• Docs: bdshare.readthedocs.io
• Bugs / Features: GitHub Issues
• Discussion: GitHub Discussions

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License

MIT — see LICENSE for details.

Disclaimer

bdshare is intended for educational and research use. Always respect DSE's terms of service. The authors are not responsible for financial decisions made using this library.