bdfinance 0.5.0

High-performance async Bangladesh stock market data library with batch processing

bdfinance

High-performance async Python library for fetching Bangladesh stock market data from the Dhaka Stock Exchange (DSE).

Inspired by yfinance, bdfinance provides a familiar Ticker-based API backed by async HTTP, automatic caching, structured logging, and Pydantic-validated models.

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Features

• Async-first — built on httpx with HTTP/2 and connection pooling
• Ticker API — unified interface per symbol: quote, history, info, depth, news, fundamentals
• Batch processing — fetch data for multiple symbols concurrently
• Caching — pluggable memory or Redis cache with configurable TTL
• Pydantic models — strongly typed, auto-validated data models
• Retry & rate limiting — exponential backoff with configurable rate limits
• Structured logging — powered by structlog
• Treasury bonds — coupon rates, approximate YTM calculations

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Installation

Requires Python 3.12+.

pip install bdfinance

Or install from source:

git clone https://github.com/your-username/bdfinance.git
cd bdfinance
pip install .

Dependencies

Package	Purpose
httpx[http2]	Async HTTP client with HTTP/2
beautifulsoup4 + lxml	HTML parsing
pandas	DataFrames for historical/tabular data
pydantic + pydantic-settings	Data models and configuration
tenacity	Retry logic
structlog	Structured logging
aiocache + msgpack	Caching layer

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Quick Start

import asyncio
from bdfinance import BDStockClient

async def main():
    async with BDStockClient() as client:
        ticker = client.ticker("GP")

        # Current quote
        quote = await ticker.quote()
        print(f"{quote.symbol}: LTP {quote.ltp}, Change {quote.change}")

        # Historical data (returns pandas DataFrame)
        df = await ticker.history(period="1y")
        print(df.head())

        # Company information
        info = await ticker.info()
        print(info.basic_information.company_name)

        # Market depth
        depth = await ticker.depth()
        print(f"Buy: {depth.buy_price} | Sell: {depth.sell_price}")

        # Fundamentals (quote + info + PE combined)
        fundamentals = await ticker.fundamentals()

asyncio.run(main())

Multiple Tickers

async with BDStockClient() as client:
    tickers = client.tickers(["GP", "BEXIMCO", "ACI"])

    for symbol, ticker in tickers.items():
        quote = await ticker.quote()
        print(f"{symbol}: {quote.ltp}")

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Configuration

Both ClientConfig and CacheConfig use pydantic-settings and can be configured via constructor arguments or environment variables (prefixed with BDFINANCE_ / BDFINANCE_CACHE_).

from bdfinance import BDStockClient, ClientConfig, CacheConfig

config = ClientConfig(
    timeout=60.0,
    max_retries=5,
    rate_limit=20,
    enable_cache=True,
)

cache_config = CacheConfig(
    backend="memory",   # or "redis"
    ttl=600,            # seconds
)

async with BDStockClient(config=config, cache_config=cache_config) as client:
    ...

Env Variable	Default	Description
BDFINANCE_TIMEOUT	30.0	Request timeout (seconds)
BDFINANCE_MAX_RETRIES	3	Max retry attempts
BDFINANCE_RATE_LIMIT	10	Max requests per second
BDFINANCE_ENABLE_CACHE	true	Enable response caching
BDFINANCE_CACHE_BACKEND	memory	memory or redis
BDFINANCE_CACHE_TTL	300	Cache TTL (seconds)
BDFINANCE_CACHE_REDIS_URL	redis://localhost:6379/0	Redis URL

See docs/configuration.md for the full reference.

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Documentation

Document	Description
Client & Ticker	BDStockClient and Ticker API reference
Configuration	ClientConfig and CacheConfig details
Models	Pydantic data models (trading, market, news, company)
Repositories	Low-level TradingRepository, MarketRepository, NewsRepository
Caching	Cache backends, key generation, TTL management
Utilities	Data cleaners, date helpers, HTML parsers

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Usage Examples

Historical Data

# By date range
df = await ticker.history(start="2024-01-01", end="2024-12-31")

# By period shorthand
df = await ticker.history(period="6mo")   # 6 months
df = await ticker.history(period="1y")    # 1 year
df = await ticker.history(period="30d")   # 30 days

Market Data (via repositories)

async with BDStockClient() as client:
    # Market overview
    overview = await client.market.get_market_overview(period="7d")

    # Latest P/E ratios
    pe = await client.market.get_latest_pe()

    # Top stocks / gainers / losers
    top = await client.market.get_top_stocks()
    gainers = await client.market.get_top_10_gainers()
    losers = await client.market.get_top_10_losers()

    # Sector listing
    sectors = await client.market.get_sector_listed()

    # Treasury bond yields
    bonds = await client.market.get_tbond_yields(tenor="10Y")

News

async with BDStockClient() as client:
    ticker = client.ticker("GP")

    # Company-specific news
    news = await ticker.news()

    # AGM news for this company
    agm = await ticker.agm_news()

    # All DSE news (via repository)
    all_news = await client.news.get_all_news()

Cache Management

async with BDStockClient() as client:
    # Clear all cached data
    await client.clear_cache()

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Project Structure

bdfinance/
├── __init__.py          # Public API exports
├── client.py            # BDStockClient entry point
├── ticker.py            # Unified Ticker interface
├── config.py            # ClientConfig / CacheConfig
├── constants.py         # DSE URLs and endpoints
├── http_client.py       # Async HTTP client
├── cache.py             # Cache backends (memory / redis)
├── parsers.py           # HTML parsing utilities
├── models/
│   ├── trading.py       # CurrentTradeData, BasicHistoricalData, ...
│   ├── market.py        # MarketInfo, MarketDepth, CompanyInfo, TBondInfo
│   ├── news.py          # AGMNews, News
│   └── company.py       # DSECompanyData and sub-models
├── repositories/
│   ├── base.py          # BaseRepository with get_data / get_dataframe
│   ├── trading.py       # TradingRepository
│   ├── market.py        # MarketRepository
│   └── news.py          # NewsRepository
└── utils/
    ├── common.py         # Dict helpers
    ├── data_cleaners.py  # clean_float, clean_int, clean_symbol, ...
    ├── date_helper.py    # Period parsing, date conversion
    └── parse_com_info.py # DSE company page parser

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License

MIT