financeindia 0.1.0a3

A high-performance, lightweight Python library written in Rust for fetching Indian financial market data (NSE).

financeindia

financeindia is a high-performance, lightweight Python library written in Rust for fetching Indian financial market data (NSE) with ease.

Why financeindia?

• Blazing Fast: Powered by Rust, providing efficient data parsing and networking.
• Lightweight: Minimal dependencies, focus on performance and reliability.
• Session Caching: Intelligent session management to avoid redundant requests and minimize rate-limiting risk.
• Comprehensive: Access to equity lists, historical data, bhavcopies, indices, and real-time quotes.

Installation

pip install financeindia

Quickstart

import financeindia

# Initialize the client
client = financeindia.FinanceClient()

# Recommended: initialize the session once
client._initialize_session()

# Get market status (returns a dict)
status = client.get_market_status()
print(status['marketState'][0]['marketStatus'])

# Fetch historical data for a stock (returns a list of dicts)
data = client.price_volume_data("RELIANCE", "01-03-2026", "05-03-2026")
print(data[0])

# Fetch current quote (returns a dict)
quote = client.get_equity_quote("RELIANCE")
print(quote['priceInfo']['lastPrice'])

# Fetch Corporate Financial Results
# Returns a list of dicts with filing metadata
results = client.get_financial_results("RELIANCE", "01-01-2025", "07-03-2026", "Annual")

# Parse the XBRL detail for a specific filing
if results:
    xbrl_url = results[0]['xbrl']
    financial_details = client.get_financial_details(xbrl_url)
    print(financial_details) # Highly granular financial data points

Supported Endpoints (32+)

financeindia provides exhaustive coverage of NSE data. All methods return structured Python objects (lists/dicts).

📊 Market Macro

• get_market_status(): Current status of all market segments.
• get_holidays(): Trading holiday calendar.
• get_fii_dii_activity(): Daily FII and DII trading activity.
• get_market_turnover(): Market-wide turnover statistics.
• get_fii_stats(date): Detailed FII statistics (returns raw XLS bytes).

📈 Equities

• get_equity_list(): All active equities listed on NSE.
• price_volume_data(symbol, from, to): Historical price and volume.
• deliverable_position_data(symbol, from, to): Delivery percentage stats.
• bhav_copy_equities(date): Daily equity bhavcopy.
• bulk_deal_data(from, to): Tracking large institutional bulk deals.
• block_deals_data(from, to): Tracking block deal transactions.
• short_selling_data(from, to): Daily short selling activity.
• get_52week_high_low(type): Stocks hitting new highs or lows.
• get_top_gainers() / get_top_losers(): Intraday performance leaders.
• get_most_active(index): Most active securities by volume/value.
• get_advances_declines(): Market breadth analysis.
• get_monthly_settlement_stats(year): Monthly settlement data.
• get_equity_quote(symbol): Real-time price and order book.

📉 Indices

• get_all_indices(): Real-time snapshot of all NSE indices.
• get_index_constituents(index): Stocks within a specific index (e.g., 'NIFTY 50').
• get_index_history(index, from, to): Historical index levels.
• get_index_yield(index, from, to): P/E, P/B, and Dividend Yield of indices.
• get_india_vix_history(from, to): Historical volatility index data.
• get_total_returns_index(index, from, to): Total Returns Index (TRI) data.

⛓️ Derivatives & SLB

• get_option_chain(symbol, is_index): Full real-time option chain with Greeks.
• bhav_copy_derivatives(date, segment): Daily F&O/Currency/Commodity bhavcopy.
• get_fo_sec_ban(): Securities currently under F&O ban period.
• get_span_margins(date): Daily SPAN margin files for risk analysis.
• get_slb_bhavcopy(date): Securities Lending & Borrowing market data.

🏢 Corporate Actions

• get_corporate_actions(): Latest dividends, bonuses, splits, etc.
• get_financial_results(symbol, from, to, period): Metadata for financial filings.
• get_financial_details(xbrl_url): Deep-dive into XBRL filings (500+ data points).

Performance Optimizations

financeindia is built for high-scale quant pipelines:

• Zero-Serialization CSV: CSV data is parsed directly into Python objects in Rust, bypassing heavy string allocations.
• Concurrent-Safe: Uses RwLock and optimized connection pooling for multi-threaded usage.
• Blazing Fast: Up to 3x faster than traditional JSON-based wrappers.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.