SharedData
A comprehensive ultrafast Python library for financial data.
📖 Table of Contents
- 🏗️ Core Features
- ⚡ Quick Start
- 🔧 Configuration
- 🚀 Advanced Usage
- 🔄 Development & Documentation
- 📄 License
- 🤝 Contributing
🏗️ Core Features
SharedData provides a comprehensive set of features for high-performance financial data management:
- 🗃️ Database Schema & Indexing - Optimized schemas for financial data types
- 🌐 Storage & Integration - Multi-storage support (Local, S3, MongoDB, Redis)
- 📈 Performance & Scalability - Parallel processing and advanced querying
- 📊 Data Containers - Tables, Collections, Time Series, Streams, Cache, Metadata
- ⚡ Multiprocessing & Parallel Computing - Sophisticated parallel processing library
- 🤖 Distributed Worker System - Automated task execution and job scheduling
- 📋 Comprehensive Logging System - Enterprise-grade logging with multiple destinations
- 🌐 Remote API Client - REST API for remote data access and operations
📚 Read the complete Core Features guide →
⚡ Quick Start
Get up and running with SharedData in minutes:
Installation
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Install SharedData
pip install -r requirements.txt
pip install -e .
Basic Usage
import pandas as pd
from SharedData.SharedData import SharedData
# Initialize SharedData
shdata = SharedData(__file__, user='master')
# Quick example - Tables
dates = pd.date_range('2025-01-01', '2025-01-10', freq='D')
symbols = ['AAPL', 'GOOGL', 'MSFT']
idx = pd.MultiIndex.from_product([dates, symbols], names=['date', 'symbol'])
df = pd.DataFrame({'price': 100, 'volume': 1000}, index=idx)
# Write and read data
tbl = shdata.table('MarketData', 'D1', 'TEST', 'PRICES', value=df)
tbl.write()
data = tbl.loc['2025-01-05':, 'AAPL'] # Fast symbol lookup
print(f"Retrieved {data.shape[0]} rows")
📚 Read the complete Quick Start guide →
🔧 Configuration
Configure SharedData with environment variables for your specific needs:
# Required Variables
SHAREDDATA_SECRET_KEY="your-secret-key"
SHAREDDATA_TOKEN="your-auth-token"
AWS_ACCESS_KEY_ID="your-aws-access-key"
S3_BUCKET="s3://your-bucket-name"
MONGODB_HOST="your-mongodb-host"
SHAREDDATA_ENDPOINT="http://your-server:port"
📚 Read the complete Configuration guide →
🚀 Advanced Usage
For power users and complex scenarios:
- Advanced Data Operations - Complex queries and aggregations
- Performance Optimization - Memory management and parallel processing
- Distributed Computing - Worker pools and distributed data processing
- Custom Extensions - Custom data containers and worker types
- Production Deployment - High availability and monitoring
- Monitoring & Debugging - Performance profiling and health checks
📚 Read the complete Advanced Usage guide →
🧭 Data by Symbol
Fetch data by what it is instead of where it is stored. The server picks the source, the container and the partitions, and every answer says where its rows came from:
bars = shdata.dataframe('marketdata', 'AAPL', period='M15', start='2026-10-05')
bars.attrs['provenance']['dataset'] # which container answered
from SharedData.IO.ClientAPI import ClientAPI
surface = ClientAPI.get_vol('AAPL', product='tenor', tenor='1M')
Market data, signals, portfolios, risk factors and volatility are served this way over
GET /api/data/.... This is a convenience, not the fast path: producers and other
performance-critical code keep using table(), timeseries() and collection().
📚 Read the Data by Symbol guide →
🔄 Development & Documentation
For developers contributing to SharedData:
Building Documentation
# Generate documentation from docstrings
make gitea
# Alternative methods
python generate_docs.py
python update_docs.py
make all
Running Tests
python -m pytest tests/
📚 Read the complete Development guide →
📄 License
MIT: free to use, change and distribute, commercially too. See the LICENSE file.
🤝 Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Update documentation:
make gitea - Submit a pull request
📚 Read the complete Contributing guide →
📚 Documentation Index
- 🏗️ Core Features - Comprehensive overview of all SharedData capabilities
- ⚡ Quick Start - Get started with SharedData in minutes
- 🔧 Configuration - Complete configuration reference
- 🚀 Advanced Usage - Advanced patterns and optimization
- 🔄 Development - Contributing and development guidelines
1 maintainer
Metadata
1 maintainer
Release files for shareddata 6.85.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| shareddata-6.85.6.tar.gz | 738.7 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| shareddata-6.85.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.6 MB
Security for shareddata 6.85.6
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
