mc-postgres-db 1.0.10

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MC Postgres DB

A Python package containing ORM models for a PostgreSQL database that powers a personal quantitative trading and investment analysis platform.

Overview

This package provides SQLAlchemy ORM models and database utilities for managing financial data, trading strategies, portfolio analytics, and market research. The database serves as the backbone for a personal "quant hedge fund" project, storing everything from market data and content data.

Features

• Asset Models: AssetType and Asset tables for categorizing and managing financial instruments and various fiat and digital currencies
• Provider Models: ProviderType and Provider tables for handling data sources and exchanges
• Market Data Models: ProviderAssetMarket table for storing OHLCV and bid/ask price data
• Order Models: ProviderAssetOrder table for tracking trading orders between assets
• Content Models: ContentType, ProviderContent, and AssetContent tables for managing news articles and social content
• Relation Models: ProviderAsset table for mapping relationships between providers and assets

Installation

From PyPI

pip install mc-postgres-db

From Source

# Clone the repository
git clone <repository-url>
cd mc-postgres-db

# Install using uv (recommended)
uv sync

Database Setup

1. PostgreSQL Setup: Ensure PostgreSQL is installed and running
2. Environment Variables: Set up your database connection string

   export SQLALCHEMY_DATABASE_URL="postgresql://username:password@localhost:5432/mc_trading_db"
   

Usage

from sqlalchemy import create_engine, select
from sqlalchemy.orm import Session
from mcpdb.tables import Asset, Provider, ProviderAssetMarket

# Create database connection
url = "postgresql://username:password@localhost:5432/mc_trading_db"
engine = create_engine(url)

# Query assets
with Session(engine) as session:
    stmt = select(Asset).where(Asset.is_active)
    assets = session.scalars(stmt).all()
    asset_pairs = {asset.id: asset.name for asset in assets}
    print("Available assets:")
    for asset_id, asset_name in asset_pairs.items():
        print(f"{asset_id}: {asset_name}")

# Query market data
with Session(engine) as session:
    stmt = (
        select(ProviderAssetMarket)
        .where(
            ProviderAssetMarket.asset_id == 1,  # Bitcoin for example
            ProviderAssetMarket.provider_id == 2,  # Binance for example
        )
        .order_by(ProviderAssetMarket.timestamp.desc())
        .limit(10)
    )
    market_data = session.scalars(stmt).all()
    for data in market_data:
        print(f"Timestamp: {data.timestamp}, Close: {data.close}, Volume: {data.volume}")

# Get assets from a provider
with Session(engine) as session:
    stmt = select(Provider).where(Provider.id == 1)
    provider = session.scalars(stmt).one()
    provider_assets = provider.get_all_assets(engine)
    print(f"Assets available from {provider.name}:")
    for provider_asset in provider_assets:
        print(f"Asset code: {provider_asset.asset_code}")

Models Overview

Core Models

• AssetType: Categorizes assets (e.g., stocks, bonds, cryptocurrencies) with names and descriptions
• Asset: Represents financial instruments with references to asset types, symbols, and optional underlying assets
• ProviderType: Categorizes data providers (e.g., exchanges, news services) with names and descriptions
• Provider: Represents data sources with references to provider types and optional underlying providers
• ProviderAsset: Maps the relationship between providers and assets with asset codes and active status
• ProviderAssetOrder: Tracks orders for assets from providers including timestamp, price, and volume
• ProviderAssetMarket: Stores OHLCV (Open, High, Low, Close, Volume) market data and bid/ask prices
• ContentType: Categorizes content (e.g., news articles, social media posts) with names and descriptions
• ProviderContent: Stores content from providers with timestamps, titles, descriptions, and full content
• AssetContent: Maps the relationship between content and assets

Database Schema Features

• Inheritance Support: Assets and providers can reference underlying entities for hierarchical relationships
• Timestamped Records: All tables include creation and update timestamps
• Soft Delete Pattern: Uses is_active flags to mark records as inactive without deletion
• Time Series Data: Market data is organized by timestamp for efficient time-series operations
• Cross-Reference Tables: Enables many-to-many relationships between assets, providers, and content

Development

Setting up Development Environment

# Install development dependencies using uv
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check src/mcpdb/
uv run ruff format src/mcpdb/

Database Migrations

# Generate new migration
uv run alembic revision --autogenerate -m "Description of changes"

# Apply migrations
uv run alembic upgrade head

# Rollback migration
uv run alembic downgrade -1

Project Structure

mc-postgres-db/
├── src/                 # Source code directory
│   └── mcpdb/           # Package directory
│       └── tables.py    # SQLAlchemy ORM models
├── tests/               # Unit and integration tests
├── scripts/             # Database maintenance scripts
├── pyproject.toml       # Project configuration and dependencies
├── uv.lock             # Locked dependency versions
└── README.md           # Project documentation

Data Sources

This database integrates with various financial data providers:

• Market data APIs (Alpha Vantage, IEX Cloud, etc.)
• Fundamental data providers
• Alternative data sources
• Custom scraped data

Security & Compliance

• Database connections use SSL encryption
• Sensitive data is encrypted at rest
• Access controls and audit logging implemented
• Regular backups and disaster recovery procedures

Performance Considerations

• Optimized indexes for common query patterns
• Partitioned tables for large time-series data
• Connection pooling for high-throughput operations
• Caching layer for frequently accessed data

Contributing

This is a personal project, but suggestions and improvements are welcome:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Submit a pull request

License

This project is for personal use and learning purposes.

Disclaimer

This software is for educational and personal use only. It is not intended for production trading or investment advice. Use at your own risk.