mc-postgres-db 1.1.9

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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
• Sentiment Models: SentimentType and ProviderContentSentiment tables for analyzing content sentiment
• Asset Group Models: ProviderAssetGroup, ProviderAssetGroupMember, and ProviderAssetGroupAttribute tables for grouping provider assets and calculating aggregated statistical values
• 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 mc_postgres_db.models 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.from_asset_id == 1,  # Bitcoin for example
            ProviderAssetMarket.to_asset_id == 2,    # USD for example
            ProviderAssetMarket.provider_id == 3,    # 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 for asset pairs
• 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
• SentimentType: Categorizes sentiment analysis methods (e.g., PROVIDER, NLTK, VADER) with names and descriptions
• ProviderContentSentiment: Stores sentiment analysis results for content with positive, negative, neutral, and overall sentiment scores
• ProviderAssetGroup: Groups provider assets for calculating aggregated statistical values between members. Each group contains provider asset pairs that share statistical relationships for cointegration analysis, mean reversion modeling, and linear regression calculations.
• ProviderAssetGroupMember: Maps provider asset pairs to statistical groups for aggregated calculations. Each record represents a pair of assets (from_asset_id, to_asset_id) from a specific provider that belong to a statistical group. Optional order field allows sequencing within groups for hierarchical analysis.
• ProviderAssetGroupAttribute: Stores aggregated statistical calculations for provider asset groups across multiple time windows. Contains cointegration analysis results, Ornstein-Uhlenbeck process parameters for mean reversion modeling, and comprehensive linear regression statistics including coefficients, fit measures, and significance tests.

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
• Statistical Group Support: Specialized tables for grouping provider assets and calculating aggregated statistical measures across multiple time windows
• Composite Primary Keys: Ensures uniqueness across multiple dimensions (timestamp, provider, asset group, lookback window)

Development

Setting up Development Environment

# Install development dependencies using uv
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check
uv run ruff format

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
│   └── mc_postgres_db/        # Main package directory
│       ├── __init__.py
│       ├── models.py
│       ├── operations.py
│       ├── prefect/
│       │   ├── __init__.py
│       │   ├── tasks.py
│       │   └── asyncio/
│       │       ├── __init__.py
│       │       └── tasks.py
│       └── testing/
│           ├── __init__.py
│           └── utilities.py
├── tests/                    # Unit and integration tests
├── alembic/                  # Database migrations
├── 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

Testing Utilities

This package provides a robust testing harness for database-related tests, allowing you to run your tests against a temporary SQLite database that mirrors your PostgreSQL schema. This is especially useful for testing Prefect flows and tasks that interact with the database, without requiring a live PostgreSQL instance or extensive mocking.

postgres_test_harness

The postgres_test_harness context manager (found in mc_postgres_db.testing.utilities) creates a temporary SQLite database file, initializes all ORM models, and patches the Prefect tasks used to obtain the SQLAlchemy engine (both sync and async) so that all database operations in your flows and tasks are transparently redirected to this SQLite database.

Key benefits:

• No need to change or mock every Prefect flow or task that uses the database engine.
• All Prefect tasks that call get_engine (sync or async) will automatically use the temporary SQLite database.
• The database is created fresh for each test session or function (depending on fixture scope), ensuring isolation and repeatability.
• At the end of the test, the database and all tables are cleaned up.

Usage with Pytest

You can use the harness as a fixture in your tests. For example:

import pytest
from mc_postgres_db.testing.utilities import postgres_test_harness

@pytest.fixture(scope="function", autouse=True)
def postgres_harness():
    with postgres_test_harness():
        yield

def test_my_flow():
    # Any Prefect task that calls get_engine() will use the SQLite test DB
    ...

If you are also testing Prefect flows, the postgres harness will already use Prefect's harness to ensure isolation:

import pytest
from mc_postgres_db.testing.utilities import postgres_test_harness

@pytest.fixture(scope="session", autouse=True)
def postgres_harness():
    with postgres_test_harness(prefect_server_startup_timeout=45):
        yield

Now, all your tests (including those that run Prefect flows) will use the temporary SQLite database, and you don't need to modify your flows or tasks to support testing.

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.