mc-postgres-db 1.3.1

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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

Testing Dependencies

For testing, you'll also need Docker installed and running:

# Install Docker (if not already installed)
# On macOS with Homebrew:
brew install --cask docker

# On Ubuntu/Debian:
sudo apt-get update
sudo apt-get install docker.io

# Start Docker daemon
# On macOS: Start Docker Desktop
# On Linux: sudo systemctl start docker

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

Basic Queries

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}")

Efficient Relationship Loading with joinedload

The ORM models are optimized for efficient querying using SQLAlchemy's joinedload functionality. All relationships are unidirectional (singular) to avoid N+1 query problems and enable efficient eager loading:

from sqlalchemy.orm import Session, joinedload
from mc_postgres_db.models import (
    Asset, Provider, ProviderAssetMarket, ProviderAssetOrder,
    ProviderAssetGroup, ProviderAssetGroupMember, ProviderAssetGroupAttribute
)

# Load Asset with its AssetType (no additional queries)
with Session(engine) as session:
    asset = session.query(Asset).options(
        joinedload(Asset.asset_type)
    ).filter_by(id=1).first()
    
    print(f"Asset: {asset.name} ({asset.asset_type.name})")
    # No additional query needed - asset_type is already loaded

# Load ProviderAssetOrder with all related objects
with Session(engine) as session:
    order = session.query(ProviderAssetOrder).options(
        joinedload(ProviderAssetOrder.provider),
        joinedload(ProviderAssetOrder.from_asset),
        joinedload(ProviderAssetOrder.to_asset)
    ).filter_by(id=1).first()
    
    print(f"Order: {order.from_asset.name} -> {order.to_asset.name}")
    print(f"Provider: {order.provider.name}")
    # All relationships loaded in a single query

# Load ProviderAssetGroup with members and attributes
with Session(engine) as session:
    group = session.query(ProviderAssetGroup).options(
        joinedload(ProviderAssetGroup.asset_group_type),
        joinedload(ProviderAssetGroup.members)
    ).filter_by(id=1).first()
    
    print(f"Group: {group.name}")
    print(f"Type: {group.asset_group_type.name}")
    print(f"Members ({len(group.members)}):")
    for member in group.members:
        print(f"  - {member.from_asset.name} -> {member.to_asset.name} (order: {member.order})")

# Load ProviderAssetGroupAttribute with related group
with Session(engine) as session:
    attribute = session.query(ProviderAssetGroupAttribute).options(
        joinedload(ProviderAssetGroupAttribute.provider_asset_group)
    ).filter_by(
        provider_asset_group_id=1,
        lookback_window_seconds=86400
    ).first()
    
    print(f"Group: {attribute.provider_asset_group.name}")
    print(f"Cointegration p-value: {attribute.cointegration_pvalue}")
    print(f"OU Process - mu: {attribute.ou_mu}, theta: {attribute.ou_theta}, sigma: {attribute.ou_sigma}")

Asset Group Management

# Create a new asset group for pairs trading
with Session(engine) as session:
    # Create asset group type
    asset_group_type = AssetGroupType(
        name="Pairs Trading",
        description="Groups for pairs trading strategies",
        is_active=True
    )
    session.add(asset_group_type)
    session.flush()
    
    # Create the asset group
    asset_group = ProviderAssetGroup(
        provider_id=1,
        asset_group_type_id=asset_group_type.id,
        name="BTC-ETH Pairs",
        description="Bitcoin and Ethereum pairs for mean reversion",
        is_active=True
    )
    session.add(asset_group)
    session.flush()
    
    # Add members to the group
    member1 = ProviderAssetGroupMember(
        provider_id=1,
        provider_asset_group_id=asset_group.id,
        from_asset_id=1,  # Bitcoin
        to_asset_id=2,    # USD
        order=1
    )
    member2 = ProviderAssetGroupMember(
        provider_id=1,
        provider_asset_group_id=asset_group.id,
        from_asset_id=3,  # Ethereum
        to_asset_id=2,    # USD
        order=2
    )
    session.add_all([member1, member2])
    session.commit()
    
    print(f"Created asset group: {asset_group.name}")
    print(f"Members: {len(asset_group.members)}")

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
• AssetGroupType: Categorizes asset group types (e.g., "Pairs Trading", "Mean Reversion") for organizing statistical groups
• 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. The 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)
• Optimized Relationships: Unidirectional relationships designed for efficient joinedload operations, eliminating N+1 query problems
• Asset Group Management: Comprehensive support for pairs trading and statistical analysis with ordered member sequences

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 PostgreSQL database running in Docker. This is especially useful for testing Prefect flows and tasks that interact with the database, without requiring a live PostgreSQL instance or extensive mocking.

Prerequisites

The testing harness requires Docker to be installed and running on your system:

# Check if Docker is installed and running
docker --version
docker ps

If Docker is not available, you may need to install Docker Desktop or start the Docker daemon.

postgres_test_harness

The postgres_test_harness context manager (found in mc_postgres_db.testing.utilities) creates a temporary PostgreSQL database using Docker, initializes all ORM models, and updates the Prefect secret that stores the database URL. This ensures that all Prefect tasks which retrieve the database engine (both sync and async) will use this temporary PostgreSQL database during your tests, without requiring any patching or changes to task code.

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 PostgreSQL database.
• The database is created fresh for each test session or function (depending on fixture scope), ensuring isolation and repeatability.
• Uses ephemeral storage (no volume mounting) for complete isolation between test runs.
• Comprehensive safety checks to prevent accidental connection to production databases.
• At the end of the test, the Docker container and all tables are cleaned up.

Safety Features:

• Validates that the database connection is to localhost only
• Ensures the database uses test credentials (testuser/testpass/testdb)
• Checks database size to prevent accidental connection to production databases
• Verifies Docker container environment variables match expected test configuration

Configuration:

• PostgreSQL version can be controlled via the POSTGRES_VERSION environment variable (defaults to latest)
• Test database uses ephemeral storage for complete isolation
• Automatic port selection to avoid conflicts

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 PostgreSQL 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 PostgreSQL database, and you don't need to modify your flows or tasks to support testing.

Additional Testing Utilities

The package also provides a clear_database function for manually clearing test databases:

from mc_postgres_db.testing.utilities import clear_database
from sqlalchemy import create_engine

# Clear a test database (with safety checks)
engine = create_engine("postgresql://testuser:testpass@localhost:5432/testdb")
clear_database(engine)  # Drops and recreates all tables

Note: The clear_database function includes comprehensive safety checks to prevent accidental clearing of production databases.

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.