foliotrack 0.0.10

Tool to keep balance a portfolio of securities while investing.

FolioTrack

FolioTrack is a robust, modular Python library for modern portfolio management. It helps you manage, optimize, rebalance, and backtest multi-currency investment portfolios with ease.

Designed primarily for DIY passive investors, FolioTrack automates the mathematical heavy lifting of maintaining a "lazy" portfolio. It ensures your asset allocation remains perfectly balanced with minimal effort, helping you stick to your long-term strategy without the spreadsheet headaches.

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🚀 Why FolioTrack?

• 🧠 Smart Optimization: Uses Mixed-Integer Quadratic Programming (MIQP) to calculate the best integer number of shares to buy/sell to reach your target allocation, respecting constraints like minimum order size or maximum position count.
• 🌍 Multi-Currency Native: Seamlessly handles portfolios with assets in different currencies (USD, EUR, GBP, etc.). Real-time exchange rates (ECB) ensure your valuations are always accurate.
• 🏗️ Clean Architecture: Built with Domain-Driven Design principles. Your core portfolio logic is decoupled from external data providers, making the system testable and extensible.
• 🔌 Pluggable Data: Comes with support for yfinance and ffn, but you can easily plug in your own market data provider.
• 📈 Built-in Backtesting: Validate your strategies against historical data before investing a cent.

✨ Features

• Portfolio Management

  • Track stocks, ETFs, and other securities.
  • JSON-based persistence for easy saving/loading.
  • Transaction history logging.

• Advanced Rebalancing

  • Set target weights (e.g., "60% Stocks, 40% Bonds").
  • Mathematical solver finds the optimal trades to minimize tracking error.
  • New: Cardinality constraints (limit number of positions).

• Data Sources

  • yfinance (Yahoo Finance) support out of the box.
  • ffn support for straightforward financial time series.
  • Extensible MarketService architecture.

🛠️ Installation

FolioTrack uses uv for fast, reliable dependency management.

# Clone the repository
git clone [e-mail]:PhDFlo/foliotrack.git
cd foliotrack

# Sync dependencies and create virtual env
uv sync

# Activate environment
source .venv/bin/activate

⚡ Quick Start

1. Command Line Interface (CLI)

You can run the included main.py entry point to see Foliotrack in action immediately:

# Create a portfolio from scratch, optimize it, and backtest it
uv run main.py --action scratch

# Use an existing portfolio JSON file
uv run main.py --action existing

# Use a different data provider (if installed)
uv run main.py --provider ffn

2. Python API

FolioTrack's new modular API is intuitive. Here is a classic "60/40" portfolio example:

from foliotrack.domain.Portfolio import Portfolio
from foliotrack.services.MarketService import MarketService
from foliotrack.services.OptimizationService import OptimizationService
from foliotrack.services.BacktestService import BacktestService
from foliotrack.storage.PortfolioRepository import PortfolioRepository

# 1. Setup Services
market_service = MarketService(provider="yfinance")
optimizer = OptimizationService()
repo = PortfolioRepository()

# 2. Create Portfolio
portfolio = Portfolio("Retirement Fund", currency="EUR")

# Buy classic ETFs (Stocks + Bonds)
portfolio.buy_security("IDDA.AS", volume=50.0) # iShares MSCI World (Stocks)
portfolio.buy_security("AGGH.AS", volume=50.0) # iShares Global Agg Bond (Bonds)

# 3. Enrich with Market Data
market_service.update_prices(portfolio)

# 4. Set Targets (60% Stocks, 40% Bonds) & Optimize
portfolio.set_target_share("IDDA.AS", 0.6)
portfolio.set_target_share("AGGH.AS", 0.4)

# Calculate optimal buys to invest an additional 5000 EUR
optimizer.solve_equilibrium(portfolio, investment_amount=5000.0)

# 5. Save Work
repo.save_to_json(portfolio, "my_portfolio.json")

🏛️ Architecture

FolioTrack follows a clean, layered architecture:

• domain/: Pure Python data entities (Portfolio, Security). No external dependencies or I/O here.
• services/: Business logic and external adapters.
  • MarketService: Fetches prices.
  • OptimizationService: Runs the solver.
  • BacktestService: Runs simulations.
• storage/: Handles file persistence (PortfolioRepository).

This structure ensures that your portfolio data remains safe and stable, regardless of how market APIs or file formats change over time.

🤝 Contributing

Contributions are welcome! Please run the test suite before submitting a PR:

uv run pytest

📄 License

Apache License 2.0. See LICENSE for details.