FactorLab - Professional Portfolio Analysis Tool

A comprehensive portfolio factor analysis and risk management tool with Interactive Brokers (IBKR) integration. Built for professional portfolio management with institutional-grade analytics.

Features

Core Capabilities

• Factor Analysis: Fama-French factors, momentum, value, quality, and custom factor definitions
• Portfolio Optimization: Mean-variance, risk parity, volatility targeting, and Black-Litterman
• Risk Management: Covariance estimation, risk decomposition, and hedging strategies
• IBKR Integration: Live data feeds, position management, and trade execution
• ETF Replication: Factor-based hedging using liquid ETFs

Web Dashboard

• Responsive web interface optimized for desktop and mobile
• Real-time portfolio analysis and optimization
• Interactive factor exposure visualization
• ETF-based hedging recommendations
• Multiple data sources: IBKR live data, custom tickers, manual input, demo data

Installation

1. Install dependencies:

pip install -r requirements.txt

2. Configure environment variables:

cp .env.example .env
# Edit .env with your settings

Usage

Web Dashboard

# Run the dashboard
python -m factorlab.ui.dashboard

# Access at http://localhost:8052

Python API

from factorlab import fetch_prices, optimize_portfolio, run_backtest

# Fetch data
prices = fetch_prices(['AAPL', 'MSFT', 'GOOGL'], '2020-01-01', '2023-01-01')

# Optimize portfolio
weights = optimize_portfolio(returns, cov_matrix, objective='max_sharpe')

# Run backtest
results = run_backtest(weights, prices, start_date, end_date)

Architecture

factorlab/
├── data/           # Data ingestion and processing
├── factors/        # Factor definitions and calculations
├── risk/           # Risk models and covariance estimation
├── optimize/       # Portfolio optimization algorithms
├── sizing/         # Position sizing methods
├── hedging/        # ETF replication and hedging
├── backtest/       # Backtesting engine
├── ui/             # Web dashboard
└── utils/          # Utilities and helpers

Key Modules

• factorlab.data.ingest - Fetch prices and factor data
• factorlab.factors.definitions - Calculate momentum, value, quality factors
• factorlab.optimize.solvers - Portfolio optimization algorithms
• factorlab.risk.cov - Covariance estimation and risk decomposition
• factorlab.hedging.etf_replication - ETF-based hedging strategies
• factorlab.backtest.engine - Strategy backtesting

IBKR Integration

FactorLab supports live integration with Interactive Brokers:

1. Install and run IBKR TWS or Gateway
2. Enable API connections in TWS
3. Configure connection settings in .env
4. Use live data mode in the dashboard

Requirements

• Python 3.8+
• Interactive Brokers TWS/Gateway (optional, for live data)
• Market data subscriptions (for comprehensive factor data)

License

MIT License - see LICENSE file for details.