Trademan

A Python library and CLI tool for gathering market data and generating optimal portfolios using modern portfolio theory.

Features

• 📊 Market Data Collection: Automated downloading and caching of S&P 500 and ETF data via yfinance
• 🎯 Portfolio Optimization: Multiple optimization strategies (Sharpe ratio, minimum volatility, etc.)
• 📈 Visualization: Generate beautiful portfolio allocation charts
• 🚀 CLI Interface: Easy-to-use command-line tools for quick analysis
• 🐍 Python API: Full programmatic access for custom workflows
• 💾 Smart Caching: Intelligent data caching to minimize API calls

Installation

Install from PyPI:

pip install trademan

Install from source:

pip install git+https://github.com/ottermatics/trademan.git

Quick Start

CLI Usage

1. Download Market Data:

market_dl  # Downloads S&P 500 and ETF data

2. Generate a Portfolio:

# Create a Sharpe-optimized portfolio with $10,000 allocation
trademan -cls stocks -alloc 10000 -opt sharpe

# Minimum volatility ETF portfolio  
trademan -cls etfs -opt min_volatility -alloc 100000 -in QQQ,SPY,VTI

Python API

import trademan

# Get stock data for specific tickers
data = trademan.get_tickers(['AAPL', 'MSFT', 'GOOGL'])

# Create optimized portfolio
weights = trademan.make_portfolio(
    data, 
    opt='sharpe',           # Optimization method
    risk='ledoit_wolf',     # Risk model  
    allocate_amount=10000   # Dollar amount
)

# Visualize the portfolio
fig, ax = trademan.plot_portfolio(weights)

CLI Options

Configuration

Set environment variables to customize data storage:

export TRADEMAN_DATA_DIR="/path/to/data"      # Market data cache
export TRADEMAN_MEDIA_DIR="/path/to/charts"   # Generated charts

Examples

1. Best Performing S&P 500 Stocks

Create a portfolio favoring established companies with cycle penalties:

trademan -cls stocks -gamma 1 -alloc 10000 -cycl-err 10 -max-wght 0.2

2. Low Volatility ETF Portfolio

Generate a conservative ETF allocation:

trademan -cls etfs -gamma 0.1 -alloc 100000 \
  -in QQQ,SCHG,VGT,SLV,VIG,SPY,VOO,VUG,IAU,PAVE \
  -opt min_volatility

How It Works

1. Data Collection: Downloads historical price data using yfinance
2. Risk Modeling: Calculates covariance matrices with Ledoit-Wolf shrinkage
3. Return Estimation: Uses mean historical returns with optional adjustments
4. Optimization: Applies modern portfolio theory via PyPortfolioOpt
5. Allocation: Converts weights to discrete share quantities
6. Visualization: Creates publication-ready portfolio charts

Dependencies

• PyPortfolioOpt: Portfolio optimization algorithms
• yfinance: Market data source
• pandas/numpy: Data manipulation
• matplotlib: Visualization
• diskcache: Data caching
• scikit-learn: Additional analytics

Development

Install development dependencies:

pip install -e .[dev]

Run tests:

pytest

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please read CONTRIBUTING.rst for guidelines.