portfolio-lib documentation
Welcome to portfolio-lib’s documentation!
portfolio-lib is a comprehensive Python library for quantitative finance and portfolio management. It provides 129 advanced technical indicators, portfolio analytics, risk metrics, and trading tools for both individual investors and institutional users.
Getting Started:
User Guide:
API Reference:
What is portfolio-lib?
portfolio-lib is designed for:
Technical Analysis: 129 technical indicators with mathematical formulas
Portfolio Management: Advanced risk analytics and position sizing
Risk Assessment: VaR, CVaR, drawdown analysis, and more
Performance Attribution: Sector and factor-based analysis
Trading Strategies: Backtesting and strategy development tools
Key Features
- ✨ Technical Indicators
Complete set of indicators including SMA, EMA, RSI, MACD, Bollinger Bands, Stochastic, Williams %R, ATR, ADX, CCI, OBV, MFI, Ichimoku, Parabolic SAR, and more.
- 📊 Portfolio Analytics
Advanced portfolio analytics including risk metrics, performance attribution, position sizing algorithms, and comprehensive risk management tools.
- 🎯 Risk Management
Value at Risk (VaR), Conditional VaR, maximum drawdown, Sharpe ratio, alpha, beta, tracking error, and many other risk measures.
- 📈 Visualization
Built-in plotting capabilities with matplotlib integration for creating professional charts and analysis reports.
- 🔧 Easy to Use
Clean, intuitive API design with comprehensive documentation and examples.
Quick Start Example
import numpy as np
from portfolio_lib.indicators import TechnicalIndicators
from portfolio_lib.portfolio import RiskMetrics
# Sample price data
prices = np.array([100, 102, 101, 103, 105, 104, 106, 108, 107, 109])
# Calculate technical indicators
sma = TechnicalIndicators.sma(prices, 5)
rsi = TechnicalIndicators.rsi(prices, 14)
# Calculate risk metrics
returns = np.diff(prices) / prices[:-1]
metrics = RiskMetrics(returns)
print(f"Latest SMA: {sma[-1]:.2f}")
print(f"Latest RSI: {rsi[-1]:.1f}")
print(f"Portfolio Volatility: {metrics.var_95(returns):.2%}")
Installation
Install portfolio-lib using pip:
pip install portfolio-lib
For development installation:
git clone https://github.com/NeuralNinja110/Portfolio-lib.git
cd portfolio-lib
pip install -e .
Support
Documentation: Complete guides and API reference
Examples: 30+ practical examples and use cases
Community: GitHub discussions and issue tracking
Professional Support: Available for enterprise users
License
portfolio-lib is released under the MIT License. See the LICENSE file for details.