SquareQuant

Professional-grade financial risk metrics and portfolio analysis

What is SquareQuant?

SquareQuant is a comprehensive Python library for quantitative finance that provides institutional-grade risk metrics, performance analysis, and visualization tools. Designed for both industry professionals and quantitative researchers, it offers a powerful yet intuitive interface for portfolio analysis and risk management.

Key Features

📊 Extensive Risk Metrics

Standard Metrics: Sharpe ratio, Sortino ratio, volatility, maximum drawdown
Advanced Risk Measures: historical and parametric Value at Risk (VaR), Conditional Value at Risk (CVaR), Entropic Risk Measure (ERM), Conditional Drawdown at Risk (CDaR)
Drawdown Analysis: Maximum drawdown, average drawdown, Ulcer Index
Advanced Statistics: Semi-deviation, mean absolute deviation, and more

📈 Data Integration & Management

Seamless integration with financial market data sources
Efficient handling of time series with proper alignment and validation
Custom date range filtering and data transformation

🔍 Visualization Tools

Portfolio weight allocation charts
Risk contribution analysis
Returns distribution visualization
Correlation heatmaps
Drawdown comparison tools
Rolling metric dashboards

⚡ Performance Optimized

Memory-efficient vectorized calculations
Optimized for large datasets and extended time series
Batch processing capabilities for resource-intensive operations

Quick Example

import pandas as pd
import squarequant as sq

# Download data
config = sq.DownloadConfig(start_date='2020-01-01', end_date='2023-01-01')
data = sq.download_tickers(['AAPL', 'MSFT', 'GOOGL', 'AMZN'], config)

# Calculate risk metrics
assets = ['AAPL', 'MSFT', 'GOOGL', 'AMZN']
sharpe_ratio = sq.sharpe(data, assets)
volatility = sq.vol(data, assets)
max_drawdown = sq.mdd(data, assets)

# Visualize risk comparison
sq.plot_risk_comparison(data, assets, risk_metrics=['vol', 'mdd', 'var', 'semidev'])

Who Should Use SquareQuant?

Portfolio Managers: Monitor and analyze portfolio risk across multiple dimensions
Quant Researchers: Implement and validate complex risk models
Risk Analysts: Generate comprehensive risk reports with detailed visualizations
Financial Advisors: Provide clients with advanced risk insights and portfolio analysis
Students & Academics: Learn and apply quantitative finance concepts with production-grade tools

Getting Started

Installation

pip install squarequant

Basic Usage

import pandas as pd
import matplotlib.pyplot as plt
import squarequant as sq

# Set up configuration and download data
config = sq.DownloadConfig(start_date='2022-01-01', end_date='2023-01-01')
data = sq.download_tickers(['AAPL', 'MSFT', 'GOOGL'], config)

# Calculate risk metrics
assets = ['AAPL', 'MSFT', 'GOOGL']
volatility = sq.vol(data, assets)

# Generate visualization
fig, ax = plt.subplots(figsize=(10, 6))
sq.plot_rolling_metrics(data, assets, metrics=['vol', 'mdd'])
plt.show()

Roadmap

SquareQuant is actively being developed with the following modules planned for upcoming releases:

Release	Expected Date	Features
v0.2.0	Q2 2025	Monte Carlo Simulation Module • Scenario generation • Risk factor simulation • Portfolio stress testing
v0.3.0	Q3 2025	Portfolio Optimization Tools • Mean-variance optimization • Risk parity allocation • Factor-based optimization
v0.4.0	Q4 2025	Pricing Module • Options pricing • Fixed income valuation • Derivatives modeling

Stay tuned for these exciting additions! We welcome community feedback on prioritizing these features.