squarequant 0.1.0

A Python package for quantitative finance

SquareQuant

A professional-grade Python package for financial risk analysis, advanced portfolio metrics, and investment visualization with a focus on performance and usability.

Features

Data Management

• Seamless download and integration with Yahoo Finance data
• Flexible data filtering and preprocessing utilities
• Configurable date ranges and interval selection

Risk Metrics

• Standard Metrics:
  • Sharpe & Sortino Ratios
  • Volatility & Semi-Deviation
  • Maximum Drawdown (MDD) & Average Drawdown
  • Value at Risk (VaR) & Conditional Value at Risk (CVaR)
• Advanced Metrics:
  • Mean Absolute Deviation (MAD)
  • Ulcer Index
  • Entropic Risk Measure (ERM)
  • Entropic Value at Risk (EVaR)
  • Conditional Drawdown at Risk (CDaR)
  • Entropic Drawdown at Risk (EDaR)

Visualization

• Interactive risk metrics dashboards
• Correlation matrix heatmaps
• Drawdown analysis charts
• Return distribution visualization
• Risk contribution analysis
• Portfolio weight allocation diagrams

Implementation Advantages

• Memory-efficient vectorized calculations
• Dual API approaches: functional & object-oriented
• Configurable window sizes for all metrics
• Minimal external dependencies

Installation

pip install squarequant

Quick Start Guide

import squarequant as sq
import matplotlib.pyplot as plt

# Download stock data
tickers = ["AAPL", "MSFT", "GOOGL"]
config = sq.DownloadConfig(
    start_date="2020-01-01",
    end_date="2023-01-01",
    interval="1d"
)
data = sq.download_tickers(tickers, config)

# Calculate risk metrics
assets = ["AAPL_Close", "MSFT_Close", "GOOGL_Close"]

# Sharpe ratio with a 1-year rolling window
sharpe_ratios = sq.sharpe(data, assets, window=252)

# Plot the results
plt.figure(figsize=(12, 8))
sharpe_ratios.plot()
plt.title("Rolling Sharpe Ratios")
plt.show()

Advanced Usage Examples

Multiple Risk Metrics Visualization

# Create a visualization of multiple risk metrics
sq.plot_rolling_metrics(
    data=data,
    assets=assets,
    metrics=['sharpe', 'vol', 'mdd', 'var'],
    windows={'vol': 20, 'mdd': 252}
)
plt.show()

Risk Comparison Dashboard

# Create a risk comparison dashboard
sq.plot_risk_comparison(
    data=data,
    assets=assets,
    risk_metrics=['vol', 'mdd', 'var', 'cvar', 'semidev', 'ulcer'],
)
plt.show()

Performance Metrics Table

# Generate a performance metrics table
sq.plot_performance_metrics(
    data=data,
    assets=assets,
    risk_free=0.02,  # 2% risk-free rate
)
plt.show()

Object-Oriented Interface

SquareQuant provides an object-oriented interface for more advanced use cases:

from squarequant.core.metrics import EntropicValueAtRisk

# Create an EVaR calculator
calculator = EntropicValueAtRisk(
    data=data,
    assets=assets,
    alpha=0.95,  # 95% confidence level
    window=252,
    start="2021-01-01",
    end="2022-01-01"
)

# Calculate EVaR
evar_values = calculator.calculate()

Configuration Options

Custom Risk-Free Rate

# Using a constant value
sharpe_ratios = sq.sharpe(data, assets, freerate_value=0.02)

# Using a column in your data
sharpe_ratios = sq.sharpe(data, assets, freerate="TREASURY_YIELD")

Custom Confidence Levels

# Calculate VaR with 99% confidence
var_values = sq.var(data, assets, confidence=0.99)

Documentation

For full documentation, visit https://squarequant.readthedocs.io

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

Author

Gabriel Bosch