squarequant 0.1.1

A Python package for financial risk metrics and stock data analysis

SquareQuant

SquareQuant is a comprehensive Python package for financial risk metrics and stock data analysis. It provides a suite of tools for quantitative finance, including risk metrics calculation, data retrieval, and visualization.

Features

• Financial Data Access: Easy-to-use interface for downloading historical price data
• Extensive Risk Metrics: Comprehensive set of traditional and advanced risk measures
• Performance Analysis: Tools for evaluating investment performance
• Visualization: Built-in plotting functions for visual analysis
• Flexible API: Consistent function-based and object-oriented interfaces

Installation

pip install squarequant

Quick Start

import squarequant as sq
import matplotlib.pyplot as plt

# Configure data download
config = sq.DownloadConfig(
    start_date="2020-01-01",
    end_date="2023-12-31",
    interval='1d',
    columns=['Close']
)

# Download data
data = sq.download_tickers(['AAPL', 'MSFT', 'GOOGL'], config)

# Calculate volatility
vol = sq.vol(data=data, assets=['AAPL', 'MSFT', 'GOOGL'], window=21)

# Calculate Sharpe ratio
sharpe = sq.sharpe(data=data, assets=['AAPL', 'MSFT', 'GOOGL'], window=252)

# Visualize results
plt.figure(figsize=(12, 6))
plt.plot(vol)
plt.title('21-Day Rolling Volatility')
plt.legend(vol.columns)
plt.show()

Available Risk Metrics

SquareQuant provides a comprehensive set of risk metrics, including:

Metric	Function	Description
Sharpe Ratio	sq.sharpe()	Risk-adjusted return using standard deviation
Sortino Ratio	sq.sortino()	Risk-adjusted return using downside deviation
Volatility	sq.vol()	Standard deviation of returns
Maximum Drawdown	sq.mdd()	Largest peak-to-trough decline
Value at Risk	sq.var()	Maximum expected loss at given confidence level
Conditional Value at Risk	sq.cvar()	Expected loss beyond the VaR threshold
Semi-Deviation	sq.semidev()	Downside volatility below target return
Average Drawdown	sq.avgdd()	Mean of all drawdowns in a period
Ulcer Index	sq.ulcer()	Square root of mean squared drawdown percentage
Mean Absolute Deviation	sq.mad()	Average absolute deviation from mean
Entropic Risk Measure	sq.erm()	Risk measure based on information theory
Entropic Value at Risk	sq.evar()	VAR using entropy concepts
Conditional Drawdown at Risk	sq.cdar()	Expected drawdown beyond threshold
Entropic Drawdown at Risk	sq.edar()	Drawdown risk using entropy concepts

Advanced Usage Examples

Analyzing Multiple Risk Metrics

import squarequant as sq
import matplotlib.pyplot as plt

# Download data
config = sq.DownloadConfig(
    start_date="2018-01-01",
    end_date="2023-12-31",
    interval='1d'
)
data = sq.download_tickers(['SPY', 'QQQ'], config)

# Calculate multiple risk metrics
vol = sq.vol(data=data, assets=['SPY', 'QQQ'], window=21)
mad = sq.mad(data=data, assets=['SPY', 'QQQ'], window=252)
mdd = sq.mdd(data=data, assets=['SPY', 'QQQ'], window=252)
ulcer = sq.ulcer(data=data, assets=['SPY', 'QQQ'], window=252)

# Visualize comparison
sq.plot_risk_comparison(
    metrics=[vol, mdd, ulcer],
    labels=['Volatility', 'Max Drawdown', 'Ulcer Index'],
    asset='SPY'
)

Portfolio Risk Analysis

import squarequant as sq
import pandas as pd

# Example portfolio weights
weights = pd.Series({'AAPL': 0.3, 'MSFT': 0.3, 'AMZN': 0.2, 'GOOGL': 0.2})

# Download data
config = sq.DownloadConfig(start_date="2020-01-01", end_date="2023-12-31")
data = sq.download_tickers(weights.index.tolist(), config)

# Calculate portfolio risk measures
portfolio_var = sq.var(
    data=data,
    assets=weights.index.tolist(),
    confidence=0.95,
    window=252,
    holding_period=10
)

# Visualize weight allocation
sq.plot_weight_allocation(weights)

Using Advanced Risk Measures

import squarequant as sq

# Download data
config = sq.DownloadConfig(start_date="2018-01-01", end_date="2023-12-31")
data = sq.download_tickers(['SPY'], config)

# Calculate Entropic Risk Measure with different risk aversion parameters
erm1 = sq.erm(data=data, assets=['SPY'], z=0.5, confidence=0.95)
erm2 = sq.erm(data=data, assets=['SPY'], z=1.0, confidence=0.95)
erm3 = sq.erm(data=data, assets=['SPY'], z=2.0, confidence=0.95)

# Compare with traditional VaR and CVaR
var = sq.var(data=data, assets=['SPY'], confidence=0.95)
cvar = sq.cvar(data=data, assets=['SPY'], confidence=0.95)

print(f"Latest ERM (z=0.5): {erm1.iloc[-1, 0]:.4f}")
print(f"Latest ERM (z=1.0): {erm2.iloc[-1, 0]:.4f}")
print(f"Latest ERM (z=2.0): {erm3.iloc[-1, 0]:.4f}")
print(f"Latest VaR (95%): {var.iloc[-1, 0]:.4f}")
print(f"Latest CVaR (95%): {cvar.iloc[-1, 0]:.4f}")

Documentation

For detailed documentation on each function and class, please see the official documentation.

License

MIT License

Credits

SquareQuant was developed to provide financial analysts, quantitative researchers, and portfolio managers with a comprehensive toolkit for risk assessment and performance analysis.