quantspace 0.1.1

Quantitative finance solutions in Python

quantspace is a Python library designed specifically for quantitative finance, with a primary focus on supporting research and education in the field.

• Risk Management
• Portfolio Optimization
• Anomaly Detection

Features

1. Quantitative Research Tools

quantspace offers a comprehensive set of tools for conducting quantitative research in finance. From statistical analysis to advanced modeling techniques, the library provides the building blocks for developing and testing sophisticated financial strategies.

2. Educational Resources

Designed with education in mind, quantspace includes resources and examples to help users understand and apply quantitative finance concepts. The library serves as a valuable resource for students, providing hands-on experience with real-world financial data and models.

3. Data Integration

quantspace seamlessly integrates with popular financial data sources, allowing users to access and analyze real-time and historical market data. This feature enhances the library's capabilities for both research and educational purposes.

4. Flexibility and Customization

The library is designed to be flexible and customizable, accommodating a wide range of financial modeling requirements. Whether you are developing algorithms for risk management, portfolio optimization, or derivative pricing, quantspace provides a robust foundation for implementation.

Getting Started

To get started with quantspace, follow these simple steps:

1. Installation:

   pip install quantspace
   

2. Getting Started:

• Risk measure:

  from quantspace.risk import summary_risk
  
  returns = [0.065, 0.0265, -0.0593, -0.001, 0.0346]
  summary_risk(returns=returns, risk_free_rate=0.05)
  

• Portfolio optimization:

  from quantspace.utils.datasets import random_portfolio
  from quantspace.portfolio import MarkowitzFrontier
  
  import numpy as np
  np.random.seed(1234)
  
  n_assets = 5 # number of assets in portfolio
  n_obs = 1000 # number of observations in data
  return_vec = np.random.randn(n_assets, n_obs) # generate random returns for each asset
  
  n_portfolios = 500 # number of portfolios to simulate
  means, stds = np.column_stack([
     random_portfolio(return_vec) 
     for _ in range(n_portfolios)
  ])
  
  # instantiate MarkowitzFrontier class
  m_frontier = MarkowitzFrontier(return_vec, stds, means)
  
  # plot efficient frontier
  m_frontier.plot_frontier()
  

  

Contributions

We welcome contributions from the community to enhance and expand quantspace. If you have ideas for new features, improvements, or find any issues, please feel free to contribute by submitting a pull request or opening an issue on our GitHub repository.

Support and Community

For support or to connect with other users and contributors, join our community forums. We encourage discussions, knowledge sharing, and collaboration within the quantspace community.

License

quantspace is released under the MIT License, making it open and accessible for a wide range of users.

Disclaimer

quantspace is provided "as is" without any warranty, express or implied. Use it at your own risk, and carefully review and test any code before deploying it in a production environment.

Happy quantifying with quantspace!