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This GitHub repo is full of ressources for data science and applied finance. It's packed with tools and insights on portfolio management, numerical finance methods, econometrics, and forecasting. Perfect for anyone keen to sharpen their finance skills with data science.

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Gimkhana/Empirical_Analysis_FM

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GitHub for empirical analysis of financial markets

About this GitHub repository

This repository full of ressources in financial analysis using different quantitative techniques.

What you'll find

  • Quantitative portfolio management: Strategies, backtesting frameworks, and optimization algorithms for crafting portfolios that stand the test of market volatility.
  • Numerical methods in finance: From Monte Carlo simulations to finite difference methods, explore computational techniques that power modern financial engineering.
  • Econometric analysis: Tools and notebooks dedicated to uncovering economic insights from data, employing everything from regression analysis to vector autoregressions (VAR).
  • Time series forecasting: Cutting-edge models for predicting financial markets, LSTM networks, MLP, Random Forest with detailed and well commented scripts.

Getting started

  1. Download the necessary Python/RStudio environment: Make sure to have all the necessary code environment set up in order to run the scripts properly.
  2. Clone the repository: Get your local copy.
  3. Explore the notebooks: Each notebook comes with detailed explanations and code comments.
  4. Join the community: Participate in discussions, share your findings, or contribute to ongoing projects.

Contribution

Got a project or an idea that can enrich our repository? We welcome contributions of all forms - new models, improvements to existing ones, or educational content.


About

This GitHub repo is full of ressources for data science and applied finance. It's packed with tools and insights on portfolio management, numerical finance methods, econometrics, and forecasting. Perfect for anyone keen to sharpen their finance skills with data science.

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