AliHabibnia / CMDA_4984_Data_Science_for_Quantitative_Finance
This course in applied data science covers the theoretical foundations of advanced quantitative approaches in machine learning, econometrics, risk and portfolio management, algorithmic trading, and financial forecasting. (first taught at Virginia Tech in 2019)
☆25Updated 5 months ago
Alternatives and similar repositories for CMDA_4984_Data_Science_for_Quantitative_Finance:
Users that are interested in CMDA_4984_Data_Science_for_Quantitative_Finance are comparing it to the libraries listed below
- This collects the scripts and notebooks required to reproduce my published work.☆44Updated last week
- Here I am collecting the scripts I have used to prepare my book "Adventures in Financial Data Science" and to support my other writing, s…☆62Updated 5 months ago
- Regime Based Asset Allocation with MPT, Random Forest and Bayesian Inference☆25Updated 2 years ago
- Resources for the AI in Finance Workshop at Texas State University (October 2023).☆48Updated last year
- Predictive yield curve modeling in reduced dimensionality☆43Updated last year
- Quant Research☆67Updated last month
- A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and…☆24Updated 3 years ago
- This course will introduce the student to classic machine learning algorithms and deep neural network structures. The style will be first…☆17Updated 10 months ago
- ☆50Updated last year
- Jupyter Notebooks and code for the book Financial Theory with Python (O'Reilly) by Yves Hilpisch.☆80Updated last year
- ☆57Updated last year
- Algo Trading Research & Documentation☆15Updated 7 months ago
- Implements different approaches to tactical and strategic asset allocation☆29Updated 3 weeks ago
- On this repository you'll find tools used for Quantitative Analysis and some examples such: MonteCarlo Simulations, Linear Regression, Ge…☆23Updated last year
- demonstration of quantstats library☆13Updated 3 years ago
- This contains notebooks and scripts used to support my writing in WILMOTT Magazine.☆16Updated 8 months ago
- Jupyter notebooks on portfolio construction and analysis - EDHEC☆42Updated 5 years ago
- Using Dask, a Python framework, I handle 900 million rows of S&P E-mini futures trade tick data directly on a local machine. Through expl…☆37Updated 9 months ago
- Projects are developed for implementing the knowledge gained in the courses studied at World Quant University and meeting the requirement…☆26Updated 4 years ago
- Resources for the Machine Learning for Finance workshop at Texas State University (November 2022).☆16Updated 2 years ago
- Solutions for selected exercises from Advances in Financial Machine Learning by Marcos Lopez De Prado☆50Updated 2 years ago
- Option pricing with various models (Black-Scholes, Heston, Merton jump diffusion, etc) and methods (Monte Carlo, finite difference, Fouri…☆72Updated 3 years ago
- ☆14Updated 2 years ago
- This notebook is devoted to exploring some aspects of the Capital Asset Pricing Model (CAPM) using Python☆18Updated 5 years ago
- Created a continuous, homogeneous, and structured 10 GB dataset from self obtained collections of unstructured intraday financial data. G…☆61Updated 4 years ago
- Python code for pricing European and American options with examples for individual stock, index, and FX options denominated in USD and Eu…☆22Updated 2 weeks ago
- ☆19Updated 3 years ago
- Yield curve Interpolation using cubic spline and nelson Seigel model☆15Updated 5 years ago
- Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strid…☆73Updated 4 years ago