Welcome to my GitHub repository for the Applied Finance in Python course! In this course, I learned how to enhance my Python financial skills and manipulate data to make better data-driven decisions. I used powerful libraries such as SciPy, statsmodels, scikit-learn, TensorFlow, Keras, and XGBoost to examine and manage risk, and applied what I learned to answer questions commonly faced by financial firms.
The course covered the following topics:
- Evaluating portfolios
- Mitigating risk exposure
- Using the Monte Carlo simulation to model probability
- Rebalancing a portfolio using neural networks
- Examining and managing risk using powerful libraries, including SciPy, statsmodels, scikit-learn, TensorFlow, Keras, and XGBoost
- Applying machine learning and financial techniques to answer questions commonly faced by financial firms
- Creating GARCH models
- Analyzing real datasets featuring Microsoft stocks, historical foreign exchange rates, and cryptocurrency data
In this repository, you'll find all the code I wrote while taking the course, as well as any additional notes or resources I found helpful. The code is organized by topic and is well-documented for easy reference.
To get started, simply clone this repository to your local machine and open the code files in your preferred text editor. You'll need to have Python and the necessary libraries installed to run the code. You can install the libraries by running pip install -r requirements.txt in your terminal or command prompt.
By the end of this course, I had gained a solid understanding of how to enhance my Python financial skills and manipulate data to make better data-driven decisions. I had also gained practical experience working with real financial datasets and applying machine learning and financial techniques to answer complex financial questions. I hope that this repository will be a helpful resource for others looking to improve their Python financial skills.
If you have any questions or feedback about this repository, feel free to contact me.