In financial markets, volatility captures the amount of fluctuation in prices. For trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product.In this Kaggle competition,we had built models that predict short-term volatility for hundreds of stocks across different sectors. Our models will be evaluated against real market data collected in the three-month evaluation period after training.
- Kaggle platform.
- Python.
- Scikit learn.
- LGBM
To get a local copy up and running follow these simple example steps.
- Open terminal
- Clone this project by the command:
$ git clone git@github.com:Taher-web-dev/Optiver-Realized-Volatility-Prediction.git
- Then go to the main folder using the next command:
$ cd Optiver-Realized-Volatility-Prediction
- IDE to edit and run the code (We use Jupyter Notebook 🔥).
- Git to versionning your work.
- Data scientist practioner
- For anyone interested by Finance topics.
👤 Taher Haggui
- GitHub: @TaherHaggui
- LinkedIn: @TaherHaggui
Contributions, issues, and feature requests are welcome!
Give a ⭐️ if you like this project!
- kaggle plarform 💘 (https://www.kaggle.com/)
- My family's support 🙌
This project is Optiver licensed.