This project contains my two submissions for Kaggle competition : Two Sigma: Using News to Predict Stock Movements.
Achieved top 2% in leaderboard.
- Refer to "Featured Notebooks/Analysis/Deliverables" section for fully executed Jupyter Notebooks
The purpose of this project is predict a signed confidence value that's correlated with stock price movement.
Therefore, predicted signed confidence value can be used by the competition host to make better decisions on stock trading.
- Two Sigma is hosting this competition through Kaggle.
- In this competition, market data is provided by Intrinio and news data is provided by Thomson Reuters.
- Exploratory Data Analysis
- Deep Learning
- Data Visualization
- Predictive Modeling
- Python
- Jupyter Notebook
- Pandas
- Numpy
- Matplotlib
- Seaborn
- etc.
You can find base code in .ipynb format and its executed output by clickin "- executed" link.
Data used in this executions are only accessible through Kaggle's kernel, which is a virtual environment within server for copyrights reasons. Therefore, it is not possible to replicate this work.
To learn more about the data, you can view it here.