Sensefolio is a personal project designed to analyze stock market news and trends, aiming to provide data-driven insights into the short-term behavior of a company's stock.
🛠️ This project is under active development and not yet production-ready.
Sensefolio combines real-time financial news with sentiment analysis and historical stock data to identify patterns that may influence stock performance. The goal is to build a system that provides a probability-based indication of whether a stock’s value may experience upward or downward momentum over a selected time frame.
It currently uses the VADER sentiment model to analyze news headlines and assess market sentiment.
In today’s fast-moving markets, news plays a critical role in shaping investor sentiment. Sensefolio helps users by:
- Aggregating and organizing company-specific financial news from trusted sources
- Extracting sentiment from news articles using natural language processing (currently via VADER)
- Preparing the foundation for analyzing correlations between sentiment trends and stock price movements
- Laying the groundwork for building predictive models to assist in decision making
Whether you're an investor, a data enthusiast, or simply curious, Sensefolio aims to be a useful tool for exploring the relationship between media sentiment and stock trends.
- Python 3.8+
- A free Finnhub API key
# 1. Clone the repository
git clone https://github.com/your-username/sensefolio.git
cd sensefolio
# 2. Set up a virtual environment
python -m venv venv
source venv/bin/activate # or .\venv\Scripts\activate on Windows
# 3. Install dependencies
pip install -r requirements.txt
# 4. Configure environment variables
# Copy the example file and insert your actual API key
cp .env.example .env
# Then edit .env and replace the placeholder with your API key# To be completed- Fetch latest financial news from Finnhub API
- Perform sentiment analysis on news headlines using VADER
- Fetch historical stock price data from Yahoo! Finance
- Perform sentiment analysis on news summaries using VADER
- Visualize the sentiments and the price
- Build a frontend interface for user interaction (comming soon)
- Implement CI/CD pipeline for automated testing and deployment (comming soon)
- Deploy a simple version of the projcet (comming soon)
- Analyze the relation between sentiment and price movement (planned)
- Generate short-term movement probabilities (planned)
- Expose functionality via a REST API (planned)
- Deploy the complete application (planned)
I'm @sorooshsorkhani, a data scientist who enjoys using data to build meaningful, useful things.
I'm learning as I go, and this project is very much a work in progress — thanks for checking it out!
This is a personal learning project and not intended as financial advice. Always conduct your own research before making investment decisions.