https://github.com/razamehar/financial-stock-analysis-and-clustering
Analyzed 157 US Energy stocks (Jan-Dec '23), identified Bullish/Bearish trends and risk categories. Used KMeans, Hierarchical, Spectral Clustering, revealing balanced returns and low volatility. Integrated data with Kafka for seamless subscriptions.
https://github.com/razamehar/financial-stock-analysis-and-clustering
financial-analysis hierarchical-clustering kafka-producer kmeans-clustering pandas python rdd stock-analysis
Last synced: 2 months ago
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Analyzed 157 US Energy stocks (Jan-Dec '23), identified Bullish/Bearish trends and risk categories. Used KMeans, Hierarchical, Spectral Clustering, revealing balanced returns and low volatility. Integrated data with Kafka for seamless subscriptions.
- Host: GitHub
- URL: https://github.com/razamehar/financial-stock-analysis-and-clustering
- Owner: razamehar
- License: mit
- Created: 2024-02-12T12:48:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-16T15:03:02.000Z (about 1 year ago)
- Last Synced: 2024-11-26T15:50:30.215Z (6 months ago)
- Topics: financial-analysis, hierarchical-clustering, kafka-producer, kmeans-clustering, pandas, python, rdd, stock-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 4.34 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Financial Stock Analysis and Clustering
## Project Overview
In a thorough analysis of 157 US Energy stocks from January to December '23, I employed advanced clustering techniques, including KMeans, Hierarchical, and Spectral Clustering, to identify bullish and bearish trends. The clustering methodology revealed overbought and overpriced stocks, profitable investments, and stocks facing losses. Additionally, a focus on volatility analysis pinpointed the most volatile stocks in the dataset. This comprehensive approach enables investors to make informed decisions and manage risks effectively. Furthermore, seamless integration with Kafka ensures real-time updates and subscriptions for stakeholders, enhancing accessibility to critical market insights.
### Analysis Steps
1. **Data Distribution Analysis:**
- Analyzing the distribution of data for insights into stock performance.
2. **Feature Engineering:**
- Enhancing data features to improve the accuracy of subsequent analyses.
3. **Technical Indicators:**
- Employing technical indicators to evaluate stocks as Bullish, Bearish, or Overbought/Oversold.
4. **Risk Analysis:**
- Categorizing stocks as Profitable, Unprofitable, or Volatile.
5. **Cluster Analysis**
- Utilizing clustering techniques like KMeans, Agglomerative Hierarchical, and Spectral Clustering to identify distinct groups among the analyzed stocks.
### Observations
#### Stock Data Distribution Analysis
#### Top and Bottom 10 Stocks
#### Clusters Visualization
### Findings
The analysis reveals a common theme among energy sector stocks, offering a balance of medium to high returns with relatively low volatility.
### Integration with Kafka
The processed data is seamlessly integrated with Kafka, enabling subscription by other systems for further analysis.
## Usage
### Prerequisites
- Python 3.10.12
- Required Python packages:
- kafka-python
- yfinance
- pyspark
- TA-Lib
### Environment Setup
1. Install the required libraries using the following commands:
```bash
pip install kafka-python
pip install yfinance --upgrade --no-cache-dir
pip install pyspark
pip install TA-Lib
2. Download and instal TA-Lib using the following instructions:
```bash
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzvf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
make install
cd /content
## License
This project is licensed under the Raza Mehar License. See the [LICENSE.md](LICENSE.md) file for details.
## Contact
For any questions or clarifications, please contact Raza Mehar at [[email protected]], Pujan Thapa at [[email protected]] or Syed Najam Mehdi at [[email protected]].