This project analyzes the stock price trends of Nvidia Corporation using historical data to derive insights into market performance, volatility, and investment opportunities.
Download the dataset from Kaggle: Nvidia Stocks Data
- Introduction
- Data Preparation
- Analytical Techniques
- Visualizations
- Comparative Analysis
- Key Insights
- Technologies Used
- Future Work
The primary objective of this project is to conduct a comprehensive analysis of Nvidia's stock prices, providing insights into trends and potential trading strategies.
- Loaded and cleaned historical stock price data from a CSV file.
- Converted the 'Date' column to datetime format and set it as the index.
- Created additional features, including:
- Daily Returns: Percentage change from the previous closing price.
- Volatility: 30-day rolling standard deviation of daily returns.
- Moving Averages: Implemented 50-day and 200-day moving averages to identify market trends.
- Bollinger Bands: Analyzed price volatility and potential reversals.
- Cumulative Returns: Evaluated overall investment growth over time.
- Developed various visualizations to represent:
- Daily returns distribution and volatility trends.
- Trading volume over time.
- Stakeholder visualizations combining price trends and volume.
- Compared Nvidia's stock performance with Advanced Micro Devices (AMD) to assess market positioning.
- Identified key trends and potential trading signals based on moving averages and volatility.
- Delivered actionable insights for stakeholders to guide investment strategies.
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly
- Explore advanced statistical modeling and machine learning techniques for predictive analysis.
- Expand the analysis to include additional semiconductor companies for a broader market view.
This project is licensed under the MIT License.