This project provides a comprehensive analysis of NVIDIA Corporation's (NASDAQ: NVDA) stock performance over several years. The analysis includes various statistical methods, time series forecasting, and advanced machine learning techniques to understand the trends, seasonality, and volatility of the stock prices.
The dataset used in this analysis includes daily market performance data for NVIDIA Corporation. It contains the following columns:
- Date: The date of the market data.
- Open: The opening price of NVIDIA stock on that day.
- High: The highest price reached by NVIDIA stock during the day.
- Low: The lowest price reached by NVIDIA stock during the day.
- Close: The closing price of NVIDIA stock on that day.
- Adj Close: The adjusted closing price, accounting for corporate actions like stock splits and dividends.
- Volume: The volume of NVIDIA stock traded on that day.
The Jupyter Notebook in this repository performs the following analyses:
- Exploratory Data Analysis (EDA): Initial exploration of the dataset with summary statistics and visualizations.
- Moving Averages: Calculation and plotting of 20-day, 50-day, and 200-day moving averages.
- Correlation Analysis: Calculation and visualization of the correlation matrix.
- Volatility Analysis: Calculation and plotting of daily returns and rolling volatility.
- Seasonality Analysis: Using Fourier transforms and seasonal decomposition to identify seasonal patterns.
- Autocorrelation and Partial Autocorrelation: Analyzing time series dependencies.
- ARIMA Modeling: Time series forecasting using the ARIMA model.
- LSTM for Time Series Prediction: Advanced time series forecasting using LSTM neural networks.
- GARCH Model for Volatility Clustering: Understanding volatility clustering with the GARCH model.
- Event Analysis: Impact of major events on stock prices.
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Seasonality and Trends:
- Significant periodic components indicate the presence of seasonality.
- Clear trends and seasonal patterns are observable in NVIDIA's stock prices.
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Autocorrelation and Partial Autocorrelation:
- Significant lags suggest past prices influence future prices.
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ARIMA Modeling:
- Reasonable fit for predicting future stock prices, but more complex models might be needed for higher accuracy.
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LSTM Modeling:
- LSTM models capture underlying patterns in stock prices better than linear models.
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Volatility Analysis:
- GARCH model reveals periods of high and low volatility, crucial for risk management and trading strategies.
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Event Analysis:
- Major events like earnings reports and product launches significantly impact stock prices.
To run the Jupyter Notebook, you need the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- statsmodels
- scikit-learn
- tensorflow
- arch
You can install these libraries using pip:
pip install pandas numpy matplotlib seaborn statsmodels scikit-learn tensorflow arch-
Clone the repository:
git clone https://github.com/debjit-mandal/NVIDIA-Stock-Analysis.git cd NVIDIA-Stock-Analysis -
Ensure you have all the required libraries installed:
pip install -r requirements.txt
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Open the Jupyter Notebook:
jupyter notebook NVDA_Advanced_Analysis.ipynb
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Run the cells in the notebook to perform the analysis.
.
├── NVDA_Advanced_Analysis.ipynb # Jupyter Notebook with the analysis
├── data/NVDA.csv # Dataset
├── LICENSE # License
├── README.md # Project description and instructions
└── requirements.txt # List of required libraries
Contributions are welcome! If you have any suggestions or improvements, feel free to create a pull request or open an issue.
- Fork the repository.
- Create a new branch:
git checkout -b my-branch-name. - Make your changes and commit them:
git commit -m 'Add some feature'. - Push to the branch:
git push origin my-branch-name. - Submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.