This project performs a comprehensive data analysis on a dataset of stock information for various tech companies. The dataset includes the following labels: stock_symbol, date, open, high, low, close, adj_close, and volume. The analysis includes statistical calculations, visualizations, moving averages, daily returns, correlation analysis, and candlestick charts.
- Data Loading and Preparation: Load dataset from a CSV file and convert date columns to datetime format.
- Statistical Analysis: Calculate basic statistical measures such as mean, median, and standard deviation for each stock symbol.
- Visualization: Create line plots, histograms, and heatmaps using Matplotlib and Seaborn.
- Moving Averages: Implement and visualize moving averages (20-day and 50-day) to identify trends in stock prices.
- Daily Returns: Calculate and visualize daily returns to understand day-to-day stock performance.
- Correlation Analysis: Compute and visualize the correlation matrix between different stock symbols.
- Candlestick Charts: Create candlestick charts for detailed visualization of stock price movements.
- Investment Decision Making: Help investors make informed decisions about buying, holding, or selling tech stocks based on historical price trends and correlations.
- Risk Management: Assist financial analysts in assessing the risk associated with tech stocks by analyzing volatility and historical returns.
- Sector Comparison: Enable comparative analysis of different tech stocks to understand sector-wide trends and performance benchmarks.
- Portfolio Optimization: Aid portfolio managers in optimizing portfolios by diversifying across tech stocks with low correlation or by using insights from moving averages and other technical indicators.
- Enhanced Decision Making: Provides actionable insights into stock performance and trends.
- Risk Mitigation: Helps identify and manage risks associated with tech stock investments.
- Efficiency in Analysis: Streamlines the analysis process, allowing for quicker identification of trends and patterns.
- Educational Value: Acts as a useful tool for learning about financial data analysis methods and their applications in real-world situations.
- Strategic Planning: Facilitates strategic planning for investors and financial professionals by offering insights into market dynamics and sector-specific trends.
- Python 3.x
- pandas
- matplotlib
- seaborn
- mplfinance
- Stock Symbol: AMZN
- Moving Averages: 20-day and 50-day
- Daily Returns: Calculated and plotted
- Correlation Matrix: Computed and visualized
- Candlestick Chart: Created for detailed price movement visualization
