# Stock Market Time Series Dataset - Project Structure ## Complete Project Structure ``` stock-time-series/ │ ├── data/ # Stock market data files │ ├── AAPL.csv # Apple Inc. stock data (252 trading days) │ ├── GOOGL.csv # Alphabet Inc. stock data │ ├── MSFT.csv # Microsoft Corp. stock data │ ├── AMZN.csv # Amazon.com Inc. stock data │ └── TSLA.csv # Tesla Inc. stock data │ ├── metadata/ # Metadata and documentation │ ├── stock_info.json # Detailed stock information │ └── indicators.json # Technical indicators documentation │ ├── scripts/ # Python analysis scripts │ ├── load_data.py # Data loading utilities │ ├── analyze.py # Statistical analysis tools │ ├── forecast.py # Time series forecasting │ └── visualize.py # Visualization utilities │ ├── index.html # Professional demo webpage ├── README.md # Main documentation ├── requirements.txt # Python dependencies ├── LICENSE # License information ├── CHANGELOG.md # Version history ├── .gitignore # Git ignore rules ├── PROJECT_STRUCTURE.md # This file └── stock-time-series.png.txt # Image placeholder instructions ``` ## File Descriptions ### Data Files (CSV Format) Each CSV file contains **252 trading days** from 2020 with the following columns: - **Date**: Trading date (YYYY-MM-DD) - **Open**: Opening price - **High**: Highest price of the day - **Low**: Lowest price of the day - **Close**: Closing price - **Volume**: Trading volume - **Adj Close**: Adjusted closing price - **MA_20**: 20-day Moving Average - **MA_50**: 50-day Moving Average - **RSI**: Relative Strength Index - **MACD**: Moving Average Convergence Divergence ### Metadata Files **stock_info.json**: Contains detailed information about each stock including: - Company name and description - Sector and industry - Exchange and country - CEO, headquarters, employees - Data quality metrics - Contact information **indicators.json**: Complete documentation of technical indicators including: - Indicator definitions and formulas - Interpretation guidelines - Usage recommendations - Calculation parameters ### Python Scripts **load_data.py** (StockDataLoader class): - Load single or multiple stocks - Merge stock data - Filter by date range - Calculate returns - Summary statistics - Caching support **analyze.py** (StockAnalyzer class): - Comprehensive summary statistics - Price history visualization - Returns distribution analysis - Technical indicator plotting - Correlation analysis - Risk metrics (Sharpe ratio, max drawdown) **forecast.py** (StockForecaster class): - Simple Moving Average forecasting - Exponential Smoothing - ARIMA-based forecasting - Confidence intervals - Forecast comparison tools - Visualization of predictions **visualize.py** (StockVisualizer class): - Candlestick charts - Multiple stock comparisons - Correlation matrices - Volume analysis - Returns heatmaps - Comprehensive dashboards ## Data Statistics ### Coverage - **Time Period**: January 2, 2020 - December 31, 2020 - **Trading Days**: 252 days - **Number of Stocks**: 5 (AAPL, GOOGL, MSFT, AMZN, TSLA) - **Total Data Points**: 1,260 (252 days × 5 stocks) - **Columns per Stock**: 11 (Date + 10 data columns) ### Stocks Included | Symbol | Company | Sector | Industry | |--------|---------|--------|----------| | AAPL | Apple Inc. | Technology | Consumer Electronics | | GOOGL | Alphabet Inc. | Technology | Internet Content | | MSFT | Microsoft Corp. | Technology | Software Infrastructure | | AMZN | Amazon.com Inc. | Consumer Cyclical | Internet Retail | | TSLA | Tesla Inc. | Consumer Cyclical | Auto Manufacturers | ## Features ### Technical Indicators 1. **MA_20**: 20-day Simple Moving Average 2. **MA_50**: 50-day Simple Moving Average 3. **RSI**: Relative Strength Index (14-period) 4. **MACD**: Moving Average Convergence Divergence ### Analysis Capabilities - ✅ Time series forecasting - ✅ Technical analysis - ✅ Portfolio optimization - ✅ Risk assessment - ✅ Correlation analysis - ✅ Volatility analysis - ✅ Trend identification - ✅ Pattern recognition ### Visualization Options - 📊 Candlestick charts - 📈 Line charts with moving averages - 📉 Volume analysis - 🔥 Correlation heatmaps - 📊 Returns distribution - 📈 Technical indicator plots - 🎯 Multi-stock comparisons - 📊 Comprehensive dashboards ## Usage Examples ### Quick Start ```python # Load a single stock import pandas as pd df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date') print(df.head()) ``` ### Using Data Loader ```python from scripts.load_data import StockDataLoader loader = StockDataLoader() aapl = loader.load_stock('AAPL') stats = loader.get_summary_stats(aapl) ``` ### Analysis ```python from scripts.analyze import StockAnalyzer analyzer = StockAnalyzer('data/AAPL.csv') analyzer.summary_statistics() analyzer.plot_price_history() analyzer.plot_technical_indicators() ``` ### Forecasting ```python from scripts.forecast import StockForecaster forecaster = StockForecaster('data/AAPL.csv') forecast = forecaster.exponential_smoothing_forecast(days=30) forecaster.plot_forecast(forecast) ``` ### Visualization ```python from scripts.visualize import StockVisualizer visualizer = StockVisualizer() visualizer.plot_candlestick('data/AAPL.csv') visualizer.create_dashboard('data/AAPL.csv') ``` ## Dependencies ``` pandas>=2.0.0 numpy>=1.24.0 matplotlib>=3.7.0 seaborn>=0.12.0 scikit-learn>=1.3.0 statsmodels>=0.14.0 prophet>=1.1.0 ``` Install all dependencies: ```bash pip install -r requirements.txt ``` ## File Sizes (Approximate) | File | Size | Records | |------|------|---------| | AAPL.csv | ~80 KB | 252 rows | | GOOGL.csv | ~80 KB | 252 rows | | MSFT.csv | ~80 KB | 252 rows | | AMZN.csv | ~80 KB | 252 rows | | TSLA.csv | ~80 KB | 252 rows | | stock_info.json | ~5 KB | - | | indicators.json | ~8 KB | - | | **Total Dataset** | ~450 KB | 1,260 rows | ## License Educational and Research License - ✅ Free for educational purposes - ✅ Free for academic research - ✅ Free for personal learning - ⚠️ Commercial use requires permission ## Contact Information **Author**: Molla Samser **Organization**: RSK World **Designer & Tester**: Rima Khatun **Website**: [https://rskworld.in/](https://rskworld.in/) **Email**: help@rskworld.in **Phone**: +91 93305 39277 **Address**: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147 ## Version Information **Current Version**: 1.0.0 **Release Date**: January 1, 2024 **Last Updated**: January 1, 2024 See [CHANGELOG.md](CHANGELOG.md) for version history and updates. ## Support For issues, questions, or suggestions: - 📧 Email: help@rskworld.in - 🌐 Website: https://rskworld.in/ - 📱 Phone: +91 93305 39277 --- **© 2024 RSK World. All rights reserved.** Visit [rskworld.in](https://rskworld.in/) for more datasets and data science resources!