README.md

# Stock Market Time Series Dataset

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Author: Molla Samser
Website: https://rskworld.in/
Email: help@rskworld.in
Phone: +91 93305 39277
Organization: RSK World
Designer & Tester: Rima Khatun
Address: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147
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## Overview

This dataset contains historical stock market data with OHLCV (Open, High, Low, Close, Volume) prices, trading volumes, and technical indicators for multiple stocks. Perfect for time series forecasting, technical analysis, portfolio optimization, and financial modeling.

## Features

- **OHLCV Price Data**: Complete open, high, low, close prices and volume
- **Trading Volumes**: Historical trading volumes for liquidity analysis
- **Multiple Stocks**: Data for various stocks across different sectors
- **Technical Indicators**: Pre-calculated indicators for quick analysis
- **Time Series Ready Format**: Data formatted for immediate time series analysis

## Technologies

- CSV
- JSON
- Pandas
- Time Series Analysis
- Python

## Difficulty Level

**Intermediate** - Suitable for data scientists with basic knowledge of time series analysis and financial markets.

## Dataset Structure

```
stock-time-series/
├── data/
│ ├── AAPL.csv # Apple Inc. stock data
│ ├── 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/
│ ├── stock_info.json # Stock metadata and information
│ └── indicators.json # Technical indicators description
├── scripts/
│ ├── load_data.py # Data loading utilities
│ ├── analyze.py # Basic analysis scripts
│ ├── forecast.py # Time series forecasting
│ └── visualize.py # Visualization utilities
├── index.html # Demo page
├── README.md # This file
└── requirements.txt # Python dependencies
```

## Data Format

Each CSV file contains the following columns:

| Column | Description |
|--------|-------------|
| Date | Trading date (YYYY-MM-DD) |
| Open | Opening price |
| High | Highest price |
| Low | Lowest price |
| 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 |

## Use Cases

1. **Time Series Forecasting**: Predict future stock prices using ARIMA, LSTM, Prophet
2. **Technical Analysis**: Analyze trading patterns and indicators
3. **Portfolio Optimization**: Build and optimize investment portfolios
4. **Risk Assessment**: Calculate volatility, VaR, and other risk metrics
5. **Machine Learning**: Train ML models for price prediction
6. **Algorithmic Trading**: Develop and backtest trading strategies

## Getting Started

### Prerequisites

```bash
pip install -r requirements.txt
```

### Loading Data

```python
import pandas as pd

# Load stock data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')
print(df.head())
```

### Quick Analysis

```python
from scripts.analyze import StockAnalyzer

analyzer = StockAnalyzer('data/AAPL.csv')
analyzer.summary_statistics()
analyzer.plot_price_history()
```

### Time Series Forecasting

```python
from scripts.forecast import StockForecaster

forecaster = StockForecaster('data/AAPL.csv')
predictions = forecaster.arima_forecast(days=30)
forecaster.plot_forecast(predictions)
```

## Sample Analysis

```python
import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')

# Calculate returns
df['Returns'] = df['Close'].pct_change()

# Plot closing prices
df['Close'].plot(figsize=(12, 6), title='AAPL Closing Prices')
plt.ylabel('Price ($)')
plt.show()

# Calculate volatility
volatility = df['Returns'].std() * (252 ** 0.5) # Annualized
print(f"Annualized Volatility: {volatility:.2%}")
```

## Data Period

- **Start Date**: January 1, 2020
- **End Date**: December 31, 2024
- **Frequency**: Daily
- **Total Trading Days**: ~1260 per stock

## Technical Indicators Included

1. **Moving Averages (MA)**: 20-day and 50-day
2. **Relative Strength Index (RSI)**: Momentum oscillator
3. **MACD**: Trend-following momentum indicator
4. **Bollinger Bands**: Volatility indicator (calculable)

## License

This dataset is provided for educational and research purposes.

## Contact

- **Author**: Molla Samser
- **Organization**: RSK World
- **Designer & Tester**: Rima Khatun
- **Email**: help@rskworld.in
- **Phone**: +91 93305 39277
- **Website**: [https://rskworld.in/](https://rskworld.in/)
- **Address**: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147

## Acknowledgments

Data sourced from public financial markets. All prices are in USD.

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