Stock Market Time Series Dataset
Historical OHLCV Data with Technical Indicators for Multiple Stocks
Overview
This comprehensive 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.
Key Features
OHLCV Price Data
Complete open, high, low, close prices and trading volume for comprehensive analysis.
Technical Indicators
Pre-calculated MA_20, MA_50, RSI, and MACD indicators for quick analysis.
Multiple Stocks
Data for AAPL, GOOGL, MSFT, AMZN, and TSLA covering the entire 2020.
ML Ready
Time series formatted data ready for machine learning and forecasting models.
Python Scripts
Complete analysis, forecasting, and visualization scripts included.
Documentation
Comprehensive documentation and metadata for all indicators and stocks.
Stocks Included
Technology • Consumer Electronics
Leading technology company known for iPhones, Mac, and innovative products.
Technology • Internet Services
Parent company of Google, leader in search, advertising, and cloud services.
Technology • Software Infrastructure
Software giant with Windows, Office, Azure, and enterprise solutions.
Consumer Cyclical • Internet Retail
E-commerce leader with AWS cloud services and diverse business portfolio.
Consumer Cyclical • Auto Manufacturers
Electric vehicle pioneer and clean energy company led by Elon Musk.
Data Format
| Column | Description | Type |
|---|---|---|
| Date | Trading date (YYYY-MM-DD format) | Date |
| Open | Opening price of the trading day | Float |
| High | Highest price during the trading day | Float |
| Low | Lowest price during the trading day | Float |
| Close | Closing price of the trading day | Float |
| Volume | Total number of shares traded | Integer |
| Adj Close | Adjusted closing price (splits/dividends) | Float |
| MA_20 | 20-day Simple Moving Average | Float |
| MA_50 | 50-day Simple Moving Average | Float |
| RSI | Relative Strength Index (momentum indicator) | Float |
| MACD | Moving Average Convergence Divergence | Float |
Use Cases
- Time Series Forecasting (ARIMA, LSTM, Prophet)
- Technical Analysis & Trading Strategies
- Portfolio Optimization & Asset Allocation
- Risk Assessment & Volatility Analysis
- Machine Learning Model Training
- Algorithmic Trading & Backtesting
Quick Start
Python Example
import pandas as pd
# Load stock data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')
# Display first few rows
print(df.head())
# Calculate daily returns
df['Returns'] = df['Close'].pct_change()
# Calculate volatility
volatility = df['Returns'].std() * (252 ** 0.5)
print(f"Annualized Volatility: {volatility:.2%}")Using Provided Scripts
# Load and analyze data
from scripts.load_data import StockDataLoader
from scripts.analyze import StockAnalyzer
loader = StockDataLoader()
aapl = loader.load_stock('AAPL')
analyzer = StockAnalyzer('data/AAPL.csv')
analyzer.summary_statistics()
analyzer.plot_price_history()Download Dataset
Get the complete dataset with all stock data, metadata, and Python scripts.
Download Dataset View on GitHubContact Information
Author: Molla Samser
Organization: RSK World
Designer & Tester: Rima Khatun
Address: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147