RSK World - Stock Market Time Series Dataset | OHLCV | LSTM | Portfolio Optimization | RSK World

Stock Market Time Series Dataset - RSK World

Stock Market Time Series OHLCV Technical Indicators ML Ready CSV

This project features a comprehensive Stock Market Time Series dataset designed for professional financial analysis, algorithmic trading, and machine learning. The dataset includes 252 trading days (full year 2020) of historical OHLCV (Open, High, Low, Close, Volume) data for 5 major stocks: AAPL (Apple), GOOGL (Alphabet), MSFT (Microsoft), AMZN (Amazon), and TSLA (Tesla). Beyond basic data, you get 15+ pre-calculated technical indicators including Moving Averages, RSI, MACD, Bollinger Bands, ATR, Stochastic Oscillator, and more. The package includes 9 advanced Python scripts: load_data.py, analyze.py, forecast.py, visualize.py, ml_models.py (LSTM + Random Forest), backtesting.py (3 trading strategies), portfolio.py (Modern Portfolio Theory + Monte Carlo), interactive_dashboard.py (Plotly charts), and advanced_indicators.py. Features include LSTM neural networks with 20+ engineered features, complete backtesting framework with Sharpe ratio and drawdown analysis, portfolio optimization with efficient frontier calculation (5,000 portfolios), Monte Carlo simulation (1,000+ scenarios), and interactive Plotly dashboards with candlestick charts. Complete with comprehensive documentation, metadata JSON files, Jupyter notebook tutorials, and professional demo webpage. Perfect for data scientists, quantitative analysts, traders, researchers, and students learning financial modeling, time series forecasting, algorithmic trading strategies, risk management, and machine learning applications in finance.

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Dataset Overview

Complete historical stock market data with OHLCV prices, 15+ technical indicators, and ML-ready format for 5 major stocks.

Date, Open, High, Low, Close, Adj Close prices
Trading Volume for all 5 stocks
5 stocks: AAPL, GOOGL, MSFT, AMZN, TSLA
252 trading days per stock (full year 2020)
1,260 total data points across all stocks
Moving Averages (MA_20, MA_50) pre-calculated
RSI, MACD, Bollinger Bands included
CSV format ready for Pandas/NumPy
Metadata JSON files with stock info
Clean data - no missing values
Perfect for time series forecasting & ML

Data Quality & Loading

Clean, validated stock market data with professional loading utilities and comprehensive data quality checks.

StockDataLoader class for easy loading
Date parsing with automatic indexing
No missing values - 100% complete dataset
Validated OHLCV price consistency
Chronologically ordered trading days
Support for single/multiple stock loading
Merge multiple stocks into one DataFrame
Filter by custom date ranges
Calculate returns (daily, cumulative)
Caching support for faster reloads
Summary statistics built-in
Metadata JSON with company details

Technical Analysis

StockAnalyzer class with comprehensive statistical analysis and technical indicator evaluation tools.

Comprehensive summary statistics
Price history visualization with MA overlays
Returns distribution analysis
Technical indicator plotting (RSI, MACD)
Correlation analysis between stocks
Risk metrics: Sharpe ratio calculation
Maximum drawdown analysis
Volatility and standard deviation
Volume analysis and liquidity metrics
Moving average crossover signals
Pattern recognition tools
Beautiful Matplotlib/Seaborn visualizations

Time Series Forecasting

StockForecaster class with multiple forecasting models including ARIMA, Exponential Smoothing, and Prophet.

Simple Moving Average forecasting
Exponential Smoothing (Holt-Winters)
ARIMA models with auto-parameter tuning
Prophet for trend and seasonality
Walk-forward validation support
Rolling window forecasting
Multi-step ahead predictions (30+ days)
Confidence intervals calculation
Forecast comparison tools
Performance metrics (RMSE, MAE, MAPE)
Visualization of predictions vs actuals
Residual analysis and diagnostics

Machine Learning Models

Advanced ML models with LSTM neural networks, Random Forest, and automated feature engineering.

LSTM neural network (3 layers + Dropout)
Keras/TensorFlow implementation
Random Forest for baseline comparison
Automated feature engineering (20+ features)
Returns, log returns, momentum features
Volatility features (20 & 50 day)
Bollinger Bands (4 components)
Average True Range (ATR)
On-Balance Volume (OBV)
8 lag features for temporal patterns
Model training with validation split
Performance metrics (RMSE, MAE, R²)
Training history visualization
30+ day future price predictions

Backtesting Framework

Complete backtesting framework with multiple trading strategies and performance metrics.

Complete backtesting engine
Moving Average Crossover strategy (MA20/MA50)
RSI trading strategy (30/70 thresholds)
MACD signal line strategy
Custom strategy support framework
Commission simulation (adjustable %)
Slippage modeling for realistic results
Sharpe Ratio calculation
Maximum Drawdown (MDD) analysis
Win rate and profit factor
CAGR (Compound Annual Growth Rate)
Total return and profit tracking
Trade execution history
Visual performance comparison charts

What You Get

Complete package with 30+ files for professional stock market analysis and financial modeling.

5 CSV files (AAPL, GOOGL, MSFT, AMZN, TSLA)
9 Python scripts (3,500+ lines of code)
Metadata JSON files (stock_info, indicators)
Jupyter notebook tutorials
Professional demo webpage (index.html)
Comprehensive README.md
ADVANCED_FEATURES.md documentation
PROJECT_STRUCTURE.md guide
CHANGELOG.md version history
VALIDATION_REPORT.md
Requirements.txt for easy setup
Educational License included

Portfolio Optimization

PortfolioOptimizer class implementing Modern Portfolio Theory with Monte Carlo simulation.

Modern Portfolio Theory (MPT) implementation
Efficient Frontier calculation (5,000 portfolios)
Maximum Sharpe Ratio portfolio
Minimum Volatility portfolio optimization
Monte Carlo simulation (1,000+ scenarios)
Value at Risk (VaR) at 95% confidence
Conditional VaR (CVaR) calculation
Correlation matrix between stocks
Expected returns calculation
Volatility and risk metrics
Best/worst case scenario analysis
Optimal portfolio weights
Visual efficient frontier plotting
Risk-return trade-off analysis

Interactive Dashboards

StockDashboard class with interactive Plotly visualizations and professional charts.

Plotly-based interactive charts
Candlestick charts with zoom and pan
4-panel technical analysis dashboard
Price chart with MA overlays (MA20, MA50)
Volume analysis with colored bar charts
RSI panel with overbought/oversold zones (30/70)
MACD histogram with signal line
Returns analysis dashboard
Volume profile visualization
Multi-stock comparison tools
Export to PNG/HTML/JSON formats
Hover tooltips with detailed information
Toggle series visibility
Range selector for time periods

Advanced Indicators

AdvancedIndicators class with 15+ technical indicators beyond basic MA/RSI/MACD.

Bollinger Bands (Upper, Lower, Middle, Width)
Average True Range (ATR) for volatility
Stochastic Oscillator (%K and %D lines)
Commodity Channel Index (CCI)
Money Flow Index (MFI)
Williams %R momentum indicator
Parabolic SAR for trend detection
Ichimoku Cloud (5 lines: Tenkan, Kijun, Senkou A/B, Chikou)
On-Balance Volume (OBV)
Customizable parameters for all indicators
Batch processing for multiple stocks
Enhanced dataset export with all indicators
Professional indicator documentation

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
Pandas - Data manipulation (BSD License)
NumPy - Numerical computing (BSD License)
Scikit-learn - Machine learning (BSD License)
TensorFlow/Keras - LSTM models (Apache 2.0)
Matplotlib - Visualization (PSF License)
Plotly - Interactive charts (MIT License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
Educational License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Stock Market Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Stock Market Time Series Dataset

Dataset Overview

Data Quality & Loading

Technical Analysis

Time Series Forecasting

Machine Learning Models

Backtesting Framework

What You Get

Portfolio Optimization

Interactive Dashboards

Advanced Indicators

Credits & Acknowledgments

Support & Contact

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