Historical, fundamental, technical, and market-wide datasets for the Nepal Stock Exchange (NEPSE), designed for researchers, investors, quantitative analysts, students, and developers.
Features • Datasets • Research • Installation • Contributing
NepseDataHome is a comprehensive data platform focused on collecting, organizing, validating, and serving Nepal Stock Exchange (NEPSE) market data.
The project aims to solve one of the largest challenges in Nepal's financial ecosystem:
Lack of accessible, structured, research-ready market data.
Instead of scattered spreadsheets, manually downloaded reports, and fragmented data sources, NepseDataHome provides a unified repository of market information that can be used for:
- Quantitative research
- Algorithmic trading research
- Portfolio analysis
- Academic studies
- Financial journalism
- Machine learning
- Time-series forecasting
- Market surveillance
- Investment research
Nepal's capital market has experienced significant growth over the past decade.
However, access to clean, structured, machine-readable datasets remains limited.
Researchers often spend more time collecting data than analyzing it.
NepseDataHome aims to become:
- Nepal's largest open market dataset
- A foundation for quantitative finance research
- A source for AI and machine learning projects
- A public resource for students and researchers
- A developer-friendly market data platform
Access historical trading information across listed securities.
Includes:
- Open price
- High price
- Low price
- Close price
- Last traded price
- Volume traded
- Turnover
- Number of transactions
Structured information for listed companies.
Examples:
- Symbol
- Company name
- Sector
- Listing information
- Market category
Analyze performance across sectors.
Examples:
- Commercial Banks
- Hydropower
- Microfinance
- Life Insurance
- Non-Life Insurance
- Hotels & Tourism
- Finance
- Manufacturing
- Investment
Track historical performance of:
- NEPSE Index
- Sensitive Index
- Float Index
- Sector Indices
Repository for:
- Bonus shares
- Cash dividends
- Rights offerings
- FPOs
- Mergers
- Acquisitions
- Stock splits
Financial indicators including:
- EPS
- Book Value
- Net Worth
- Dividend History
- PE Ratio
- Market Capitalization
Data is organized for:
- Python
- R
- SQL
- Excel
- Power BI
- Tableau
NepseDataHome/
├── data/
│ ├── companies/
│ ├── prices/
│ ├── sectors/
│ ├── indices/
│ ├── fundamentals/
│ ├── dividends/
│ ├── rights/
│ ├── announcements/
│ └── market_summary/
│
├── notebooks/
│ ├── analysis/
│ ├── forecasting/
│ ├── machine_learning/
│ └── visualization/
│
├── scripts/
│ ├── collectors/
│ ├── cleaners/
│ ├── validators/
│ └── exporters/
│
├── api/
│
├── docs/
│
└── README.md
| Category | Description |
|---|---|
| Daily Prices | Historical OHLCV data |
| Indices | Market and sector indices |
| Fundamentals | Financial statements and ratios |
| Dividends | Dividend distributions |
| Rights Shares | Rights issue history |
| Market Summary | Daily market statistics |
| Corporate Actions | Events affecting securities |
| Sector Data | Industry classifications |
| Company Profiles | Listed company metadata |
NepseDataHome can support:
- Momentum strategies
- Mean reversion
- Factor investing
- Portfolio optimization
- Risk modelling
- Price prediction
- Volatility forecasting
- Classification models
- Regime detection
- Market anomaly detection
Potential areas:
- Market efficiency
- Behavioral finance
- Emerging markets research
- Liquidity studies
- Event studies
Useful for:
- Election-related economic coverage
- Market investigations
- Corporate reporting
- Economic trend analysis
import pandas as pd
df = pd.read_csv("data/prices/NABIL.csv")
print(df.head())df["returns"] = df["close"].pct_change()df["ma50"] = df["close"].rolling(50).mean()sector_returns = (
df.groupby("sector")
.returns
.mean()
)- Historical market datasets
- Company metadata
- Sector classification
- Data validation pipeline
- Public API
- Automated updates
- Data explorer dashboard
- Download center
- Real-time feeds
- Quantitative research toolkit
- Portfolio analytics
- Screening engine
- AI-powered market assistant
- Research paper generation
- Forecasting models
- Institutional datasets
Learning finance, economics, and data science.
Studying emerging capital markets.
Analyzing listed companies.
Investigating financial stories.
Building market tools and applications.
Training predictive models.
Contributions are welcome.
Areas where contributors can help:
- Data validation
- Historical data collection
- Documentation
- API development
- Visualization tools
- Research notebooks
If you use NepseDataHome in research:
@dataset{nepsedatahome,
title={NepseDataHome},
author={Sakshyam Banjade},
year={2026},
publisher={GitHub},
url={https://github.com/sakshyambanjade/NepseDataHome}
}MIT License
See LICENSE for details.
Sakshyam Banjade
AI Researcher • Quantitative Systems Builder • Developer
GitHub: https://github.com/sakshyambanjade
Project: https://github.com/sakshyambanjade/NepseDataHome
To become the most comprehensive open data platform for Nepal's capital markets and enable the next generation of quantitative finance, AI research, and investment analysis in Nepal.