Indian Mutual Fund Dataset 📊

This repository hosts a daily-updated dataset focusing on Indian Mutual Fund schemes. It is comprised of two main data files:

1. mutual_fund_data.csv: Contains the latest snapshot of scheme details, including Net Asset Value (NAV), Asset Management Company (AMC), Assets Under Management (AUM), scheme categories, and more.
2. mutual_fund_nav_history.parquet: Provides extensive historical Net Asset Value (NAV) data for time-series analysis.

The data is automatically fetched and processed daily via a Kaggle Notebook.

Table of Contents

• 📜 Description
• 💻 Explore the Data Online
• 💾 How to Use
• 🔍 What's Inside
  • Current Scheme Details: mutual_fund_data.csv
  • Historical NAV Data: mutual_fund_nav_history.parquet
• 📈 Calculable Metrics & Analyses (from Historical NAV Data)
• ⏱️ Update Frequency
• 💡 Potential Uses
• 🤝 Contributing
• 🙏 Acknowledgements
• 📄 License

📜 Description

This dataset provides a comprehensive view of the Indian mutual fund market.

• The mutual_fund_data.csv file offers a snapshot of over 9,000+ mutual fund schemes, featuring the latest Net Asset Value (NAV) & Assets Under Management (AUM) data, refreshed daily.
• The mutual_fund_nav_history.parquet file contains over 20 Million+ historical NAV records for in-depth performance analysis, with 6,000+ new NAV records added daily.

Together, these files serve as a valuable resource for financial analysis, comparison, backtesting, and tracking of the Indian mutual fund landscape.

💻 Explore the Data Online

Instantly explore, filter, and sort the mutual_fund_data.csv (scheme details) directly in your browser:

(Note: The Parquet file is best explored after downloading due to its size and format.)

💾 How to Use

1. Download: Clone the repository or download the desired files (mutual_fund_data.csv, mutual_fund_nav_history.parquet) directly.

2. Load: Use your favorite data analysis tool.

   • For mutual_fund_data.csv (Scheme Details):

     import pandas as pd
     df_schemes = pd.read_csv('mutual_fund_data.csv')
     print("Scheme Details Data:")
     print(df_schemes.head())

   • For mutual_fund_nav_history.parquet (Historical NAVs): You'll likely need pandas and potentially pyarrow or fastparquet installed.

     import pandas as pd
     # Ensure you have pyarrow or fastparquet installed:
     # pip install pandas pyarrow
     # or
     # pip install pandas fastparquet
     df_nav_history = pd.read_parquet('mutual_fund_nav_history.parquet')
     print("\nHistorical NAV Data:")
     print(df_nav_history.head())

3. Analyze: Explore the data based on your requirements!

🔍 What's Inside

📋 Current Scheme Details: mutual_fund_data.csv

This file provides a daily snapshot of various details for each mutual fund scheme.

• Scheme_Code: Unique code assigned to a mutual fund scheme.
• AMC: The Asset Management Company that manages the mutual fund.
• Scheme_Name: Name of the mutual fund scheme.
• Scheme_NAV_Name: Detailed name of the scheme often indicating the specific plan (e.g., Growth, IDCW/Dividend).
• ISIN_Div_Payout/Growth: Unique ISIN (International Securities Identification Number) for dividend payout or growth option of the scheme.
• ISIN_Div_Reinvestment: Unique ISIN for dividend reinvestment option of the scheme.
• ISIN_Div_Payout/Growth/Div_Reinvestment: Comprehensive ISINs covering dividend payout, growth, or dividend reinvestment options.
• Launch_Date: Date when the mutual fund scheme was launched.
• Closure_Date: Date when the mutual fund scheme was closed (if applicable).
• Scheme_Type: How the fund is structured (e.g., Open Ended, Close Ended).
• Scheme_Category: Classification of the scheme based on its investment strategy (e.g., Equity Large Cap, Debt Liquid Fund).
• NAV: Latest Net Asset Value per unit of the fund scheme.
• Latest_NAV_Date: Date on which the latest NAV was declared.
• Scheme_Min_Amt: Minimum investment amount required to invest in the scheme.
• AAUM_Quarter: The quarter for which the average AUM is reported (e.g., January - March 2025).
• Average_AUM_Cr: Average assets under management in crores for the scheme.

⏳ Historical NAV Data: mutual_fund_nav_history.parquet

This file contains the daily time-series of Net Asset Values, crucial for performance analysis and backtesting. It includes:

• Scheme_Code: 🔑 Unique code assigned to a mutual fund scheme. (Links to mutual_fund_data.csv)
• Date: 📅 The specific date for which the NAV is reported (e.g., YYYY-MM-DD).
• NAV: 💰 The Net Asset Value per unit of the fund scheme on the given Date.

This file is designed for efficient storage and quick loading of large historical datasets.

📈 Calculable Metrics & Analyses (from mutual_fund_nav_history.parquet)

The historical NAV data empowers you to quickly derive powerful insights like:

• Performance & Returns 🚀:
  • Absolute & Annualized (CAGR) Returns
  • Rolling & Point-to-Point Returns
  • Daily/Log Returns
• Risk & Volatility 📉:
  • Standard Deviation
  • Sharpe & Sortino Ratios (needs risk-free rate)
  • Max Drawdown
  • Beta & Alpha (needs benchmark data)
• Trends & Momentum 📊:
  • Moving Averages (SMA, EMA)
  • Rate of Change (ROC)
• Comparisons & Market View 🧐 (when combined with scheme data):
  • Fund Performance Rankings
  • Correlations Between Funds
• Basic Stats 🔢:
  • Highest/Lowest NAV over periods
  • Average/Median NAV over periods

⏱️ Update Frequency

• Daily Updates: Both mutual_fund_data.csv (scheme details) and mutual_fund_nav_history.parquet (new daily NAVs) are automatically refreshed every day via a scheduled Kaggle Notebook.
• Data typically reflects the NAV from the previous trading day.
• The historical NAV file (mutual_fund_nav_history.parquet) grows daily with new NAV records for all tracked schemes.

💡 Potential Uses

• ✅ Scheme Discovery & Comparison: Use mutual_fund_data.csv to filter and compare funds based on AMC, category, AUM, etc.
• ✅ Performance Backtesting: Leverage mutual_fund_nav_history.parquet to test investment strategies over historical periods.
• ✅ Trend Analysis: Analyze NAV movements and calculate momentum indicators from the historical data.
• ✅ Risk Assessment: Calculate volatility, Sharpe ratio, and other risk metrics for individual funds.
• ✅ Market Overview: Get a quick snapshot of the Indian mutual fund market structure using the scheme details.
• ✅ Dashboard Building: Create visualizations of the Indian MF landscape, tracking NAVs and performance.

🤝 Contributing

While the data is updated automatically, contributions to improve the README, add analysis examples (e.g., in a separate notebook), or suggest enhancements are welcome! Please feel free to open an issue or submit a pull request.

🙏 Acknowledgements

• Data is sourced from the Association of Mutual Funds in India (AMFI).
• This dataset is compiled for educational and analytical purposes.
• Always consult a financial advisor before making investment decisions.

📄 License

This dataset is shared under the MIT License.