⚡ Python library for getting publically available Mutual Funds data in India

Parser for Consolidated Account Statements (CAS) generated from CAMS/Karvy/Kfintech

⚡ This Project gives you an overall idea for Forecasting Mutual Funds

Indian Mutual Funds Portfolio Manager

A POC for a mutual fund listing app with a user signup/signin flow that displays multiple mutual funds in a scrollable list. Certain essential data displayed on every item in this list. On click of any of these items, the user will be redirected to a product details page containing additional details of that specific mutual fund.

ISIN database for casparser

📊 Data-driven mutual fund allocation planner for Indian investors. Input your age, income, risk profile to receive personalized asset allocation recommendations across equity (large/mid/small-cap) and debt funds. Built with Streamlit + Python for offline financial planning.

🚀 MCP Server for publicly available real-time Indian Mutual Funds data

To analyze expected return/loss of several investment plans

The CAS Parser Agent Toolkit is a comprehensive collection of skills, templates, and documentation designed to empower AI coding agents to build, integrate, and manage Fintech applications using CAS Parser

historical-mf-nav-data is a high-performance, automated data engine and Java library designed to provide seamless access to historical Mutual Fund (MF) Net Asset Value (NAV) data for India. It bridges the gap between raw regulatory data (AMFI) and developer-ready applications by providing a pre-indexed SQLite database and a Spring Boot auto config

CAS Parser allows you to track Consolidated Account Statement (CAS PDF) portfolios from NSDL, CDSL, CAMS, KFintech - CAS Parser API Client - PHP

CAS Parser allows you to track Consolidated Account Statement (CAS PDF) portfolios from NSDL, CDSL, CAMS, KFintech - CAS Parser API Client - NPM

📈 Manage your investments effortlessly with this open-source Android app, offering real-time fund tracking and analysis tools for smarter decisions.

🛠️ Parse NTFS on-disk structures directly to access hidden files and metadata, empowering red teams with advanced data retrieval capabilities.