🚀 IndiaQuant MCP – AI-Powered Market Intelligence System

Production-ready AI system for real-time stock analysis, trading signals, and portfolio simulation using FastAPI and MCP architecture.

⚡ Built with:

• FastAPI backend

• Real-time APIs (yfinance, NewsAPI)

• Options analytics + Black-Scholes Greeks

• Portfolio simulation (SQLite)

• AI-agent compatible MCP tools

👉 Designed as a modular system for real-world financial intelligence applications

IndiaQuant MCP is a real-time AI-powered market intelligence system built using the Model Context Protocol (MCP).
It provides live stock data, trading signals, options analytics, sentiment analysis, and portfolio simulation using 100% free APIs.

The system exposes these capabilities as MCP-compatible tools so an AI agent (like Claude Desktop) can query and analyze financial markets in real time.

Project Architecture

AI Agent (Claude / AI Assistant)
        │
        ▼
MCP Tool Server (FastAPI)
        │
        ├── Market Data Engine
        ├── Signal Generator
        ├── Options Analyzer
        ├── Greeks Calculator
        ├── Portfolio Manager
        ├── Sentiment Analyzer
        ├── Market Scanner
        └── Sector Heatmap
        │
        ▼
External APIs
   ├── Yahoo Finance (yfinance)
   ├── NewsAPI
   └── Alpha Vantage (optional)

The system is designed as a modular financial intelligence platform, where each component provides a specific capability.

Project Structure

indiaquant-mcp
│
├── app
│   ├── market_data
│   ├── signals
│   ├── options
│   ├── analytics
│   ├── portfolio
│   ├── decision          # decision layer v1 (normalize → fuse → validate)
│   └── mcp
│       ├── mcp_server.py    # FastAPI + OpenAPI
│       └── stdio_server.py  # native MCP stdio (Claude Desktop / Cursor)
│
├── docs
│   └── decision_layer_first_draft.md
│
├── tests
│   └── test_decision_engine.py
│
├── .github/workflows
│   └── ci.yml
│
├── screenshots
│   ├── live_price.png
│   ├── signal.png
│   ├── trade.png
│   └── heatmap.png
│
├── main.py
├── pytest.ini
├── CHANGELOG.md
├── requirements.txt
└── README.md

MCP Tools Implemented

The following MCP / HTTP tools are implemented.

All tools return live market data using free APIs where applicable. See docs/decision_layer_first_draft.md for schema, examples, and fusion rules; CHANGELOG.md summarizes decision-layer v1. Run tests: pytest (see pytest.ini). CI: .github/workflows/ci.yml.

Core Modules

Market Data Engine

Uses yfinance to fetch real-time market data.

Capabilities:

• Live stock prices

• Historical OHLC data

• Volume and price change analysis

• Supports NSE and global stocks

Example response:

{
"symbol": "RELIANCE",
"price": 1418.6,
"change_percent": 3.17,
"volume": 34897
}

AI Trade Signal Generator

Generates trading signals using technical indicators:

Indicators used:

• RSI

• MACD

• Bollinger Bands

Signal output: BUY / SELL / HOLD confidence score

Example:

{
"symbol": "RELIANCE",
"signal": "BUY",
"confidence": 40
}

Options Chain Analyzer

Retrieves options data and performs analysis including:

• Open Interest tracking

• Max Pain calculation

• Options volume comparison

• Unusual activity detection

This helps identify potential institutional trading behavior.

Greeks Calculator

Implements the Black-Scholes model from scratch.

Greeks calculated:

• Delta

• Gamma

• Theta

• Vega

Example:

{
  "delta": 0.2265,
  "gamma": 0.026248,
  "theta": -0.06355,
  "vega": 0.172592
}

Portfolio Risk Manager

Simulates a virtual trading portfolio using SQLite.

Features:

• Place virtual buy/sell trades

• Track portfolio positions

• Real-time PnL calculation

• Trade history storage

Example: POST /place_virtual_trade

{
"symbol": "RELIANCE",
"qty": 1,
"side": "BUY"
}

Sentiment Analysis

Uses NewsAPI to analyze market sentiment from financial news.

Process:

1. Fetch recent headlines

2. Score sentiment based on keywords

3. Generate sentiment signal

Example output:

{
  "symbol": "RELIANCE",
  "sentiment_score": 2,
  "signal": "POSITIVE"
}

Market Scanner

Scans multiple stocks to find oversold opportunities.

Criteria: RSI < 30

Example response:

[
{
"symbol": "AAPL",
"RSI": 28.3,
"signal": "OVERSOLD"
}
]

Sector Heatmap

Analyzes sector performance by aggregating stock movements.

Example output:

[
{"sector": "IT", "change_percent": 0.35},
{"sector": "BANKING", "change_percent": -2.82},
{"sector": "ENERGY", "change_percent": -0.78},
{"sector": "AUTO", "change_percent": -4.6}
]

Technologies Used

Core stack:

• Python

• FastAPI

• SQLite

• yfinance

• pandas

• numpy

• NewsAPI

Libraries: fastapi uvicorn pandas numpy yfinance newsapi-python sqlite3

Installation

Clone repository

git clone https://github.com/sowjanya5751/indiaquant-mcp.git

cd indiaquant-mcp

Create virtual environment

Windows

python -m venv venv venv\Scripts\activate

Linux / Mac

python -m venv venv source venv/bin/activate

Install dependencies

pip install -r requirements.txt

Running the MCP Server

Related MCP server: finstack-mcp

Option A — FastAPI (HTTP tools + OpenAPI)

Start the server:

cd indiaquant-mcp
pip install -r requirements.txt
uvicorn app.mcp.mcp_server:app --reload

Server will start at:

http://127.0.0.1:8000

Option B — Native MCP (stdio, Claude Desktop / Cursor)

The repo also exposes an official MCP server over stdio using the Python mcp SDK (FastMCP), including fuse_market_decision and core market tools.

From the repo root:

PYTHONPATH=. python -m app.mcp.stdio_server

Example Claude Desktop (claude_desktop_config.json) fragment:

{
  "mcpServers": {
    "indiaquant": {
      "command": "python3",
      "args": ["-m", "app.mcp.stdio_server"],
      "cwd": "/absolute/path/to/indiaquant-mcp",
      "env": {
        "PYTHONPATH": ".",
        "NEWSAPI_KEY": "your-key-optional"
      }
    }
  }
}

API Endpoints

API Documentation

Interactive API documentation is available at:

http://127.0.0.1:8000/docs

Swagger UI allows testing all MCP tools directly.

Design Decisions

FastAPI was chosen because:

• High performance async framework

• Automatic API documentation

• Ideal for MCP tool integration

SQLite was used because:

• Lightweight database

• Perfect for portfolio simulation

• Easy local deployment

yfinance provides:

• Free stock market data

• Historical price access

• Options chain support

Future Improvements

Possible extensions:

• Real-time WebSocket streaming

• Machine learning trading models

• Redis caching for faster data retrieval

• Cloud deployment (AWS / GCP)

• Advanced portfolio risk analytics

Assignment Requirements Fulfilled

✔ Real-time market data
✔ 10 MCP tools implemented
✔ Options analysis and Greeks calculation
✔ Sentiment analysis using NewsAPI
✔ Virtual trading portfolio
✔ Modular system architecture
✔ API-based MCP server compatible with AI agents

API Demo

Live Price

Trade Signal

Portfolio Trade

Sector Heatmap

Conclusion

IndiaQuant MCP demonstrates how AI agents can interact with financial markets through modular tools and real-time data pipelines.

The system combines quantitative analysis, market intelligence, and AI integration into a unified platform capable of supporting advanced trading insights.