📊 Institutional Tracker Dashboard (Smart Money Flow)

The Institutional Tracker Dashboard is a powerful analytical tool built with Streamlit that provides real-time, aggregated insights into institutional (or "Smart Money") buying and selling activity across defined index scopes (e.g., Nifty 50, Nifty 200, Nifty 500).

It visualizes a proprietary Institutional Score derived from volume, delivery, open interest, momentum, and VWAP-based indicators — helping traders and analysts identify high-conviction accumulation trends and sector rotation opportunities.

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✨ Features

📈 Ticker Scorecard

• Detailed, sortable table of all stocks with computed Institutional Score.
• Includes supporting metrics:
  • Momentum30 – 30-day price thrust
  • Delivery % – Genuine accumulation measure
  • OI Change % – Futures open interest trend
  • OBV Trend – Volume-based confirmation
  • VWAP Gap % – Institutional cost basis proximity
• Color-coded Smart Money Trend to highlight strong vs. weak accumulation.

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🔄 Sector Rotation Graph

• An RRG-style (Relative Rotation Graph) scatter plot that compares:
  • X-Axis: Average 30-day Momentum
  • Y-Axis: Average Sector Institutional Score
• Identifies sectors in four phases:
  • 🟢 Leading – Strong accumulation & momentum
  • 🔵 Improving – Accumulation rising, momentum yet to confirm
  • 🔴 Lagging – Weak in both
  • 🟡 Waning – Momentum high, accumulation cooling

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🌐 Market Breadth Overview

• Aggregated analysis comparing Institutional Score vs. SMT Trendline.
• Detects whether overall accumulation is accelerating or decelerating.
• Highlights the number of stocks above key score thresholds (55, 75).

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💡 Advanced Insights

• Flags mean-reversion setups based on VWAP divergence.
• Evaluates accumulation persistence by comparing OI trend and Delivery%.
• Enables sector-level relative strength tracking for swing or positional strategies.

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🛠️ Installation and Setup

Prerequisites

• Python 3.9+
• Internet connection (for live NSE data via nselib)

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Step 1: Clone the Repository

git clone <your-repository-url>
cd institutional_tracker

Step 2: Create a Virtual Environment (Recommended)

On Linux/macOS

python -m venv venv
source venv/bin/activate

On Windows

python -m venv venv
.\venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Prepare the Data

Before running the dashboard, generate the institutional report using:

python main.py --index-scope 500 or 200 or 100 or 50

This will produce a file like:

exports/nifty500_INSTITUTIONAL_REPORT_20251011.xlsx

🚀 Running the Dashboard

Once setup is complete, launch the dashboard from the project root:

streamlit run src/dashboard.py

The dashboard will automatically open in your web browser.

📂 Project Structure

institutional_tracker/ ├── main.py ├── README.md ├── requirements.txt │ ├── config/ │ ├── config.py │ ├── data/ │ ├── delivery/ # Historical delivery % data │ ├── oi/ # F&O Open Interest snapshots │ ├── sectors/ # Sector-wise aggregated data │ │ ├── nifty50_sectors.csv │ │ ├── nifty100_sectors.csv ← auto-generated if missing │ │ ├── nifty200_sectors.csv ← auto-generated if missing │ │ ├── nifty500_sectors.csv ← auto-generated if missing │ ├── tickers/ # Nifty constituent CSVs │ │ ├── ind_nifty50list.csv │ │ ├── ind_nifty100list.csv │ │ ├── ind_nifty200list.csv │ │ ├── ind_nifty500list.csv │ ├── exports/ # Generated institutional score reports │ ├── nifty50_INSTITUTIONAL_REPORT_20251011.xlsx │ ├── src/ │ ├── dashboard.py # Streamlit-based dashboard │ ├── institutional_score.py # Institutional score computation logic │ ├── sector_aggregator.py # Sector-wise aggregation and RS calculation │ ├── data_loader.py # Data loading and auto-generation logic │ ├── utils/ │ │ ├── data_processing.py # Helper functions and transformations │ │ ├── init.py │ ├── init.py

📊 Scoring Model Overview

Metric	Weight	Range	Interpretation
Delivery %	30%	40–80	Genuine accumulation
OI Change %	25%	-5–10	F&O build-up strength
Momentum (30D)	15%	-10–20	Short-term price thrust
OBV Trend	15%	-0.5–0.5	Volume confirmation
VWAP Gap %	15%	-2–+2	Institutional cost proximity

Final Institutional Score = Weighted normalized composite (0–100 scale)

🧠 Future Enhancements (Planned)

• 📅 Multi-timeframe scoring (Weekly, Monthly)

• 🔔 Alert generation for strong accumulation breakouts

• 🧭 Watchlist integration with TradingView links

• 📈 Trend persistence and Smart Money reversal visualization

• ☁️ Optional cloud-based update scheduler

🙌 Acknowledgments

• NSE India API (via nselib)
• Yahoo Finance (yfinance) for price and volume data
• Plotly & Streamlit for visualization

🧑‍💻 Author

Aryashree Pritikrishna

Principal Architect | Full Stack, Cloud & Data Engineering

🔗 LinkedIn | GitHub