The problem DhanSetu - NSE Intelligent Investor solves

What can people use it for?

This platform helps retail investors make smarter and safer investment decisions using AI-powered portfolio intelligence. It analyzes real-time market data, insider trades, bulk deals, corporate filings, and technical chart patterns to identify high-potential investment opportunities.

Users can:

-Discover promising stocks using AI-driven signals
-Optimize portfolios with LSTM-based predictions
-Get personalized buy/sell/hold recommendations
-Understand portfolio risk through simple dashboards
-Detect bullish and bearish chart patterns automatically
-Receive plain-English explanations instead of complex financial jargon

The system combines:

-Opportunity Detection
-Chart Pattern Intelligence
-AI Portfolio Optimization
to reduce emotional investing and improve decision-making accuracy.

How does it make existing tasks easier and safer?
Traditional investing requires manually analyzing huge amounts of financial data, which is difficult for beginners and retail investors. Our platform automates this process using AI and real-time analytics.

It makes investing easier by:
-Saving hours of manual research
-Simplifying technical analysis
-Automatically calculating optimal portfolio allocations
-Providing real-time risk analysis and rebalancing suggestions

It makes investing safer by:
-Using multi-signal confirmation before recommendations
-Monitoring risk metrics like Sharpe Ratio, CVaR, and volatility
-Encouraging diversification and disciplined investing
-Providing transparent, explainable AI insights

The goal is to give everyday investors access to institutional-grade portfolio intelligence in a simple and accessible way.

Challenges we ran into

-Integrating multiple real-time financial data sources such as NSE APIs, SEBI filings, bulk deals, insider trades, and market sentiment feeds into a unified pipeline.
-Training and optimizing the LSTM model for accurate stock return prediction while avoiding overfitting and maintaining stable performance during volatile market conditions.
-Combining deep learning predictions with Modern Portfolio Theory (MPT) for dynamic portfolio optimization and risk-adjusted allocation.
-Processing large datasets efficiently and generating real-time signals with low latency.
-Building explainable AI outputs so that complex financial metrics and predictions could be understood easily by retail investors.
-Detecting reliable chart patterns and validating them using historical market data.
-Handling asynchronous backend workflows for optimization, risk analysis, and portfolio rebalancing.
-Designing a beginner-friendly UI that simplifies advanced financial analytics without overwhelming users.
-Ensuring scalability, data reliability, and secure handling of sensitive portfolio information.