Built an AI Stock Advisor That Tracks, Analyzes, and Remembers, Cutting Manual Research by 75%

The Problem: Too Many Tools, Too Much Time

A high-net-worth investor came to Product Siddha with a clear problem:

They were spending too much time switching between apps, websites, and spreadsheets just to track their portfolio.

Here’s what wasn’t working:

• Manual research across platforms like Groww and Screener
• No smart system to track financial ratios or market trends
• No way to remember personal investment preferences
• No personalized advice based on real portfolio data
• Repetitive tasks and API overuse, leading to unnecessary costs

What the client wanted was simple:

A smart, memory-aware AI stock advisor that could save time, reduce research work, and give custom investment insights based on their risk profile.

The Solution: A Personalized AI Investment Assistant

Product Siddha designed a lightweight, cost-efficient AI system built specifically for the Indian equity market. It was more than just a dashboard; it was a true assistant that remembered, learned, and adapted to its users’ needs.

Here’s how we did it:

1. Live Portfolio Tracking via Groww API
   We connected the client’s brokerage account to pull real-time data on holdings, stock prices, and order history, automatically and securely.
2. Fundamentals via Screener.in Scraper
   To track stock health, the system pulled data like:
   • • P/E Ratio
     • ROCE
     • Debt-to-Equity
     • Dividend Yield

   This allowed smart filtering of high-risk or low-return stocks.

3. Built-In Technical Analysis Engine
   Custom code to calculate:
   • • RSI (Relative Strength Index)
     • MACD (Moving Average Convergence Divergence)
     • SMA (Simple Moving Average)
     • These helped identify overbought or oversold conditions, giving timely signals for entries and exits.

   These helped identify overbought or oversold conditions, giving timely signals for entries and exits.

4. Conversational AI Using OpenAI 4.1
   We trained the AI to understand:
   • The client’s risk tolerance
   • Investment goals (capital preservation + dividend income)
   • Preferences for industry sectors and stock types
   • This made every recommendation personal and context-aware.

   This made every recommendation personal and context-aware.

5. Smart Memory via Supabase (Postgres)
   Every user interaction, stock preference, and past recommendation was stored securely.That way, the assistant could say things like:”You previously reduced Tata Motors due to cyclicality. Do you want to review that decision again based on new signals?”
6. Automation Layer with n8n
   We added automation to:
   • Orchestrate workflows
   • Control API usage (to avoid overbilling)
   • Trigger insights only when market conditions change

   This kept the system efficient, scalable, and low-cost.

Real Use Cases: Smart Actions, Not Just Alerts

The AI assistant didn’t just pull data; it gave smart, actionable suggestions.

Reduce exposure to Tata Motors & JSW Steel

• Both were showing cyclical risk and overbought signals (via RSI/MACD)
• AI flagged and suggested gradual exits

Increase allocation to Power Grid, HDFC Bank, and ITC

• Low debt, stable income, and consistent dividends
• Matched the client’s conservative investment goals

Adaptive re-entry alerts

• If technical indicators improved, the AI suggested when to consider buying back, while reminding the user of their original risk profile

The Outcome: Research Time Cut by 75%, Confidence Boosted

With this system in place, the investor no longer needed to:

• Jump between platforms
• Track charts manually
• Second-guess buy/sell decisions

Key Wins:

• Daily stock insights customized to client holdings
• Tailored recommendations for lower-risk, high-dividend picks
• Clear visibility into stock fundamentals and technical indicators
• 75% reduction in time spent on portfolio research
• Minimal API usage, low cost, high performance
• History tracking: See how and why decisions were made over time

Bonus: Ready for Other Markets Too

This AI stock advisor was built for Indian stocks, but the same system works in other markets.

For U.S. Markets, we can plug in:

• Broker APIs like Robinhood, Alpaca, and Interactive Brokers
• Data from Yahoo Finance, Alpha Vantage, and Finviz
• News and fundamentals via Seeking Alpha

The architecture remains the same, making it easy to adapt for global equity, ETFs, or crypto.

Conclusion: Personalized Investment Automation That Learns

With Product Siddha’s smart AI setup, this investor gained:

• More time
• More confidence
• Smarter decisions

All without hiring a portfolio manager.

If you’re an investor looking to automate your stock research, reduce manual work, and get insights tailored to you, Product Siddha can help.

Let’s build an AI stock advisor that works like your second brain.