DEV Community: Kiaan K

What I Learned Building My First AI-Powered Stock Research App

Kiaan K — Mon, 10 Aug 2026 06:07:48 +0000

Building an AI-powered application sounds straightforward at first.

Get some data.
Send it to an AI model.
Display the answer.

Then you actually build it. 😅

I recently built an AI Stock Research Assistant using Python, Streamlit, Plotly, and financial data APIs.

What started as an idea turned into a much bigger learning experience.

Getting the data is only the beginning

Financial data isn't always as clean or predictable as you expect.

I had to think about things like:

Missing data
API responses
Different time periods
News availability
Stocks with incomplete information
Making sure the application doesn't break when something isn't available

One of the biggest lessons I learned was:

A good application shouldn't assume that every API response will be perfect.

Visualization matters

I added interactive charts so users can actually explore the stock rather than just read numbers.

Using Plotly allowed me to build things like:

Candlestick charts
Volume charts
Interactive price data
Different time periods

This was one of my favorite parts because I could immediately see how a small change in the code affected the entire user experience.

AI analysis needs context

Adding AI to an application isn't just about asking an LLM a question.

The quality of the analysis depends heavily on what information you give the model.

I experimented with structuring the application so that the AI analysis could use relevant stock information rather than simply generating a generic response.

That made me realize something important:

AI is only as useful as the context and data surrounding it.

Building the UI taught me more than I expected

I used Streamlit to turn the Python code into an interactive application.

This also introduced me to problems I hadn't encountered before:

Managing Streamlit components
Handling multiple interactive charts
Debugging UI errors
Organizing application logic
Making the interface understandable for someone who didn't write the code

Some bugs took much longer to fix than I expected. 😅

But honestly, those were some of the most useful parts of the project.

I added an Education Mode

One feature I'm particularly interested in is an Education Mode.

The idea is to make the application useful not only for someone researching a stock, but also for someone learning how financial markets work.

I'm interested in the intersection of:

AI + Finance + Education

and this project has given me a way to experiment with that intersection.

What I'd like to build next

The current version is far from finished.

Some things I'd like to explore next include:

More advanced financial analysis
Better AI reasoning
More educational features
Improved data visualization
Additional research tools
More ways to compare companies

I'm learning as I go, so I'm sure the project will continue changing.

The project

If you'd like to see the code or try it yourself:

GitHub — AI Stock Research Assistant

I'd especially love feedback from developers who have built AI applications, financial tools, or Streamlit projects.

What would you add to this project?

[Boost]

Kiaan K — Mon, 10 Aug 2026 05:43:26 +0000

Kiaan K

Aug 9

I Built an AI-Powered Stock Research Assistant 🚀

#ai #python #machinelearning #opensource

1 min read

What I Learned Building My First AI-Powered Stock Research App

Kiaan K — Sun, 09 Aug 2026 16:07:02 +0000

Building an AI-powered application sounds straightforward at first.

Get some data.
Send it to an AI model.
Display the answer.

Then you actually build it. 😅

I recently built an AI Stock Research Assistant using Python, Streamlit, Plotly, and financial data APIs.

What started as an idea turned into a much bigger learning experience.

Getting the data is only the beginning

Financial data isn't always as clean or predictable as you expect.

I had to think about things like:

Missing data
API responses
Different time periods
News availability
Stocks with incomplete information
Making sure the application doesn't break when something isn't available

One of the biggest lessons I learned was:

A good application shouldn't assume that every API response will be perfect.

Visualization matters

I added interactive charts so users can actually explore the stock rather than just read numbers.

Using Plotly allowed me to build things like:

Candlestick charts
Volume charts
Interactive price data
Different time periods

This was one of my favorite parts because I could immediately see how a small change in the code affected the entire user experience.

AI analysis needs context

Adding AI to an application isn't just about asking an LLM a question.

The quality of the analysis depends heavily on what information you give the model.

I experimented with structuring the application so that the AI analysis could use relevant stock information rather than simply generating a generic response.

That made me realize something important:

AI is only as useful as the context and data surrounding it.

Building the UI taught me more than I expected

I used Streamlit to turn the Python code into an interactive application.

This also introduced me to problems I hadn't encountered before:

Managing Streamlit components
Handling multiple interactive charts
Debugging UI errors
Organizing application logic
Making the interface understandable for someone who didn't write the code

Some bugs took much longer to fix than I expected. 😅

But honestly, those were some of the most useful parts of the project.

I added an Education Mode

One feature I'm particularly interested in is an Education Mode.

The idea is to make the application useful not only for someone researching a stock, but also for someone learning how financial markets work.

I'm interested in the intersection of:

AI + Finance + Education

and this project has given me a way to experiment with that intersection.

What I'd like to build next

The current version is far from finished.

Some things I'd like to explore next include:

More advanced financial analysis
Better AI reasoning
More educational features
Improved data visualization
Additional research tools
More ways to compare companies

I'm learning as I go, so I'm sure the project will continue changing.

The project

If you'd like to see the code or try it yourself:
https://github.com/KiaanKothari/AI-Stock-Research-Assistant

I'd especially love feedback from developers who have built AI applications, financial tools, or Streamlit projects.

What would you add to this project?

[Boost]

Kiaan K — Sun, 09 Aug 2026 11:19:43 +0000

Kiaan K

Aug 9

I Built an AI-Powered Stock Research Assistant 🚀

#ai #python #machinelearning #opensource

1 min read

I Built an AI-Powered Stock Research Assistant 🚀

Kiaan K — Sun, 09 Aug 2026 05:29:32 +0000

Hi everyone! 👋

I’m a high school student interested in AI, programming, and quantitative research, and I’ve been working on building projects that combine these areas.

Recently, I built an AI Stock Research Assistant using Python, Streamlit, Plotly, and financial data APIs.

The goal was to create a tool that makes stock research more accessible by bringing different pieces of information together in one place, including:

📈 Stock price and performance data
📊 Interactive charts
📰 Financial news
🤖 AI-powered analysis
🎓 An education mode for students learning about financial markets

I built the project from scratch and have been continuously adding features and improving it based on what I learn.

🔗 GitHub: https://github.com/KiaanKothari/AI-Stock-Research-Assistant

This is also my first time sharing one of my projects on DEV Community, so I’d love to hear what you think!

If you’re also building projects with AI, Python, finance, or data science, feel free to share what you're working on. I'd love to check them out and learn from the community. 🚀