DEV Community

Cover image for Vibe Coded AI Stock Analyst with Amazon Q in 1 Hour!
Amit Wani
Amit Wani

Posted on

Vibe Coded AI Stock Analyst with Amazon Q in 1 Hour!

This is a submission for the Amazon Q Developer "Quack The Code" Challenge: Crushing the Command Line & Exploring Possibilities

What I Built 🛠️📈

I have built Stock Pulse. Stock Pulse is a Python tool that analyzes stocks using financial metrics, news trends, and AI-powered sentiment analysis. It provides investment recommendations (BUY, SELL, HOLD) with detailed reasoning based on comprehensive analysis.

I have used Amazon Q Developer CLI to Vibe Code this in under 1 hour without writing a single line of code. I have recorded my full vibe-coding session. It can be found in the Demo section below.

Features ✨

  • 🔍 Analyze multiple stocks at once
  • 💹 Fetch financial metrics and news data using yfinance
  • 🤖 Utilize OpenAI's GPT-4o-mini model for intelligent analysis
  • 📈 Generate investment signals with detailed reasoning
  • ⚠️ Identify key factors and potential risks
  • 💻 Command-line interface and Streamlit web UI

Screenshots 🖼️

Stock Pulse CLI

Stock Pulse CLI - Analysis Output

Stock Pulse CLI - Stock News

Stock Pulse CLI - Metrics

Stock Pulse UI - Analysis Output 1

Stock Pulse UI - Analysis Output 2

Stock Pulse UI - Stock News

Demo 🎬

Streamlit Link: here
Vibe-Coding Session Demo:

Code Repository 🗃️

GitHub logo mtwn105 / stock-pulse

Feel the pulse of the market with AI-powered stock insights. Vibe Coded with Amazon Q Developer CLI in 1 hour!

Stock Pulse

Feel the pulse of the market with AI-powered stock insights.

Vibe Coded with Amazon Q Developer CLI in 1 hour!

Overview

Stock Pulse is a Python tool that analyzes stocks using financial metrics, news trends, and AI-powered sentiment analysis. It provides investment recommendations (BUY, SELL, HOLD) with detailed reasoning based on comprehensive analysis.

Features

  • Analyze multiple stocks at once
  • Fetch financial metrics and news data using yfinance
  • Utilize OpenAI's GPT-4o-mini model for intelligent analysis
  • Generate investment signals with detailed reasoning
  • Identify key factors and potential risks
  • Command-line interface and Streamlit web UI

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/stock-pulse.git
    cd stock-pulse
    
  2. Install dependencies using Poetry:

    poetry install
    
  3. Create a .env file with your OpenAI API key:

    cp .env.example .env
    

    Then edit the .env file to add your OpenAI API key.

Usage

Command Line Interface

Analyze stocks using the command line:

# Analyze a single stock
poetry run python
Enter fullscreen mode Exit fullscreen mode

How I Used Amazon Q Developer 🤖⚡

I have used Amazon Q Developer CLI to Vibe Code this project in under 1 hour without writing a single line of code manually.

Initial Prompt 📝

I kickstarted the development by giving Amazon Q a single, well-crafted prompt:

i want to create a stock analysis script in python using langchain and langgraph with using openai llm with gpt-4o-mini model. 

use poetry for deps management. poetry is already installed on the system. 

use yfinance tools from langchain for news and price data. use last 1 year price return. 
also use other financial metrics from yfinance like roce, roe, etc. and llm will analyze and return recommendation with signal - BUY,SELL,HOLD and reasoning for it. 

accept list of tickers as argument to the script. 

use poetry to setup project and add deps. dont create pyproject.toml by yourself. 

use python best practices and folder structure for a cli.
Enter fullscreen mode Exit fullscreen mode

Amazon Q responded by generating an organized, ready-to-run CLI project using Poetry, fetching real-time stock data, and integrating GPT-4o-mini for analysis and recommendations.

Iterative Development with Q 🔁💡

I continued to use Amazon Q Developer CLI throughout the process for:

  • Debugging and resolving errors by simply pasting the error logs into Q.
  • Requesting code refactors and feature enhancements (e.g., adding streamlit UI or news items in output).
  • Running Git commands and pushing my code without switching terminals.

Zero Manual Edits ✋❌

From project initialization to the final commit, I did not write or modify any code manually. Every change—big or small—was executed through Amazon Q Developer CLI.

To show full transparency and the incredible capability of Amazon Q, I’ve recorded my entire coding session, which you can find in the demo video above.

Why Amazon Q Was a Game-Changer 💥🎯

Amazon Q isn’t just another coding assistant—it’s an AI developer. It allowed me to stay in flow, innovate rapidly, and push creative boundaries, eliminating friction from the development lifecycle.

From initializing the project with Poetry to handling dependencies, coding logic, bug fixing, git commands, and even feature iterations—Amazon Q handled everything like a true coding copilot. I never had to touch the code manually.

I’ve also recorded my entire coding session (watch the demo video above) to showcase how seamlessly Q guided me through the entire development lifecycle.

Conclusion ✅🎉

Participating in the Amazon Q Developer "Quack The Code" Challenge was a fun and productive experience. I built an intelligent UI & CLI-based stock analysis tool using LangChain, LangGraph, and OpenAI—all within an hour—thanks to the power of Amazon Q Developer CLI.

This project proved that with the right tools and a powerful AI assistant like Amazon Q Developer, building complex and intelligent applications becomes fast, efficient, and even enjoyable.

Top comments (0)