Real-Time Stock Analytics Dashboard

Overview

A real-time stock analytics system that streams stock data from Chartink, stores it in MongoDB, and visualizes it with Streamlit. Detects anomalies in stock prices using moving averages and provides both live and historical views.

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Features

• ⚡ Real-time streaming with Apache Kafka
• 🗃️ Persistent storage using MongoDB
• 📊 Interactive dashboard with Streamlit & Plotly
• 🧮 Anomaly detection via moving averages
• 🕒 View live or historical stock data

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Tech Stack

Component	Purpose
Python	Core programming language
Apache Kafka	Real-time message streaming
MongoDB	NoSQL storage for historical stock data
Streamlit	Web-based visualization
Plotly	Interactive charts

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Setup

Prerequisites

• Python 3.8+
• Kafka broker (local or Docker)
• MongoDB server running locally or in Docker

Installation

# Clone the repo
git clone https://github.com/your-username/real-time-stock-analytics.git
cd real-time-stock-analytics

# Install dependencies
pip install -r requirements.txt

Running the Project

1. Start Kafka & MongoDB
2. Run Producer

python stock_producer.py

3. Run Consumer

python stock_consumer.py

4. Launch Dashboard

streamlit run app.py

Open your browser at http://localhost:8501

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Project Structure

real-time-stock-analytics/
│
├── stock_producer.py      # Fetches data & sends to Kafka + MongoDB
├── stock_consumer.py      # Consumes Kafka data & stores in MongoDB
├── app.py                 # Streamlit dashboard
├── requirements.txt       # Dependencies
└── README.md              # Documentation

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Results

• Live streaming of stock data every 60 seconds
• Automatic moving average calculations
• Anomalies highlighted in dashboard
• Interactive charts for trends & analysis