Real-Time Footprint Chart for Stock Market Analysis

This project is a web-based, real-time footprint chart visualization tool for stock market data. It is built with a Python Flask backend that connects to a broker's WebSocket for live tick data, processes it into candles and footprint bars, and streams the updates to a modern, interactive frontend built with Lightweight Charts.

Features

• Real-Time Data Streaming: Connects to brokers via WebSocket using a decoupled architecture for ultra-low latency processing.
• Advanced Orderflow Visualization: Detailed footprint bars, volume profiles, delta modes, and diagonal imbalances.
• Optimized Performance: Backend powered by eventlet and SocketIO for handling high-frequency tick data smoothly.
• Historical Data Management: Robust tick-level storage in SQLite with built-in CSV import tools for offline analysis.
• Interactive Navigation: "Goto Date" feature and persistent UI settings for a seamless analysis workflow.

Technology Stack

• Backend: Python, Flask, eventlet (Production-grade asynchronous server)
• Real-time Communication: SocketIO (Main), Server-Sent Events (Legacy support)
• Broker Integration: SmartAPI
• Database: SQLite (Tick-level storage)
• Frontend: HTML5, JavaScript, Lightweight Charts v4.2+

Setup and Installation

Follow these steps to get the application running on your local machine.

1. Clone the Repository

git clone https://github.com/Alex-dev-angel/order-flow-chart
cd order-flow-chart

2. Create a Virtual Environment

It is highly recommended to use a virtual environment to manage project dependencies.

# For Windows
python -m venv venv
venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

Install all the required Python packages using the requirements.txt file.

pip install -r requirements.txt

4. Configure Environment Variables

The project uses a .env.example file as a template for the required credentials.

First, make a copy of this file and name it .env:

# For Windows
copy .env.example .env

# For macOS/Linux
cp .env.example .env

Next, open the newly created .env file and replace the placeholder values with your actual broker credentials and settings.

# Contents of your .env file
API_KEY="YOUR_API_KEY"
CLIENT_CODE="YOUR_CLIENT_CODE"
PASS="YOUR_LOGIN_PASSWORD"
AUTH_TOKEN="YOUR_2FA_SECRET_KEY"  # The secret key from your 2FA app, not the 6-digit code
INSTRUMENT_TOKEN="53001" # Example: NIFTY Token
LOTSIZE=75 # Example: Lot size for the instrument
DB_NAME="trading_data_ticks.db"

Note: The .env file contains sensitive information and should never be committed to version control. The .gitignore file should already be configured to ignore it.

6. Run the Application

Start the optimized application using the following command:

python app_optimized.py

The application will be accessible at http://localhost:5002.

Data Management

Importing Historical Data

You can import historical tick data from CSV files into the database:

python import_csv_to_db.py --csv "your_data.csv" --instrument "NIFTY" --lotsize 75

See CSV_IMPORT_GUIDE.md for more details.

How It Works

1. Decoupled Ingestion: Raw tick data is received from the broker and instantly queued.
2. Tick Processing: A dedicated background worker processes the queue, calculating trade direction and delta.
3. Batch Storage: Processed ticks are written to the database in batches to minimize disk I/O and prevent lag.
4. Interactive Frontend: The UI provides specialized orderflow modes (Delta, Profile, Dual Side) and communicates via SocketIO for real-time updates.

---