Footprint Trading Dashboard

YEAR

2024

MY ROLE

Full-Stack Development and Deployment

STACK USED

Python | Dash | Plotly

MySQL

HTML/CSS

Linux

INDUSTRY

Finance

View Demo ↗

Project Overview

This Dash-based trading analytics dashboard visualizes candlestick and footprint data in real-time, offering traders deep insight into buy/sell imbalances, volume deltas, and high-activity price zones. I leveraged AI-assisted coding to streamline the development process—particularly for chart rendering, database interactions, and pattern analysis. I architected the MySQL database from scratch, creating the schema, tables, and indexes optimized for time-series market data. The application fetches and processes that data efficiently and renders it using Plotly in an interactive web UI. Deployed on a self-managed Linux server, this self-hosted dashboard offers powerful, fast, and scalable insights tailored to internal trading operations.

Key Features

Real-Time Charting with Dash & Plotly

Displays candlestick charts with volume delta annotations and buy/sell footprint zones.

Interactive controls for symbol selection, timeframes, and manual refresh.

Custom Database & Schema Design

Designed and implemented a custom MySQL schema for storing candlestick and footprint data.

Optimized table structure and indexing for time-series querying and analytical performance.

AI-Assisted Development & Linux Deployment

Used AI tools to accelerate development of layout logic, SQL query handling, and data transformation patterns.

Deployed the entire app on a Linux server, managing environment setup, firewall config, and dependency isolation.

Modular Processing & Visualization Engine

Clean separation of concerns between chart rendering, volume analysis, and database access logic.

Custom Plotly theme, color gradients, and annotation logic for rich visualization output.

Challenges Solved

End-to-End Infrastructure

Managed the full setup from database schema to front-end rendering—including secure deployment and logging for debugging and observability.

Real-Time Volume Profile Visualization

Processed high-frequency trading data and displayed imbalance levels, footprint volume, and price support zones using optimized aggregation logic.

Error Tolerance & Fallback Modes

Implemented dummy data fallbacks, debug info panels, and try-except boundaries for continuous availability even when live data fails.

Project Impact

The project delivered a fully self-hosted, scalable analytics platform tailored for internal trading teams. It improved visibility into price movement patterns, reduced manual market analysis time, and showcased full-stack ownership—from data architecture to deployment and real-time interactivity.

Check other similar projects

Room Booking Platform

This room booking platform offers a seamless online experience where users can easily search and reserve rooms. Once a booking is made, they receive an automatic notification along with a unique booking number, which allows them to manage their reservations effortlessly. Additionally, the platform features an admin dashboard that provides comprehensive tools for monitoring room availability, managing bookings, and accessing detailed analytics for efficient operations.

Time-Tracking App

TimeTracker delivers a comprehensive solution for team time management and absence tracking. Users can log their working hours through an intuitive interface, choosing between duration-based entry or start/end times with break tracking. Time entries can be categorized by projects, with detailed notes for activity context. The application features a robust absence management system, including vacation requests, sick leave tracking, and administrative approval workflows. The admin dashboard provides real-time visibility into team status, working hours, and planned absences, enabling better resource planning. Built with Python/Flask and MySQL for the backend with SQLAlchemy ORM, the application combines Tailwind CSS interface with JavaScript interactions for a seamless user experience.

Portfolio Website

This portfolio website serves as a personal showcase for my technical expertise, projects, and certifications. It features a fully dynamic structure built with Flask and SQLAlchemy, enabling easy updates to content like projects, skills, and achievements. The responsive design ensures optimal viewing across all devices, while visitors can explore categorized projects, view detailed case studies, and get in touch through an integrated contact form. The admin side leverages modular Flask architecture, making the platform scalable and maintainable.

Email Signature Generator

This internal email signature generator was built with Python and Flask to streamline the process of creating consistent, branded email signatures. The web-based tool allows employees to enter or update their details through a simple form, which are then rendered into responsive HTML email templates. This eliminated the need for manual editing or technical support involvement. The system also supports updates to formatting or branding at the template level, automatically applying them across all generated signatures. It has become a self-serve productivity tool that reduces friction and ensures brand consistency in internal and external communications.

Notion API Integration

The Python-Notion Integration with Document-Merge project automates the creation of customized documents by integrating data from multiple Notion databases with a dynamic document generation system built in Python. It fetches and processes participant, event, and venue data, then merges this information into pre-formatted Word document templates using MailMerge. Custom post-processing is applied to enhance formatting, ensuring documents meet business standards while reducing manual effort.

Bank Export Automation Tool (CSV to Lexware)

This Flask-based CSV (Bank Export) Processor streamlines the transformation of exported bank transaction files into a structured accounting-ready format. The app applies custom financial classification logic to parse, categorize, and enhance transaction data—automatically generating seven additional columns, including SKR04 codes, counter-accounts, and period metadata. It produces a cleaned, enriched CSV file nearly ready for direct import into Lexware, effectively replacing manual spreadsheet work. Built with Python, Pandas, and Flask, the tool has eliminated hundreds of hours of repetitive accounting labor.