Client Background

  • Client Name: Confidential (Fintech / EdTech)
  • Industry Type: Financial Services
  • Products & Services: Trading Education Platform
  • Organization Size: 100+
  • About Client: An educational platform providing risk-free trading simulations for aspiring investors.

The Problem

The client needed a realistic trading environment to simulate market actions without risking real capital. They required real-time data ingestion and professional-grade charting tools to mimic a live brokerage account.

Our Solution

I built a simulation platform using FastAPI for high-speed backend processing and Jinja2 for server-side rendering.

  • Real-Time Data: Integrated the Databento API to fetch and stream live market data to the platform.
  • Visualization: Embedded and configured TradingView Widgets on the frontend.
  • Simulation Engine: Developed backend logic to execute “paper trades” against live market prices.

Solution Architecture

  • Frontend: Jinja2 Templates + TradingView JS Library.
  • Backend: FastAPI (Python).
  • Data Feed: Databento WebSocket API.
  • State Management: Redis (for session data).

Deliverables

  • Functional Trading Dashboard.
  • Paper Trading Backend Engine.
  • Market Data Integration Module.

Tech Stack

  • Framework used: FastAPI, Jinja2
  • Language/techniques used: Python, WebSockets
  • Models used: Time-Series Data Processing
  • Skills used: Fintech Development, WebSocket Management
  • Databases used: PostgreSQL (Trade Logs), Redis (Live Data)
  • Web Cloud Servers used: NA

What are the technical challenges faced during project execution

  • Managing WebSocket stability to ensure continuous data streaming from Databento without packet loss.
  • Synchronizing the frontend TradingView charts with the backend simulation engine execution prices.

How the Technical Challenges were Solved

  • Implemented an auto-reconnect logic with exponential backoff for the WebSocket connections to handle network jitter.
  • Used a shared Redis cache to store the “Last Traded Price” (LTP), ensuring both the chart and the trade engine referenced the exact same data point.

Business Impact

Created a high-fidelity simulation environment that allows users to practice trading strategies with real-time market accuracy, effectively serving as a risk-free training ground.

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