Quant Trading Labs

Universal Multi‑Ticker Intraday Engine (v1.0)

This project is part of my Quant Trading Labs series, designed to build universal, modular, and reproducible trading research environments. It includes multi‑ticker support, intraday charting, backend API integration, and secure environment variable workflows for professional‑grade quant development.

🔍 Overview

Quant Trading Labs is my flagship initiative to build universal, modular, and reproducible trading research environments. This project introduces a multi‑ticker intraday engine designed for real‑time market tracking, secure data access, and professional‑grade quant experimentation.

It blends Python, Streamlit, backend APIs, and PowerShell automation into a single, elegant research workflow.

🎯 Objectives

  • Develop a ticker‑agnostic quant engine for intraday and historical analysis
  • Provide reliable, secure market data through a backend API
  • Enable modular research workflows for rapid experimentation
  • Support charting, CSV export, and candlestick visualization
  • Create a foundation for future quant tools, bots, and strategy engines

🛠️ Tech Stack

  • Python (data processing, charting, API integration)
  • Streamlit (UI, charts, CSV export)
  • Custom Backend API (data reliability, bypassing network blocks)
  • PowerShell (automation rituals, environment setup)
  • Environment Variables (API key protection)

📦 Key Features

1. Multi‑Ticker Dropdown

Select any ticker and instantly load intraday data.

2. Intraday Line Charts

Clean, responsive charts for real‑time analysis.

3. Candlestick Visualization

Professional‑grade OHLC charts for deeper insights.

4. CSV Export

Download processed intraday data with a single click.

5. Secure API Access

Environment variables ensure zero key leakage.

6. Modular Architecture

Every component is isolated, reusable, and production‑ready.

🧱 Architecture

Code

/data/        → Raw + processed datasets  
/scripts/     → Automation utilities (PowerShell + Python)  
/api/         → Backend data provider for reliable market access  
/utils/       → Helper functions (formatting, validation, logging)  
/reports/     → Academic + client deliverables

This structure ensures clarity, reproducibility, and scalability.

📊 Workflow Summary

  1. User selects ticker
  2. Backend API fetches intraday data
  3. Python processes + cleans data
  4. Streamlit renders charts
  5. User downloads CSV or continues analysis
  6. Logs + reports stored for academic or client use

📸 Screenshots

(Upload these in your WordPress editor)

  • Dashboard home
  • Multi‑ticker dropdown
  • Intraday line chart
  • Candlestick chart
  • CSV export panel

📜 Academic Deliverables (GNDU‑Style)

This project includes a full academic documentation suite:

  • Certificate Page
  • Abstract
  • Introduction
  • System Design
  • Code Explanation
  • Output Screenshots
  • Conclusion
  • Viva Questions

Perfect for students, trainees, and client submissions.

🚀 Deployment

  • Streamlit Cloud (primary)
  • Vercel (optional frontend)
  • Local PowerShell automation for reproducible runs
  • Backend API for global reliability

📌 Status

Active — v1.0 released Backend API v2.0 in development for faster, more reliable data.

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