Built a fully autonomous 24/7 automation platform that runs on a remote Linux server with zero human intervention.

The system scrapes live data from a heavily obfuscated web application, where CSS class names are randomized on every single page load. I built a custom DOM traversal algorithm using Selenium that bypasses this by using relational element positioning and SVG path analysis instead of standard selectors.

On top of the scraper, I built a statistical decision engine backed by SQLite that tracks 10,000+ historical events in real time, calculates live win probabilities, and applies a modified Kelly Criterion formula to dynamically adjust risk parameters per session completely automatically.

The platform manages 4 independent headless browser sessions concurrently on a 1GB RAM cloud server, with a custom Flask web dashboard for remote monitoring and configuration.

WHAT I BUILT: → Custom Selenium DOM traversal engine (bypasses dynamic obfuscation) → Multi-threaded account manager (4 concurrent headless sessions) → SQLite statistical risk engine with Kelly Criterion staking → Auto-recovery system (handles disconnects, logins, popups) → Flask remote dashboard (live stats, balances, logs, strategy config) → Optimized for 1GB RAM Linux server (systemd, swap, Chromium flags)

RESULTS: ✓ 99.9% uptime over extended deployment periods ✓ Sub-400ms execution latency on all critical actions ✓ 10,000+ live events processed and analyzed ✓ Runs for weeks completely without manual input

TECH STACK: Python • Selenium • SQLite • Flask • JavaScript • Linux • Bash • systemd • Chromium (Headless)