About Qunex Trade

An AI-powered penny stock intelligence platform that uses advanced machine learning to identify high-probability trading opportunities with 70%+ success rate.

What is Qunex Trade?

Qunex Trade is an advanced machine learning system designed to predict penny stock surges (50%+ intraday gains) with exceptional accuracy. Using ensemble learning and 115+ engineered features, the model achieves a 0.9789 ROC-AUC score and maintains a 70%+ success rate in real-world trading scenarios.

0.9789
ROC-AUC Score
70%+
Success Rate
522
Stocks Tracked
115+
Features

How It Works

1. Data Collection & Feature Engineering

The system continuously monitors 522 penny stocks, collecting historical price, volume, and technical indicator data. Advanced feature engineering creates 115+ predictive features including volatility patterns, momentum indicators, and price-volume relationships.

2. Machine Learning Ensemble

Four state-of-the-art ML models work together:

The models are weighted and combined to achieve a final ensemble ROC-AUC of 0.9789.

3. Signal Generation (4:05 PM Daily)

Every day at 4:05 PM ET (after market close), the system automatically:

4. Performance Tracking

The system tracks every signal's actual performance by comparing predicted vs. actual results. Success is measured by 50%+ intraday gains (buy at open, sell at close). All signals remain active over weekends and holidays until the next trading day.

Key Features

🤖

Fully Automated

Signals generated daily at 4:05 PM automatically. No manual intervention required.

📊

Real-Time Tracking

Every signal is tracked and performance is recorded automatically with success/failure status.

🎯

High Precision

Only signals with 90%+ confidence are displayed (0.90 threshold for optimal signal volume).

📈

Proven Backtest

19 months of rigorous backtesting (Apr 2024 - Oct 2025) with 70%+ success rate.

💎

Transparency

All signals, results, and statistics are openly displayed on the dashboard.

📅

Smart Scheduling

Automatically handles weekends and US market holidays. Signals persist until next trading day.

Technology Stack

Machine Learning

Python 3.14 XGBoost 2.0 LightGBM 4.1 Scikit-learn 1.3 Pandas 2.1 NumPy 1.24

Data & APIs

yFinance Yahoo Finance API Real-time Market Data

Web Application

Flask 3.0 HTML5 / CSS3 JavaScript Responsive Design

Automation

Python Schedule Task Scheduler Automated Pipeline

Created By

Kwangui Chung
University of Illinois at Urbana-Champaign (UIUC) Student

Developed as a personal project to explore machine learning applications in financial markets.
Combining academic knowledge with practical trading strategies.

Disclaimer

⚠️

This system is for educational and informational purposes only.

Trading penny stocks involves substantial risk of loss. Past performance does not guarantee future results. The 70%+ success rate is based on historical backtesting and may not reflect future performance. Always conduct your own research and consult with a financial advisor before making investment decisions.

This is not financial advice. Use at your own risk.