🚀 QuantumEdge-Trading — My Advanced Algorithmic Trading Toolkit

Hey dev.to! I’m an 11-year-old aspiring developer and I love building real tools that do cool things — not just toy apps 😄

Today I want to share another one of my projects: QuantumEdge-Trading — a set of open-source algorithmic trading strategies and tools that you can use to explore machine learning and smart money concepts in markets!

🔗 Check out the repo here: https://github.com/pookiejames634-dotcom/quantumedge-trading 

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💡 What QuantumEdge-Trading Is

QuantumEdge-Trading is a collection of professional-grade trading strategies and supporting tools written in Python and TradingView Pine script, designed to help traders and developers learn and experiment with: • Machine learning-based strategy — a clean ML approach using K-Nearest Neighbors • Smart Money Concepts / Institutional-level strategy — advanced indicators and market structure • Bonus tools like a lead scraper and sentiment engine for market research and automation 

It’s free, open source, and you can use it both for learning and practical experimentation — perfect if you’re curious about quant trading or how machine learning can drive decision-making. 

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🧠 What’s Inside

Here are the highlights straight from the repo:

📊 Strategies • ML Algo Pro (Machine Learning) — a minimal yet powerful ML strategy that outputs clean signals and confidence scores.  • QuantumEdge Pro (Smart Money Concepts) — institutional-grade indicators like order blocks, fair value gaps, volume profile, and Wyckoff pattern detection. 

🛠 Tools • News Sentiment Engine — Python script that analyzes market sentiment from news and data.  • Business Lead Scraper — a scraper to collect leads from Yellow Pages and other sources (bonus real-world automation tool). 

📈 How to Use • Paste the Pine strategies into TradingView to start paper-trading signals.  • Run the Python tools locally with pip install -r requirements.txt. 

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🛠 What I Learned

Working on this taught me: • How trading strategies are structured and coded • How to mix machine learning with financial signals • How to organize a large, multi-part repository • How developers can share tools so others can learn too

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🚀 Next Steps

I’m planning to: • Add more ML strategies and research new indicators • Improve documentation and examples • Explore live tracking dashboards in future versions

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Thanks for reading! If you’re into quant trading, ML, or just love tinkering with open-source tools, check it out and drop some feedback — I’m constantly learning and building 😄

Stay curious, — [neil]