Algorithmic Cryptocurrency Trading by Hanguk Quant

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Algorithmic Cryptocurrency Trading by Hanguk Quant – Build, Backtest & Automate Robust Quantitative Crypto Strategies

Learn how professional quantitative traders build, test, and execute algorithmic trading systems in crypto markets with Algorithmic Cryptocurrency Trading by Hanguk Quant — a 5+ hour, 25-lecture masterclass in data-driven, risk-managed, and empirically tested strategy design.

This course is a practical deep dive into quantitative finance, covering everything from data retrieval and regression modeling to backtesting, portfolio construction, volatility targeting, and full exchange integration (Binance, Hyperliquid, Bybit, and more).

If you’re a trader or developer ready to elevate your skills to the professional level, this course gives you the complete pipeline from idea → code → simulation → live execution.

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Why This Course Is Different

Unlike surface-level “bot trading” tutorials, Hanguk Quant’s course is built for serious learners who want to understand the science and discipline of quantitative trading.

It emphasizes robust frameworks, empirical validation, and risk awareness, giving you a real-world foundation in algorithmic strategy design.

Here’s what makes it stand out:

💡 End-to-End Framework — From data collection and signal design to backtesting and exchange automation.

📈 Professional Quant Mindset — Learn to think in inefficiencies and risk premiums, not chart patterns or retail indicators.

🧮 Mathematical Rigor — Covers regressions, simulations, and volatility targeting in detail.

💻 Hands-On Code — Implement and test every concept directly in Python/NinjaTrader environments.

⚙️ Exchange Integration Ready — Practical modules for deploying strategies on Binance and other major exchanges.

🎓 Quantitative & Programming Depth — Created by a quant developer with a formal background in statistics, finance, and computer science.

This isn’t a beginner’s course — it’s an initiation into professional algorithmic trading.

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Course Overview

Duration: 5h 12m
Content: 4 Sections • 25 Lectures
Skill Level: Intermediate–Advanced

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Section 1: Introduction (7 Lectures • 1 Hour)

• Course Introduction – Learn the goals and structure of the course.

• Imported Functionalities – Essential Python packages and data-handling utilities.

• Data Retrieval – Accessing and cleaning market data for quantitative analysis.

• Utility SDK – Overview of reusable tools for your trading pipeline.

• Exchange Universe – Introduction to supported crypto exchanges and APIs.

• Inefficiencies vs Risk Premiums – Understand the two monetizable effects in quant trading and why most “indicators” aren’t either.

🎯 Outcome: Build your foundation for systematic thinking and technical setup.

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Section 2: Regressions (6 Lectures • 1h 4m)

• Regression from both calculus and algebraic perspectives.

• Learn how regression modeling drives trend-following and signal generation.

• Understand Winsorization and data normalization for robustness.

• Practical lessons on avoiding overfitting and misinterpretation of correlation.

📊 Outcome: Model and evaluate quantitative relationships statistically.

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Section 3: Backtesting & Portfolio Management (8 Lectures • 2h 25m)

• Build trend-following simulations and write backtest logic.

• Learn volatility parity and volatility targeting — essential risk tools for modern portfolios.

• Compare continuous vs discrete systems and their implications on trading cost.

• Design portfolio abstractions and diversification strategies.

• Apply real-world portfolio rebalancing with dynamic risk management.

💼 Outcome: Create realistic simulations that withstand market noise and bias.

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Section 4: Exchange Integration & Extensions (4 Lectures • 44m)

• Integrating with Binance: API connections, order execution, and data feeds.

• Extensions: Implementing support for Hyperliquid, Bybit, and other exchanges.

• Automation & Cronjobs: Scheduling recurring strategy execution tasks.

• Bug Support: Debugging common integration and execution issues.

🧠 Outcome: Deploy your quantitative strategies live across multiple exchanges.

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What You’ll Learn

By the end of the course, you’ll be able to:

✅ Design quantitative strategies based on inefficiencies and risk premiums.

✅ Retrieve and preprocess market data for robust testing.

✅ Apply regression models to generate and evaluate signals.

✅ Run backtests and manage portfolios with volatility targeting.

✅ Implement diversification and rebalancing based on empirical insights.

✅ Integrate your system with live exchanges like Binance and Bybit.

✅ Build an end-to-end quant trading pipeline from idea to execution.

This course equips you with the exact thinking, coding, and analytical frameworks used by professional quants and hedge funds.

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Requirements

🔹 Intermediate quantitative literacy — familiarity with probability, statistics, and regression concepts.
🔹 Programming methodology — experience with Python or ability to learn with AI assistance.
🔹 Financial literacy — understanding of market instruments and trading logic.

Not for beginners. Those new to programming or finance should first study Python, data science, and financial basics.

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Who This Course Is For

This course is ideal for:

• Traders ready to move from discretionary setups to algorithmic systems.

• Developers interested in building and automating quantitative strategies.

• Aspiring quants seeking a rigorous introduction to strategy testing and simulation.

• Analysts exploring volatility targeting, portfolio design, and data-driven trading.

• Crypto professionals looking to automate cross-exchange and market-neutral systems.

If you’re serious about developing your own quant trading operation, this course is your blueprint.

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About the Instructor

Hanguk Quant is a quantitative researcher, developer, and educator specializing in algorithmic and systematic trading.

With formal education in Computer Science and Statistics (Summa Cum Laude) and years of experience as a quant trader and developer, Hanguk bridges the gap between academic rigor and market application.

He writes on the HangukQuant Newsletter, discussing the intersection of mathematics, finance, and automation, helping professionals and traders transition into quant-level strategy design.

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Final Thoughts

Algorithmic Cryptocurrency Trading by Hanguk Quant is more than just a coding course — it’s a full quantitative trading curriculum distilled into a concise, actionable format.

You’ll walk away with the tools, frameworks, and mindset to build and deploy your own algorithmic trading systems in crypto markets confidently.

🧠 Think like a quant. 💻 Build like a developer. 📈 Trade like a professional. Your journey into algorithmic trading starts here.