RippleTechFlow

Bangkok Region

Study Materials for Algorithmic Trading

We've spent years figuring out what actually works in algorithmic trading education. Not the theoretical stuff you'd find in dusty textbooks, but the practical knowledge that helps you understand market mechanics and build real trading systems. Our materials reflect what we wish we'd had when we started—clear explanations, honest examples, and no unnecessary complexity.

What You'll Find Here

Strategy Fundamentals

Most people jump straight into coding without understanding why certain strategies work. We break down momentum, mean reversion, and statistical arbitrage with real market examples from 2024 and early 2025. You'll see what actually happens when theory meets live data.

Core Concepts

Risk Management Guides

Position sizing isn't exciting, but it's the difference between surviving your first drawdown and blowing up your account. We cover Kelly Criterion, volatility scaling, and portfolio heat—things that sound complicated but are surprisingly straightforward once explained properly.

Essential Skills

Backtesting Practices

Everyone's first backtest looks amazing. Then reality hits. Our materials walk you through common pitfalls like look-ahead bias, survivorship bias, and overfitting. You'll learn to build tests that actually tell you something useful about your strategy's potential.

Practical Methods

Market Microstructure

Order books, spread dynamics, liquidity patterns—this stuff matters more than most beginners realize. Understanding how markets actually function at a mechanical level changes how you think about entry and exit timing. We keep it accessible without dumbing it down.

Advanced Topics

Code Examples

You'll find Python implementations of common trading algorithms with detailed annotations. Not production-ready systems, but working examples that demonstrate core concepts. Think of them as learning tools rather than plug-and-play solutions.

Hands-On

Data Analysis Tutorials

Working with financial data has its quirks. Missing values, adjusted prices, timezone handling—small details that can mess up your analysis if you're not careful. We cover the practical side of cleaning, storing, and analyzing market data efficiently.

Technical Skills

Structured Learning Paths

We've organized materials into logical progressions based on what we've seen work for people with different backgrounds and goals.

Foundation Track

8-10 weeks

For people new to algorithmic trading but comfortable with programming. You'll build fundamental understanding of market mechanics, statistical analysis, and system architecture. By the end, you should be able to implement and test basic strategies independently.

Market Basics Python for Trading Statistical Testing Simple Strategies

Intermediate Development

10-12 weeks

You already understand the basics and want to build more sophisticated systems. This covers advanced strategy types, portfolio construction, execution optimization, and professional backtesting frameworks. Expect to spend significant time on projects.

Multi-Asset Strategies Portfolio Theory Execution Algorithms Advanced Backtesting

Specialized Topics

Variable

Deep dives into specific areas like machine learning applications, high-frequency patterns, options strategies, or cryptocurrency markets. These assume strong fundamentals and focus on specialized knowledge you won't find in general courses.

ML in Trading Options Strategies Crypto Markets HFT Concepts

Ready to Start Learning?

Our next cohort begins in September 2025. We keep groups small to ensure everyone gets proper attention and feedback on their work.

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