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Total Size: ~38.7 MB | Instant Cloud Access

If you’re serious about quantitative trading, algorithmic backtesting, and financial data engineering, this bundle from HangukQuant is one of the most practical collections you’ll find online.

No fluff. No theory overload. Just real Python code, real strategies, and real quant workflows.

1. Costful Trading 📁 Size: 32.0 MB

This course dives deep into cost-aware trading strategies — one of the most overlooked aspects of quant finance. Most beginners ignore transaction costs, slippage, and execution fees. This course teaches you how real traders account for these hidden costs when building and testing strategies. If you want your backtests to actually reflect live performance, this is essential.

2. Flirting with CPUs – Advanced Backtesting in Python (with Code) 📁 Size: 4.1 MB

This is where things get seriously fast. Learn how to optimize backtesting performance using CPU-level techniques in Python — vectorized operations, multiprocessing, and low-latency code design. Perfect for quants who are tired of slow backtests eating up hours of compute time.

3. Retrieval of Financial Data and Implementation of a Quant (MongoDB) Database 📁 Size: 2.6 MB

A must-have skill in 2025 — building your own quant financial database using MongoDB. This course walks you through retrieving market data from APIs, storing it efficiently, and querying it for backtesting pipelines. No more depending on expensive data vendors.

🎯 Who Is This For?

• Python developers moving into algorithmic trading
• Finance students learning quantitative analysis
• Traders who want to build automated strategies
• Anyone learning MongoDB for financial data

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