HangukQuant's Research

QT410: Elementary Mid-Frequency Funding Arbitrage

Theory and design in the implementation of a multi-exchange, multi asset funding arbitrage enterprise in Python. Code written from scratch. Demonstration on binance - hyperliquid.

quantpylib repo pass

Access pass to Github repository see project: https://hangukquant.github.io/

QT203: Development of Robust Python Packages for Quantitative Trading

Learn how to create your own Python modules and libraries for quant trading. On writing flexible, robust and extensible code that lasts the test of time. Work with exchange documentations, repositories - and build you a better one.

Lifetime Pass

Lifetime access to all current and future quant lectures listed on hangukquant.thinkific.com.

Principal Quantitative Tools for Systematic Trading

Learn how to engineer quant trading and research development tools. From backtesting equities to option strategies with comprehensive infrastructure, statistical models and hypothesis tests to advanced mathematical parsers that boost trading process.

QT402: Advanced Options Backtesting

Integrate concepts in QT201, QT301 to QT202 (Options Backtesting) for advanced tools in building trading systems.

QT202: Quantitative Options Backtesting

Develop in Python a powerful, vectorised quantitative engine to test out any options strategy, complete with automatic contract selection, rebalancing and delta hedging features.

QT101,QT201,QT301 (no waitlist, no prereq)

Access to QT101,QT201,QT301 lectures without waiting list or certificate of completion in prerequisites.

QT101,QT201 (no waitlist, no prereq)

Access to QT101 and QT201 lectures without waiting list or certificate of completion in prerequisites.

Essentials in Quantitative Trading (QT*01)

Access to QT101,QT201,QT301,QT401 lectures without waiting list or certificate of completion in prerequisites.

QT401: Applied Alpha Research and Quantitative Trading

Learn Artificial Intelligence techniques for building Alpha Research factories with a Genetic Programming overlay. Applications in data mining and quantitative research.

QT301: Modern Techniques in Quantitative Trading

Build a hyper-efficient no-code, first-in-class quantitative research engine for the modern systematic trader. A must for advanced quant devs or evolving quants to take their systematic infra to a new height with elegant alpha modelling techniques.

(3) 5.0 average rating

QT201: Statistical Methods in Quantitative Trading

Learn how to compute performance metrics for your quant strategies, with hypothesis tests for single strategy and multi strategy systems.

(2) 5.0 average rating

QT101: Introductory Lectures in Quantitative Trading

Learn what constitutes a trading hypothesis, simulate one and write advanced Python code for efficient and extensible testing libraries.

(9) 5.0 average rating

Costful Trading

Learn how to incorporate trading execution costs into your test. Learn techniques in cost and alpha analysis for quant trading with efficient code. Full paper available for all readers. Access to code and code files require purchase.