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Exploring the Essential Features of “Essentials in Quantitative Trading (QT*01) – HangukQuant’s”
Essentials in Quantitative Trading (QT*01)
Access to QT101,QT201,QT301,QT401 lectures without waiting list or certificate of completion in prerequisites.
Bundle includes
Here are all the courses that are included in your bundle.
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.
Course curriculum
QT101 Introductory Lectures in Quantitative Trading
- DISCLAIMER
- Introducing QT101 – Who Should be Interested?
- Retrieving OHLCV with the yfinance API
- Python Multithreading
- Python Object Pickling
- Implementing a Random Alpha Unit
- Implementing Alpha Unit 1
- Implementing Alpha Unit 2
- Implementing Alpha Unit 3
- Objected Oriented Programming and Implementing a Generic Alpha Unit
- Adapting the Code to the Generic Alpha Unit
- Relative Position Sizing – Instrument Volatility Targeting
- Absolute Position Sizing – Strategy Volatility Targeting
- Implementing the Portfolio
- Git for Version Tracking and Python Decorators
- Function Profiling
- Line Profiling
- Vectorization and Memory Locality
- Handling Non-Linearity with Vectorization
- Python Generators
- Vectorization of the Alpha Library
- Bit Masking and Manipulation
- Type Compatibility
- Alpha Units Refactorization
- Wrapping Up
- Support Lecture (Common Issues and Bug Fixes)
QT201: Statistical Methods in Quantitative Trading
Learn how to compute performance metrics for your quant strategies, with p-value hypothesis tests for single strategy and multi strategy systems for strategy validation.
New Chapter
- DISCLAIMER
- Course Introduction
- Foundational Concepts
- Economics of Multiple Assets
- Portfolio Metrics
- Implementation of the Portfolio Metrics
- Implementation of the Portfolio Metrics
- Basics of Hypothesis Testing
- t-tests and sign tests for portfolio return mean/median
- Confidence Intervals and Signed Rank test
- Permutation of Price Data
- Permutation of OHLCV Bars
- Adjustments for Dynamic Universe of Assets
- Data Shuffle Implementation
- Introduction to the Monte Carlo Permutation Test
- Overfit Detection, Asset Timing and Asset Picking, Skill Hypothesis Tests
- Implementation of Non-Permutation Based Hypothesis Tests
- Decision Shuffling
- Decision Shuffling
- Implementation and Computation of the p-values
- Multiple Hypothesis Testing with FER Control
- Implementation of the Marginal Family Tests
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.
Course curriculum
New Chapter
- DISCLAIMER
- Introducing QT301
- Alpha Modelling
- Machine Encoding and Recursion
- Alpha String Parser
- Alpha String Deparsing
- Alpha Visualization
- Graph Traversal Algorithms
- Post-Order No-Code Evaluator
- Indexing for Dynamic Data
- Behavioural Polymorphism and Union Indexing Implementation
- Implementation of Further Primitives
- Time-Series Operations
- More Time Series Implementations
- Signal Transformations and Cross Sectional Operations
- Our First No-Code Backtest
- Branching and Specialised Logic
- Modelling Considerations
- Encoding our Alpha Set
- Compound Functions and Syntactic Sugar
- Computations with Alternative Data
- Support Lecture (Common Issues and Bug Fixes) set15
QT401: Applied Alpha Research and Quantitative Trading
Quantitative Trading Learn Artificial Intelligence techniques for building Alpha Research factories with a Genetic Programming overlay. Applications in data mining and quantitative research.
Course curriculum
New Chapter
- DISCLAIMER
- Introduction to QT401
- Artificial Intelligence is Search
- Genetic Programs as Intelligent Systems
- GP Iterations
- Specifying the Primitive Set
- Ephemeral Constant Generation
- Brute Force Numerical Trees
- Brute Force Boolean Trees
- Simulating the Brute Force Alphas
- Genetic Operators
- Crossover Implementation
- Mutation Implementation
- GP Implementation Overview
- Warm Start Initialization
- Elitism
- NaN Proof Marginal Significance
- Evolution; Recombination
- Evolution; Mutation
- Simulation Walkthrough
- Multi Objective Optimization
- k-Pareto Optimality Measure
- GP Bloat, Kruskal Wallis and Conover Iman tests
- Covariant Parsimony Pressure
- Verifying the Parsimony Coefficients
- Adding Proprietary Datasets
- Advanced GP Extensions
- Support and Bug Fix Lecture
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