Essentials in Quantitative Trading (QT*01)

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

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

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

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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