Essentials in Quantitative Trading (QT01)
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.
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.
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.
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.
What You’ll Learn In Essentials in Quantitative Trading (QT01)?
QT101 Introductory Lectures in Quantitative Trading
Course curriculum
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
Course curriculum
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
Course curriculum
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
Course curriculum
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
Find out more Stock Trading courses
