Essentials in Quantitative Trading (QT*01)

• 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 (QT*01)?

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

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