Syllabus

Provide a strong foundation in the tools and techniques used in algorithmic trading.
Cover everything from basic programming concepts to advanced trading strategies and methods for research into new alpha sources.
Apply everything in hands-on projects throughout the course.

1. Introduction

Trading Basics:

Types of Trading
Trading Strategies
Introduction to Algorithmic Trading

Industry Overview:

Industry Structure
Size and growth
The different sub-sectors

Approaches:

Some simplified frameworks
Full On Approaches
Execution styles

Trading Platform Architecture:

Data
Features
Models

Data:

Market data sources – Aggregators and Exchanges.
Scraped Data.
Alternative Data sources.

2. Statistics and Time Series

ARIMA Models:

Autoregressive, Moving Average Models and Integrated Models
Detecting model types using Autocorrelation functions
Solving Difference Equations
Seasonality and State-Space ModelsTypes of stationarity and Brownian motion

Model Selection:

Hypothesis Testing, Sequential Testing
Covariance Penalties and Criteria
Drop-out and Cross-Validation
Sparsity, and Regularisation

Forecasting:

Batch Updates
Online Learning – Adaptive Filters – RLS and Kalman Filters
GARCH
Updating schemes and Stability
Learning Rates and Cross Validation

Change-points and Regime Shifts:

Change points vs Regime Shifts
Endogenous Regimes / HMMs – EM method
Regimes and weighted LS
Chow, CUSUM and CUSUM-SQR tests
Bayesian Changepoint Detection
Breaks in Stationarity
Changepoints and Stability

Multivariate Models:

Causality – Granger and Otherwise
Integration and Cointegration
VAR / VARMA
Exogenous restrictions / Conditional forecasting

3. Features and Factors

Feature Creation:

Automating Features
Feature Stores
Best Practices from MLOps

Feature Selection:

Feature Relevance – Sequential Filtering and Testing
Feature Stability
Examples : Lags, Movavs, etc

Exogenous Features:

Economic Data
Fundamental Data
Releases and Surprises, Revisions and Corrections
Changing the Frequency – Nowcasts and MIDAS
Market Data
Text and Sentiment
Causality and Spillovers

Factor Investing: CAPM and APT

Fama-French factors – Market, Value, Size
Other Factors – Quality, Profitability, Leverage,
Factor Proliferation and the Factor ZooAnomalies, Alpha and Time-Decay
Risk Premia Investing

4. Trend Following

Momentum:

Design
Approaches and properties
Impact on design
Option value vs Reactivity
Sharpe vs Skewness
Continuous Time – Power Options and Trend
Discrete time – skewness term-structure, autocorrelation and volatility

Nonlinear Momentum:

Non-linear filter – Impact and benefits
Risk and Return
Returns distribution engineering, limitations and further direction

Momentum signals in practice:

Time Series vs Cross- sectional Momentum
Design Choices: MA, CMA, Z-scores, etc
General Convolution Filters
Technical indicators
ARIMA models

Cross-Sectional Momentum:

Relation with Time-series
Fitting into APT/Fama-French
Equities Quant (as a subsector) and Equities Factor Investing
Turnover and Rebalancing Costs
Mean-Reversion and Trend (and Mean-Reversion)

Momentum Breaking:

Ultra Long-horizon
Momentum breaking and Value

5. Carry and Volatility Strategies

Carry:

Rationale – P vs Q Measures
Definitions – Carry in Bonds, Futures, FX.
Mathematical / Statistical Properties
Negative Skewness Premium and Currency Crashes
Diversification of Carry and Momentum

Mean-Reversion or pseudo market-making:

Mean Reversion
Rationales: Liquidity provision or Overreaction?
Relation to other liquidity measures
Stationary vs Non-Stationary processes
Cointegration – Johansen, CCA and PCA
Testing – Univariate and Multivariate Tests.
Var-Ratio (Tests) vs Sharpe Ratio
Shortcomings – Time-variation / Breaks
“Optimal” MR TradingTiming

Relative Value Trading:

RV in Delta-one space – Pairs, Spreads, Butterflies, Boxes and BasketsI(1) vs I(0): RV vs Trend
Timing Entry points and mean reversion. Optimising
Stationarity: Are RV trades stationary?
Statistical arbitrage and Pairs Trading

Short-Gamma Trading:

Variance risk premium
Models of implied vol evolution and the risk/reward of short positions
Expressions in different markets
Hedging methods and new techniques
Risk and signals
Skewness and Scaling

6. Machine Learning and other New Techniques

Nonparametric Statistics

Kernel Methods
Empirical Densities and Bayesian Techniques
Nonparametric Regression and Classification

Trees, Forests and Boosting

Tuning the Parameters
Feature Selection
Boosting and Bagging

Neural Networks:

Deep Neural Networks
Recurrence and Memory
Deep vs Wide – Efficiency and Stability
Autoencoders

Reinforcement Learning:

Decision Processes
Stochastic Control

Genetic Algorithms:

Genetic Representation, Fitness and Operators.
Applications to Strategy Selection.
Bandits and hyperparameter optimization.

7. Trading and Execution

Market Microstructure:

Electronic vs Voice
Limit Order Book
RFQs vs Streaming Quotes
Order types – Market, Limit, Spread, etc
Other actions – Fills, Cancellation, etc

Execution Issues:

VWAP and TWAP
Auctions
Transaction Costs
Price Impact.

Optimising Execution:

Stochastic Control
Optimal market order placement
Inventory constraints
Extensions
Optimal Market making and extensions
Practical Implementation
Fill probabilities
Limit Order Book Simulations
Reinforcement Learning

8. Backtesting and Performance Measurement

Performance:

Sharpe & Sortino ratio etc.
Distribution of Drawdowns.
Impact of Leverage and Margining.
Accounting for Funding and other Costs.
Strengths and shortcomings of different approaches.

Back-testing, Simulations, and Hyper-parameter Tuning:

Approaches to back-testing.
Using Time Windows
Training/Testing/Holdout Sets.
Cross-validation.
Possible biases.

Optimization:

Classical Optimization
Bayesian Optimization
Bandits, Stopping Rules and Genetic algorithms.
Multi-Objective Optimization.
Off the shelf tools.

9. Allocation and Risk Management

Mean-variance optimization:

Efficient Frontier.
Allocation in Reality – Impact of volatility and Frictions.
Quadratic Programming and Convex Optimization.

Constraints:

Margin and Leverage.
Funding
Limits – Sector, sub-sector, strategy, name

Correlations:

Approaches to Estimating the Correlation Matrix.
PCA and Factor Analysis
Dealing with Numerical Issues such as Sparseness.
Shrinkage Methods.

Market & Liquidity Risk:

Measuring Exposures to the Overall Market, Individual Sectors and Specific Factors.
Correlation Estimation.
Principal Component Analysis.
Simulation and Stress Testing.

Credit Risk:

Modelling Default Intensity and Recovery.
Counterparty Credit Risk.
Managing Credit Risk across the Firm.

Limits:

Value at Risk and Expected Shortfall
Setting Stop-Loss and other Limit Orders.
Limits to Gross/Net Exposure to Rates/FX etc.

Operational & Legal/Regulatory Risk:

Important to Capture as much as possible using Quantitative Techniques.
Avoidance vs Mitigation.