World Business StrategiesServing the Global Financial Community since 2000

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.
  • Discount Structure
  • Early bird discount
    10% until 6th October 2023

  • 70% Academic Discount
    (FULL-TIME Students Only)

Only four days left to claim this discount!
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