Advanced Algorithmic Trading Course Curriculum

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Introduction 1 Topic

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Unit 1 – Your guide to Advanced Algorithmic Trading

Risk management 1: measuring performance 9 Topics

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Unit 1 – Introduction

Unit 2 – Introduction to trade and returns analysis

Unit 3 – Trade distribution analysis

Unit 4 – Maximum adverse and favourable excursion

Unit 5 – Trade summary statistics and returns correlation

Unit 6 – Trade statistics by period

Unit 7 – Drawdown analysis and random processes

Unit 8 – Risk

Unit 9 – Measuring risk and reward

Risk management 2: quantitative portfolio management 8 Topics

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Unit 1 – Introduction to quantitative portfolio contruction

Unit 2 – Loop functions: Zorro’s shortcut to building portfolios

Unit 3 – Modern portfolio theory

Unit 4 – Optimal F and The Kelly Criterion

Unit 5 – Performance-based allocation

Unit 6 – Reinvesting and other capital allocation methods

Unit 7 – When to pull out: a quantitative approach

Unit 8 – Portfolios: conclusions

Working with time 11 Topics

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Unit 1 – Your reference framework: arbitrary or principled?

Unit 2 – The basics of working with time

Unit 3 – Multiple perspectives: analyzing data at different timeframes

Unit 4 – Seasonal effects

Investigating Seasonal Patterns using Zorro

Correlograms

Unit 7 – Exploiting seasonality

Unit 8 – Exploiting seasonality: Stock Index ETF monthly patterns

Exploiting Seasonality Example: Overnight Effects

Exploiting Seasonality: Non-Farm Payrolls Drift

Unit 11 – Conclusions

Advanced analytical tools for traders 22 Topics

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Unit 1 – Advanced analytical tools for traders: introduction

Unit 2 – Regression 1: an introduction

Unit 3 – Regression 2: smoothing trend estimation

Unit 4 – Regression 3: polynomial regression

Unit 5 – Regression 4: beta

Unit 6 – Regression 5: further considerations

Unit 7 – Spearman Rank correlation

Unit 8 – Pattern recognition with the Frechet Distance

Unit 9 – Shannon Entropy

Unit 10 – Machine learning 1: introduction to machine learning in Zorro

Unit 11 – Machine learning 2: a brief overview

Unit 12 – Machine learning 3: first steps with Zorro

Unit 13 – Machine learning 4: decision trees

Unit 14 – Machine learning 5: ensembling machine learning models in Zorro

Unit 15 – Machine learning 6: perceptrons, the simplest neural networks

Unit 16 – Machine learning 7: data mining for predictive patterns

Unit 17 – The insidious threat of data mining bias

Unit 18 – Dealing with data mining bias: the empirical approach

Unit 19 – Dealing with data mining bias using synthetic price data

Unit 20 – Dealing with data mining bias using bootstrapped backtests

Unit 21 – Applications of digital signal processing to trading

Unit 22 – Oversampling for getting more out of data

Advanced utilities for algo traders 11 Topics

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Unit 1 – Introduction to algo trading utilities

Unit 2 – Introduction to string manipulation

Unit 3 – String manipulation example 1: reading in a binary balance curve

Unit 4 – String manipulation 2: exporting data to a CSV file

Unit 5 – String manipulation 3: scraping sentiment data from the web

Unit 6 – Advanced script flow control

Unit 7 – The command line: enabling an efficient and productive workflow

Unit 8 – Sending free email and SMS from a trading algorithm

Unit 9 – External input 1: sliders

Unit 10 – External input 2: control panels

Conclusion: Algo trading utilities

Quick start guide to R: the language of statistical computing 16 Topics

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Unit 1 – Leveraging R: the free software for statistical computing

Unit 2 – The basics: installing, calculating, commenting and getting help

Unit 3 – Variable types, assignment and data structures

Unit 9 – Flow control

Unit 10 – Functions

Unit 11 – Vectorization

Unit 12 – Basic plotting in R

Unit 13 – Packages

Unit 14 – Data manipulation with dplyr

Unit 15 – Managing an R installation with Installr

Unit 16 – Conclusions and my favourite R references

Early stage strategy evaluation: practical research in the R enviromnent 9 Topics

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Unit 1 – Introduction

Unit 2 – Preliminaries

Unit 3 – Getting and preparing data

Unit 4 – Vectorized backtesting

Unit 5 – Vectorized backtesting of a simple trading strategy

Unit 6 – Performance of a single vectorized backtest

Unit 7 – Parameter permutation

Unit 8 – Parameter selection

Unit 9 – Conclusions, code and a warning

Extending Zorro with R 13 Topics

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Unit 1 – Integrating Zorro and R: Introduction

Unit 2 – Configuring Zorro to communicate with R

Unit 3 – The R bridge functions

Unit 4 – Mean reversion trading 1: stationarity

Unit 5 – Mean reversion trading 2: timing of mean reversion

Unit 6 – Mean reversion trading 3: implementing mean reversion strategies

Unit 7 – Practical pairs trading

Unit 8 – Harnessing external machine learning algorithms in Zorro

Unit 9 – Predicting market direction with k-Nearest Neighbours

Unit 10 – Tips and tricks for better machine learning

Unit 11 – XGBoost and its application to the markets

Unit 12 – Deep learning trading algorithms

Unit 13 – Better machine learning with ensembles

Intra-trade management: all about trade management functions 5 Topics

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Unit 1 – Intra-trade management: all about trade management functions

Unit 2 – Introduction to trade management functions

Unit 3 – Structs and their importance for trade management functions

Unit 4 – Structs built into Zorro

Unit 5 – Getting practical with trade management functions

Practical examples of trade management functions 12 Topics

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Unit 1 – Examples of trade management functions

Unit 2 – Move stops depending on trade profit

Unit 3 – Move stops with a technical indicator – AssetVars

Unit 4 – Controlling script behaviour using TMFs

Unit 5 – Calculating ATR inside a TMF

Unit 6 – Scale into a position

Unit 7 – One-cancels-other orders

Unit 8 – Stop-and-reverse orders

Unit 9 – The price() functions inside a TMF

Unit 10 – Typecasting trade-specific variables (why printf() is not working!)

Unit 11 – Cycling through the trade list

Unit 12 – User-defined trade-specific variables

Advanced Algorithmic Trading Course Curriculum

Course Content

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