Lessons
 Unit 1  Introduction
 Unit 2  Introduction to trade and returns analysis
 Unit 3  Trade distribution analysis
 Unit 4  Maximum adverse and favourable correlation
 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
 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  Performancebased allocation
 Unit 6  Reinvesting and other capital allocation methods
 Unit 7  When to pull out: a quantitative approach
 Unit 8  Portfolios: conclusions
 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: NonFarm Payrolls Drift
 Unit 11  Conclusions
 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
 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
 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 4  Vectors
 Unit 5  Matrices
 Unit 6  Factors
 Unit 7  DataFrames
 Unit 8  Lists
 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
 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
 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 kNearest 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
 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  Onecancelsother orders
 Unit 8  Stopandreverse orders
 Unit 9  The price() functions inside a TMF
 Unit 10  Typecasting tradespecific variables (why printf() is not working!)
 Unit 11  Cycling through the trade list
 Unit 12  Userdefined tradespecific variables
Advanced Algorithmic Trading
Course Content
Lessons
Status
2
Risk management 1: measuring performance

Unit 1  Introduction

Unit 2  Introduction to trade and returns analysis

Unit 3  Trade distribution analysis

Unit 4  Maximum adverse and favourable correlation

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
 Unit 1  Introduction
 Unit 2  Introduction to trade and returns analysis
 Unit 3  Trade distribution analysis
 Unit 4  Maximum adverse and favourable correlation
 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
3
Risk management 2: quantitative portfolio management

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  Performancebased allocation

Unit 6  Reinvesting and other capital allocation methods

Unit 7  When to pull out: a quantitative approach

Unit 8  Portfolios: conclusions
 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  Performancebased allocation
 Unit 6  Reinvesting and other capital allocation methods
 Unit 7  When to pull out: a quantitative approach
 Unit 8  Portfolios: conclusions
4
Working with time

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: NonFarm Payrolls Drift

Unit 11  Conclusions
 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: NonFarm Payrolls Drift
 Unit 11  Conclusions
5
Advanced analytical tools for traders

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
 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
6
Advanced utilities for algo traders

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
 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
7
Quick start guide to R: the language of statistical computing

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 4  Vectors

Unit 5  Matrices

Unit 6  Factors

Unit 7  DataFrames

Unit 8  Lists

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
 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 4  Vectors
 Unit 5  Matrices
 Unit 6  Factors
 Unit 7  DataFrames
 Unit 8  Lists
 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
8
Early stage strategy evaluation: practical research in the R enviromnent

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
 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
9
Extending Zorro with R

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 kNearest 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
 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 kNearest 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
11
Practical examples of trade management functions

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  Onecancelsother orders

Unit 8  Stopandreverse orders

Unit 9  The price() functions inside a TMF

Unit 10  Typecasting tradespecific variables (why printf() is not working!)

Unit 11  Cycling through the trade list

Unit 12  Userdefined tradespecific variables
 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  Onecancelsother orders
 Unit 8  Stopandreverse orders
 Unit 9  The price() functions inside a TMF
 Unit 10  Typecasting tradespecific variables (why printf() is not working!)
 Unit 11  Cycling through the trade list
 Unit 12  Userdefined tradespecific variables