Advanced Algorithmic Trading

Course Curriculum

Course Content

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Lessons Status

1

Introduction

Unit 1 - Your guide to Advanced Algorithmic Trading

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

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 - Performance-based 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: Non-Farm 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

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

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

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

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 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

10

Intra-trade management: all about trade management functions

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

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 - 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