This e-learning workshop will get delegates quickly up-to-speed on how to use artificial intelligence techniques for trading and investing in the financial markets. Delegates will learn how to apply the various machine learning algorithms to identify predictive variables that can be used for successful trading strategies.
Our e-learning workshop is designed to give you everything you need to know to get started and includes:
- Online course module (6 hours)
- MATLAB software (1 month) with MATLAB tutorial (3 hours) and sample data
- Direct access to the trainer for assistance
- Course duration: 6 hours
- Access period: 3 months
- Trainer: Ernest Chan
- Course Fee: £895 + VAT
General paradigm of machine learning
- Features selection
- Training vs test sets
- Cross validation
- Hyperparameters optimization
- Data snooping bias
- Accuracy, confusion matrix, recall vs precision, F1-score, log-loss
Programming tutorial (available as pre-recorded session)
Setting up the problem with multiple linear regression as the learning model
- Exercise: Predicting 1-day SPY returns using simple technical indicators
Exercise: Predicting SPY returns using various learning algorithms
Stepwise linear regression
Classification and regression trees (CART)
- Stopping criteria for tree growing
- Using the whole tree or selecting certain nodes for prediction?
- Reducing overfitting by cross-validation
- Increasing training sample size by bootstrapping/bagging
- Decreasing number of predictors: random subspace
- Random forest
- Learning from past errors: boosting
- Which technique gives most accurate predictions?
- Improving accuracy with weighted samples, priors, and hyperparameters optimization
Support vector machine (SVM)
- Predicting sign of returns
Neural networks (NN)
- Neural network as nonlinear function fitting
- What network architecture to pick?
- Drawback of using NN for financial predictions
An extended exercise on features selection
- Building a multifactor stock selection model using fundamental factors
- Techniques: multiple regression, stepwise regression, and CART
- What fundamental factors are most useful for predicting stock portfolio returns?
Frequently Asked Questions
WHAT IS THE TIMELINE FOR THE COURSE?
This course is pre-recorded and available indefinitely for viewing on Adobe Connect.
HOW LONG SHOULD IT TAKE ME TO COMPLETE THE COURSE?
We provide indefinite access to the online material to give students a chance to take things at their own pace, but we expect most people to take no longer than a week to complete the course.
WHAT KIND OF MATLAB ASSISTANCE AM I PROVIDED WITH?
There is a MATLAB tutorial included in the workshop. If you have used MATLAB before, you should find it quite easy to complete the exercises, which do not require extensive programming. No prior knowledge of MATLAB is in fact necessary, although some general experience in programming using other simple languages would be useful.
WHEN WILL MY MATLAB LICENCE BEGIN?
You are advised to go through the online material first, then let us know when you are ready to start the course in conjunction with the MATLAB exercises, at which point we will request that your MATLAB licence begin. Please bear in mind it may take a couple of days to set you up with MATLAB.
IS THE COURSE STILL USEFUL IF I HAVE NO INTENTION OF USING MATLAB?
Yes, the main aim of the course is to communicate the principles of machine learning. Furthermore, MATLAB programming code can be adapted fairly easily into ‘R’, an open-source alternative.
WHAT DOES THE ONLINE COURSE MATERIAL COMPRISE?
The online course material is made up of two modules. The first module is the main course and consists of slides with the trainer’s narration. The second module consists of a tutorial covering the essentials of MATLAB programming.
WILL I BE ABLE TO ASK QUESTIONS AS I GO ALONG?
Unlimited Q&A will be conducted on the course Slack channel.