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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

Algorithmic trading has been used for a long time, the rise of artificial intelligence and machine learning has accelerated the advancement of algorithmic trading. With the help of various algorithms, machine learning and artificial intelligence recognise the profitable pattern and make a trade at high frequency. The Oxford Algorithmic Trading Programme online course introduces the rules that drive hedge funds and algorithmic trading strategies.

The Oxford Algorithmic Trading Programme syllabus provides a grounded introduction to behavioural finance and financial theory. The course teaches to evaluate the algorithm training models by which artificial intelligence recognises trade patterns. The course covers various factors which affect the market and creates exploitable patterns.

The Oxford Algorithmic Trading Programme training focuses on investment and trading space. The course discusses the key issues faced by algorithmic traders and the future of Artificial intelligence and machine learning algorithmic trading. The course teaches about various experimental models of algorithmic trading.

The Highlights

Case-study based learning
Split fee option available
Course assessments and projects
6 weeks duration
Online Learning
University of Oxford offering
Course provider Getsmarter
Downloadable resources
6 modules
7-10 hours learning/week
Shareable certificate

Programme Offerings

assessments
Certification
quizzes
Offline resources
insights
Case Studies
Projects
online learning

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Said Business School, Oxford

Fee type	Fee amount in INR
Algorithmic Trading Programme fees	Rs. 96,337

The Algorithmic Trading Programme fees are Rs. 96,337 . Getsmarter offers a split option of payment in which learners can pay the fee in 2 instalments of Rs. 87,390.00 per month. A 3% admin fee will be charged as an admin fee for split payments.

Eligibility Criteria

Certification Qualifying Details

To get qualified for the Oxford Algorithmic Trading Programme certification, learners will have to complete the series of practical assessments along with all the modules included in the online course. Learners will have to meet all the requirements written in the coursebook for certification.

What you will learn

Knowledge of AlgorithmsFinancial knowledge

After completing the Oxford Algorithmic Trading Programme online training, candidates will gain knowledge of algorithmic training models. Candidates will learn about 4 key rules of algorithmic training strategies. Learning will gain knowledge of emerging technologies like machine learning, artificial intelligence.

Who it is for

Traders, financial professionals, and investors who want to gain knowledge of Algorithmic trading and understand algo-trading strategy can join this course.
Business leaders who want to take advantage of algorithmic trading in their companies can join the Oxford Algorithmic Trading Programme course for beginners.
Technology and IT professionals who want to know about the strategies behind algorithmic training models can enroll in this course.
New hires and MBA graduates who want to learn about automatic trading techniques.

Admission Details

To get admission to the Oxford Algorithmic Trading Programme online training by the University of Oxford, follow the steps written below:

Step 1. Go through the official link to open the course webpage.

Step 2. Click the ‘Register Now’ button on the course page to start the registration

Step 3. Provide required details to create a profile on the Getsmarter

Step 4. Provide billing address and choose the method of payment

Step 5. Pay the fee either by bank transfer or with a debit/credit card and begin your class

The Syllabus

Review the fundamental principles of the efficient frontier
Define the efficient market hypothesis and other concepts related to classic finance theory
Discuss how behavioral biases impact the market
Discuss how stock market trends are identified in practice
Define terminology associated with stock market trends
Identify rules or principles that can be used to build and evaluate an algorithmic trading model

Identify the characteristics of hedge funds and their history
Review the principles of systematic trading within hedge funds
Identify which types of investors tend to invest in hedge funds
Identify the difference between large systematic trading organisations and smaller players
Discuss the factors that contribute to the success or failure of large systematic trading organisations
Interpret the historical and current opportunities and challenges of the systematic trading industry

Review appropriate measures of model performance, including their advantages and disadvantages
Identify the characteristics of out-of-sample testing and backtesting as methods of statistical verification
Discuss how the Sharpe ratio is used to measure a model’s validity
Interpret the risks associated with algorithmic trading at the management and programming levels
Identify the origins of trading rules and the importance of futures in algorithmic trading
Identify the role of APIs and other programming considerations in the modelling of algorithmic trading strategies
Articulate the benefits and challenges of cross-disciplinary collaboration in financial modelling

Review important considerations for selecting appropriate in-sample data
Review the process of building and optimising a simple trend model
Articulate useful techniques for testing whether a trend model is functional or viable
Reflect on the applications of an algorithmic trading model to a live environment or real-world market circumstance
Test whether a trend model can be applied to a different set of data
Test the functionality and viability of a trend model, given changes in the underlying data

Discuss the different avenues for growth that can help to further develop or improve an algorithm model
Discuss the opportunities and challenges faced by different participants in the algorithm-trading ecosystem
Evaluate whether or not a fund is worth investing in based on the four rules for fund evaluation
Investigate the strengths and weaknesses of a systematic fund in order to analyze how it is likely to perform

Investigate the opportunities and challenges of utilising robot-advisers and algorithmic technology for systematic trading
Review the current state of the digital finance technology industry
Review the ways in which AI and machine learning are utilized for algorithmic trading
Formulate a view on future trends in algorithmic trading and the relationship between these trends and new technologies
Assess the real value and impact of AI and machine learning on the algorithmic trading industry