Trading with Machine Learning: Classification and SVM

Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.

No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.

Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community

Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.

There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.

Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.

We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.

Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.

No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.

You can learn with or without coding knowledge. If you would like to do the analysis on excel, we would suggest you to start with course on Statistical Arbitrage in Trading. You can create and test your trading strategies using excel.

Alternatively, you can do the course on Python for Trading which will help you gain knowledge in all these fields: Python, Analysis and Financial markets.

Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.

Lines are separators that are often used in separating groups of points in classification problems. Points on one side become one class and the points on the other become the other class. The generalisation of lines in three dimensions is called Planes. Planes are two dimensioned. They are used by various models to separate points in a 3d-coordinate space. A generalisation of Planes to N number of dimensions is called a Hyperplane. Hyperplane means an N-1 dimensioned plane. Hyperplanes are always one or more dimensions less than the dimensions of the coordinate space they lie in. The concept of Hyperplane is an important one for models like margin classifiers because they use hyperplanes to classify or categories data points.
This concept has been dealt with in Section 4 of this course.

SVM stands for Support Vector Machine. In the SVM algorithm, each data-point is plotted as a point in the coordinate space. Each data feature is a dimension in this coordinate space. The algorithm then involves finding hyperplane or n-dimensioned plane that differentiates the many classes of data points the best. New data points are classified based on this 'learned' hyperplane.

Supervised learning is a statistical problem where we find the combined relationship between a set of independent variables (or predictor) to derive another variable called the dependent variable (or target). Supervised learning is of two kinds; Regression and Classification.
When the dependent variable is a continuous real value like 5.0, -0.007 etc, the problem is called Regression.
When the dependent variable is a categorical nominal value such as large, small etc, the problem is called Classification.
Classification is covered in this course in section 2 and 3.

Yes, SVMs can be used for multiclass classification. SVMs are inherently binary classifiers, but multiclass classification can be done by framing the problem as a one-vs-rest problem, where any one class will be the first class and all other classes will be the second class. We will do the same for all classes and build as many binary SVM classifiers as the number of classes. This is covered in section 5 of this course.