Introduction to Machine Learning for Trading

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.

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.

Quants use machine learning for various reasons. Some of these are forecasting and inference. Forecasting is the prediction of the prices in the future. This is generally modelled as a regression problem. Inference or analysis is the process of finding the variables or indicators that most impact the price movement.

Financial data is usually time-series data which has to be extrapolated for the future to make decisions. Machine learning algorithms use historical data to forecast or predict parameter values in the future. These parameters can be anything like prices, returns or volatility.

Reinforcement learning is a paradigm within Machine learning. In this paradigm, we have an agent and an environment. The environment is always in a particular state. The agent can take a set of actions on this state of the environment to go to a new state. After every action, the agent gets rewarded or penalised for taking a given action. The aim of learning is to find the optimal action at each state such that the long term reward is maximised.

This course is a perfect place to start when you have no programming background. It will give a nice overview of machine learning, with research paper discussions around implementing it in financial markets.
If you enjoy this course and are ready to learn to do 'How to', we recommend this mini learning track which will teach you Python and two popular machine learning techniques.

There are hundreds of Machine Learning algorithms classified into different types depending on how they work. For example, machine learning regression algorithms are used to model the relationship between dependent and independent variables.

In contrast, decision tree algorithms construct a model of decisions used in classification or regression problems in trading. Of these, some algorithms have become popular among quants.

Below are the most used Machine Learning algorithms for quantitative trading:

• Linear Regression
• Logistic Regression
• Random Forests (RM)
• Support Vector Machine (SVM)
• k-Nearest Neighbor (KNN)
• Classification and Regression Tree (CART)
• Deep Learning algorithms