Data & Feature Engineering 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.

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.

Generally, Survivorship Bias is the error of looking at entities that survived a situation while overlooking the ones that did not. After that, making strategies and taking decisions based on this error. This often happens while looking at a group of listed stocks which did well for a period of time while ignoring the ones that didn't and making strategies for future periods based on this logical error.
This has been covered in section 6 in detail.

Alternative data in finance is any non-market data set which can be used to enhance market prediction accuracy. For example, data such as news data which isn't price or market data is increasingly being used to predict the price movement of financial instruments. News data which is alternate data has been covered in section 8 and 9 of this course.

Technically, outliers are data points that are abnormally away from other points in the population or dataset. They might affect the predictive power of the model given the algorithm used to create the model. There are a couple of ways they can be identified and removed. One way is to calculate the z-score of the particular point to find the likelihood of the point belonging to a given population. Another way is to find the distance of the data point from a chosen function of the inter-quartile range.

Data for sentiment analysis can be procured from online vendors like webhose. Similar data can also be scraped from social media sites like Twitter etc. It can also be collected on the fly.
This has been covered in section 8 and 9 of this course.

Fundamental analysis is a method of measuring an instrument's value by examining related economic and financial factors. This is done by studying macroeconomic factors such as the state of the economy and industry conditions to microeconomic factors like the earnings of the company and its sector. Various indicators are used for the same like P/E ratio etc.
This has been covered in section 7 of this course.

Fundamental analysis refers to the analysis of financial aspects of business like financial statements, financial ratios and other factors like economics and others affecting the business to analyze the fair market value of its share/security. Technical analysis refers to the analysis of share/security fair price by examining and analyzing the past trends and changes in the price of shares and by studying historical information of the business.
This has been covered in various sections of this course.

Look-ahead bias occurs by using data in a backtest or simulation that wouldn't be available during the period being analysed in the test or simulation. This often leads to good results in the test or simulation which aren't correct.
This is covered in section 12 of this course.