Natural Language Processing in 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.

There are various applications of natural language processing (NLP). Sentiment analysis is one of the widely used applications of NLP. The goal of sentiment analysis is to identify sentiment from the text. This can be further extended to making trading decisions. Other areas where NLP is associated are machine translation, automatic summarization, text classification, question answering etc.
Refer to Section 2 of Natural Language Processing in Trading course.

You can learn about natural language processing (NLP) and its application in the Natural Language Processing in Trading course. In the course, you learn to do sentiment analysis on the news headline data and predict stock and bond prices. You also learn about various embedding methods such as Bag of Words, TF-IDF, Word2Vec and BERT. Along with NLP techniques, you learn to use a machine learning algorithm to generate sentiment from news headlines.

Word embedding is a technique or model to map reinfor sentences or a group of words to a vector of numerical values. These vectors can be fed as input to the machine learning algorithm. This can further be used for sentiment analysis, translations, and other applications. There are different word embedding methods such as Bag of Words, TF-IDF, Word2Vec, and BERT.
You can learn about these word embedding techniques and their applications in the Natural Language Processing in Trading course.

There are various Python APIs such as Webhose, NewsAPI, GoogleNews which aggregate news headlines from different media sources. We have used Webhose API in our course for the illustration purpose, which provides access or free trial for ten days. Also, for various other sources API requests are limited to a certain number of requests per day or month. Once the limit is reached, one needs to purchase the API access for further use.

No. We used free data sources for news headlines data in this course.

The news headlines data is collected at Computer Science & Engineering Department, Indian Institute of Technology, Roorkee, and provided in CSV format for the illustration purpose in the course. To access the data, you can visit: https://sites.google.com/view/headlinedataset/home

This resource is subject to a CC-BY 2.5 IN license.

You can also access free news headlines data from various Python APIs such as Webhose, NewsAPI, GoogleNews. However, these resources have API requests limit per day or month. Once the limit is reached, one needs to purchase the API access for further use.