Agentic AI 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.

This course is for traders, analysts, or students who want to use modern AI to automate the research and strategy development process. It focuses on practical implementation using no-code and low-code tools.

While a single AI model (like a standard LLM) can answer questions or provide code for predicting a price move, it requires repeated prompting. Agentic AI operates as a coordinated team, with different "specialists". For example, one agent will focus on data retrieval and performing checks on data, while another agent focuses on backtest code.

There are indeed several robust and well-established backtesting engines available, and rebuilding the mathematical logic of a backtester from scratch is often unnecessary.

The purpose of this course is not to create a new calculation engine, but to automate the workflow of a Quantitative Researcher.

Why this course still matters:

• Solves the idea-to-code bottleneck:
  Even with existing backtesters, trading ideas must be manually translated into Python code, data must be prepared, and errors must be debugged. This course teaches you to build agentic workflows that convert a plain-English trading idea directly into a complete, executable backtesting script.

• Focuses on orchestration, not just coding:
  You learn to design and manage multiple AI agents that handle strategy formalization, data sourcing, backtesting, and notebook assembly, similar to how a real research team operates.

           Agent 1 (Hypothesis): Formalizes the strategy.

           Agent 2 (Data Scout): Finds & preps the data.

           Agent 3 (The Backtester): Writes the simulation code.

           Agent 4 (Assembler): Compiles it all into a Jupyter Notebook.

          You become the manager of an AI team that does the heavy lifting, allowing you to test multiple ideas in the time it used to take to test one.

• Builds future-proof skills:
  The concepts you learn, multi-agent orchestration and tool-using AI, are transferable beyond backtesting and can later be applied to live execution or risk management workflows.

In short: you’re not learning to build a backtester, you’re learning to automate quantitative research.

Yes. The curriculum is designed in such a that beginners can understand the concepts clearly before moving to implementation.

While the course covers technical implementation, it does not require advanced python knowledge, though a basic understanding of Python will be helpful when you are looking at the generated code of the agents.

You will build a comprehensive Agentic Quant Team. This includes:

• Hypothesis Designer Agent: To convert vague ideas to structured hypothesis.
• Data Scout Agent: To find and prepare market data.
• Backtester Agent: To simulate your strategies.
• Performance Evaluation Agent: To analyse results.
• AI Assembler Agent: To coordinate the entire system