Trading Using LLM: Concepts and Strategies
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Comprehend the foundational aspects of large language models
- Evaluate the process of training an LLM and prompt engineering
- Extract sentiment score from transcripts using LLM
- Develop entry and exit trading signals by using sentiment scores generated using LLM
- Design and backtest an intraday trading strategy based on sentiment scores generated by LLM. Analyse performance and conduct trade-wise analysis.
Skills Covered
Python
- Pandas
- NumPy
- finBERT
- Matplotlib
Concepts & Trading
- Transformers
- Prompt Engineering
- Sentiment Score Generation
Strategies
- Sentiment Analysis
- Sentiment Analysis With Price Trend
- Sentiment Analysis Post FOMC Meeting
learning track 5
This course is a part of the Learning Track: Artificial Intelligence in Trading Advanced
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Trade & Learn Together
Trade and Learn Together
- Get Certified
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Prerequisites
Fluency with Python, including Python libraries like Pandas, Numpy, and Matplotlib. You can enrol for the ‘Python for Trading: Basic’ course on Quantra to attain a basic level of understanding of Python. You can also check our course on ‘Introduction to Machine Learning for Trading’ if you are not familiar with machine learning concepts.
Syllabus
- IntroductionThis section serves as a preview of the course and introduces the course contents. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions regarding LLMs. This section explains the course structure as well as the various teaching tools used in the course, such as videos, quizzes, and coding exercises.
Introduction to the Course4m 54s
Inspiration for the Course2m
Course Features2m
Course Structure5m
Quantra Features and Guidance2m 38s
FAQs5m
- Introduction to Generative AIIn this section, you will discover what generative AI is, how it differs from traditional machine learning models, and explore Large Language Models (LLMs) and their rapid evolution over the years.Part Overview: Evolution of AI2m
Generative AI2m
Define Generative AI3m
Generative AI vs Traditional ML Models3m
Define Large Language Models3m
Input Accepted by LLMs3m
Output Generated by LLMs3m
FAQs on Generative AI5m
Additional Reading on Generative AI5m
- Machine Translation and LLMsDid you know that LLMs were inspired by machine translation models? In this section, we'll explore the history of machine translation, from early rule-based systems to modern data-driven approaches. You'll learn how these models evolved thereby improving accuracy and laying the foundation for today's powerful LLMs.Machine Translation2m
First Attempts3m
Issues With the Initial Approach3m
Improvement in Machine Translations3m
Recent Developments in Machine Translation3m
Evolution of Machine Translation Models3m
How Do LLMs Work?2m
Category of Model3m
Converting Text to Numbers3m
Multiple Layers in LLM3m
Operations in LLMs3m
Final Vector3m
FAQs on Machine Translation Models and LLMs5m
Additional Reading5m
Test on Generative AI, Machine Translation, and LLMs10m
- Progression of Neural NetworksEver wondered how neural networks evolved from simple models to sophisticated systems capable of handling complex sequences? In this section, you will learn how neural networks have evolved over time, starting with Recurrent Neural Networks (RNNs) and progressing to Long Short-Term Memory networks (LSTMs).Recurrent Neural Networks2m
Type of Data for RNNs3m
Output at Each Time Step3m
RNN for Long Sequence of Data3m
Relationship Between Input and Output3m
Limitations of RNN3m
RNN vs. LSTM2m
Limitation of RNN3m
LSTM vs. RNN3m
Gates in LSTMs3m
Translation Tasks3m
Role of Encoder3m
Role of Decoder3m
FAQs on Progression of Neural Networks5m
Additional Reading on Progression of Neural Networks5m
- Attention MechanismIn this section, you will be introduced to attention mechanisms and how they were used to enhance LSTMs, addressing long-term dependency issues. You'll learn how attention helps models focus on crucial information across longer sequences, improving their ability to retain context over time.LSTM With Attention Mechanism2m
Attention Mechanism3m
Primary Function of the Encoder3m
LSTM Modification3m
Traditional Encoder-Decoder Structure3m
FAQs on Attention Mechanism5m
Additional Reading on Attention Mechanism5m
Test on Neural Networks and Attention Mechanisms10m
- Introducing TransformersIn this section, you will be introduced to the use of attention mechanisms all by itself, also known as Transformers. We'll briefly explore the groundbreaking "Attention Is All You Need" paper to understand how this led to the development of Transformers.The Beginning of Transformers2m
Innovation in "Attention is All You Need"3m
Limitation of RNNs and CNNs3m
Benefit of the Transformer Model3m
CNNs and Long-Term Memory3m
Challenges of Deeper CNNs3m
- Transformers and Its ElementsCurious about what makes Transformers so powerful? Here, you will examine the key elements of the Transformer architecture. You’ll learn about its core components, including self-attention mechanisms, positional encoding, and the encoder-decoder structure, which together enable Transformers to process and generate sequences efficiently.Transformer Architecture: Encoder Elements2m
Role of Input Embedding3m
Role of Positional Encoding3m
Input of the Multi-Head Attention3m
Role of the Multi-Head Attention3m
Role of the Feedforward Network3m
Meaning of Nx3m
Transformer Architecture: Decoder Elements2m
Difference Between Encoder and Decoder3m
Masked Multi-Head Attention3m
Linear Layer3m
Softmax Layer3m
- Transformers vs. Sequence ModelsIn this section, you will explore how Transformers overcame the challenges of sequence models. We'll discuss how their architecture addresses issues like long-term dependencies and parallel processing, making them more effective and efficient for handling complex sequences.Transformers vs. RNNs and LSTMs2m
Limitations of RNNs and LSTMs3m
Processing Time in RNNs and LSTMs3m
Processing in Transformers3m
Long-Range Dependencies in Transformers3m
Advantage of Transformers3m
State-of-the-Art models in NLP3m
Improving Translation Quality3m
FAQs on Transformers5m
Summary5m
Additional Reading on Transformers5m
Test on Transformers10m
- Process of Training an LLMThis section offers a high-level overview of training Large Language Models (LLMs), covering the use of internet data to build foundational models and the role of reinforcement learning through human feedback (RLHF) to align these models with human expectations.Part Overview: LLM Training and Prompt Engineering2m
Process of Training an LLM2m
Definition of an LLM3m
Training of LLM3m
Training Process of LLM3m
Purpose of Human Feedback3m
Prediction of Multiple Answers3m
Ultimate Goal of LLM3m
FAQs on Process of Training an LLM5m
Summary5m
Additional Reading5m
- Brief Overview of Prompt EngineeringIn this section, you will learn how to use prompt engineering with examples from the trading domain, including question answering, iterating, summarising, sentiment analysis, and translation. By mastering these techniques, you'll be able to use LLMs effectively when you are creating trading strategies.Brief Overview of Prompt Engineering2m
Definition of Prompt Engineering3m
Prompt Similar to Human Interaction3m
Iteration in Prompt Engineering3m
Condensation of Financial Prompts3m
Determination of Tone in Passage3m
LLM and Strategy Creation3m
Practical Use of Translation3m
Importance of Prompt Engineering3m
FAQs on Prompt Engineering5m
Summary of Prompt Engineering5m
Additional Reading5m
Test on LLM Training and Prompt Engineering16m
- Applications of LLMs in FinanceExplore the idea of generating Python code based on research papers using LLMs and list examples of financial LLMs.Part Overview: Applications of LLM2m
Applications of LLMs in Finance2m
Experience of Creating Strategy Code Using LLM3m
Effectiveness of LLM Generated Code3m
Usage of LLMs by Traders3m
General Purpose LLM3m
Specialised LLM3m
JP Morgan LLM3m
Feature of BloombergGPT3m
FAQs5m
Summary5m
Additional Reading5m
- Overview of FinBERT ModelIllustrate the differences between finBERT and BERT, the specific training process of finBERT, and why finBERT is better suited for financial sentiment analysis.Overview of FinBERT Model2m
Creation of FinBERT3m
Difference Between BERT and FinBERT3m
Pre-train FinBERT Model3m
Fine-tune FinBERT Model3m
Advantage of FinBERT3m
Primary Focus of FinBERT3m
FAQs5m
Summary5m
Additional Readings5m
Test on Applications of LLMs12m
- Sentiment Analysis Trading ProcessThis section covers the sentiment analysis trading process, starting with data collection from sources like FOMC transcripts and earnings calls, then moving on to data preprocessing, where we convert and clean the data for sentiment scoring using specialised models like FinBERT and finally applying applying sentiment scores in trading strategies and analysing their performance.Sentiment Analysis Trading Process2m
First Step in the Sentiment Analysis Trading3m
Preprocessing Stage3m
Generating Sentiment Scores3m
Setting Thresholds3m
Raw Audio Data3m
Performance Analysis3m
Additional Reading on Sentiment Analysis Process5m
- Data Collection and PreprocessingWe'll transform raw FOMC transcripts into clean, actionable data by extracting key parts of the FED Chairman’s speech and removing irrelevant content. The transcript is then segmented into minute intervals to capture detailed sentiment shifts. Finally, we'll pull in price data and set the stage for backtesting sentiment-driven strategies.Data Collection5m
Data Preprocessing2m
Extracting Text3m
Header and Footer Text3m
Python Library for Extracting Text3m
Irrelevant Q&A3m
Number of Words per Minute3m
Poor Performance3m
Generate Alpaca API Keys5m
Data Collection and Preprocessing5m
Header/Footer Filtering Function3m
Data Merging3m
- Sentiment Analysis of FOMC TranscriptsThe FinBERT model can be used to analyse the financial data and generate sentiment scores. In this section, you will learn to score the FOMC meeting transcripts at a one-minute frequency.
Score The Sentiment of FOMC Transcripts2m
Understanding FinBERT for Sentiment Analysis3m
Functions in the FinBERT Python File3m
Using FinBERT Functions for Sentiment Scoring3m
Sentiment Score Range and Interpretation3m
Implementing FinBERT for FOMC Transcripts3m
Sentiment Score of FOMC Transcripts5m
Function to Load FinBERT Model3m
Function to Score Sentiment of Multiple Sentences3m
FAQs on Sentiment Analysis of FOMC Transcripts5m
Summary of Score the Sentiment of FOMC Transcripts5m
Additional Reading on Scoring the Sentiment5m
- Trade FOMC Meeting Using Sentiment ScoreThe sentiment scores of the FOMC meetings at a one-minute frequency can be used to generate trading signals. In this section, you will learn a trading strategy to trade FOMC meetings based on sentiment score, backtest it and analyse the performance.Trading Strategy Based on Sentiment Score2m
Strategy Based on Sentiment Score3m
Understanding Rolling Sentiment Score3m
Defining Thresholds for Trade Signals3m
Exiting a Short Position3m
Strategy Flow Diagram5m
Trading Strategy Based on Sentiment Score Threshold5m
Use of Signal Information3m
Calculate Trade Level Analytics5m
FAQs on Trading Strategy Based on Sentiment Score5m
Summary of Trade FOMC Meeting5m
Additional Reading on Trading FOMC Meeting5m
Test on Scoring and Trading Sentiment Score14m
- Strategy Variations to Trade the FOMC MeetingThere are many variations of the strategy based on sentiment score to trade the FOMC meetings. This section covers the variations of strategy based on the time to enter and exit and considers additional conditions such as price trends.Variations for Sentiment-Based Strategies2m
Understanding the First Sentiment-Based Strategy3m
Rolling Text and Its Use in Strategy3m
Adding Price Trend to the Strategy3m
Exiting the Trade3m
Post-FOMC Trading Variation3m
Trading Strategy Based on Rolling Text5m
Calculate the Rolling Text3m
Trading Strategy With Sentiment Score and Price Trend5m
Conditions to Enter Positions3m
Trading Strategy Post FOMC Meeting and Price Trend5m
Trading Strategy Entry Post Report5m
Function to Update Signal Column3m
FAQs on Variations for Sentiment-Based Strategies5m
Summary of Strategy Variations5m
Additional Reading on Variations of Strategy5m
Test on Strategy Variations14m
- Sentiment Analysis Using Audio DataIn this section, you will try to list the process of converting audio files to a transcript format which can be read by an LLM.
Sentiment Analysis Using Audio Data2m
Transcribing Audio to Text3m
Purpose of Audio Transcription3m
First Step of Whisper Model3m
Conversion of Video Format3m
Step After Audio Transcription3m
Accuracy of Whisper Model3m
Speech to Text5m
FAQs on Sentiment Analysis Using Audio Data5m
Summary of Sentiment Analysis Using Audio Data5m
Additional Reading5m
- Challenges and Deployment in ProductionThis section explores key challenges in using and deploying large language models (LLMs), including contextualization, quality assurance, etc. It also covers best practices for deploying LLMs, comparing enterprise solutions, and discussing open-source models like Meta's LLaMA.LLM: Challenges2m
Improving LLM Responses3m
Benefits of Retrieval-Augmented GPT Models3m
LLM Concerns3m
Mitigating Challenges3m
LLMs Without RAG3m
LLM Deployment Essentials2m
RLHF in Enterprise Platform3m
Open-Source LLM by Meta3m
Third-Party Packages in LLM Deployment3m
Efficient Fine-Tuning of Large LLMs3m
Optimising LLM Deployment for Performance3m
Knowledge Distillation3m
- Capstone ProjectIn this section, you will apply the knowledge you have gained throughout the course. You will work on a capstone project where the goal is to generate sentiment scores from the earnings call audio of publicly listed companies.Getting Started5m
Problem Statement5m
Capstone Project Model Solution5m
Capstone Data Files2m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra5m
Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips for Setting-up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Summary of the CourseIn this section, we will summarise all the learning from the course. All the data files and code used in this course can be downloaded from the downloadable unit of this section.Course Summary2m
Summary and Next Steps5m
Python Codes and Data2m
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Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
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.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
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.
- Is there a refund available?
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.
- Is the course downloadable?
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.
- Can the python strategies provided in the course be immediately used for trading?
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.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
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.
- If I plug in the Quantra code to my trading system, am I sure to make money?
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.
- What does "lifetime access" mean?
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.
- How to use LLM in trading?
Large Language Models (LLMs) can be used in trading to analyse vast amounts of financial data, generate trading signals, and automate trading strategies. They can process news, social media, and financial reports to provide insights and predictions.
- Can LLMs predict the stock market?
LLMs have shown potential in predicting stock market movements by analysing textual data like news headlines and social media posts. However, their predictions are not always accurate and should be used with caution.
- Is LLM good for sentiment analysis?
Yes, LLMs are highly effective for sentiment analysis. They can understand and interpret the nuances of human language, making them capable of accurately detecting sentiments in text data.
- How do I develop my own trading strategy with LLM?
To develop your own trading strategy with an LLM, you can start by using an LLM model which is trained on financial data or fine-tune a pre-trained model on financial data. Use this model to analyse market trends and generate trading signals. Backtest your strategy using historical data to evaluate its performance before deploying it in real-time trading.
- Which LLMs does the course cover? Does the course combine them with reinforcement learning?
The course utilizes the pre-trained financial LLM, FinBERT, which is specifically designed for financial applications. Other LLMs can also be applied. While the course does not cover reinforcement learning with human feedback (RLHF), users can explore Google Vertex AI, which offers an RLHF Tuning feature.