Trading Using LLM: Concepts and Strategies
₹7000/-₹27999/-
75% OFF
Get for ₹5600 with Course Bundle
- 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
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
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 54sInspiration for the Course2mCourse Features2mCourse Structure2mQuantra Features and Guidance2m 38sFAQs2m
Introduction to Generative AI
In 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 AI2mGenerative AI2mDefine Generative AI5mGenerative AI vs Traditional ML Models5mDefine Large Language Models5mInput Accepted by LLMs5mOutput Generated by LLMs5mFAQs on Generative AI2mAdditional Reading on Generative AI2m- 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 Translation2mFirst Attempts5mIssues With the Initial Approach5mImprovement in Machine Translations5mRecent Developments in Machine Translation5mEvolution of Machine Translation Models5mHow Do LLMs Work?2mCategory of Model5mConverting Text to Numbers5mMultiple Layers in LLM5mOperations in LLMs5mFinal Vector5mFAQs on Machine Translation Models and LLMs2mAdditional Reading2mTest 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 Networks2mType of Data for RNNs5mOutput at Each Time Step5mRNN for Long Sequence of Data5mRelationship Between Input and Output5mLimitations of RNN5mRNN vs. LSTM2mLimitation of RNN5mLSTM vs. RNN5mGates in LSTMs5mTranslation Tasks5mRole of Encoder5mRole of Decoder5mFAQs on Progression of Neural Networks2mAdditional Reading on Progression of Neural Networks2m
- 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 Mechanism2mAttention Mechanism5mPrimary Function of the Encoder5mLSTM Modification5mTraditional Encoder-Decoder Structure5mFAQs on Attention Mechanism2mAdditional Reading on Attention Mechanism2mTest 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 Transformers2mInnovation in "Attention is All You Need"5mLimitation of RNNs and CNNs5mBenefit of the Transformer Model5mCNNs and Long-Term Memory5mChallenges of Deeper CNNs5m
- 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 Elements2mRole of Input Embedding5mRole of Positional Encoding5mInput of the Multi-Head Attention5mRole of the Multi-Head Attention5mRole of the Feedforward Network5mMeaning of Nx5mTransformer Architecture: Decoder Elements2mDifference Between Encoder and Decoder5mMasked Multi-Head Attention5mLinear Layer5mSoftmax Layer5m
- 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 LSTMs2mLimitations of RNNs and LSTMs5mProcessing Time in RNNs and LSTMs5mProcessing in Transformers5mLong-Range Dependencies in Transformers5mAdvantage of Transformers5mState-of-the-Art models in NLP5mImproving Translation Quality5mFAQs on Transformers2mSummary2mAdditional Reading on Transformers2mTest 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 Engineering2mProcess of Training an LLM2mDefinition of an LLM5mTraining of LLM5mTraining Process of LLM5mPurpose of Human Feedback5mPrediction of Multiple Answers5mUltimate Goal of LLM5mFAQs on Process of Training an LLM2mSummary2mAdditional Reading2m
- 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 Engineering2mDefinition of Prompt Engineering5mPrompt Similar to Human Interaction5mIteration in Prompt Engineering5mCondensation of Financial Prompts5mDetermination of Tone in Passage5mLLM and Strategy Creation5mPractical Use of Translation5mImportance of Prompt Engineering5mFAQs on Prompt Engineering2mSummary of Prompt Engineering2mAdditional Reading2mTest 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 LLM2mApplications of LLMs in Finance2mExperience of Creating Strategy Code Using LLM5mEffectiveness of LLM Generated Code5mUsage of LLMs by Traders5mGeneral Purpose LLM5mSpecialised LLM5mJP Morgan LLM5mFeature of BloombergGPT5mFAQs2mSummary2mAdditional Reading2m
- 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 Model2mCreation of FinBERT5mDifference Between BERT and FinBERT5mPre-train FinBERT Model5mFine-tune FinBERT Model5mAdvantage of FinBERT5mPrimary Focus of FinBERT5mFAQs2mSummary2mAdditional Readings2mTest 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 Process2mFirst Step in the Sentiment Analysis Trading5mPreprocessing Stage5mGenerating Sentiment Scores5mSetting Thresholds5mRaw Audio Data5mPerformance Analysis5mAdditional Reading on Sentiment Analysis Process2m
Data Collection and Preprocessing
We'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 Collection2mData Preprocessing2mExtracting Text5mHeader and Footer Text5mPython Library for Extracting Text5mIrrelevant Q&A5mNumber of Words per Minute5mPoor Performance5mGenerate Alpaca API Keys2mData Collection and Preprocessing5mHeader/Footer Filtering Function5mData Merging5mSentiment Analysis of FOMC Transcripts
The 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 Transcripts2mUnderstanding FinBERT for Sentiment Analysis5mFunctions in the FinBERT Python File5mUsing FinBERT Functions for Sentiment Scoring5mSentiment Score Range and Interpretation5mImplementing FinBERT for FOMC Transcripts5mSentiment Score of FOMC Transcripts5mFunction to Load FinBERT Model5mFunction to Score Sentiment of Multiple Sentences5mFAQs on Sentiment Analysis of FOMC Transcripts2mSummary of Score the Sentiment of FOMC Transcripts2mAdditional Reading on Scoring the Sentiment2mTrade FOMC Meeting Using Sentiment Score
The 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 Score2mStrategy Based on Sentiment Score5mUnderstanding Rolling Sentiment Score5mDefining Thresholds for Trade Signals5mExiting a Short Position5mStrategy Flow Diagram2mTrading Strategy Based on Sentiment Score Threshold5mUse of Signal Information5mCalculate Trade Level Analytics5mFAQs on Trading Strategy Based on Sentiment Score2mSummary of Trade FOMC Meeting2mAdditional Reading on Trading FOMC Meeting2mTest 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 Strategies2mUnderstanding the First Sentiment-Based Strategy5mRolling Text and Its Use in Strategy5mAdding Price Trend to the Strategy5mExiting the Trade5mPost-FOMC Trading Variation5mTrading Strategy Based on Rolling Text5mCalculate the Rolling Text5mTrading Strategy With Sentiment Score and Price Trend5mConditions to Enter Positions5mTrading Strategy Post FOMC Meeting and Price Trend5mTrading Strategy Entry Post Report5mFunction to Update Signal Column5mFAQs on Variations for Sentiment-Based Strategies2mSummary of Strategy Variations2mAdditional Reading on Variations of Strategy2mTest on Strategy Variations14m
Sentiment Analysis Using Audio Data
In 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 Data2mTranscribing Audio to Text5mPurpose of Audio Transcription5mFirst Step of Whisper Model5mConversion of Video Format5mStep After Audio Transcription5mAccuracy of Whisper Model5mSpeech to Text5mFAQs on Sentiment Analysis Using Audio Data2mSummary of Sentiment Analysis Using Audio Data2mAdditional Reading2m- 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: Challenges2mImproving LLM Responses5mBenefits of Retrieval-Augmented GPT Models5mLLM Concerns5mMitigating Challenges5mLLMs Without RAG5mLLM Deployment Essentials2mRLHF in Enterprise Platform5mOpen-Source LLM by Meta5mThird-Party Packages in LLM Deployment5mEfficient Fine-Tuning of Large LLMs5mOptimising LLM Deployment for Performance5mKnowledge Distillation5m
- 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 Started2mProblem Statement2mCapstone Project Model Solution5mCapstone Data Files2m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra2mPython Installation Overview1m 59sFlow Diagram10mInstall Anaconda on Windows10mInstall Anaconda on Mac10mKnow your Current Environment2mTroubleshooting Anaconda Installation Problems10mCreating a Python Environment10mChanging Environments2mQuantra Environment2mTroubleshooting Tips for Setting-up Environment10mHow to Run Files in Downloadable Section?10mTroubleshooting 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 Summary2mSummary and Next Steps2mPython Codes and Data2m
WEBINAR
Trading Using LLM | Generative AI & Sentiment Analysis for Finance

Registered Successfully!
You will receive webinar joining details on your registered email
Would you like to start learning immediately?
about author



Why quantra®?
- More in Less Time
Gain more in less time
- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
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.