Complete Webinar Recording: https://www.quantinsti.com/chatgpt-machine-learning-trading-webinar-22-march-2023 ----------------------------------------- About the Session This session covers the basics of ChatGPT and the need for machine learning in trading. Attendees will learn how to integrate ChatGPT and machine learning in trading, along with successful real-world examples. The session will also discuss potential challenges and risks. The session concludes with a Q&A session for participants to engage with the presenter. ----------------------------------------- Session Outline - Introduction - Understanding ChatGPT - Machine Learning in Trading - Integration of ChatGPT and Machine Learning in Trading - Challenges and Risks - Interactive Q&A ----------------------------------------- Pre-Requisites There are no specific prerequisites for attending this webinar. However, a basic understanding of trading and machine learning concepts would be helpful. ----------------------------------------- About the Speaker Varun Kumar Pothula (Quantitative Analyst at QuantInsti) Varun holds a Masters degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and also an algo trading strategist. Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading. ----------------------------------------- Useful Resources: 📈 Algo Trading with ChatGPT https://blog.quantinsti.com/algorithmic-trading-chatgpt/ ✅ Free Resources to Learn Algorithmic Trading https://blog.quantinsti.com/free-resources-list-compilation-learn-algorithmic-trading/ 📜 FREE courses | Quantra https://quantra.quantinsti.com/ 📄 Blogs and Tutorials https://blog.quantinsti.com/ 🎞️ Complete webinar recordings https://blog.quantinsti.com/tag/webinars/ ----------------------------------------- This event was conducted on: Wednesday, March 22, 2023 09:30 AM EST | 07:00 PM IST | 09:30 PM SGT ----------------------------------------- Like and Follow us on: Our Blog: https://blog.quantinsti.com/ Facebook @ https://www.facebook.com/quantinsti/ Twitter @ https://twitter.com/QuantInsti LinkedIn @ https://www.linkedin.com/school/quantinsti/ Instagram @ https://www.instagram.com/quantinstian/ E-mail us @ sales@quantinsti.com

1. ChatGPT and ML in Trading
Varun Pothula
Quantitative Analyst at QuantInsti

2. Agenda
● Introduction
● Understanding Chat GPT
● Machine Learning in Trading
● Integration of Chat GPT and Machine Learning in Trading
● Challenges and Risks
● Q&A

3. Chat GPT and ML in Trading
● Significant increase of data
● Market trends and patterns
Algo Trading:
● Develop and optimize trading
strategies
● Predict future market movements
● Make more informed decisions

4. Chat GPT and ML in Trading
Natural language processing (NLP)
models
● ChatGPT
● Analyze and understand text
● News articles, social media posts,
and other textual sources

5. Chat GPT and ML in Trading
ChatGPT, developed by OpenAI
● Natural-sounding responses to text
prompts
● Detailed responses about trading
opportunities

6. Chat GPT and ML in Trading
Expectations of Finance Community
● Analyze news articles and social
media posts
● Identify trends and market
sentiment
● Generate trading signals
● Insights based on textual data
● Develop and optimize trading
strategies

7. Chat GPT and ML in Trading

8. Understanding Chat GPT

9. Understanding Chat GPT
● Generative Pre-trained Transformer
(GPT) architecture
● Neural network that is trained on
large amounts of text data
● Coherent and contextually
appropriate responses
● Sentiment analysis, text
classification, and text generation

10. Machine Learning in Trading

11. Machine Learning

12. Machine Learning
● Subset of AI
● Learns from data
● Make predictions and decisions

13. ML For Algo Trading
Data

14. ML For Algo Trading
Data
● Price
● Fundamental
● Economic
● News
● Social media data

15. ML For Algo Trading
Data Preprocessing

16. ML For Algo Trading
Data
● Removal of unwanted observations
● Fixing Structural errors
● Managing Unwanted outliers
● Handling missing data
Model
Strategy
Preprocessing

17. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering

18. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering
● Converting raw data into features/attributes

19. ML For Algo Trading
Feature Engineering

20. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering
Data Split

21. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering
Data Split
● Input dataset X
● Output dataset y
● Train Data
● Test Data

22. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering
Data Split
Train Data
ML Model

23. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
engineering
Data Split
Train Data
ML Model
Test Data
ML Model

24. ML For Algo Trading
Data
Model
Strategy
Preprocessing
Feature
Engineering
Data Split
Train Data
ML Model
Test Data
ML Model
Predictions

25. Integration of Chat GPT and
Machine Learning in Trading

26. Problem Statement
● Predict High and Low of next trading
day
● Linear regression

27. I’m creating a linear regression model to predict the high and low of gold prices
for the next trading day.
Can you give me complete pipeline to make the algorithm deployment ready?
Give me a list of steps that need to be done as a quant would do
Prompt

33. Quantra Notebooks

34. ML Trading Model

35. Give me python code to split the input data for a linear regression model into the
train and test data with a ratio of 70:30
Prompt

38. Quantra Notebooks

39. How to study the performance of the machine learning based algo trading
algorithm?
Prompt

43. Quantra Notebooks

44. Challenges and Risks

45. What are the challenges and risks of using chat gpt for ML based algo trading?
Prompt

51. Compare the yearly financial statements of
Apple and Microsoft for the year 2020

55. $ 3.28

56. $3.28
≠
$13.03

57. Key Takeaways
● Chat GPT is a neural network that is trained on large amounts of text data
● Can generate ideas to apply ML for trading
● Can produce wrong code due to lack of domain expertise
● Can generate erroneous information

58. Complete Recording