Generative AI for Asset Managers Workshop
Sept 30 - Oct 1
2 day, online workshop
Online
8:00 am - 11:00 am ET
Hosted By
Dr. Chan & Dr. Hunter
Questions?
Email us: info@predictnow.ai
Generative AI for Asset Managers is a 2-day online workshop to demonstrate how we construct a discretionary trading strategy using an LLM.
The difference between algorithmic and discretionary traders is that the latter use qualitative, unstructured data as input to their brains, form high level understanding and intuition, and make trading decisions. With the advent of Generative AI and Large Language Models (LLMs), we can now systematize and deploy at scale discretionary trading strategies – essentially creating “George Soros on a chip”.
During the workshop, we will demonstrate how asset managers and traders can use Google’s BARD to turn unstructured data such as the audio feed of the Federal Reserve’s Chair’s speech into high frequency trading signals and backtest such strategies, all at minimal cost. We will then break into small groups to explore and experiment with variations and improvements on the basic code, as well as brainstorm other use cases of LLM for asset management.
Workshop Speakers
This workshop will be hosted by Dr. Ernest Chan, Founder and CEO of Predictnow.ai, and Dr. Roger Hunter, Chief Technology Officer at QTS Capital Management, and Dr. Hamlet Medina, Chief Data Scientist at Criteo. We are honoured to be joined by Dr. Lisa Huang, Head of AI Investment Management at Fidelity Investments who will present as a keynote speaker.
Host
Dr. Ernest Chan
Founder and CEO of Predictnow.ai
Host
Dr. Roger Hunter
CTO of QTS Capital Management
Host
Dr. Hamlet Medina
Chief Data Scientist at Criteo
Keynote speaker
Dr. Lisa Huang
Head of AI Investment Management, Fidelity Investments.
Intended Audiences
Asset Managers
Venture Investors
Entrepreneurs
Product Developers
Regulators
Finance & AI Researchers
While the intended audience for this event includes, asset managers, venture investors, entrepreneurs, product developers, regulators, finance and AI researchers. If you fall into another category but feel like you would be a good fit for this workshop, send us an email at info@predictnow.ai and we can help clarify any questions you have.
Workshop Outline
01
Large Language Models (LLMs) & Generative Pre-trained Transformers (GPT)
- Introduction to LLM: BARD, ChatGPT, and other large language models
- Typical Applications of LLMs
- How LLMs work
- Using BARD/PaLM on the web through their API
02
Building Applications
- Overview of Prompt Engineering
- Building applications such as text generation, summarization, etc.
- Few-shot learning with BARD
- Introduction to embeddings
- Overview of the BARD embeddings API and its usage
03
Risks Associated with LLMs
- Understanding main risks with LLMs, such as, hallucinations, bias, consent and security
- Methods for reducing the risks of Hallucinations, such as, retrieval augmentation, prompt engineering, and self-reflection
- Methods to detect and address hallucinations, including reinforcement learning from human feedback (RLHF) and model-based approaches
04
Using LLMs for trading Federal Reserve Chair’s speeches
- Why we chose the BARD family among the many available LLMs
- Evaluating BARD’s native performance
- Improving performance with embeddings
- Worked example: computing sentiment ratings on public companies using embeddings
- Test data: Video archives of the press conferences of the Federal Reserve Chair.
- Backtesting a discretionary trading strategy using the sentiment output of a LLM.
05
Deploying LLMs in Production
- Best Practices for Deploying LLMs in Production
- Overview of alternative generative models such as ChatGPT, BART, Cohere, Alpaca, etc.