In this episode of ODSC’s Ai X Podcast, Dr. Yves J. Hilpisch, founder and CEO of The Python Quants (http://tpq.io), and founder and CEO of The AI Machine (http://aimachine.io), joins us to discuss reinforcement learning for finance. 

Yves is also the author of the book "Reinforcement Learning for Finance” and has a diploma in Business Administration and a Ph.D. in Mathematical Finance. Yves is also an adjunct professor for Computational Finance at the Miami Herbert Business School.

Show Topics:

• Overview of The Python Quants
• The speaker's new book, “Reinforcement Learning for Finance” and why the focus on reinforcement learning
• Dynamic time problems
• Markov decision processes
• Key types of reinforcement learning models
• Deep Q-Learning (DQL) and how it relates to Q-Learning
• How deep Q-Learning be applied to financial contexts, such as trading strategies or portfolio management
• Issues associated with using static historical time series data for training DQL agents in finance
• End-of-day data vs tick data
• Adding white noise to historical time series data to improve the training of DQL agents
• Key differences between the noisy time series data and the simulated time series data approaches 
• Generative Adversarial Networks (GANs) utility for generating synthetic financial time series data
• GANs’ advantages over traditional Monte Carlo simulations in generating financial data
• How to check the quality of synthetic data
• The role of Kolmogorov-Smirnov (KS) test in evaluating the synthetic data generated by GANs
• How the chapter compare the effectiveness of GAN-generated data to real financial data
• The primary goal of the trading agent 
• The role of buy bots 
• The role of agentic AI
• Topic analysis and sentiment analysis 
• Overview of the “Researchers Find AI Model Outperforms Human Stock Forecasters ‘Financial Statement Analysis with Large Language Models’” paper
• Yves’ session at ODSC Europe 

SHOW NOTES

Monte Carlo Simulation in Finance: https://www.investopedia.com/articles/investing/112514/monte-carlo-simulation-basics.asp

Python Quants: https://home.tpq.io/

Certificate in Python for Finance: https://home.tpq.io/certificate/

Markov decision process: https://en.wikipedia.org/wiki/Markov_decision_process

Black Scholes model: https://www.investopedia.com/terms/b/blackscholes.asp

Deep Q Learning: https://www.tensorflow.org/agents/tutorials/0_intro_rl

Backtesting: https://www.investopedia.com/terms/b/backtesting.asp

Model collapse: https://en.wikipedia.org/wiki/Model_collapse

GANS: https://en.wikipedia.org/wiki/Generative_adversarial_network

Black Swan Events: https://www.investopedia.com/terms/b/blackswan.asp

Kamograve Smirnov test: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test

Delta Hedging: https://www.investopedia.com/terms/d/deltahedging.asp

Hedging strategies: https://www.investopedia.com/trading/hedging-beginners-guide/ 

Option Replication: https://www.cfainstitute.org/en/membership/professional-development/refresher-readings/option-replication-put-call-parity 

Geometric Brownian motion: https://en.wikipedia.org/wiki/Geometric_Brownian_motion 

Jump Diffusion: https://en.wikipedia.org/wiki/Jump_diffusion

Heston model: https://en.wikipedia.org/wiki/Heston_model  

Bates Mode: https://en.wikipedia.org/wiki/Stochastic_volatility_jump

Gain Fallacy (A loss of 70% requires a 300% gain to break even): https://www.rgbcapitalgroup.com/preserving-capital 

Prime Brokers: https://www.investopedia.com/terms/p/primebrokerage.asp

Algorithmic trading: https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp 

Financial statement analysis, with large language models: https://arxiv.org/pdf/2407.17866 

This episode was sponsored by:  

Ai+ Training ⁠https://aiplus.training/⁠ 

Home to 600+ hours of on-demand, self-paced AI training, live virtual training, and certifications in in-demand skills like LLMs and prompt engineering.

And created in partnership with ODSC ⁠https://odsc.com/⁠ 

The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps

Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31. 

Never miss an episode, subscribe now