How Reinforcement Learning can be Applied to Quantitative Finance w/ Dr. Tom Starke

Показать описание

In our latest video, “How Reinforcement Learning can be Applied to Quantitative Finance”, the CEO of AAA Quants, Dr. Tom Starke, discusses reinforcement learning with Quantopian VP of Growth, Delaney MacKenzie. Over the course of the video, they discuss different uses of reinforcement learning, different types of machine learning, and how they relate in finance.

About the Speaker:
Dr. Tom Starke has a Ph.D. in Physics and works as an algorithmic trader at a proprietary trading company in Sydney. He has a keen interest in mathematical modeling and machine learning in the financial markets. He has previously lectured computer simulation at Oxford University and lead strategic research projects for Rolls-Royce Plc.

Tom is very active in the quantitative trading community, running workshops for Quantopian, teaching people quantitative analysis techniques, and organizing algorithmic trading meetup groups such as Cybertraders Syd.

Disclaimer
Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice.

More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian.

In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

Рекомендации по теме

Комментарии

I am developing a modeless TD app for the ES which is merely scalping ticks throughout the day. Using an 800 tick rollup rather than a time based roll up has helped me immensely. I am also using Bollinger Bands on that data measuring the delta between the tick and the distance from the 3 bands (upper lower and ma20), Q learning

devonk

FinRL: A Deep Reinforcement Learning Library for Quantitative Finance.

aifinance_byfintech

Nice interview. Good info - multi-factor model weighted by a non-linear relationship learned by the likes of Inverse RL to be used in financial multifactor models in modelling risk, demand etc.

sj_life_and_science

Is their a video game that teaches quantitative finance?

hume

How Reinforcement Learning can be Applied to Quantitative Finance w/ Dr. Tom Starke

An introduction to Reinforcement Learning

Reinforcement Learning Basics

Reinforcement Learning: Crash Course AI #9

How Reinforcement Learning can be Applied to Quantitative Finance w/ Dr. Tom Starke

Reinforcement Learning: Machine Learning Meets Control Theory

Reinforcement Learning from scratch

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

AI Learns to Ride a Motorcycle (Deep Reinforcement Learning)

AI Agent 101: Unleashing the Power of Reinforcement Learning Step by Step

Reinforcement Learning, by the Book

AI Learns to Walk (deep reinforcement learning)

What Is Reinforcement Learning?

The power of reinforcement learning and robotics

Deep Reinforcement Learning Tutorial for Python in 20 Minutes

Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn

Reinforcement Learning Series: Overview of Methods

Solving a Maze with Reinforcement Learning

Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar

Q-Learning Explained - Reinforcement Learning Tutorial

Reinforcement Learning through Human Feedback - EXPLAINED! | RLHF

How does Reinforcement Learning Work | Environment and Agent in Reinforcement Learning Mahesh Huddar

A. I. Learns to Play Starcraft 2 (Reinforcement Learning)

Deep Reinforcement Learning with Real-World Data

Chip Placement with Deep Reinforcement Learning (Paper Explained)