Machine learning for trading
Gordon Ritter applies reinforcement learning to dynamic trading strategies with market impact
CLICK HERE TO VIEW THE PDF
In multiperiod trading with realistic market impact, determining the dynamic trading strategy that optimises the expected utility of final wealth can be difficult. Gordon Ritter shows that, with an appropriate choice of reward function, reinforcement learning techniques (specifically Q-learning) can successfully handle the risk-averse case
In this article, we show how machine learning can be applied to the problem of discovering and implementing dynamic trading
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact [email protected] to find out more.
You are currently unable to copy this content. Please contact [email protected] to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email [email protected]
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email [email protected]
More on Asset management
7 days in 60 seconds
Autocallables, Mifexit and the value of HFT
The week on Risk.net, September 19-25, 2020
Receive this by email