{"id":69237,"date":"2020-12-22T10:09:00","date_gmt":"2020-12-22T15:09:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=69237"},"modified":"2022-11-21T09:46:47","modified_gmt":"2022-11-21T14:46:47","slug":"reinforcement-learning-in-trading-part-v","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/reinforcement-learning-in-trading-part-v\/","title":{"rendered":"Reinforcement Learning in Trading \u2013 Part V"},"content":{"rendered":"\n

See Part I<\/a>, Part II<\/a>,  Part III<\/a> and Part IV<\/a> to get started.<\/em><\/p>\n\n\n\n

Bellman Equation<\/h2>\n\n\n\n
\"Bellman<\/figure>\n\n\n\n

In this equation, s <\/strong>is the state, a <\/strong>is a set of actions at time t <\/strong>and ai <\/strong>is a specific action from the set. R is the reward table. Q <\/strong>is the state action table but it is constantly updated as we learn more about our system by experience. \u03b3  is the learning rate<\/p>\n\n\n\n

We will first start with the q-value for the Hold action on July 30.<\/p>\n\n\n\n

  1. The first part is the reward for taking that action. As seen in the R-table it is 0<\/li>
  2. Let us assume that \u03b3 = 0.98. The maximum Q-value for sell and hold actions on the next day, i.e. 31 July, is 1.09<\/li>
  3. Thus q-value for Hold action on 30 July is 0 + 0.98 (1.09) = 1.06<\/li><\/ol>\n\n\n\n

    In this way, we will fill the values for the other rows of the Hold column to complete the Q table.<\/p>\n\n\n\n

    Date<\/strong><\/td>Sell<\/strong><\/td>Hold<\/strong><\/td><\/tr>
    23-07-2020<\/td>0.95<\/td>0.966<\/td><\/tr>
    24-07-2020<\/td>0.95<\/td>0.985<\/td><\/tr>
    27-07-2020<\/td>0.98<\/td>1.005<\/td><\/tr>
    28-07-2020<\/td>0.96<\/td>1.026<\/td><\/tr>
    29-07-2020<\/td>0.98<\/td>1.047<\/td><\/tr>
    30-07-2020<\/td>0.99<\/td>1.068<\/td><\/tr>
    31-07-2020<\/td>1.09<\/td>1.090<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    The RL model will now select the hold action to maximise the Q value. This was the intuition behind the Q table. This process of updating the Q table is called Q learning. Of course, we had taken a scenario with limited actions and states. In reality, we have a large state space and thus, building a q-table will be time-consuming as well as a resource constraint.<\/p>\n\n\n\n

    To overcome this problem, you can use deep neural networks. They are also called Deep Q networks or\u00a0DQN<\/a>. The deep Q networks learn the Q table from past experiences and when given state as input, they can provide the Q-value for each of the actions. We can select the action to take with the maximum Q value.<\/p>\n\n\n\n

    Stay tuned for the next installment to learn how to train artificial neural networks.<\/em><\/p>\n\n\n\n

    Visit QuantInsti to download practical code: https:\/\/blog.quantinsti.com\/reinforcement-learning-trading\/<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    Join Ishan Shah from QuantInsti for an overview of Bellman Equation and how to apply it in Reinforcement Learning models.<\/p>\n","protected":false},"author":517,"featured_media":36684,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,338,350,341,344],"tags":[8577,8902,2105,8576,4922,1006,852,8575,8579,8578,494,7258],"contributors-categories":[13654],"class_list":{"0":"post-69237","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-asia-pacific","10":"category-quant-development","11":"category-quant-regions","12":"tag-alphazero","13":"tag-bellman-equation","14":"tag-deep-learning","15":"tag-delayed-gratification","16":"tag-econometrics","17":"tag-fintech","18":"tag-machine-learning","19":"tag-mean-reverting-strategy","20":"tag-q-learning","21":"tag-q-table","22":"tag-quant","23":"tag-reinforcement-learning","24":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nReinforcement 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