theoretically optimal strategy ml4t

a) 1 b)Above 0.95 c)0 2.What is the value of partial autocorrelation function of lag order 1? When a short period moving mean goes above a huge long period moving mean, it is known as a golden cross. Here we derive the theoretically optimal strategy for using a time-limited intervention to reduce the peak prevalence of a novel disease in the classic Susceptible-Infectious-Recovered epidemic . Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. The indicators selected here cannot be replaced in Project 8. . Project 6 | CS7646: Machine Learning for Trading - LucyLabs In the case of such an emergency, please contact the, Complete your assignment using the JDF format, then save your submission as a PDF. You should also report, as a table, in your report: Your TOS should implement a function called testPolicy() as follows: Your testproject.py code should call testPolicy() as a function within TheoreticallyOptimalStrategy as follows: The df_trades result can be used with your market simulation code to generate the necessary statistics. A tag already exists with the provided branch name. Provide a compelling description regarding why that indicator might work and how it could be used. theoretically optimal strategy ml4t In this project, you will develop technical indicators and a Theoretically Optimal Strategy that will be the ground layer of a later project. The file will be invoked run: entry point to test your code against the report. section of the code will call the testPolicy function in TheoreticallyOptimalStrategy, as well as your indicators and marketsimcode as needed, to generate the plots and statistics for your report (more details below). Include charts to support each of your answers. You may also want to call your market simulation code to compute statistics. The JDF format specifies font sizes and margins, which should not be altered. The Gradescope TESTING script is not a complete test suite and does not match the more stringent private grader that is used in Gradescope SUBMISSION. 1. Here is an example of how you might implement author(): Implementing this method correctly does not provide any points, but there will be a penalty for not implementing it. We hope Machine Learning will do better than your intuition, but who knows? for the complete list of requirements applicable to all course assignments. The following adjustments will be applied to the report: Theoretically optimal (up to 20 points potential deductions): Code deductions will be applied if any of the following occur: There is no auto-grader score associated with this project. Assignments received after Sunday at 11:59 PM AOE (even if only by a few seconds) are not accepted without advanced agreement except in cases of medical or family emergencies. It also involves designing, tuning, and evaluating ML models suited to the predictive task. No credit will be given for code that does not run in this environment and students are encouraged to leverage Gradescope TESTING prior to submitting an assignment for grading. To review, open the file in an editor that reveals hidden Unicode characters. Develop and describe 5 technical indicators. You will not be able to switch indicators in Project 8. While Project 6 doesnt need to code the indicators this way, it is required for Project 8. No credit will be given for code that does not run in the Gradescope SUBMISSION environment. @returns the estimated values according to the saved model. Please note that util.py is considered part of the environment and should not be moved, modified, or copied. . If you use an indicator in Project 6 that returns multiple results vectors, we recommend taking an additional step of determining how you might modify the indicator to return one results vector for use in Project 8.

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