This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.<\/span><\/p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”1 Overview” module_id=”overview” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.16″ _module_preset=”02aeb796-358c-446a-8d9e-9cdcece0467f” global_colors_info=”{}” theme_builder_area=”post_content”] In this project, you will develop technical indicators and a Theoretically Optimal Strategy that will be the ground layer of a later project<\/span> (i.e., project 8)<\/span>. The technical indicators you develop here will be utilized in your later project to devise an intuition-based trading strategy and a Machine Learning based trading strategy. <\/span>The <\/span>Theoretically Optimal Strategy will give a baseline to gauge your later project\u2019s performance. We hope Machine Learning will do better than your intuition, but who knows? You will submit the code for the project <\/span>to<\/span> <\/span>Gradescope<\/span> SUBMISSION. The report will be submitted to Canvas.<\/span><\/span>\u00a0<\/span><\/p>[\/et_pb_text][et_pb_text admin_label=”1.1 Learning Objectives” _builder_version=”4.16″ _module_preset=”02aeb796-358c-446a-8d9e-9cdcece0467f” global_colors_info=”{}” theme_builder_area=”post_content”] The specific learning objectives for this assignment are focused on the following areas:<\/span>\u00a0<\/span><\/p>\n Please keep in mind that the completion of this project is pivotal to Project 8 completion. <\/span><\/span>The indicators <\/span>that are <\/span>selected here cannot be replaced in Project 8. <\/span><\/span>We encourage spending time finding and research<\/span>ing<\/span> indicators, including examining how they might later be combined to form trading strategies. Considering how multiple indicators might work together during Project 6 will help you complete the later project.<\/span><\/span>\u00a0<\/span><\/p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”3 Your Implementation” module_id=”implementation” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.16″ _module_preset=”02aeb796-358c-446a-8d9e-9cdcece0467f” global_colors_info=”{}” theme_builder_area=”post_content”] This project has two main components: <\/span><\/span><\/span>First, you will develop a theoretically optimal strategy (TOS), which represents the maximum amount your portfolio can theoretically return.\u00a0 Note that this strategy does not use any indicators.\u00a0 Second<\/span><\/span><\/span>, you will research and identify five market indicators.<\/span><\/span><\/span><\/p>\n Before the deadline, make sure to pre-validate your submission using Gradescope TESTING. Once you are satisfied with the results in testing, submit the code to Gradescope SUBMISSION. <\/span>Only code submitted to Gradescope SUBMISSION will be graded. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0).<\/span><\/b>\u00a0<\/span><\/p>[\/et_pb_text][et_pb_text admin_label=”3.1 Getting Started” _builder_version=”4.19.4″ _module_preset=”02aeb796-358c-446a-8d9e-9cdcece0467f” global_colors_info=”{}” theme_builder_area=”post_content”] This framework assumes you have already set up the <\/span>local environment<\/span><\/a> and <\/span>ML4T Software<\/span><\/a>.\u202fThere is no distributed template for this project.<\/span>\u00a0<\/span><\/p>\n You will have access to the ML4T\/Data directory data, but you should use ONLY the API functions in util.py to read it.\u202fYou may create a new folder called indicator_evaluation to contain your code for this project.<\/span>\u00a0<\/span><\/p>\n You should create the following code files for submission. They should contain ALL code from you that is necessary to run your evaluations.\u202f<\/span>\u00a0<\/span><\/p>\n For both sections:<\/span><\/p>\n Theoretically Optimal Strategy only:<\/span><\/p>\n In the Theoretically Optimal Strategy, assume that you can see the future<\/span><\/i>. You are constrained by the portfolio size and order limits as specified above. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. We have you do this to have an idea of an upper bound on performance, which can be referenced in Project 8.\u00a0\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n Note: The Theoretically Optimal Strategy does not use the indicators developed in the next section.\u00a0<\/span>\u00a0<\/span><\/p>\n Use a revised market simulator based on the one you wrote earlier in the course to determine the portfolio valuation.\u00a0 For this activity, use $0.00 and 0.0 for commissions and impact, respectively.\u00a0<\/span>\u00a0<\/span><\/p>\n Provide a chart that reports:\u00a0<\/span>\u00a0<\/span><\/p>\n You should also <\/span>create a table <\/span>in your report<\/span> that <\/span>shows<\/span><\/span> t<\/span><\/span><\/span>o<\/span> 6 digits to the right of the decimal<\/span><\/span><\/span>:<\/span><\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n Your TOS should implement a function called \u2018testPolicy()\u2019 as follows:\u00a0<\/span>\u00a0<\/span><\/p>[\/et_pb_text][et_pb_code _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]1 Overview<\/h2>\n
1.1 Learning Objectives<\/h3>\n
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2 About the Project<\/h2>\n
3 Your Implementation<\/h2>\n
3.1 Getting Started<\/h3>\n
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<\/span>Code implementing your indicators as functions that operate on <\/span>DataFrames<\/span>. There is no defined API for indicators.py, but when it runs, the main method should generate the charts that will illustrate your indicators in the report.<\/span><\/span><\/span><\/li>\n
<\/span>An improved version of your marketsim code accepts a \u201ctrades\u201d DataFrame (instead of a file). More info on the trades data frame is below. It is OK not to submit this file if you have subsumed its functionality into one of your other required code files.\u202f<\/span>This file has a different name and a slightly different setup than your previous project. However, that solution can be used with several edits for the new requirements.<\/span><\/i>\u202f<\/span>\u00a0<\/span><\/li>\n
<\/span>Code implementing a TheoreticallyOptimalStrategy (details below). It should implement testPolicy(), which returns a trades data frame (see below).\u202f\u202f<\/span>\u00a0<\/span><\/li>\n
<\/span>This file should be considered the entry point to the project. The\u202f<\/span>if \u201c__name__\u201d == \u201c__main__\u201d:<\/span><\/i>\u202fsection 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).<\/span><\/li>\n<\/ul>\n<\/ul>[\/et_pb_text][et_pb_image src=”https:\/\/lucylabs.gatech.edu\/ml4t\/wp-content\/uploads\/2021\/09\/Screen-Shot-2021-09-25-at-5.21.40-PM.png” title_text=”Screen Shot 2021-09-25 at 5.21.40 PM” _builder_version=”4.16″ _module_preset=”758e35fc-a92e-4572-9046-d349d3a7226f” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”3.2 Task & Requirements” _builder_version=”4.16″ _module_preset=”02aeb796-358c-446a-8d9e-9cdcece0467f” global_colors_info=”{}” theme_builder_area=”post_content”]
3.2 Data, Details, Dates & Rules<\/h3>\n
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3.3 Implement Part 1: Theoretically Optimal Strategy<\/h3>\n
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