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You are allowed unlimited resubmissions to Gradescope TESTING. They take two random samples of 15 months over the past 30 years and find. This movement inlines with our indication that price will oscillate from SMA, but will come back to SMA and can be used as trading opportunities. This is a text file that describes each .py file and provides instructions describing how to run your code. Please submit the following files to Gradescope SUBMISSION: You are allowed a MAXIMUM of three (3) code submissions to Gradescope SUBMISSION. You may create a new folder called indicator_evaluation to contain your code for this project. You are allowed to use up to two indicators presented and coded in the lectures (SMA, Bollinger Bands, RSI), but the other three will need to come from outside the class material (momentum is allowed to be used). An indicator can only be used once with a specific value (e.g., SMA(12)). Include charts to support each of your answers. It is not your 9 digit student number. The report will be submitted to Canvas. The, number of points to average before a specific point is sometimes referred to as, In our case, SMA aids in smoothing out price data over time by generating a, stream of averaged out prices, which aids in suppressing outliers from a dataset, and so lowering their overall influence. Create a Theoretically optimal strategy if we can see future stock prices. @param points: should be a numpy array with each row corresponding to a specific query. Code that displays warning messages to the terminal or console. Provide a compelling description regarding why that indicator might work and how it could be used. Close Log In. We can calculate Price/SMA (PSMA) values and use them to generated buy or, and above can indicate SELL. Code provided by the instructor or is allowed by the instructor to be shared. No credit will be given for coding assignments that do not pass this pre-validation. Are you sure you want to create this branch? Maximum loss: premium of the option Maximum gain: theoretically infinite. You may find our lecture on time series processing, the. Introduce and describe each indicator you use in sufficient detail that someone else could reproduce it. Code implementing a TheoreticallyOptimalStrategy object (details below). Fall 2019 ML4T Project 6. to develop a trading strategy using technical analysis with manually selected indicators. The implementation may optionally write text, statistics, and/or tables to a single file named p6_results.txt or p6_results.html. Course Hero is not sponsored or endorsed by any college or university. We have you do this to have an idea of an upper bound on performance, which can be referenced in Project 8. Use only the data provided for this course. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0). PowerPoint to be helpful. For each indicator, you should create a single, compelling chart (with proper title, legend, and axis labels) that illustrates the indicator (you can use sub-plots to showcase different aspects of the indicator). We do not anticipate changes; any changes will be logged in this section. You must also create a README.txt file that has: The following technical requirements apply to this assignment. You are constrained by the portfolio size and order limits as specified above. (up to -100 points), Course Development Recommendations, Guidelines, and Rules. For the Theoretically Optimal Strategy, at a minimum, address each of the following: There is no locally provided grading / pre-validation script for this assignment. . June 10, 2022 Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. . Late work is not accepted without advanced agreement except in cases of medical or family emergencies. For each indicator, you will write code that implements each indicator. You are constrained by the portfolio size and order limits as specified above. Ten pages is a maximum, not a target; our recommended per-section lengths intentionally add to less than 10 pages to leave you room to decide where to delve into more detail. Packages 0. Explicit instructions on how to properly run your code. 'Technical Indicator 3: Simple Moving Average (SMA)', 'Technical Indicator 4: Moving Average Convergence Divergence (MACD)', * MACD - https://www.investopedia.com/terms/m/macd.asp, * DataFrame EWM - http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html, Copyright 2018, Georgia Institute of Technology (Georgia Tech), Georgia Tech asserts copyright ownership of this template and all derivative, works, including solutions to the projects assigned in this course. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. Are you sure you want to create this branch? 2/26 Updated Theoretically Optimal Strategy API call example; 3/2 Strikethrough out of sample dates in the Data Details, Dates and Rules section; Overview. You should create a directory for your code in ml4t/manual_strategy and make a copy of util.py there. To review, open the file in an editor that reveals hidden Unicode characters. You may not use stand-alone indicators with different parameters in Project 8 (e.g., SMA(5) and SMA(30)). Develop and describe 5 technical indicators. A tag already exists with the provided branch name. Citations within the code should be captured as comments. df_trades: A single column data frame, indexed by date, whose values represent trades for each trading day (from the start date to the end date of a given period). . Zipline is a Pythonic event-driven system for backtesting, developed and used as the backtesting and live-trading engine by crowd-sourced investment fund Quantopian. 6 Part 2: Theoretically Optimal Strategy (20 points) 7 Part 3: Manual Rule-Based Trader (50 points) 8 Part 4: Comparative Analysis (10 points) . Transaction costs for TheoreticallyOptimalStrategy: In the Theoretically Optimal Strategy, assume that you can see the future. Technical indicators are heuristic or mathematical calculations based on the price, volume, or open interest of a security or contract used by traders who follow technical analysis. The performance metrics should include cumulative returns, standard deviation of daily returns, and the mean of daily returns for both the benchmark and portfolio. Readme Stars. All work you submit should be your own. These should be incorporated into the body of the paper unless specifically required to be included in an appendix. SMA can be used as a proxy the true value of the company stock. Benchmark: The performance of a portfolio starting with $100,000 cash, investing in 1000 shares of JPM, and holding that position. Use the revised market simulator based on the one you wrote earlier in the course to determine the portfolio valuation. Floor Coatings. Ensure to cite any sources you reference and use quotes and in-line citations to mark any direct quotes. If you need to use multiple values, consider creating a custom indicator (e.g., my_SMA(12,50), which internally uses SMA(12) and SMA(50) before returning a single results vector). In Project-8, you will need to use the same indicators you will choose in this project. We hope Machine Learning will do better than your intuition, but who knows? While Project 6 doesnt need to code the indicators this way, it is required for Project 8, 3.5 Part 3: Implement author() function (deduction if not implemented). ) You are encouraged to develop additional tests to ensure that all project requirements are met. This class uses Gradescope, a server-side autograder, to evaluate your code submission. ML4T is a good course to take if you are looking for light work load or pair it with a hard one. This is the ID you use to log into Canvas. You also need five electives, so consider one of these as an alternative for your first. This is the ID you use to log into Canvas. This Golden_Cross indicator would need to be defined in Project 6 to be used in Project 8. For this activity, use $0.00 and 0.0 for commissions and impact, respectively. Please answer in an Excel spreadsheet showing all work (including Excel solver if used). Simple Moving average Second, you will research and identify five market indicators. You are allowed to use up to two indicators presented and coded in the lectures (SMA, Bollinger Bands, RSI), but the other three will need to come from outside the class material (momentum is allowed to be used). manual_strategy/TheoreticallyOptimalStrategy.py Go to file Cannot retrieve contributors at this time 182 lines (132 sloc) 4.45 KB Raw Blame """ Code implementing a TheoreticallyOptimalStrategy object It should implement testPolicy () which returns a trades data frame You are allowed unlimited submissions of the report.pdf file to Canvas. import TheoreticallyOptimalStrategy as tos from util import get_data from marketsim.marketsim import compute_portvals from optimize_something.optimization import calculate_stats def author(): return "felixm" def test_optimal_strategy(): symbol = "JPM" start_value = 100000 sd = dt.datetime(2008, 1, 1) ed = dt.datetime(2009, 12, 31) However, it is OK to augment your written description with a, Do NOT copy/paste code parts here as a description, It is usually worthwhile to standardize the resulting values (see. : You will develop an understanding of various trading indicators and how they might be used to generate trading signals. Also, note that it should generate the charts contained in the report when we run your submitted code. D) A and C Click the card to flip Definition Provide a chart that illustrates the TOS performance versus the benchmark. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Textbook Information. You may also want to call your market simulation code to compute statistics. Of course, this might not be the optimal ratio. About. ML4T / manual_strategy / TheoreticallyOptimalStrateg. 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 . Compute rolling mean. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). You are encouraged to submit your files to Gradescope TESTING, where some basic pre-validation tests will be performed against the code. egomaniac with low self esteem. It should implement testPolicy () which returns a trades data frame (see below). A tag already exists with the provided branch name. We encourage spending time finding and researching indicators, including examining how they might later be combined to form trading strategies. Our experiments show that the R-trees produced by the proposed strategy are highly efficient on real and synthetic data of different distributions. Read the next part of the series to create a machine learning based strategy over technical indicators and its comparative analysis over the rule based strategy, anmolkapoor.in/2019/05/01/Technical-Analysis-With-Indicators-And-Building-Rule-Based-Trading-Strategy-Part-1/. result can be used with your market simulation code to generate the necessary statistics. You are constrained by the portfolio size and order limits as specified above. Please submit the following file(s) to Canvas in PDF format only: You are allowed unlimited submissions of the. 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. . It has very good course content and programming assignments . This file has a different name and a slightly different setup than your previous project. Enter the email address you signed up with and we'll email you a reset link. Your report should useJDF format and has a maximum of 10 pages. 0 stars Watchers. However, that solution can be used with several edits for the new requirements. By looking at Figure, closely, the same may be seen. 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. a)Equal to the autocorrelation of lag, An investor believes that investing in domestic and international stocks will give a difference in the mean rate of return. We have you do this to have an idea of an upper bound on performance, which can be referenced in Project 8. These commands issued are orders that let us trade the stock over the exchange. In this case, MACD would need to be modified for Project 8 to return your own custom results vector that somehow combines the MACD and Signal vectors, or it would need to be modified to return only one of those vectors. You may find our lecture on time series processing, the Technical Analysis video, and the vectorize_me PowerPoint to be helpful. Only code submitted to Gradescope SUBMISSION will be graded. specifies font sizes and margins, which should not be altered. (-2 points for each item), If the required code is not provided, (including code to recreate the charts and usage of correct trades DataFrame) (up to -100 points), If all charts are not created and saved using Python code. You may find our lecture on time series processing, the Technical Analysis video, and the vectorize_me PowerPoint to be helpful. Code implementing your indicators as functions that operate on DataFrames. While Project 6 doesnt need to code the indicators this way, it is required for Project 8. Please note that util.py is considered part of the environment and should not be moved, modified, or copied. Considering how multiple indicators might work together during Project 6 will help you complete the later project. You will have access to the ML4T/Data directory data, but you should use ONLY the API functions in util.py to read it. Describe the strategy in a way that someone else could evaluate and/or implement it. Watermarked charts may be shared in the dedicated discussion forum mega-thread alone. Charts should be properly annotated with legible and appropriately named labels, titles, and legends. In Project-8, you will need to use the same indicators you will choose in this project. Introduces machine learning based trading strategies. stephanie edwards singer niece. A Game-Theoretically Optimal Defense Paradigm against Traffic Analysis Attacks using Multipath Routing and Deception . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Short and long term SMA values are used to create the Golden and Death Cross. that returns your Georgia Tech user ID as a string in each . The main part of this code should call marketsimcode as necessary to generate the plots used in the report. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). However, it is OK to augment your written description with a. In this case, MACD would need to be modified for Project 8 to return your own custom results vector that somehow combines the MACD and Signal vectors, or it would need to be modified to return only one of those vectors. Our bets on a large window size was not correct and even though the price went up, the huge lag in reflection on SMA and Momentum, was not able to give correct BUY and SELL opportunity on time. 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. (You may trade up to 2000 shares at a time as long as you maintain these holding requirements.). The Theoretically Optimal Strategy will give a baseline to gauge your later projects performance. Since the above indicators are based on rolling window, we have taken 30 Days as the rolling window size. Experiment 1: Explore the strategy and make some charts. Please address each of these points/questions in your report. Develop and describe 5 technical indicators. You may set a specific random seed for this assignment. However, it is OK to augment your written description with a pseudocode figure. Please refer to the. We will learn about five technical indicators that can. 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. We want a written detailed description here, not code. The optimal strategy works by applying every possible buy/sell action to the current positions. Please refer to the Gradescope Instructions for more information. When optimized beyond a, threshold, this might generate a BUY and SELL opportunity. SUBMISSION. PowerPoint to be helpful. The value of momentum can be used an indicator, and can be used as a intuition that future price may follow the inertia. If simultaneously have a row minimum and a column maximum this is an example of a saddle point solution. The file will be invoked using the command: This is to have a singleentry point to test your code against the report. Anti Slip Coating UAE Backtest your Trading Strategies. Your report should useJDF format and has a maximum of 10 pages. In addition to submitting your code to Gradescope, you will also produce a report. The report will be submitted to Canvas. Theoretically, Optimal Strategy will give a baseline to gauge your later project's performance. Assignments should be submitted to the corresponding assignment submission page in Canvas. Performance metrics must include 4 digits to the right of the decimal point (e.g., 98.1234), You are allowed unlimited resubmissions to Gradescope TESTING. Provide a table that documents the benchmark and TOS performance metrics. Before the deadline, make sure to pre-validate your submission using Gradescope TESTING. Your report and code will be graded using a rubric design to mirror the questions above. Buy-Put Option A put option is the opposite of a call. The main method in indicators.py should generate the charts that illustrate your indicators in the report. Fall 2019 ML4T Project 6 Resources. The report is to be submitted as report.pdf. As an, Please solve these questions.. PBL SESSION 1: REVENUE CYCLE ZARA Son Bhd is a well-known manufacturing company supplying Baju Kurung and Baju Melayu, a traditional costume of the Malays. Since the above indicators are based on rolling window, we have taken 30 Days as the rolling window size. It should implement testPolicy() which returns a trades data frame (see below). As will be the case throughout the term, the grading team will work as quickly as possible to provide project feedback and grades. Charts should also be generated by the code and saved to files. You will have access to the data in the ML4T/Data directory but you should use ONLY . be used to identify buy and sell signals for a stock in this report. Purpose: Athletes are trained to choose the pace which is perceived to be correct during a specific effort, such as the 1500-m speed skating competition. At a minimum, address each of the following for each indicator: The total number of charts for Part 1 must not exceed 10 charts. Considering how multiple indicators might work together during Project 6 will help you complete the later project. Note that this strategy does not use any indicators. The report is to be submitted as. Create a Theoretically optimal strategy if we can see future stock prices. Complete your assignment using the JDF format, then save your submission as a PDF. The indicators selected here cannot be replaced in Project 8. You should submit a single PDF for the report portion of the assignment. 1 TECHNICAL INDICATORS We will discover five different technical indicators which can be used to gener- ated buy or sell calls for given asset. This algorithm is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks. To review, open the file in an editor that reveals hidden Unicode characters. (Round to four decimal places) Find the, What is the value of the autocorrelation function of lag order 0? df_trades: A single column data frame, indexed by date, whose values represent trades for each trading day (from the start date to the end date of a given period). . This is the ID you use to log into Canvas. All work you submit should be your own. Develop and describe 5 technical indicators. You will submit the code for the project to Gradescope SUBMISSION. indicators, including examining how they might later be combined to form trading strategies. Please address each of these points/questions in your report. or reset password. You are allowed unlimited submissions of the p6_indicatorsTOS_report.pdf. The specific learning objectives for this assignment are focused on the following areas: Please keep in mind that the completion of this project is pivotal to Project 8 completion. You may also want to call your market simulation code to compute statistics. @returns the estimated values according to the saved model. This assignment is subject to change up until 3 weeks prior to the due date. 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. Second, you will develop a theoretically optimal strategy (TOS), which represents the maximum amount your portfolio can theoretically return. This is an individual assignment. . This file has a different name and a slightly different setup than your previous project. Code implementing a TheoreticallyOptimalStrategy object, It should implement testPolicy() which returns a trades data frame, The main part of this code should call marketsimcode as necessary to generate the plots used in the report, possible actions {-2000, -1000, 0, 1000, 2000}, # starting with $100,000 cash, investing in 1000 shares of JPM and holding that position, # # takes in a pd.df and returns a np.array. The. You are encouraged to develop additional tests to ensure that all project requirements are met. It also involves designing, tuning, and evaluating ML models suited to the predictive task. Assignments should be submitted to the corresponding assignment submission page in Canvas. Individual Indicators (up to 15 points potential deductions per indicator): Is there a compelling description of why the indicator might work (-5 if not), Is the indicator described in sufficient detail that someone else could reproduce it? Neatness (up to 5 points deduction if not). Stockchart.com School (Technical Analysis Introduction), TA Ameritrade Technical Analysis Introduction Lessons, (pick the ones you think are most useful), A good introduction to technical analysis, Investopedias Introduction to Technical Analysis, Technical Analysis of the Financial Markets. that returns your Georgia Tech user ID as a string in each .py file. It is OK not to submit this file if you have subsumed its functionality into one of your other required code files. technical-analysis-using-indicators-and-building-rule-based-strategy, anmolkapoor.in/2019/05/01/technical-analysis-with-indicators-and-building-rule-based-trading-strategy-part-1/, Technical Analysis with Indicators and building a ML based trading strategy (Part 1 of 2). This class uses Gradescope, a server-side auto-grader, to evaluate your code submission. BagLearner.py. You may not use an indicator in Project 8 unless it is explicitly identified in Project 6. and has a maximum of 10 pages. An indicator can only be used once with a specific value (e.g., SMA(12)). For your report, use only the symbol JPM. 7 forks Releases No releases published. We hope Machine Learning will do better than your intuition, but who knows? which is holding the stocks in our portfolio. Calling testproject.py should run all assigned tasks and output all necessary charts and statistics for your report. 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. 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. Be sure you are using the correct versions as stated on the. They can be calculated as: upper_band = sma + standard_deviation * 2, lower_band = sma - standard_deviation * 2. or. 64 lines 2.0 KiB Raw Permalink Blame History import pandas as pd from util import get_data from collections import namedtuple Position = namedtuple("Pos", ["cash", "shares", "transactions"]) def author(): return "felixm" def new_positions(positions, price): You may not modify or copy code in util.py. Learn more about bidirectional Unicode characters. We have you do this to have an idea of an upper bound on performance, which can be referenced in Project 8. To facilitate visualization of the indicator, you might normalize the data to 1.0 at the start of the date range (i.e., divide price[t] by price[0]). . The main part of this code should call marketsimcode as necessary to generate the plots used in the report. You may also want to call your market simulation code to compute statistics. , where folder_name is the path/name of a folder or directory. Considering how multiple indicators might work together during Project 6 will help you complete the later project. The following textbooks helped me get an A in this course: Description of what each python file is for/does. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. Include charts to support each of your answers. The directory structure should align with the course environment framework, as discussed on the local environment and ML4T Software pages. All work you submit should be your own. You may not use any code you did not write yourself. Use only the functions in util.py to read in stock data. Your report and code will be graded using a rubric design to mirror the questions above. Note: The format of this data frame differs from the one developed in a prior project. Please note that requests will be denied if they are not submitted using the Fall 2021 form or do not fall within the timeframes specified on the Assignment Follow-Up page. The. For our discussion, let us assume we are trading a stock in market over a period of time. Note: Theoretically Optimal Strategy does not use the indicators developed in the previous section. specifies font sizes and margins, which should not be altered. We hope Machine Learning will do better than your intuition, but who knows? Please keep in mind that the completion of this project is pivotal to Project 8 completion. Charts should also be generated by the code and saved to files. Bonus for exceptionally well-written reports (up to 2 points), Is the required report provided (-100 if not), Are there five different indicators where you may only use two from the set discussed in the lectures (i.e., no more than two from the set [SMA, Bollinger Bands, RSI])? It is usually worthwhile to standardize the resulting values (see https://en.wikipedia.org/wiki/Standard_score). Code implementing your indicators as functions that operate on DataFrames. While Project 6 doesnt need to code the indicators this way, it is required for Project 8, In the Theoretically Optimal Strategy, assume that you can see the future. While such indicators are okay to use in Project 6, please keep in mind that Project 8 will require that each indicator return one results vector. A) The default rate on the mortgages kept rising. Considering how multiple indicators might work together during Project 6 will help you complete the later project. In Project-8, you will need to use the same indicators you will choose in this project. Create testproject.py and implement the necessary calls (following each respective API) to indicators.py and TheoreticallyOptimalStrategy.py, with the appropriate parameters to run everything needed for the report in a single Python call. This copyright statement should not be removed, We do grant permission to share solutions privately with non-students such, as potential employers. No credit will be given for code that does not run in the Gradescope SUBMISSION environment. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Charts should also be generated by the code and saved to files. More info on the trades data frame below. 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. You should submit a single PDF for this assignment. Legal values are +1000.0 indicating a BUY of 1000 shares, -1000.0 indicating a SELL of 1000 shares, and 0.0 indicating NOTHING. Once grades are released, any grade-related matters must follow the. In your report (described below), a description of each indicator should enable someone to reproduce it just by reading the description. After that, we will develop a theoretically optimal strategy and. Theoretically Optimal Strategy will give a baseline to gauge your later projects performance. For large deviations from the price, we can expect the price to come back to the SMA over a period of time.