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. Learn more about bidirectional Unicode characters. This project has two main components: First, you will research and identify five market indicators. A position is cash value, the current amount of shares, and previous transactions. 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. Theoretically Optimal Strategy will give a baseline to gauge your later project's performance against. Use the time period January 1, 2008, to December 31, 2009. This assignment is subject to change up until 3 weeks prior to the due date. Charts should also be generated by the code and saved to files. If the report is not neat (up to -5 points). Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). For grading, we will use our own unmodified version. Citations within the code should be captured as comments. This process builds on the skills you developed in the previous chapters because it relies on your ability to 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. We have you do this to have an idea of an upper bound on performance, which can be referenced in Project 8. or reset password. The report will be submitted to Canvas. import datetime as dt import pandas as pd import numpy as np from util import symbol_to_path,get_data def 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. Develop and describe 5 technical indicators. More info on the trades data frame is below. You signed in with another tab or window. 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). It is usually worthwhile to standardize the resulting values (see Standard Score). Learn more about bidirectional Unicode characters. At a minimum, address each of the following for each indicator: The total number of charts for Part 1 must not exceed 10 charts. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. 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. 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. Considering how multiple indicators might work together during Project 6 will help you complete the later project. (Round to four decimal places) Find the, What is the value of the autocorrelation function of lag order 0? This framework assumes you have already set up the. Performance metrics must include 4 digits to the right of the decimal point (e.g., 98.1234). Your, # code should work correctly with either input, # Update Portfolio Shares and Cash Holdings, # Apply market impact - Price goes up by impact prior to purchase, # Apply commission - To be applied on every transaction, regardless of BUY or SELL, # Apply market impact - Price goes down by impact prior to sell, 'Theoretically Optimal Strategy vs Benchmark'. Watermarked charts may be shared in the dedicated discussion forum mega-thread alone. Any content beyond 10 pages will not be considered for a grade. selected here cannot be replaced in Project 8. The library is used extensively in the book Machine Larning for . Note: Theoretically Optimal Strategy does not use the indicators developed in the previous section. We should anticipate the price to return to the SMA over a period, of time if there are significant price discrepancies. For grading, we will use our own unmodified version. In Project-8, you will need to use the same indicators you will choose in this project. Charts should be properly annotated with legible and appropriately named labels, titles, and legends. 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/. You should create the following code files for submission. Are you sure you want to create this branch? Please submit the following file(s) to Canvas in PDF format only: Do not submit any other files. In your report (described below), a description of each indicator should enable someone to reproduce it just by reading the description. 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. The performance metrics should include cumulative returns, standard deviation of daily returns, and the mean of daily returns for both the benchmark and portfolio. 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). 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. In your report (described below), a description of each indicator should enable someone to reproduce it just by reading the description. Any content beyond 10 pages will not be considered for a grade. Note: The Sharpe ratio uses the sample standard deviation. (-10 points if not), Is the chart correct (dates and equity curve), including properly labeled axis and legend (up to -10 points if not), The historical value of benchmark normalized to 1.0, plotted with a green line (-5 if not), The historical value of portfolio normalized to 1.0, plotted with a red line (-5 if not), Are the reported performance criteria correct? Legal values are +1000.0 indicating a BUY of 1000 shares, -1000.0 indicating a SELL of 1000 shares, and 0.0 indicating NOTHING. Thus, these trade orders can be of type: For simplicity of discussion, lets assume, we can only issue these three commands SHORT, LONG and HOLD for our stock JPM, and our portfolio can either be in these three states at a given time: Lets assume we can foresee the future price and our tasks is create a strategy that can make profit. 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. Assignments should be submitted to the corresponding assignment submission page in Canvas. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. specifies font sizes and margins, which should not be altered. We can calculate Price/SMA (PSMA) values and use them to generated buy or, and above can indicate SELL. Only code submitted to Gradescope SUBMISSION will be graded. Calling testproject.py should run all assigned tasks and output all necessary charts and statistics for your report. It is usually worthwhile to standardize the resulting values (see https://en.wikipedia.org/wiki/Standard_score). This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). Introduce and describe each indicator you use in sufficient detail that someone else could reproduce it. By analysing historical data, technical analysts use indicators to predict future price movements. The report is to be submitted as. 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. Provide a compelling description regarding why that indicator might work and how it could be used. Code must not use absolute import statements, such as: from folder_name import TheoreticalOptimalStrategy. However, sharing with other current or future, students of CS 7646 is prohibited and subject to being investigated as a, -----do not edit anything above this line---, # this is the function the autograder will call to test your code, # NOTE: orders_file may be a string, or it may be a file object. Fall 2019 ML4T Project 6. to develop a trading strategy using technical analysis with manually selected indicators. Create a Manual Strategy based on indicators. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). TheoreticallyOptimalStrategy.py Code implementing a TheoreticallyOptimalStrategy object (details below).It should implement testPolicy () which returns a trades data frame (see below). Our Challenge (-5 points if not), Is there a chart for the indicator that properly illustrates its operation, including a properly labeled axis and legend? Any content beyond 10 pages will not be considered for a grade. SUBMISSION. Be sure to describe how they create buy and sell signals (i.e., explain how the indicator could be used alone and/or in conjunction with other indicators to generate buy/sell signals). A) The default rate on the mortgages kept rising. You may also want to call your market simulation code to compute statistics. This class uses Gradescope, a server-side auto-grader, to evaluate your code submission. 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). 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. This can create a BUY and SELL opportunity when optimised over a threshold. Let's call it ManualStrategy which will be based on some rules over our indicators. Why there is a difference in performance: Now that we have found that our rule based strategy was not very optimum, can we apply machine learning to learn optimal rules and achieve better results. 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. Code implementing your indicators as functions that operate on DataFrames. Describe the strategy in a way that someone else could evaluate and/or implement it. The secret regarding leverage and a secret date discussed in the YouTube lecture do not apply and should be ignored. Students are allowed to share charts in the pinned Students Charts thread alone. You are encouraged to perform any tests necessary to instill confidence in your implementation, ensure that the code will run properly when submitted for grading and that it will produce the required results. The JDF format specifies font sizes and margins, which should not be altered. # def get_listview(portvals, normalized): You signed in with another tab or window. If this had been my first course, I likely would have dropped out suspecting that all . 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. An improved version of your marketsim code accepts a trades DataFrame (instead of a file). The indicators should return results that can be interpreted as actionable buy/sell signals. This length is intentionally set, expecting that your submission will include diagrams, drawings, pictures, etc. Learning how to invest is a life skill, as essential as learning how to use a computer, and is one of the key pillars to retiring comfortably. Your report should useJDF format and has a maximum of 10 pages. It is OK not to submit this file if you have subsumed its functionality into one of your other required code files. In the case of such an emergency, please contact the Dean of Students. ML4T is a good course to take if you are looking for light work load or pair it with a hard one. Find the probability that a light bulb lasts less than one year. 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). In the case of such an emergency, please, , then save your submission as a PDF. Gradescope TESTING does not grade your assignment. (up to -100 points), Course Development Recommendations, Guidelines, and Rules. By looking at Figure, closely, the same may be seen. Here are my notes from when I took ML4T in OMSCS during Spring 2020. However, it is OK to augment your written description with a. Please note that there is no starting .zip file associated with this project. 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 should submit a single PDF for this assignment. This length is intentionally set, expecting that your submission will include diagrams, drawings, pictures, etc. View TheoreticallyOptimalStrategy.py from CS 4646 at Kenesaw Secondary School. Allowable positions are 1000 shares long, 1000 shares short, 0 shares. 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]). You may not use the Python os library/module. Code in Gradescope SUBMISSION must not generate any output to the screen/console/terminal (other than run-time warning messages) when verbose = False. You should have already successfully coded the Bollinger Band feature: Another good indicator worth considering is momentum. You should have already successfully coded the Bollinger Band feature: Another good indicator worth considering is momentum. After that, we will develop a theoretically optimal strategy and. 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. Email. 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). While Project 6 doesnt need to code the indicators this way, it is required for Project 8. This file has a different name and a slightly different setup than your previous project. 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 must also create a README.txt file that has: The secret regarding leverage and a secret date discussed in the YouTube lecture do not apply and should be ignored. At a minimum, address each of the following for each indicator: The total number of charts for Part 1 must not exceed 10 charts. If you want to use EMA in addition to using MACD, then EMA would need to be explicitly identified as one of the five indicators. These should be incorporated into the body of the paper unless specifically required to be included in an appendix. Lastly, I've heard good reviews about the course from others who have taken it. Floor Coatings. You should create the following code files for submission. The indicators selected here cannot be replaced in Project 8. . 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. fantasy football calculator week 10; theoretically optimal strategy ml4t. 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. This file should be considered the entry point to the project. (-2 points for each item if not), Is the required code provided, including code to recreate the charts and usage of correct trades DataFrame? It is OK not to submit this file if you have subsumed its functionality into one of your other required code files. All charts must be included in the report, not submitted as separate files. Make sure to answer those questions in the report and ensure the code meets the project requirements. Code that displays warning messages to the terminal or console. Code implementing a TheoreticallyOptimalStrategy (details below). Introduces machine learning based trading strategies. Usually, I omit any introductory or summary videos. (You may trade up to 2000 shares at a time as long as you maintain these holding requirements.). Assignments should be submitted to the corresponding assignment submission page in Canvas. All work you submit should be your own. To review, open the file in an editor that reveals hidden Unicode characters. (up to -5 points if not). Benchmark: The performance of a portfolio starting with $100,000 cash, investing in 1000 shares of JPM, and holding that position. Describe the strategy in a way that someone else could evaluate and/or implement it. The main part of this code should call marketsimcode as necessary to generate the plots used in the report. Since the above indicators are based on rolling window, we have taken 30 Days as the rolling window size. Values of +2000 and -2000 for trades are also legal so long as net holdings are constrained to -1000, 0, and 1000. Now we want you to run some experiments to determine how well the betting strategy works. Password. You will have access to the data in the ML4T/Data directory but you should use ONLY the API . In your report (described below), a description of each indicator should enable someone to reproduce it just by reading the description. For our discussion, let us assume we are trading a stock in market over a period of time. Your project must be coded in Python 3.6. and run in the Gradescope SUBMISSION environment. You should create a directory for your code in ml4t/manual_strategy and make a copy of util.py there. This is the ID you use to log into Canvas. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0). We want a written detailed description here, not code. For this activity, use $0.00 and 0.0 for commissions and impact, respectively. Provide a table that documents the benchmark and TOS performance metrics. Explicit instructions on how to properly run your code. 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]). Here are the statistics comparing in-sample data: The manual strategy works well for the train period as we were able to tweak the different thresholds like window size, buy and selling threshold for momentum and volatility. When optimized beyond a, threshold, this might generate a BUY and SELL opportunity. Momentum refers to the rate of change in the adjusted close price of the s. It can be calculated : Momentum[t] = (price[t] / price[t N])-1. I need to show that the game has no saddle point solution and find an optimal mixed strategy. It should implement testPolicy(), which returns a trades data frame (see below). 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 . . In my opinion, ML4T should be an undergraduate course. Maximum loss: premium of the option Maximum gain: theoretically infinite. In the case of such an emergency, please contact the, Complete your assignment using the JDF format, then save your submission as a PDF. 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).