Theoretically optimal (up to 20 points potential deductions): Is the methodology described correct and convincing? Fall 2019 ML4T Project 6. to develop a trading strategy using technical analysis with manually selected indicators. Legal values are +1000.0 indicating a BUY of 1000 shares, -1000.0 indicating a SELL of 1000 shares, and 0.0 indicating NOTHING. To review, open the file in an editor that reveals hidden Unicode characters. Maximum loss: premium of the option Maximum gain: theoretically infinite. Ensure to cite any sources you reference and use quotes and in-line citations to mark any direct quotes. You also need five electives, so consider one of these as an alternative for your first. By looking at Figure, closely, the same may be seen. result can be used with your market simulation code to generate the necessary statistics. Provide one or more charts that convey how each indicator works compellingly. Any content beyond 10 pages will not be considered for a grade. Code implementing your indicators as functions that operate on DataFrames. Please keep in mind that the completion of this project is pivotal to Project 8 completion. They take two random samples of 15 months over the past 30 years and find. You should have already successfully coded the Bollinger Band feature: Another good indicator worth considering is momentum. For your report, use only the symbol JPM. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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). 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. # Curr Price > Next Day Price, Price dipping so sell the stock off, # Curr Price < Next Day Price, stock price improving so buy stock to sell later, # tos.testPolicy(sd=dt.datetime(2010,1,1), ed=dt.datetime(2011,12,31)). Some may find it useful to work on Part 2 of the assignment before beginning Part 1. SMA can be used as a proxy the true value of the company stock. 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. We should anticipate the price to return to the SMA over a period, of time if there are significant price discrepancies. . 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 . 6 Part 2: Theoretically Optimal Strategy (20 points) 7 Part 3: Manual Rule-Based Trader (50 points) 8 Part 4: Comparative Analysis (10 points) . You should create a directory for your code in ml4t/indicator_evaluation. The, Suppose that the longevity of a light bulb is exponential with a mean lifetime of eight years. Languages. The report will be submitted to Canvas. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. There is no distributed template for this project. You may not use the Python os library/module. Are you sure you want to create this branch? You are constrained by the portfolio size and order limits as specified above. Code that displays warning messages to the terminal or console. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A position is cash value, the current amount of shares, and previous transactions. The indicators selected here cannot be replaced in Project 8. . Here are my notes from when I took ML4T in OMSCS during Spring 2020. 7 forks Releases No releases published. def __init__ ( self, learner=rtl. Compute rolling mean. This assignment is subject to change up until 3 weeks prior to the due date. Any content beyond 10 pages will not be considered for a grade. 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. Gradescope TESTING does not grade your assignment. (-2 points for each item if not), Is the required code provided, including code to recreate the charts and usage of correct trades DataFrame? However, that solution can be used with several edits for the new requirements. Of course, this might not be the optimal ratio. Charts should also be generated by the code and saved to files. No credit will be given for coding assignments that do not pass this pre-validation. Describe the strategy in a way that someone else could evaluate and/or implement it. Gradescope TESTING does not grade your assignment. In Project-8, you will need to use the same indicators you will choose in this project. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). In the Theoretically Optimal Strategy, assume that you can see the future. The Theoretically Optimal Strategy will give a baseline to gauge your later projects performance. Readme Stars. Buy-Put Option A put option is the opposite of a call. This file has a different name and a slightly different setup than your previous project. You are allowed unlimited submissions of the p6_indicatorsTOS_report.pdf. 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? 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. These should be incorporated into the body of the paper unless specifically required to be included in an appendix. Also, note that it should generate the charts contained in the report when we run your submitted code. The directory structure should align with the course environment framework, as discussed on the local environment and ML4T Software pages. After that, we will develop a theoretically optimal strategy and compare its performance metrics to those of a benchmark. It also involves designing, tuning, and evaluating ML models suited to the predictive task. It should implement testPolicy(), which returns a trades data frame (see below). Are you sure you want to create this branch? A tag already exists with the provided branch name. Backtest your Trading Strategies. Deductions will be applied for unmet implementation requirements or code that fails to run. You may find our lecture on time series processing, the Technical Analysis video, and the vectorize_me PowerPoint to be helpful. Please address each of these points/questions in your report. This class uses Gradescope, a server-side autograder, to evaluate your code submission. For grading, we will use our own unmodified version. You are constrained by the portfolio size and order limits as specified above. You can use util.py to read any of the columns in the stock symbol files. Explicit instructions on how to properly run your code. No packages published . Code implementing your indicators as functions that operate on DataFrames. This copyright statement should not be removed, We do grant permission to share solutions privately with non-students such, as potential employers. Code provided by the instructor or is allowed by the instructor to be shared. The implementation may optionally write text, statistics, and/or tables to a single file named p6_results.txt or p6_results.html. Enter the email address you signed up with and we'll email you a reset link. Considering how multiple indicators might work together during Project 6 will help you complete the later project. compare its performance metrics to those of a benchmark. You may find our lecture on time series processing, the Technical Analysis video, and the vectorize_me PowerPoint to be helpful. Floor Coatings. Considering how multiple indicators might work together during Project 6 will help you complete the later project. Assignments should be submitted to the corresponding assignment submission page in Canvas. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. Introduce and describe each indicator you use in sufficient detail that someone else could reproduce it. Description of what each python file is for/does. 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). 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): The indicators should return results that can be interpreted as actionable buy/sell signals. The tweaked parameters did not work very well. 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. Please submit the following file(s) to Canvas in PDF format only: You are allowed unlimited submissions of the. The report is to be submitted as. Benchmark (see definition above) normalized to 1.0 at the start: Plot as a, Value of the theoretically optimal portfolio (normalized to 1.0 at the start): Plot as a, Cumulative return of the benchmark and portfolio, Stdev of daily returns of benchmark and portfolio, Mean of daily returns of benchmark and portfolio, sd: A DateTime object that represents the start date, ed: A DateTime object that represents the end date. It is not your, student number. Make sure to cite any sources you reference and use quotes and in-line citations to mark any direct quotes. 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. 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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