As will be the case throughout the term, the grading team will work as quickly as possible to provide project feedback and grades. Textbook Information. 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. For this activity, use $0.00 and 0.0 for commissions and impact, respectively. Since the above indicators are based on rolling window, we have taken 30 Days as the rolling window size. Please submit the following files to Gradescope, Important: You are allowed a MAXIMUM of three (3) code submissions to Gradescope, Once grades are released, any grade-related matters must follow the, Assignment Follow-Up guidelines and process, alone. 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). It is not your 9 digit student number. You should submit a single PDF for this assignment. Remember me on this computer. We do not anticipate changes; any changes will be logged in this section. Of course, this might not be the optimal ratio. Please keep in mind that the completion of this project is pivotal to Project 8 completion. Any content beyond 10 pages will not be considered for a grade. 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. . An improved version of your marketsim code accepts a trades DataFrame (instead of a file). 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. You are allowed unlimited resubmissions to Gradescope TESTING. In Project-8, you will need to use the same indicators you will choose in this project. Following the crossing, the long term SMA serves as a. major support (for golden cross) or resistance (for death cross) level for the stock. 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). When optimized beyond a, threshold, this might generate a BUY and SELL opportunity. In the case of such an emergency, please, , then save your submission as a PDF. Enter the email address you signed up with and we'll email you a reset link. We encourage spending time finding and researching indicators, including examining how they might later be combined to form trading strategies. We want a written detailed description here, not code. (-2 points for each item if not), Is the required code provided, including code to recreate the charts and usage of correct trades DataFrame? This project has two main components: First, you will develop a theoretically optimal strategy (TOS), which represents the maximum amount your portfolio can theoretically return. Complete your assignment using the JDF format, then save your submission as a PDF. These should be incorporated into the body of the paper unless specifically required to be included in an appendix. Code implementing your indicators as functions that operate on DataFrames. It is usually worthwhile to standardize the resulting values (see, https://en.wikipedia.org/wiki/Standard_score. Use only the data provided for this course. We encourage spending time finding and research. Provide a chart that illustrates the TOS performance versus the benchmark. other technical indicators like Bollinger Bands and Golden/Death Crossovers. This length is intentionally set, expecting that your submission will include diagrams, drawings, pictures, etc. We should anticipate the price to return to the SMA over a period, of time if there are significant price discrepancies. The file will be invoked run: entry point to test your code against the report. 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. 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. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). Make sure to cite any sources you reference and use quotes and in-line citations to mark any direct quotes. Three examples of Technical indicators, namely Simple moving average, Momentum and Bollinger Bands. () (up to -100 if not), All charts must be created and saved using Python code. that returns your Georgia Tech user ID as a string in each .py file. A position is cash value, the current amount of shares, and previous transactions. Note that an indicator like MACD uses EMA as part of its computation. Any content beyond 10 pages will not be considered for a grade. Gradescope TESTING does not grade your assignment. You are encouraged to perform any unit tests necessary to instill confidence in your implementation. Benchmark: The performance of a portfolio starting with $100,000 cash, investing in 1000 shares of JPM, and holding that position. It should implement testPolicy(), which returns a trades data frame (see below). If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0). This is an individual assignment. TheoreticallyOptimalStrategy.pyCode implementing a TheoreticallyOptimalStrategy object (details below). You may not use an indicator in Project 8 unless it is explicitly identified in Project 6. Legal values are +1000.0 indicating a BUY of 1000 shares, -1000.0 indicating a SELL of 1000 shares, and 0.0 indicating NOTHING. However, it is OK to augment your written description with a. For our discussion, let us assume we are trading a stock in market over a period of time. Please refer to the Gradescope Instructions for more information. 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 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. We hope Machine Learning will do better than your intuition, but who knows? (-15 points each if not), Does the submitted code indicators.py properly reflect the indicators provided in the report (up to -75 points if not). To review, open the file in an editor that reveals hidden Unicode characters. Only code submitted to Gradescope SUBMISSION will be graded. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Citations within the code should be captured as comments. The report will be submitted to Canvas. Using these predictions, analysts create strategies that they would apply to trade a security in order to make profit. Assignments should be submitted to the corresponding assignment submission page in Canvas. . Please keep in mind that the completion of this project is pivotal to Project 8 completion. 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. . Provide a table that documents the benchmark and TOS performance metrics. Your report should useJDF format and has a maximum of 10 pages. Make sure to answer those questions in the report and ensure the code meets the project requirements. 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. diversified portfolio. import pandas as pd import numpy as np import datetime as dt import marketsimcode as market_sim import matplotlib.pyplot SUBMISSION. You are not allowed to import external data. Compare and analysis of two strategies. Any content beyond 10 pages will not be considered for a grade. You will not be able to switch indicators in Project 8. We propose a novel R-tree packing strategy that produces R-trees with an asymptotically optimal I/O complexity for window queries in the worst case. (up to -100 points), Course Development Recommendations, Guidelines, and Rules. Charts should also be generated by the code and saved to files. This is a text file that describes each .py file and provides instructions describing how to run your code. Your report and code will be graded using a rubric design to mirror the questions above. 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 are encouraged to develop additional tests to ensure that all project requirements are met. Only code submitted to Gradescope SUBMISSION will be graded. Use only the functions in util.py to read in stock data. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Please note that there is no starting .zip file associated with this project. . At a minimum, address each of the following for each indicator: The total number of charts for Part 1 must not exceed 10 charts. The report is to be submitted as p6_indicatorsTOS_report.pdf. If the required report is not provided (-100 points), Bonus for exceptionally well-written reports (up to +2 points), If there are not five different indicators (where you may only use two from the set discussed in the lectures [SMA, Bollinger Bands, RSI]) (-15 points each), If the submitted code in the indicators.py file does not properly reflect the indicators provided in the report (up to -75 points). Please submit the following file(s) to Canvas in PDF format only: Do not submit any other files. Only code submitted to Gradescope SUBMISSION will be graded. 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. It is usually worthwhile to standardize the resulting values (see Standard Score). 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 will have access to the ML4T/Data directory data, but you should use ONLY the API functions in util.py to read it. 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). To review, open the file in an editor that reveals hidden Unicode characters. These metrics should include cumulative returns, the standard deviation of daily returns, and the mean of daily returns for both the benchmark and portfolio. By looking at Figure, closely, the same may be seen. 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]). This assignment is subject to change up until 3 weeks prior to the due date. The indicators should return results that can be interpreted as actionable buy/sell signals. The secret regarding leverage and a secret date discussed in the YouTube lecture do not apply and should be ignored. specifies font sizes and margins, which should not be altered. This project has two main components: First, you will research and identify five market indicators. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0). optimal strategy logic Learn about this topic in these articles: game theory In game theory: Games of perfect information can deduce strategies that are optimal, which makes the outcome preordained (strictly determined). Ensure to cite any sources you reference and use quotes and in-line citations to mark any direct quotes. When utilizing any example order files, the code must run in less than 10 seconds per test case. You are constrained by the portfolio size and order limits as specified above. 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). Please submit the following files to Gradescope SUBMISSION: Important: You are allowed a MAXIMUM of three (3) code submissions to Gradescope SUBMISSION. 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. 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. Now we want you to run some experiments to determine how well the betting strategy works. Citations within the code should be captured as comments. You should submit a single PDF for the report portion of the assignment. A Game-Theoretically Optimal Defense Paradigm against Traffic Analysis Attacks using Multipath Routing and Deception . About. Code must not use absolute import statements, such as: from folder_name import TheoreticalOptimalStrategy. No packages published . Our Challenge Note: Theoretically Optimal Strategy does not use the indicators developed in the previous section. (You may trade up to 2000 shares at a time as long as you maintain these holding requirements.). 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. fantasy football calculator week 10; theoretically optimal strategy ml4t. Within each document, the headings correspond to the videos within that lesson. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. 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. (up to -100 points), If any charts are displayed to a screen/window/terminal in the Gradescope Submission environment.
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