We do not anticipate changes; any changes will be logged in this section. This length is intentionally set, expecting that your submission will include diagrams, drawings, pictures, etc. 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])? 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). You may also want to call your market simulation code to compute statistics. Considering how multiple indicators might work together during Project 6 will help you complete the later project. . We will be utilizing SMA in conjunction with a, few other indicators listed below to optimize our trading strategy for real-world. SMA helps to iden-, tify the trend, support, and resistance level and is often used in conjunction with. Stockchart.com School (Technical Analysis Introduction), TA Ameritrade Technical Analysis Introduction Lessons, (pick the ones you think are most useful), Investopedias Introduction to Technical Analysis, Technical Analysis of the Financial Markets, A good introduction to technical analysis. June 10, 2022 You will have access to the ML4T/Data directory data, but you should use ONLY the API functions in util.py to read it. 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) Theoretically, Optimal Strategy will give a baseline to gauge your later project's performance. This is the ID you use to log into Canvas. You are constrained by the portfolio size and order limits as specified above. More info on the trades data frame below. You are not allowed to import external data. We hope Machine Learning will do better than your intuition, but who knows? This means someone who wants to implement a strategy that uses different values for an indicator (e.g., a Golden Cross that uses two SMA calls with different parameters) will need to create a Golden_Cross indicator that returns a single results vector, but internally the indicator can use two SMA calls with different parameters). Rules: * trade only the symbol JPM Once you are satisfied with the results in testing, submit the code to Gradescope SUBMISSION. 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. Please note that util.py is considered part of the environment and should not be moved, modified, or copied. This class uses Gradescope, a server-side autograder, to evaluate your code submission. For large deviations from the price, we can expect the price to come back to the SMA over a period of time. Please note that there is no starting .zip file associated with this project. 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. Transaction costs for TheoreticallyOptimalStrategy: Commission: $0.00, Impact: 0.00. def __init__ ( self, learner=rtl. Theoretically Optimal Strategy will give a baseline to gauge your later projects performance. The average number of hours a . Include charts to support each of your answers. Languages. Theoretically Optimal Strategy will give a baseline to gauge your later projects performance. In the case of such an emergency, please contact the Dean of Students. Calling testproject.py should run all assigned tasks and output all necessary charts and statistics for your report. Charts should also be generated by the code and saved to files. Code implementing your indicators as functions that operate on DataFrames. Please submit the following file(s) to Canvas in PDF format only: Do not submit any other files. Introduces machine learning based trading strategies. 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). You may find our lecture on time series processing, the Technical Analysis video, and the vectorize_me PowerPoint to be helpful. 0 stars Watchers. This assignment is subject to change up until 3 weeks prior to the due date. The file will be invoked. . Another example: If you were using price/SMA as an indicator, you would want to create a chart with 3 lines: Price, SMA, Price/SMA. You should have already successfully coded the Bollinger Band feature: Another good indicator worth considering is momentum. Describe the strategy in a way that someone else could evaluate and/or implement it. By analysing historical data, technical analysts use indicators to predict future price movements. They should comprise ALL code from you that is necessary to run your evaluations. The indicators selected here cannot be replaced in Project 8. . You may not use an indicator in Project 8 unless it is explicitly identified in Project 6. The indicators should return results that can be interpreted as actionable buy/sell signals. The report is to be submitted as. Here is an example of how you might implement author(): Create testproject.py and implement the necessary calls (following each respective API) to. 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). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. After that, we will develop a theoretically optimal strategy and compare its performance metrics to those of a benchmark. We will discover five different technical indicators which can be used to gener-, ated buy or sell calls for given asset. Note: Theoretically Optimal Strategy does not use the indicators developed in the previous section. ML4T / manual_strategy / TheoreticallyOptimalStrateg. In addition to submitting your code to Gradescope, you will also produce a report. Only code submitted to Gradescope SUBMISSION will be graded. For example, Bollinger Bands alone does not give an actionable signal to buy/sell easily framed for a learner, but BBP (or %B) does. Once you are satisfied with the results in testing, submit the code to Gradescope SUBMISSION. 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. In your report (described below), a description of each indicator should enable someone to reproduce it just by reading the description. (-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. Code must not use absolute import statements, such as: from folder_name import TheoreticalOptimalStrategy. 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). Note that an indicator like MACD uses EMA as part of its computation. Contribute to havishc19/StockTradingStrategy development by creating an account on GitHub. SUBMISSION. These should be incorporated into the body of the paper unless specifically required to be included in an appendix. It should implement testPolicy () which returns a trades data frame (see below). Code implementing a TheoreticallyOptimalStrategy (details below). Only code submitted to Gradescope SUBMISSION will be graded. Charts should also be generated by the code and saved to files. : You will also develop an understanding of the upper bounds (or maximum) amount that can be earned through trading given a specific instrument and timeframe. You may not use an indicator in Project 8 unless it is explicitly identified in Project 6. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0). Include charts to support each of your answers. You are allowed unlimited resubmissions to Gradescope TESTING. selected here cannot be replaced in Project 8. For each indicator, you will write code that implements each indicator. 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. 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. 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]). Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. Floor Coatings. Please submit the following file(s) to Canvas in PDF format only: You are allowed unlimited submissions of the. In this project, you will develop technical indicators and a Theoretically Optimal Strategy that will be the ground layer of a later project. You may also want to call your market simulation code to compute statistics. The main method in indicators.py should generate the charts that illustrate your indicators in the report. Any content beyond 10 pages will not be considered for a grade. Assignments should be submitted to the corresponding assignment submission page in Canvas. Second, you will research and identify five market indicators. Provide a compelling description regarding why that indicator might work and how it could be used. 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). Assignments should be submitted to the corresponding assignment submission page in Canvas. 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). Develop and describe 5 technical indicators. In Project-8, you will need to use the same indicators you will choose in this project. Do NOT copy/paste code parts here as a description. The main part of this code should call marketsimcode as necessary to generate the plots used in the report. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You should create a directory for your code in ml4t/indicator_evaluation. Calling testproject.py should run all assigned tasks and output all necessary charts and statistics for your report. (-5 points if not), Is there a chart for the indicator that properly illustrates its operation, including a properly labeled axis and legend? Also, note that it should generate the charts contained in the report when we run your submitted code. (Round to four decimal places) Find the, What is the value of the autocorrelation function of lag order 0? 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. Experiment 1: Explore the strategy and make some charts. This length is intentionally set, expecting that your submission will include diagrams, drawings, pictures, etc. 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/. SMA can be used as a proxy the true value of the company stock. We hope Machine Learning will do better than your intuition, but who knows? ML4T is a good course to take if you are looking for light work load or pair it with a hard one. In addition to testing on your local machine, you are encouraged to submit your files to Gradescope TESTING, where some basic pre-validation tests will be performed against the code. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). To review, open the file in an editor that reveals hidden Unicode characters. The indicators that are selected here cannot be replaced in Project 8. You are encouraged to develop additional tests to ensure that all project requirements are met. In Project-8, you will need to use the same indicators you will choose in this project. result can be used with your market simulation code to generate the necessary statistics. The report is to be submitted as report.pdf. This is an individual assignment. We do not provide an explicit set timeline for returning grades, except that all assignments and exams will be graded before the institute deadline (end of the term). 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). Complete your assignment using the JDF format, then save your submission as a PDF. 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. Some indicators are built using other indicators and/or return multiple results vectors (e.g., MACD uses EMA and returns MACD and Signal vectors). Considering how multiple indicators might work together during Project 6 will help you complete the later project. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. You are not allowed to import external data. Are you sure you want to create this branch? For your report, use only the symbol JPM. 6 Part 2: Theoretically Optimal Strategy (20 points) 7 Part 3: Manual Rule-Based Trader (50 points) 8 Part 4: Comparative Analysis (10 points) . For example, you might create a chart showing the stocks price history, along with helper data (such as upper and lower Bollinger Bands) and the value of the indicator itself. This file has a different name and a slightly different setup than your previous project. Your report should useJDF format and has a maximum of 10 pages. Create a set of trades representing the best a strategy could possibly do during the in-sample period using JPM. Please submit the following file to Canvas in PDF format only: Please submit the following files to Gradescope, We do not provide an explicit set timeline for returning grades, except that everything will be graded before the institute deadline (end of the term). In the case of such an emergency, please contact the Dean of Students.
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