代写Project 1: Martingale代做Python编程

日期：2024-09-01 01:44

Project 1: Martingale (Report)

Instructions for Project 1: Martingale

Revision History

This assignment is subject to change up until 3 weeks before the due date. We do not anticipate changes; any changes will be logged in this section.

1. Published - Start of Term

2. [082524] - Added recommendation to set a starting seed.

3. [082624] - Added clarification that .txt and .html files are optional

4. [082724] - Revised "all work should be your own" to "all work must be your own" to align that statement with course and Institute policy.

1. Overview

In this course, you are building a simplified AI-based trading system. This system is constructed over the course of 8 projects, where each project you complete is a step towards building an intricate system that synthesizes your knowledge of machine learning with practical algorithmic trading strategies. For detailed context and an overview of how each piece fits into the broader system, please refer to the

Machine Learning for Trading Project page

(https://gatech.instructure.com/courses/411838/pages/machine-learning-for-trading-project) .

In this project, you will write software that will perform probabilistic experiments involving an American Roulette wheel .(https://en.wikipedia.org/wiki/Roulette) . The project will help provide you with some  initial feel for risk, probability, and “betting.” Purchasing a stock is, after all, a bet that the stock will increase (or, in some cases, decrease) in value. You will submit the code for the project to Gradescope SUBMISSION. You will also submit to Canvas a report that discusses your experimental findings.

1.1 Learning Objectives

The specific learning objectives for this assignment are focused on the following areas:

· Mathematical Tools: Developing an understanding of common probabilistic and statistical tools associated with machine learning, including expectations, standard deviations, sampling, minimum values, maximum values, and convergence.

· Research: Experience researching additional material (conceptual and programming) to ensure the successful completion of the assignment.

· Programming & Academic Writing: Each assignment will build upon one another. The techniques  around experimentation, graphs, interpretation (and so on) will play important roles in this and future projects.

· Course Conduct: Developing and testing code locally in the local Conda ml4t environment,

submitting it for pre-validation in the Gradescope TESTING environment, and submitting it for grading in the Gradescope SUBMISSION environment.

2. About the Project

In this project, you will build a Simple Gambling Simulator. Specifically, you will revise the code in the martingale.py file to simulate 1000 successive bets on the outcomes (i.e., spins) of the American roulette wheel using the betting scheme outlined in the pseudo-code below. Each series of 1000 successive bets is called an “episode.” You should test for the results of the betting events by making successive calls to  the get_spin_result(win_prob) function. Note that you will have to update the win_prob parameter according to the correct probability of winning. You can figure that out by thinking about how roulette works (see Wikipedia link below).

In this project, you will evaluate Professor Balch’s actual betting strategy at roulette when he goes to Las Vegas.

Here is the pseudocode of the strategy:

episode_winnings = $0 while episode_winnings < $80: won = False bet_amount = $1 while not won wager bet_amount on black won = result of roulette wheel spin if won == True: episode_winnings = episode_winnings + bet_amount else: episode_winnings = episode_winnings - bet_amount bet_amount = bet_amount * 2

Additional details regarding how roulette betting works: Betting on black (or red) is considered an “even money” bet. That means that if you bet N chips and win, you keep your N chips, and you win another N    chips. If you bet N chips and you lose, then those N chips are lost. The odds of winning or losing depend on betting at an American wheel or a European wheel. For this project, we will be assuming an American wheel. You can learn more about roulette and betting here: https://en.wikipedia.org/wiki/Roulette.

3. Your Implementation

You will develop an implementation leveraging the pseudocode above that conducts several experiments. Conduct the following experiments, then write your report. Before the deadline, make sure to pre-validate your submission using Gradescope TESTING. Once you are satisfied with the results locally and in Gradescope TESTING, submit the code to Gradescope SUBMISSION. Only code submitted to Gradescope SUBMISSION will be graded. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0).

3.1 Getting Started

You will be given a starter framework to make it easier to get started on the project and focus on the concepts involved. This framework assumes you have already set up the ML4T Development

Environment (https://gatech.instructure.com/courses/411838/pages/ml4t-development-environment-setup) . The framework for Project 1 can be obtained from the Martingale_2024Fall.zip

(https://gatech.instructure.com/courses/411838/files/53096803?wrap=1)

(https://gatech.instructure.com/courses/411838/files/53096803/download?download_frd=1)file on Canvas or Dropbox (https://www.dropbox.com/scl/fi/2qisdfrikezwsm7yaws6w/martingale_2024Fall.zip?

rlkey=xl4zcpde5o2bkqmpz60e1qyc8&st=g31fzvvb&dl=1) . Extract its contents into the base directory (e.g., ML4T_2023Fall, although “ML4T_2021Summer” is shown in the image below). This will add a new ” martingale ” folder to the directory structure.

Within the martingale folder is a single file: martingale.py

You will modify the martingale.py file to implement the necessary functionality for this assignment. The    existing code in the martingale.py file may contain ideas for functions and methods that could be used in your implementations. This file must remain and run from within the martingale directory using the following command:

PYTHONPATH= . ./: . python martingale.py

3.2 Experiment 1 – Explore the strategy and create some charts

In this experiment, you will develop code that performs experiments using Professor Balch’s original betting strategy. You will run some experiments to determine how well the betting strategy works. The approach we are going to take is called Monte Carlo simulation. The idea is to run a simulator repeatedly with randomized inputs and assess the results in aggregate. Your implementation will produce the following charts (i.e., figures):

· Figure 1: Run your simple simulator 10 episodes and track the winnings, starting from 0 each time.

Plot all 10 episodes on one chart using Matplotlib functions. The horizontal (X) axis must range from 0 to 300,the vertical (Y) axis must range from –256 to +100. We will not be surprised if some of the  plotlines are not visible because they exceed the vertical or horizontal scales.

· Figure 2: Run your simple simulator 1000 episodes. (Remember that 1000 successive bets are one  episode.) Plot the mean value of winnings for each spin round using the same axis bounds as Figure 1. For example, you should take the mean of the first spin of all 1000 episodes. Add an additional line above and below the mean, at mean plus standard deviation, and mean minus standard deviation of the winnings at each point.

· Figure 3: Use the same data you used for Figure 2 but plot the median instead of the mean. Add an additional line above and below the median to represent the median plus standard deviation and median minus standard deviation of the winnings at each point.

For all the above figures and experiments, if the target of $80 winnings is reached, stop betting, and allow the $80 value (or whatever value greater than $80 you have obtained) to persist from spin to spin (e.g., fill the data forward with a value of $80 or the value greater than $80 you have obtained).

The charts created by the experiments must be included in your report, along with your supporting analysis and discussion. All charts must be properly titled, have appropriate axis labels, use consistent axis ranges, and have legends.

3.3 Experiment 2 – A more realistic gambling simulator

You may have noticed that the original strategy performed in experiment 1 works well, maybe better than you expected. One reason for this is that we were allowing the gambler to use an unlimited bankroll. In this experiment, we retain the upper limit of $80 in winning retained but make things more realistic by giving the gambler a $256 bankroll. This will require a modification to the original strategy since if he or she runs out of money: bzzt, that’s it. Repeat the experiments, as above, with this new condition. Note that once the player has lost all their money (i.e., episode_winnings reach -256), stop betting and fill that number (-256) forward. An important corner case to handle is the situation where the next bet should be $N, but you only have $M (where M

· Figure 4: Run your realistic simulator 1000 episodes and track the winnings, starting from 0 each

time. Plot the mean value of winnings for each spin using the same axis bounds as Figure 1.Add an additional line above and below the mean at mean plus standard deviation and mean minus standard deviation of the winnings at each point.

· Figure 5: Use the same data you used for Figure 4 but plot the median instead of the mean. Add an additional line above and below the median to represent the median plus standard deviation and median minus standard deviation of the winnings at each point.

The charts created by the experiments will be included in your report, along with your supporting analysis and discussion. All charts must be properly titled, have appropriate axis labels, use consistent axis ranges, and have legends. You should use Python’s Matplotlib library.

3.4 Technical Requirements

The following technical requirements apply to this assignment:

1. The martingale.py file must implement this API specification

(https://gatech.instructure.com/courses/411838/pages/api-specifications) .

2. All winnings must be tracked by storing them in a NumPy array. You might call that array winnings

where winnings[0] should be set to 0 (just before the first spin). The entry in winnings[1] should reflect the total winnings after the first spin and so on.

3. Use the population standard deviation. The standard deviation is plotted as two lines, the upper standard deviation (e.g., mean +stdev) and the lower standard deviation (e.g., mean –stdev).

4. You may set a specific random seed for this assignment. If a specific random seedisused, it can

only be called once, and it must use your GT ID as the numeric value. To promote the reproducibility of results on different machines, we strongly recommend setting a starting seed,

5. The implementation may optionally write text, statistics, and/or tables to a single file named

p1_results.txt or p1_results.html. These files can be created by each student so that they do not need to write output to the terminal (or screen). The use of such files is entirely optional (i.e., left to each student). If used, this file (or both files) should be created in the same folder as the .py file being run.

6. You must implement the author() and study_group() APIs.