Ml4t martingale

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I ever saw pandas. One slight interpretation change is you need to mark a loss as a 1 and a win as a 0.

You then need to take the difference of the sizes of the steps and shove those values back into the original data. When you take a cumsum of toss2 it gives you the current length of your losing streak. This is the pure numpy version my native language is it were. There is a bit of fineagling to get the arrays to line up that pandas does for you. Pandas is going to get it's biggest efficiency wins when you can use vectorized operations, but I think this problem requires iteration.

A solution using pandas:. Learn more. Implement a classic martingale using Python and Pandas Ask Question. Asked 7 years, 2 months ago. Active 7 years, 2 months ago.

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Viewed 2k times. I want to implement a classic martingale using Python and Pandas in a betting system. DataFrame np. Initial stake is 1. I don't know how to proceed! Maybe I'm wrong and using shift function is not a good idea. Active Oldest Votes. DanB DanB 2, 4 4 gold badges 18 18 silver badges 21 21 bronze badges.

It works fine. But as you said I will be better to use vectorized operations I am very skeptical that there is a clean vectorized solution.This post is a first effort at gathering the info necessary to assemble a self-study plan, so that I do everything I can to maximize my likelihood of successfully completing a top 10 CS masters degree. The degree requires completion of 30 units, and each course is 3 units.

The specialization that I would prefer given my long-term career interests is the Machine Learning specialization. To continue the program, the OMSCS program requires newly admitted students to complete two foundational courses in the first 12 months following matriculation. This is obviously a critical hurdle to pass. The machine learning specialization consists of the following courses. Passing five of these six is required, and with the revamp of DVA I may complete all six.

And, since the coursework in my undergrad CS minor stopped just short of operating systems, IOS would be helpful to fill out my CS knowledge. To make up for a lack of software engineering coursework during my non-CS undergraduate degree, I may want to pursue graduate level software engineering courses.

A final consideration is that it may be prudent to select courses that allow me to pursue my interests while still minimizing the total programming languages used in the degree. Also included are the average work and average difficulty as reported by other students, and sourced from OMSCentral. Also listed is the programming language utilized. His planned course of study is similar to mine, with the exception of apparently pursuing a double-specialization in both ML and computing systems.

I have colored in the nodes representing the courses applicable to me in red. Please note that this is list is completely based on fantasy and may not be practical by any measure due to factors such as non-availability, potential for failing miserably to meet the foundational requirements, sanity prevailing etc.

Read at your own risk and excuse my poor English. First, it will put you in the right mindset and prep you for the upcoming rigor of the program. Secondly, the concepts learned in this course are useful for the rest of the course. AI4R AI4R probably has the best introduction to Probability and Linear Algebra which, along with algorithms, form the basis of everything you learn in the following courses.

AI Offers everything to catch up on the classic AI from the 60s onward to 90s and 00s. ML4T Unholy! This is probably one of the most important classes in the program in terms of gratification-effort ratio. DVA Offers valuable practical data science perspective such as cleaning raw data, visualizations and report making as well as more Machine learning practice.

After this class you will be chewing through Kaggle datasets one after the other. ML Finally the big one. Heavy emphasis on synthesis of Machine learning, Reinforcement Learning algorithms and Learning theory. RL OK, why are we even doing this class at this point? Even though there is a heavy overlap with ML, this course offers a wondrous journey though academic papers and advanced concepts and can be a rewarding experience.

Best of luck to you all! My research resulted in a several page word document full of notes. I decided to host that information as a public service. If you're not, it definitely isn't, but congratulations on finding it! This page is consistently my most viewed page and the site's most common entry point. Rumor is that it will use Python instead of R, which I am thrilled about.

The original version of this post "crossed out" various courses on the basis of my notes at the bottom of the post. Given the popularity of this page and the fluid nature of OMSCS coursework, I've decided to remove such explicit condemnations of courses.

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I feel that leaving that in would do a disservice to the program. The insights and opinions that led me to reach conclusions about my own planned course of study are still available in my notes below though. I invite readers of this page to do their research and draw their own conclusions about the best courses for them to pursue. I wish you all the best of luck!This assignment is subject to change up until 3 weeks prior to the due date.

We do not anticipate changes; any changes will be logged in this section. The purpose of this assignment is to get you started programming in Python right away and to help provide you some initial feel for risk, probability and "betting. In this project you will evaluate the actual betting strategy that Professor Balch uses at roulette when he goes to Las Vegas. Here it is:.

Here are some 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 whether you're betting at an American wheel or a European wheel.

For this project we will be assuming an American wheel. The base directory structure is used for all projects in the class, including supporting data and software are will be set up correctly when you follow those instructions. This project is available here: Filespring martingale. Once you've done this, you should see the following directory structure:. You should change only martingale. All of your code should be in that one file.

Do not create additional files.

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Leave the copyright information at the top intact. You should also update this information the comments section at the top. Revise the code in martingale. You can figure that out by thinking about how roulette works see wikipedia link above. Track your winnings 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. Now we want you to run some experiments to determine how well the betting strategy works.

The approach we're going to take is called Monte Carlo simulation where the idea is to run a simulator over and over again with randomized inputs and to assess the results in aggregate.I choose to enroll in this course in an effort to gain more experience with applying machine learning techniques to other real world problems. As someone who already took, and loved, the primary machine learning course it made a lot of sense to apply those same skills to round them out further.

The first part of the course focused on utilizing Python Pandas, numpy, and scipy on stock data. There were a number of assignments to import data, clean it, manipulate it, and calculate various items on it. The second part of the course was focused on building financial indicators, understanding market mechanisms, and doing technical analysis. In this part of the course we also analyzed various strategies that are used to generate a portfolio.

For example, holding one stock and trying to micromanage it versus holding large numbers of stocks and letting it ride out the ups and downs. The final section of the course was utilizing actual machine learning algorithms against portfolio data. Here I used KNN and linear regression algorithms in order to make predictions as to whether to buy or sell. Scikit-learn, another Python library, was leveraged in order to do some of these calculations.

Machine Learning for Trading CS Introduction I choose to enroll in this course in an effort to gain more experience with applying machine learning techniques to other real world problems. Data Manipulation in Python The first part of the course focused on utilizing Python Pandas, numpy, and scipy on stock data. Computational Investing The second part of the course was focused on building financial indicators, understanding market mechanisms, and doing technical analysis.

Machine Learning Algorithms The final section of the course was utilizing actual machine learning algorithms against portfolio data. References Python Pandas scikit-learn. Numeric Data Duplication Detection 18 July ai python numeric data vectors.The purpose of this assignment is to get you started programming in Python right away and to help provide you some initial feel for risk, probability and "betting.

In this project you will evaluate the actual betting strategy that Professor Balch uses at roulette when he goes to Las Vegas. Here it is:. Here are some 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 whether you're betting at an American wheel or a European wheel.

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For this project we will be assuming an American wheel. The base directory structure is used for all projects in the class, including supporting data and software are will be set up correctly when you follow those instructions.

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This project is available here: Filefall martingale. Once you've done this, you should see the following directory structure:. You should change only martingale. All of your code should be in that one file. Do not create additional files.

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Leave the copyright information at the top intact. You should also update this information the comments section at the top.

Revise the code in martingale. You can figure that out by thinking about how roulette works see wikipedia link above. Track your winnings 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. Now we want you to run some experiments to determine how well the betting strategy works. The approach we're going to take is called Monte Carlo simulation where the idea is to run a simulator over and over again with randomized inputs and to assess the results in aggregate.

Skip to the "report" section below to which specific properties of the strategy we want you to evaluate. For the following charts, and for all charts in this class you should use python's matplotlib library. Your submitted project should include all of the code necessary to generate the charts listed in your report.

Best Binary Options Martingale Strategy - What Results Can You Expect from Martingale Mastered?

You should configure your code to write the figures to. Do not allow your code to create a window that displays images. If it does you will receive a penalty.

You may have noticed that the strategy actually works pretty well, maybe better than you expected. One reason for this is that we were allowing the gambler to use an unlimited bank roll. If he or she runs out of money, bzzt, that's it. Repeat the experiments above with this new condition. Note that once the player has lost all of their money i. Here are the two charts to create:.

Do not submit any other files. Note that your charts should be included in the report, not submitted as separate files.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Can anyone provide some pseudo code for a roulette selection function? How would I implement this: I don't really understand how to read this math notation. I want General algorithm to this. The other answers seem to be assuming that you are trying to implement a roulette game.

I think that you are asking about roulette wheel selection in evolutionary algorithms. Here is some Java code that implements roulette wheel selection.

Assume you have 10 items to choose from and you choose by generating a random number between 0 and 1. You divide the range 0 to 1 up into ten non-overlapping segments, each proportional to the fitness of one of the ten items.

For example, this might look like this:. This is your roulette wheel. Your random number between 0 and 1 is your spin. If the random number is 0. If it's 0. There are 2 steps to this: First create an array with all the values on the wheel.

This can be a 2 dimensional array with colour as well as number, or you can choose to add to red numbers. Then simply generate a random number between 0 or 1 depending on whether your language starts numbering array indexes from 0 or 1 and the last element in your array. Most languages have built-in random number functions. In Javascript it is Math.

Final note : don't forget to seed your random number generator or you will get the same sequence of draws every time you run the program. First, generate an array of the percentages you assigned, let's say p[ Here is a really quick way to do it using stream selection in Java.

It selects the indices of an array using the values as weights. No cumulative weights needed due to the mathematical properties. This could be further improved using Kahan summation or reading through the doubles as an iterable if the array was too big to initialize at once.This assignment is subject to change up until 3 weeks prior to the due date.

We do not anticipate changes; any changes will be logged in this section. The purpose of this assignment is to get you started programming in Python right away and to help provide you some initial feel for risk, probability and "betting. In this project you will evaluate the actual betting strategy that Professor Balch uses at roulette when he goes to Las Vegas. Here it is:. Here are some 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 whether you're betting at an American wheel or a European wheel. For this project we will be assuming an American wheel. The base directory structure is used for all projects in the class, including supporting data and software are will be set up correctly when you follow those instructions.

This project is available here: Filefall martingale. Once you've done this, you should see the following directory structure:. You should change only martingale. All of your code should be in that one file. Do not create additional files. Leave the copyright information at the top intact. You should also update this information the comments section at the top. Revise the code in martingale.

You can figure that out by thinking about how roulette works see wikipedia link above. Track your winnings 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. For the following charts, and for all charts in this class you should use python's matplotlib library. Your submitted project should include all of the code necessary to generate the charts listed in your report. You should configure your code to write the figures to.

If it does you will receive a penalty. Now we want you to run some experiments to determine how well the betting strategy works. The approach we're going to take is called Monte Carlo simulation where the idea is to run a simulator over and over again with randomized inputs and to assess the results in aggregate. Skip to the "report" section below to which specific properties of the strategy we want you to evaluate. You may have noticed that the strategy actually works pretty well, maybe better than you expected.

One reason for this is that we were allowing the gambler to use an unlimited bank roll. If he or she runs out of money, bzzt, that's it. Repeat the experiments above with this new condition. Note that once the player has lost all of their money i. Here are the two charts to create:. Do not submit any other files. Note that your charts should be included in the report, not submitted as separate files.

Also note that if we run your submitted code, it should generate all 5 figures as png files.