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Options trading machine learning

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options trading machine learning

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information learning to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations. Students should have strong coding skills and some familiarity with equity markets. No finance or machine learning experience is assumed. Here's a short test to check if you have strong programming skills: If you don't do well on that quiz, you should either drop the course, or be sure to plan so that you can devote extra time to the course. Note that this course serves CS major students with machine learning experience, as well as students in other majors such as ISYE, MGMT, or MATH who have different experiences. All types of students are welcome! The ML topics might be "review" for CS students, while finance parts will be review for finance students. However, even if you have experience in these topics, you will find that we consider them in a different way than you might have seen before, in particular with an eye towards implementation for trading. In most cases I expect all code that you trading was written by you. I will present some libraries learning class that you are allowed to use such as pandas and numpy. Otherwise, all source code, images and write ups you provide should have been created by you alone. Thursday 21 Aug Overview of Project 1 N files, leave one out Instructor will outline one solution in class: Not required that you follow it Bag of words model Hashing trick tf-idf. Tuesday 26 August Project 1A due tonight, questions? Project 1B definition tf t,d definition idf t,D definition Project 1C definition Overall: How would instructor solve the problem. Thursday 28 August How to solve Project 1B Words by documents size matrix: Review of solutions to Project 1A. Tuesday 2 September Review of reference solution to Project 1B Intro to ML: Sort SP and longshort other Thursday 4 September How supervised training works for time series data Roll forward cross validation Example ML trading strategies? Thursday 11 September Book Any questions about Project 1C due next Tuesday? Required Readings Andrew Moore's slides on KNN [ [1] ]. Tuesday 16 September Review: Overview of Fin part Module So you want to be a fund manager? Common metrics to assess fund performance. Thursday 18 September Module Common metrics, pt 2 Module Excel demo of metrics. Required Readings Murphy 1. Optional Machine Slide decks [ [2] ]. Thursday 25 September Module Market Mechanics Module Order Book, Short Selling Module How Hedge Funds Exploit, Market Mechanics. Optional Readings Slide decks [ [3] ]. Tuesday 7 October Module The computing inside a hedge fund Module Where does info come from? Thursday 9 October 3 ways to assess company value: Book, Market Cap, Intrinsic Module Intrinsic Value Options Fundamental analysis of company value. Optional Readings Slide decks [ [4] ]. Thursday 16 October Module Review Events, and Survivor Bias How to tell how much money we'd make? Fundamental analysis of company value Options to: Optional Readings Slide decks [ [5] ] Hints on how to build marketsim: Tuesday 21 October Module How news affects prices Module Capital Assets Pricing Model Module What is Beta Module How Hedge Funds use CAPM Module Data Scrubbing Checking options Sanity. Thursday 23 October Module The Inputs and Outputs of a Portfolio Optimizer Module The Importance of Correlation and Covariance in daily returns Module The Efficient Machine Module Optional Learning Slide decks [ [6] ]. Tuesday 28 October Module Coin Flipping Module Fundamental Law Part 1 Module Fundamental Law Part 2 Module CAPM recap, overview for portfolios Module Thursday 30 October Module Example Information Feeds Module Trading to Technical Analysis Module Some Example Technical Indicators Module Optional Readings Slide decks [ [7] ]. Thursday 6 November Recap of regression learning Introduction to decision trees Animal guessing game How to use a decision tree if you have one. Tuesday 11 November ML Project 2: KNN defined and released How to build a decision tree, according to Quinlan. Required Readings Seminal paper on trees: Quinlan Paper Perfect Ensembles of Random Trees Cutler. Optional Machine Ensemble learners wikipedia Bagging wikipedia Boosting wikipedia Bagging and boosting trees Diettrich Section Tuesday 18 November Guest Speaker: Prof Soohun Kim media: Thursday 20 November Converting spatial learners to timeseries learners. Overview of last assignment. Optional Readings Slide decks [ [8] ]. Tuesday 2 December Tips and tricks on how to make the sine wave learner work. Retrieved from " http: Views Page Discussion View source History. Personal tools Log in. Navigation Main page Computational Investing I Computational Investing II GT CS QSTK Recent changes this sidebar Help. Tools What links here Related changes Special pages Printable trading Permanent link. Contents 1 Machine Learning for Trading 1.

Machine Learning for Quantitative Trading Webinar with Dr. Ernie Chan

Machine Learning for Quantitative Trading Webinar with Dr. Ernie Chan

5 thoughts on “Options trading machine learning”

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