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Mixture Model Trading (Part 2 - Gaussian Mixtures)

Post Outline * Introduction * Links * Notebook * Next Steps Introduction This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: “The primary goal of this repo is to demonstrat

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Mixture Model Trading (Part 1 - Motivation)

Post Outline * Introduction * Links * Notebook * Next Steps Introduction This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic trading strategy. Below is a quote taken from the README of the github repo: I will be presenting each of the notebooks

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Computing Option Skews with Dask

Post Outline * Introduction * Links + Datasets * Notebook * Next Steps Introduction This article series provides an opportunity to move towards more interactive analysis. My plan is to integrate more Jupyter notebooks and Github repos into my research/publishing workflow. For datasets that are too big to share through github I will provide a download link both here and in the github readme. I will be posting the notebooks into this blog using iframes. If you experience any issues with f

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How To Get Free Intraday Options Data With Pandas-DataReader

Post Outline * Purpose * Intuitive explanation * Code * Next Steps Purpose This is a simple reference article for readers that might wonder where I get/got my options data from. In this regard I would like to shout out the contributors to the pandas-datareader, without their efforts this process would be much more complex. Intuitive Explanation So this code consists of three components. The first is the actual script that wraps the pandas-datareader functions and downloads the options

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Exploring Our Scraped Options Data Bid-Ask Spreads (Part-2)

Post Outline * Notes on Part-2 * The Data * Bid-Ask Spread Analysis * How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? * How Do Bid-Ask Spreads Vary with Volume? * How Do Bid-Ask Spreads Vary with Volatility? * Summary Conclusions Notes on Part-2 Some astute readers in the comments noted that analysis based on the absolute difference in bid-ask price is not robust when considering the price of the underlying option and can lead to spurious conclusions. They recomme

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Exploring Our Scraped Options Data Bid-Ask Spreads

Post Outline * The Objective * The Data * Basic Data Analysis * Bid-Ask Spread Analysis * How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? * How Do Bid-Ask Spreads Vary with Volume? * How Do Bid-Ask Spreads Vary with Volatility? * Summary Conclusions The Objective Compared to the equity market, the options market is a level up in complexity. For each symbol there are multiple expiration dates, strike prices for each expiration date, implied volatilities, and that'

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How to Build a Sequential Option Scraper with Python and Requests

Post Outline * Recap * The Problem * The Solution * Barchart Scraper Class * Barchart Parser Class * Utility Functions * Putting it all together * The Simple Trick * Next Steps Recap In the previous post I revealed a web scraping trick that allows us to defeat AJAX/JavaScript based web pages and extract the tables we need. We also covered how to use that trick to scrape a large volume of options prices quickly and asynchronously using the combination of aiohttp and asyncio. The Pr

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How to Scrape and Parse 600 ETF Options in 10 mins with Python and Asyncio

Post Outline * Intro * Disclaimers * The Secret to Scraping AJAX Sites * The async_option_scraper script * first_async_scraper class * expirys class * xp_async_scraper class * last_price_scraper class * The option_parser Module * The Implementation Script * References Intro This is Part 1 of a new series I'm doing in semi real-time to build a functional options data dashboard using Python. There are many underlying motivations to attempt this, and several challenges to imp

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Can We Use Mixture Models to Predict Market Bottoms? (Part 3)

Post Outline * Recap * Webinar Hypothesis * Anaylsis/Conclusions * Jupyter (IPython) Notebook * Github Links and Resources Recap Thus far in the series we've explored the idea of using Gaussian mixture models (GMM) to predict outlier returns. Specifically, we were measuring two things: 1. The accuracy of the strategy implementation in predicting return distributions. 2. The return pattern after an outlier event. During the exploratory phase of this project there were some interestin

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Can We Use Mixture Models to Predict Market Bottoms? (Part 2)

Post Outline * Recap * Model Update * Model Testing * Model Results * Conclusions * Code Recap In the previous post I gave a basic "proof" of concept, where we designed a trading strategy using Sklearn's implementation of Gaussian mixture models. The strategy attempts to predict an asset's return distribution such that returns that fall outside the predicted distribution are considered outliers and likely to mean revert. It showed some promise but had many areas in need of improvement.

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Can We Use Mixture Models to Predict Market Bottoms?

Post Outline * Recap * Hypothesis * Strategy * Conclusion * Caveats and Areas of Exploration * References Recap In Part 1 we learned about Hidden Markov Models and their application using a toy example involving a lazy pet dog. In Part 2 we learned about the expectation-maximization algorithm, K-Means, and how Mixture Models improve on K-Means weaknesses. If you still have some questions or fuzzy understanding about these topics, I would recommend reviewing the prior posts. In those po

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Intro to Expectation-Maximization, K-Means, Gaussian Mixture Models with Python, Sklearn

Post Outline * Part 1 Recap * Part 2 Goals * Jupyter (IPython) Notebook * References part 1 recap In part 1 of this series we got a feel for Markov Models, Hidden Markov Models, and their applications. We went through the process of using a hidden Markov model to solve a toy problem involving a pet dog. We concluded the article by going through a high level quant finance application of Gaussian mixture models to detect historical regimes. part 2 goals In this post, my goal is to impar

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