Box-cox transformation

These are reminder notes about Box-cox transformation. One of the problems that box-cox transformation tries to solve is “heteroscedasticity” (non-constant variance). This article explains the problem where you can apply box-cox transformation to solve it: SciPy has added an inverse Box-Cox transformation: Does Box-cox always work? The answer is NO. Box-cox does not … Read more

Time Series notes

I have done this course proposed by Kaggle, and I would like to take some notes. The trend component of a time series represents a persistent, long-term change in the mean of the series. We mainly have: Time dependent properties: trends and seasonality. Serial dependent properties: Cycles and lagged series 1. Trends The trend component of a time series … Read more

Intermediate Machine Learning, by Kaggle

Some notes of this course offered by Kaggle, for my poor memory. Cross validation Cross-validation gives a more accurate measure of model quality, which is especially important if you are making a lot of modeling decisions.  Use pipelines for doing cross-validation, you will save a lot of time. XGBoost = Gradient boosting We refer to … Read more

Looking for a machine learning model that hits S&P 500 daily change using market breadth data, DIX, GEX, VIX.

This is the third version of a code I have started to write while learning the concepts of Machine Learning. Changes with respect the previous versions Shift(-1) have been removed for the SPX price, I consider is an error to add it. I have added data related to DIX, GEX (since 2011) and VIX (since … Read more

Using Machine Learning to predict the S&P 500 price change, using the dark pool indicators Dix and Gex

This is the first version of an analysis I wanted to perform with the main purpose of learning. By this reason I have limited the number of input data and operations to the minimum. To do something different I will be looking for correlations between the two main dark pool indexes: DIX and GEX Indexes. … Read more