We consider the equilibrium drawdown distribution for a biased random walk — in the context of a repeated investment game, the drawdown at a given time is how much has been lost relative to the maximum capital held up to that time. We show that in the tail, this is exponential. Further, when mean drift is small, this has an exponent that is universal in form, depending only on the mean and standard deviation of the step distribution. We give simulation examples in python consistent with the results.

# Author: Jonathan Landy

## Compounding benefits of tax protected accounts

Here, we highlight one of the most important benefits of tax protected accounts (eg Traditional and Roth IRAs and 401ks). Specifically, we review the fact that not having to pay taxes on any investment growth that occurs while the money is held in the account results in compounding / exponential growth with a larger exponent than would be obtained in a traditional account.

## Linear compression in python: PCA vs unsupervised feature selection

We illustrate the application of two linear compression algorithms in python: Principal component analysis (PCA) and least-squares feature selection. Both can be used to compress a passed array, and they both work by stripping out redundant columns from the array. The two differ in that PCA operates in a particular rotated frame, while the feature selection solution operates directly on the original columns. As we illustrate below, PCA always gives a stronger compression. However, the feature selection solution is often comparably strong, and its output has the benefit of being relatively easy to interpret — a virtue that is important for many applications.

## linselect demo: a tech sector stock analysis

This is a tutorial post relating to our python feature selection package, `linselect`

. The package allows one to easily identify minimal, informative feature subsets within a given data set.

Here, we demonstrate `linselect`

‘s basic API by exploring the relationship between the daily percentage lifts of 50 tech stocks over one trading year. We will be interested in identifying minimal stock subsets that can be used to predict the lifts of the others.

This is a demonstration walkthrough, with commentary and interpretation throughout. See the package docs folder for docstrings that succinctly detail the API.

Contents:

- Load the data and examine some stock traces
- FwdSelect, RevSelect; supervised, single target
- FwdSelect, RevSelect; supervised, multiple targets
- FwdSelect, RevSelect; unsupervised
- GenSelect

The data and a Jupyter notebook containing the code for this demo are available on our github, here.

The `linselect`

package can be found on our github, here.

## Integration method to map model scores to conversion rates from example data

This note addresses the typical applied problem of estimating from data how a target “conversion rate” function varies with some available scalar score function — e.g., estimating conversion rates from some marketing campaign as a function of a targeting model score. The idea centers around estimating the integral of the rate function; differentiating this gives the rate function. The method is a variation on a standard technique for estimating pdfs via fits to empirical cdfs.

## Gaussian Processes

We review the math and code needed to fit a Gaussian Process (GP) regressor to data. We conclude with a demo of a popular application, fast function minimization through GP-guided search. The gif below illustrates this approach in action — the red points are samples from the hidden red curve. Using these samples, we attempt to leverage GPs to find the curve’s minimum as fast as possible.

Appendices contain quick reviews on (i) the GP regressor posterior derivation, (ii) SKLearn’s GP implementation, and (iii) GP classifiers.