A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 3)

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 3)

Recap

This is an update to the original blog series that explored a simple strategy of being long UPRO and TMF in equal weight, inverse volatility and inverse-inverse volatility. This strategy crushed the cumulative and risk-adjusted returns of the benchmark SPY etf. However through our research we determined that this strategy is heavily dependent on the correlation between the two assets. This strategy works best when correlations are positive and prices are trending positively, however, theoretically it is most stable when correlations are negative. Previously we determined the strategy is most exposed when correlations are positive or rising and prices are declining. The problem is that we don’t know ex-ante if, during periods of increasing correlations, prices will trend up or down, which exposes our capital to large risks. In the past I eluded to a potential workable solution to this issue. In this blog post and associated materials we will explore some potential solutions to this problem.

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Labeling and Meta-Labeling Returns for ML Prediction

Labeling and Meta-Labeling Returns for ML Prediction

This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado.  In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and  also meta-labeling which is a technique designed to address several issues.

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COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/31/2015)

NEW LAYOUT: 

Going forward I will be testing a new organizational format for the charts. I have created a chart description page which details how each plot type is commonly interpreted (used). I will provide a link to the chart description page in the caption of each chart. The primary change is all the chart types will be grouped by the referenced time period. It is my hope that grouping the data this way will allow for easier analysis. Each time period grouping will display the charts in the following order: 

  1. Composite ETF Cumulative Returns Momentum Bar plot and associated Tables

  2. Composite ETF Cumulative Returns Line plot

  3. Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean)

  4. Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot)

 

YEAR-TO-DATE LAST 217 TRADING DAYS

LAST 63 TRADING DAYS

LAST 21 TRADING DAYS

LAST 10 TRADING DAYS