Is it Possible to Know the Daily High or Low Intraday with 80% Accuracy?

Is it Possible to Know the Daily High or Low Intraday with 80% Accuracy?

This is an old concept concerning the opening range. The idea is that the opening range often sets the day’s high or low within the first hour of cash equities trading (9:30 am - 10:30 am EST). Recently a trader on [Youtube] made the claim that you can know with 88% probability the high or low of the day after the first hour of trading. He managed to successfully re-popularize the idea of using the opening range in a a more specific way than other methods.

In this article I set out trying to validate or reject this claim with the available intraday data I have. Ideally, if this claim is true, there should be a methodology or mechanical trading approach to exploit this phenomenon.

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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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