Blackarbs Retirement Strategy Algorithm Debut (Part 1)

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Blackarbs Retirement Strategy Algorithm Debut (Part 1)

Blackarbs current mission is to create automated strategies with the goal of beating the market with superior risk adjusted returns. Originally, I wanted to illuminate some of the more hidden aspects of markets and investing that I found interesting and of value. Over time, that goal crystallized into creating a strategy or strategies that made (potential) superior performance accessible to investors of all types and demographics. To this end I believe we have finally created a flagship algorithm. 

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

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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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Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

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Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations.

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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 4)

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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 4)

Introduction

In this blog post we will review the simulated performances of a few UPRO/TMF strategy implementations using the Quantconnect platform. If you’re not familiar with the platform, it is an algorithmic trading platform that provides backtesting and live trading across of variety of asset classes including: equities, futures, forex, options, and cryptocurrencies. I like using the platform because of the access to a large number of asset classes, the development team is responsive and you can code strategies in Python (even though the underlying platform is built in C). The strategies’ performances are evaluated using pyfolio and ffn. Note that in some cases their calculations are slightly different.

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

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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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