AWS Trading Part 2 - The Strategy

AWS Trading Part 2 - The Strategy

In part 1 we covered the data pipeline portion of the AWS trading bot architecture. I demonstrated how to set up your AWS environment, including creating a simple dynamoDB database to hold our price and strategy data. Then we walked through the data pipeline code in detail including how to grab the data and populate our db with it. In this post we’ll cover the strategy that we will be implementing.

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Blackarbs Retirement Strategy Algorithm Debut (Part 2)

Blackarbs Retirement Strategy Algorithm Debut (Part 2)

In part 1 of the series, I introduced the blackarbs retirement algorithm, a long only leveraged ETF strategy meant to perform at or better than SPY (the market benchmark) with less volatility. I discussed the goals I set for the algo and how thus far in simulated backtests and live trading it has met those goals.  In this post I want to talk about the development process and robustness testing of the algorithm.

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

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)

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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Exploring the Relationship Between SPY and TLT

Exploring the Relationship Between SPY and TLT

In this post I examine the relationship between the SPY and TLT ETFs. This can be considered Part 2.5 of my series exploring the 2-Asset Leveraged ETF portfolio of UPRO and TMF. Thus far I've posted results of the strategy using two implementations: "Inverse Risk-Parity" and "Risk-Parity". I've also covered some key concepts behind investing in leveraged ETFs including convexity, and beta-slippage/decay. Now we can explore the strengths and weaknesses of the strategy.

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