Tag: algorithmic trading

How I Automated My Trading Strategy Using AWS Cloud for Free (Part 1)

This year I launched a strategy subscription service for a long-only ETF strategy developed in house. I learned a lot through this process but I made several mistakes that pushed me to learn new skills and improve the product offering. In this series I will discuss my initial mistakes, and how correcting them led me to automate the system using AWS cloud and how you can too. Mistake #1 First mistake was not considering automation in the beginning. I had the script, I ran it daily, used the s

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Blackarbs Retirement Strategy Algorithm Debut (Part 2)

Join the growing Blackarbs Research Group Discord community here Get access to the strategy that has returned 48% live trading since November 2023 here (Updated: 2024-Mar-02) Recap 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.

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Mixture Model Trading (Part 5 - Algorithm Evaluation with pymc3)

Post Outline * Recap * Chapter Goals and Outline * Links * Embedded Jupyter Notebook Recap See <Mixture Model Trading (Part 1, Part 2, Part 3, Part 4, Part 5, Github Repo)>. This research demonstrates a systematic trading strategy development workflow from theory to implementation to testing. It focuses on the concept of using Gaussian Mixture Models as a method for return distribution prediction and then using a simple market timing strategy to take advantage of the predicted asset retu

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Mixture Model Trading (Part 4 - Strategy Implementation)

Post Outline * Chapter Goals and Outline * Links * Introduction * Mixture Model Trading Algorithm Outline * GMM Algorithm Implementation * Next Steps Chapter Goals and Outline * Use Part 3 - strategy research as a basis for algorithmic trading strategy. * Implement strategy using the Quantconnect platform. Links * Mixture Model Trading Github Repo * Part 4 Jupyter Notebook Link * Full Algorithm Python Script Introduction This notebook will walkthrough the algorithm implement

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Can We Use Mixture Models to Predict Market Bottoms? (Part 2)

Post Outline * Recap * Model Update * Model Testing * Model Results * Conclusions * Code Recap In the previous post I gave a basic "proof" of concept, where we designed a trading strategy using Sklearn's implementation of Gaussian mixture models. The strategy attempts to predict an asset's return distribution such that returns that fall outside the predicted distribution are considered outliers and likely to mean revert. It showed some promise but had many areas in need of improvement.

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Can We Use Mixture Models to Predict Market Bottoms?

Post Outline * Recap * Hypothesis * Strategy * Conclusion * Caveats and Areas of Exploration * References Recap In Part 1 we learned about Hidden Markov Models and their application using a toy example involving a lazy pet dog. In Part 2 we learned about the expectation-maximization algorithm, K-Means, and how Mixture Models improve on K-Means weaknesses. If you still have some questions or fuzzy understanding about these topics, I would recommend reviewing the prior posts. In those po

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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. The strategy works because

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

In Part 1, and Part 1.5 I introduced a simple 2-asset portfolio that substantially outperformed the SPY ETF since 2009. In Part 1 I examined the performance of an "inverse risk-parity" approach where the ETF with the largest volatility contribution to the portfolio was weighted more heavily. In Part 1.5 I examined the performance of the actual "risk-parity" approach, where the ETF with the smallest volatility contribution is weighted more heavily. In this post I will examine some of the conceptu

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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1.5)

In Part 1 of this series I shared a simple strategy which showed outsized performance relative to the SPY ETF since 2009. I made a small error in the implementation. The previous portfolio was not rebalanced according to a risk-parity framework. It was actually the inverse. The strategy was rebalanced such that the ETF responsible for the highest percentage of the portfolio's volatility was weighted more heavily! Surprisingly this error did nothing to substantially alter the performance of the p

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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1)

I'm going to share a portfolio with you that has absolutely annihilated the performance of the market (as proxied by SPY) since the recovery began in 2009*. The strategy has not had a down year since. This portfolio maintains constant exposure, has 1 un-optimized parameter and wins on a risk-adjusted basis even after considering reasonable transaction costs. I can't claim credit for the general idea. I found it in the comments section while reading a SeekingAlpha article written by Jonathan Kin

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