Profitable Insights into Financial Markets
Misinformation is everywhere. Many people believe the key to successful short selling is simply the inversion of a successful long strategy. I also used to believe this, among other short selling myths.
This article will demonstrate the impact, just one of the revealed secrets to short selling, can have on your algorithmic strategy development.
Read MoreMy initial insample performance targets for this portfolio are a sharpe ratio of 1.5 or better, max drawdown less than 15%, and only 1 losing year every ten years. I aim to develop variations of this portfolio that can scale from 100K to several million.
To support this development effort and still stay true to my original goal of sharing my work, I’ve created a subscription tier for premium content. I’m calling this the Take Profits Strategy Subscription.
Over the next 12 months, every month (including February), I will share one new algorithmic strategy with my subscribers. My goal is to have each strategy be fully functional on the Quantconnect platform out of the box. This will allow you to backtest and live trade the strategy immediately if you choose.
The code will be documented in a way that practitioners can build on and customize the strategy. The code documentation will also provide in-depth explanations behind the purpose and function of each component. I will also provide any additional research documents and notebooks, that helped me in the development process. Ultimately, I will pick the best combination of these strategies to implement in the multi-strategy portfolio.
Read MoreThis 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.
Read MoreIn 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.
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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