Own-research

Why has been alternative data recently so much popular? The answer most of the time hovers around the notion of “seeking the new alpha sources”. First, the hunt for alpha is huge due to the low yield world and is getting only bigger. Secondly, some of the more popular strategies can become crowded, leading to diminishing alpha or the risk of a sudden reversal in performance (all of us remember this year’s growth vs. value switch).

We at Quantpedia don’t create nor manage any alternative data sets. But we are aware of this trend, and we strive hard to find new alpha opportunities which may lie in these new data sources. From the database of almost 700 quantitative investment strategies Quantpedia has gathered, almost 100 strategies are based on alternative datasets. Today, we picked just 6 of them to give you a little taste of how these alternative strategies may look like, what kind of datasets they utilize and how they perform.

Quantpedia started ten years ago in October 2011, and it was built out of necessity. The following article is a short recollection of the journey it has traveled. It’s a thought on the connection between creativity and our need to collect, analyze and categorize abstract ideas and how does it all help in the design of systematic trading strategies.

Each change in a strategic asset allocation of a professionally managed portfolio comes only after meticulous analysis. Firstly, we must understand the current status of the portfolio – how it behaved in the past, the strong and weak points of current allocation, and the main risk factor exposures. Then we can think about the future. We can decide how active we want to be, how large a risk budget we have at our disposal, and what asset classes we want to continue to focus on in our tactical models. Afterward comes the time for creativity – we can analyze opportunities and look for ideas for new models that complement what we already have. That’s time for Quantpedia Pro, and we will use this short case study and walk you through the few features that simplify the process of finding new ideas for trading strategies that fit your individual case.

This is the third and final article from the clustering series. If you’ve missed the previous parts, here you can find the first and second parts of the series. This section examines trading strategies based on previously introduced clustering methods. The complete Portfolio Clustering report will be available for our Quantpedia Pro clients next week.

October’s is coming, and we continue our short series of introductory articles about portfolio clustering methods we will soon use in our new Quantpedia Pro report. In the previous blog, we introduced three clustering methods and discussed the pros and cons of each one. Additionally, we showed a few examples of clustering, and we presented various methods for picking an optimal number of clusters.

This section demonstrates the Partitioning Around Medoids (PAM) – a centroid-based clustering method, Hierarchical Clustering, which uses machine learning and Gaussian Mixture Model based on probability distribution and applies all three methods to an investment portfolio that consists of eight liquid ETFs.

This blog post is the continuation of series about Quantconnect’s Alpha market strategies. Part 1 is here and Part 2 can be found here. This part is related to the factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but in this part, the investment universe is reduced based on the insights of the last part. So, without further ado, we continue where we have left last time.