It is indisputable that the data are changing financial markets – computing power has increased, allowing to rise the trends of ML/AI and big data (number of possible predictors or granularity) or HFT strategies. Indeed, not all the datasets are worth the time of academics, investors or traders, but we are always keen to analyze the novel and unique datasets. Of course, if we believe that the analysis is worthy of sharing, we are happy to do so. This post offers a shorter version of our newest research about Synthetic lending rates and subsequent market return. We hope that you find it enriching; enjoy the reading!

This blog post is the continuation (and finale) of series about Quantconnect’s Alpha market strategies. This part is related to the multi-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 the investment universe is reduced based on the insights of the previous part. So, without further ado, we continue where we have left last time.

Today we will again talk more about the portfolio management theory, and we will focus on techniques for combining quantitative strategies into one multi-strategy portfolio. So, let’s imagine we already have a set of profitable investment strategies, and we need to combine them. The goal of such “strategy allocation” usually is to achieve the best risk-adjusted return possible. There is no single correct solution to this task, but there are a few methods that we can try.

The “appropriate combination” highly depends on the type of strategies we are about to combine. Are we combining equity and bond strategies together? Are we combining equity strategies, with each one having an entirely different logic? Or do we rather need to assign weights to strategies that are similar in nature yet still different? We will focus this article on the last option – combining similar yet different strategies.

Understanding the risks of any quantitative trading strategy is one of the pillars of successful portfolio management. Of course, we can hope for good future performance, but to survive market whipsaws, we must have tools for sound risk management. The “Value at Risk” measure is such a standard tool used to assess the riskiness of trading and investment strategies over time. We plan to unveil our new “Value at Risk” report for Quantpedia Pro clients next week, and this article is our introduction to different methodologies that can be used for VaR calculation.

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.