Grid trading is an automated currency trading strategy where an investor creates a so-called “price grid”. The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting and covering). We will explore the basics and show favorable and unfavorable scenarios in the first article about this trading style. Later articles will dig deeper and investigate how Grid trading is related to other systematic trading strategies.

Our new article investigates “rebalancing premium” or “diversification return” in cryptocurrencies which can be achieved by periodically rebalancing portfolios. We analyze whether the daily/ monthly rebalanced portfolios outperform a simple buy-and-hold portfolio of cryptocurrencies and under which conditions. Additionally, we also look at the various combinations of volatile cryptocurrency portfolios with low-risk bonds.

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.