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.

NFTs are taking the cryptocurrency trading world by storm. NFTs stand for the non-fungible tokens which have emerged as another possible usage of blockchain technology. NFT can be used to record/verify/track the ownership of a unique – hence the non-fungible asset. Commonly, NFTs are connected with art (visual art, music, etc.), but there are also several decentralized finance or gaming-related projects.
Same as for the other blockchain-related projects, the critics are easy to find, so a research paper with hard data concerning the NFTs can be of great importance. The research paper by Mazur (2021) studies the NFT startups traded in the crypto markets. Therefore, the paper does not analyze the individual NFTs (such as some piece of art), but rather the whole projects and their tokens traded on the Binance crypto exchange.

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!

Hello all,

What’s new in November’s update of Quantpedia’s services?

– A new Benchmark Portfolio feature for Quantpedia Pro service
– A new Algo Trading Discounts table
– 10 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 2+5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month