Have you ever wondered if your trading asset trends or mean-reverts? Everyone involved in trading or investments daily solves the task of – What trading strategy should I apply to my assets to generate profits? As always, we at Quantpedia will try to help you a bit with this never-ending task with our new tool/report, which will be unveiled next week for all Quantpedia Pro subscribers. The following article serves as an introduction to the methodology we will use to find new trading edges for you automatically.

Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the theme of our previous articles, we decided to extend historical data of a new factor, the Dollar Factor. This article explains how to combine multiple data sources to create a 100-year daily data history for the Dollar Factor (the value of the United States Dollar relative to its most important trading partners’ currencies), introduces data sources, and explains the methodology.

Every year, Quantpedia’s team investigates thousands of academic research papers to bring you the most promising ideas from the academic world. We read papers, identify ideas and backtest them to build our unique database. As a result, we have already identified hundreds of factors and built tools to help you orient better in the broad universe of trading strategies and systematic investment factors.

And now, we are opening the possibility to all external researchers, quants, and portfolio managers to contribute to Quantpedia.

A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

When someone mentions a financial crisis, most people immediately think of the global financial crisis of 2007-2008. Even though this is the most significant economic crisis in recent years, there have been many more significant crisis periods in the past 100 years. This article examines the biggest crises in three asset classes: stocks, bonds, and commodities, during the past century. Additionally, we analyze the behavior of our trend-following strategy during each of the crisis periods and propose it as a hedge for the stock, bond, and/or commodity markets.