Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements, which can help reduce the portfolio’s overall risk. Following the theme of our previous article Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?, we decided to extend the data and conduct a more in-depth analysis of our earlier findings. This article explores potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market.

In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. In this article, we revisit our research to address the forward-looking bias in our final market timing strategy. Upon careful examination, we identified a bias in our macroeconomic trading signal based on the U.S. S&P Composite dividends. To eliminate the issue, we have replaced the signal from U.S. S&P Composite dividends with Housing Starts Growth sourced from FRED, ensuring the strategy is no longer biased.

The unbiased version of our TrendYCMacro strategy, which uses the HOUSE signal, yields an annual excess return of 6.59%, slightly below the 7.10% of the biased version with the DIVIDEND signal. Interestingly, the unbiased version experiences slightly lower annualized volatility at 11.87% compared to the 11.89% of the biased version. Both versions have suffered the same maximal drawdown of -25.13% and exhibit comparable risk-adjusted returns, with the unbiased version having a Sharpe ratio of 0.56 and the biased version having a Sharpe ratio of 0.60.

Some of the more vague terms in Technical Analysis are really hard to quantify as nearly every TA user defines and interprets them differently. We mean mainly TA patterns like supports, resistances, trend lines, double tops, double bottoms, and/or more complex patterns like head-and-shoulders. Now, what we can do with that? We tried to spend some time and fought a little with some of these TA terms, and the following article/study results from our attempts to quantify a tiny subset of the world of Technical Analysis patterns.

Science has been in a “replication crisis” for more than a decade. But what does it mean to us, investors and traders? Is there any “edge” in purely academic-developed trading strategies and investment approaches after publishing, or will they perish shortly after becoming public? After some time, we will revisit our older blog on this theme and test the out-of-sample decay of trading strategies. But this time, we have hard data – our regularly updated database of replicated quant strategies.

Skewness is one of the less-known but practical measures from statistics that can be used in trading. It is defined as a measure of the asymmetry of the probability distribution of a random variable around its mean. The goal of this analysis is to explore the commodity skewness trading strategy and perform the battery of robustness tests to see how sensitivity analysis changes overall results regarding performance, volatility, and Sharpe ratios.

At the end of 2018, researchers at Google AI Language made a significant breakthrough in the Deep Learning community. The new technique for Natural Language Processing (NLP) called BERT (Bidirectional Encoder Representations from Transformers) was open-sourced. An incredible performance of the BERT algorithm is very impressive. BERT is probably going to be around for a long time. Therefore, it is useful to go through the basics of this remarkable part of the Deep Learning algorithm family.