I just came across an interesting debate about "Sell in May" — the classic investment strategy that many still believe in. Today, I want to share some thoughts on this, especially whether it can be applied to crypto.

First, what is "Sell in May"? Basically, it’s a rule that suggests investors should sell stocks at the beginning of May to avoid the weak season, then buy back in November. This strategy originated in 17th-century England, when aristocrats and bankers would leave London during the hot summer to attend horse racing events, then return at the end of the year. In the U.S., they applied a similar approach by avoiding the market from Memorial Day in May to Labor Day in September.

What does the U.S. stock market data from 1950 to 2013 show? The Dow Jones index only increased by an average of 0.3% from May to October, but rose up to 7.5% from November to April. Sounds reasonable, but when accounting for taxes and trading costs, the actual profit is only about 0.7% higher per year than a normal strategy, according to Barron’s research. Not an overly impressive figure.

Now, the important question: does "Sell in May" work in the crypto market? I checked crypto price data over the past 13 years, and the results are quite interesting — there are 7 years where May was a bullish month and 6 years where it was bearish. That means about 54% of Mays are up, and 46% down. This clearly shows that the "Sell in May" strategy does not follow a consistent pattern in crypto. It’s a market too new and volatile to follow the historical rules of stocks.

This is the point I want to emphasize: don’t blindly apply strategies from one market to another. Stocks and crypto operate under different logics. You can’t just transplant technical analysis theories from Forex or stocks into crypto without adjustments. Each market has its own characteristics, and while the fundamental knowledge is shared, the application must be flexible.

Conclusion? Be cautious with overly absolute "golden rules." The crypto market is still too young to have predictive models that are so certain.