A Technical Analyst’s View of Crypto

For some, technical analysis (TA) represents a material technique rooted in an asset price. For others, it warrants no attention at all because it’s just lines on a screen and self-fulfilling prophecies.

During my time in traditional finance (TradFi), the answer was often the latter. Fundamental analysis took precedence over all publicly available data on earnings, debt, management statements, etc. The underlying assumption was that if it was good enough for Warren Buffett, it’s good enough for you.

Admittedly, I am biased in the opposite direction. I completed the Chartered Market Technician (CMT) designation in 2018, while still covering traditional stocks, no less. I find it useful every day. I do not subscribe to the “random walk” theory of prices, which states that changes in an asset’s price are completely unpredictable; data, and the charts that represent it, can have predictive power.

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TA helps me answer this key question: What if I’m completely wrong? It allows you to unemotionally (well, mostly) decide whether it’s time to leave a position, for example. If you buy something for $10 and it drops to $5, you can do the opposite move and buy more. “If you like it at $10, you’ll love it at $5.” TA can act as a reality check. “I thought the price would go up to $15. I was wrong. Time to get out.”

What is TA? I see it as a graphical representation of investor behavior, with certain patterns and indicators that give a clue as to what is coming next. It has an important place in the three-legged stool of fundamental, technical and quantitative analysis.

While fundamental data is prevalent in TradFi, it does not exist to the same extent in crypto. In fact, the allure of cryptocurrencies for many is the decentralized nature of many of the assets. There is no CEO for bitcoin (BTC), nor is there a balance sheet or cash flow statement for it. So there is one leg of the analytical stool missing; it makes sense to consider the other two.

TA encompasses a wide range of topics that go far beyond looking at lines on a chart and subjective assessments. During my preparation for the CMT, the weight stood out for using hard numbers when making a decision.

For example, I know very quickly that BTC has broken the upper area of ​​the Bollinger Band three times in the last 25 days. When that happened in January, 30 days later BTC was up 11%. I also know that it has been 24 days since BTC’s volume was at least double its 20-day moving average, but despite that, the volume on that day does not rank within BTC’s top 50 days since 2015.

From a risk management perspective. I often use the ATR value (Average True Range) to measure an asset’s volatility.

Taking things a step further, I like to look at an asset’s return, as well as its standard deviation of returns, by comparing one asset to another—essentially distilling what’s seen on a chart into a different format.

Doing so in the following chart shows the risk-to-return ratio since January for BTC, ether (ETH), Avalanche’s AVAX, and Binance’s BNB. The S&P 500 (GSPC), Nasdaq (IXIC), Google (GOOG), and Amazon (AMZN) were also added just out of curiosity.

This chart highlights BTC’s better performance while having a slightly lower standard deviation (risk) than ETH. AVAX’s high performance level is also shown, but with significantly greater risk.

This should be kept in mind when looking at other technical indicators, especially momentum and volume. I will also be inclined to perform the same exercise over different time frames.

All in all, TA represents an analysis of data, starting with what you see specifically for price. In many ways, it allows you to ignore the noise or verbal sales pitch that can come along with an asset. That’s possibly even more important as key figures for cryptographers run into trouble this year.

Whether you believe in technical analysis or not, it’s hard to ignore the price. And it’s even harder to ignore the market’s general reaction to it. In crypto markets it will always be worth watching.

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