To some, technical analysis (TA) represents a substantive technique rooted in an asset’s price. To others, it warrants no attention at all because it’s just lines on a screen and self-fulfilling prophecies.
During my stint in traditional finance (TradFi), oftentimes the response was the latter. Fundamental analysis took precedence given all the publicly available data on revenue, debt, managerial statements, etc. The underlying assumption was that if it was good enough for Warren Buffett, then it’s good enough for you.
I’m admittedly biased in the opposite direction. I completed the Chartered Market Technician (CMT) designation in 2018, while still covering traditional equities, no less. I find it useful every day. I don’t subscribe to the “random walk” theory of prices, which states that changes in an asset’s price are completely unpredictable; data, and the charts representing it, can have predictive power.
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TA helps me answer this key question: What if I’m completely wrong? It lets you emotionlessly (well, mostly) decide whether it’s time to exit a position, for instance. If you buy something at $10 and it sinks to $5, you might do the contrarian move and buy more. “If you like it at $10, you love it at $5.” TA can act as a reality check. “I thought the price would rise to $15. I was wrong. Time to get out.”
What is TA? I view it as a graphical representation of investor behavior, with certain patterns and indicators providing a clue about what’s 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 doesn’t exist to the same extent in crypto. In fact, the allure of cryptocurrencies to many is the decentralized nature of many of the assets. There is no CEO of bitcoin (BTC), nor is there a balance sheet or statement of cash flows for it. So that’s one leg of the analysis stool that’s missing; it makes sense to pay attention to the other two.
TA encompasses a wide range of topics that goes well beyond looking at lines on a chart and subjective judgments. During my time preparing for the CMT, the emphasis on using hard numbers when making a decision stood out.
For instance, I know very quickly that BTC has breached the upper range of its Bollinger Band three times over the past 25 days. When that happened in January, 30 days later BTC was up 11%. I also know that it’s been 24 days since BTC’s volume was at least twice as high as its 20-day moving average but, despite that, volume on that day doesn’t rank within BTC’s top 50 days since 2015.
From a risk-management perspective. I often use the Average True Range (ATR) metric, to measure an asset’s volatility.
Taking things a step further, I like to look at an asset’s returns, as well as its standard deviation of returns, in 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 versus return relationship 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 added as well just out of curiosity.
This chart highlights BTC’s outperformance while having a slightly lower standard deviation (risk) than ETH. Also displayed is AVAX’s high level of performance, but with significantly more risk.
This should be kept in mind when looking at other technical indicators, specifically momentum and volume. I would also be inclined to perform the same exercise over different time frames.
Whether you believe in technical analysis or not, it’s difficult to ignore price. And it’s even more difficult to ignore the market’s overall reaction to it. In crypto markets, that will always be worth looking at.
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