BIC’s Video News Show: Top 3 Beginner Crypto Trading Strategies for 2021

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In Brief
  • The first beginner trading strategy is dollar-cost averaging, buying a small amount over time to negate volatility.

  • Fundamental analysis requires researching the crypto project in order to tease out its intrinsic value.

  • Finally, the Relative Strength Index (RSI) in Technical Analysis measures buy and sell-side momentum in the market.

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In this episode of the BeInCrypto video news show, host Jessica Walker will introduce our top three cryptocurrency trading strategies for beginners. Next week we will cover another 3 more advanced strategies traders who want to go to the next level so stay tuned!

Remember that no crypto trading strategy will always be a winner, successful traders don’t succeed at every single trade. Expert traders just employ techniques that have them making a profit more often than not.

Dollar-Cost Averaging

The first is Dollar-Cost Averaging (DCA), which is the practice of performing regular, usually smaller, purchases of an asset over time. For instance, let’s say you want to buy $1,000 worth of Bitcoin and hodl it for several years, as you believe it will continue to appreciate. 

You could just buy $1,000 today at whatever price today, or wait for a significant dip, assuming you believe one is coming. Buying all at once, or gradually are both fair options but rely a lot on uncertainty, chance, and fate.

An alternative to this is to Dollar-Cost Average. In this scenario, you take that same $1,000 and buy, for example, $100 a month for 10 months. Or about $20 per week. By doing this you can significantly offset volatility in the market. This means that while you didn’t probably buy at the best lows, you also likely didn’t buy all the highs either. It never hurts to secure a little more peace of mind.

Crypto Fundamental Analysis 

Fundamental Analysis or FA traditionally has been used to look for the intrinsic value of crypto projects. This is different from Technical Analysis, or TA, where traders work with price action only.

One starts analyzing a project by learning about the team behind it. They ideally should be transparent about their credentials and history. If you give this team your money, and they just disappear, how would you find them? This is why it is generally very important to learn at least a bit about the major players on a team before investing in anything. Stay away from projects that lack headline names and faces, as lacking these things is a huge red flag.

A good project also has pretty solid and clear documentation available, presumably from either a team website or a community repository. See what people are saying on social media, particularly on Twitter and Reddit. Then ask yourself if you understand what kind of asset this is and if there is a real demand for it. This is also when you want to be looking at some hard numbers.

Generally speaking, there won’t be “earnings reports” like there are for a traditional company. But even if there are, they may not be as relevant to the validity of the project as they are with stocks. However, it can be important to look at the current price, circulating supply, market capitalization, and trading volume. 

RSI Divergences

Moving into Technical Analysis, Divergences are the most popular technical approach according to by many traders, used in all timeframes and with many other indicators. In this case, we will talk about the Relative Strength Index (RSI), which is a popular indicator that basically charts buy and sell-side momentum in the market.

Assuming you already know how to spot and draw trend lines, a divergence occurs when the trend line you drew for the price action does not correlate with the trend line you spotted on the RSI. The price action was still hitting lower lows, but the RSI was starting to hit higher lows. All in all, divergences are what you are looking for, be patient because they show up under certain conditions and there is no guarantee the market will give you one right away. 


All the information contained on our website is published in good faith and for general information purposes only. Any action the reader takes upon the information found on our website is strictly at their own risk.
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Nick is a data scientist who teaches economics and communication in Budapest, Hungary, where he received a BA in Political Science and Economics and an MSc in Business Analytics from CEU. He has been writing about cryptocurrency and blockchain technology since 2018, and is intrigued by its potential economic and political usage.

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