Best Trading Strategy for Crypto: Bitcoin vs. Altcoin – Backtesting, Rules, and Performance Insights

The world of cryptocurrencies is drawing more and more traders, and as of today, crypto traders have about 25,000 different symbols to choose from. That is a lot! But which trading strategy is best for crypto, Bitcoin or altcoin?

Bitcoin is better than altcoin. Backtests reveal that cryptocurrencies with higher market caps outperform their small-cap competitors across several different trading strategies, creating huge ramifications for any cryptocurrency trader.

When you contrast this with the mere seven cryptocurrencies that were around ten years ago or the 6 000 stocks that you can trade publicly on the NYSE and Nasdaq today, it gives a sense of how much the cryptocurrency market has grown and expanded. It’s a lot to choose from, to say the least.

Below we do some backtests of a few backtests we published a couple of years ago and see how they perform on Bitcoin vs. altcoins

Related reading: – Different types of trading systems

Which trading strategy is best for crypto? Bitcoin or altcoin?

Despite the diversity within crypto, Bitcoin gets the lion’s share of attention from the general public and traders alike. Most of the remaining attention goes to Ethereum and a few other large altcoins. 

Is this focus on the big players a wise decision by traders, or are there hidden gems among the smaller altcoins? In this article, we do the backtesting required to shed light on this matter.

Crypto market capitalization

In simple terms, an asset’s market capitalization, or ‘market cap’ for short, is a way to measure its total value by combining the value of all its shares or other units. For example, multiplying the current value of Apple Inc. (AAPL) stock (≈$175) by the number of shares in the company (≈15.8 billion) gives us a market cap of ≈$2.77 trillion for the entire company.

The entire cryptocurrency market cap is currently about $1.12 trillion, less than half the value of Apple Inc!

While the market cap of companies gives us some insights about them, they also churn out more detailed financial data, like quarterly earnings reports, for us to analyze.

However, in the world of cryptocurrencies, such data is typically unavailable, making market cap the primary method for gauging their value. When we perform these backtests, we will do so on cryptocurrencies with various market caps.

Which Trading Strategy Is Best For Crypto? Bitcoin or Altcoin?

10 largest cryptocurrencies by market cap ($) and market share (%), as of May 28, 2023

Backtesting crypto strategy

Let’s find out which crypto is best for trading. We will backtest the following cryptocurrencies:

  1. BTC (Bitcoin), by far the largest cryptocurrency by market cap, is currently sitting at a 46% share of the entire crypto market.
  2. ETH (Ethereum) is currently the 2nd largest cryptocurrency by market cap
  3. TRX (TRON) is currently the 13th largest cryptocurrency by market cap
  4. ZEC (Zcash) is currently the 80th largest cryptocurrency by market cap
  5. NMR (Numeraire) is currently the 140th largest cryptocurrency (and, somewhat interestingly to us, a machine learning project for market predictions)

We chose these five based on their longevity in the market, and collectively, they should give us a peek into how our trading strategies fare across various market cap levels in backtesting.

How do you backtest a Bitcoin or crypto trading strategy?

We have decided to use two straightforward strategies for our backtesting needs. Our aim is not to discover the ultimate, optimized strategy for a specific timeframe or symbol, but to find out some indications of what works best. This is not investment advice in any way for form and is only meant for informational purposes.

We’re looking to answer a broader question: Do cryptocurrencies with larger or smaller market caps perform better in backtesting?

This is what de did:

For these purposes, we will use two strategies already described on Quantified Strategies, with some small variations. We backtest from September 13, 2017, until today for all the mentioned symbols. We will be backtesting on the 12-hour timeframe, allowing us to take advantage of crypto’s round-the-clock nature. 

Price action momentum strategy on bitcoin

The first strategy is a modified version of our Price Action Momentum Strategy, taken from our article 3 Momentum Strategies and with 2 years out of sample. We will stick with the same number of periods (settings) as in the article, only changing the backtesting period and timeframe.

To reiterate the trading logic and trading rules:

  • When the close crosses above the 25-period high of the close, we go long at the close.
  • When the close crosses below the 25-period high of the close, we sell at the close.

Backtesting on BTC for this period with the Price Action Momentum Strategy yields the following results:

Bitcoin (BTC) [#1 in Market Cap], Price Action Momentum

Bitcoin or altcoin - what is best?

These results let us see some of this strategy’s visible pros and cons:

  • Net Profit is decent, ending in almost exactly the same place as Buy and Hold, but with much less volatility and smaller drawdowns.
  • We get a decent number of total trades (242), meaning we can see some tendencies.
  • The Win Ratio (percent profitable) is quite low (37%). We want it to be much closer to 50% to avoid trading biases
  • Profit Factor is also a bit on the low side. It should preferably be above 1.75.
  • Drawdowns look decently low. We like it below 25%, which it is.
  • The average gain per trade (0.89%) is somewhat low in relation to the number of trades, meaning that transaction costs will probably eat a lot of the profits over time. Transaction costs are complex to measure as it varies.

Probably due to cutting the timeframe in half, and moving from the daily to 12h timeframe, our results have declined from the original Price Action Momentum Strategy. 

Overall, the results are decent, but with a lot of room for improvement. We would never consider this strategy suitable for live trading in its current state. However, this under-optimized strategy is perfect for the comparative backtesting we are doing here, where we aim for generalizations.

(In a future article, we will return to the Price Action Momentum Strategy, exploring the potential of TradingView’s Pine Script in creating a more functional variation of the strategy for cryptocurrencies.)

Let’s look at how the current strategy performs on various altcoins:

Price action momentum strategy on altcoins

We’ll move through the backtesting results for the Price Action Momentum Strategy for the altcoins one-by-one, comparing and commenting on each one before summarizing at the end. We use the same trading rules as we did for Bitcoin.

Let’s start with the biggest altcoin – Ethereum:

Ethereum (ETH) [#2 in Market Cap], Price Action Momentum

How do you backtest a Bitcoin or crypto trading strategy?

The results for Ethereum are comparable to those of Bitcoin. Total net profits are higher than for Bitcoin, and so is the average gain per trade. This comes at the cost of a reduced Profit Factor and a much worse Max Drawdown. Overall, we would say that the results somewhat underperform those of Bitcoin.

Let’s look at TRON (TRX):

TRON (TRX) [#13 in Market Cap], Price Action Momentum

How to backtest crypto and Bitcoin

TRON’s results display a significant rise in net profit compared to Bitcoin (+2482% vs. +552%) and a higher average gain per trade (+2.6% vs. +0.89%).

However, we need to remember that there are always tradeoffs in trading. The price we pay for these higher returns over a shorter timespan is a degree of uncertainty unsuitable for long-term trading. The Profit Factor is low – too low for our liking. The Max Drawdown is also alarmingly high, at -42.6%. Moreover, the strategy has been in a drawdown since October 2020.

Let’s look at ZEC (Zcash):

ZEC (Zcash) [#80 in Market Cap], Price Action Momentum

In ZEC, we get our first example of an altcoin that is completely unworkable with the current strategy. In this lower-cap altcoin, our strategy flat-out loses money. No further comment is required.

We switch to NMR (Numeraire):

NMR (Numeraire) [#140 in Market Cap], Price Action Momentum

As we reach the bottom of our market cap list with Numeraire, our results with this strategy deteriorate even further. We’re not just dealing with losses, but a near-total loss of our entire investment, which we would probably never be able to recover from. 

Summary of price action momentum strategy

These backtests might offer some insights. Bitcoin stands out as the best performer, with its higher Profit Factor and lower Max Drawdown ensuring more consistent profits over time, likewise with Ethereum.

Total Net Profits increased drastically in the case of TRON, but its low metrics in other areas suggest this might be due to luck. Its early success initially led to substantial profits, but the strategy has been in a prolonged drawdown for years. This pattern, we suspect, will be seen in many altcoins in the coming years, but with the majority of them being launched only in the past few years, there is still too little data to confirm this.

As we move into coins with lower market caps, our strategy’s reliability decreases before eventually dissolving and failing. We’ll score this as 1-0 in favor of trading higher market caps cryptocurrencies.

Still, this can all be due to factors specific to this strategy. Let’s try this again with another strategy:

Short-term trend following strategy on Bitcoin

The second strategy is a short-term trend-following strategy and very similar to a Litecoin Trading Strategy. We will modify it to use an exponential moving average instead of a simple moving average and change the length of the moving average to 30 to decrease the number of false signals. The trade logic and rules then look like this:

  • When the close breaks above the 30-period exponential moving average, we go long at the close.
  • When the close breaks below the 30-period exponential moving average, we sell at the close.

Backtesting on BTC for this period with the Short-Term Trend Following Strategy gives us the following results:

Bitcoin (BTC) [#1 in Market Cap], Short-Term Trend Following

Compared to the previous strategy, this one also has its pros and cons:

  • Net Profit is very good and outperforms Buy and Hold by a wide margin.
  • The total amount of trades is 179.
  • Win Rate (Percent Profitable) is egregiously low, at 25.7%(!).
  • Profit Factor is again a bit low, about the same as the previous strategy.
  • Max Drawdown is way too high.
  • The average gain per trade is much better, especially considering the number of trades, leading to reduced transaction costs.

Again, we have a somewhat decent, if flawed, strategy. Again, it is not a tradable strategy in its current state. And again, this is not the point here, as we are doing something even more fundamental: to identify where any strategy is more likely to be profitable in the first place.

The important thing right now is that the strategies behave differently from one another, which they certainly seem to do so far. Let’s see how this strategy performs on the altcoins.

Short-term trend following strategy on altcoins

We start with Ethereum:

Ethereum (ETH) [#2 in Market Cap], Short-Term Trend Following

Trading strategy for altcoins

In this case, Ethereum outperforms Bitcoin slightly across most aspects. This is interesting, as ETH is tit-for-tat with BTC across the two strategies we tested.

This could indicate that it isn’t necessarily the case that having the largest market cap leads to the best trading performance, but that market cap only needs to be above a certain threshold. This would imply that other cryptocurrencies could reach this level in the future as their market cap increases, making them more viable for trading.

Let’s look at TRON:

TRON (TRX) [#13 in Market Cap], Short-Term Trend Following

Statistically proven crypto trading strategies

This is a good illustration of a situation where, if diligently applying this strategy to TRX, you could break even after transaction fees are considered. Due to behavioral mistakes resulting from the massive drawdown, however, you would probably be looking at a loss at the end of the day.

As with the previous strategy, we are starting to see a deterioration in performance as we go lower in terms of market cap.

ZEC (Zcash) [#80 in Market Cap], Short-Term Trend Following

backtesting crypto strategy

In Zcash, the strategy’s performance decreases further, maintaining the pattern that seems apparent: lower market caps lead to worse trading performance.

NMR (Numeraire) [#140 in Market Cap], Short-Term Trend Following

What are the best altcoins for trading and day trading?

NMR completes the low-cap/low-performance pattern, showing the same disastrous equity curve with this strategy as it did with the last one.

Summary of short-term trend following strategy

Bitcoin is yet again a high-performer, but has been dethroned by Ethereum with this specific strategy. This is interesting, as it indicates that while many altcoins have a market cap too low for effective trading, it is not necessarily the case that bigger is always better. It might be the case that each cryptocurrency needs to meet a certain market cap threshold to become viable, but that after these criteria are met, other aspects become more important. We would need more backtesting of the highest ranked currencies to find out more.

Even with this in mind, we can still see the general trend, where the lower the market-cap, the more unreliable the strategy becomes. 2-0 to higher market caps.

Survivorship bias when backtesting crypto

All these backtesting examples seem to suggest the same thing: trading cryptocurrencies with a larger market cap tends to yield better long-term results, irrespective of the specific strategy used. 

Cryptocurrencies with small market caps simply lack sufficient liquidity and trade volume, resulting in high volatility and a lot of statistical noise. Also, we can expect most of them to fail, as is typical. Only a few survive.

This makes predicting future price movements through a trading strategy extremely challenging. This also jives with the behavior of stocks, where low-cap stocks tend to be more unpredictable and volatile. At least in this respect, stocks and crypto seem to be similar. 

We anticipate that this might get better for those altcoins that continue to grow and prosper in the future, but traders should be very mindful of survivorship bias here, as most altcoins have historically trended towards zero over time.

Bitcoin or altcoin? Which is best for trading? Lessons to be learned

Do these backtests prove that traders should always stick to Bitcoin when trading crypto? Not really.

These backtests merely provide us with general indications. Conclusive proof does not exist in trading except in hindsight, and we can only rely on probabilities. 

However, we think that these backtests demonstrate that the probability of high-cap cryptocurrencies outperforming small-cap altcoins across most trading strategies is higher than the reverse, perhaps simply because they’ll survive (most altcoins won’t).

Simply put, traders should consider sticking to the big players in crypto, looking for high market caps in combination with a proven track record of good trading performance

Which trading strategy is best for crypto? Final thoughts

We’ll wrap up with some reflections about the future of crypto trading. We have seen that the higher-cap currencies such as BTC and ETH appear quite tradable, and if the crypto market keeps growing, a few other altcoins will also reach comparable market cap thresholds. If this happens, these cryptocurrencies might become tradable. 


Quantified Strategies (SIA Lofjord) is not an investment advisor. The content and information provided are educational and should not be treated as financial advisory services or investment advice. Trading and investment in securities involve substantial risk of loss and is not recommended for anyone that is not a trained trader or investor – it shall be conducted at your own risk. It is recommended that you never risk more than you are willing to lose. Leverage can lead to substantial losses. Any use of leverage, margin, or shorting is at your discretion. Quantified Strategies (SIA Lofjord) is not responsible for any losses that occur as a result of its content and information.

Hypothetical or simulated performance results have certain limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, Since the trades have not been executed, the results may have under or overcompensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs, in general, are also subject to the fact that they are designed with the benefit of hindsight. No representations are made that any account will or is likely to achieve profit or losses similar to those shown.


Why focus on Bitcoin and altcoins in trading strategies?

The cryptocurrency market has witnessed significant growth, with traders now having access to around 25,000 different symbols. This diversity presents both opportunities and challenges for traders. Bitcoin and altcoins represent different segments of the cryptocurrency market. Understanding the dynamics between them helps traders make informed decisions based on market trends and behaviors.

What is the significance of backtesting in cryptocurrency trading strategies?

Backtesting is a valuable tool for assessing the historical performance of trading strategies. Backtesting allows traders to evaluate the historical performance of a trading strategy. In the context of cryptocurrencies, it provides insights into how different strategies have performed over time.

Why is market capitalization crucial in evaluating cryptocurrency assets?

Market capitalization, or ‘market cap,’ is a key metric in assessing the total value of a cryptocurrency. Understanding market cap helps traders gauge the significance of a cryptocurrency within the overall market. Survivorship bias is explained in the context of crypto trading backtests, emphasizing the importance of considering this bias when interpreting historical results. Traders can learn to navigate potential pitfalls in their analyses.

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