In this article, we will explore the effectiveness of an intraday breakout strategy based on volatility on Ethereum, which represents the second cryptocurrency by market capitalization, behind only Bitcoin. Ethereum, in reality, is more than just a simple cryptocurrency; it is a decentralized platform, launched in 2015 by Vitalik Buterin, that allows developers to create and deploy smart contracts and many other innovative applications.
For our analysis, we will use the ETH/USDT pair, one of the most popular and traded in the cryptocurrency markets, with historical data provided by Binance, currently the largest exchange in the world for virtual currencies. This pair represents the value of Ethereum (ETH) expressed in Tether (USDT), a stablecoin pegged to the value of the US dollar.
How the strategy works on Ethereum (ETHUSDT): Intraday breakout on volatility
Right from the start, considering also the substantial impossibility for Binance customers residing in Italy to be able to short on the spot exchanges of cryptocurrencies, the decision was made to continue development only for the long side, certainly more in line with the nature of this cryptocurrency.
The system that will be tested will be based on the concept of volatility as the “engine” for the strategy’s entries, as already discussed for Bitcoin in the December 2023 article. Specifically, as a measure of it, the value of the “Average True Range” indicator of the last N days will be calculated at the beginning of each session and multiplied by a coefficient, which will then be added to the previous day’s close to determine the valid entry level for the entire current session. Therefore, if the price of the underlying exceeds this level, a long position will be opened (Figure 1).
Figure 1 – Graphic illustration of the logic on which the intraday breakout strategy on Ethereum (ETHUSDT) is based.
As the main time frame of the trading system, five-minute bars will be used while for the calculation of the ATR, Daily bars will be used. The backtest of the strategy will start from 2018, in order to have cleaner historical data, and will last until 31/07/2024.
It will be assumed to operate with a fixed countervalue of $10,000 per operation and with an initial Stop Loss of 5% equal to $500. All trades will still be closed at the end of the session, which conventionally will be assumed to start at 00:00 GMT and end at 23:59 GMT, to make it coincide with the solar day. Cryptocurrencies, in fact, neither have their own session, being quoted 24 hours a day, nor a physical reference exchange, as is the case for other regulated financial instruments.
Optimization of the strategy: the Average True Range indicator
As the first initial test, we proceed to optimize the “heart” of the operational engine, that is, the number of days with which the ATR is calculated and the relative coefficient with which it is then multiplied.
Figure 2 – Optimization of the number of periods to use for the calculation of the Average True Range indicator.
Analyzing the results obtained, the combination len_atr=2 and multiplier=0.7 is chosen; essentially, the Average True Range indicator will be calculated over the last two days and then multiplied by the coefficient 0.7. The value obtained, as previously indicated, will be added to the previous day’s close to establish the long entry level for the current session.
Applying the values chosen from the optimization, it is possible to see how this still rough system would have behaved from 2018 to today (Figure 3). The equity line immediately appears pleasant and linear, a sign that the instrument reacts well to this type of entry. Even the average trade of about $72 is decent, although not yet sufficient to cover the operational costs of real trading.
Figure 3 – Equity Line and Strategy Performance Report of the breakout strategy on Ethereum (ETHUSDT) after optimizing the Average True Range.
Optimization of the optimal operating window for trading on Ethereum (ETHUSDT)
One of the possible development paths could be to define an operational window different from the current 24 hours; in fact, at the moment the system is free to make entries throughout the day without any limitation. By optimizing the start and end times of the operations, it is found that by operating from the beginning of the session (00.00 GMT) and anticipating the end by a few hours, such as at 19:00 (Figure 4), the metrics remain substantially unchanged, but with improvements in the average trade which increases to about $87, an interesting value that starts to approach what could be hypothesized as the minimum requirement to be used in real, namely 1% of the fixed size (in this case 0.01*10,000$=100$).
It is clear that stopping early with the possibility of making entries gives greater chances for trades to develop positively, having still a few hours of trading ahead, compared to operations originated perhaps only close to the session’s closure.
Therefore, this combination is chosen, but it is still decided to cautiously delay the opening of the operational window from 00:00 to 00:15 by 15 minutes, in order to avoid operating in the first session bars following the calculation of the daily entry levels.
Figure 4 – Results of the optimization of the operating window of the breakout strategy on Ethereum (ETHUSDT).
Optimization of the strategy: analysis of price patterns capable of improving performance
At this point, to improve the strategy, one could delve into the potential application of some patterns through proprietary lists to identify only the market situations where it is more favorable to operate, considering that the system still generates a significant number of operations (480), which should definitely be filtered.
Figure 5 – Results of the optimization of price Patterns for the long side of the breakout strategy on Ethereum (ETHUSDT).
If, for example, one were to choose PtnLY=4 (Figure 5), it would be possible to increase the average trade up to $107 and the net profit up to $44,744, with a slight worsening for the max drawdown which would go from $3,531 to $4,084, still remaining very contained.
This pattern identifies the days when the market shows uncertainty and little directionality; in fact, in this case, the “body” (open-close) of the daily candle must not be greater than 75% of its total range (high-low), demonstrating a situation of lateral movement and indecision where the sessions do not close either at the highs or the lows. For a system that is based on the increase of volatility, evidently identifying if there was congestion the day before greatly increases the probability that the trade will go in the right direction.
Figure 6 aims to represent precisely the concept expressed by pattern no.4, not necessarily maintaining the proportions of the case in question.
Figure 6 – Illustration of the logic of Pattern Long no.4.
Optimization of automatic exits of the strategy on Ethereum: Stop Loss and Profit Target
As the final step, it remains to optimize the Stop Loss, initially set at 5% of the countervalue (500$) and a possible Profit Target, not yet inserted.
Figure 6 – Results of the optimization of Stop Loss and Profit Target values of the breakout strategy on Ethereum (ETHUSDT).
The optimization of these two parameters highlights that the 5% value of Stop Loss remains the best solution while the use of a Profit Target at $1,800 allows for further improvement, even if slightly, of the metrics. Obviously, the application of a Take Profit at a rather high level like this has minimal impact on a system that will still close positions at the end of the day.
Figure 7 – Final equity line of the intraday breakout strategy on Ethereum (ETHUSDT).
Figure 8 – Chart showing the Buy & Hold of Ethereum (ETHUSDT).
Final considerations on the automated strategy to take advantage of Ethereum price movements
This strategy has also confirmed that it is possible to develop automatic trading systems on crypto to operate profitably and with acceptable risk levels, as demonstrated by the comparison between this trading system and Buy & Hold on the same underlying asset (Figure 7 and 8). The gain of the strategy, with the same invested value, is lower, however, the fluctuations compared to simple holding are also very limited.
It can also be concluded that the use of volatility, in this case through the Average True Range indicator, as a driver of automatic trading systems on virtual currencies is a good choice and produces excellent results, as confirmed in this article on Ethereum and in the past on Bitcoin.
It is obvious that in the years when the underlying tends to be less volatile or in any case not bullish like in 2022, the system produces fewer results while still remaining profitable. It will be interesting to observe if in the near future, with the probable return of bullish momentum across the entire crypto world induced by the recent Halving on Bitcoin, there will be new “fuel” for the trading system to confirm the good work done so far.
See you next time!
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Andrea Unger
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