Simple Signals, Superior Returns: The EWMAC Crypto Strategy - Research Article #68
How Shorter-Term Moving Average Combinations Outperformed Bitcoin and Altcoin Benchmarks
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I was reading Rob Carver’s book “Systematic Trading: A Unique New Method for Designing Trading and Investing Systems”, and there he mentioned how he uses the EWMAC (exponential weighted moving average crossover) signal for his trend models.
It’s a simple rule for trend-following models, but as usual, I like to test things I read, so I decided to just test it and add to our book of signals here at Trading Research Hub.
The best of the variations of the rule did generate some attractive performance that I will outline below.
With such a simple signal, the model achieved significantly higher risk adjusted returns:
Bitcoin Sharpe: 0.94
Altcoins Benchmark Sharpe: 0.87
Strategy Sharpe: 1.27
Also the drawdowns as you can see from the picture above, fall way below the maximum of 80%+ that the crypto market offers.
Let’s get into the full analysis of this model!
(This article is for informational purposes only and contains hypothetical data. Past performance does not guarantee future results.)
Index
Introduction
Strategy Thesis
Data and Methodology
Performance Analysis
Conclusion
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Strategy Thesis
This strategy is designed to catch market trends by using two moving averages that react to price changes at different speeds (called EWMA crossovers). When the faster EWMA crosses above the slower one, the strategy signals a buy (long); when it crosses below, it signals a sell (short). The system has four versions, each using longer moving averages to spot bigger or slower trends as it evolves. This approach works because markets often show momentum—prices that start moving in one direction tend to keep going for a while. By systematically following these crossovers, the strategy aims to ride those trends while avoiding the noise of random price movements.