Cross-asset Trading Strategy Development & Backtest
In this article, we will share an very interesting crypto strategy backtesting on CorrAI and take this opportunity to introduce the core concepts of strategy development.
Let's start with a series of questions:
Why does market sentiment turn to panic, and what causes this panic?
It stems from the trend becoming unclear. Starting from the two logical chains—when the trend becomes uncertain and when the trend is relatively clear—we chose the moving average indicator (SMA for instance).
If the price remains above the moving average, we buy and hold; otherwise, we sell. The moving average is a line connecting the average price over a period, reflecting the trend of the average price level over a specific timeframe.
Now that we have a trading idea, how should we set up the specific execution strategy?
First, in the CorrAI platform, select Ethereum as the target asset, choose the four-hour chart, and select the closing price.
Based on the trading logic, we need to use the moving average of the closing price, specifically:
A 7-period moving average as the fast line;
A 25-day moving average as the medium line;
A 200-day moving average as the long line.
For the entry condition, when the 7-day moving average exceeds the 25-day moving average, it indicates that the short-term price has surpassed the medium-term average. At the same time, if the 25-day moving average exceeds the 200-day moving average, it confirms that the price is above the 200-day line in the medium-to-long term.
For the exit logic, our approach assumes that if the price falls below the moving average, it may trigger panic among retail investors, prompting an exit. When the price sharply drops below the long-term moving average, the trend becomes unclear, and retail investors lose confidence in the market’s future, leading them to exit. Therefore, we set the exit condition as follows: when the closing price falls below the 200-day moving average, we exit.
Using Ethereum as the backtest target, we initiate our long strategy and conduct a backtest. The backtest results are now available, and you can intuitively see the strategy’s overall performance from the chart:
The white curve represents the return of holding the spot asset (ETH);
The green curve represents the strategy’s performance.
As you can see, the final return is similar to simply holding the spot asset. However, evaluating a strategy’s quality involves many other criteria. We will explain in detail how to assess a strategy’s performance in our course.
Now that we have the backtest results, let’s revisit our trading logic. Please consider what these results mean for our trading logic and how we can use them to evaluate its applicability.
Take a moment to think about this question, and we will provide detailed explanations in subsequent contents.
Here’s an assumption worth considering:
Ethereum holds a leading position among public blockchain projects.
Is it possible that when Ethereum’s trend is relatively clear, other public blockchain projects might experience lagged price increases in response?
Theoretically, this possibility exists! On one hand, Ethereum’s leading status means it has significant market influence. Its price movements and trend changes often attract widespread attention, and when Ethereum’s trend is clear, investors may develop more positive expectations for the entire blockchain sector.
Now, let’s test this hypothesis:
We modified the entry and exit logic, selecting Solana as the second blockchain coin after Ethereum. Assuming Ethereum’s trend is clear and ultimately drives Solana’s price upward, the logic is that Ethereum, as the blockchain leader, uplifts the entire sector, causing Solana’s price increase to lag behind.
The exit simulation assumes the opposite: if Ethereum’s trend becomes uncertain, it leads to a decline in Solana’s price. Using the same signals as before, we now change the backtest target to SOL. Let’s take a look at the backtest results together.
The results show that this strategy significantly outperforms the one using Ethereum as the target. This is the strategy we introduced earlier, which achieved an astonishing return of over 400 times from 2021 to 2024. From the chart, it’s clear that the strategy effectively avoided SOL’s largest drawdown and entered the market decisively during uptrends, demonstrating strong adaptability and effectiveness.
We can also replace Ethereum’s moving average with SOL’s moving average and conduct another backtest to observe the results. The backtest results show that the returns are significantly lower when using SOL’s moving average as the entry and exit condition compared to using Ethereum’s moving average.
From this, we can preliminarily conclude that the strategy’s excess returns diminish significantly when using SOL’s moving average as the entry and exit condition.
Through the strategy development process—from trading logic to backtest results—I believe you’ve discovered an approach to developing strategies. The moving average indicator is widely known in the market, but the lagged price relationship between Ethereum and SOL, both being blockchain coins, is less obvious than the first layer of the moving average.
This additional layer of understanding makes a significant difference in strategy performance. It fully demonstrates that enhancing market insight in strategy development can lead to more advantageous trading strategies, resulting in better returns in a complex and volatile market.
I’m sure you’re all wondering: with this strategy, can we just close our eyes and make money? First, it’s important to clarify that the above returns are based on backtest results, and real-world trading involves various challenges. Our backtest assumes a transaction fee of 0.03% and does not account for market capacity or execution deviations, which can significantly impact real-world returns.
We will discuss these impacts and the correct countermeasures in detail in future lessons. Most importantly, a single strategy is destined to fail eventually. How does a strategy fail? Stay tuned for the next CorrAI quantitative strategy development content~
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