The Moonshot strategy utilizes statistical analysis and price-based indicators to identify the beginning of a potential bull trend. My tests have shown that using statistical analysis of volatility along with technical indicators produces superior results compared to using indicators alone. The combination of both statistical analysis and technical indicators allows this strategy to have a more holistic view of the market and better identify major trend changes.
How exactly does it work?
Let’s break down exactly what the strategy is doing and why. It starts by calculating the standard deviation of price movement over a short-term time window (30–100 hours). It then converts that value into a ‘percent of closing price’ value. This gives us a 1 standard deviation of price volatility as percentage of closing price over ‘x’ period.
Next it calculates a long-term average of ‘x’ standard deviations of the above calculation. This smoothed and adjusted average is performed over a much larger period of time (1000–4000 hours). This smoothed average is important because it give the micro value context by comparing it to the volatility behavior over the past 1–3+ months. This then becomes a signal for when the short-term value crosses over the smoothed value.
The final step is to look at the price direction and magnitude which it does using the Relative Strength Indicator. This indicator is calculated using the same ‘x’ period as the short-term volatility calculation described above. At this point the strategy has everything it needs (context on price volatility, direction and magnitude)to make a decision on whether to enter a new trade or close an existing trade.
Specifically what criteria are used for entries and exits?
When the strategy is looking for an entry it needs to see that the short-term standard deviation percentage has crossed above the smoothed long-term average of the same value. Once this crossover has occurred then the volatility behavior is seen as being significant. The next step is to check if the RSI has crossed above its entry threshold. If it has then a trade is entered. In summary, the strategy is looking for significant volatility movement that is also accompanied by strong bullish price movement and magnitude.
For a trade to be exited the strategy first looks if the short-term standard deviation percentage has crossed below the smoothed long-term average of the same value. If it has then the volatility behavior is seen as returning to normal. The next step is to check if the RSI has crossed below its exit threshold. If it has then the current position is closed. In summary, the strategy is looking for volatility behavior to return to normal and for the price direction and magnitude to break down before exiting its position.
Will there be further development on this?
I will be locking down the strategies current logic to release for active trading but this strategy will be under continued development. A few things I have experimented with that need further testing are is position scaling and using other indicators such as the Relative Volatility Index. While these didn’t make the cut for release they could be in a future version of this strategy.
What makes this strategy different?
What this strategy does differently than most automated strategies is analyze long-term volatility behavior of the market, not just the short term price movements, in determining the significance of a price movement.
How often does this strategy trade?
Backtesting shows that trades will be taken on average once per month. The length of each trade can last anywhere from a few days up to a few weeks, depending on how long the trend lasts.
When will this strategy overperform/underperform?
This strategy should overperform in market conditions where there are solid trends in place (both bullish and bearish). It will likely underperform when the markets are exceptionally choppy with a lot of head fakes and no clear trend.
What is this strategies weakness?
This strategy will attempt to identify the start of a bull trend using the holistic analysis of price behavior as in the beginning of this article. It will use the same analysis when deciding to close a trade. It attempts to find the “sweet-spot” in price behavior by not being too strict and missing out on trends or being too lenient and being whipsawed. This strategy should improve over time through further market analysis and improvements made to its algorithm.
What does this strategy do when there are no trading opportunities?
When a trade comes to an end this strategy will re-balance into cash (USDC). In future versions of this strategy it will re-balance into cUSDC to earn interest while not in the market.