How Aaron Kruger Takes Advantage of Crypto’s Wild Volatility— Set Social Trader Spotlight

Anthony Sassano
Jan 6 · 6 min read

Welcome to the Set Social Trader Spotlight Series. In these posts, we’ll give you an insight into our launch traders that’ll be on the Set Social Trading platform once it goes live in the next few weeks. Learn more about Social Trading here.

Today, we’ve got an interview with Aaron Kruger for all of you to enjoy.


Hi Aaron, nice to have you. Can you give us an intro of yourself?

My name is Aaron Kruger and I’m a full stack software developer. I’m the lead developer for an industrial IoT monitoring and analytics software company.

I began trading in 2013 in the FX spot markets. Over time I have included other instruments in my trading such as futures, options, options on futures and equities. My private trading involves swing trading and beta-neutral, short volatility options strategies.

You told me about your ‘Moonshot’ strategy and how you use those strategies to trade the market. Can you tell us about how they work?

First I want to offer the disclaimer that this strategy has never been ran live in these markets before. These markets are very young and most automated strategies offered are being ran for the first time. My hope is that this strategy can build a positive, profitable history.

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 (50–100 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.

How well did the strategy perform?

You can find performance charts below of the strategy across both BTC and ETH.

This chart compares the hypothetical performance of the BTC Moonshot Strategy compared to a buy-and-hold strategy from January 1st, 2017 to December 19th, 2019.
This chart compares the hypothetical performance of the ETH Moonshot Strategy compared to a buy-and-hold strategy from January 1st, 2017 to December 19th, 2019.
The ETH Moonshot trade log history.

It’s important to keep in mind that the charts above are based on a number of assumptions and are only meant for illustrative purposes. As always, past performance is not indicative of future performance.

If you want to learn more about this strategy and see its theoretical performance you can check ETH out here and BTC here. You can also learn more about my latest strategy (ETH Moonshot X) which is tailored towards the volatility seen in the Ethereum markets here.

That’s impressive. How did you find out about those strategies, and when did you realize that they could work?

As a software developer who works in the field of big data and analytics, I naturally began studying price behavior as soon as I began trading. I have been searching for an automated approach that would hopefully help limit downside losses and catch some of the upside gains to reduce the massive volatility found in crypto. That is the goal that the Moonshot strategy is aiming for.

Is this a strategy that you’d be offering on the Set Social Trader? If so what assets will be inside and what’s the average trade interval?

This will be the first strategy that I offer and it’s fully automated. In theoretical testing over the past 3 years, it would have performed well on both ETH and BTC and for that reason I will offer this strategy on both assets. This will allow users to have the choice of getting exposure to either one asset or both.

Backtesting shows that trades will happen 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 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.

What is something that people don’t know about you that you’d like to share?

I am a husband and father of 4 beautiful children (soon to be 5). We live in the frigid north-central US where it can reach -60F (-51C) or colder during the winter. The crazy thing is that I love where I live and wouldn’t want to be anywhere else.

Anything else you’d like to tell us or your potential followers?

My hope is to get traders and investors to adopt a more hands-off/automated approach for their crypto portfolio. With the extreme volatility of the crypto markets I want to see people be able to avoid some of that volatility and feel a little better about holding long-term positions. I hope the Moonshot strategy that I have developed is able to deliver on that.

Conclusion

We hope you enjoyed reading this interview! Aaron will be one of initial launch traders when Set Social Trading goes live in the next few weeks. If you’d like to be notified when Aaron’s Set goes live, head here to sign up!

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Set Labs

Grow your crypto using automated asset management strategies

Anthony Sassano

Written by

Marketing at https://www.setprotocol.com/ | Co-founder of https://ethhub.io and Co-host of the Into the Ether podcast

Set Labs

Set Labs

Grow your crypto using automated asset management strategies

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