Robotics and AI Trading Strategies (BOTZ ETF Backtest – Rules, Settings Analysis)

The advent of Artificial Intelligence is here, and is forecast to combine with and revolutionize many industries in the years to come. One such combined industry is ‘Robotics and AI’, and traders are increasingly seeking ways to position themselves to make use of this trend as adoption rates of robotics and AI increase. What can we do, if we want to trade robotics and AI trading strategies?

In this article, we use optimization to find profitable robotics and AI trading strategies (for the AI industry).

Related reading: Looking for a library of trading systems? (We have hundreds)

Global X Robotics and Artificial Intelligence ETF (BOTZ)

As of yet, there are few good trading vehicles available for traders wishing to participate in the robotics and AI industry. Trading the stocks of startup companies is possible, but these usually have a very short price history and are very volatile, making them unsuitable for trading and besides, most IPOs fail.

In order to find a viable trading edge, we need to find a way to trade the industry as a whole. This is where exchange-traded funds (ETFs) come into play. An ETF contains a variety of assets, and can thus track an entire industry by including many different assets from that industry. 

The Global X Robotics & Artificial Intelligence ETF (ticker name BOTZ) is an ETF that invests in companies that stand to benefit from increased adoption of robotics and artificial intelligence.

The BOTZ ETF was founded in 2016. Contrary to many new robotics and AI startup companies, BOTZ now has enough price data and trading volume for us to start designing a functioning trading strategy around it.

Donchian Channels Strategy Overview

The BOTZ trading strategy in this article uses the indicator from our Donchian Channels article. We recommend you read that article for further details on the strategy itself. 

Donchian Channels (DC) is normally a trend following indicator, with a length setting of 20 (for example). In this article, we experiment with using short-term settings that are more appropriate for the price fluctuations in BOTZ, turning it into something closer to a momentum indicator.

Exponential Moving Average (EMA)

The second component of our BOTZ trading strategy is to use an Exponential Moving Average to reduce the number of false signals we encounter. Our previous article on the EMA goes into details on its calculation and use cases.

In this article we experiment with various long-term settings, and use the EMA as a complement to filter bad trades from our short-term Donchian Channels strategy.

BOTZ Strategy Trading Rules

The trading logic and rules for the BOTZ Donchian Channel/EMA Strategy is as follows:

Trading Rules


This is a long only strategy, where we only take long (bullish) trades.

Displayed on a chart the trading strategy will look something like this:

Robotics and AI Trading Strategies (BOTZ ETF

The green and red lines are the Donchian Channels. The blue line is the EMA. Blue arrows show where long positions are opened (‘ChBrkLE’), while red arrows show where positions are closed (‘Exit Long’).

BOTZ trading strategy optimization

Optimizing a trading strategy is an important step in trading strategy development, but not for the reason you might think.

Optimization in trading, which is the process of testing different input values for each of our variables, is not about finding the perfect value. Instead, it is mainly about testing the robustness of our strategy.

What we’re looking for is a strategy that holds up well even when we change the values and settings across a fairly broad range, without our results taking a big hit. If our backtesting results change significantly when the number is only varied slightly, it is likely that our strategy is curve fitted. If it holds up well under changing variables, it indicates that our strategy is more robust.

In the BOTZ Donchian Channel/EMA Strategy, we have only two variables: The length of the EMA, and the length of the Donchian Channels lookback period. Thus, this a rather simple model.

Let’s start by comparing the results for various Donchian Channels length inputs, without any EMA filter. We have backtested on BOTZ (on Nasdaq), on a daily timeframe, from September 13, 2016 (the furthest back the BOTZ ETF goes) until today:

DC lengthNo. of tradesWin RatioProfit FactorMax DrawdownAvg. trade

There’s no need to test with higher inputs right now, as the number of trades would be too small to provide us with meaningful results. This is because BOTZ has only been around since 2016. As time goes on, we’ll accumulate enough data to backtest these higher length settings as well.

The key observation here is that nearly all settings lead to results that are at least profitable on paper (Profit Factor 1.25 or above), indicating that this part of our strategy is reasonably robust.

For now, we’ll set our Donchian Channels length to 3. This is not the optimal result we found, but is still profitable, and has a decent win ratio. It also gives us a decent number of trades (157) to work with before the EMA filter reduces the number further.

Let’s continue by comparing the results of Donchian Channels length 3 with various EMA inputs:

EMANo. of tradesWin RatioProfit FactorMax DrawdownAvg. trade
No EMA filter15749.04%1.52738.58%+0.70%

If we compare this to the results of using no EMA filter in the top of the table, we can clearly see that using the EMA as a filter improves our results across the board using almost any length setting. This in turn indicates that this part of our strategy is also reasonably robust.

While it is possible to scan through every input number to find the optimal one (we found it at EMA145), this is a mostly pointless exercise, as all you’re doing is curve fitting. It is highly unlikely that this exact number will continue to be the optimal input in the future.

We’ll stick with EMA200 for now, as this is a common setting used by many traders. This is again not the optimal result. Choosing relatively underperforming inputs in this way helps increase the realism of our backtesting results.

BOTZ Donchian Channel Strategy Backtest Equity Curve

We can now view the equity curve for the BOTZ DC/EMA Strategy we just created, using a Donchian Channel setting of 3 and EMA 200 as inputs. 

BOTZ trading strategy backktest

Overall, the results look good. Our strategy also beats Buy and Hold (blue line) by a good margin, and with much less volatility.

The BOTZ DC/EMA Strategy exhibits many of the traits we value as traders: 

  • Good Win Ratio (Percent Profitable) (very few traders tolerate a low win rate and subsequently risk many losers in a row).
  • Good Profit Factor. Should preferably be above 1.75, which it is.
  • Good Max Drawdown. It should preferably be below 25%, which it is. It is even low enough to allow for some Leverage, if we are seeking ways to increase Net Profits.

The strategy so far has a relatively low total number of trades (87). This is again due to BOTZ only being around since 2016. This makes our equity curve less statistically reliable for the time being, but we could also potentially have found an early entry point into a profitable strategy. This is important, as all strategies stop working eventually.

The small number of trades increases the chances that our strategy could be Curve Fitted. If you are considering adopting this strategy in some form for live trading, please remember that nothing on the website is investment advice. This article serves just as an example.

We strongly encourage you to do Out-of-Sample Backtesting. The best way to do this, especially considering the small amount of data we have so far, is to conduct an Incubation Period.

BOTZ Donchian Channel Short-Term Strategy?

We will round off this article by simply noticing that our BOTZ DC/EMA Strategy also seems to have good results on lower time frames. The following are the results for the 2-hour time frame:

BOTZ trading strategies rules and settings

Here we observe a very smooth equity curve, with results that are nearly as good as those on the daily time frame. Have we found a good Short-Term Trading Strategy in BOTZ? Maybe, but we should be wary of drawing any such conclusions too early.

Short-term trading is difficult, and most good trading opportunities are found on long-term time frames. The above equity curve should serve mostly as food for thought, and something to pay attention to into the future. Only if it proves itself through out-of-sample backtesting during an incubation period, should it be considered as a viable short-term strategy.


How can I trade robotics and AI strategies?

Trading robotics and AI strategies involves exploring trading vehicles like stocks or ETFs. In this article, we focus on the BOTZ ETF and discuss a trading strategy using Donchian Channels and Exponential Moving Averages. The BOTZ ETF is an exchange-traded fund that invests in companies poised to benefit from the increased adoption of robotics and artificial intelligence.

What are Donchian Channels and how do they work in trading?

Donchian Channels are a technical analysis tool used to identify trends and potential entry and exit points in the market. In this strategy, we adapt them for short-term trading in the context of the BOTZ ETF. The 200 EMA is used in the BOTZ trading strategy to filter out false signals and enhance the overall effectiveness of the strategy.

Should I consider adopting the BOTZ trading strategy for live trading?

While the strategy shows promising results, it’s essential to conduct out-of-sample backtesting and an incubation period for validation. Live trading decisions should be made cautiously, considering market changes and risks. Findings suggest positive results on a 2-hour time frame, but caution is advised. Short-term trading is challenging, and further validation through out-of-sample backtesting is recommended before considering it as a viable strategy.

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