Clean Energy Trading Strategy – Setup, Rules, Backtest, ETF (PBW)

After decades of relying on fossil fuels to power the economy, there is increasing demand for Clean Energy such as solar, wind, and geothermal power. As the world seeks to transition into a green energy future, many traders now seek to participate. Can we create a clean energy trading strategy that trades the clean energy ETF (PBW) profitably?

In this article, we have an example of a trading strategy that so far has made more than +1000% in net profits. Let’s explain the strategy to you:

Related reading:- We have published many more quantitative trading strategies

Invesco WilderHill Clean Energy ETF (PBW)

To find a viable trading edge, we must find a way to trade the clean energy market. This is where exchange-traded funds (ETFs) come into play. 

The Invesco WilderHill Clean Energy ETF (ticker name PBW) is such an ETF that invests in companies that stand to benefit from increased adoption of clean energy technologies. We’ll be backtesting this one in this article.

Clean Energy Trading Strategy – indicators and components

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

In this article, we experiment with short-term settings that are more appropriate for the price fluctuations in PBW, turning it into something closer to a momentum indicator.

The second component of our PBW trading strategy is to use an Exponential Moving Average to reduce the number of false signals we encounter. 

Clean Energy Trading Strategy – Trading Rules

The trading logic for this PBW Donchian Channel/EMA Strategy is as follows:

Trading Rules

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This means that we are using the daily timeframe, but trades are entered or exited immediately when the price crosses the Donchian Channels band during the day (thus, we enter intraday, not at the close of the day). We do not wait for the daily close. We achieve this by placing stop orders one tick outside the Donchian Channel band.

This is a ‘long only’ strategy, where we don’t take any short trades.

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

Clean Energy Trading Strategy

PBW Strategy Optimization

Optimizing our trading strategy is an important step in trading strategy development, and is mainly about testing the robustness of our strategy. Contrary to what many think, strategy optimization is helpful, but only if done correctly and not misused.

If the strategy holds up well under changing variables, it indicates that our strategy is robust, and is not curve fitted.

In the PBW Donchian Channel/EMA trading strategy, we have only two variables: The length of the EMA, and the length of the Donchian Channels lookback period. 

Let’s start by comparing the results for various Donchian Channels length inputs, without any EMA filter. We have backtested on PBW, on a daily timeframe, from March 3, 2005, until today:

DC lengthNo. of tradesWin RatioProfit FactorMax DrawdownAvg. trade
256745.15%1.42735.55%+0.73%
339045.90%1.49243.59%+1.08%
432145.79%1.27454.81%+0.77%
526841.79%1.20854.36%+0.70%
621743.32%1.31253.90%+0.93%
718644.09%1.33848.24%+1.21%
817139.77%1.23051.76%+1.01%
914943.62%1.50450.86%+1.52%
1013844.20%1.29955.19%+1.23%

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 robust.

We’ll set our Donchian Channels length to 3. Let’s compare the results of Donchian Channels length 3 with various EMA inputs:

EMANo. of tradesWin RatioProfit FactorMax DrawdownAvg. trade
No EMA filter39045.90%1.49243.59%+1.08%
5024247.11%2.14121.70%+1.34%
10021644.91%2.40815.13%+1.49%
15020146.45%2.04522.31%+1.35%
20018647.85%2.58620.42%+1.49%
25012544.80%2.24716.93%+1.62%
30012346.34%2.96815.00%+1.88%

Using the EMA as a filter improves our results across the board using almost any length setting. This indicates that this part of our strategy is also reasonably robust.

Scanning through every input number to find the optimal one is a mostly pointless exercise in curve fitting. It is doubtful that this exact number will continue to be optimal in the future.

We’ll stick with EMA200 for now, as this is a standard set many traders use.

Clean Energy Trading Strategy – Backtest And Equity Curve

We can now view the equity curve for the PBW Donchian Channel/EMA Strategy we created using DC3 and EMA200 as inputs. 

Clean energy trading strategy backtest

Overall, the results look good with a very strong “finish”.

The clean energy strategy (Donchian Channel /EMA) exhibits many of the traits we value as traders: 

  • Good Win Ratio (Percent Profitable) is above our 50% target (to avoid trading bises – we all love winners, not losers, no matter the size).
  • Good Profit Factor. It should preferably be above 1.75, which it is.
  • Good Max Drawdown. It should preferably be below 25%, which it is.

Importantly, our short-term trading strategy blows the buy and hold strategy (blue line) out of the water, turning an otherwise unprofitable long-term investment into a profitable one.

If you are considering adopting this strategy for live trading, you should do Out-of-Sample Backtesting. The best way to do this is to conduct an Incubation Period. This is no investment advice. Always do your own research!

Clean Energy and the trader’s portfolio

Can short-term trading have a positive social and environmental impact?

While the effects of each trade are minimal, each time we trade Clean Energy ETFs, we support the transition to a clean energy future in a variety of ways:

  1. Increasing Liquidity: Active short-term trading can increase the liquidity of the ETF. Higher liquidity can make the ETF more attractive to other investors, leading to more investment in the ETF overall, benefiting the clean energy companies involved.
  2. Raising Awareness and Visibility: Trading clean energy ETFs brings attention to the clean energy sector. This can indirectly support the sector by attracting more investors.
  3. Supporting Ethical Investment Trends: By trading a clean energy ETF, traders send a signal to the market that there is demand for presumed ethical investments. This can influence financial institutions to offer more ethical investment products, leading to greater capital allocation towards industries with positive environmental and social impact.
  4. Enhancing Bullish Price Development: Using a ‘long only’ strategy, such as the one detailed here, helps support the price of clean energy ETFs in a bullish direction.

Our impact increases or declines along with our capital. If we are able to trade clean energy ETFs profitably, the positive impact of our trading will increase over time. The clean energy trading strategy for the ETF with the ticker code PBW detailed in this article has been designed for exactly these purposes.

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