In building an ETF strategy, we can refine our selection by adding filters. As part of our Xplore ETF offering, we give our users access to a number of metrics that are derived from the underlying ETF market data. These metrics enable users to filter their ETFs by a strict set of rules and to automate their own investment strategy. The inputs are grouped by Volatility, Momentum, and Technical metrics.

Users can apply up to 3 filters (factors) as standard with the core plans Beginner, Established and Xplorer. If you would like to unlock more filters you will need to augment your core plan with Xpert.

Filters operate very much like a set of criteria that must be obeyed and adhered to and they are an integral part of any investment strategy design. The more filters that are used in the design of your investment strategy, the more complex and constrained the strategy becomes. Apply filters wisely, as too many or overly strict filters could mean the bar is set too high for any ETFs to pass.

All filters chosen by a user must be satisfied - for example, if an ETF satisfies 2 out of 3 filters then it still will not be included in your strategy as it still failed to pass the test on 1 filter.

We split our filter metrics into 3 groups - Volatility, Momentum, and Technical. There are multiple metrics within each group.

For example:

• Volatility 3-month is a Volatility metric.
• Momentum-3-month is a Momentum metric.
• Simple Moving Average is a Technical metric.

Volatility

Volatility, or to put it another way risk, is a key consideration for investors. Assuming all else is equal, most investors quite naturally would prefer lower volatility for their strategies or portfolios than higher volatility. Volatility metrics are based on the dispersion of returns for a given ETF. Volatility is a measure of how variable the returns are and how far from the average value they are over a historical period. It is technically measured by calculating the standard deviation of the returns for the given historical period. This result is then annualised into a yearly number by multiplying it by the square root of 252, the number of trading days in a year.

For example:

• Volatility 3-month is calculated by measuring the standard deviation of returns for the previous 3 months of trading.
• Volatility 6-month is calculated by measuring the standard deviation of returns for the previous 6 months of trading.

Momentum

Momentum metrics are popular among investors who want a structured and longer term approach to investing. Stocks and ETFs with strong upward trends will have good momentum and its easier to go with the crowd than against. Momentum is calculated by measuring the price change in an ETF across a specifed historical period. Stocks with the highest momentum are trending the most.

For example:

• Momentum 3-month is calculated by measuring the change in today's ETF price versus the price from 3 months ago. For example, if the SPY today is priced at 440 and the SPY 3 months ago was 400, then the momentum 3-month would be 10% based on the calculation (440-400)/400. You can then calculate the momentum score across all ETFs and choose the ones with the highest return, indicating the highest momentum.

Technical

Investors use technical metrics or indicators which examine the available market data and provide them with buy or sell signals. There are numerous technical metrics that are available to users of the Xplore ETF tool, these indicators can be used in filters and sorts to refine the universe down to a smaller subset of ETFs that may outperform and do better.

For example:

• Moving Average Convergence / Divergence (MACD) is a technical metric provided in STRATxAI Xplore ETF tool. MACD helps investors to determine the trend direction and the momentum within a trend.
• Other technical metrics include Relative Strength Index (RSI), Exponential Moving Average (EMA), and Simple Moving Average (SMA).

STRATxAI offers a simple way to leverage these filters in your investment strategy, Let's take a look at how we might employ some filters to create a reversion entry point (for example, waiting for a small reversal or pullback in an overall trend for your entry) into our strategy.

The above represents a simple strategy utilising 3 filters. The filters are as follows:

• Positive 3-month momentum
• Positive 6-month momentum
• RSI is in the best 50% (less than the 50% percentile)

Finally, we rank the remaining ETFs by their lowest RSI. We use the default strategy parameters with a portfolio size of 3. The results are impressive and this incredibly simple strategy outperforms the S&P 500 on a risk-adjusted basis (Sharpe ratio). It also has a nice diversification benefit as the correlation (beta) to the S+P is only 35%.

We can see in the cumulative return chart above, that there were periods where the equity went horizontally across, which might suggest that there were no ETFs passing the strategy filters at this time. We can confirm this in the backtest tearsheet.

It is visible from this chart that there were three times when the size of the universe fell to 0, which indicates that no ETFs passed the filters during these 3 periods.

Our Xplore ETF tool is designed to be very dynamic and robust enough to investigate all sorts of investment strategy design decisions. Please let us know any feedback and enjoy building your own strategies!