Tactical Trading on Your Portfolio

Chessboard

There’s a lot of talk out there about implementing complex trading strategies to achieve better returns while minimizing risk. But even simple solutions can deliver attractive results. In this post, we’ll explore how to use simple trading rules to better manage your portfolio.

You may have read my post Portfolio Diversification: How Does It Work?, which explains why it’s important to diversify your portfolio. This holds true without question. However, the types of strategies many people use when diversifying their portfolio force them to buy-and-hold various investments for a long period of time. In that scenario, your portfolio is fully invested all of the time in good and bad market conditions. Isn’t there a way we could try to keep the investments in our portfolio active during good markets and on the sidelines during bad markets?

A common way to do this is to use technical indicators to help decide when it’s a good time to enter and exit the market. The most used indicators by far are moving averages, which in a nutshell use a specific amount of past price values to determine an average price value.

The most well-known moving average indicator is the simple moving average (SMA). Simply put, this indicator calculates the average price (often the close price) using a certain number of preceding observations.

For example, if you wanted to know the SMA of the SPDR S&P 500 ETF (SPY) over the last 50 trading days, you would use the following formula:

SMA(50) formula

And 200 trading days would look like this:

SMA(200) formula

Pretty simple, right? Now how can you use the SMA to hold your investments during good market conditions and get rid of them during back market conditions?

One way to achieve this is by buying an investment when its SMA(50) goes above its SMA(200) and selling when its SMA(50) goes below its SMA(200). Why would you do this? For one, SMA(50) reacts much faster to new market behavior than SMA(200). Also, SMA(200) contains more information representing the past than SMA(50). Both of these points imply that when these two indicators cross, the new market behavior is either potentially better (i.e., upward cross) or worse (i.e., downward cross) than it was in the past.

Tactical Trading in Action

Now for an example! We’ll piggyback off the one used in the post Diversification: How Does It Work? where we had a quarterly rebalanced portfolio containing 50% of the SPDR S&P 500 ETF (SPY) and 50% of the Vanguard Total Bond Market ETF (BND). The strategy used was buy-and-hold, but we’ll change that so we can buy when SMA(50) > SMA(200) and sell when SMA(50) < SMA(200).

We also need to know how much of our overall portfolio value to allocate to both SPY and BND when buy and sell signals are generated per asset.We can use weighting schemes to do this. We’ll use the “uniform all-in weights” scheme, which means at any given time the following portfolio allocations will exist:

SMA strategy trading rules

Now we’ll run our strategy through a backtester and see how it does. You’ll notice in the performance chart below that we compare our strategy with a benchmark set to SPY, which is simply a buy-and-hold strategy of that asset.

SMA backtest price performance

Looks pretty good! We weren’t able to beat the benchmark to-date, but we did beat it some of the time and even avoided several major drawdowns. That’s cause for celebration!

We even improved upon our buy-and-hold strategy from the previous post.

SMA Strategy

SMA backtest statistics


Buy-And-Hold Strategy

Buy-and-hold backtest statistics

You can see from the tables above that we not only increased our total returns by almost 20%, we also reduced our volatility by about 2% and our maximum drawdown by a whopping 18%! That means we were able to generate greater returns without fearing the financial grim reaper.

If you check out how our annual returns did over time, you’ll notice that in 2008 when the benchmark lost a whole lot of money (~31%), our strategy actually made some (~6%).

SMA backtest returns

I’d say we did pretty well without having to rack our brains over some complex trading strategy. What do you think?

Closing Thoughts

While this was a pretty straightforward example of how tactical trading can improve the risk and return profile of a simple portfolio, there are endless examples out there that are potentially better fitting for any investor or trader. Getting your hands dirty by trying out different assets, indicators, and weighting schemes can really help in discovering new and interesting trading strategies.