Algorithmic and Mechanical Forex Strategies | OneStepRemoved

Are You Sabotaging Your Projected Trend Following Profits?

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It is widely understood that most of the profits from trend following and momentum strategies come from a select few big winners. Despite that understanding, traders generally don’t appreciate exactly how few trades make up that select few.

As quantitative traders, we willingly forfeit the desire to pick and choose between signals that our strategy triggers. Even though we aren’t making discretionary decisions about entries and exits, there is still a level of respect that needs to be paid to the importance of the select few trades that will drive our performance.

trend following

Missing just one trade can have a severe impact on your overall return, so if you aren’t committed to taking every trade you might be better off with a buy-and-hold strategy.

The Dorsey Wright Money Management blog published a post earlier this month that did a tremendous job of breaking down exactly how the top 20% of returns of a momentum strategy were relative to the other 80%. The post also showed how each quintile performed relative to a buy-and-hold benchmark.

Breaking Down Performance of Trades

The article took a basic sector rotation strategy that trades S&P 500 sub-sectors and broke its returns into five groups that each made up 20% of the total trades after sorting all trades by performance. They charted the performance of each of these groups and compared it to a chart of a equal-weight strategy that held all of the sub-sectors and rebalanced monthly.

The charts show that, regardless of lookback period, the bottom 60% of the strategy’s trades underperform the benchmark. The next 20% of trades just barely outperform the benchmark, and more or less match it after transaction costs. The only group that significantly outperforms the benchmark is the very top 20% of trades.

The Best Trades Are Critically Important

The point that the post is trying to make is that you absolutely must be willing and able to take every single trade that your trend following or momentum strategy produces. Omitting even one trade from the top 20% could cripple your overall performance because. Because we have no way of knowing what trades will end up producing the best profits, we cannot afford to miss out on any of them.

The article sums up this theme quite nicely:

If you are unwilling to constantly cut the losers and buy the winners because of some emotional hangup, it is extremely difficult to outperform.

Even the Best Traders Learn the Hard Way

Market Wizard Tom Basso tells a great story that fits in well here. Early in his trading career, Tom took a day off to spend the day with his parents who had come to visit him. On that day, he missed a signal for a silver trade that would have ended up being hugely profitable. That single trade would have made the difference between a profitable year and a losing year.

The moral of the story is that if you are going to trade a trend following or momentum strategy, you absolutely must be willing and able to take every single trade signal. There are no exceptions, because just one exception could ruin your performance.

A Case Study in Salvaging a Bad Strategy

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The idea of developing a multi-system strategy is very appealing. Every strategy will have its own collection of strengths and weaknesses, but the theory behind using multiple strategies is that the flaws will cancel each other out, leaving a robust strategy that can perform in any market environment.

An ideal multi-strategy approach would contain strategies that trade an assortment of different markets over multiple time frames. The ability to trade a diverse collection of markets on multiple time frames should provide an incredible level of diversification.

time frame

There are significant advantages that can be found through adding multiple strategies and time frames to your trading system.

Cesar Alvarez, who is known for creating short-term mean reversion strategies, seems to be headed in the direction of multiple systems and time frames. He recently published a post discussing an Intermediate Term Stock Rotation Strategy. In that post, he states that he is looking to add an intermediate term rotation strategy that trades S&P 500 stocks to his portfolio of strategies this year.

Cesar’s Stock Rotation Strategy

Cesar begins by explaining that he recently read some material that mentioned that past performance seemed to be a better indicator using older data. The article he read suggested that using previous intermediate term performance from 7-12 months ago was a better indicator than using previous intermediate term performance from today’s date.

From this idea, he crafted a quarterly rotational strategy that ranks the stocks in the S&P 500 on the first day of each quarter. The stocks are ranked by six month performance, as of six months ago. The strategy buys the top ten stocks according to the rankings and then sits tight until the first day of the next quarter.

Backtesting Cesar’s Strategy

Cesar backtested this strategy from January 1, 2001 through December 31, 2013. During that time, the strategy produced an annual return of 5.88% with a maximum drawdown of 70.78%.

Cesar also tested a version that rebalanced only twice each year, in January and June. That strategy fared even worse, producing an annual return of 3.09% with a maximum drawdown of 74.34%.

Clearly, this strategy is all but dead in the water. Even if the annual returns were ten times bigger, traders still wouldn’t be able to survive with that big of a maximum drawdown. 

Optimizing Cesar’s Strategy

Not one to give up easily, Cesar spend some time attempting to optimize this strategy into something that might be worth trading. He tested a wide range of combinations of different values for the number of months for the lookback period and the number of months for the delay of the performance data.

The most profitable strategy he came up with at this point used a 6-month lookback period and a 3-month delay. That produced an annual return of 8.62%, but still contained a maximum drawdown greater than 70%.

After looking at the returns by individual years, Cesar noticed that the strategy was really suffering during the bad years for the S&P 500. This gave him the idea of implementing a trend filter. The filter he chose was that the strategy would only enter new positions when the S&P 500 was above its 200-day simple moving average (SMA).

This increased the annual returns and reduced the maximum drawdowns for each variation of the strategy. The best performing version at this point used a lookback period of 6-months with a delay of zero. That version produced an annual return of 16.49% with a maximum drawdown on 37.42%.

At this point, Cesar had put together a strategy with a respectable annual return. The only problem was that its maximum drawdown was still high. He suggests that adding some form of a stop might be able to help reduce the drawdown, but at this point he is still brainstorming other ideas.

 

Should Your Equities Strategy Focus on Relative Strength or Weakness?

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One of the long-running debate for designers of equities-based quantitative strategies can be boiled down to three words: strength vs. weakness.

The strength side of the argument maintains that stocks that are breaking into new highs are statistically more likely to continue higher. On the flip side, the weakness argument advocates buying stocks at their lows because they are priced at a discount to their actual value.

There are investing and trading icons on both side of this argument going all the way back to guys like Jesse Livermore and Benjamin Graham. There have also been plenty of successful examples of both strategies over the past hundred years.

relative strength

Cesar Alvarez put together a simple rotational system that allows us to compare relative strength to weakness.

In one of his recent posts, Cesar Alvarez decided to look into designing a simple rotational strategy that would use the S&P 500 stocks as its universe and rebalance its positions on a monthly basis. The interesting debate that he faced was whether to focus on holding the best performing stocks or the worst performing stocks.

Of course, the best way to determine the answer to that debate was to actually test both strategies. Here are the rules and parameters as he defined them:

Test Parameters

  • From 1/1/2001 to 10/31/2013
  • $.01/share for commission
  • 3 month T-bill interest rate used for cash

Best Performing Stocks

  • It is the first trading day of the month
  • Stock is a member of the S&P500
  • Rank stocks from the highest to lowest by (6,12) month return
  • Buy the top 20 ranked stocks at the close
  • Optional market timing rule: SPX close > 200 day moving average of SPX closes on the entry day
  • Hold until next month

Worst Performing Stocks

  • It is the first trading day of the month
  • Stock is a member of the S&P500
  • Rank stocks from the lowest to highest by (6,12) month return
  • Buy the top 20 ranked stocks at the close
  • Optional market timing rule: SPX close > 200 day moving average of SPX closes on the entry day
  • Hold until next month

The Backtesting Results

Alvarez lists the stats for each version of this simple system, but we can boil those down with a few quick observations. First, the systems that utilize the optional market timing rule always outperformed the systems that did not utilize the rule. Second, in each instance, using a 6-month return produced better returns than using a 12-month return. 

After processing those observations, it is clear that we just need to look at two systems. The system that held the best performing stocks each month produced a CAR of 13.05% with a maximum drawdown of 27.88%. The system that held the worst performing stocks each month produced a CAR of 10.48% with a maximum drawdown of 42.22%.

While the numbers indicate that holding the best stocks would have produced a better and less volatile return than holding the worst stocks, both strategies dramatically outperformed a simple buy and hold approach.

Looking at the equity chart that Alvarez provides, you can see that the Worst Stocks Strategy performs just as well (if not better) than the Best Stocks Strategy for most of the twelve year period. The key differences are that the Worst Stocks Strategy got crushed around 2008, and the Best Stocks Strategy have performed phenomenally well this past year.

Alvarez suggests that he would also repeat the test changing the number of stocks held and trying different lookback periods to rank the stocks. I would be curious to see how adding a stop-loss that allowed the strategy to exit positions mid-month would affect the drawdowns.