Using Probability Cones to Test for Strategy Death

The most common question I get is how do you determine that a strategy is no longer working. It is also the question that I don’t have a good answer for. I have written several posts about this: Trading the Equity Curve, How to turn off a strategy using historical volatility, Broken Strategy or Market Change. During his How to detect a failing trading strategy presentation, Kevin Davey discusses how he uses probability cones to determine when to stop trading a strategy. I had not investigated this concept and was very intrigued.

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Slippage and low liquidity stocks

Recently, I have been working on a strategy that trades stocks with low dollar turnover. The initial performance was attractive and I was liking the strategy. But there were two issues that I needed to deal with in the backtesting. How much slippage to add to these stocks. The strategy enters and exits on the open and while looking over the trade list, I noticed some trades entered at the low of the day and exited at the high of the day. From my trading, I knew this would not be a realistic price. Should these cases get extra slippage? What follows is how I try to account for these issues.

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Using strength to exit a mean reversion trade

I had a long-time reader, Cristian Franchi, send me a mean-reversion strategy that he wanted me to test and write about.  What caught my attention was the rules differing from what I typically see and use. Different ways of measuring strength of a sell-off and volatility expansion. Along with a different type of exit being used on a mean reversion strategy. Not simply waiting for the bounce.

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Be Careful of Big Years

While doing research on a mean reversion strategy, I was really happy with the Compounded Annual Returns (CAR) of 51%. I was thinking, I may have a new strategy to add to my stable of trading of trading strategies. A big fact I liked was the strategy used no market regime filter.

Then I looked at the yearly returns. The 2020 return through July 31 as 444%! How much did the CAR depend on this year’s numbers?

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The importance of testing different exits

When developing a strategy, exits are often not given a second thought. If you are creating a mean reversion, you may default to using Close greater than the 2-period RSI. If you are trading a trend strategy, you may default to trailing exit using 14-day ATR. You try a bunch of entry filters but rarely try a different exit. Or maybe a slight change in the exit.

If you are having success, with your strategy. You think great and don’t change the exit. If you are not getting anywhere, you think the idea did not work and stop testing.

A slight change in your exit can have a huge impact on the results as was driven into me during some recent research. I am guilty of not be as thorough in my testing of exits as I should be. Hopefully, this will convince you to look at them more at the beginning of your research.

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How to turn off a strategy using historical volatility

A very common question I get, is “when should I turn off a strategy?” Given the very volatile markets we have had the last few months, I can relate. Some strategies can thrive in these high volatility markets. While others can suffer.

In the June 2020 issue of Technical Analysis of Stocks and Commodities, Perry Kaufman writes an article about using the historical volatility of the equity curve to decide when to turn off a strategy. I always read Perry’s articles because they are full of good ideas and this was another one that I liked and had not tried before.

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S&P 500 Dividend Aristocrats

Back in 2018, I wrote a post, Backtesting a Dividend Strategy, which was conceptually based on the S&P 500 Dividend Aristocrats. Just recently, Norgate Data started offering historical constituent data for the S&P 500 Dividend Aristocrats index. This would be a much ‘cleaner’ version compared to what I was trying to do in my original post. Would using this index produces better results?

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Market Cap vs. Crash Severity

Has the market sell-off and subsequent bounce treated all stocks the same? A good portion of the bull market move from 2009 to 2019 has been led by the big-cap stocks. Did they hold up better during the March sell-off? What about with the bounce? Did the smaller-cap stocks have a bigger bounce?

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Correlations go to One

There is a saying: “in bear markets correlations go to one.” I wanted to see how true that is for both stocks and a basket of ETFs. Now they don’t go to exactly one, not that I expected that, but they take some large steps towards one.

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Escalator on way up. Broken elevator on the way down.

I have been waiting for a close under 2350 to write this post. Today the $SPX closed at 2304.92.

Markets slowly grind up. But crash quickly. How quickly? I will be looking at each new drawdown low since the market top on February 19, 2020 and then seeing how many days of market gains were erased since the previous time

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