Average True Range, Part1

This week will be the first in a series that explores various implementations of Average True Range (ATR) for both entry and exit techniques. For those not familiar with ATR, I suggest reading THIS. I personally find ATR to be one of the most robust and elegant ways of measuring price movement primarily because it is incredibly adaptive across multiple time scales, markets and market conditions. The way we will utilize ATR in these studies is by measuring price movement as a multiple of its average ATR for both breakout entry criteria and next week, for protective and profit exit criteria. Unlike percentage changes, a system built around ATR multiples can be used quite effectively not only for stocks, but also for currencies, ETFs, commodities, etc. Let’s use the following example.

If the daily volatility of any underlying issue is ½%, then a 5% move is obviously much more substantial than a 5% move on an underlying that has an average volatility of 3%. A 5% daily move is a very rare occurrence for most indexes or forex markets while not unusual at all for many stocks. However, if we use a multiple applied to average volatility, we get a technique that is very effective at capturing moves of equal significance across any market or time frame. It just makes a lot of intuitive sense. Would you place equal significance on a certain percentage move if trading QQQQ and QLD? I want to be clear, I am not dismissing percentage moves as invalid and they are certainly much easier to implement. My argument is that ATR offers a more complete picture of the significance of a percentage move.

One more example, ATVI had a 5.49% gap up on Friday, unusual but certainly not an incredibly rare event. However, the ATR(10) multiple of that gap was 4.9x due to the extremely tight range that stock had been trading in. That is nearly 3 times as rare and far more significant.

As usual, we will run the test on three separate portfolios to increase our confidence that the results are systemic and all tests will be run from 01/01/1998-12/31/2007.

Our entry criteria will a liquidity condition requiring 10-day average daily volume to be greater than 100,000 shares. In addition, we will look at both volume as a percentage surge over 10-day average daily volume using a >= condition, and an ATR multiple price breakout over the 10-day average ATR in a >= condition. Trades will be entered at open with a market order the day following the entry signal.

Like last week, we will use 3D surface graphs to visualize both volume and ATR multiples simultaneously. We will begin by looking at the EDR results for the three portfolios.

EDR=Expectancy per dollar risked. This is my primary evaluation ratio and has been covered extensively in previous posts. It is calculated by Average Trade/Average Loss. The results are in dollars with .28 representing 28 cents per dollar risk.The “ATR multiple” axis of the graphs represent the ATR multiple change in price, while the “Volume%” axis of the chart represents the volume surge. This is consistent in all tests. The z-axis represents our various evaluation ratios. For clarity, some of the graphs have been rotated so please pay attention to the axis when comparing results across multiple tests, particularity when compared to the previous study.

Let’s begin by focusing on the EDR ratio. First, compared to the percentage price action studies from a few weeks ago, ATR breakouts provide us with peak expectancy figures that are 84% higher for our IBD portfolio, 28% higher for our Nasdaq100 portfolio, and 24% higher for our S&P500 portfolio. Furthermore, when compared to the percentage study, the average expectancy across all ATR tests was 43% higher for the IBD portfolio, 21% higher for the Nasdaq100 portfolio and 16% higher for the S&P500 portfolio. From this, we can begin to conclude that ATR does provide us with an effective way of evaluating price movement, perhaps more so than single daily percentage changes in the creation of a breakout trading strategy.

Also, when compared to the previous study, we see a much more implicit increase in expectancy as we move along the ATR-axis of the graphs. While the price action axis of the percentage breakout tests offered little to no expectancy edge on any of the 3 portfolios and a clear disadvantage on the IBD portfolio, leaving most of the work to volume, here we see a clear advantage in 2 of the 3 portfolios and no negative impact on any of them. Very encouraging results.

A word of caution. While I go to great pains to avoid presenting overly curve-fit results, the spike you see along the 4.0 ATR multiple of the IBD portfolio is a clear warning sign. What we look for in these tests are smooth trends as we modify variables and a sudden spike in the results is always a concern. What it means is that particular number on these particular stocks happen to jive well together and while that does not invalidate the results, it means that future test will probably not perform anywhere near those levels. What I would do in a situation like this is use an average of the 3.5x & 4.5x results along that axis as a better indication of what we would more likely experience in real-time. That is exactly what we will do next week when evaluating our entry criteria to use with ATR multiple exits next week.

Below, I will post the EER and DDR results for these tests. I will not comment on them but am making them available for those motivated to dig a little deeper. If anyone has any questions, feel free to ask.

EER=Efficient Expectancy Ratio. This is our EDR/Avg. Days in Winning trades. This number is meant to reveal the most efficient use if capital as it calculates expectancy per dollar risk per day in trade. The results are in dollars with .01 representing 1 cent per dollar risk per day in trade







DDR= Drawdown ratio. This number is calculated by Net Profit/Maximum Drawdown. A higher number is better.