We traders have all heard the above saying, haven’t we? (And if you haven’t, don’t worry, you soon will. The saying is a staple of most trading methodologies).  It is easy enough to see the basis for the saying; the trend is, after all, what Wyckoff and Livermore called,  the ‘line of least resistance’.  In other words, trading with the trend is the easiest way to make money  - if the trend continues. 

 

To illustrate what I mean, let’s look at Figures 1 and 2, where Figure 1 is an example of trading against the trend  i.e. trading a corrective move,  and Figure 2 is an example of trading with the trend. 

 

 

 

Depending on where you enter in Figure 1, you can either:

 

• make money, 

• scratch the trade, or 

• lose money. 

 

In short, when you trade a corrective move, profitability depends upon an optimal entry.

 

Figure 2 shows that when trading with the trend (also called an impulse trade), our entries need not be as precise i.e. we have a greater margin for error.  In addition, the profits from trades with the trend tend to be greater than in trades against it.

 

 

 

One question immediately pops up:  if it were so easy to make money (all we need to do is trade with the trend), then why is it that so many trades are unprofitable?  Figure 3 shows the problem.   But, before turning to Figure 3, let’s first define the types of trends a trader can encounter:

 

• an uptrend to be "higher highs and higher lows", and 

• a downtrend to be "lower lows and lower highs", and 

• a sideways trend to be "almost equal highs and lows".

 

 

With that in mind, what would you say the trend is in Figure 3? Now most would say an uptrend. But, if that were the case, how about the areas marked by a rectangle(s): don't these show (a) lower lows and lower highs?

 

The problem can be resolved by remembering that markets are fractal. 

 

In any given chart, we have trends of different timeframes. The challenge for the trader is to separate and identify "the trend of his trading timeframe" and then ask the question:  “is the trend likely to continue or change?”  With the answer to these questions, the trader has his strategy.

 

Have a look at Figure 4. It’s the same chart with Barros Swings applied.  We now know that the monthly trend (18-day swing) is up. The apparent downtrends took place in the 5-day swing; and these took place when the 18-day underwent a correction. 

 

 

 

Most technical analysis tools rely on some form of moving averages to identify the trend. The problem is, moving averages have a number of drawbacks:

 

• they fail to distinguish between a change in trend of the timeframe and a correction to that timeframe. 

• It is difficult to correct the lag that is the hallmark of a moving average.

 

In Figure 5 (same chart as Figure 4) a single moving average cross-over system and the MACD give premature change in trend signals for ongoing 18-day uptrend.  (I used a 34-period Simple Moving Average and ‘5,25, 5’ for the MACD).

 

This then is the main drawback with most trend identifying technical tools: they distinguish between trend structures of different timeframes. 

 

So, in an effort to find a more effective trend identification tool, I turned to swing charts. At the time I took up the challenge, there were two types of swing charts in vogue. The first was Arthur Merrill's "Percentage Swing Charts"; and the second was W. D. Gann's Swing Charts. Unfortunately, for my purposes I found that both of these had serious drawbacks.

 

I wanted my swing charts to identify the trend of a calendar timeframe, for example, the monthly trend. 

 

But, with a Percentage Swing Chart, the question was  "how do I to tell what percentage defined a specific calendar, e.g. monthly, trend?"

 

 I found the percentage would depend not only on the calendar timeframe but also on the volatility of the market at any given time. (By calendar timeframe I mean the frequency of the bar we are viewing, e.g. a monthly bar, a weekly bar etc.). In short, Percentage Swing Charts would not define trends of a calendar period. 

 

Gann Swing Charts used time as their construction foundation.  However, I found that they did not take price into consideration. As a result I saw situations where bars of a large magnitude failed to turn the line, whereas bars of a smaller magnitude did this. Since the purpose of the swing Chart is to define a timeframe's trend (and in so doing identify key support and resistance), I saw this as a major weakness. 

 

Figure 6 shows what I mean.

 

 

 

(Note that at ‘B’ where the Gann Swing turned down, we saw a $35.00 range; but at ‘C’ where we have a $38.00, the Gann Swing has still to turn down)

 

For this reason, I decided to create a swing Chart of my own that would be dependent on both time and price. 

 

I might add here that another technician Joseph Hart came up with a similar idea. There are differences in the construction of the Hart Swings and Barros Swings but they serve the same function and use similar parameters.  In fact, I have taken the liberty of borrowing some of Hart’s nomenclature and ideas e.g. LCC (Hart call this the ‘Line Continuation Count’), I prefer the name, ‘Line Change Count’ but the idea is the same: it is a momentum filter to confirm a breakout. 

 

So, now you have an idea of what I want my swing charts to do. The next steps are to:

 

1. Define how they are constructed: to see how I construct my Barros Swings, go to: http://www.tradingsuccess.com/free-stuff.html

 

2. Once we know how to construct the swings, we see how I use them. 

 

To understand how I use Barros Swings, you first need to know how I define the trends of the calendar timeframe: 

 

• Yearly Trend: 12-month swing. This means a 12-period swing on a monthly chart.

• Quarterly Trend: 13-week swing. This means a 13-period swing on a weekly chart.

• Monthly Trend: 18-day swing. This means an 18-period swing on a daily chart.

• Weekly Trend: 5-day swing. This means a 5-period swing on a daily chart. 

• For intraday timeframes, I divide the dominant period by five, e.g. for the S&P I divide the 8:30 to 4:00 PM (EST) by 5. My first time period is 80-minutes and the second (80/5), 15-minutes. You'll note that time periods are not exact; I tend to round off the quotient. 

 

When determined this way, I found that the 5-period swing represented the 1-period swing of the next higher timeframe, and the 18-period swing represents the 5-period swing of the next higher timeframe. 

 

For example, in the S&P, the 5-period swing on the 80-minute represents the 1-period swing on the daily chart; and the 18-period swing on the 80-minute represents the 5-period swing on the daily.

 

Armed with this knowledge, you can define the trend of any timeframe at a glance. For example if we see a series of higher highs and higher lows in the 5-day swing, we can say we have an uptrend. We can also tell:

 

• If the uptrend is in line with the 18-day trend or 

• Whether it is merely reflecting the 5-day trend within an 18-period correction. 

 

Moreover, once you have the Barros Swings in place, it is easy to define when a trend has ended; e.g. in an uptrend: 

once a timeframe’s swing low is breached then whatever trend you may have in that timeframe, you no longer have uptrend.

 

However, the Barros Swings are far more useful than this observation. I’ll consider some of these in an article to follow. 

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