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Happy Monday Bruce,
Wanted to revisit another past topic. With the PCF language's new capabilities the past few years, I was wondering if ConnorsRSI was finally doable. There's a lot to it, but if anybody can, ... . If not, can we add this to the developer's Honey-Do list? ... ConnorsRSI is pretty popular, and I know Larry used to do a webinar or two with Michael - I'm sure he'd love it if Worden implemented ConnorsRSI.
Excerpt reprinted from An Introduction to ConnorsRSI from Connors Research,
LLC, 2012.
Now let’s turn our attention back to ConnorsRSI. As mentioned previously,
ConnorsRSI combines three components, and as you might guess, they are
all elements that our research has repeatedly shown to have significant
predictive ability:
Price Momentum: As we just discussed, RSI is an excellent way to measure
price momentum, i.e. overbought and oversold conditions. By default,
ConnorsRSI applies a 3-period RSI calculation to the daily closing prices of
a security. We will refer to this value as RSI(Close,3).
Duration of Up/Down Trend: When the closing price of a security is lower
today than it was yesterday, we say that it has “closed down”. If yesterday’s
closing price was lower than the previous day’s close, then we have a
“streak” of two down close days. Our research has shown that the longer
the duration of a down streak, the more the stock price is likely to bounce
when it reverts to the mean. Likewise, longer duration up streaks result
in larger moves down when the stock mean reverts. In effect, the streak
duration is another type of overbought/oversold indicator.
The problem is, the number of days in a streak is theoretically unbounded,
though we could probably place some practical limits on it based on past
experience. For example, we might observe that there have been very few
instances of either an up streak or a down streak lasting for more than 20
days, but that still doesn’t get us to a typical oscillator-type value that varies
between 0 and 100.
The solution is two-fold. First, when we count the number of days in a
streak, we will use positive numbers for an up streak, and negative numbers
for a down streak. A quick example will help to illustrate this:
The closing price on Day 2 is higher than on Day 1, so we have a one-day
up streak. On Day 3, the price closes higher again, so we have a two-day up
streak, i.e. the Streak Duration value is 2. On Day 4, the closing price falls,
giving us a one-day down streak. The Streak Duration value is negative (-1)
because the price movement is down, not up. The downward trend continues
on Days 5 and 6, which our Streak Duration reflects with values of -2
and -3. On Day 7 the closing price is unchanged, so the Streak Duration is
set to 0 indicating neither an up close nor a down close. Finally, on Day 8
the closing price rises again, bringing the Streak Duration value back to 1.
The second aspect of the solution is to apply the RSI calculation to the set
of Streak Duration values. By default, ConnorsRSI uses a 2-period RSI for
this part of the calculation, which we denote as RSI(Streak,2). The result
is that the longer an up streak continues, the closer the RSI(Streak,2) value
will be to 100. Conversely, the longer that a down streak continues, the
closer the RSI(Streak,2) value will be to 0. Thus, we now have two components
-- RSI(Close,3) and RSI(Streak,2) -- that both use the same 0-100 scale
to provide a perspective on the overbought/oversold status of the security
we’re evaluating.
Relative Magnitude of Price Change: The final component of ConnorsRSI
looks at the size of today’s price change in relation to previous price changes.
We do this by using a Percent Rank calculation, which may also be referred
to as a “percentile”. Basically, the Percent Rank value tells us the percentage
of values in the look-back period that are less than the current value.
For this calculation, we measure price change not in dollars and cents, but
as a percentage of the previous day’s price. This percentage gain or loss is
typically referred to as the one-day return. So if yesterday’s closing price
was $80.00, and today’s price is $81.60, the one-day return is ($81.60 - $80.00)
/ $80.00 = 0.02 = 2.0%.
To determine the Percent Rank, we need to establish a look-back period.
The Percent Rank value is then the number of values in the look-back
period that are less than the current value, divided by the total number
of values. For example, if the look-back period is 20 days, then we would
compare today’s 2.0% return to the one-day returns from each of the previous
20 days. Let’s assume that three of those values are less than 2.0%.
We would calculate Percent Rank as:
Percent Rank = 3 / 20 = 0.15 = 15%
The default Percent Rank look-back period used for ConnorsRSI is 100, or
PercentRank(100). We are comparing today’s return to the previous 100 returns,
or about 5 months of price history. To reiterate, large positive returns
will have a Percent Rank closer to 100. Large negative returns will have a
Percent Rank closer to 0.
The final ConnorsRSI calculation simply determines the average of the
three component values. Thus, using the default input parameters would
give us the equation:
ConnorsRSI(3,2,100) = [RSI(Close,3) + RSI(Streak,2) + PercentRank(100)] / 3
The result is a very robust indicator that is more effective than any of
the three components used individually. In fact, ConnorsRSI also offers
some advantages over using all three components together. When we use
multiple indicators to generate an entry or exit signal, we typically set a
target value for each one. The signal will only be considered valid when all
the indicators exceed the target value. However, by using an average of the
three component indicators, ConnorsRSI produces a blending effect that allows
a strong value from one indicator to compensate for a slightly weaker
value from another component. A simple example will help to clarify this.
Let’s assume that Trader A and Trader B have agreed that each of the following
indicator values identify an oversold condition:
• RSI(Close,3) < 15
• RSI(Streak,2) < 10
• PercentRank(100) < 20
Trader A decides to take trades only when all three conditions are true.
Trader B decides to use ConnorsRSI to generate her entry signal, and uses
a value of (15 + 10 + 20) / 3 = 15 as the limit. Now assume we have a stock
that displays the following values today:
• RSI(Close,3) = 10
• RSI(Streak,2) = 8
• PercentRank(100) = 21
• ConnorsRSI = (10 + 8 + 21) / 3 = 13
Trader A will not take the trade, because one of the indicators does not
meet his entry criteria. However, Trader B will take this trade, because the
two low RSI values make up for the slightly high PercentRank value. Since
all three indicators are attempting to measure the same thing (overbought/
oversold condition of the stock) by different mechanisms, it makes intuitive
sense to take this “majority rules” approach. More importantly, our
research has shown ConnorsRSI to be superior to any other momentum
indicator that we’ve tested.
To receive a free copy of the full report on ConnorsRSI, go to www.tradingmarkets.
com and click on the ConnorsRSI link in the menu.
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