LOG BRICK VALUE


In the earlier examples, we discussed the plotting of Renko charts using

absolute brick values, such as 5 or 10. Actually, Renko charts can be plotted

with any number. But often a chart moves in a wide range which makes

defining the brick value difficult. Take, for instance, a stock like Aban

Offshore that was trading at 5,000 in January 2008 and subsequently

crashed to 300 in March 2009, and then traded below 200 in January 2016.

A brick value of 50, when the stock was trading at 5,000, would have

represented 1% of the prevailing price. But if the chart were plotted with the

same brick value when the price had dropped to 200, the brick value of 50

would represent 25% of the prevailing price.

This example illustrates the problem associated with using absolute brick

values for Renko charts, especially when studying a long term chart. There

are many examples of stocks moving in a wide price range where it would be

illogical to use a single brick value to sensibly capture the price action over

time.

Technology came as a saviour in dealing with this issue. With the advent of

computers, it’s become easier to use log scale, or log brick values, to plot

Renko charts. So, if we use a 1% log scale, a bullish brick will be plotted

when the price moves higher by 1%, irrespective of its absolute value.

Similarly, the next bullish brick will get plotted when the price again moves

higher by 1%. On the other hand, a bearish brick will be plotted if the price

drops below the low of the previous brick. For a reversal from a bearish to a

bullish brick, the price must move past the high of the previous brick.

To summarise, while the manner of chart construction remains the same but a

log brick value is used while instead of the absolute brick value. This makes

the chart consistent in terms of brick value, and price patterns too become

more relevant.

Remember, the X-axis in a Renko chart does not capture time. Therefore, a

series of bullish or bearish bricks can represent the price action of many days.

In all the charts in this book, the period covered is therefore specifically

mentioned in the chart.


Figure 1.15 is the Renko chart of BHEL plotted with a brick value of 5. It

aptly highlights why log bricks are of great advantage while studying longer

term charts.

Figure 1.16 is a Renko chart of BHEL plotted with a daily 5% brick value for

the same period as in the case of Figure 1.15.

The brick value changes as per the price level due to the log scale, which is

logical for analysing longer term trends. Notice that in the chart in Figure

1.16, the earlier bricks are larger because the price level was high and they

later turn smaller as the price fell to lower levels.


Log scale allows the user to plot consistent charts and, more importantly, it

makes the chart tradable in practice. Figure 1.17 is the BHEL chart plotted

with 1.5% brick value for the same period. Compare it with the chart in

Figure 1.15 and notice the difference in the latter part of the chart when the

stock started falling.


A huge advantage of log brick charts is that they lend themselves to screening

for opportunities across a universe, or a group, of stocks. A brick value of 5

point for a stock trading at 1,000 and for another one trading at 100 is

incompatible and hence scanning is rendered meaningless with absolute brick

values. But 1% across the charts remains 1% of prevailing prices, irrespective

of the absolute price level a stock is trading at.

We can therefore run a scanner for a given strategy or pattern across a group

of stocks or instruments using a log brick value. The use of log brick value

also facilitates the testing of a trading system.

You would have notice that the chart plotted with brick value of 5 has more bricks than a chart

with a brick value of 20. The number of bricks plotted in a chart depends on the chosen brick

value.

By now it must be clear that changing the brick value alters the amount of information captured

in the chart. This possibility of changing brick values on the same time frame (data frequency)

is a key feature of these charts. To look at the larger degree price setups, higher brick value can

be used. To look at the short term price structure, lower brick values are useful.


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