The original Tick index was introduced by the New York Stock Exchange many years ago. Since then, it has been employed by traders to analyze market momentum. Still, despite the index’s popularity, it lends itself well to innovation. Our focus here is its application to high-frequency day-trading.
The Tick index shows the difference between the number of up-tick issues and the number of down-tick issues. An issue is considered as having an up tick (+1) if its last trade is traded at a price higher than the previous trade, and vice versa for the identification of a down tick (-1). If the last trade is executed at the same price as the previous one, then the up/down tick status stays the same.
The Tick index helps to detect short-term (sub-minute level) extreme overbought conditions. It is also a good tool for detecting possible trend changes in intraday trading when its strength or weaknesses diverge from the futures contract being traded.
Consider “Tick index usage.” Almost every extreme high/low reading of the Tick index (the data series identified by the symbol $TICK) marks the short-term top/bottom in the E-mini S&P 500 index futures (shown on the top half of the chart). For many swing traders looking for significant moves in the index futures, such information is probably not useful at all. On the other hand, if you are a day-trader and your interest is capturing a few ticks per trade, then the ability to foretell a possible short-term top or bottom is extremely valuable.
Focus on the arrows marking the series of extreme low readings in the Tick index. Notice that in a downtrend within the day, the Tick index keeps generating extreme lows (the red arrows) at around the same level. Then, as the downtrend is about to finish, the Tick index stops producing such extreme lows. At the double bottom of the index futures contract, you get a divergence reading from the Tick index with higher lows (green arrows).
TICK INDEX PROBLEMS
When the Tick index fails to work, however, it gets much more difficult to interpret. For example, in our chart, when the index futures start to trade higher in the afternoon, the Tick index starts to swing to extremes in both directions, making it hard to tell if the up trend will continue.
The weaknesses in the Tick index can be summarized as follows:
There is no objective way to tell if an extreme low is indeed an extreme low. Back in the 1980s, the usual extreme low readings were -400. In the 1990s, they expanded to -800. In the current trading environment, extreme low readings are more along the lines of -1200. This is due to the increase in listings at the exchanges. As there are more stocks traded at the stock exchanges, the range of the Tick index will keep expanding.
For some issues that are rarely traded during the day, their last trade could have happened many minutes ago, but those trades still affect the current value of the Tick index.
The standard Tick index includes too many symbols that are likely not part of the specific index futures being traded. This creates some incompatibility risks between the Tick index and the actual futures contract.
Since the Tick index was created, real-time stock market activities have grown 20 fold. Collecting the last tick value across all the stocks may not be enough to provide stable reading all the time.
BUILDING OUR OWN
To improve on the Tick index, we can design our own breadth data, called Tick 16 (click to download), that overcomes many of these problems. It has two distinct features. First, we restrict data collection to the relevant index components, making the breadth data directly relevant to our index future counterpart. Second, we will collect the sum of the last 16 up/down tick values for each component, as opposed to just the last one. This method will smooth out the breadth data.
Consider the typical five-day chart (see “Tick index distribution”). This chart shows the E-mini S&P 500 futures contract, the Tick index and the Tick 16 for the S&P 500, all in 15-second resolution. Distribution plots of both the Tick index and the Tick 16 are shown for comparison.
For the Tick index, the data are skewed toward the negative side by 13.8%, together with long and thick tails on both ends. In short, the Tick index cannot normalize the data as we would expect.
With the Tick 16, however, the distribution is clearly well balanced and its distribution is much closer to normal distribution. That implies that we can use the distribution behavior to our advantage to deal with the correlated price data — in this case, the E -mini S&P — in real-time trading.
Keep in mind, though, that there is always a trade off when it comes to working with designed breadth data. With the Tick 16, you can expect that in the long run, the data will be stable within the prescribed range and trading models built around the data will not throw off the map over time. However, the superior data quality requires regular maintenance of the underlying data collection process that can be time consuming. (For more information on customizing breadth data, see “Using market depth in trading systems,” Futures, January 2008.)
FIELD TESTING
To test the effectiveness of this new breadth data for intraday trading, we can use it to develop a basic trading system. This day-trading system uses the Tick 16 as our only signal to trade the E-mini S&P. The system is based on 15-second bars, using data from 9:30 a.m. to 4 p.m. EDT.
Long signal: Tick 16 has a reading at or above 2800. Within a minute, Tick 16 declines below and then crosses above 600.
Short signal: Tick 16 has a reading at or below -2800. Within a minute, Tick 16 rises above and then crosses below -600.
Trade management: The profit target is 2.75 points with a fixed stop loss at 5.25 points.
Basically, the system tries to capture a move in the direction where Tick 16 is heading within a two-minute window (see “Tick 16 code”). This system is not for everyone. It is a short-term, day-trading application and your risk parameters and execution abilities need to accommodate this style.
By testing the system in January, a total of 292 trades were generated (see “System results,” below). The overall performance of the system is impressive (see “Inside the numbers,” above). The system is well balanced with a similar number of long and short trades. Drawdown is controlled by the stop loss. Overall, it is a good foundation for further development.
One final note: In live trading, it can be difficult to follow this system on a discretionary basis. Without an automated execution solution, the trader would have to be incredibly agile to execute the orders without error.
That said, the Tick index is a good trading tool and will continue to be one for many years. However, due to its changing nature over time, it is hard to develop stable trading models based on its data. With the Tick 16 concept, we can solve the weaknesses of the Tick index and make it possible and rewarding to use this breadth data in mechanical trading systems.
Lawrence Chan is a trader and principal designer of NeoTicker. He can be contacted at Lawrence@tickquest.com.
