Using Two Popular Oscillators: Slow Stochastics and Relative Strength Two of the more popular computer-generated technical indicators are the Slow Stochastics and Relative Strength Index (RSI) oscillators. (An oscillator, defined in market terms, is a technical study that attempts to measure market price momentum?such as a market being overbought or oversold.)
I?ll define and briefly discuss these two oscillators, and then I?ll tell you how I use them in my market analysis and trading decisions.
Slow Stochastics:
George Lane has been called the father of the stochastic indicator. I met this gentleman a few years ago. He and his wife still attend and participate in trading seminars around the U.S. Lane?s basic premise is as follows: During periods of price decreases, daily closes tend to accumulate near the extreme lows of the day. Periods of price increases tend to show closes accumulating near the extreme highs of the day. The stochastic study is an oscillator designed to indicate oversold and overbought market conditions.
Some technical analysts, including me, prefer the slow stochastic rather than the normal stochastic. The slow stochastic is simply the normal stochastic smoothed via a moving average technique. The slow stochastic, like the normal stochastic study, generates two lines. They are %K and %D. The stochastic has overbought and oversold zones. Lane suggests using 80 as the overbought zone and 20 as the oversold zone. Some technicians prefer 75 and 25. I like to use the 80-20 figures.
Lane also contends the most important signal is divergence between %D and the commodity. He explains divergence as the process where the stochastic %D line makes a series of lower highs while the commodity makes a series of higher highs. This signals an overbought market. An oversold market exhibits a series of lower lows while the %D makes a series of higher lows.
When one of the above patterns appears, you should anticipate a market signal. You initiate a market position when the %K crosses the %D from the right-hand side. A right-hand crossover is when the %D has bottomed or topped and is moving higher or lower and the %K crosses the %D line. According to Lane, the most reliable trades occur with divergence and when the %D is between 10 and 15 for a buy signal and between 85 and 90 for a sell signal.
Relative Strength Index:
The Relative Strength Index (RSI) is a J. Welles Wilder, Jr. trading tool. The main purpose of the study is to measure the market's strength or weakness. A high RSI, above 70, suggests an overbought or weakening bull market. Conversely, a low RSI, below 30, implies an oversold market or dying bear market. While you can use the RSI as an overbought and oversold indicator, it works best when a failure swing occurs between the RSI and market prices. For example, the market makes new highs after a bull market setback, but the RSI fails to exceed its previous highs
Figure 1 - RSI
Another use of the RSI is divergence. Market prices continue to move higher/lower while the RSI fails to move higher/lower during the same time period. Divergence may occur in a few trading intervals, but true divergence usually requires a lengthy time frame, perhaps as much as 20 to 60 trading intervals.
Selling when the RSI is above 70 or buying when the RSI is below 30 can be an expensive trading system. A move to those levels is a signal that market conditions are ripe for a market top or bottom. But it does not, in itself, indicate a top or a bottom. A failure swing or divergence accompanies the best trading signals.
The RSI exhibits chart formations as well. Common bar chart formations readily appear on the RSI study. They are trendlines, head and shoulders, and double tops and bottoms. In addition, the study can highlight support and resistance zones.
How I employ Slow Stochastics and the RSI:
First of all, these two oscillators, especially the RSI, tend to be over-used by many traders. As you just read above, some traders use these oscillators to generate buy and sell signals in markets - even as an overall trading system. However, I treat the RSI and Slow Stochastics as just a couple more trading tools in my trading toolbox. I use them in certain situations, but only as ?secondary? tools. I tend to use most computer-generated technical indicators as secondary tools when I am analyzing a market or considering a trade. My ?primary? trading tools include chart patterns, fundamental analysis and trend lines.
Oscillators tend not to work well in markets that are in a strong trend. They can show a market at either an overbought or oversold reading, while the market continues to trend strongly. Another example of oscillators not working well is when a market trades into the upper boundary of a congestion area on the chart and then breaks out on the upside of the congestion area. At that point, it?s likely that an oscillator such as the RSI or Slow Stochastics would show the market as being overbought and possibly generate a sell signal?when in fact, the market is just beginning to show its real upside power.
I do look at oscillators when a market has been in a decent trend for a period of time, but not an overly strong trend. I can pretty much tell by looking at a bar chart if a market is ?extended? (overbought or oversold), but will employ the RSI or Slow Stochastics to confirm my thinking. I also like to look at the oscillators when a market has been in a longer-term downtrend. If the readings are extreme, say a reading of 10 or below on Slow Stochastics or RSI, that is a good signal the market is well oversold and could be due for at least an upside correction. However, I still would not use an oscillator, under this circumstance, to enter a long-side trade in straight futures, as that would be trying to bottom-pick.
These two oscillators are not perfect and are certainly not the ?Holy Grail? that some traders continually seek. However, the RSI and Slow Stochastics are useful tools to employ under certain market conditions.
Forecasted Moving Averages: Creating Leading Indicators through Intermarket Analysis When you review all of the technical indicators available to traders in today's analytical software, moving averages are still one of the most popular and widely-used indicators to help identify market trends. Moving averages form the basis of many single-market, trend-following trading strategies, ranging from the popular 4-9-18-day moving average "crossover" approach to the widely followed 50-day and 200-day simple moving averages used to highlight the underlying trend direction of broad market indexes and individual stocks.
Moving averages, calculated according to precise mathematical formulae, are an objective (quantitative) way to ascertain the current trend direction of a market and develop expectations about its future direction. Moving averages filter out the random "noise" in past price data by "smoothing" or "averaging" out the fluctuations in price movement.
Lagging Behind
However, traditional moving averages have one very serious deficiency: They are a '"lagging" technical indicator. By virtue of their mathematical construction (averaging prices over a number of prior periods), they have to rely on past prices that have already occurred so tend to lag behind the current market price.
"Making trades based upon the analysis of moving averages typically results in getting into and out of the market late when you compare the points at which the market's price actually makes a top or bottom and when it changes trend direction," points out Lou Mendelsohn, developer of VantagePoint Intermarket Analysis software. "Depending on the market's price movement and the type and size of moving average used, this lag effect can be substantial, causing the difference between trading success and failure in today's highly volatile, global financial markets."
Notice, for example, how the moving average lags behind the market at major turning points on Figure 1, a chart of daily prices of the U.S. Dollar Index futures contract with its actual 10-day simple moving average.
Figure 1 - Daily prices of the 30 Year U.S. Treasury Bonds with its actual 10-day simple moving average. Source: VantagePoint Intermarket Analysis Software (
www.TraderTech.com)
This lag is the Achilles' heel of moving averages. Technical analysts have spent years on research in a futile effort to eliminate this lag while still retaining the beneficial "smoothing" effects of moving averages. As a result, numerous types of moving averages have been devised, each with its own mathematical construction and effectiveness at discerning the underlying market trend and ability to minimize the lag effect.
Complexity Doesn't Help
Moving averages are often incorporated into more complex technical indicators, such as moving average crossover strategies, to improve their effectiveness. One such approach involves two simple moving averages of different lengths, such as a 5-day and a 10-day average. When the short moving average value is greater than the long moving average value, the underlying trend is assumed to be up. When the short moving average value is less than the long moving average value, the trend is assumed to be down.
The chart of the New York light crude oil futures contract provides an example of how the shorter 5-day moving average is more responsive to current price action than the longer 10-day simple moving average (see Figure 2), but both still lag behind the market at major turning points.
Figure 2 - Chart of daily prices of the U.S. Dollar Index with its actual 5-day and 10-day simple moving averages. Source: VantagePoint Intermarket Analysis Software (
www.TraderTech.com)
An inherent assumption behind moving averages is that once a trend is underway, it tends to persist. Therefore, until the long moving average is penetrated by the short moving average in the direction opposite from the prevailing trend, an uptrend is assumed to remain intact.
Traditional moving average crossover strategies are effective at identifying the current market direction in strongly trending markets. In non-trending, sideways markets, however, and even in trending markets when very short moving averages may be overly sensitive to abrupt price fluctuations, these approaches are subject to whipsaws. This results in erroneous trading signals at market tops and bottoms. So, while traders can make money in trending markets using moving averages, it is the choppy markets, increasingly more common today, that can cause substantial trading losses.
Various crossover strategies have been created in a further effort to overcome this deficiency. One popular approach compares an actual price, such as the daily close, with a moving average value. Other commonly-used approaches attempt to minimize whipsaws and filter out faulty signals by using bands surrounding the moving averages, using three or more moving averages, or combining moving averages with other single-market technical indicators for additional confirmation.
With today's unprecedented intraday and interday market volatility, caused in no small measure by the globalization of the markets and resulting effects of related markets on one another, traders can no longer rely solely on single-market lagging indicators such as moving averages. Knowing that a market made a top or bottom several days ago is no longer an effective way to make trading decisions, if it ever was. Even a one-day lag in today's fast-paced, globally interconnected markets is too long to wait for this information.
"It is imperative that traders adopt an intermarket perspective and incorporate intermarket data into their current trading strategies, so they can develop effective leading indicators that correspond to how today's global financial markets really exist," Mendelsohn contends.
New Way to Forecast
The purpose of technical analysis is to identify the underlying market trend and forecast (or at the very least extrapolate) its future course for the purpose of making profitable trading decisions. Therefore, it seems logical that this goal could best be achieved through applying leading indicators that use both single-market and intermarket data, rather than by continuing to rely upon trend-following indicators such as traditional moving averages that are computed solely on past single-market data.
Theoretically, a predicted moving average value for a future date, if it were 100% accurate, would have, by definition, no lag whatsoever. Since this is impossible, something else must be done to bring this widely-used trend-following indicator into the 21st century of trend forecasting.
One innovative solution to this dilemma transforms moving averages into a leading indicator by using both single-market and intermarket price, volume and open interest futures data as inputs into the design of neural networks, which are then trained to make short-term forecasts of moving averages.
"Neural networks are a mathematical technology from the field of artificial intelligence and can be trained to find reoccurring patterns and relationships within both single-market and intermarket data that can be applied to market forecasting," Mendelsohn explains.
"These forecasted moving averages are then incorporated into predictive moving average crossover strategies that identify market trend direction of individual financial markets with very high accuracy."
For instance, to forecast the short-term trend direction of 30-year U.S. Treasury bond futures for the next several days, neural networks can be trained on past single-market data on the 30-year U.S. Treasury bond itself, in addition to intermarket data from various related markets. Sophisticated software can be used to analyze related markets to forecast several moving averages of different lengths and of different forecast time horizons on 30-year Treasury bond futures. This could include a 5-day average for two days in the future and a 10-day average for four days in the future. The related markets would include the 10-year U.S. Treasury notes, U.S. Dollar Index, Euro FX, Comex Gold Index, S&P 500 Index, Japanese Yen, Eurodollar, Bridge/CRB Index and New York light crude oil.
Figure 3 shows a crossover of a predicted 10-day simple moving average for four days in the future with today's actual 10-day moving average for 30-year T-bond futures. Notice that the predicted moving average, because it is forecasted in advance, does not lag behind the market, while the actual 10-day average lags behind both the market and the predicted average.
Figure 3 - Chart of daily prices of the Australian Dollar / Japanese Yen Forex pair with a 10-day predicted and actual moving average crossover. This charts shows that the market has moved over 600 pips total for the three moves depicted. 600 pips equals about $6900. Source: VantagePoint Intermarket Analysis Software (
www.TraderTech.com)
Each day as the neural networks are updated with the most recent single-market and intermarket data, new moving average forecasts are made, and the difference in value between each predicted and actual moving average of the same length is determined.
Crossover Signals That Pay Off
In this simple example, when the predicted moving average crosses the actual moving average from above to below (the difference goes from positive to negative), the market trend is expected to turn down within the forecast time horizon. When the difference reaches a maximum negative value and starts to narrow (indicating that the downward trend is beginning to lose strength), this is an early warning that the market is poised to make a bottom and turn up.
Rather than wait for the crossover itself to occur, trading decisions can be based on a narrowing in the difference. For instance, when the difference reaches a maximum negative value and starts to narrow, you can act on this information in a number of ways depending on your account size, risk propensity, trading style and objectives. Mendelsohn cites just three possible scenarios that can be pursued (assuming that you are in a short position):
? If the difference reaches a maximum negative value and narrows by even a small amount, you can close out your short position and stand aside. Then you can wait for the difference to narrow further before going long.
? If the difference reaches a maximum negative value and narrows by even a small amount, you can tighten your stop and stay in your short position until the next day.
? If the difference reaches a maximum negative value and narrows by a minimal amount, you can close out your short position and go long. This strategy is the most aggressive of the three because it involves reversing positions at the earliest indication that the current market trend is expected to make a bottom and change direction.
No Financial Crystal Ball
By employing leading indicators, using both single-market and intermarket data such as forecasted moving averages, early warnings of imminent changes in trend direction become apparent days before they show up on traditional price charts or can be identified by single-market trend-following indicators that lag the market.
Admittedly, it is impossible to create leading trend forecasting indicators that can forecast future market direction with 100% accuracy. This elusive Holy Grail is the financial market equivalent of a desert mirage. In reality, no more than 80%-85% predictive accuracy can ever be achieved, given the randomness and unpredictable events that are inherent in today's globally interdependent financial markets, as well as due to the daunting task of creating effective forecasting tools that can stay current with rapidly evolving, complex financial markets.
As the futures and equities markets become even more intertwined and more traders incorporate intermarket analysis into their trading strategies, powerful leading indicators, such as the predictive moving average crossover strategies that expand upon the concepts of popular trend-following indicators, are a must for serious traders. These indicators will allow traders to seize trading opportunities based on predictive information derived from the hidden relationships and complex patterns among related global markets.