more conveniently, spot traders need to access this information on their own. It is available at many web sites, as well as fromthird-party software providers. One such provider, Track ’n Trade, generates CoT chartswith effective visualizations of the data. Lan Turner, the developer of Track ’n Tradesoftware, observed the following (see Figure 12.2):Following the Commitment of Traders, as it relates to the March U.S. dollarcontract traded on the New York Board of Trade, you can see how the large
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speculators, depicted in the indicator windows below our chart [Figure 12.2], hasbeen following the ebb and flow of the market as the value of the dollar increasesand decreases. The large speculators, as represented by the histogram, consist oflarge banks and hedge funds, organizations who manage large amounts of moneyfor speculative investment. It’s often asked: Is this a case of the dog wagging thetail, or the tail wagging the dog? Because these organizations manage and controlsuch large amounts of cash, do they react to the market, or does the market reactto them?What we’re seeing represented in this graph is the percentage of large speculators who are long versus short the market. For example, looking at the abovechart, you notice that the large speculators represented by the high point duringmid-October is over 85 percent, which means that over 85 percent of reported largespeculators were trading long or had long positions during that time frame. Alsonotice that as the market begins to drop, how quickly the large speculators reversetheir positions, where we then see a pictation where the majority of large speculators are now short the U.S. dollar during the month of December. Also take a closelook at the open interest line as it increases in volume; this is an indication of anincreased number of short positions being added to the market. It is very commonto see the open interest line increase in relationship to the market trend during astrong downtrend, and decrease during a strong uptrend.An emerging source of volume data is exchange-traded funds (ETFs) on currencies.One of the leading traders in the country, John Person, effectively uses these volume datasources. ETFs on currencies are fairly new opportunities for forex and equity traders.
TREND-RELATED INDICATORSWhile trend lines remain the most effective ways of determining whether a trend is inplace, many indicators have arisen that go further in detecting changes in the trend. Moving averages and their variations are often used by traders. Let’s consider some of themand how to apply them to help understand the trend.The basic technical indicator used to help interpret the trend line is the moving average. It has many variations. The most popular is the simple and exponential movingaverage. Other variations, such as the weighted and adaptive, have been developed toprovide traders different views on filtering the price information. What is important is torealize what the moving average does and what it cannot do. Moving averages are inherently late to the party. They lag behind the information and therefore have limitations ontheir use as triggers to put on a trade. But they are valuable in mapping the contours ofchanges in the trend.
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FIGURE 12.2 Commitment of Traders Chart. Chart courtesy of Track ’n Trade Pro. Copyright ©2007 Gecko Software, Inc.
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The simple moving average acts as an easy way to smooth out the price data. It’sa basic algebraic average that adds a new period and drops the first as time moves on.In the simple moving average, each period gets the same level of importance. This hasdisturbed some traders because earlier periods are overvalued. The exponential movingaverage provides greater weight to the most recent periods being considered, and thisreduces the lag of the resulting curve. There are many variations emerging on how tominimize the inherent lag of moving averages, such as adaptive moving average, fractal adaptive moving average, median adaptive moving average, and triangular movingaverage. The triangular moving average weights middle periods more than the earlierand later periods. Traders are always coming up with new moving average versions totest out.Which moving average is better? That depends, of course, on how the use of themoving average contributes to trading success. In Figure 12.3, compare the simple moving average to the exponential moving average and the triangular moving average. It’shard to answer that question. They are very close. For the trader, consistency in use ismore important.
FIGURE 12.3 Comparison of Moving Average Types.
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A consideration for the trader is to choose which time period to use for the moving average. The 50-period is considered an important hurdle, and when it is probed orpenetrated, the trader needs to pay closer attention because it is considered important.Combining two moving averages where one period is longer than the other createsthe moving average crossover. The idea behind it is to overcome the lag of one movingaverage. There are many variations of periods to use in a crossover. The 55-period versus the 5 and the 13 versus the 5 offer examples of how to use them. When a crossoveroccurs, traders will look to enter a position or exit one that assumed the previous direction, providing further confirmation is achieved.
MOVING AVERAGE CROSSOVERSCrossovers provide several uses in interpreting price action. We can see in the chartfor the GBPUSD 30 minutes (Figure 12.4) that the crossovers were of limited value insideways patterns, but did provide buy zones when the crossover turned up. Also note
FIGURE 12.4 Moving Average Crossover Followed by 50 Pip Move.Source: www.tradesignal.com.
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that the moving average acted as a firm support later on. Notice that the moving averageslook ready to cross over again, pointing down. If the candle probes below these movingaverages, traders looking to sell will have strong confirmation.Which crossover periods should be used? In the previous example, the 21 versus8 simple moving averages was used. But the selection of the moving averages is quitearbitrary unless they are back-tested. The experience of the trader is the most importantvariable to use.
MOVING AVERAGE RAINBOWS—MULTIPLEMOVING AVERAGESA very effective utilization of moving averages occurs when several are plotted at thesame time. The effect is to reveal areas of major convergence and also display visualization of changes in momentum. We can see in Figure 12.5 how a cluster of movingaverages provided important clues to a shift in trend direction and momentum.
FIGURE 12.5 Multiple Moving Averages Converge.Source: www.tradesignal.com.
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MOMENTUM INDICATORS AND OSCILLATORSAnother group of technical indicators available for forex trading are those that measurechanges in momentum by comparing extremes in value. These are also called oscillators.The goal of using indicators from this group is to identify whether a currency pair isapproaching extremes of being overbought or oversold.Moving average convergence divergence (MACD) is a popular indicator that givesthe trader a sense of a change in momentum by comparing two exponential movingaverages (see Figure 12.6). The settings used are the 24-period exponential movingaverages and the 12-period exponential moving averages. So what we have is a moving average crossover. MACD goes further and adds a 9-period exponential movingaverage of the difference between these two. The result is a visual clue when thereis a turn in the sentiment from being bullish to bearish, and vice versa. When usedwith other confirming indicators, MACD increases the confidence of a new trader forhigh-probability trades. The MACD tool is a standard part of the set of indicators, but
FIGURE 12.6 Example of MACD Histogram or Forest Chart.Source: www.tradesignal.com.
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there are variations in how it is presented. The difference between the two exponentialmoving averages can be displaced alone. Also, the MACD indicator can be presentedwith bars (histogram) showing more clearly whether the momentum is changing. Thislater version of MACD is also known as MACD Forest and is very agreeable and easyto use.Note that Figure 12.6 is an hour chart, and as a result the trader has to wait longerto detect a shift in the MACD condition. However, we will see that by shifting to ashorter time frame, this waiting period can be reduced. Figure 12.7 shows a 15-minutechart, where the MACD histograms provide, in effect, permission to enter a trade on thebuy side.An important observation for the trader is to spot whether there is a divergencebetween the MACD indicator direction and the price. When this occurs, the traderneeds to be careful in assuming that the price will continue in its direction. Figure 12.8shows the price’s being essentially flat while MACD is shifted down. Traders who arealready in a buying position would be looking to get out and sellers would be looking toget in.
FIGURE 12.7 Buy and Sell Signals Using MACD Forest.
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FIGURE 12.8 Divergence between Price and MACD.Source: www.tradesignal.com.
RELATIVE STRENGTH INDICATOR (RSI)The relative strength indicator (RSI) provides clues to whether the currency pair is overbought or oversold. It compares averages of period closes that are up against periodcloses that are down. It is an oscillator that has a range of 0 to 100. Traders using RSIshould note that when RSI reaches below 20 or above 80, it is considered to be peaking inits strength. But these levels do not mean they are automatic buy or sell signals. Applying
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FIGURE 12.9 Trade Signals with RSI.Source: www.tradesignal.com.
trend lines to the RSI indicator is a more precise approach to when and where one canput on a buy or sell trade. In Figure 12.9, the trend-line break in the RSI coincided withthe failure of the price to retrace above the Fib line. This provides added confirmation ofa weakening condition, and it is appropriate to apply trend lines on indicators that areoscillators, such as RSI. We see a similar RSI trend break failure in Figure 12.10.RSI has default settings at 14 periods, but traders can reduce this period to 9 to eliminate some of the lag and make the indicator a bit more sensitive to the recent actions.Traders should also note that there are variations in the RSI index formula producing different RSIs. But they all are relatively close, and personal preference will decide whichone you choose.
THE STOCHASTIC GROUP OF INDICATORSStochastic analysis is a general term describing a process of sampling random data togenerate information. In evaluating price action and in forex, the stochastic group ofindicators has the goal of providing insight into whether the price has lost its ability to
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FIGURE 12.10 Break of RSI’s Trend Lines.Source: www.tradesignal.com.
close at the highest or lowest level. In simple terms, when a currency pair is strong, oneaspect of this strength is that it achieves closes that are higher. At some point, it losesits strength and creates closes away from the top or bottom. The formula shows thatthe stochastic compares the most recent closing price with the low of a preset periodof periods (14) and compares it against the high of the same period. The result is theequation called %K:%K = 100[(C − L14)/(H14 − L14)]Then the stochastic indicator creates a moving average of the %K this is called %D.A three-period moving average is usually used. A crossover effect is generated when the%K line crosses the %D line.
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Once again, a technical indicator is trying to smooth out the data to show somethingof value. A high %K, for example, of 81 means that the price closing is percentage-wise81 percent of the range of the period measured. So the ability of the price to stay thathigh will be more difficult.Stochastics, like any indicator, are not predicting anything. They provide a usefulmeasure of the power of the trend. There are several variations of stochastic indicators. Most common are slow stochastics and fast stochastics. The difference is that slowstochastics are less sensitive to price movements and therefore provide less risk of toomany crosses. The settings for the stochastic indicator are usually defaulted at 14 periods, and 3 for the moving averages. Adjusting the settings to a smaller period of time,such as 8 or even 5 periods, can provide more sensitivity to changes.The use of the stochastics as supporting a trading decision can be seen inFigure 12.11. If a trader were looking for a buy or sell entry and saw that the stochastic indicator crossed (in the direction of the trade), it would be a green light to buy. Ifit did not cross, then further confirmation would be necessary. The stochastic indicator
FIGURE 12.11 Stochastic Crossover.Source: www.tradesignal.com.
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acts like a traffic signal. It’s always necessary to watch all the traffic, but if the indicatorhas crossed, then the trader could be focused on pulling the trigger.The following section explains how the stochastic formula was calculated in moredetail (adapted from http://help.geckosoftware.com/40manual/new/use indicators/fsto/faststo.htm).
CalculationParameters:
r Overall Period (3)—the number of periods used to determine the highest high andlowest low.
r %D MA Period (14)—the number of periods used to determine the moving averagefor the %D value.Formula:The first step in computing the stochastic indicator is to determine the n period high andlow. For example, suppose you specified 20 periods for the stochastic. Determine thehighest high and lowest low during the last 20 trading intervals. It determines the tradingrange for that time period. The trading range changes on a continuous basis.The calculations for the %K are as follows:%Kt = ((Closet − Lown )/(Highn − Lown )) ∗ 100%Kt: The value for the first %K for the current time period.Closet : The closing price for the current period.Lown : The lowest low during the n periods.Highn : The highest high during the n time periods.n: The value you specify.Once you obtain the %K value, you start computing the %D value which is an accumulative moving average. Since the %D is a moving average of a moving average, it requiresseveral trading intervals before the values are calculated properly. For example, if youspecify a 20-period stochastic, the software system requires 26 trading intervals before itcan calculate valid %K and %D values. The formula for the %D is:%DT = ((%DT − 1 ∗ 2) + %Kt )/3%DT: The value for %D in the current period.%DT – 1: The value for %D in the previous period.%Kt: The value for %K in the current period.
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VOLATILITY/RESISTANCE AND SUPPORT INDICATORSUnderstanding the volatility conditions of the market will significantly contribute to trading success because when prices are at extreme volatility they cannot sustain themselves. These conditions generate many kinds of trades. Trades can trade a reversal ofthe move, or anticipate a reversal and wait for the price to retrace and then enter atrade. There are several good technical indicators that provide quick visualizations andmeasurement of volatility. This set of indicators includes the classic Bollinger bands,volatility envelopes, STARC (Stoller average-range channels), and Linear regressionchannels.Bollinger bands provide an easy-to-see map of whether the price is at its upper orlower ranges. Simply stated, Bollinger bands are a statistical envelope around a presetmoving average period of 20. The bands represent 2 standard deviations from this movingaverage. In other words, the price is considered to be 96 percent of the time rangingbetween the two Bollinger bands. The Bollinger bands are simply a version of the 100year-old statistical bell curve that shows probability distributions of a sample population.When a price is probing an upper or lower Bollinger band, the trader should consider itan alert that the price may be getting tired and will return.Note that this is not a prediction that the price will reverse. In fact, the price maycontinue to hang on at the bands (this is called “hugging the band”) and it may also breakbeyond it. But the trader using the bands can sense the potential. The shape of the bandsbecomes important. If the bands are sideways, prices can bounce off the top and bottommore easily. If they are tilted, reversals are less likely. We will also see that when used incombination with other indicators, setups for trades start formulating.Figure 12.12 shows a Bollinger band with the default setting. Now let’s considerthe same chart with the addition of another band (see Figure 12.13). On the secondband, the setting is 13 periods with the standard deviation of 2.618. This is called anextreme Bollinger band. Jea Yu and Russell Lockhart in Secrets of the UndergroundTrader (McGraw-Hill, 2003) use the concept of extreme Bollinger bands with great success. The addition of this extra outside band provides an outer bounder for sensingwhether the price has reached unsustainable levels. Remember that at a 2.618 setting,almost 99 percent of the time the price is between the upper and lower Bollinger bands.If the trading platform does not allow for settings at 2.618, one can approximate with asetting of 3. Its use is most effective when the price reaches an extreme Bollinger bandand then proceeds to go back under the original Bollinger band. The forces that put theprice at an extreme have changed, and the likelihood of its going back to an extremeare much lower. It will require some new energy. In forex, we can invoke a version ofNewton’s third law: Prices stay in their pattern unless news moves them out of their pattern. The extreme Bollinger band also can act as an area for stops, which we will explorelater on.
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FIGURE 12.12 Standard Bollinger Bands and Stochastics Crossover.Source: www.tradesignal.com.
FIGURE 12.13 Extreme Bollinger Bands.Source: www.tradesignal.com.
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FIGURE 12.14 Extreme Bollinger Bands and Stochastic Crossover.Source: www.tradesignal.com.
We can see that the addition of the extreme Bollinger band adds a depth of understanding to the forex trader. It provides an entry condition as well. If the price moves toan extreme Bollinger band and then moves back below the standard Bollinger band (asshown in Figure 12.13), then the trader can look to enter a trade. In Figure 12.14 we cansee an example of the price reaching the lower extreme Bollinger band and coming backtoward the regular Bollinger band, but we see the stochastic also turned, giving extraconfidence that the price is ready to reverse (with extra confidence when the stochastichas turned as well!).There are other volatility indicators that use envelopes similar to Bollinger bands.The STARC band provides another boundary where the price has a tendency to staybetween the bands. For example, the same EURUSD chart shown with the extremeBollinger bands can be illustrated with STARC bands (see Figure 12.15). Notice howthe STARC bands form a channel and appear to provide support and resistance.The linear regression channel is a popular graphic tool that provides another envelope around the prices. If available on your platform, it can be used to project a potentialsupport or resistance area. The channel should be drawn so it extends into the future so
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FIGURE 12.15 STARC Channels Act Like Bollinger Bands.Source: www.tradesignal.com.
the trader can view the upper and lower resistance areas. In Figure 12.15, we see that theprice has probed the lower support channel, and watching carefully what it will do canpay off for the trader looking to buy on a bounce off this pattern or looking to enter a sellon the failure of the price to come back. Figures 12.16 and 12.17 are snapshots of howthis linear regression channel pattern worked recently on the EURUSD 15-minute chart.In Figure 12.16, we have drawn the linear regression channel from point A to point B.Point C is the future, and the price has not reached there yet. As stated earlier, the linearregression channel points to a zone of support around 1.3153.The trader does nothing but wait. Using the linear regression channel adds anotherlayer of confirmation. If the price is probing a linear channel, it is also very likely to beprobing support or resistance. The idea for the trader is to get as much confirmation aspossible.
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FIGURE 12.16 Linear Channel Pattern Probed by Price.Source: www.tradesignal.com.
FIGURE 12.17 Linear Channel Pattern Support Probed but Holds.Source: www.tradesignal.com.
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The use of any envelope type of indicator serves to provide an ability to increaseconfidence that a trade is worthwhile. No single one is itself sufficient.
OPTIMIZING YOUR USE OF INDICATORSThe developers of indicators offer default settings that are intended to be general in theiruse. However, the settings can be altered. Many traders try to alter the settings withouta real understanding of the basis of the alteration. Simple changes, such as increasingor reducing the periods used, aren’t particularly controversial. Generally, traders usethe default settings on the indicators. These defaults are there because a shorter periodmakes the indicators more sensitive to price changes, while a longer period smoothsout the indicator and increases its robustness. But the question arises as to what is theoptimum setting. For example, is 5, 3, 3 on the slow stochastic a better fit to the data thanthe default 14, 3, 3? It is possible to answer that question if an optimization program orback-testing program was available.Many platforms and charting services provide an ability to optimize the settings. Thefirst step in optimizing an indictor setting is to identify the time frame for the optimization. A period of price action too far back runs the risk of the trader’s optimizing againstconditions that are no longer there. Geopolitical and economic conditions change frequently, particularly in forex. This means that the period of optimization should be perhaps very recent, such as the one to three months. Also important is a selection of whichcandle chart to optimize. Will it be trading off day candles or shorter ones such as the15-minute candle? Additional challenges face traders using optimizing approaches. A keyfactor is the stop loss. Even after settings are optimized for gaining the most profits froma trading period, the use of stops and limits will ultimately affect the results.Once you have developed a trading idea, back-testing is the next step before turningyour idea into real, live trades. While paper trading with a demo or game account ishighly recommended for getting used to the real-time nature of markets, back-testingcan save you an incredible amount of time. By definition, back-testing is a simulation ofwhat would happen if you had traded your ideas in the past. You can test a day’s worthof trades up to many years, going back as far as there was a market for the instrumentyou are trading.
TURNING A TRADING IDEA INTO A TRADING SYSTEMBefore back-testing may begin, your trading idea has to be turned into trading rules thatare objective, reproducible, and able to be optimized. One common mistake is to try toback-test a trading strategy or idea that is based on subjectivity. Many popular methods
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leave out important parameters that you have to guess at. For example, methods underthe umbrella of “Elliott wave counting” are notorious for being difficult to back test,because where the wave is measured from profoundly affects the back-test outcome farmore so than the technique itself.As you develop trading rules, you will be amazed how many trading slogans such as“The trend is your friend” become meaningless, since they can be objectified into hard,cold trading rules.
Finding the Fittest SystemOnce an initial set of trading rules is established, you can begin simulating what wouldhappen if they were strictly followed over a period of time. The time series is the collection of dates and times when you will be testing the trading system. The fitness function is a function or measure that you use to compare systems and the basis on whichyou optimize a system’s parameters. For example, a fitness function could be net profitor loss.More sophisticated measures, such as the Sharpe ratio, Sortino ratio, and riskadjusted return, compare different systems as to their performance measured byvolatility, loss volatility, and portfolio risk, respectively.
Quick Back-Testing with ExcelInitial back-tests can be easily done in Excel. Simply paste your historical time seriesinto Excel, enter your formula, and apply it to all cells in the time series. The easiestway to express this is by assigning each type of market position by a –1 (sell), 0 (outof market), or a 1 (buy). Then calculate profit or loss, subtracting out a spread and/ortransaction cost.I recommend mastering Excel thoroughly before buying an expensive back-testingtool. This ensures that you know how back-testing works from the ground up.Typical articles on back-testing typically suggest two contradictory rules for the sizeof your historical data set. On the one hand, they suggest you use as large a data set aspossible, in order to “prove” that your trading system can work under any circumstances.Additionally, it is often said that you should only test your trading system under conditions similar to the current market. Subtly enough, these suggestions again introducesubjectivity. Instead of the trading rules’ being subjective to the trading system owner,now market conditions become entirely subjective. For example, you read typically ona web site about a trading system that has an annual return of 22 percent. It has hada consistent winning record over the last 12 months, and you’re ready to purchase thesystem (probably for far too much!) After you buy the system, you trade the systemrules exactly. When you fail to realize a 22 percent return and perhaps even a negative
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return, you are told that the market conditions have changed! So the trading system rulescannot predict market conditions any more than they can predict future prices based onthe past!This phenomenon reveals another common mistake made when back-testing. Curvefitting is a term taken from statistics, usually used to refer to nonlinear regression. I willexplain with an example. You are back-testing a simple trading idea that takes two parameters. The first time you run the back-test, you get a negative return. However, asyou continue to change the parameters, you notice that certain values produce higher,positive returns. If you choose the two parameters that, together, produce the highestreturns, you are essentially predicting that the time series of market data will look exactly like your historical test in the future. So, counterintuitively, the harder you work atback-testing, the worse your results in live markets. How do you mitigate this inherentproblem?There are several techniques for reducing curve fitting in a back-test. The first technique is to keep your trading idea intact. If you are unable to express your trading idea notonly in market actions, but also market action sizes, you need to go back to the drawing board and continue work on your trading idea. Additionally, you can back-test onmultiple markets and move the window of the back-test forward and backward to seekout market conditions, setups, or patterns that are ideal for your system. For example,you may want to back-test only on days where a certain economic indicator is released.Back-testing on the most recent data can capitalize on recent market “shocks.”Advanced mathematics provides many back-testing methodologies that are producing results pointing to the fact that volatility and volume