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Indicator | VWAP (Volume Weighted Average Price)
Category | Institutional Trading
Date - 8/6/2009 | Size 2.83 KB | Download Free MetaTrader VWAP Indicator Code Link
Description: Standard VWAP for MetaTrader
Indicator | DOUBLE CCI
Category | Statistics
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Indicator | Duel Volatility Bands
Category | Volatility / Probability
Date 7/28/2009 | Size - 4.59 KB | Link Temporarily Unavailable
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Using separate volatility band time frames, traders are able to identify price action volatility breakouts, while also identifying consolidation through short-term volatility collapsing back underneath long-term volatility.
Transcending Markets through Volatility and Probability
Almost all financial markets display relentless volatility for traders attempting to capitalize on trading within shorter-term timeframes. Because of the underlying “volatility paradigm”, many traders -both new and seasoned– stand significant risk of unforeseen losses should their trades move against larger order flow. Moreover, while order flow may not seem like a reasonably transparent variable in Forex, in reality, through descriptive statistics, we may not only predict when institutional order flow might start, but where such could end as well. In the end, through volatility and probability, savvy traders will learn to ‘ride the waves’ of order flow within markets.
At some level, I expect these concepts to be met with some sense of resistance, as traders find difficulty in leaving behind the ‘old notions’ of technical analysis behind. By this I mean, often when ‘the accepted standard of things’ is challenged, some have trouble letting go of the information that has been taught as reliable for so long. As the 19th Century playwright Henrik Ibsen stated in the 1882 play An Enemy of the People (1882) “the majority is always wrong.”
When truly looking into the markets, one thing is for sure; technicals can often lie to us by providing false signals or even worse, incredibly confusing signals as they unfold in real time. Regardless, many people continually take simple occurrences within technical analysis as fact, without ever actually considering the true paradigm of why the events are occurring on the charts anyway.
Really, technical analysis is only true, so far as enough people are acting on the same information at the same time. However, as we saw in Chapter Two, technicals are actually failing in today’s market, because too many people are acting on the same ‘pre-set’ information at the same time.
Moreover, in terms of the ’self-fulfilling prophecy of technical analysis,’ what if the majority are wrong most of the time already? Did you know about 95 percent of all retail Forex traders lose money?
However, if we are able to take a moment to step back from the larger concepts of technical and fundamental analysis, we just may very well be able to see that regardless of the action unfolding, at the end of the day, the outcome is nothing more than data.
In addition, ‘the data’ is actually telling us more than just a few simple facets about markets; the data is actually providing significant insights into probability, volatility and the fundamental underpinnings- truly moving markets.
Unfortunately, it is here that many may have trouble breaking away from the larger mindset of accepted technicals, fundamentals and economics, to truly begin to see that really, all markets have the capacity to predict volatility based on nothing more than probability itself. Let me step back for a moment and clarify. I’m not asking traders to completely let go of traditional fundamental and technical analysis, but instead, to simply understand that both economics and technicals are not only transparent through probability, but perhaps even predictable.
Fact is, technical analysis generally only perceives events in the past. What this means is that technical analysis is really the measurement of information that has already occurred, without regard to the probability of what could transpire in the future. In essence, technical analysis is nothing more than a study of history. However, there are those who will still argue technical analysis can predict the future…
While this may be true in some circumstances, fact is, technicals often lie. Have you ever entered a trade based on an overbought or oversold indicator, only to see the technicals breakdown and the trade move against you?
Case in point, in the amazing book Technical Analysis for the Trading Professional by Constance Brown, in Chapter she inquires, “How did I miss such an obvious signal like that one?”
Constance Brown is truly correct, because many traders really do not ever look into ‘why’ what is happening – is actually happening. Repeatedly, I see traders completely disregard the philosophical, fundamental, mathematical and common sense reasoning behind the charts they trade from. I can assure you that when these people grow sick and tired of losing money, they will finally either quit, or start putting in the time. Regardless, trading purely off technicals is similar to looking for Braille in the drive-thru.
The reason behind why technicals lie is simply that when one infers an outcome- based on their chart(s), the real money driving the outcome truly feels an entirely different way. (The aforementioned is simple I know, but it is the truth.) What’s more, at times, traders who have no real reason for being in position in the first place will jump ship at the first sign of trouble, thus creating additional circumstances of extreme volatility.
The same logic applies to fundamentals, in that enough people have to believe in a future outcome for the actual trading action to mimic the “outcome perceived” by those same people. However, when fundamentals shift, many technical traders are not even aware that the occurrence has taken place and thus, find themselves not only on the wrong side of the trade, but often stopped out at exact high and low prints of the relative range- as well.
There is a different way to trade though… When we begin to understand that descriptive statistics not only allow us to intuitively ‘feel’ the true fundamental sentiment behind the market, while also overstepping the simple ‘hope’ that often comes with charting, we begin to see that both technical and fundamental perceptions of the larger market are actually transparent within the data unfolding before our very eyes.
Descriptive statistics are defined as:
[1] Descriptive Statistics are used to describe the basic features of the data gathered from an experimental study in various ways. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. It is necessary to be familiar with primary methods of describing data in order to understand phenomena and make intelligent decisions.
Various techniques that are commonly used are classified as:
• Graphical displays of the data in which graphs summarize the data or facilitate comparisons.
• Tabular description in which tables of numbers summarize the data.
• Summary statistics (single numbers) which summarize the data.
My question for readers is this: How can anyone make an intelligent decision in markets, without understanding the data at hand?
See, so many traders use technical analysis to decipher their entry and exits without truly evaluating the data presented before them. What’s more, tack on complete oversight of the common sense economic fundamentals and truly, it’s no wonder their trades are often stopped out at the top, or bottom. Economic fundamentals aside, let’s discuss how technical analysis completely overlooks the “real data” traders need. Foremost, technical analysis is a “lagging event,” meaning the visual representation we see on our charts can only exist insofar as the event has already occurred. In short, an equity, index, option, commodity, futures contract, or even currency must have already witnessed a trade ‘print’, before the data ever even shows up on a chart. What this means is that virtually all traditional technical analysis is that of a lagging event to predict a future occurrence…
Really, the true wealth in technical analysis is the identifiable and/or representative display of descriptive statistics. When we change the way we think about technical analysis from the start…and understand that it the technicals occurring are not a simple ‘chart, or indicator’, we begin to see that the visual representation of charts on our screens are really ‘historical data’ we can use to statistically measure volatility and probability.
For the purposes of this chapter, we will be using normal distributions within our statistical measurement of trading action to imply volatility, trend and/or reversals. The application of a normal distribution applies to trading insomuch as the data we are measuring is constantly moving with the periods studied. Because the mean (seen through moving averages) of the data in constantly in motion as prices rise and fall, the ‘data’ (prices) will never stay skewed on one side of the mean, or another. Eventually, the data will cross back over the mean (moving average), because the moving average eventually travels in the same direction as the data. The ‘traveling factor’ behind a moving mean proves that the data measured will eventually return to the mean and likely cross above, or below, possibly even extending significantly in the opposite direction.
It is the concept of the mobile mean (where old data drops off – i.e. the 1st day of a 50-period Simple Moving Average being replaced by each new day of data presented) that justifies measuring volatility through standard deviations under a normal Gaussian curve. (Really, we’re talking about the Central Limit Theorem.)
What’s more, as John Bollinger points out in his book Bollinger on Bollinger Bands, the Central Limit Theorem tells us that even when data is not normally distributed (as is the case with virtually every financial instrument, including Forex), “a random sampling will produce a normally distributed subset for which the statistical rules will hold.”
In short, smaller samples within the market will not produce the kurtosis of the larger data set.
Gaussian Curve Revisited
Within descriptive statistics, we will use a random normal distribution (Gaussian Curve) to measure volatility via standard deviations. In terms of the Gaussian Curve, we are looking at a normalized distribution, where the sum of all values of x, are meant to equal 1. What this means is that if we are measuring price movement within 21-periods on a 5-minute chart (for example), the result should equal a probability of 1.
In other words, the sum of all values that have transpired in the measured period will give us a probability of 1 (an empirical absolute) that the events have happened. What we’re referring to here is a probably of 1 that all of the data presented will rest within our chart.
Moreover, by using historically measured occurrences of data, we can also use the same data to infer probability of future events.
In short, measuring probability and volatility through mobile means (moving averages) and standard deviations, we can infer volatility induced trending, volatility consummated consolidation and total potential ranges, before they ever even surface. Additionally enticing, when extended volatility strikes our measured probability ‘cut off’ we will also know to begin looking for a reversal, though we will also know to watch for a dangerous continuation at the same time. In short, we are using historical data to infer the probability of future events, based on quantitative probability, not just traditional technical analysis ‘hope’.
How will we do all of this?
As you are likely already aware, data within a Gaussian Curve is measured in ’standard deviations’ from the mean.
The arithmetic mean, of course, is the average of all prices recorded in the period we are studying. Thus, by being able to calculate the average prices for a particular period, we can also measure probability of movement away from the mean through standard deviations.
If you remember your old statistics days, you may also recollect that the majority of all data occurrence probability falls within three standard deviations of either side of the mean. What we know is that 49.86% of all the data should rest within three standard deviations of each side of the mean.
In translation, measuring three standard deviations on both sides of the mean, equates to 99.72% of the data within measured period.
In other words, there is a 99.72% probability that all of the data will fall within three standard deviations of the mean.
Breaking the standard deviations down, there is a 34.13% probability our data should rest on one side (above, or below) of the mean.
Moving out a little further, there is a 47.72% probably that all of the data will sit within two standard deviations of one side of the mean, and a 49.86% probability that all of the data will rest within three standard deviations of one side of the mean.
However, while there is one standard deviation above the mean, there is also one standard deviation below, and thus, know to multiplying all of our probabilities by two, to calculate all data on either side of the mean.
There is a 68.26% probability all of the data will lye within one standard deviation of both sides of the mean, 95.44% probability of all of the data will rest within two standard deviations of both side of the mean and a 99.72% probability of all data in our period measured will reside within three standard deviations of both sides of the mean.
In terms of measuring the ‘mean’ for this chapter, we will only be using Simple Moving Averages and not Exponential Moving Averages (which put more weight on near-term price data of the latter.) It’s true that Exponential Moving Averages track true price action more closely, however, we’re going to just ‘keep it simple.’
I think Mr. Bollinger presents a great argument for using Simple Moving Averages in his book, asserting that we are simply adding one more variable to an already complicated scenario by using Exponential Moving Averages. I’m not saying that you shouldn’t use EMA’s, but for these pages (at least for now), we’re going to keep things clean and simple.
As many likely already know, a Simple Moving Average (SMA) is created by summing the data (close prices, for example) for any given period and then dividing by the total number of periods summed. For example, on a five minute chart, a 20-period SMA will present the arithmetic mean, or average of all closing prices for the past 20 5-minute bars.
The Simple Moving Average, then, is the ‘mean’, which we are basing our standard deviations from, when attempting to measure total ‘probability’ of breadth, or ‘wingspan’ of a given data curve.
The above chart shows a 20-SMA, which will be the basis of our analysis here. Please note that what you’re seeing is ‘more than just a moving average’, instead, we are seeing a visual representation of the “mean data range” of the past 20 periods, which we will derive statistical data, volatility and probability from.
In essence, we are visually seeing the middle point of our data curve, by charting a Simple Moving Average.
With the mean identified, what we must now imagine (and map) is the curve of data that sits on either side of our average. We are talking about the ‘distribution’ of data that sits on either side of the mean…
The distribution is really a sort of teeter-totter (around the mean), where we are able to actually see how the data is unfolding on our chart.
We also know that there should be a 99.7% probability that all of the data will sit within three standard deviations of the mean…
As a picture tells a thousand words, just below you will see the actual visual representation of the Gaussian Curve on a chart.
Please note that I am using a 50-period Simple Moving Average (SMA) to measure the Gaussian Curve in the larger data set. Why the 50-SMA? Because measuring 50-periods generally allows us to capture enough data that we are able to infer larger trends within virtually every charting time frame.
What’s more, because the 50-SMA is highly coveted by many traders, we are actually putting the “self-fulfilling” prophecy side of technicals in our corner, attempting to not only accurately measure volatility, but also understand the greater mentality of retail traders at any given moment.
Looking at the actual chart (below), traders will notice that when we simply overlay three representative curves (denoting the 1st, 2nd an 3rd standard deviations) with the mean being the 50-SMA, we begin to see how the Gaussian Curve looks when mapping actual data.
Now though, let us think of the Gaussian Curve in another format… Let us imagine the curve as an actual “teeter-totter” like scale, which will move from left to right as the data that sits on it moves from side to side.
The question is though, how will we measure the data shifting from left to on the teeter-totter, thus providing some insight to ascending, or descending prices?
Stay tuned… More to come!
“Exit Is Everything…”
Disclaimer
U.S. Government Required Disclaimer - Commodity Futures Trading Commission Futures and Options trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures and options markets. Don't trade with money you can't afford to lose. This is neither a solicitation nor an offer to Buy/Sell futures or options. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this web site. The past performance of any trading system or methodology is not necessarily indicative of future results.
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