In some respects, this is nothing more than Economics 101 – examining the supply and demand curves, and the point at which they intersect. Since supply and demand are each a function of both quantity and price, it would seem counterintuitive to notice any fluctuations in the one without paying attention to (and assuming a relationship with) the other.

**Using The MACD**

So what is the best way to reconcile increasing or decreasing volume with price fluctuations? No one has a definitive answer, but Marc Chaikin has come close. A septuagenarian stock trader and analyst out of Philadelphia who has created several indicators throughout his distinguished career, many of Chaikin’s inventions are staples of Wall Street technical analysis today. His most famous and most used is the Chaikin Oscillator.

The Chaikin Oscillator is a third-derivative technical analysis indicator – an indicator of an indicator, the latter of which is derived from the stock price. Chaikin’s oscillator builds on the concept of "Moving Average Convergence/Divergence"or MACD (pronounced “mack dee.”)

MACD is derived from the moving average, which is the mean price of an issue over a certain period. If stock XYZ closed at $22 two days ago, $23 yesterday and $24 today, that’s a moving average of $23, notwithstanding that a moving average is typically calculated over a longer period than just three days.

The above example is a

*simple*moving average. Its complicated sibling, the exponential moving average, weighs recent prices more heavily. While the simple moving average is $23, the exponential moving average would be somewhat higher. The actual value depends on which exponent you use.

In practice the exponent is notated as a “smoothing factor,” a coefficient between 0 and 1 that indicates the relative weight given to the most recent prices when constructing the moving average. A smoothing factor of 1 would ignore all but the latest closing price, while a smoothing factor of 0 would weigh all days equally (and thus give you nothing beyond the simple moving average.) A standard smoothing factor for calculating exponential moving average is 0.05 or 0.1. Many analysts use both and then take the average of those.

As for MACD, it’s just the 26-day exponential moving average subtracted from its 12-day counterpart. MACD is a measure that tries to distinguish short-term from long-term pricing trends and then use the difference between them to make an educated guess as to what a stock is going to do.

**But Wait, There's More: How Do You Get From MACD to Chaikin?**

To get from the MACD to the Chaikin Oscillator takes a couple more steps. The Chaikin Oscillator was created in reference to the "accumulation/distribution line," another brainchild of Marc Chaikin’s. (Note: Even though it’s 10 syllables long, resist the temptation to shorten the accumulation/distribution line’s name to the “A/D line”. That’s synonymous with the advance/decline line, which is something vastly different.)

We’ll get to the accumulation/distribution line in a second, because it first rests on the notion of yet another Chaikin creation, the "money flow multiplier," which attempts to quantify the amount of money coming into the market with respect to the price of the stock. As elemental as the idea of thBut e money flow multiplier is, at this point we have no choice but to finally break down and use equations instead of prose to explain. It is simply the following:

{(2 x close) - high - low)}/(high - low)

Over whichever period you want. If the stock in our previous example, XYZ, had peaked at $25 during the aforementioned three days and fell as low as $21 at some point, that’d be a money flow multiplier of 0.5. Then, multiply that by the quantity of stocks traded over the period to get the "money flow volume." Plot the running total of this over time and that’s your accumulation/distribution line. The final step is to apply the accumulation/distribution line to the MACD. That’s all.

To summarize:

1. Determine a stock’s high, low and closing prices over a certain period.

2. Add, subtract and divide them as above, then multiply by the volume over that period.

3. Do this again, for every day in the period, then measure the progress (or regress, as the case may be.)

4. Take the 26-day and 12-day exponential moving averages of the trading issue and subtract the former from the latter. Plot that.

5. Subtract the 3-day exponential moving average from the 10-day exponential moving average of

*that*.

The result is a value that, regardless of its starting point or the price of the underlying security, has an origin of 0. (A perfectly inert market would mean neither negative nor positive values for the Chaikin Oscillator.)

What the oscillator lacks in simplicity, it makes up for in authority. By measuring the momentum of the accumulation/distribution line using the MACD, the oscillator should anticipate when the line will change direction. By now we’re several levels removed from the stock price itself, but Chaikin devotees argue that such distance is necessary to determine the importance of certain volume and price changes. Also, the three- and 10-day values are mere convention, not necessity. Using six- and 20-day exponential moving averages will result in a Chaikin Oscillator that changes direction less abruptly.

**The Bottom Line**

At its most basic level – and “basic” is a relative term here – the Chaikin Oscillator is supposed to offer straightforward advice: buy when positive, sell when negative. However, the judicious adopter of technical analysis will use the Chaikin Oscillator only in conjunction with other indicators. When they all recommend the same strategy, that’s a stronger signal to buy, sell or hold than is the Chaikin Oscillator acting on its own.