We read Market Timer from left to right – just like we read the newspaper.

Ignoring the Date column, we will start with the

Average Volume per Share (AVS) and move left to right on the spreadsheet to the LT Signal and the Price Anomaly, column CM, (not shown below).

Let’s take a closer look at each column:

(1) Average Volume per Share (AVS)

The Average Volume per Share or AVS column is simply the total NYSE or Nasdaq volume for time period divided by the total number of shares traded that day.

We make a mental note of its level. A normal range is 1000 to 1500 (x1000) on the NYSE and 650 to 950 on the Nasdaq (x1000) at the close of trading.

The NYSE it usually only goes above 2000 when there’s a lot of volatility in the market like the period from April – June 2010:

The AVS usually only goes below 1000 when it’s near a US holiday (Christmas, Easter, Thanksgiving, New Year’s etc.) or when trading is light during the height of the summer vacation period in July and August.

The only time AVS becomes useful as an indicator of future price action is when the market has been in a steep decline for some time and then suddenly finds a ‘bottom’ at which time it starts to decline from 2000+ levels to normal levels mentioned above.

This gives us an indication that volatility will likely decline, and the market may be ready to enter a new bull phase.

Of course, we look for additional confirmation from MT signals before taking any action.

(2) SPX / Dow Change

Instead of looking at the level of the DOW we simply note the price change for the day positive or negative. We chart the level of the DOW with our LT signal which is included in each weekly Insights newsletter.

(3) MT Supply/Demand Index (MTSD)

If MT Volatility is our barometer for the market environment (bullish or bearish), then MTSD is the mercury. MTSD is the measurement of the supply/demand liquidity and the amount of risk (on or off) assumed by market participants.

In other words, the more bullish the herd is the higher MTSD will register.

The more bearish the herd is the lower MTSD will go.

Extreme reading +100 or higher or -100 or lower sometimes indicates a new trend or the end of the trend – must be analyzed in the context of MTVol levels and BoP.

(4) MT Volatility (MTVol)

Mentioned above, the trend is as important as the level of MTVol. Watch the trend – is it higher or lower over the last 3-7 days? What level is it at? Under 100 over 100? Sharp increases or decreases (5% -10% or more in a single day) sometimes signal a change in trend of the herd.

(5) Pipeline

Provide ‘advanced’ MTVol data on whether the herd is moving toward selling or buying. Watch the ‘3rd column’ as most MTVol trends start there… ‘seeing red’ in the third column typically means there are pockets of seller starting to herd in the market and MTVol will start rising… when it turns from ‘red’ to ‘white’ in the 3rd column it typically means we are starting to see pockets of buyers herding in the market and MTVol will start declining.

(6) Flow

As mentioned above, FLOW is the average f the Pipeline columns so in a single glance it can inform us of what MTVol might do in the next few days or weeks. But we’ve also created a very basic ‘trading system’ around the FLOW column (see below).

We watch for ‘bottoms’ – typically in the 40%-60% range and peaks in the 150%+ range.

(7) Balance of Power (BoP)

The BoP is how we ‘visualize’ bullish and bearish herding moving through the markets. We use it to see if there’s more bullish or bearish herding for a given period of time, look at herding trends, identify anomalies such as ‘long tails, and ‘bull’ or ‘bear traps’, ‘orphans’ and more (see below).

We also look to see if the herding is ‘spotty’ – meaning that some columns are filled in while others are not. This typically happens when the bullish or bearish crowd lacks conviction and alerts us to a potential reversal or the end of a trend.

Below is an example of ill-formed bullish herding and ‘spotty’ gaps which gave way to strong bearish herding:

When we do our daily analysis, we look for ‘well formed’ herding. If we don’t see well-formed herding then we can deduct that the bullish or bearish herding is weak.

We also look to see if the herding on one side or the other is ‘thinning’. Thinning is when the only herding is in the 5th and 6th column (counting from left to right) on either side of the BoP.

Here’s an example:

In the example above, the bullish side of the BoP started to get ‘thin’ (restricted to the 5th and 6th columns) setting up the ‘bull trap’ and possible take over by the bears.

(8) BoP Curve

The newest of the indictors for Market Timer, the BoP Curve gives us an idea if the BoP will see bullish or bearish herding in the future.

BoP Bullish Curve
When the bullish curve is rising it indicates we will see price rise as well and see more bullish herding.

When the bullish curve is declining it indicates there’s a temporary decline in bullish herding and there could be a pullback in prices.

BoP Bearish Curve
When the bearish curve is rising it indicates we will see prices decline as well and see more bearish herding.

When the bearish curve is declining it indicates there’s a temporary decline in bearish herding and there could be an advance in prices.

(9) Strength Trend Index (ST)

As stated above, ST helps determine the intensity of market movements. It’s a our ‘momentum’ indicator.

When we do our analysis, we check the reading to see if it’s outside the benchmark ranges. If it is it sometimes indicates a move in that direction could start a new trend.

(10) Swing Signal Algorithm

The Swing Signal algorithm is sensitive to shifts in the market typically before they happen. Often, we will see the Swing Signal turn ‘up’ or ‘down’ a few days before we see the market turn.

We want to see if the Swing Signal algorithm changes direction or turns ‘neutral’ at important market swing highs and lows.

(11) DOW Cycle Points: 21 vs 27 Day SUM’s

We use a 21 Day vs a 27 Day SUM optimized cycles for the DOW. Rather than an average, we add each day’s net price change to the last for 21 days cumulative and 27 days cumulative and compare them on the graph. We chose the 21 days and 27 days based on our market cycle studies.

You’ll see our Cycle Points analysis occasionally in the Insights newsletter when it gets to extreme levels.

We like it because the SUM’s provide real-time price action data, without smoothing. Price average indicators such as moving averages, stochastics, MACD and other price average indicators smooth price action and lag the market. These studies can give us a better indication of overbought/oversold conditions in the market using real-time price data without lagging or smoothing.

(12) LT Signal

Our Long-Term Signal algorithm can identify important market tops and bottoms.

When doing our analysis, we look at the rate at which the signal is increasing or decreasing. If it starts moving sideways with little to no progress in either direction it can often point to a change in market direction.

(13) Price Anomaly and Price Expected

Price Anomaly is also the difference of the Dow Price Change and the Price Expected but it’s expressed as an absolute value. In other words, the difference is always a positive number. We made it an absolute number because it’s not important if it’s positive or negative only if it is a small or large number as described below.

Price Anomaly (PA) measures the elasticity of prices based on our Price Expected (PE) algorithm. The PE predicts what the price change should have been based on the data we input on the spreadsheet. When the PE and the actual price change in the index is out of alignment the PA is ‘stretched’ causing a spike like the one’s on the chart below and the response is typically a ‘snap back’ in the opposite direction for prices. The majority of large PA’s come on declines, but not always.

A normal ‘level’ of the PA is ≤100. A level under 100 means that the internal market data and prices are positively correlated or in ‘alignment’. Anything ≥200 is a significant ‘stretch’ and a ‘snap back’ is anticipated. A PA level between 100 and 200 is still significant but has fewer ‘snap back’ success.

When prices are declining, and volatility is high the PA can be stretched daily and that’s why we often see the market up by 400 points one day and down 400 points the next which we call a ‘snap back’.

We asked the question, “Is there any way to anticipate these ‘snap backs’ during periods of high volatility?”

Let’s see…

The example above shows the DOW advancing +673 points on 3/23 and the PA was 280.

This indicated a ‘snap back’ could be anticipated. The PE algorithm based on the internal market data we entered into the spreadsheet said the prices should have been up 393 points not 673 so the move was out of alignment with the PE and the market needed an ‘adjustment’ in prices.

The ‘adjustment’ of prices that are out of alignment with the data meant that prices would, in this case, ‘snap back’ by declining.

A ‘snap back’ is always opposite direction of the day in which the PA occurred.

The next day prices declined -344 points.

In the same example above we see a second instance of a ‘stretch’ and ‘snap back’ – this time in reverse starting on 3/29.

The Dow declined -458 points, but our PE algorithm indicated the price should have been lower by -1100 points. The PA was 641, a very high number. Another ‘snap back’ could be anticipated. In this case the Dow would ‘snap back’ the next day by going higher.

The next day the Dow went up +389 points.

In the next example below from 4/18 to 5/2 we see that prices were declining, for the most part, but the PA each day was less than 100. This indicates that prices are in alignment with the data and no snap backs are likely to occur. This is simply normal price action and the data and the PA algorithm are aligned.

Using PA to anticipate ‘snap backs’ in high volatility market environments provides us with another critical tool for trading success – and can sometimes ‘forecast’ the next day’s change.

In the example above at point “1” we see a PA of 141. We got a ‘snap back’ the next day and the DOW declined 301 points.

At point ‘2”, we also see a high PA… this time with a reading of 177. The next day the DOW advanced 171 points – almost the exact PA reading of 177 from the previous day.

It’s infrequent but sometimes a ‘price projection’ of the next day’s closing prices could be forecast using the previous days PA reading.

RE: ‘DOW Price Expected’ Column

We get a lot of questions about the “Price Expected” column to the far right in the spreadsheets – mostly from those who want us to ‘predict’ the next day’s closing values!

But the column is simply the price change that the DOW should have made that day according to the algorithms predictions based on our model.

We do not use the column as a signal. The purpose of the calculation is to act as a ‘self-correcting’ correlation algorithm for our signals.

In other words, it’s a dynamic adjustment for the algorithms that generate the MT signals.

The result is we are always correlated to the market and we don’t have to manually correct or adjust our algorithms based on changing market conditions.