Stock Market: How To Use Divergence In Stock Market Trading?

What is Divergence Analysis?

Divergence, when used in conjunction with other indicators, offers a way of forecasting potential reversals and continuations in price and stock market action.

Welcome back, traders! After learning about Fibonacci levels and fan lines in our last blog post, let's move forward with the divergence concept and how we can improve our stock market trading using it.

Few Words About Divergence

Divergence can also be found on all wave degrees and timeframes. It is particularly useful when identified between non‐correlated data series.

To determine if there is divergence, we compare primary data direction with supporting data direction. Supporting data series can be more than one.

Generally speaking, divergence occurs when primary data and supporting data are going in opposite directions(one of the three directions). 

Primary Data = Price

Supporting Data = Oscillators, Volume, Indicators, Open Interest, Sentiment Data

Both the data series may not always need to go in opposite directions for the divergence to exist. One of the other data series can go in a sideways direction as well.

Let’s see what it means in opposite directions.

Below are six use case scenarios in divergence:

 

1. One data series is up while the other is sideways

2. One data series is up while the other is down

3. One data series is sideways while the other is up

4. One data series is sideways while the other is down

5. One data series is down while the other is up

6. One data series is down while the other is sideways

Below are scenarios in non-divergence:

1. Both data series are moving up

2. Both data series are moving sideways

3. Both data series are moving down

To technically find out if the divergence is occurring we may check:

• The sequence of peaks and troughs (or swing highs and swing lows, respectively) to identify the direction of the data series over a specified number of cycles or oscillations.

• Slope (or angle) between two data points to identify the direction of the data series over a specified range or duration of data (especially in the absence of visible peaks and troughs).

Understanding of wave cycle(amplitudes & periods) will be helpful here in understanding the divergence analysis.

Please have a look at the below drawings for the definitions of directions in divergence:

The above definitions are based on the peaks while the below definitions are based on troughs:

You can figure out the other combinations with respect to peaks and troughs between the main and supporting data series. Described in the below drawings:

In the above drawing, we can see the concept of confirmation with peaks and troughs moving in same direction in main and supporting data series.

In the above drawing, we can see the concept of non-confirmation with peaks and troughs moving in opposite directions in main and supporting data series.

If you can see this closely, confirmation and non-confirmation have taken place in the same wave cycle.

Reversal and Continuation in terms of Wave Degrees

The below drawing shows the reversal of the current larger trend in terms of wave degree.

Please observe the peaks of both the data series in the same wave degree(between vertical dotted lines).

Standard Bearish Divergence

It is also called standard bearish divergence, look at the data at supporting data series which is signaling early trend change. This is why oscillators are known as leading indicators(sometimes earlier than the price which is the main data series).

A standard bearish divergence occurs when only price, and not the supporting

data series is making equal or higher peaks, or when the slopes between the

two data series are diverging.

But this is not always the case. See the below drawing of the continuation of a current larger trend.

Reverse Bearish Divergence

In the example below, the supporting data series is showing the reversal however the main data series keeps on continuing in the direction of the larger trend.

Reverse bearish divergence also occurs when only price, and not the supporting data series, is making equal or lower peaks. But in this case, price is expected to continue to make lower peaks and troughs, establishing the subsequent downtrend.

The core for bearishness here is as follows: although rising peaks in the supporting data series indicate increasing momentum, price still fails to rise above its previous peak, which is potentially bearish for price.

We call it reverse bearish divergence. This is the reason we should always use more than one supporting data series and they should be noncollinear.

Standard Bullish And Reverse Bullish Divergence

Please observe the below drawings closely to understand the divergences. 

Standard Bullish Divergence

A standard bullish divergence occurs when only price, and not the supporting

data series is making equal or lower troughs, or when the slopes between the

two data series are converging.

Reverse Bullish Divergence

This type of divergence may also be likened to a failed Bear Setup. 

The core for bullishness here is as follows: although falling troughs in the supporting data series indicate diminishing momentum, price still fails to penetrate below its previous trough, which is potentially bullish for price. 

As in all reverse divergences, price moves in opposition to the direction indicated by the supporting data series at non‐confirmation.

Price Confirmation In Divergence Analysis

**To further increase the potential strength and reliability of the qualifying action, price confirmation is potentially most reliable when:

1. Accompanied by at least three weakly or non‐correlated oscillators or indicators.

2. All three non‐correlated oscillators and indicators indicate divergence and are in agreement with each other.

3. All three non‐correlated oscillators and indicators also indicate non‐conflicting divergences.

4. The expected time of breakout for price confirmation occurs in proximity to projected cycle lows for upside breakouts and cycle highs for downside breakouts.

**Price confirmation in the main data series is best determined by a penetration

or violation of any significantly clear and obvious price barriers, which include:

1. Support or resistance levels

2. Indicator overlay barriers

3. Psychological levels (e.g., double‐ and triple‐zero‐digit prices)

We will now look at the above points with respect to price confirmation and divergence in the below drawing.

I am just making one drawing to let everyone understand how using non-correlated data series is advantageous.

You can take reference of below for other divergence types.

This is the advantage of using non-correlated supporting data series. In the above drawing, two out of three supporting data series are indicating an upside direction which is confirmed by price giving breakout.

The use of weakly or non‐correlated data series will also help increase the probability of a divergent signal being more reliable and reducing the misleading effects of multicollinearity.

Divergence analysis is a widely used concept in stock market trading and investing. On longer timeframes, it will be more beneficial as there will be no noise.

Our lesson on divergence analysis ends here. We will meet again with the next blog post with supply and demand analysis.

Let's move to our stock market book alert for this post.

Book Alert:

Two Roads Diverged: Trading Divergences (Trading with Dr. Elder Book 2)

-BY DR. ALEXANDER ELDER

KEY TAKEAWAYS

• Divergence is a disagreement between the patterns of indicators/oscillators and prices.

• Divergence works as a leading indicator.

• It is always best to obtain price confirmation when using divergence.

• For better signals, the use of non-correlated supporting data series must be used with the main data series.

• Observation is the key: observe peaks and troughs with wave cycles and degrees.