It’s number crunching time!!!!!

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One of the basic tools that every trader interested in technical analysis employs is theĀ *moving average*. I know math can be boring, but if you’re going to look at trends, and want to dig into how a stock’s price has performed over time, you need to see the best representation of that data that you can get!

Problem is, understanding stock-price behavior is a pursuit fraught with the peril of a lot of extraneous noise. There’s investor psychology at work, there’s the impact on the market real-time business news, and a slew of factors besides. Moving averages are one way to look at stock prices and strip away some of that static.

**Moving averages: three types to start your charts**

Trades tend to focus on two types of moving averages: theĀ **simple**Ā and theĀ **exponential**. Both will serve as go-to tools in pursuit of your next informed decision on the market.

Let’s have a look at what they do.

**Simple Moving Average**

To plot a simple moving average, first gather stock closing prices from the period of time you wish to examine. Let’s look at our fictional model company, Lilo Chemicals.

For our purposes in this example, Lilo’s closing prices over the span of 10 days will be as follows: **$112**; **$114**; **$116**; **$113**; **$114**; **$115**; **$117**; **$116**; **$118**; **$119**. Say you want to see a simple moving average for seven days. To calculate the first of these SMAs — one that represents Day One of seven — take the first week of closing prices, add them together and divide by seven.

**The result is $114.43**, in this case.

To get Day Two’s SMA, redo the computation, but start your addition one closing price down the line. Instead of beginning your addition with $112, you begin with Day Two: $114. And so on.

As you build your simple moving average, one of the things you’re doing is “smoothing out” the impact of any variation in price. That is, when Lilo Chemical’s stock fell from $116 to $113 a share on Day Four, your simple moving average will take into account the higher prices of the preceding three days.

This is one way to get an accurate understanding of a stock’s real average, over time.

**Exponential Moving Average**

Exponential moving averages give higher importance toĀ **recent**Ā changes in a stock price. If you want a snapshot of what a stock price is doing in the near present, this is probably the tool for you.

Let’s say you wanted to run an eight-day exponential moving average on the same Lilo prices we looked at above. First, you’ll need what’s called aĀ *multiplier āĀ *something that can give that extra weight to the most recent prices.

**The Multiplier**

To get it, take your number of days (8), add 1, and then use that number to divide the number 2. In our example, this means: 2 divided by 9, making our multiplier is 0.22222

**2/(8+1) = 0.22222**

Now, start with a closing price. Let’s measure from Day Two using the example above, meaning **$114**. We also need the previous day’s simple moving average (which we already computed): **$114.43**.

Notice something? Yes, your first data point will be built on a simple moving average. How else are you going to start? After the first computation, though, it’s all exponential.

**The Equation**

ToĀ find your first EMA, run this equation:

**($114 – 114.43) x 0.222 + $114.43**

We’re subtracting the Day One simple moving average from our Day Two closing price. Then we’re multiplying what we get by **0.2222** and adding the product to the Day One simple moving average. The result is our first exponential moving average: **$114.33**.

To build your set of EMAs after this, just swap in the Day Three closing price and the new EMA you’ve just created. The third EMA uses the Day Four closing price and your second-created EMA, and so on.

**Weighted Moving Average**

The concept of weighted moving average is grounded in the belief that recent data should carry more weight than older data. Investors are always asking their stocks, “What have you done for me lately?”

But how do we assign a weight to each data point?

Here’s where it gets interesting. We know that taking the average involves adding numbers together and dividing by the number of data points used. If we add the same data point to the mix multiple times, the average will be weighted more towards that number.

Assume the closing stock prices for the last 5 days of a Stock A are **$100** (oldest), **$101**, **$102**, **$104**, and **$103** (newest). Ā Computing for the weighted moving average requires that we place the heaviest weight on **$103** because it is the most recent data point. The **$104** data point would have the second highest weight and so on down to **$100** which has the lowest weight.

We can assign a weight of Ā **1**, **2**, **3**, **4**, and **5** for **$100**, **$101**, **$102**, **$104**, and **$103**, respectively. Ā Each data point will be multiplied by their respective weights, the results of which will be totaled. Ā The total amount will then be divided by the sum total of the weights. Here’s what we mean:

The underlying principle behind the weights makes sense. Ā Consider the fact that despite the trust you have in a friend, youāll find it hard to forgive them for betraying your trust recently. Why? Ā The betrayal was the most recent one ā itās the most relevant.

Feeling like you’re in it for a long haul? Build your moving averages along a couple hundred days.

Looking for the medium-term trend? Try the popular 50-day moving average.

Anything under 20 is a short-term situation. You’ll best decide which applies to the technical analysis you want to perform based on your market strategy, as it develops.

One use for these moving averages it to help discern the nature of a stock’s levels ofĀ *supportĀ *andĀ *resistance*. While we’ve touched on those three terms throughout this post, get a more in depth look intoĀ technical analysis, with the WSS courses: Technical Analysis 1 and the more advanced Technical Analysis 2 .

Until then, work on those computations, traders!