// We use compose() to piece our function together using the small helpers. // Extract the popularity scores so we just have an array of numbers. Our old friend .reduce() is a good candidate here. The center of the data is where most of the values in the data are located. Averages are measures of the location of the center.
The average of all elements will be sum/length where length is the size of the given array. You don’t sum all the elements, which may result in large number that cannot be stored in a 64-bit float. And of course in server side JS, e.g. node.js, then naturally eval() might introduce security issues. But this looks perfect for any well-structured client-side uses. It’s possible to divide the value each time the reduce method is called.. Callthe same function on the partial result and the third element of the array to create a new result.
Before we do that, let’s quickly understand what the terms ‘Average’ & ‘Array’ mean. As you can see, this piece of code is way shorter than the for-loop approach. To get the sum of an array of numbers, for example, you can reduce or fold it.
Now, let’s use the reduce() to find the average of a function. For example, you can reduce or fold an array of numbers to get the sum of it. This function works the same way asfoldingin mathematics. Reducing means you fold a group of numbers into a single value.
The moving average is calculated over a specific time span which is customizable to your liking. You can calculate it for 10 days, 20 days, etc. The moving average is a method to analyze data by creating a series of averages of it.
We call this style ‘point-free’, or ‘tacit’ programming. But to make it work, we need a lot of helper functions. But it gets harder if you have a more complicated data structure. And you need to extract some numeric value from the object?
In this case, we do all the heavy lifting in compose(). Reading from bottom up, we start by filtering on the found property. Then we extract the popularity score with map(). And then we use the magical lift function to make two calculations for the same input.
- It’s relatively easy to calculate but still widely used, especially in finance.
- You’re getting element 9 and then you’re done as you have it now.
- The first thing to do is to choose a window, e.g. 5 days.
This is why you calculate the SMA for the first day, and for the next, you calculate the EMA using the SMA of the previous day as EMAy. When you’re done, the SMAs can be interpreted. Either by some automatism or by you, looking at a chart, like the one you see below.
Easy mode: Filter, map, and sum
The blue line shows the closing prices of company ABC’s stock. You could also work with the daily highs or lows. The moving average doesn’t care about the value you use, as long as you use it persistently. It doesn’t make much sense to use the closing price for day one, but then the daily high, and then the closing price again, etc. This could lead to some very wrong signals in your analysis. Especially in finance, the moving average is used a lot to give buy or sell signals for stocks.
To access the previous EMA on each iteration, you can make use of the result array that already contains everything you need. And by keeping a separate index that tracks the number of EMAs calculated, you can access the previous result within that array. The next part is the same as it already was for the SMA calculation. You need a loop to go over all values within the price array. In this article, we will cover the simple and exponential moving average.
Compute a moving arithmetic mean and unbiased sample variance incrementally. A Moving Average is a good way to get a feeling about a trend for prices. It’s relatively easy to calculate but still widely used, especially in finance. It is, of course, not the only tool you should use, but one of the first ones, as it has such an impact. With this knowledge at hand, you can calculate the EMAs for all days, except the first one where you use the SMA. And all that can be plotted so you can analyze the stock of company ABC.
The array methods, .filter(), .map() and .reduce(), do the heavy lifting for us. Those array methods tell us more about the intent of the code than a for-loop can. Pay special attention to our addScores function and the line where we call .reduce(). The first, runningTotal, is known as an accumulator. It’s updated each time around the loop when we call return. The second parameter, popularity, is the individual array item that we’re processing.