# APPLYING DATA ANALYTICS TO TECHNICAL ANALYSIS1

Written by Shatu Mshelia · 2 min read

Applying Data Analytics (DA) to technical analysis provides a technical trader with necessary tools; to navigate the charts, and make informed trade and investment decisions. In this write-up, we will be focusing on one of the moving averages. There are two types of moving averages;

1. Simple Moving Average (SMA)
2. Exponential Moving Average (EMA)

New traders often confuse the application and use of both. As there endless argument about which is best.

## WHY THEY ARE NOT THE SAME

Both are moving averages however, they measure different things. Simply put, it is futile to argue that an allen key is better than a screw driver. Because, though both may have similar applications, the uses differ. Similarly, we apply the Simple Moving Average and Exponential Moving Average, for two distinct yet similar purpose. As they both offer two different perspectives on price’s momentum.

## COMMON USES FOR BOTH

• Both tell us a story about price.
• They both help provide foresight and reduce the tendencies to trade blindly.
• The foresight they give us a glimpse into understanding the psychology of the market.

## SMA/MA

Moving averages are generally represented by a line, on the charts. Every time the chart prints a new candle; the SMA extends to tell us the current average price. The number attached to it shows the number of data points represented by candles involved; counting backward from the most current. For example, 7SMA, 21SMA, 100SMA.

It is important to note that these candles, represent different time spans. These spans are totally dependent on the timeframe which the analysis is dissected . To illustrate, on the daily timeframe(tf) each candle portrays the price action for a given day. Similarly, on the 4 hour, 3 minutes, and monthly time-frames, an individual candle represents price action every 4 hours, 3 minutes and month, respectively.

The images above depict price action across different time-frames at a particular point in time.

### HOW TO APPLY DATA ANALYTICS TO THE SMA

In the image above we see that price on the daily time-frame just broke and is currently hovering over the 20MA. The image reveals that in at least the last 20days (because we are analyzing the daily time-frame) price has been living underneath the 20MA. This shows that something has shifted and informs traders to pay attention. If this does not turn out being a false break-out, then it is a hint to a change in price’s momentum towards the upside.

### DEALING WITH OUTLIERS

Sometimes, we come across outliers. Because the SMA is simply an average of data points, it does not make provision to sort out outliers. Hence, you must learn to manually treat this by;

1. Increasing the number of data points so that the outlier has less impact on your results.
2. Simply changing the number of of data points to preclude the outlier. For instance, If you are analyzing using the 10SMA and the outlier falls on the 8th candle, you can decide to use the &SMA instead.

Long term traders seek to eliminate the noise of price action by leveraging on larger sets of data points to make investment and trading decisions.

On the whole, the larger the number of data points, the stronger the impact of the story it tells. Consequently, the higher the probability of such an analysis to hold than not. This is why analysis on the longer time frames are more dependable on.

#MMBA3 #AVANTE-GARDE

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