A moving average smooths out price data by continuously calculating the average price over a set number of recent periods — for example, a 20-day moving average recalculates, each day, the average closing price of the last 20 days. As new data comes in and old data drops off the window, the average "moves" — hence the name.

Why smooth the price out at all

Raw price data is noisy — it moves up and down constantly on short timeframes, which can make the broader direction hard to see. A moving average filters out a lot of that short-term noise, making the underlying trend easier to read at a glance.

Simple vs. exponential

• A Simple Moving Average (SMA) weights every period in the window equally.
• An Exponential Moving Average (EMA) weights more recent periods more heavily, so it reacts a bit faster to new price changes than an SMA covering the same window.

Neither is universally "better" — an SMA's smoother, slower response and an EMA's quicker, twitchier one both trade off responsiveness against noise differently, and different traders prefer different tradeoffs for different purposes.

A common way traders use it

One widely cited way to use a moving average is simply comparing the current price to it: price sitting above its moving average is often read as a sign of upward bias over that window, and price below as downward bias — exactly the kind of comparison the Pair Analysis Tool in this section calculates automatically from real recent price history. Traders also sometimes compare two moving averages of different lengths (e.g. a shorter one crossing above a longer one) as a way of gauging shifts in trend.

The honest limitation

A moving average is, by definition, based entirely on past prices — it describes where the price has been, not where it's going. It's a genuinely useful tool for organizing and summarizing price history, but it isn't a forecast, and treating it as one is a common and costly misunderstanding.