Mean Absolute Deviation (MAD) in Trading - Everything You Need to Know

You’ve looked into standard deviation. But you also tried Bollinger Bands. Then came ATR for checking price swings. Each one tracks how prices spread out. They’re useful - right up until they fail.

Deviation values get squared before they’re averaged out. That math trick gives big moves more weight. Just one wild price jump can stretch your volatility number way up. Bands start spreading wider as a result. You end up taking smaller positions. Trade triggers just stop showing up. Blame it on that single weird candle messing up everything.

You’ve been there - your solid plan vanishes when the volatility tool freaks out over random spikes. So you’re stuck on the sidelines. Price takes off anyway. No entry means losses pile up slowly.

Many traders go along with this flaw. Since they figure standard deviation is the most solid way to track spread, they put up with its weak spot on extremes - other options feel either too shaky or just don’t work well enough.

MAD flips that idea completely - shifting how we see it from the start.

What Does Mean Absolute Deviation Mean?

Mean Absolute Deviation (MAD) shows how spread out the numbers are from the middle point - usually the average or median. Instead of squaring differences as standard deviation does, it just takes their actual size. Because of this small twist, its behaviour in analysis turns out quite distinct.

The math isn't complicated. Start by picking the middle value - this could be the average or the median - from your list of numbers. Next, find how far each number is from that centre, ignoring minus signs. After that, add up those distances and divide by to get the overall spread. This gives a clear idea of variation without overreacting to outliers.

In trading, MAD shows how much prices usually drift from balance. So here’s what it tackles: once price wanders off its usual level, just how big is that shift most times?

Standard deviation tackles a separate issue - something tied to squared gaps plus averages under a square root. That math setup does certain stats jobs well, yet often misses how traders actually see market moves.

Mean Absolute Deviation From the Median Formula

The formula changes based on the middle value you pick. If you go with the median, here’s how it works instead

MAD = (1/n) × Σ|xᵢ - median(X)|

Where:

• n = number of observations
• xᵢ = individual data point
• X = the complete dataset
• |...| = absolute value

This idea shows something key - the median is the middle price, with just as many numbers higher as there are lower. Checking how much values stray from it tells us how much prices usually move away from balance.

The median handles wild numbers better than the average - it won't budge much even if one price is way off. Because of this, your MAD stays steady. Since it's built around a solid middle value, the spread measurement doesn't freak out. That steadiness sticks through each step of the math.

A price list goes like this: 100, then 101, followed by 102, next 103, and finally 150. Most values cluster low - yet the average jumps to 111.2 because that last number pulls it up. But the middle point stays at 102, showing what prices usually are. When measuring spread using that midpoint, only one big gap shows up across five numbers. However, if you base it on the high average, squaring the jump from 150 makes everything seem way more scattered than it really is.

Median Absolute Deviation Formula in Practice

Traders usually adjust MAD so it lines up with the standard deviation. That adjustment - using a number close to 1.4826 - turns MAD into something similar when data follows a normal pattern:

Scaled MAD = 1.4826 × median(|xᵢ - median(X)|)

See that middle-value step inside? You’re using the middle of those distance numbers, not an average. Doing this twice makes it super tough for wild points to mess things up. Just a few crazy prices won’t wreck how you see movement.

This strength counts when markets change. As swings shift, prices show uneven patterns - quiet times broken by sudden jumps. Instead of seeing those jumps as real signs of more chaos, standard deviation views them that way. On the other hand, MAD sees them for what they usually are: random bursts that don’t reflect lasting movement.

You’ve seen this before - over and over. A press announcement hits, price jumps fast. Then it snaps back right away. Volatility shoots up out of nowhere. Your risk model sees that surge and thinks it's something real. So you scale down - or just wait. But truth is, nothing’s different underneath; that flash wasn’t changed, just noise.

MAD-based tools notice this difference - they react when prices spread out for good, but skip short-lived spikes. Because they pick up only what sticks, they match how you really see usable volatility.

Why MAD Matters for Trading

Old-school measures expect price swings to follow a bell curve. But that idea keeps breaking down when you look at actual trading. You see fatter tails instead - big jumps happen way more often than those models say they should. When prices trend hard, things get lopsided. The peakiness shifts depending on how wild the market feels.

Standard deviation brings along the flaws of those math-based rules. Because it works best where data follows a bell curve. Yet financial markets rarely act like that. Instead, they swing wildly beyond predictable patterns.

MAD doesn't need assumptions about data shape. It runs without preset patterns. Whether returns are normal, exponential, uniform, or something else, results stay reliable. That adaptability helps, since real-market behaviour is always uncertain.

The real-world effects show up like this - through changes here, also adjustments there, while some outcomes appear elsewhere:

• Firmer trend zones show up when you use MAD instead of typical spread math. Since it skips wild one-bar jumps, the lines shift only when real price swings happen. They stretch or shrink based on actual market shifts - not fake signals from skewed calcs.
• Better control over trade size. When volatility measures get skewed by extreme values, position sizing goes off track - happens more than it should. Instead of relying on shaky averages, use MAD - it handles wild data points better. That means fewer mistakes in how much to risk.
• Cleaner hints that prices might snap back. To trade reversals, you need spots where price drifts way off balance. Z-scores built on MAD work well here since the middle value - the median - and spread size - MAD - don't get messed up by wild data points.
• Better at spotting trends. Figuring out market layout means finding real floors and ceilings in price action. Using volume-adjusted MAD shows balance points across different trade activity - spots where buying or selling really piles up.

Volume-Weighted Median Absolute Deviation

Average MAD counts every price point the same way. But a small trade - just 100 contracts - isn't really like one with 10,000. So treating them alike misses something real. Big trades often show stronger intent.

Volume-weighted calculations adjust for this. They weight each price by its associated trading activity. High-volume prices—where substantial commitment occurred—influence the calculation more than low-volume prices where few participants engaged.

Once you add volume into the median math, you spot the price acting like a gravity centre for trading activity. Not the average midpoint across prices - this is different. Instead, it’s where trades split evenly: one side buys more above, the other sells less below. That specific level? Where buying pressure meets selling flow, face to face.

Measuring how much prices usually drift from this weighted middle point shows when they might stall or pick up again. That gap turns into real levels - spots where traders have acted before, so they could step back in.

This method fixes an issue you’ve probably seen with regular bands - they show levels based on math, not real market behaviour. When the price hits the bottom band, often nobody steps in to buy. The reason? Those lines track how far prices spread out, ignoring where traders actually place orders. So even if it looks like support, there’s no real crowd backing it up.

Volume-based MAD bands include this detail - widening near prices with heavy action, narrowing where activity is low. These areas match up better with real trading choices people make. So they reflect actual decision spots in the market.

MADBands Pro: Automatic Equilibrium Zone Calculation

You might do these math steps by yourself. Or try coding the volume-based median values from scratch. Another option is building measures for variation. Instead, sketch the ranges and areas straight onto your graphs.

Or you could use MADBands Pro - Drawing Tool for NinjaTrader 8.

MADBands Pro takes care of everything automatically. So you can concentrate on your trades, it simplifies the tough math behind the scenes. Instead of guessing zones, it finds balance areas using volume-based shifts. Because it runs on smart logic, you skip manual calculations.

The tool shows where prices settle based on how the market's acting right now. Because it uses a volume-based midpoint, you get the real centre value. While tracking usual swings around that level, it spots ranges where past trading activity might push shifts in price direction.

This isn’t just another version of Bollinger Bands using tweaked math. Rather, it’s a whole new way to see how prices move. While typical methods assume price swings around an average point, this one sees price being pulled toward spots where traders agree - places backed by real trading activity showing strong interest.

The drawing tool fits right into NinjaTrader 8 without hassle. Because you skip exporting data, doing math elsewhere, or bringing numbers back in. Since it auto-adjusts for volume weight, no manual tweaks are needed. Even when fresh data shows up, everything updates on its own. As a result, MADBands Pro takes care of every step behind the scenes.

Traders creating step-by-step systems find this auto setup useful - try it with solid balance areas to test tactics. Instead of guessing, fine-tune when to get in or out based on high-volume spots. Rely on MAD-driven trade sizes without coding complex math yourself.

With discretionary trading, clear visuals make a difference. Because you can spot where the price is compared to the average based on volume. This shows how much it's drifted from normal levels lately. Where past action hints at areas traders cared about before. All that pops up right on your chart - no number crunching needed.

Implementing MAD-Based Analysis

You’ve made tools like this already. Because you’re familiar with the whole process - starting from an idea, then writing code, fixing errors, running tests, and making tweaks. It takes longer than expected. Ever found weird glitches messing up your math? That’s frustrating. Also annoying is speeding things up so your tool runs smoothly when markets are open.

MAD-based signals add extra layers. Because volume adjustments depend on how data’s organised, you’ve got to watch the setup closely. For medians to work fast, sorting needs smart methods that keep up live. When fresh candles come in, deviations adjust step by step - no full recalcs. To plot zones through code, you first need a grip on how NinjaTrader draws stuff.

Many coders struggle with a decision - spend ages creating custom tools themselves, yet some ditch MAD checks completely since setting them up feels like more trouble than it’s worth.

This decision means giving up something else. Since you’re setting up systems, strategy gets ignored. Because you’re fixing display issues, market trends go unchecked. As calculations get faster, fresh ideas stay untested.

MADBands Pro cuts out the wait. This tool delivers ready-to-use MAD insights right away. Get volume-adjusted balance areas without coding at all. See if using MAD analysis boosts your trades - before diving into building something from scratch.

If the method works well, you’ll clearly see its benefits. Should you choose to tweak it yourself later, the main idea’s already been tested. In case MAD analysis doesn’t fit how you trade, you find out early - no big time or effort wasted.