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SMA, EWMA, KAMA and VWMA on NVIDIA’s Price Chart

When traders first encounter technical analysis, the chart can feel chaotic.

Candles rise and fall, trends form and vanish, and it’s easy to mistake randomness for opportunity. That’s where moving averages come in.

They help us see what the price has been doing over time, in a way that makes patterns visible without overcomplicating the picture.

This article walks through four essential moving averages used by traders and analysts alike, each with a distinct approach to interpreting price action:

  • Simple Moving Average (SMA)

  • Exponentially Weighted Moving Average (EWMA)

  • Kaufman Adaptive Moving Average (KAMA)

  • Volume Weighted Moving Averages (VWMA)

Each one has a distinct way of interpreting price action, and each reveals something different about the market’s rhythm.

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Simple Moving Average (SMA)

NVIDIA daily price with a 50-day Simple Moving Average.

The Simple Moving Average is the most straightforward way to smooth price data. It takes a set number of past prices, adds them up, and divides by that number. The result is a rolling line that moves gradually as new prices are added and old ones drop out.

Traders often use the SMA to identify trends. If the price is consistently above the SMA, it suggests upward momentum. When it’s below, it may indicate a downward trend. The longer the window you choose, for example, 50 or 200 days, the smoother the line becomes, and the slower it reacts to recent price changes.

The SMA doesn’t try to predict. It simply reflects what has already happened in a clear and stable way. That’s what makes it such a trusted foundation for many trading strategies.

Exponentially Weighted Moving Average (EWMA)

NVIDIA daily price with a 50-day Exponentially Weighted Moving Average.

The Exponentially Weighted Moving Average improves on the simplicity of the SMA by giving more importance to recent prices. Instead of treating all points equally, it applies exponential weighting so that yesterday’s data matters more than data from a month ago.

This makes the EWMA more responsive. When the market shifts direction, it adjusts faster, allowing traders to notice emerging trends earlier. However, that speed also makes it more sensitive to short-term volatility. In fast-moving markets, it can generate signals that appear early, sometimes too early.

Used thoughtfully, the EWMA is a great companion to the SMA. Together, they can help confirm whether a change in direction is gaining strength or just part of normal fluctuation.

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Kaufman Adaptive Moving Average (KAMA)

NVIDIA daily price with Kaufman Adaptive Moving Average.

The Kaufman Adaptive Moving Average takes adaptability to another level. Unlike the SMA or EWMA, which use fixed formulas, KAMA adjusts itself depending on how efficiently the market is moving.

When prices are trending smoothly, KAMA speeds up, staying close to the action. When the market becomes choppy and directionless, it slows down, filtering out unnecessary noise. The calculation involves an efficiency ratio that measures how directly price has moved compared to its volatility, in other words, how “purposeful” the market’s movement is.

The result is a line that breathes with the market’s pace. For traders who dislike constant false signals in sideways conditions, KAMA provides a more stable and realistic picture of underlying movement.

Volume Weighted Moving Average (VWMA)

NVIDIA daily price with Volume Weighted Moving Average.

The Volume Weighted Moving Average adds another layer of meaning: it accounts for how much trading activity is happening. Instead of treating every price equally, VWMA gives more weight to prices that occur during high-volume periods.

This makes it especially insightful when analyzing market participation. A price move that happens on strong volume carries more significance than one that happens in quiet trading. By incorporating volume, the VWMA can confirm whether a trend is supported by real interest or just drifting on low liquidity.

It’s particularly useful in stocks or cryptocurrencies where volume spikes often precede strong price moves. Many traders use VWMA alongside other averages to check whether a breakout is backed by genuine momentum.

Seeing the Bigger Picture

Each moving average tells a slightly different story about the same price data. The SMA gives stability, the EWMA brings responsiveness, KAMA adapts intelligently, and VWMA introduces the reality of market activity.

When combined, they reveal layers of structure within what often appears to be chaos. Prices no longer move as random noise; they show phases, shifts, and slow transitions that the naked eye might miss.

For traders, understanding these differences is a way of learning to read markets more clearly. And once you can see those patterns, you can make decisions with a calmer mind, guided but by a more complete view of how prices evolve over time.

The code used to generate the diagrams in this article can be found in my GitHub Repository. Check it out to understand more about how these strategies work.

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