Table of Contents
- What Is a Linearly Weighted Moving Average?
- Key Takeaways
- The Formula for the Linearly Weighted Moving Average (LWMA)
- How to Calculate the Linearly Weighted Moving Average (LWMA)
- What Does the Linearly Weighted Moving Average (LWMA) Tell You?
- Advantages of Linearly Weighted Moving Averages
- Downsides to Linearly Weighted Moving Averages
- Common Finance Cases for Linearly Weighted Moving Average
- LWMA and Timeframes
- Frequently Asked Questions
- The Bottom Line
What Is a Linearly Weighted Moving Average?
Let me explain what a linearly weighted moving average (LWMA) is. It's a calculation for moving averages that puts more emphasis on recent price data. The latest price gets the highest weight, and each earlier price gets less weight in a linear drop-off. This makes LWMAs faster at responding to price shifts compared to simple moving averages (SMA) or exponential moving averages (EMA).
Key Takeaways
You can use a linearly weighted moving average just like you would an SMA or EMA. It helps you define price trends and reversals more clearly, gives trade signals from crossovers, and points out potential support or resistance areas. If you're a trader looking for a moving average with less lag than an SMA, consider the LWMA.
The Formula for the Linearly Weighted Moving Average (LWMA)
Here's the formula for LWMA: LWMA = [(P_n * W_1) + (P_{n-1} * W_2) + (P_{n-2} * W_3) ...] / ∑W, where P is the price for the period, n is the most recent period, n-1 is the prior one, and so on, and W is the weight assigned to each period, starting highest and decreasing linearly based on the number of periods.
How to Calculate the Linearly Weighted Moving Average (LWMA)
Start by choosing a lookback period, which determines how many values go into the LWMA. Then, assign linear weights to each period—one simple way is to give the first value the weight equal to the number of periods, like 100 for a 100-period lookback, then 99 for the next, and so on. Multiply each price by its weight, sum them up, and divide by the total of all weights.
For example, if you're calculating the LWMA for a stock's closing prices over the last five days, multiply today's price by 5, yesterday's by 4, the day before by 3, and continue down to the first day multiplied by 1. Add those up and divide by the sum of the weights (5+4+3+2+1).
Suppose the prices are: Day 5: $90.90, Day 4: $90.36, Day 3: $90.28, Day 2: $90.83, Day 1: $90.91. The calculation gives ((90.90*5) + (90.36*4) + (90.28*3) + (90.83*2) + (90.91*1)) / 15 = 90.62. So, the LWMA is $90.62.
What Does the Linearly Weighted Moving Average (LWMA) Tell You?
The LWMA calculates an asset's average price over a period, weighting recent data more heavily to analyze market trends. If the price is above a rising LWMA, it confirms an uptrend; if below a falling LWMA, it confirms a downtrend. A price crossing the LWMA might signal a trend change, like shifting from up to down.
Consider the lookback period when assessing trends—a short one like five periods tracks price closely for small trends, while a longer one like 100 periods shows broader trends with space between price and LWMA. Like other moving averages, LWMA can indicate support or resistance; if prices bounce off it repeatedly, it acts as support, and failure might mean a trend shift.
Advantages of Linearly Weighted Moving Averages
There are benefits to using LWMA. It weights recent data more, so it's sensitive to short-term price moves, helping you react quickly in fast markets or for spotting very short trends. It still provides some smoothness, balancing responsiveness and stability, and you can adjust the weighting to fit your trading style. Plus, it works on various data like prices, volume, or metrics across assets from stocks to cryptocurrencies.
Downsides to Linearly Weighted Moving Averages
LWMA has drawbacks too. It's more affected by outliers, which can distort signals if they're heavily weighted. Calculations are more complex than SMA, requiring effort to set weights and update. The choice of weights adds subjectivity, risking inconsistency. It might lag on major reversals, produce false signals in ranging markets, or lead to overfitting if tuned too much to past data.
Alternatives to Linearly Weighted Moving Average
- Simple Moving Average (SMA): Equal weights for all points, smoother and less responsive, good for long-term trends.
- Exponential Moving Average (EMA): More weight to recent data with exponential decay, quick like LWMA but different scheme.
- Smoothed Moving Average (SMMA): Equal weights in a smoothing period for a smoother curve, used for medium-term trends.
- Weighted Moving Average (WMA): Custom weights, similar to LWMA but fully customizable.
- Triangular Moving Average (TMA): Triangular weights with highest in the center, for smoothing and trend direction.
Common Finance Cases for Linearly Weighted Moving Average
In finance, LWMA helps with investment strategies. Use it for trend identification by comparing short- and long-term LWMAs—crossovers signal uptrends or downtrends. It provides entry/exit signals: buy on upward crossovers, sell on downward. Measure volatility by the gap between LWMA and price; narrow gaps mean low volatility. LWMA acts as dynamic support/resistance, and combines with other tools to confirm trend strength.
LWMA and Timeframes
Apply LWMA across timeframes. Short-term ones like 5- or 10-day give quick insights for day trading. Medium-term, such as 20- or 50-day, balance for assessing market direction over weeks. Long-term, like 100- or 200-day, reveal broad trends for strategic decisions over months or years.
Frequently Asked Questions
What is LWMA? It's a moving average weighting recent data more, reducing lag compared to SMA.
How does LWMA differ from SMA? LWMA weights recent points higher, making it more responsive but prone to noise.
How is LWMA calculated? Multiply data by linear weights, sum, and divide by total weights.
How does LWMA identify trends? Crossovers between short- and long-term LWMAs signal trend changes.
The Bottom Line
The linearly weighted moving average smooths price data by weighting recent prices higher, making it responsive to changes. Remember, like any indicator, it doesn't guarantee outcomes, so use it carefully in your decisions.
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