Linearly Weighted Moving Average (LWMA) And Its Functionality

Functionality of LWMA

Functionality of LWMA

The Linearly Weighted Moving Average (LWMA) is a technical analysis tool used to analyze and predict price trends in financial markets. It is a type of moving average that assigns more weight to recent data points, making it more responsive to recent price changes.


The LWMA calculation involves assigning weights to each data point in the time series, with the most recent data points receiving the highest weights. The formula for calculating the LWMA is as follows:

LWMA = (P1 * W1 + P2 * W2 + … + Pn * Wn) / (W1 + W2 + … + Wn)


  • LWMA is the Linearly Weighted Moving Average
  • P1, P2, …, Pn are the prices of the data points
  • W1, W2, …, Wn are the weights assigned to each data point

The weights assigned to each data point follow a linear progression, with the most recent data point receiving the highest weight. For example, if we have a time series of 5 data points, the weights assigned could be 5, 4, 3, 2, and 1, respectively.


The LWMA is primarily used to identify trends and potential reversals in price movements. Traders and analysts use it to generate buy and sell signals based on the crossover of the LWMA with the price chart.

When the price is above the LWMA, it suggests a bullish trend, indicating that the market is in an uptrend. Conversely, when the price is below the LWMA, it indicates a bearish trend, suggesting that the market is in a downtrend.

The LWMA can also be used to identify potential support and resistance levels. When the price approaches the LWMA from below and bounces off, it can be considered a support level. On the other hand, when the price approaches the LWMA from above and fails to break through, it can be seen as a resistance level.

Traders often use the LWMA in conjunction with other technical indicators to confirm signals and improve the accuracy of their trading decisions. It is important to note that no indicator is perfect, and false signals can occur. Therefore, it is recommended to use the LWMA in combination with other tools and analysis techniques for better results.

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