(on/off button)
In the future, I'll try to combine all timeframe buttons into 1 indicator.
ChatGPT wrote:Quadratic Weighted Moving Average (QWMA) is a method used for smoothing time series data by assigning weights to the historical data points in a quadratic manner. In traditional weighted moving averages, each data point in the time series is assigned a weight, and the average is computed by multiplying each data point with its corresponding weight and then summing up these weighted values. QWMA extends this concept by applying quadratic weights instead of linear weights.
The formula for calculating the Quadratic Weighted Moving Average for a particular time period "n" is as follows:
QWMA(n) = (a * y(n)) + (b * y(n-1)) + (c * y(n-2))
Where:
y(n) is the current data point at time period "n."
y(n-1) is the data point at the previous time period.
y(n-2) is the data point at the time period before the previous one.
"a," "b," and "c" are the quadratic weights assigned to the respective data points.
The quadratic weights "a," "b," and "c" are typically chosen in a way that gives more emphasis to recent data while still considering some of the past data. This helps in smoothing out the noise present in the time series data and capturing trends more effectively.
QWMA can be useful in scenarios where there is a need to place varying degrees of importance on different historical data points, giving more significance to recent observations while still taking into account older ones. However, the choice of weights involves a certain level of subjectivity and domain knowledge to strike a balance between recent and past data in the smoothing process.
It's important to note that while Quadratic Weighted Moving Average can provide smoother results compared to simpler moving average methods, the specific choice of weights and parameters may vary based on the characteristics of the data and the goals of analysis.
