This code snippet demonstrates how to calculate and plot weekly Bollinger Bands on a daily chart using ProBuilder language. Bollinger Bands are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity, using a formulaic method involving moving averages and standard deviation.

//Weekly Bollinger Bands On Faster Time Frame chart
//PRTv11
//By Vonasi
//Date: 20200415

p = 20
deviations = 2

//store each week's close
if opendayofweek < opendayofweek[1] then
    a = a+1
    $price[a] = close[1]
endif

if a >= p then
    //get a mean of last p weeks' close price
    total = 0
    for b = a downto a-p+1
        total = total + $price[b]
    next
    avg = total/p

    //calculate standard deviation
    total = 0
    for b = a downto a-p+1
        total = total + (square($price[b]-avg))
    next
    stdev = sqrt(total/p)

    //calculate bands
    upper = avg + (stdev*deviations)
    lower = avg - (stdev*deviations)
endif

return upper as "upper", lower as "lower", avg as "avg"

The code snippet above is structured to perform the following steps:

• Initialization: Set the period (p) to 20 and the number of standard deviations (deviations) to 2. These are typical settings for Bollinger Bands.
• Storing Weekly Close Prices: It checks if the current day is the start of a new week by comparing the opening day of the week with the previous day. If true, it increments a counter a and stores the previous day’s close price in an array $price.
• Calculating the Moving Average: Once there are at least p weeks of data, it calculates the average close price of the last p weeks.
• Calculating Standard Deviation: It then calculates the standard deviation of the close prices over the same period, which measures the volatility.
• Calculating Bollinger Bands: Using the average and standard deviation, it calculates the upper and lower Bollinger Bands.
• Output: Finally, the code returns the upper band, lower band, and the average as outputs, which can be plotted on a chart.

This example is useful for understanding how to manipulate time series data and perform statistical calculations in ProBuilder language to create dynamic trading indicators.