Coefficient-of-variation

As suggested, just use the coefficient of variation formula to compute it as a TimeSeries.

CODE:
   public class MyStrategy : UserStrategyBase
   {
      const int coeffVarPeriod = 10;
      TimeSeries coeffVar;
      
      public override void Initialize(BarHistory bars)
      {
         coeffVar = new StdDev(bars.Close, coeffVarPeriod) / new SMA(bars.Close, coeffVarPeriod);
      }

      public override void Execute(BarHistory bars, int idx)
      {
         if (HasOpenPosition(bars, PositionType.Long))
         {
            //code your sell conditions here
         }
         else
         {
            //code your buy conditions here
            if (coeffVar[idx] < 0.1)
               PlaceTrade(bars, TransactionType.Buy, OrderType.Market, 0.0);
         }
      }
   }

But as statistics guys, we both know the standard deviation is unreliable with noisy data that contains outliers. So using a more robust measurement is really required here. Wikipedia writes "A more robust [metric] is the quartile coefficient of dispersion, half the interquartile range, (Q3 - Q1) / 2, divided by the average of the quartiles, (Q1 + Q3) / 2." https://en.wikipedia.org/wiki/Coefficient_of_variation

And, in practice, using the quartile coefficient of dispersion would be the way to do it for noisy stock prices. Hmm, I thought finantic.Indicators had the quartile calculations for you, but I don't see them there--oops. https://www.wealth-lab.com/extension/detail/finantic.Indicators#screenshots Maybe MP, MP2, MP3, MP4 are the quartiles, but I could be wrong.

Well, the Math.NET numerical analysis package has them (which is what I use). Check out https://numerics.mathdotnet.com/DescriptiveStatistics

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Another approach would be to simply use the Beta indicator WL has with the S&P500 as the index. If the short-term Beta is over two or so, then I wouldn't buy the stock.