For an intro into variance...check out these posts:

Problem with variance is calculating it in the traditional sense. Its costly to compute across a time series. It can be quite a drag on your simulation engine's performance. The way to reduce the cost is to calculate the running variance. And that's when you get into quite a briar patch - loss of precision and overflow issues. See John D. Cook's post covering the variance briar patch:

And a few more posts by John covering different variance formulas and their outcomes:

- Comparing three methods of computing standard deviation
- Theoretical explanation for numerical results

- Reduced the precision loss issue as much as possible;
- Allowed an easy way to window the running variance;
- Allowed an easy way to memoize the call.

So, let's start with the formula for the Power Sum Average (\(PSA\)):

\( PSA = PSA_{yesterday} + ( ( (x_{today} * x_{today}) - x_{yesterday} ) ) / n) \)

Where:

- \(x\) = value in your time series
- \(n\) = number of values you've analyzed so far

Once you have the \(PSA\) and \(SMA\); you can tackle the Running Population Variance (\(Var\) ):

\(Population Var = (PSA_{today} * n - n * SMA_{today} * SMA_{today}) / n \)

Now, one problem with all these formulas - they don't cover how to window the running variance. Windowing the variance gives you the ability to view the 20 period running variance at bar 150. All the formulas I've mentioned above only give you the running

**cumulative**variance. Deriving the running windowed variance is just a matter of using the same SMA I've posted about before and adjusting the Power Sum Average to the following:

\( PSA = PSA_{yesterday} + (((x_{today} * x_{today}) - (x_{yesterday} * x_{yesterday}) / n) \)

Where:

- \(x\) = value in your time series
- \(n\) = the period

\(Sample Var = (PSA_{today} * n - n * SMA_{today} * SMA_{today}) / (n - 1) \)

Okay, on to the code.

**Code for the Power Sum Average**:

def powersumavg(bar, series, period, pval=None): """ Returns the power sum average based on the blog post from Subliminal Messages. Use the power sum average to help derive the running variance. sources: http://subluminal.wordpress.com/2008/07/31/running-standard-deviations/ Keyword arguments: bar -- current index or location of the value in the series series -- list or tuple of data to average period -- number of values to include in average pval -- previous powersumavg (n - 1) of the series. """ if period < 1: raise ValueError("period must be 1 or greater") if bar < 0: bar = 0 if pval == None: if bar > 0: raise ValueError("pval of None invalid when bar > 0") pval = 0.0 newamt = float(series[bar]) if bar < period: result = pval + (newamt * newamt - pval) / (bar + 1.0) else: oldamt = float(series[bar - period]) result = pval + (((newamt * newamt) - (oldamt * oldamt)) / period) return result

**Code for the Running Windowed Variance**:

def running_var(bar, series, period, asma, apowsumavg): """ Returns the running variance based on a given time period. sources: http://subluminal.wordpress.com/2008/07/31/running-standard-deviations/ Keyword arguments: bar -- current index or location of the value in the series series -- list or tuple of data to average asma -- current average of the given period apowsumavg -- current powersumavg of the given period """ if period < 1: raise ValueError("period must be 1 or greater") if bar <= 0: return 0.0 if asma == None: raise ValueError("asma of None invalid when bar > 0") if apowsumavg == None: raise ValueError("powsumavg of None invalid when bar > 0") windowsize = bar + 1.0 if windowsize >= period: windowsize = period return (apowsumavg * windowsize - windowsize * asma * asma) / windowsize

**Example call and results:**

list_of_values = [3, 5, 8, 10, 4, 8, 12, 15, 11, 9] prev_powersumavg = None prev_sma = None prev_sma = None period = 3 for bar, price in enumerate(list_of_values): new_sma = running_sma(bar, list_of_values, period, prev_sma) new_powersumavg = powersumavg(bar, list_of_values, period, prev_powersumavg) new_var = running_var(bar, list_of_values, period, new_sma, new_powersumavg) msg = "SMA=%.4f, PSA=%.4f, Var=%.4f" % (new_sma, new_powersumavg, new_var) print "bar %i: %s" % (bar, msg) prev_sma = new_sma prev_powersumavg = new_powersumavg ---------------------------------------------------------- Results of call: bar 0: SMA=3.0000, PSA=9.0000, Var=0.0000 bar 1: SMA=4.0000, PSA=17.0000, Var=1.0000 bar 2: SMA=5.3333, PSA=32.6667, Var=4.2222 bar 3: SMA=7.6667, PSA=63.0000, Var=4.2222 bar 4: SMA=7.3333, PSA=60.0000, Var=6.2222 bar 5: SMA=7.3333, PSA=60.0000, Var=6.2222 bar 6: SMA=8.0000, PSA=74.6667, Var=10.6667 bar 7: SMA=11.6667, PSA=144.3333, Var=8.2222 bar 8: SMA=12.6667, PSA=163.3333, Var=2.8889 bar 9: SMA=11.6667, PSA=142.3333, Var=6.2222Of course, as I said in the beginning of this post, just take the square root of this Running Windowed Variance to obtain the Standard Deviation.

Later Trades,

MT

## 5 comments:

Great article,thanks. I think there's an error in line 28 of running_var. The line:return (apowsumavg * windowsize - windowsize * asma * asma) / windowsizeshould be:return (apowsumavg * windowsize - windowsize * asma * asma) / (windowsize-1)

You are correct if you want to obtain the sample variance instead of the population variance - which most people including myself do.

You are correct if you want to obtain the sample variance instead of the population variance – which most people including myself do.

Thanks for the algo.

Is there a way to calculate the value without requiring the

series[bar - period] data point ? This requires me to store previous data points in a sliding window calculation.

No way I know of to avoid storing the previous data point in order to obtain the running variance. Just have to decide where you'd like to store it. You could store it and then use it in the call to your function. Or you could create a class for your variance logic and memoize the previous data point (cache the value) in the class. Then the object will keep track of it for you through the window.

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