Larry - I said that I'd follow up with an array implementation and I'm told that I'm a "bottom line" kind of guy, so here it is - all 5 lines of it. I'll put some additional info below it -
// Get the average price and the scaled volume Avgprice = ( O + H + L + C ) / 4; // Get the bars since the first tick bsince = BarsSince( Day() != Nz( Ref( Day(), -1 ) ) ); // Get the sum of the volume for the day Daytotvol = Sum( V, bsince + 1 ); // Get the vwap for each tick of the day Dayvwap = Sum( Avgprice * V, bsince + 1 ) / Daytotvol; // Get the running variance and standard deviation for each tick of the day // NOTE - the use of Daytotvol outside of the Sum() function is a subtle, but important point Daystdev = sqrt( Sum( ( ( Avgprice - Dayvwap ) ^ 2 ) * V, bsince + 1 ) / Daytotvol ); I isolated just the code that did the work so I could offer a few comments. I know that array logic can look daunting at first. I've taught it to a fair number of people. It will help if you can visualize each bar as a column in a spreadsheet and each variable as a row. The full explanation is beyond the scope of this post, but if you keep that analogy in mind, it should help. So, what is going on above. The first 3 statements are straightforward. avgprice - just a version of average price bsince - this is an array of bars since the first bar of a day daytotvol - this is an array of the running total of the daily volume initialized at the start of each day Statement 4 utilizes the variable period lookback available to the Sum() function. It get the running sum of the avgprice X volume for each day and divides that result by the running total volume. This gives a volume weighting X average price or VWAP. Statement 5 is the "heavy lifting". If you unpack it, it uses the same V/daytotvol weighting technique to derive a "running" variance. You may need to look at this a little while. FWIW, it is not unusual for a single array statement to take quite a while to analyze. In conclusion, I thought it might be interesting to look at performance (on a slow machine). Your original code had a SetBarsRequired(), but remember that AFL must first pass the code to get this, and if you are using 100,000+ bars, this was painful. Let's just look at the performance or the original after AFL has analyzed the original version and is using SetBarsRequired(), then the corrected version that I posted earlier, and also the one above - Original version for 5000 bars - 1.7 seconds Correct original version for 5000 bars - 32 milliseconds Array implementation for 5000 bars - 13 milliseconds Now, the weighted variance makes this more complex than normal array app's. But, just to show off the power of what Tomasz has done with fine-tuning array functions. 50000 bars with the array version is - Array implementation for 50000 bars - 150 milliseconds That is why I leave you with this thought. Un-nested for loops are fine for prototyping, but for production performance - JUST SAY NO TO LOOPING :-) BruceR --- In [email protected], "bruce1r" <bru...@...> wrote: > > Larry - > > Good first attempt. I thought I'd tackle this for two reasons. First, > you did a fair amount of work before asking. But, second, I had been > about to write an article for a web site about array processing and > happened upon your post. If you don't mind, I'd like to use a variation > of it as an example for that. There are several great teaching points > in the performance issue that you ran into. > > There are two classes of issues in your AFL - an algorithm/math issue > and a looping issue. Let's deal with the major one first. > > As you might suspect the inner loop is the problem. If you assume > approx. 400 x 1 minute bars per day, then on average for each bar, you > will execute the inner (j) loop 200 times. This has to go ! > > Here's the original code for reference - > > // now the hard part...calculate the variance... > // a separate calc from the start of each day - note it requires the > vwap from above > // also note, we calculate starting at the first bar in the new day to > today to the curent bar > Variance = 0; > > for ( j = newdayindex; j < i; j++ ) > { > AvgPrice = ( O[j] + H[j] + L[j] + C[j] ) / 4; > Variance += ( Volume[j] / totVolume ) * > ( Avgprice - Vwap2temp ) * ( Avgprice - Vwap2temp ); > } > > The way to get rid of it may not be what you expect, though. You are > calculating a variance at each bar by restarting the calculation from > the first bar of the day. At first glance, it probably appeared that > this was required because of the volume weighting. > > With some fairly straightforward algebraic manipulation, the formula can > be converted into a "running" variance calculation. I'll just show the > result, and you can work through it. Replace the code above with - > > variance = ( prevvar * prevtotvol / totvolume ) + > ( Volume[i] / totvolume ) * > ( Avgprice - Vwap2temp ) * ( Avgprice - Vwap2temp ); > > prevtotvol = totvolume; > prevvar = Variance; > > Finally, to support this code, there are two areas of initialization. I > did it this way to minimize changes to your original code. Insert the > following code in two places - > > prevtotvol = 0; > prevvar = 0; > > Put it once above the outer (i) loop before the for( i=0; i<Barcount; > i++). Then, put it in the newday initialization found in the if ( > newday[i] == True) block. > > This should yield a improvement of over two orders of magnitude. > Actually, a little more could be wrung out as you have a few > calculations that could be streamlined. I didn't post the entire code, > because the endgame is really an AFL with NO LOOPS that uses purely > array processing. That will yield about an additional minimum 2x > improvement at the smaller intervals (5000 bars) that you are probably > using, but much greater as bars increase. As importantly, it is only 7 > or 8 lines of code in total. I'll show you that next and later. > > -- BruceR > > > --- In [email protected], "shakerlr" ljr500@ wrote: > > > > I just created the following code to calculate the VWAP + std > deviation bands, but have found that it is extrememly slow. I posted > the original code to the amibroker study site and was wondering if > anyone has any suggestions to speed it up for display on 1 minute > charts. > > > > Also, I noticed that if I DO NOT USE: > > SetBarsRequired( 1000, 0 ); > > > > The bands show up incorrect...(sometimes expanding/shrinkking as I > scroll on the 1 minute chart) > > > > Note that I have about 100000 bars in my stock/ticker being > studied...so that may be the reason it is slow... > > > > ---- > > /// VWAP code that also plots standard deviations...if you want a > 3rd...it > > should be fairly simple to add > > // > > // NOTE: the code is SLOOOOWWWW...can someone help speed it up? > > // I tried my best, but can't really do much with the two for-loops... > > // > > // LarryJR > > > > > > SetBarsRequired( 1000, 0 ); > > > > // this stores true/false based on a new day... > > newday=Day() != Ref(Day(), -1); > > > > SumPriceVolume=0; > > totVolume=0; > > Vwap2=0; > > stddev=0; > > newdayindex=0; > > Variance =0; > > > > // we must use a loop here because we need to save the vwap for each > bar to > > calc the variance later > > for( i= 0; i < BarCount; i++ ) > > { > > // only want to reset our values at the start of a new day > > if (newday[i]==True) > > { > > SumPriceVolume=0; > > totVolume=0; > > newdayindex=i; // this is the index at the start of a new day > > Variance=0; > > //Vwap2=0; > > } > > AvgPrice=(O[i] + H[i] + L[i] + C[i])/4; > > > > // Sum of Volume*price for each bar > > sumPriceVolume += AvgPrice * (Volume[i]); > > > > // running total of volume each bar > > totVolume += (Volume[i]); > > > > if (totVolume[i] >0) > > { > > Vwap2[i]=Sumpricevolume / totVolume ; > > Vwap2temp=Vwap2[i]; > > } > > > > // now the hard part...calculate the variance... > > // a separate calc from the start of each day - note it requires the > vwap from > > above > > // also note, we calculate starting at the first bar in the new day > to today > > to the curent bar > > Variance=0; > > for (j=newdayindex; j < i; j++) > > { > > AvgPrice=(O[j] + H[j] + L[j] + C[j])/4; > > Variance += (Volume[j]/totVolume) * > > (Avgprice-Vwap2temp)*(Avgprice-Vwap2temp); > > } > > stddev_1_pos[i]=Vwap2temp + sqrt(Variance); > > stddev_1_neg[i]=Vwap2temp - sqrt(Variance); > > > > stddev_2_pos[i]=Vwap2temp + 2*sqrt(Variance); > > stddev_2_neg[i]=Vwap2temp - 2*sqrt(Variance); > > } > > Plot (Vwap2,"VWAP2",colorDarkGrey, styleLine); > > Plot (stddev_1_pos,"VWAP_std+1",colorGrey50, styleDashed); > > Plot (stddev_1_neg,"VWAP_std-1",colorGrey50, styleDashed); > > Plot (stddev_2_pos,"VWAP_std+2",colorGrey40, styleDashed); > > Plot (stddev_2_neg,"VWAP_std-2",colorGrey40, styleDashed); > > >
