Hi Dick, I use it in places that an MA, EMA, WMA, or AMA would typically be used. I don't just use it by itself for trading. It would be up to you to test it in your own trading styles to see if it adds value during your backtests. It is very good for de-trending, as it tries to slice right through the middle of the swings, and can adapt to a reasonable range of frequencies within a timeframe. It reduces whipsaws during congestion.
I am using the latest beta AB V5.30.4 If you want to plot it, You must create the global variables in your system first, or change the function directly. Off the top of my head: Global g_FirstBar, g_LastBar; g_FirstBar = 0; g_LastBar = BarCount-1; Periods = 20; Price = C; Plot( ALFilter( Price, Periods ), "ALF", colorRed ); You might want to create a generic AFL that plots MA, EMA, WMA, ALF for comparison. Use a param for periods for each one separately so you can try to adjust them on top of each other and see what you get for following errors and smoothness. If you don't know how to do that, then you are not ready to use this function in your formulas yet. Perhaps if it were rewritten as a drag and drop formula. I will consider that later, or someone else can do that and post it also (I don't use D&D in my formulas). BR, Dennis On Aug 6, 2010, at 10:37 AM, Richard wrote: > Dennis, > You have done some good work here. Would it be possible to explain exactly > how you use for trading. Also, there is no PLOT so one could see a chart of > the adaptive MA. > > Dick > > --- In [email protected], Dennis Brown <se...@...> wrote: >> >> Hello, >> >> I spent a good deal of effort creating a very useful smooth moving average >> filter that was also fast. It is based on the John Ehlers Adaptive Laguerre >> Filter. I started with some code from "Mich" in the AB library a couple of >> years ago. >> >> http://www.amibroker.com/library/detail.php?id=772&hilite=PLOT >> >> The general problem with this adaptive filter is that is uses a loop within >> a loop for each bar. The time to execute is the number of bars times the >> look-back period. This made long periods impractically slow. >> >> I came up with a feedback method that worked well in a single loop. The >> speed became a normal linear function of the number of bars to process. >> >> This filter is excellent for de-trending. It is also very smooth. I use it >> as the basis of many indicators. I would not trade without it. Because the >> Periods input to the function can also be an array, it can be further >> adapted with inputs like volume. This makes some indicators even better. >> >> However, since I use it so often I would like it to run even faster. There >> are two different ways that could happen: >> >> 1. Convert the algorithm to run with array operations instead of a loop. I >> don't know how to do that with this algorithm. Perhaps someone with more >> experience with doing everything in arrays can look at this code and render >> an opinion on if it can be done (or even post the method). >> >> 2. Convert the inner loop of the algorithm into a DLL plugin. The guts of >> the loop is fairly straight forward and should not be hard to code in C++, >> but it is beyond my current level of development expertise. It would take >> me some time to do that myself. I am also in the process of converting to >> 64 bit W7. >> >> In any case, I am posting the AFL code for my prized filter for all to use. >> Hopefully someone will improve it with one of the above means and share it >> with me. >> >> Best regards, >> Dennis >> >> //================================================================ >> //AdaptiveLaGuerre function Last Updated 8/5/2010 by Dennis Brown >> //================================================================ >> // Smooth Adaptive Moving Average Filter, by Dennis Brown 9/6/2007 >> // Laguerre Filter section, by John Ehlers >> // Adaptive coeficient filter algorithm, by Dennis Brown >> // This adaptive filter runs orders of magnitudes faster than the original >> Ehlers version. >> // The speed of the original slowed geometrically by the feedback lookback >> bars. >> // There is no looking back in this version, so speed is linear to number >> of bars. >> // >> // decay is the decay time for historical error feedback significance --99 >> is a good starting number >> // smooth is smoothing factor of the error feedback signal --0.8 is a good >> starting number >> // gain is a number (or array for additional adaptive inputs) that sets the >> base filtering --1 is a good number >> // Periods is an array of periods for the filter that is translated to a >> gain array. >> //================================================================ >> >> function ALF( array, decay, smooth, gain ) >> { >> global g_FirstBar, g_LastBar; // global bar range to apply all loops due to >> lack of full control over SBR >> lastResult = lastL0 = lastL1 = lastL2 = lastL3 = array[g_FirstBar]; >> lastErrorFactor = 0.5; >> HH = 0.1; >> LL = 0; >> >> for ( i = g_FirstBar; i <= g_LastBar; i++ ) >> { >> //following error >> error = abs( array[i] - lastResult ); >> //recent highest error (decays over time) >> if ( error > HH ) { HH = error; } >> else { HH *= decay; } >> //recent lowest error (decays over time) >> if ( error < LL ) { LL = error; } >> else { LL *= decay; } >> //get ema of error position in range of error >> if ( HH != LL ) { errorFactor = smooth * lastErrorFactor + (1 - smooth) >> * ((error - LL) / (HH - LL)); } >> else { errorFactor = 1; } // for x/0 case >> lastErrorFactor = errorFactor ; >> // LaGuerre Filter >> G = errorFactor * gain[i]; >> L0 = G * array[i] + (1 - G) * lastL0; >> L1 = -(1 - G) * L0 + lastL0 + (1 - G) * lastL1; >> lastL0 = L0; >> L2 = -(1 - G) * L1 + lastL1 + (1 - G) * lastL2; >> lastL1 = L1; >> L3 = -(1 - G) * L2 + lastL2 + (1 - G) * lastL3; >> lastL2 = L2; >> lastL3 = L3; >> result[i] = lastResult = (L0 + 2 * L1 + 2 * L2 + L3) / 6; >> } >> return result; >> } >> >> function ALFilter( PriceArray, Periods ) >> { >> gain = (1 / ((Periods / 2) ^.6666) + 2 / (Periods / 2)) / 2; // translate >> Periods to the non-linear gain term >> decay = 0.99; // error decay factor >> smooth = 0.8; // error smoothing factor >> return ALF( PriceArray, decay, smooth, gain ); >> } >> > > > > > ------------------------------------ > > **** IMPORTANT PLEASE READ **** > This group is for the discussion between users only. > This is *NOT* technical support channel. > > TO GET TECHNICAL SUPPORT send an e-mail directly to > SUPPORT {at} amibroker.com > > TO SUBMIT SUGGESTIONS please use FEEDBACK CENTER at > http://www.amibroker.com/feedback/ > (submissions sent via other channels won't be considered) > > For NEW RELEASE ANNOUNCEMENTS and other news always check DEVLOG: > http://www.amibroker.com/devlog/ > > Yahoo! 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