> When we 'plot' an MA(C,10) line everything else is measured relative to this > construct.
Repeated mental constructs tend to become habitual ... moreso if we actualize them in the 'real world' == PhysicalWorld; Note also that no one in the forum complains when I use AFL as part of the English language, because we are habitualized to thinking in AFL and using in our prose is a natural extension. A labourer once said to me,"Habits are easy to manage, just start a new one". --- In [email protected], "brian_z111" <brian_z...@...> wrote: > > For the trading philosophers (on modelling): > > When we look at a chart of an index, say the monthly ^DJI, if we really > 'look' at it, without prejudice, we see apparently random datapoints, or at > least meaningless datapoints, until after we determine our perspective and > overlay it on the charts. > > When we 'plot' an MA(C,10) line everything else is measured relative to this > construct. > > > --- In [email protected], "brian_z111" <brian_z111@> wrote: > > > > Thanks for that Howard. > > > > Samantha's topic was an excellent study piece, both from a technical and > > behavioural perspective, and now it continues here. > > > > There were a lot of behavioural issues highlighted by the discussion.... it > > would take me quite a while to canvass them all and I am not sure about the > > level of interest so I will confine myself to a short summary: > > > > Self-managed investors, of which 'traders' are a subgroup, are an extreme > > minority on a world scale, even when restricted to those who are > > financially literate .... possibly they aren't even a majority in this > > forum. Speaking for myself only, I think it is safe to assume that my > > opinion isn't as important as I think it is and that my investment style > > isn't as topical as I think it is (I say this so that we can put the > > discussions on 'all trading systems are discovered and traded to failure', > > and it's derived arguments, into a rational perspective). > > > > I am sure the financial world is a different place, for those with clients > > (Financial Planners/Fund Managers etc), than it is for a self-managed > > retiree or a self-managed trader, for example. > > > > Perhaps the smile on the clients faces is the number one metric used by > > FinancialPlanners. > > > > To that end: > > > > - the study we are referencing is not Samantha's... it is from Faber's > > paper, which I find extremely well writtten and interesting ... he/she > > quotes from several interesting referenced sources > > > > - specifically, studies referenced by Faber meant that up to 2006 had to be > > considered IS and the OOS sample can only be from 2006 to the present ... > > this data is included. > > > > - for the referenced studies the IS varies ... one goes back to the turn of > > the previous century > > > > - Fabers study is conducted on a TotalReturnBasis > > > > - the chosen metric is RiskAdjustedReturn although overall the results are > > summarized by the statement that > > > > "in the five asset classes > > tested, the timing approach improves the results over buy-and-hold in each > > of the four > > metrics (return, volatility, Sharpe and drawdown) for each of the five > > asset classes." > > > > In lay terms ... trend following, timing systems outperform in bull years > > and reduce/minimize losing years/amounts. > > > > I agree with Samantha's comments that these are desirable qualities for > > retirement account investments. > > > > I am not sure if Samantha advocated this model before 2008 but certainly > > the paper itself highlights the fact that historical returns, in all asset > > classes, are marred by occassional heavy drawdowns and that in 2008 (for > > the first time?) the assumptions about diversification where tested to > > breaking point (I did say in this forum, around 2-3 years ago, that > > diversification by asset class and market was an old world financial model > > ... now we see the reality of globalisation and the (NewAge) one world > > market?) > > > > From the paper: > > > > - recently correlated returns were negative for all asset classes > > - G7 countries experienced 75% losses that need a 300% gain to get back, > > which is equivalent to 10% for 15 years compounding > > - individuals do not have sufficiently long time frame to recover > > - the correlation between negative years for the S&P > > 500 and the timing model is approximately -.37, while the correlation for > > all years is approximately .82. > > > > - timing improves risk-adjusted returns in about two-thirds of all decades > > and improves drawdown in all but one decade. > > > > Note the objectives of Faber's approach: > > > > "The attempt is not to build > > an optimization model, but rather to build a simple trading model that > > works in the vast > > majority of markets. The results suggest that a market timing solution is a > > risk-reduction > > technique that signals when an investor should exit a risky asset class in > > favor of risk-free > > Treasury bills." > > > > At the end of the paper the implications for portfolios and leveraged > > portfolios are briefly considered. > > > > IMO opinion self-managed investors/traders do not operate in a seperate > > financial universe, from others, and the principles dicussed in this paper > > apply equally to mechanical system design and diversification by system etc. > > > > > > > > On the technical aspect (not in order of significance and certainly not > > complete): > > > > (I haven't done any testing and I am relying on conceptual analysis and > > experience for this opinion). > > > > - for long strategies, in any timeframe, selling when the market is falling > > and buying our 'holdings' back, at a cheaper price, is always theoretically > > justified ... only transactional costs, exascerbated by whipsawing prices, > > negatively impact the strategy ... the strategy is always justified IF the > > cost of the round trip is less than the avoided 'losses' (nominal losses > > for system designers)... the challenge is first to achieve this in a cost > > effective way, if it can be achieved at all, and then to optimize the > > strategy from there. IMO a large part of trading effort goes into that > > pursuit (catching the falling knife). > > > > - re the small sample size available for monthly testing ... volatility > > (risk, evidenced as drawdown) can only increase with a longer sample > > period? ... so, in the long run, the 'benefit of doubt' is in favour of > > timed systems, over the B&H strategy > > > > - QP data is not dividend adjusted , nor does it take into account > > delistings? > > > > - if we test on an index we don't need to worry about delistings because, > > irrespective of the constituents, we could have actually traded the index > > at the historically recorded prices, if we were around at the time?... same > > if we test endemic patterns in RT data? > > > > - prices always 'revert to the mean' and this has two components ... > > reverting to the mean when the price is above the mean or below the mean > > ... Howard's study shows that 'fading the trend' (going short when above > > the mean) underperforms compared to going long when below the mean ... in > > the long term it always will because the stockmarket is upwardly biased (by > > earnings and or inflation?) > > > > - one way to get around small datasets, when using long timeframes, might > > be to identify and treat non-correlated assets as different datasets ... > > perhaps non-correlation might only occur in 'screaming bear years' that > > come around every 7-10 years (maybe less ... who knows ... the future might > > be different to the past?). > > > > - MA(10), chosen for the study, is one of the simplest trend following > > systems .. it is not necessarily the optimum system for meeting Faber's > > objectives > > > > - Fabers paper showed that a small range of variable MA's for the monthly > > period all improved the 5 chosen metrics in historical data > > > > - high volatility diminishes compound returns.... evaluation should be > > considered on a compounding basis ... careful selection of the metrics > > needs to be made with that end in mind. > > > > - when will MA(10) systems fail? ... who knows how many are trading this > > system and if they will trade it to failure but it will definitely fail if > > the underlying instrument (the market you are trading) stops trending i.e. > > it goes sideways or whipsaws back and forth across the MA > > > > - markets always revert to mean because moving average 'indicators' are > > momentum indicators and they always lag (when price momentum slows the MA > > will still be going up and they cross each other when the price slows > > markedly, relative to the MA). > > > > - I didn't test this but I plotted PivotLo(Close), from the Zboard > > example, on the ^DJI, using Yahoo EOD data - going back a few decades ... > > to the naked eye PivotLo (sell when the close crosses below the prev > > PivotLo(C) seems to outperform MA(C,10) when it comes to avoiding whipsaw > > on the sell (not all whipsaw, just some) ... MA10 does quite a remarkable > > job though, for a simple indicator .. no wonder it has enduring appeal ... > > Buy when C crosses above previous PivotHi(Close) ... to the naked eye MA10 > > seems to outperform PivotHi when entering.... the entry is the hardest part > > to optimize (note when I use the term optimize I don't primarily mean > > parameter optimization althought I don't rule it out). > > > > - PivotHiLo is an adaptive indicator (it is self-adjusting regarding > > dimension and timing == periods, phase, freqency, probability?) > > > > - chart traders (pattern traders) don't have reversion to mean to consider, > > or optimize parametrically > > > > - chart patterns are fractal/holographic (not sure what term to use there > > ... maybe I will coin one or be more decisive about it in the future) .... > > so trend (momentum) patterns are endemic in price charts ...therefore > > similar probabilities, for the same trade, or class of trades, can be found > > in lower timeframes ... therefore Howard is correct in moving the anlaysis > > to lowertimeframes to obtain acceptable N values ... generally (subject to > > data limitations) I find that higher timeframes (monthly bars) are biased > > towards trending (non-random behaviour) slightly more than shorter > > timeframes (randomness is slightly more prevalent in intraday data ... > > perhaps tick data is even chaotic ... I haven't worked with tick data so > > far so that is only a hunch at this stage)....anyway, as long as caution is > > applied the CoreMetrics will be similar across all timeframes (say 5min - > > monthly bars .... which is what I have checked ... core price behaviour > > might persist into 1 min or below ... I can't say without checking) > > > > - while the structure, and hence viable systems, is similar between > > timeframes, increased transactional friction will tend to impact more on > > shorter timeframes.... commission and slippage are the killers in short > > timeframes and not the unreliabilty of TA. > > > > I am sure I have missed a few interesting observations from the MA10 study > > but I am going from memory. > > > > > > > > > > > > --- In [email protected], "Howard Bandy" <howardbandy@> wrote: > > > > > > Greetings all -- > > > > > > The thread that prompted me to make this posting began with the question > > > of whether a system based on a simple-moving-average crossover was > > > profitable and would remain profitable. > > > > > > I used this code to test that: > > > // Test MA Crossover.afl > > > // > > > > > > BuyPrice = SellPrice = C; > > > ShortPrice = CoverPrice = C; > > > SetTradeDelays(0,0,0,0); > > > > > > OptimizerSetEngine("cmae"); > > > > > > > > > FastMALength = Optimize("FMAL",1,1,200,1); > > > SlowMALength = Optimize("SMAL",10,1,200,1); > > > > > > FastMA = MA(C,FastMALength); > > > SlowMA = MA(C,SlowMALength); > > > > > > Buy = Cross(FastMA,SlowMA); > > > Sell = Cross(SlowMA,FastMA); > > > Short = Sell; > > > Cover = Buy; > > > > > > e = Equity(); > > > > > > ArrowShape = Buy*shapeUpArrow + Sell*shapeDownArrow; > > > > > > Plot(C,"C",colorBlack,styleCandle); > > > PlotShapes(ArrowShape,IIf(Buy,colorGreen,colorRed)); > > > Plot(e,"equity",colorGreen,styleLine|styleOwnScale); > > > > > > ////////////////////// > > > > > > Samantha suggests Buy when the monthly price is above the 10 month SMA, > > > Go to cash when it is below. > > > > > > When tested on the 500 stocks currently in the S&P 500, using end-of-day > > > data from Quotes Plus that goes back to about 1993 (for companies with > > > history that long), the median exposure is about 50% and the median RAR > > > is about +9%. > > > > > > As asked, the question assumes that being long or cash is the correct > > > allocation. For many reasons, a trader may want to consider only long > > > positions. But recent history (1984 to the present) has been a period of > > > very strong rising markets. The future might be different. Using SPY as > > > a surrogate for the broad market, the result of that same test are an > > > exposure of 61% and an RAR of 12.6%. > > > > > > We can ask several questions: > > > 1. Using full knowledge of the history, what are the best parameters for > > > a trader who wants to use only long positions? > > > > > > First, we need to decide how to measure best. We'll make a few runs with > > > different objective functions and note the results. > > > > > > Net % Profit. > > > The best values for the parameters are 2 and 12. Net profit is 304% > > > (every initial $1.00 becomes $4.04). Maximum drawdown is 15.3%. > > > > > > CAR/MDD. > > > Best values are 116 and 15. Backwards. This implies the trade should be > > > flat when the faster moving average is above the slower, be long when it > > > is below. Net profit is 51%. Exposure is 6%, RAR is 39%. > > > > > > Maximum System Drawdown. > > > The best result is to never trade. > > > > > > Sharpe Ratio -- used by many money management firms. > > > Best values are 97 and 13. Backwards again. 64% return with 13% > > > exposure, for an RAR of 22.7%. > > > > > > RRR (Same as K-Ratio). > > > Best values are 1 and 12. 292% return, exposed 63%, RAR of 13.8%. > > > > > > The best results depend very much on the definition of best. > > > > > > 2. If the trader was able and willing to take short positions, what is > > > best for them? > > > > > > Net % profit. > > > Best values are 7 and 18. 64% return, exposed 26%, RAR of 11.7%. > > > > > > CAR/MDD. > > > Best values are 1 and 24. 63% return, exposed 25%, RAR of 12.1%. > > > > > > Maximum System Drawdown. > > > The best result is to never trade. > > > > > > Sharpe Ratio. > > > Best values are 5 and 22. 63% return, exposed 24%, RAR of 12.5%. > > > > > > RRR. > > > Best values are 7 and 18. 64% return, exposed 26%, RAR of 11.7%. > > > > > > 3. If the trader wants a system that is always exposed, long or short, > > > and a single set of values that signal reversals. > > > > > > Net % Profit. > > > Best values are 1 and 22. Net profit is 534%, exposure 88% (flat at the > > > beginning until the averages have enough data), RAR of 13.6%. Maximum > > > drawdown is 16.3%. > > > > > > CAR/MDD. > > > Best values are 1 and 23. Net profit is 534%, exposure 88%, RAR 13.6%. > > > > > > Maximum System Drawdown. > > > The best result is to never trade. > > > > > > Sharpe Ratio -- used by many money management firms. > > > Best values are 78 asnd 47. Backwards again. 76% return with 34% > > > exposure, for an RAR of 10.3%. > > > > > > RRR (Same as K-Ratio). > > > Best values are 7 and 18. 522% return, exposed 89%, RAR of 13.2%. > > > > > > However --- those are all in-sample, backward looking results. And none > > > of the results have more than a handful of trades. Beware of making > > > estimates of future performance based on in-sample results (even when > > > there are a lot of data points, but that is another topic). > > > > > > 4. What are the results when a period of several years is analyzed, the > > > best parameter values select based on those years, and the future results > > > computed? Since the moving average lengths are so long -- typically > > > around 24 months -- the in-sample period must be longer. > > > > > > Five years is a long enough in-sample period to get some results. But, > > > in part due to the edge effects of the walk forward process, one year is > > > not a long enough out-of-sample period. > > > > > > An in-sample length of six years, out-of-sample three years, starts to > > > give results, but the out-of-sample results are not profitable. > > > > > > Six years and four years gives only two steps -- one profitable, the > > > other not. > > > > > > My conclusion is that we cannot determine whether a moving average > > > crossover system based on monthly bars is likely to be profitable in the > > > future or not. Sixteen years of monthly data -- 192 data points is > > > insufficient to allow meaningful validation. > > > > > > What next? > > > > > > 1. Add more data. We might think that adding data from earlier periods > > > will help. Testing to find out what moving average lengths worked best > > > in the 1930s through the 1980s might be interesting, but it is still > > > in-sample and has no value in estimating future performance. But we > > > could run walk forward tests beginning at an earlier date and observe > > > more steps. Whether those results look promising or not, we are still > > > stuck with using the data we have from 1993 through the current. So we > > > might find out what the best lengths of the in-sample and out-of-sample > > > periods are. But we already know that the system is not sufficiently > > > profitable or stable over the past eight years or more to actually trade > > > it or to use it as a filter. > > > > > > 2. Use short bars. Moving to weekly bars increases the amount of data > > > to about 830 data points. Moving to daily bars increases the amopunt of > > > data to about 4000 data points. > > > > > > Using weekly data, walking forward, two years in-sample, one year > > > out-of-sample, long only, RRR as the objective function. The results are > > > pretty promising. The system is profitable in nine OOS periods, > > > unprofitable in three, and does not trade in two. But the values of the > > > parameters are the interesting part. In 11 of the 15 OOS periods, the > > > values are backwards. That is, be long when the faster moving average is > > > below the slower moving average, and go flat when it crosses from below > > > to above. The values of the parameters vary quite a lot, with four of > > > the steps having both lengths greater than 60 weeks. In the steps where > > > the moving average periods are both below 30, 10 of 11 have the values > > > backwards -- often the faster period is one bar. > > > > > > The conclusion I draw is that the traditional thoughts on using moving > > > average crossovers as filters for trading systems have it backwards. > > > When run as a walk forward test, which is the only way to estimate what > > > future performance is likely to be, the S&P 500 is mean reverting, not > > > trend following. > > > > > > Thanks for listening, > > > Howard > > > > > >
