Category Archives: back testing

Weekly & Daily Stochastics

The AIQ code based on Vitali Apirine’s article in the September issue of Stocks and Commodities, “Weekly and Daily Stochastics, is provided below

Using Apirine’s weekly and daily stochastic indicators and a moving average to determine trend direction, I created an example system (long only) with the following rules:

Enter long next bar at open when all of the following are true:

  1. The 200-day simple average of the NDX is greater than the day before
  2. The 200-day simple average of the stock is greater than the day before
  3. Both the weekly and daily stochastic indicators have been below 20 in the last five days
  4. Both the weekly and daily stochastic indicators are greater than the day before.

I tested three exits. Figure 8 shows a 21-day hold then exit. Figure 9 shows a three-moving-average trend-following exit. Figure 10 shows an exit using only the weekly & daily stochastic, once both are lower than the day before.

Sample Chart

FIGURE 8: AIQ, BUY and HOLD. Here is the sample equity curve (blue) compared to the NDX (red) for the test using a 21-day hold exit.

Sample Chart

FIGURE 9: AIQ, TREND-FOLLOWING EXIT. Here is the sample equity curve (blue) compared to the NDX index (red) for the test using a trend-following exit.

Sample Chart

FIGURE 10: AIQ, W and D STOCHASTIC EXIT. Here is the sample equity curve (blue) compared to the NDX index (red) for the test using the weekly and daily stochastic indicators.

The 21-day hold test showed a 11.2% return with a maximum drawdown of 29.3%. The trend-following exit test showed a 17.6% return with a maximum drawdown of 28.8%. The test using an exit based on only the weekly and daily stochastic indicators showed a return of 2.9% with a maximum drawdown of 32.5%. All the tests used the same entry rule and were run on an old 2016 list of the NASDAQ 100 stocks with the stocks that are no longer trading deleted.

!WEEKLY AND DAILY STOCHASTIC
!Author: Vitali Apirine, TASC Sept 2018
!Coded by: Richard Denning 7/7/2018
!www.TradersEdgeSystems.com

!INPUTS:
Periods is 14.
Periods1 is 3.
Pds is 70. 
Pds1 is 3.
smaLen1 is 70.
exitType is 1.

!ABBREVIATIONS:
C is [close].
H is [high].
L is [low].

!INDICATOR CODE:
STOCD is (C-LOWRESULT(L,Periods))/(HIGHRESULT(H,Periods)-LOWRESULT(L,Periods))*100. 
SD is Simpleavg(Stocd,Periods1).
StocW is (C-LOWRESULT(L,Pds))/(HIGHRESULT(H,Pds)-LOWRESULT(L,Pds))*100.
SW is Simpleavg(Stocw,Pds1).
HD if hasdatafor(1000) >= 500.
SMA200 is simpleavg(C,200).
SMA200ndx is tickerUDF("NDX",SMA200).

!SYSTEM CODE:
Buy if SMA200ndx > valresult(SMA200ndx,1)
          and SMA200 > valresult(SMA200,1)
          and SW > valresult(SW,1) 
          and SD > valresult(SD,1) 
          and countof(SW < 20,5)>=1 
          and countof(SD < 20,5)>=1 
          and HD.
smaLen2 is smaLen1*2.
smaLen3 is smaLen1*4.
SMA1 is simpleavg(C,smaLen1).
SMA2 is simpleavg(C,smaLen2).
SMA3 is simpleavg(C,smaLen3).
PD is {position days}.

!EXIT TYPE 1 USES THE INDICATOR ONLY
!EXIT TYPE 2 IS TREND FOLLOWING
Sell if (SD < valresult(SD,1) and SW < valresult(SW,1) and exitType=1)
       or (exitType = 2 
           and ((Valresult(C,PD)valresult(SMA1,PD) And Cvalresult(SMA2,PD) And Cvalresult(SMA3,PD) And C 250)).

RSS is C/valresult(C,120).
RSL is C/valresult(C,240).

—Richard Denning

info@TradersEdgeSystems.com

for AIQ Systems

When to Buy Energy Stocks

Crude oil and pretty much the entire energy sector has been crushed in recent months. This type of action sometimes causes investors to wonder if a buying opportunity may be forming.

The answer may well be, “Yes, but not just yet.”

Seasonality and Energy

Historically the energy sector shows strength during the February into May period.  This is especially true if the November through January period is negative.  Let’s take a closer look.

The Test

If Fidelity Select Energy (ticker FSENX) shows a loss during November through January then we will buy and hold FSENX from the end of January through the end of May.  The cumulative growth of $1,000 appears in Figure 1 and the yearly results in Figure 2.

1

Figure 1 – Growth of $1,000 invested in FSENX ONLY during Feb-May ONLY IF Nov-Jan shows a loss

2

Figure 2 – % + (-) from holding FSENX during Feb-May ONLY IF Nov-Jan shows a loss

Figure 3 displays ticker XLE (an energy ETF that tracks loosely with FSENX).  As you can see, at the moment the Nov-Jan return is down roughly -15%.

3

Figure 3 – Ticker XLE (Courtesy AIQ TradingExpert)

All of this suggests remaining patient and not trying to pick a bottom in the fickle energy sector. If, however, the energy sector shows a 3-month loss at the end of January, history suggests a buying opportunity may then be at end.

Summary

Paraphrasing here – “Patience, ah, people, patience”.

Jay Kaeppel

Disclaimer:  The data presented herein were obtained from various third-party sources.  While I believe the data to be reliable, no representation is made as to, and no responsibility, warranty or liability is accepted for the accuracy or completeness of such information.  The information, opinions and ideas expressed herein are for informational and educational purposes only and do not constitute and should not be construed as investment advice, an advertisement or offering of investment advisory services, or an offer to sell or a solicitation to buy any security.

Portfolio Strategy Based On Accumulation/Distribution

The AIQ code based on Domenico D’Errico’s article in the August issue of Stocks & Commodities magazine, “Portfolio Strategy Based On Accumulation/Distribution,” is shown below.

“Whether you are an individual trader or an asset manager, your main goal in reading a chart is to detect the intentions of major institutions, large operators, well-informed insiders, bankers and so on, so you can follow them. Here, we’ll build an automated stock portfolio strategy based on a cornerstone price analysis theory.”

!Portfolio Strategy Based on Accumulation/Distribution
!Author: Domenic D'Errico, TASC Aug 2018
!Coded by: Richard Denning 6/10/18
!www.TradersEdgeSystem.com
!Portfolio Strategy Based on Accumulation/Distribution
!Author: Domenic D'Errico, TASC Aug 2018
!Coded by: Richard Denning 6/10/18
!www.TradersEdgeSystem.com

!SET TO WEEKLY MODE IN PROPERTIES
!ALSO VIEW CHARTS IN WEEKLY MODE

!INPUTS:
rLen is 4.
consolFac is 75. ! in percent
adxTrigger is 30.
volRatio is 1.
volAvgLen is 4.
volDelay is 4.

!CODING ABREVIATIONS:
H is [high].
L is [low].
C is [close].
C1 is valresult(C,1).
H1 is valresult(H,1).
L1 is valresult(L,1).

!RANGE ACCUMULATION/DISTRIBUTION:
theRange is hival([high],rLen) - loval([low],rLen).
Consol if theRange < consolFac/100 * valresult(theRange,rLen).
rRatio is theRange/valresult(theRange,4)*100.

!AVERAGE TRUE RANGE ACCUMULATION/DISTRIBUTION:
avgLen is rLen * 2 - 1.	
TR  is Max(H-L,max(abs(C1-L),abs(C1-H))).
ATR  is expAvg(TR,avgLen).

ConsolATR if ATR < consolFac/100 * valresult(ATR,rLen). atrRatio is ATR / valresult(ATR,4)*100. !ADX ACCUMULATION/DISTRIBUTION: !ADX INDICATOR as defined by Wells Wilder rhigh is (H-H1). rlow is (L1-L). DMplus is iff(rhigh > 0 and rhigh > rlow, rhigh, 0).
DMminus is iff(rlow > 0 and rlow >= rhigh, rlow, 0).
AvgPlusDM is expAvg(DMplus,avgLen).
AvgMinusDM is expavg(DMminus,avgLen).           	
PlusDMI is (AvgPlusDM/ATR)*100.	
MinusDMI is AvgMinusDM/ATR*100.	
DIdiff is PlusDMI-MinusDMI. 		
Zero if PlusDMI = 0 and MinusDMI =0.
DIsum is PlusDMI+MinusDMI.
DX is iff(ZERO,100,abs(DIdiff)/DIsum*100).
ADX is ExpAvg(DX,avgLen).

ConsolADX if ADX < adxTrigger. !CODE FOR ACCUMULATIOIN/DISTRIBUTION RANGE BREAKOUT: consolOS is scanany(Consol,250) then offsettodate(month(),day(),year()). Top is highresult([high],rLen,^consolOS). Top0 is valresult(Top,^consolOS) then resetdate(). Bot is loval([low],rLen,^consolOS). AvgVol is simpleavg([volume],volAvgLen). Bot12 is valresult(Bot,12). BuyRngBO if [close] > Top
and ^consolOS <= 5 and ^consolOS >= 1
and Bot > Bot12
and valresult(AvgVol,volDelay)>volRatio*valresult(AvgVol,volAvgLen+volDelay).
EntryPrice is [close].

Sell if [close] < loval([low],rLen,1).
ExitPrice is [close].

Figure 9 shows the summary backtest results of the range accumulation breakout system using NASDAQ 100 stocks from December 2006 to June 2018. The exits differ from the author’s as follows: I used two of the built-in exits — a 20% stop-loss and a profit-protect of 40% of profits once profit reaches 10%.

Sample Chart

FIGURE 9: AIQ. Here are the summary results of a backtest using NASDAQ 100 stocks.

Figure 10 shows a color study on REGN. The yellow bars show where the range accumulation/distribution shows a consolidation.

Sample Chart

FIGURE 10: AIQ. This color study shows range consolidation (yellow bars).

—Richard Denning

info@TradersEdgeSystems.com

for AIQ Systems

A Technical Method For Rating Stocks

The AIQ code based on Markos Katsanos’ article in this issue, “A Technical Method For Rating Stocks,” is shown below.
Synopsis from Stocks & Commodities June 2018
I’s it possible to create a stock rating system using multiple indicators or other technical criteria? If so, how does it compare with analyst ratings? Investors around the world move billions of dollars every day on advice from Wall Street research analysts. Most retail investors do not have the time or can’t be bothered to read the full stock report and rely solely on the bottom line: the stock rating. They believe these ratings are reliable and base their investment decisions at least partly on the analyst buy/sell rating. But how reliable are those buy/sell ratings? In this article I will present a technical stock rating system based on five technical criteria and indicators, backtest it, and compare its performance to analyst ratings.
!A TECHNICAL METHOD FOR RATING STOCKS
!Author: Markos Katsanos, TASC June 2018
!Coded by: Richard Denning, 4/18/18
!www.TradersEdgeSystems.com

!INPUTS:
  MAP is 63. 
  STIFFMAX is 7. 
  VFIPeriod is 130. 
  MASPY is 100. 
  MADL is 100.
  SCORECRIT is 5.
  W1 is 1.
  W2 is 1.
  W3 is 1.
  W4 is 1.
  W5 is 2.
 
!VFI FORMULA: 
  COEF is 0.2.
  VCOEF is 2.5.
  Avg is ([high]+[low]+[close])/3.
  inter is ln( Avg ) - ln( Valresult( Avg, 1 ) ). 
  vinter is sqrt(variance(inter, 30 )).
  cutoff is Coef * Vinter * [Close].
  vave is Valresult(simpleavg([volume], VFIPeriod ), 1 ).
  vmax is Vave * Vcoef.
  vc is Min( [volume], VMax ).
  mf is Avg - Valresult( Avg, 1 ).
  vcp is iff(MF > Cutoff,VC,iff(MF < -Cutoff,-VC,0)).
  vfitemp is Sum(VCP , VFIPeriod ) / Vave.
  vfi is expavg(VFItemp, 3 ).

!STIFFNESS 
  ma100 is Avg. 
  CLMA if [close] < MA100.
  STIFFNESS is countof(CLMA,MAP).

!CONDITIONS:
 ! MONEY FLOW:
   COND1 is iff(VFI>0,1,0). 
 !SIMPLEAVG:
    SMA is simpleavg([close],MADL).                              
    COND2 is iff([close]>SMA,1,0).  
 !SIMPLEAVG DIRECTION:                       
    COND3 is iff(SMA>valresult(SMA,4),1,0).  
!STIFFNESS:                          
    COND4 is iff(STIFFNESS<= STIFFMAX,1,0).  
!MARKET DIRECTION:
    SPY is TickerUDF("SPY",[close]).
    COND5 is iff(EXPAVG(SPY,MASPY)>= 
 valresult(EXPAVG(SPY,MASPY),2),1,0).            

SCORE is  W1*COND1+W2*COND2+W3*COND3+
   W4*COND4+W5*COND5.

 buy if Score>=SCORECRIT and hasdatafor(300)>=268. 
Figure 11 shows the summary results of a backtest using NASDAQ 100 stocks during a generally bullish period from April 2009 to April 2018. Figure 12 shows the backtest using the same list of NASDAQ 100 stocks during a period that had two bear markets (April 1999 to April 2009). The average results are similar except that there are fewer trades during the period that contained the two bear markets. Both backtests use a fixed 21-bar exit.
Sample Chart

FIGURE 11: AIQ, BULL MARKET. Here are the summary results of a backtest using NASDAQ 100 stocks during a generally bullish period from April 2009 to April 2018.
Sample Chart

FIGURE 12: AIQ, BEAR MARKET. Here are the summary results of a backtest using NASDAQ 100 stocks during a period from April 1999 to April 2009 that contained two bear markets.
—Richard Denning info@TradersEdgeSystems.com for AIQ Systems

A Candlestick Strategy With Soldiers And Crows

ndle reversal patterns—a bullish one white soldier and a bearish one black crow—that requ

The Expert Design Studio code for Jerry D’Ambrosio and Barbara Star’s article, “A Candlestick Strategy With Soldiers And Crows,” in Stocks & Commodities October 2018 issue is shown below.”Among the more well-known candlestick reversal patterns are soldiers and crows. These occur in a three-candle pattern such as three white soldiers or three black crows. Recently, on the website Candlesticker.com, we learned of two other candle reversal patterns—a bullish one white soldier and a bearish one black crow—that require fewer candles. ”

!A CANDLESTICK STRATEGY WITH SOLDIERS AND CROWS
!Author: Jerry D'Ambrosio & Barbara Star, TASC Oct 2017
!Coded by: Richard Denning 8/05/2017
!www.TradersEdgeSystems.com

!CODING ABBREVIATIONS:
O is [open].
O1 is valresult(O,1).
C is [close].
C1 is valresult(C,1).
C2 is valresult(C,2).
H is [high].
L is [low].
V is [volume].

!INPUTS:
minPriceBull is 1.
minPriceBear is 10.
minVolume is 1000. !in hundreds
volAvgLen is 50.
dayCount is 5.
longExitBars is 7.
shortExitBars is 1.

okToBuy if simpleavg(C,50) > simpleavg(C,200) or CminPriceBull and simpleavg(V,volAvgLen)>minVolume.
BullWS if C1C1 and C>O1 and O= longExitBars.

okToSell if simpleavg(C,50) < simpleavg(C,200) or C>simpleavg(C,200)*1.1.
okToSellMkt if TickerRule("SPX",okToSell).
PVfilterBear if C>minPriceBear and simpleavg(V,volAvgLen).
BearBC if C1>C2 and C1>O1 
     and OO1 
     and countof(C1>C2,dayCount)=dayCount
     and PVfilterBear and okToSellMkt.
ExitShort if {position days} >= shortExitBars.
I ran several backtests using the NASDAQ 100 list of stocks over the period from 8/04/2000 to 8/04/2017. I varied the following inputs to find the optimum set of parameters for the candlestick patterns. For longs, the “dayCount” = 5 with an “longExitBars” = 7 produced the best results, which is shown in Figure 5. For shorts, the “dayCount” = 5 with a “shortExitBars” = 1 produced the best results, which is shown in Figure 6. Neither commission nor slippage were subtracted from the results.

Sample Chart

FIGURE 5: WINWAY. EDS summary report for longs only.

Sample Chart

FIGURE 6: WINWAY. EDS summary report for shorts only.
—Richard Denning
info@TradersEdgeSystems.com
for TradingExpert Pro

ire fewer candles. “

System Development Using Artificial Intelligence

The AIQ code based on Domenico D’Errico and Giovanni Trombetta’s article in August 2017 Stock & Commodities issue, “System Development Using Artificial Intelligence,” is shown here. You can also download the EDS file from here

Are humans or computers better at trading? This question has been around on many fronts since the era of punch cards, and as technology advances, you question whether machines have limits. It’s the same with trading, and here’s an algorithm that may shed some light on which performs better…

!ARTIFICAL INTELLIGENCE FOR SYSTEM DEVELOPMENT
!Authors: Domenico D'Errico & Giovanni Trombetta, TASC August 2017
!Coded by: Richard Denning, 6/08/2017
!www.TradersEdgeSystems.com

!INPUTS:
O is [open].
C is [close].
H is [high].
L is [low].
exitBars is 8.
exitBarsP is 6.
enterGap is -0.08.

!CODE:
AvgP is (O+C+H+L)/4.
MedP is (H+L)/2.
MedB is (O+C)/2.

AvgP1 is valresult(AvgP,1).
AvgP2 is valresult(AvgP,2).
AvgP3 is valresult(AvgP,3).

MedP1 is valresult(MedP,1).
MedP2 is valresult(MedP,2).
MedP3 is valresult(MedP,3).
MedP4 is valresult(MedP,4).

MedB1 is valresult(MedB,1).
MedB2 is valresult(MedB,2).
MedB3 is valresult(MedB,3).
MedB4 is valresult(MedB,4).

!ENTRY & EXIT RULESl
Gandalf if 
  (AvgP1exitBars-1)
 or ({position days}>=exitBars-1)
 or ({position days}>=exitBarsP-1 and (C-{position entry price}>0)).

EntryPr is min(val([low],1) + enterGap,[open]).

Buy if Gandalf and [low] <= EntryPr.

See Figure 10 for how to set up the pricing in a backtest.
Sample Chart

FIGURE 10: AIQ. This shows the EDS backtest settings for entry pricing.
—Richard Denning
info@TradersEdgeSystems.com
for AIQ Systems

Detecting Swings

The AIQ code based on Domenico D’Errico’s article in the May 2017 issue of Stocks & Commodities issue, “Detecting Swings,” is provided here.

I tested the author’s four systems using the NASDAQ 100 list of stocks on weekly bars, as did the author, from 3/16/2005 through 3/14/2017. Figure 7 shows the comparative metrics of the four systems using the four-week exit. The results were quite different than the author’s, probably due to a different test portfolio and also a 10-year test period rather than the author’s 20-year period. In addition, my test results show longs only, whereas the author’s results are the average of both the longs and shorts.

Sample Chart

FIGURE 7: AIQ. As coded in EDS, this shows the metrics for the author’s four systems run on NASDAQ 100 stocks (weekly bar data) over the period 3/16/2005 to 3/14/2007.

The Bollinger Band (Buy2) system showed the worst results, whereas the author’s results showed the Bollinger Band system as the best. The pivot system (Buy1) showed the best results, whereas the author’s results showed the pivot system as the worst. I am not showing here the comparative test results for the Sell1 thru Sell4 rules, as all showed an average loss over this test period.

!DECTECTING SWINGS
!Author: Domenico D'Errico, TASC May 2017
!Coded by: Richard Denning, 3/15/17
!www.TradersEdgeSystems.com

!Set to WEEKLY in properties

Low is  [low].
Low1  is valresult(Low,1).
Low2  is valresult(Low,2). 
High is [high].
High1  is valresult(High,1).
High2  is valresult(High,2). 
PivotLow if Low1 &lt; Low2  and Low1 &lt; Low.
PivotHigh if High1 &gt; High2  and High1 &gt; High.

Buy1 if  PivotLow.  
Sell1 if  PivotHigh.    

!Set parameter for bollinger bands to 12 with 2 sigma (weekly) in charts:
Buy2 if [close] &gt; [Lower BB] and valrule([close] &lt;= [Lower BB],1).
Sell2 if [close] &lt; [Upper BB] and valrule([close] &gt;= [Upper BB],1).

!Set parameter for Wilder RSI to 5 (weekly) in charts:
Buy3 if [RSI Wilder] &gt; 40 and valrule([RSI Wilder] &lt;= 40,1).
Sell3 if [RSI Wilder] &lt; 60 and valrule([RSI Wilder] &gt;= 60,1).

Buy4 if [RSI Wilder] &lt; 40  And Low &gt; Low1.
Sell4 if [RSI Wilder] &gt; 60  And High &lt; High1.    

Exit if {position days} &gt;= 4.

The code and EDS file can be downloaded from http://aiqsystems.com/detectingswings.EDS

—Richard Denning

info@TradersEdgeSystems.com

for AIQ Systems

One Good Reason NOT to Pick a Bottom in DIS

A better title for this article might be “How to Avoid Losing 98% in Disney.”
The recent dip in the price of Disney stock may ultimately prove to be a buying opportunity.  But for reasons detailed below I am going to let this one pass.
If you have read my stuff in the past you know that I look a lot at seasonal trends.  This is especially true for sectors and commodities – which in some cases can be tied to recurring fundamental factors.  I have occasionally looked at individual stocks (here and here and here), but tend to think that an individual company’s fundamentals can change so drastically over time that a persistent seasonal trend is less likely.
It appears that there are exceptions to every rule.
In Figure 1 below we see that after a strong run up from its 2009 low, Disney finally topped out in August of 2015. Since that time it’s been a string of large moves up and down – with the latest being down. This might prompt one to consider the latest dip as a buying opportunity.  And in fact, maybe it is. But I won’t be making that play myself based simply on a seasonal trend in DIS stock that was highlighted by Brooke Thackray in his book Thackray’s 2017 Investor’s Guide.
0Figure 1 – Is latest dip in DIS a buying opportunity?  Maybe, but history suggests we look elsewhere….(Courtesy AIQ TradingExpert)
When NOT to Own Disney Stock
In his book, Thackray highlights the period from June 5th through the end of September as an “unfavorable” period for DIS stock.  He also listed a specific “favorable” period that I’ll not mention here.  For purposes of this article I made the following changes:
*The “unfavorable” period begins at the close on the 5th trading day of June and ends at the close on the last trading day of September.
*The rest of the year – i.e., end of September until the close on the 5th trading day of June – is considered the “favorable” period.
Also, the test uses price data only.  No dividends are included nor is any interest assumed to be earned while out of DIS stock.
The results are fairly striking.  From the end of 1971 through the end of 2016:
*$1,000 invested in DIS on a buy-and-hold basis grew +8,042% to $81,422 (average annual +/- = +15.8%)
*$1,000 invested in DIS only during the “favorable” period grew +430,874% to $4,309,735 (average annual +/- = +25.0%)
*$1,000 invested in DIS only during the “unfavorable” period declined -98% to $18.89 (average annual +/- = (-6.9%))
It’s sort of hard to ignore the difference between +430,784% and -98%.
Figure 1 displays the cumulative performance during the unfavorable period from 1971 through 2016.
1
Figure 1 – Growth of $1,000 invested in DIS only from close of June Trading Day #5 through the end of September (1971-2016)
Figure 2 displays the growth of $1,000 during the favorable period (blue line) versus a buy-and-hold approach (red line).
2
Figure 2 – Growth of $1,000 invested in DIS only from the end of September through June Trading Day #5 (blue) versus Buy-and-Hold (red); 1971-2016
*The favorable period showed a net gain in 39 out of 45 years (87%)
*The unfavorable period showed a net gain in only 13 out of 45 years (29%)
*Buy-and-hold showed a net gain in 28 out of 45 years (62%)
Figure 3 displays year-by-year results.
Year Favorable Unfavorable Buy/Hold
1972 78.1 (3.5) 71.9
1973 (53.5) (12.0) (59.1)
1974 4.4 (56.4) (54.4)
1975 175.6 (12.4) 141.5
1976 5.2 (6.9) (2.0)
1977 (28.2) 19.1 (14.4)
1978 4.6 (3.3) 1.2
1979 1.2 10.5 11.9
1980 20.8 (5.8) 13.8
1981 42.8 (28.7) 1.9
1982 16.0 4.4 21.1
1983 (5.7) (11.6) (16.7)
1984 25.6 (9.6) 13.6
1985 94.4 (3.3) 88.0
1986 98.3 (23.0) 52.8
1987 14.6 20.1 37.6
1988 4.2 6.5 10.9
1989 32.7 28.3 70.3
1990 28.4 (29.4) (9.3)
1991 14.4 (1.5) 12.8
1992 51.3 (0.7) 50.2
1993 16.0 (14.5) (0.8)
1994 23.2 (12.4) 7.9
1995 27.7 0.3 28.0
1996 18.4 0.0 18.4
1997 43.3 (0.9) 41.9
1998 36.9 (33.6) (9.1)
1999 15.8 (15.8) (2.5)
2000 4.0 (4.8) (1.1)
2001 23.6 (42.1) (28.4)
2002 12.6 (30.1) (21.3)
2003 50.9 (5.2) 43.0
2004 28.9 (7.6) 19.2
2005 (2.5) (11.6) (13.8)
2006 41.8 0.8 43.0
2007 (6.2) 0.4 (5.8)
2008 (24.4) (7.0) (29.7)
2009 29.1 10.1 42.1
2010 16.1 0.2 16.3
2011 30.4 (23.4) (0.0)
2012 15.9 14.6 32.8
2013 54.3 (0.6) 53.4
2014 17.2 5.2 23.3
2015 20.4 (7.3) 11.6
2016 5.0 (5.6) (0.8)
2017 ? ? ?
# Years UP 39 13 28
# Years DOWN 6 32 17
Average % +/- 25.0 (6.9) 15.8
Figure 3 – Year-by-Year Results
Summary
Brooke Thackray found an extremely interesting and robust “unfavorable” seasonal trend in DIS stock.  Of course none of the data above guarantees that DIS stock is doomed to languish and/or decline in the months ahead.  But I for one do not intend to “buck the odds” and play the long side of DIS for a while.
Jay Kaeppel  Chief Market Analyst at JayOnTheMarkets.com and AIQ TradingExpert Pro http://www.aiqsystems.com) client. 
Disclaimer:  The data presented herein were obtained from various third-party sources.  While I believe the data to be reliable, no representation is made as to, and no responsibility, warranty or liability is accepted for the accuracy or completeness of such information.  The information, opinions and ideas expressed herein are for informational and educational purposes only and do not constitute and should not be construed as investment advice, an advertisement or offering of investment advisory services, or an offer to sell or a solicitation to buy any security.

Detecting Swings

The AIQ code based on Domenico D’Errico’s article in the May 2017 issue of Stoks Commodities, “Detecting Swings,” is provided below.

I tested the author’s four systems using the NASDAQ 100 list of stocks on weekly bars, as did the author, from 3/16/2005 through 3/14/2017. Figure 7 shows the comparative metrics of the four systems using the four-week exit. The results were quite different than the author’s, probably due to a different test portfolio and also a 10-year test period rather than the author’s 20-year period. In addition, my test results show longs only, whereas the author’s results are the average of both the longs and shorts.

Sample Chart
 
FIGURE 7: AIQ. As coded in EDS, this shows the metrics for the author’s four systems run on NASDAQ 100 stocks (weekly bar data) over the period 3/16/2005 to 3/14/2007.

The Bollinger Band (Buy2) system showed the worst results, whereas the author’s results showed the Bollinger Band system as the best. The pivot system (Buy1) showed the best results, whereas the author’s results showed the pivot system as the worst. I am not showing here the comparative test results for the Sell1 thru Sell4 rules, as all showed an average loss over this test period.

!DECTECTING SWINGS
!Author: Domenico D'Errico, TASC May 2017
!Coded by: Richard Denning, 3/15/17
!www.TradersEdgeSystems.com

!Set to WEEKLY in properties

Low is  [low].
Low1  is valresult(Low,1).
Low2  is valresult(Low,2). 
High is [high].
High1  is valresult(High,1).
High2  is valresult(High,2). 
PivotLow if Low1 < Low2  and Low1 < Low.
PivotHigh if High1 > High2  and High1 > High.

Buy1 if  PivotLow.  
Sell1 if  PivotHigh.    

!Set parameter for bollinger bands to 12 with 2 sigma (weekly) in charts:
Buy2 if [close] > [Lower BB] and valrule([close] <= [Lower BB],1).
Sell2 if [close] < [Upper BB] and valrule([close] >= [Upper BB],1).

!Set parameter for Wilder RSI to 5 (weekly) in charts:
Buy3 if [RSI Wilder] > 40 and valrule([RSI Wilder] <= 40,1).
Sell3 if [RSI Wilder] < 60 and valrule([RSI Wilder] >= 60,1).

Buy4 if [RSI Wilder] < 40  And Low > Low1.
Sell4 if [RSI Wilder] > 60  And High < High1.    

Exit if {position days} >= 4.
—Richard Denning
info@TradersEdgeSystems.com
for AIQ Systems
Editor note: The code and EDS file can be downloaded from http://aiqsystems.com/Detecting_Swings_TASC_May_2017.EDS

Exponential Standard Deviation Bands

The AIQ code based on Vitali Apirine’s article in the 2017 issue of Stocks & Commodities magazine, “Exponential Standard Deviation Bands”

Editor note: “Author Vitali Apirine presented a method intended to help traders see volatility while a stock is trending. These bands, while similar to Bollinger Bands, are calculated using exponential moving averages rather than simple moving averages. Like Bollinger Bands, they widen when volatility increases and narrow as volatility decreases. He suggests that the indicator can be used as a confirming indication along with other indicators such as the ADX. Here’s an AIQ Chart with the Upper, Lower and Middle Exponential SD added as custom indicators.”

 

 

To compare the exponential bands to Bollinger Bands, I created a trend-following trading system that trades long only according to the following rules:
  1. Buy when there is an uptrend and the close crosses over the upper band. An uptrend is in place when the middle band is higher than it was one bar ago.
  2. Sell when the low is less than the lower band.
Figure 8 shows the summary test results for taking all signals from the Bollinger Band system run on NASDAQ 100 stocks over the period 12/9/2000 to 12/09/2016. Figure 9 shows the summary test results for taking all signals from the exponential band system on NASDAQ 100 stocks over the same period. The exponential band system improved the average profit per trade while reducing the total number of trades.

Sample Chart

FIGURE 8: AIQ. Here are summary test results for taking all signals from the Bollinger Band system run on NASDAQ 100 stocks over the period 12/9/2000 to 12/09/2016.

Sample Chart

FIGURE 9: AIQ. Here are summary test results for taking all signals from the exponential band system run on NASDAQ 100 stocks over the period 12/9/2000 to 12/09/2016.
The EDS file can be downloaded from http://aiqsystems.com/EDS/Exponential_Standard_Deviation_Bands.EDS 
and is also shown here:
!Exponential Standard Deviation Bands
!Author: Vitali Apirine, TASC February 2017
!Coded by: Richard Denning 12/11/2016
!www.TradersEdgeSystems.com!INPUT:
xlen is 20.
numSD is 2.

!INDICATOR CODE:
ExpAvg is expavg([close],xlen).
Dev is [close] – ExpAvg.
DevSqr is Dev*Dev.
SumSqr is sum(DevSqr,xlen).
AvgSumSqr is SumSqr / xlen.
ExpSD is sqrt(AvgSumSqr).

!UPPER EXPONENTIAL SD BAND:
UpExpSD is ExpAvg + numSD*ExpSD.  !PLOT ON CHART

!LOWER EXPONENTIAL SD BAND:
DnExpSD is ExpAvg – numSD*ExpSD.   !PLOT ON CHART

!MIDDLE EXPONENTIAL SD BAND:
MidExpSD is ExpAvg.

!BOLLINGER BANDS FOR COMPARISON:
DnBB is [Lower BB].  !Lower Bollinger Band
UpBB is [Upper BB].  !Upper Bollinger Band
MidBB is simpleavg([close],xlen). !Middle Bollinger Band
!REPORT RULE TO DISPLAY VALUES:
ShowValures if 1.

!TRADING SYSTEM USING EXPPONENTIAL SD BANDS:
UpTrend if MidExpSD > valresult(MidExpSD,1).
BreakUp if [close] > UpExpSD.
BuyExpSD if UpTrend and BreakUp and valrule(Breakup=0,1).
ExitExpSD if [Low] < DnExpSD.  ! or UpTrend=0.

!TRADING SYSTEM USING BOLLINGER BANDS:
UpTrendBB if MidBB > valresult(MidBB,1).
BreakUpBB if [close] > UpBB.
BuyBB if UpTrendBB and BreakUpBB and valrule(BreakupBB=0,1).
ExitBB if [Low] < DnBB.  ! or UpTrend=0.

—Richard Denning
info@TradersEdgeSystems.com
for AIQ Systems