The importable AIQ EDS file based on Vitali Apirine’s article in the August, 2020 issue of Stocks & Commodities magazine, “The Compare Price Momentum Oscillator (CPMO),” can be obtained on request via email to info@TradersEdgeSystems.com.

… Here is a way you can compare at a glance the momentum of two different market indexes or securities in the same chart. It could also be used to help generate trading signals. In this first part of a three-part series, we’ll look at comparing index momentums…

The code is also available here:

!Author: Vitali Aprine, TASC August 2020
!Coded by: Richard Denning, 6/20/20
!www.TradersEdgeSystems.com

!Custom smoothing multiplier: 2 / time period
!PMO line: 20-period custom EMA of (10 × 35-period
!custom EMA of ((Today’s price – Yesterday’s price) /
!Yesterday’s price × 100))
!PMO signal line: 10-period EMA of the PMO line

Len1 is 20.
Len2 is 35.
Len3 is 10.
Ticker1 is “QQQ”.
Ticker2 is “SPY”.

C is [close].
C1 is valresult(C,1).
RC1 is (C/C1*100)-100.

custSmoLen1 is Len1 – 1.
custSmoLen2 is Len2 – 1.

CustEma is 10*expavg(RC1,custSmoLen2).
PMO is expavg(CustEma,custSmoLen1).
PMOsig is expavg(PMO,Len3).

Ticker1C is tickerUDF(Ticker1,C).
RC1ticker1 is (Ticker1C/valresult(Ticker1C,1)*100)-100.
CustEmaTicker1 is 10*expavg(RC1ticker1,custSmoLen2).
PMOticker1 is expavg(CustEmaTicker1,custSmoLen1).

Ticker2C is tickerUDF(Ticker2,C).
RC1ticker2 is (Ticker2C/valresult(Ticker2C,1)*100)-100.
CustEmaTicker2 is 10*expavg(RC1ticker2,custSmoLen2).
PMOticker2 is expavg(CustEmaTicker2,custSmoLen1).

CPMO is PMOTicker1 – PMOTicker2.
List if hasdatafor(1000) >= 900.

I coded the indicator described by the author. Figure 10 shows the indicator (QQQ,SPY,20,35) on chart of IWM. When the white line is above the red line on the CPMO indicator, this indicates that the QQQ is stronger than the SPY. Generally, it is considered bullish when the QQQ is leading in strength.

FIGURE 10: AIQ. The CPMO indicator is shown on a chart of IWM with parameters (QQQ,SPY,20,35).

In “A Simple Way To Trade Seasonality” in the September 2019 Stocks & Commodities, author Perry Kaufman describes methods he uses for measuring the seasonality in markets and approaches he uses for trading these patterns

Editors note: The full article can be obtained from Stocks & Commodities magazine at http://technical.traders.com/sub/sublog2.asp#Sep the system rules are from the article and are based on these rules

1. Average the monthly frequency of the past 4 years.

2. Find the last occurrence of the highest frequency and the last occurrence of the lowest frequency using the average frequency in step 1. That is, if both March and April have a frequency of 70, we use April.

3. Only trade if the high frequency is 75% or greater and the low frequency is 25% or lower.

4. If the high frequency comes first, sell short at the end of the month with the high frequency. Cover the short at the end of the month with the low frequency.

5. If the low frequency comes first, buy at the end of the month with the low frequency. Sell to exit at the end of the month with the high frequency

The importable AIQ EDS file and Excel spreadsheet for Perry Kaufman’s article can be obtained on request via email to info@TradersEdgeSystems.com. The code is also shown below

!A Simple Way to Trade Seasonality
!Author: Perry Kaufman, TASC September 2019
!Coded by: Richard Denning, 07/21/2019
!www.TradersEdgeSystem.com
C is [close].
year is 2019.
len is 4000.
OSD is offsettodate(month(),day(),year()).
FirstDate is firstdatadate().
EOM1 if Month()=2 and valresult(month(),1)=1 and year()=year.
EOMos1 is scanany(EOM1,len) then OSD+1.
EOMc1 is valresult(C,^EOMos1).
EOM2 if Month()=3 and valresult(month(),1)=2 and year()=year.
EOMos2 is scanany(EOM2,len) then OSD+1.
EOMc2 is valresult(C,^EOMos2).
EOM3 if Month()=4 and valresult(month(),1)=3 and year()=year.
EOMos3 is scanany(EOM3,len) then OSD+1.
EOMc3 is valresult(C,^EOMos3).
EOM4 if Month()=5 and valresult(month(),1)=4 and year()=year.
EOMos4 is scanany(EOM4,len) then OSD+1.
EOMc4 is valresult(C,^EOMos4).
EOM5 if Month()=6 and valresult(month(),1)=5 and year()=year.
EOMos5 is scanany(EOM5,len) then OSD+1.
EOMc5 is valresult(C,^EOMos5).
EOM6 if Month()=7 and valresult(month(),1)=6 and year()=year.
EOMos6 is scanany(EOM6,len) then OSD+1.
EOMc6 is valresult(C,^EOMos6).
EOM7 if Month()=8 and valresult(month(),1)=7 and year()=year.
EOMos7 is scanany(EOM7,len) then OSD+1.
EOMc7 is valresult(C,^EOMos7).
EOM8 if Month()=9 and valresult(month(),1)=8 and year()=year.
EOMos8 is scanany(EOM8,len) then OSD+1.
EOMc8 is valresult(C,^EOMos8).
EOM9 if Month()=10 and valresult(month(),1)=9 and year()=year.
EOMos9 is scanany(EOM9,len) then OSD+1.
EOMc9 is valresult(C,^EOMos9).
EOM10 if Month()=11 and valresult(month(),1)=10 and year()=year.
EOMos10 is scanany(EOM10,len) then OSD+1.
EOMc10 is valresult(C,^EOMos10).
EOM11 if Month()=12 and valresult(month(),1)=11 and year()=year.
EOMos11 is scanany(EOM11,len) then OSD+1.
EOMc11 is valresult(C,^EOMos11).
EOM12 if Month()=1 and valresult(month(),1)=12 and valresult(year(),1)=year.
EOMos12 is scanany(EOM12,len) then OSD+1.
EOMc12 is valresult(C,^EOMos12).
YEARavg is (EOMc1+EOMc2+EOMc3+EOMc4+EOMc5+EOMc6+EOMc7+EOMc8+EOMc9+EOMc10+EOMc11+EOMc12)/12.
AR1 is (EOMc1 / YEARavg-1)*100.
AR2 is (EOMc2 / YEARavg-1)*100.
AR3 is (EOMc3 / YEARavg-1)*100.
AR4 is (EOMc4 / YEARavg-1)*100.
AR5 is (EOMc5 / YEARavg-1)*100.
AR6 is (EOMc6 / YEARavg-1)*100.
AR7 is (EOMc7 / YEARavg-1)*100.
AR8 is (EOMc8 / YEARavg-1)*100.
AR9 is (EOMc9 / YEARavg-1)*100.
AR10 is (EOMc10 / YEARavg-1)*100.
AR11 is (EOMc11 / YEARavg-1)*100.
AR12 is (EOMc12 / YEARavg-1)*100.
EOMc if firstdate < makedate(1,20,2019-20).
AR if EOMc.

The EDS code is not a trading system but a way to get the data needed into an Excel spreadsheet to enable you to make the seasonal calculations. The EDS file should be run on a date after the end of the year being calculated. Each year for which data is needed must be run separately by setting the “year” variable. Multiple symbols can be run at the same time by using a list of the desired symbols. Each time a year is run, the “AR” report must be saved as a “.csv” file. Once all the years needed have been run and saved to separate “.csv” files, they all should be cut and pasted to a single Excel sheet. They then can be sorted by symbol and each symbol can be copied and pasted to a tab for that symbol.

Figure 6 shows the rolling four-year frequency for the S&P 500 ETF (SPY) and Figure 7 shows the annual trades resulting from applying the seasonal rules to the frequency data.

FIGURE 6: AIQ. Shown here is the rolling four-year frequency for the SPY.

FIGURE 7: AIQ. Shown here are the annual trades resulting from applying the seasonal rules to the frequency data for SPY.

The importable AIQ EDS file based on Vitali Apirine’s article in July 2019 Stocks & Commodities issue, “Exponential Deviation Bands,”

and a recreated Excel spreadsheet similar to the one shown in the article can be obtained on request via email to info@TradersEdgeSystems.com. The code is also shown here:

Exponential deviation (ED) bands are plotted above and below a moving average (MA) from which the bands are calculated. An exponential deviation from the moving average is used to set the bands. ED bands can be used with either a simple moving average (SMA) or an exponential moving average (EMA). The moving average dictates direction, and the exponential deviation sets band width. Breakouts from the band and changes in the band’s direction can help identify price trends and price reversals. These bands can be used on a variety of securities with its standard settings.

!EXPONENTIAL DEVIATION BANDS
!Author: Vitali Apirine, TASC July 2019
!Coded by: Richard Denning, 5/15/2019
!www.TradersEdgeSystems.com
C is [close].
Periods is 20.
MA20 is simpleavg(C,Periods). !expavg(C,Periods). !or simpleavg(C,Periods).
MDev20 is (Abs(MA20-C)+Abs(MA20-valresult(C,1))+Abs(MA20-valresult(C,2))+Abs(MA20-valresult(C,3))
+Abs(MA20-valresult(C,4))+Abs(MA20-valresult(C,5))+Abs(MA20-valresult(C,6))+Abs(MA20-valresult(C,7))
+Abs(MA20-valresult(C,8))+Abs(MA20- valresult(C,9))+Abs(MA20- valresult(C,10))+Abs(MA20- valresult(C,11))
+Abs(MA20- valresult(C,12))+Abs(MA20- valresult(C,13))+Abs(MA20- valresult(C,14))+Abs(MA20- valresult(C,15))
+Abs(MA20- valresult(C,16)) +Abs(MA20- valresult(C,17))+Abs(MA20- valresult(C,18))+Abs(MA20- valresult(C,19)))/20.
Dev is Abs(MA20-C).
Rate is 2/( Periods +1).
DaysInto is ReportDate() - RuleDate().
Stop if DaysInto >= 200.
stopEXD is iff(Stop,Mdev20, EXD).
EXD is Dev*Rate + valresult(stopEXD,1)*(1-Rate).
UpperExp is MA20+2*EXD.
MidExp is MA20.
LowerExp is MA20-2*EXD.
ShowValues if 1.

Figure 9 shows the exponential deviation bands centered on a 20-bar simple moving average on a chart of the New York Composite Index (NYA).

FIGURE 9: AIQ. Here are exponential deviation bands centered on a 20-bar simple moving average on a chart of the New York Composite Index (NYA).

The importable AIQ EDS file based on Anthony Garner’s article in May 2019 Stocks & Commodities “Backtesting A Mean-Reversion Strategy In Python,” can be obtained on request via email to info@TradersEdgeSystems.com. The code is also shown below.

I backtested the author’s mean-reversion system (MeanRev.eds) using both the EDS module, which tests every trade on a one-share basis, and also via the Portfolio Manager, which performs a trading simulation.

The short side strategy showed a loss overall in the EDS test so I tested only the long side in the Portfolio Manager. I selected trades using the z-score, taking the lowest values.

For capitalization, I used max of three trades per day with a max total of 10 open trades at one time, 10% allocated to each position. I did not deduct slippage but did deduct commissions. I used a recent list of the NASDAQ 100 stocks to run the test. The equity curve and account statistics report are shown in Figure 7.

FIGURE 7: AIQ. This shows the equity curve (blue line) from long-only trading the NASDAQ 100 list of stocks from 1999 to March 15, 2019. The red line is the NDX index.

!Backtesting a Mean-Reversion Strategy In Python !Author: Anthony Garner, TASC May 2019 !Coded by: Richard Denning 3/14/19 !www.TradersEdgeSystems.com

!ABBREVIATIONS: C is [close].

!INPUTS: meanLen is 10. longZmult is -1. shortZmult is 1. meanMult is 10.

!FORMULAS:

SMA is simpleavg(C,meanLen). LMA is simpleavg(C,meanLen*meanMult). STD is sqrt(variance(C,meanLen)). zScore is (C - SMA) / STD.

!TRADING SIGNALS & EXITS:

buyLong if zScore < longZmult and SMA > LMA. sellShort if zScore > shortZmult and SMA < LMA. exitLong if valresult(zScore,1) < -0.5 and zScore > 0.5. exitShort if valresult(zScore,1) > 0.5 and zScore < -0.5.

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:

The 200-day simple average of the NDX is greater than the day before

The 200-day simple average of the stock is greater than the day before

Both the weekly and daily stochastic indicators have been below 20 in the last five days

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.

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.

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.

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).

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%.

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.

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

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.

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.

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.

The AIQ code based on Vitali Apirine’s article in Stocks & Commodities issue, “Weekly & Daily Percentage Price Oscillator,” Modifying a traditional indicator can make you look at a chart differently. You can compare indexes, look at price movements during extended periods of time, and make trading decisions based on your observations is provided here:

!WEEKLY & DAILY PPO
!Author: Vitali Apirine, TASC Feb 2018
!Coded by: Richard Denning 12/17/17
!www.TradersEdgeSystems.com
!INPUTS:
S is 12.
L is 26.
EMA1 is expavg([Close],S).
EMA2 is expavg([Close],L).
EMA3 is expavg([Close],S*5).
EMA4 is expavg([Close],L*5).
DM is (EMA1 - EMA2)/EMA4*100.
WM is (EMA3 - EMA4)/EMA4*100.
WD_PPO is WM + DM.

Figure below shows the daily and weekly PPO indicator on a chart of the Nasdaq 100 index (NDX) from 2015 to 2017.

Here, the daily & weekly PPO is displayed on a chart of the NDX.

The AIQ code based on Vitali Apirine’s article in December 2017 issue of Stocks and Commodities magazine, “Weekly & Daily MACD,” is provided below.

The moving average convergence/divergence oscillator (MACD), developed by Gerald Appel, is one of the more popular technical analysis indicators. The MACD is typically used on a single timeframe, but what if we looked at two timeframes on one chart?

Traders can look for relative daily MACD line crossovers, weekly and daily centerline crossovers, and divergences to generate trading signals.

Figure 5 shows the daily & weekly MACD indicator on a chart of Apple Inc. (AAPL) during 2016 and 2017, when there was a change from a downtrend to an uptrend.

FIGURE 5: AIQ. Here is an example of the daily & weekly MACD on a chart of AAPL.

!WEEKLY & DAILY MACD
!Author: Vitali Apirine, TASC Dec 2017
!Coded by: Richard Denning 10/13/17
!www.TradersEdgeSystems.com
!INPUTS:
S is 12.
L is 25.
EMA1 is expavg([Close],S).
EMA2 is expavg([Close],L).
EMA3 is expavg([Close],S*5).
EMA4 is expavg([Close],L*5).
MACD is EMA1 - EMA2.
MACDW is EMA3 - EMA4.
rdMACD is MACD + MACDW.

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.

FIGURE 10: AIQ. This shows the EDS backtest settings for entry pricing.