#Using Artemis

Repository link: [https://github.com/mandradebs/artemis]

To start using Artemis bot, you must install the following R packages. By default, when loading the Bot.R file, it will try to install the required packages but if an error occur, you must install the missing pakages manually. All tests were done on a Windows 10 laptop with a 6th Generation Intel i7 processor and 16 GB of RAM.

#Load Bot.R file. It contains the Bot class.
source("Bot.R")



After loading the Bot class, you can load the Artemis bot with the following command:

load("190830artemis.rda")



Now, we can communicate with Artemis as follows:

Artemis, What’s your name? When’s your birthday?

#Translating the questions to the Artemis language:
artemis$getName()
## [1] "artemis"
artemis$getBirthday()
## [1] "2019-04-13"



Artemis, show me the first 10 tickers in your study material. How many tickers are there?

head(artemis$studyMaterial$quotes,10)
##  [1] "MMM"  "ABT"  "ABBV" "ABMD" "ACN"  "ATVI" "ADBE" "AMD"  "AAP"  "AES"
length(artemis$studyMaterial$quotes)
## [1] 503



Artemis, How many records do you have in your knowledge? How many by pattern name and class?

length(artemis$knowledge)
## [1] 1004
#class 0 means negative pattern, 1 means positive pattern
table(Reduce(f = function(x,y) rbind(x,y),x = artemis$knowledge)[,1:2])
##     class
## name   0   1
##   CH 411  91
##   DB 311 191



This copy of Artemis already contains 5 models for 2 patterns (cup with handle (CH) and double bottom (DB)). So you don’t need to start a new training process. Examples of Artemis training are:

Artemis, start your training ability for the CH pattern using the knn method and trying from 1 to 20 neighbors. Use 7 cores of my laptop. Also, start a training for DB but using a deep neural network with 3 hidden layers and 1024 neurons in each one.

set.seed(67)
artemis$train(patternName = "CH",method = "knn",nsocks = 7,
              trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum), 
              tuneGrid = expand.grid(k = seq(1,20)))

set.seed(80)
artemis$train(patternName = "DB",method = "deepNet",hidden=c(1024,1024,1024))

Full training was performed using the following sentences:

seed = 67
set.seed(seed)
artemis$train(patternName = "CH",method = "knn",nsocks = 7,
              trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum), tuneGrid = expand.grid(k = seq(1,20)))
set.seed(seed)
artemis$train(patternName = "CH",method = "deepNet",hidden=c(1024,1024,1024))
set.seed(seed)
artemis$train(patternName = "CH",method = "rf",nsocks = 7,tuneLength = 10,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))
set.seed(seed)
artemis$train(patternName = "CH",method = "svmRadial",nsocks = 7,tuneLength = 10,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))
set.seed(seed)
artemis$train(patternName = "CH",method = "glm",family=binomial(),nsocks = 7,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))

seed = 80
set.seed(seed)
artemis$train(patternName = "DB",method = "knn",nsocks = 7,
              trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum), tuneGrid = expand.grid(k = seq(1,20)))
set.seed(seed)
artemis$train(patternName = "DB",method = "deepNet",hidden=c(1024,1024,1024))
set.seed(seed)
artemis$train(patternName = "DB",method = "rf",nsocks = 7,tuneLength = 10,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))
set.seed(seed)
artemis$train(patternName = "DB",method = "svmRadial",nsocks = 7,tuneLength = 10,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))
set.seed(seed)
artemis$train(patternName = "DB",method = "glm",family=binomial(),nsocks = 7,tuneLength = 10,trControl = trainControl(classProbs =  TRUE,summaryFunction = twoClassSum))



Artemis, show me your training results.

artemis$trainSummary()
Pattern name Method Train Accuracy Train Sensitivity Train Specificity Test Accuracy Test Sensitivity Test Specificity
DB deepNet 0.97 0.98 0.98 0.83 0.83 0.82
DB svmRadial 0.88 0.82 0.92 0.87 0.81 0.91
DB knn 0.84 0.70 0.93 0.83 0.68 0.92
DB rf 0.83 0.66 0.92 0.82 0.69 0.90
DB glm 0.64 0.55 0.70 0.63 0.63 0.63
CH deepNet 1.00 1.00 1.00 0.92 0.68 0.97
CH svmRadial 0.91 0.69 0.96 0.93 0.62 0.99
CH rf 0.91 0.66 0.97 0.90 0.53 0.98
CH knn 0.88 0.46 0.99 0.89 0.35 0.99
CH glm 0.77 0.55 0.83 0.84 0.50 0.90

Note that Neural networks (deepNet) and Support vector machines (svmRadial) were the best methods according to the accuracy.



Artemis, check if the AAL time series is a known pattern.

dta <- artemis$getData(ticker = "AAL")[,"Close"]
artemis$explore(tickers = "AAL",dta = list(dta),nlast = nrow(dta),minN = nrow(dta))#use all available records and consider just one candidate
Tickers Method Pattern start Probability
AAL rf DB 2007-01-03 0.2640
AAL svmRadial DB 2007-01-03 0.0708
AAL deepNet DB 2007-01-03 0.0111
AAL glm DB 2007-01-03 0.0000
AAL knn DB 2007-01-03 0.0000
AAL glm CH 2007-01-03 1.0000
AAL deepNet CH 2007-01-03 1.0000
AAL svmRadial CH 2007-01-03 0.9121
AAL rf CH 2007-01-03 0.8440
AAL knn CH 2007-01-03 0.8333

Note that DB pattern has a low probability meanwhile CH pattern has probability 1 using neural networks (deepNet) and logistic regression (glm) models.

Artemis, make a plot of the AAL ticker.

#one way
#artemis$plot(ticker = "AAL")

#another way, since dta dataframe contains the AAL close prices
artemis$plot(dta = dta)

Note that the plot seems to be a cup with handle pattern.



Artemis, explore 100 tickers, take the names from your study material (only the names, not the time series data). Consider just the last year (240 days) with candidates length of 5 months (100 days) or more and return up to 3 candidates for each ticker (the best 3 candidates).

#This was run on May 1, 2019
artemis$explore(tickers = head(artemis$studyMaterial$quotes,100),nlast = 240,minN = 100,ncandidates = 3)



Artemis, show me the best candidates (with probability 0.99 or more using Support vector machines and Neural networks methods).

dta <- artemis$exploration$candidates[which(artemis$exploration$candidates$Probability>0.99 & artemis$exploration$candidates$Method %in% c("svmRadial","deepNet")),]
dta
Tickers Method Pattern start Probability
1 BKNG deepNet DB 2018-12-04 1.0000
2 LNT deepNet DB 2018-09-10 1.0000
3 AWK deepNet DB 2018-09-10 1.0000
4 ADSK deepNet DB 2018-06-14 1.0000
5 AGN deepNet DB 2018-11-19 1.0000
6 AMD deepNet DB 2018-06-14 1.0000
7 AVGO deepNet DB 2018-06-28 0.9999
8 CDNS deepNet DB 2018-09-10 0.9999
9 AON deepNet DB 2018-07-13 0.9998
10 AFL deepNet DB 2018-05-31 0.9998
11 APD deepNet DB 2018-05-31 0.9998
12 A deepNet DB 2018-05-31 0.9994
13 AJG deepNet DB 2018-07-27 0.9993
14 AEE deepNet DB 2018-09-10 0.9993
15 BR deepNet DB 2018-12-04 0.9986
16 BDX deepNet DB 2018-08-24 0.9970
17 AAL deepNet DB 2018-12-04 0.9960
18 CAH deepNet DB 2018-08-24 0.9946
19 CNC deepNet DB 2018-08-24 0.9940
20 AEP deepNet DB 2018-09-10 0.9926
21 CAT deepNet DB 2018-05-16 0.9908
22 BHGE deepNet CH 2018-10-22 1.0000
23 ALGN deepNet CH 2018-10-08 1.0000
24 AOS deepNet CH 2018-09-10 1.0000
25 ALL deepNet CH 2018-10-08 1.0000
26 BWA deepNet CH 2018-09-10 1.0000
27 BBT deepNet CH 2018-11-05 1.0000
28 AMD deepNet CH 2018-10-08 1.0000
29 CAH deepNet CH 2018-11-05 1.0000
30 AMG deepNet CH 2018-10-22 1.0000
31 BLK deepNet CH 2018-09-24 1.0000
32 AAPL deepNet CH 2018-10-22 1.0000
33 CELG deepNet CH 2018-08-24 1.0000
34 AMP deepNet CH 2018-10-08 1.0000
35 ALB deepNet CH 2018-11-19 1.0000
36 ANSS deepNet CH 2018-09-24 0.9999
37 AIZ deepNet CH 2018-10-08 0.9999
38 BKNG deepNet CH 2018-11-05 0.9999
39 AMAT deepNet CH 2018-08-24 0.9998
40 BA deepNet CH 2018-10-08 0.9998
41 ABC deepNet CH 2018-10-22 0.9997
42 CE deepNet CH 2018-09-24 0.9997
43 BAX deepNet CH 2018-09-24 0.9995
44 CBS deepNet CH 2018-11-05 0.9994
45 BAC deepNet CH 2018-09-10 0.9993
46 AIG deepNet CH 2018-09-24 0.9993
47 AJG deepNet CH 2018-11-05 0.9993
48 CPRI deepNet CH 2018-10-22 0.9993
49 APA deepNet CH 2018-11-05 0.9991
50 CCL deepNet CH 2018-11-05 0.9986
51 AVY deepNet CH 2018-09-24 0.9975
52 MO deepNet CH 2018-11-05 0.9973
53 AMZN deepNet CH 2018-09-10 0.9963
54 AKAM deepNet CH 2018-11-05 0.9946
55 BBY deepNet CH 2018-11-05 0.9941



Artemis, from the last table, plot the first candidate. It is supposed to be a DB pattern.

j <- 1
artemis$plot(dta = artemis$exploration$dta[[which(artemis$exploration$tickers == dta$Tickers[j])]][sprintf("%s/",dta$start[j]),])