Business Questions

What

This dashboard will show the summary of author’s personal investment position (dummy data), as well as stock swing trading recommendation using MA (Moving Average) line.

Moving average (MA) is a calculation used to analyze data points by creating a series of averages of different subset of the full data set. In asset technical analysis, MA gives information about the average value of an assets over a given period of time. Exponential Moving Average (EMA) is the average calculation that place more weight to the more recent information, as opposed to the other calculation where every information across timeline hold the same weight. It is more widely used by asset traders to assist in making trading plan, as it shows at least three informations:

Price general trend
Current price point position within market cycle
Asset buy/sell point, including its support & resistance line

Moving Average (either EMA or SMA, with each of their respective nature) is a lagging indicators, thus it is not to be used a future price predictor. In this tools, this indicator will be used as a filter to help user to choose which stocks are starting a confirmed uptrend (hence give recommendation to start buying), and which are declining so that the user can sell their asset before it drops too deeply.

Why & Who

The wide type of investment across multiple platform is often exhaustive to monitor. This dashboard will show how are they performing at any point of the day in one single click. Especially for the stock trading activity, the dashboard will show which stocks are worth checking amongst 400 listed companies before executing the buy/sell decision.

This dashboard will be helpful for people who have multiple investments, also those with financial asset trading activities.

When

This dashboard will require the following data:

Time series data of daily closing, daily high and daily low price of stocks listed in IDX. Considering the tight deadline, the data used in this paper contains only LQ45 & JII70 stocks from Jan 1, 2010.

Stock historical data are acquired from yahoofinance platform (https://finance.yahoo.com) and Indonesia Stock Exchange website (www.idx.co.id)
Gold price time series data acquired from World Gold Council website (www.gold.org)
Stock portfolio (stock name, current average price, volume in hand)
Bond portfolio (bond name, buying value, expiration date, stated return, return acquired)
Time Deposit (bank name, deposit value, due date, the stated return, return acquired)
Gold portfolio (stock name, current average price, volume in hand)

How

More detail on this in next sections: Pre-processing and Exploratory Data Analysis

Data Importing and Explanation

Importing data

In this paper, there are 2 (two) groups of data: portfolio data and industry historical data.

Portfolio data shows the (dummy) data of current assets in hand. All 4 assets will be imported separatedly, as they have different variables each. Meanwhile the industry historical data will be used to create filter and recommendation.

Portfolio Data

#Stock
stockporto <- read.csv("data/stockporto.csv")
#Bond
bondporto<- read.csv("data/bondporto.csv")
#TD
tdporto<- read.csv("data/tdporto.csv")
#Gold
goldporto<- read.csv("data/goldporto.csv")

Variables in Portfolio Data

Stock

glimpse(stockporto)

#> Rows: 13
#> Columns: 4
#> $ name      <fct> BBTN, BBCA, ACES, MIKA, MDKA, JSMR, BBRI, ADRO, BSDE, JPF...
#> $ avg_price <int> 1290, 27982, 1489, 2280, 1518, 5530, 2980, 1011, 1187, 12...
#> $ volume    <int> 2000, 1200, 1800, 1000, 2900, 1400, 3200, 700, 3500, 3000...
#> $ value     <int> 2580000, 33578400, 2680200, 2280000, 4402200, 7742000, 95...

Name: Name of stock acquired
avg_price: Average of stock acquisition price, in IDR
volume: Total of share in hand
value: Total of money invested/ avg_price x volume, in IDR

Bond

glimpse(bondporto)

#> Rows: 7
#> Columns: 9
#> $ name     <fct> ST 005, ORI 012, SBR 004, ST 007, SBR 006, SBR 007, ORI 018
#> $ face     <int> 50000000, 82000000, 45000000, 180000000, 75000000, 5750000...
#> $ price    <int> 50000000, 82000000, 45000000, 180000000, 75000000, 5750000...
#> $ coupon   <fct> 8.00%, 7.90%, 7.60%, 7.20%, 6.80%, 6.60%, 6.50%
#> $ acq_int  <dbl> 22126713.4, 29727810.0, 14289042.1, 40056168.3, 11648403.4...
#> $ exp_date <fct> 2023-05-11, 2023-10-02, 2023-12-22, 2024-06-10, 2024-10-09...
#> $ plc_date <fct> 2018-06-06, 2018-10-28, 2019-01-17, 2019-07-07, 2019-11-05...
#> $ term     <int> 1800, 1800, 1800, 1800, 1800, 1800, 1800
#> $ category <fct> government, government, government, government, government...

Name: Name of Bond
Face: Bond face value, the money amount the bond will be worth at maturity, in IDR
Price: The price at which the bond were purchased, in IDR
coupon: The stated coupon (interest/return) rate of the bond
acq_int: Portion of interest already realized, in IDR
exp_date: Bond maturity date
plc_date: Bond purchasing date
term: Bond maturity term in days
category: Bond categories: corporate/government

Time Deposit

glimpse(tdporto)

#> Rows: 5
#> Columns: 7
#> $ name        <fct> Bank 1, Bank 2, Bank 3, Bank 4, Bank 5
#> $ deposit     <int> 10000000, 82000000, 950000000, 120000000, 10000000
#> $ interest_pa <fct> 5.00%, 4.50%, 5.50%, 4.30%, 4.70%
#> $ acq_int     <dbl> 320547.9, 2032027.4, 22617808.2, 1767123.3, 27041.1
#> $ exp_date    <fct> 2020-12-13, 2021-01-15, 2021-02-27, 2021-04-01, 2020-09-17
#> $ plc_date    <fct> 2019-12-19, 2020-01-21, 2020-03-04, 2020-04-06, 2020-07-19
#> $ term        <int> 360, 360, 360, 360, 60

Name: Name of Bank
deposit: The amount of deposit locked for the specified maturity term, in IDR
interest_pa: The stated interest/return rate of the deposit
acq_int: Portion of interest already realized, in IDR
exp_date: Time Deposit maturity date
plc_date: Time Deposit placement date
term: Time Deposit maturity term in days

Gold

glimpse(goldporto)

#> Rows: 5
#> Columns: 4
#> $ name      <fct> Marketplace 1, Marketplace 2, Marketplace 3, Marketplace ...
#> $ avg_price <int> 875534, 785534, 897534, 860283, 905534
#> $ volume    <int> 15, 6, 10, 7, 4
#> $ value     <int> 13133010, 4713204, 8975340, 6021981, 3622136

Name: Name of gold marketplace, where the user got them from
avg_price: : Average of gold acquisition price, in IDR
volume: Total of gold on hand, in gr
value: Total of money invested/ avg_price x volume, in IDR

Industry Historical Data

We will only need stock and gold historical data, as these two have the biggest daily volatility. Stock historical data is collected from yahoofinance and idx, and gold one from The Gold Council.

Stock

stock_dat <- rbind.data.frame(ACES,ADRO,ANTM,ASII,BBCA,BBNI,BBRI,BBTN,BMRI,BSDE,BTPS,CPIN,CTRA,ERAA,EXCL,GGRM,HMSP,ICBP,INDF,INKP,INTP,ITMG,JPFA,JSMR,KLBF,MDKA,MIKA,MNCN,PGAS,PTBA,PTPP,PWON,SCMA,SMGR,SMRA,SRIL,TBIG,TKIM,TLKM,TOWR,UNTR,UNVR,WIKA,ZINC,WTON,WSBP,WOOD,WEGE,TPIA,TCPI,TARA,SSIA,SMSM,SMBR,SIDO,RALS,PPRO,MYOR,MTDL,MAPI,LSIP,LPPF,LPKR,LINK,KREN,KINO,KAEF,ISAT,IPTV,INCO,INAF,GIAA,DMAS,CLEO,BRPT,BRIS,BMTR,BKSL,ADHI,AALI,APLN)

stock_data <- stock_dat %>% 
  mutate(Date= ymd(Date)) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.character) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.integer) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.numeric)

#dailymaintenance
ags10 <- read.csv("data/IHSG_Ags10.csv",)
ags11 <- read.csv("data/IHSG_Ags11.csv")
ags12 <- read.csv("data/IHSG_Ags12.csv")
ags13 <- read.csv("data/IHSG_Ags13.csv")
ags14 <- read.csv("data/IHSG_Ags14.csv")
ags18 <- read.csv("data/IHSG_Ags18.csv")
ags19 <- read.csv("data/IHSG_Ags19.csv")
ags24 <- read.csv("data/IHSG_Ags24.csv")
ags25 <- read.csv("data/IHSG_Ags25.csv")
stock_data <- stock_data %>% select(Name,Date,Company,Open,High,Low,Close,Volume) 
stockupdate <- rbind(stock_data,ags10,ags11,ags12,ags13,ags14,ags18,ags19,ags24,ags25) %>% 
  mutate(Date= ymd(Date)) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.character) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.integer) %>% 
  mutate_at(vars(Open,High,Low,Close, Volume), as.numeric) %>% 
  group_by(Name) %>% 
  filter(n()>20) #to remove non-LQ45 & non-JII70 #to be updated when the number of daily patching exceed the set number

glimpse(stockupdate)

#> Rows: 180,958
#> Columns: 8
#> Groups: Name [81]
#> $ Name    <fct> ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES,...
#> $ Date    <date> 2010-01-04, 2010-01-05, 2010-01-06, 2010-01-07, 2010-01-08...
#> $ Company <fct> Ace Hardware Indonesia Tbk., Ace Hardware Indonesia Tbk., A...
#> $ Open    <dbl> 148, 149, 150, 149, 149, 150, 151, 151, 149, 148, 147, 147,...
#> $ High    <dbl> 151, 149, 150, 149, 149, 150, 151, 151, 151, 148, 147, 147,...
#> $ Low     <dbl> 147, 147, 149, 147, 148, 148, 149, 150, 147, 146, 141, 147,...
#> $ Close   <dbl> 148, 149, 150, 149, 149, 150, 151, 151, 149, 148, 147, 147,...
#> $ Volume  <dbl> 1380000, 2630000, 495000, 995000, 405000, 455000, 150000, 5...

Variables Explanation

Name: Name of stock listed
Date: Active trading day
Company: The full name of the company listed

4.Open: Stock price at the beginning of Date trading day, in IDR

High: The highest price a stock traded within Date trading period,in IDR
Low: The lowest price a stock traded within Date trading period, in IDR
Close: Stock price at the ending of Date trading period, in IDR
Volume: Number of share traded within Date trading period

Gold

golddata<- read.csv("data/goldprice.csv")

glimpse(golddata)

#> Rows: 10,851
#> Columns: 3
#> $ Date        <fct> 12/29/1978, 1/1/1979, 1/2/1979, 1/3/1979, 1/4/1979, 1/5...
#> $ spot_daily  <dbl> 0.0, 0.0, 138160.5, 138877.0, 139616.3, 142069.1, 14187...
#> $ price_in_gr <dbl> 0.000, 0.000, 4441.963, 4464.999, 4488.768, 4567.628, 4...

Variables Explanation

Date: Active trading day
Daily_price: Gold spot price per troy oz at the Date trading day, in IDR
price_in_gr: Daily_price converted to gr, in IDR

Data Exploratory & Preprocessing

Portfolio data

As the portfolio data is already quite tidy and small in number, we will only include them to show in the dashboard. Hence, we will only conduct simple adjustment to them.

Stock

str(stockporto)

#> 'data.frame':    13 obs. of  4 variables:
#>  $ name     : Factor w/ 13 levels "ACES","ADRO",..: 5 3 1 10 9 8 4 2 6 7 ...
#>  $ avg_price: int  1290 27982 1489 2280 1518 5530 2980 1011 1187 1290 ...
#>  $ volume   : int  2000 1200 1800 1000 2900 1400 3200 700 3500 3000 ...
#>  $ value    : int  2580000 33578400 2680200 2280000 4402200 7742000 9536000 707700 4154500 3870000 ...

For stock portfolio dataframe, We will only change the data type of the first variable into character, as each stocks will only be showed once

stockporto <- stockporto %>% mutate(name=as.character(name))

Gold

str(goldporto)

#> 'data.frame':    5 obs. of  4 variables:
#>  $ name     : Factor w/ 5 levels "Marketplace 1",..: 1 2 3 4 5
#>  $ avg_price: int  875534 785534 897534 860283 905534
#>  $ volume   : int  15 6 10 7 4
#>  $ value    : int  13133010 4713204 8975340 6021981 3622136

For gold portfolio dataframe, We will also only change the data type of the first variable into character, as each entry will only be showed once

goldporto <- goldporto %>% mutate(name=as.character(name))

Time Deposit

str(tdporto)

#> 'data.frame':    5 obs. of  7 variables:
#>  $ name       : Factor w/ 5 levels "Bank 1","Bank 2",..: 1 2 3 4 5
#>  $ deposit    : int  10000000 82000000 950000000 120000000 10000000
#>  $ interest_pa: Factor w/ 5 levels "4.30%","4.50%",..: 4 2 5 1 3
#>  $ acq_int    : num  320548 2032027 22617808 1767123 27041
#>  $ exp_date   : Factor w/ 5 levels "2020-09-17","2020-12-13",..: 2 3 4 5 1
#>  $ plc_date   : Factor w/ 5 levels "2019-12-19","2020-01-21",..: 1 2 3 4 5
#>  $ term       : int  360 360 360 360 60

For this entries, we will implement the following steps:

Change exp_date and plc_date from factor data type to date
Remove the ‘%’ symbol in interest_pa and later change it into numeric data type.

tdporto <- tdporto %>% mutate(exp_date = ymd(exp_date),plc_date=ymd(plc_date)) %>% 
  mutate(interest_pa=sapply(interest_pa,function(x)as.numeric(gsub("%","",x))))

Bond

str(bondporto)

#> 'data.frame':    7 obs. of  9 variables:
#>  $ name    : Factor w/ 7 levels "ORI 012","ORI 018",..: 6 1 3 7 4 5 2
#>  $ face    : int  50000000 82000000 45000000 180000000 75000000 57500000 82500000
#>  $ price   : int  50000000 82000000 45000000 180000000 75000000 57500000 82500000
#>  $ coupon  : Factor w/ 7 levels "6.50%","6.60%",..: 7 6 5 4 3 2 1
#>  $ acq_int : num  22126713 29727810 14289042 40056168 11648403 ...
#>  $ exp_date: Factor w/ 7 levels "2023-05-11","2023-10-02",..: 1 2 3 4 5 6 7
#>  $ plc_date: Factor w/ 7 levels "2018-06-06","2018-10-28",..: 1 2 3 4 5 6 7
#>  $ term    : int  1800 1800 1800 1800 1800 1800 1800
#>  $ category: Factor w/ 1 level "government": 1 1 1 1 1 1 1

For this portfolio entries, we will implement the following steps:

Change name into character data type, as each stocks will only be showed once
Change exp_date and plc_date from factor data type to date
Remove the ‘%’ symbol in coupon and later change it into numeric data type

bondporto <- bondporto %>% mutate(name=as.character(name)) %>% 
  mutate(exp_date = ymd(exp_date),plc_date=ymd(plc_date)) %>% 
  mutate(coupon=sapply(coupon,function(x)as.numeric(gsub("%","",x))))

Industry data

Gold

str(golddata)

#> 'data.frame':    10851 obs. of  3 variables:
#>  $ Date       : Factor w/ 10851 levels "1/1/1979","1/1/1980",..: 3336 1 331 662 753 783 871 902 32 62 ...
#>  $ spot_daily : num  0 0 138161 138877 139616 ...
#>  $ price_in_gr: num  0 0 4442 4465 4489 ...

For this dataframe entries, we will change the Date variable into proper format of date and remove the spot_daily as this column isn’t necessary. After that, we will remove the first two rows as it have no value.

implement the following steps:

golddata<- golddata %>% mutate(Date = mdy(Date)) %>% 
  select(Date,price_in_gr) %>% 
  filter(price_in_gr>0)

Stock

glimpse(stockupdate)

#> Rows: 180,958
#> Columns: 8
#> Groups: Name [81]
#> $ Name    <fct> ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES, ACES,...
#> $ Date    <date> 2010-01-04, 2010-01-05, 2010-01-06, 2010-01-07, 2010-01-08...
#> $ Company <fct> Ace Hardware Indonesia Tbk., Ace Hardware Indonesia Tbk., A...
#> $ Open    <dbl> 148, 149, 150, 149, 149, 150, 151, 151, 149, 148, 147, 147,...
#> $ High    <dbl> 151, 149, 150, 149, 149, 150, 151, 151, 151, 148, 147, 147,...
#> $ Low     <dbl> 147, 147, 149, 147, 148, 148, 149, 150, 147, 146, 141, 147,...
#> $ Close   <dbl> 148, 149, 150, 149, 149, 150, 151, 151, 149, 148, 147, 147,...
#> $ Volume  <dbl> 1380000, 2630000, 495000, 995000, 405000, 455000, 150000, 5...

No need any further adjustment as the format is pretty much correct

Exploratory Data Analysis

Portfolio

This section contains quick summary of each portfolio

Stock

summary(stockporto)

#>      name             avg_price         volume         value         
#>  Length:13          Min.   :  287   Min.   : 700   Min.   :  707700  
#>  Class :character   1st Qu.: 1187   1st Qu.:1400   1st Qu.: 2280000  
#>  Mode  :character   Median : 1489   Median :2500   Median : 3870000  
#>                     Mean   : 3791   Mean   :2323   Mean   : 6121808  
#>                     3rd Qu.: 2280   3rd Qu.:3000   3rd Qu.: 4782000  
#>                     Max.   :27982   Max.   :4000   Max.   :33578400

User have 13 stock code under her tab, with acquisition price ranging from IDR287 to IDR27.982 each. Total money invested for all stocks in hand is amounted to IDR 79.583.500

stockporto

stockporto %>% summarise(stock_no=n(),
                                   total=sum(value))

Bond

summary(bondporto)

#>      name                face               price               coupon     
#>  Length:7           Min.   : 45000000   Min.   : 45000000   Min.   :6.500  
#>  Class :character   1st Qu.: 53750000   1st Qu.: 53750000   1st Qu.:6.700  
#>  Mode  :character   Median : 75000000   Median : 75000000   Median :7.200  
#>                     Mean   : 81714286   Mean   : 81714286   Mean   :7.229  
#>                     3rd Qu.: 82250000   3rd Qu.: 82250000   3rd Qu.:7.750  
#>                     Max.   :180000000   Max.   :180000000   Max.   :8.000  
#>     acq_int            exp_date             plc_date               term     
#>  Min.   :  713244   Min.   :2023-05-11   Min.   :2018-06-06   Min.   :1800  
#>  1st Qu.: 8804006   1st Qu.:2023-11-11   1st Qu.:2018-12-07   1st Qu.:1800  
#>  Median :14289042   Median :2024-06-10   Median :2019-07-07   Median :1800  
#>  Mean   :17788713   Mean   :2024-05-26   Mean   :2019-06-22   Mean   :1800  
#>  3rd Qu.:25927262   3rd Qu.:2024-11-24   3rd Qu.:2019-12-21   3rd Qu.:1800  
#>  Max.   :40056168   Max.   :2025-06-30   Max.   :2020-07-26   Max.   :1800  
#>        category
#>  government:7  
#>                
#>                
#>                
#>                
#>

User have invested in 7 bond series under her tab, with value ranging from IDR45 mio to IDR180 mio each. Total money invested in bonds is amounted to IDR 572 mio, with expected interest ranging from 6.5% to 8% pa.

bondporto %>% summarise(bond_no=n(),total=sum(face))

Time Deposit

summary(tdporto)

#>      name      deposit           interest_pa     acq_int        
#>  Bank 1:1   Min.   : 10000000   Min.   :4.3   Min.   :   27041  
#>  Bank 2:1   1st Qu.: 10000000   1st Qu.:4.5   1st Qu.:  320548  
#>  Bank 3:1   Median : 82000000   Median :4.7   Median : 1767123  
#>  Bank 4:1   Mean   :234400000   Mean   :4.8   Mean   : 5352910  
#>  Bank 5:1   3rd Qu.:120000000   3rd Qu.:5.0   3rd Qu.: 2032027  
#>             Max.   :950000000   Max.   :5.5   Max.   :22617808  
#>     exp_date             plc_date               term    
#>  Min.   :2020-09-17   Min.   :2019-12-19   Min.   : 60  
#>  1st Qu.:2020-12-13   1st Qu.:2020-01-21   1st Qu.:360  
#>  Median :2021-01-15   Median :2020-03-04   Median :360  
#>  Mean   :2021-01-08   Mean   :2020-03-14   Mean   :300  
#>  3rd Qu.:2021-02-27   3rd Qu.:2020-04-06   3rd Qu.:360  
#>  Max.   :2021-04-01   Max.   :2020-07-19   Max.   :360

User have invested in 5 time deposits under her tab, with value ranging from IDR10 mio to IDR950 mio. Total money invested in bonds is amounted to IDR 1.172 mio, with expected interest ranging from 4.3% to 5.5% pa.

tdporto %>% summarise(td_no=n(),total=sum(deposit))

Gold

goldporto

summary(goldporto)

#>      name             avg_price          volume         value         
#>  Length:5           Min.   :785534   Min.   : 4.0   Min.   : 3622136  
#>  Class :character   1st Qu.:860283   1st Qu.: 6.0   1st Qu.: 4713204  
#>  Mode  :character   Median :875534   Median : 7.0   Median : 6021981  
#>                     Mean   :864884   Mean   : 8.4   Mean   : 7293134  
#>                     3rd Qu.:897534   3rd Qu.:10.0   3rd Qu.: 8975340  
#>                     Max.   :905534   Max.   :15.0   Max.   :13133010

User have bought gold from 5 marketplaces, with average acquisition value ranging from IDR785.534 to IDR 905.534 per gr. Total money invested in gold is amounted to IDR 36,4 mio.

goldporto %>% summarise(market_no=n(),total=sum(value))

Historical Data

Stock

NA Checking

table(is.na(stockupdate))

#> 
#>   FALSE    TRUE 
#> 1434369   13295

stockupdate dataframe does have 13.295 amount of NAs out of 180.958 rows. Those NAs came from the rows added by yahoofinance to announce dividend payment time. As this information is irrelevant to the data purpose, we remove those rows from analysis.

We would first analyze how each stocks are doing.

#group stock by name and return the statistics (max, min, Last Close)
stocksumm <- stockupdate %>% 
  select(Name, Date, Company, Open, High, Low, Close, Volume) %>% 
  group_by(Name) %>% 
  summarise(Last_Close=last(Close), 
            Max_Price=max(High, na.rm = T),
            Min_Price=min(Low, na.rm = T),
            Avg_Volume=median(Volume, na.rm = T))

Quick visualization of some sample stocks

#chart
aces_plot <- stockupdate[stockupdate$Name=='ACES',]
aces_plot %>% ggplot(aes(x=Date,y=Close))+
  geom_line()+
  labs(title = 'Closing Price:ACE Hardware Indonesia', x='Date', y='Price (in IDR)')+
  theme_minimal()

ACES had a quite stable price increase over time with four notable price decline around 2014, 2016, and early 2020.

#chart
bbca_plot <- stockupdate[stockupdate$Name=='BBCA',]
bbca_plot %>% ggplot(aes(x=Date,y=Close))+
  geom_line()+
  labs(title = 'Closing Price:Bank Central Asia, Tbk.', x='Date', y='Price (in IDR)')+
  theme_minimal()

BBCA had a quite stable price increase over time with barely significant drops in price. However, it faces sudden drop in early 2020, even though it’s currently crawling back to it’s last high.

#chart
apln_plot <- stockupdate[stockupdate$Name=='APLN',]
apln_plot %>% ggplot(aes(x=Date,y=Close))+
  geom_line()+
  labs(title = 'Closing Price:Agung Podomoro Land', x='Date', y='Price (in IDR)')+
  theme_minimal()

From the issuance date, APLN shows quite a consistent decline in price. The last top happened around 2019, which was already half the price of its all-time high back in 2013.

Gold

summary(golddata$price_in_gr)

#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    4344   22367   79101  183205  344009  922326

NA checking

table(is.na(golddata))

#> 
#> FALSE 
#> 21698

There are no NA in gold historical data.

#chart
goldpricechart <- golddata %>% ggplot(aes(x=Date,y=price_in_gr))+
  geom_line()+
  labs(title = 'Gold Price in IDR', x='Date', y='Price (in IDR)')+
  theme_minimal()
goldpricechart

Gold shows quite consistent increase over time, where it’s currently still reaching all-time high (trading against IDR)

Generating the EMA

Setting EMA: EMA 2, EMA 9, EMA 30, EMA 200

Setting Up the Daily Alert

If the latest closing price cross up/ higher than EMA 30 line (number), uptrend alert

If the latest closing price cross down/ lower than EMA 30 line (number), downtrend alert

All-in-One Investment Monitoring Dashboard

Dewi Niara Astuti

September 1, 2020

Business Questions

What

Why & Who

When

How

Data Importing and Explanation

Importing data

Portfolio Data

Variables in Portfolio Data

Stock

Bond

Time Deposit

Gold

Industry Historical Data

Stock

Variables Explanation

Gold

Variables Explanation

Data Exploratory & Preprocessing

Portfolio data

Stock

Gold

Time Deposit

Bond

Industry data

Gold

Stock

Exploratory Data Analysis

Portfolio

Stock

Bond

Time Deposit

Gold

Historical Data

Stock

NA Checking

Gold

NA checking

Generating the EMA

Setting Up the Daily Alert