Publications by MMD
Data Viz for Bond for 29 Jan'24
Data Data diambil dari Pasardana bond <- read_excel("data/data240126.xlsx") bond$`Tanggal Terbit` <- as.Date(bond$`Tanggal Terbit`, "%y-%m-%d") ## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%y-%m-%d' bond$`Jatuh Tempo` <- as.Date(bond$`Jatuh Tempo`, "%y-%m-%d") ## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%y-%...
65 sym
RDPU Universe per Desember 2023
1 Data mf <- read.csv("Data/pasardana_rdpu_oktober2023.csv") head(mf) ## Reksadana MI Kategori ## 1 BNP Paribas Rupiah Plus PT BNP Paribas Asset Management Konvensional ## 2 Batavia Dana Likuid PT Batavia Prosperindo Aset Manajemen Konvensional ## 3 Mandiri Investa Pasar...
744 sym R (10992 sym/15 pcs) 13 img
Forecasting Rupiah
Data rupiah_full <- read.csv("D:/Onedrive/Data Science/Forecasting Financial Market/data/threemusketeers.csv") %>% select(time, USDIDR) %>% rename(close = USDIDR) rupiah_full$time <- as.Date(rupiah_full$time, "%Y-%m-%d") rupiah_full <- rupiah_full %>% na.omit() rupiah <- rupiah_full %>% filter_by_time(.end_date = "2023-10-31")...
2844 sym R (568522 sym/1900 pcs) 1 tbl
Forecasting Yield SUN10Yr - 28 Okt 2023
Data yield <- read.csv("D:/Onedrive/Data Science/Forecasting Financial Market/data/TVC_ID10Y, 1D_64448.csv") yield$time <- as.Date(yield$time, "%Y-%m-%d") yield <- yield %>% select(time, close) yield ## time close ## 1 2011-11-29 6.8820 ## 2 2011-11-30 6.8820 ## 3 2011-12-01 6.4150 ## 4 2011-12-02 6.1570 ## 5 ...
3736 sym R (705484 sym/2864 pcs) 1 tbl
Mutual Fund Clustering
1 Data # mf <- read_excel("Data/MF Data from Bareksa.xlsx") mf <- read.csv("Data/MF.csv") head(mf) ## X6.Nov.23 X1D X1Wk X1M X3M X6M X1T ## 1 Recapital Equity 0.51 1.60 0.21 -2.76 8.67 14.47 ## 2 HPAM Syariah Ekuitas 0.23 2.91 -1.73 11.69 7.21 9.65 ## 3 ...
317 sym Python (33324 sym/21 pcs) 9 img
Forecasting JCI with Modeltime - workflow 1
Data # JKSE_from_1990_1H23 <- tq_get("^JKSE", get = "stock.prices", from = "1900-01-01", to = "2023-06-30") # JKSE_from_1990_1H23 %>% write_rds("JKSE_full") JKSE <- read_rds("JKSE_full") jkse_tbl <- tk_tbl(JKSE) ## Warning in tk_tbl.data.frame(JKSE): Warning: No index to preserve. Object ## otherwise converted to tibble successfully. ihsg_tb...
671 sym Python (33740 sym/124 pcs)
Forecasting JCI with Modeltime - workflow 2 (Resample)
Data From Part 1 ihsg_tbl <- read_rds("ihsg_tbl") jkse_current_tbl <- read_rds("jkse_current_tbl") ihsg_future_tbl <- read_rds("ihsg_future_tbl") splits <- read_rds("splits") regression_model_tbl <- read_rds("regression_model_tbl") machine_learning_model_tbl <- read_rds("machine_learning_model_tbl") boosted_model_tbl <- read_rds("boosted_mod...
360 sym 1 tbl
Forecasting JCI with Timetk and Modeltime : Workflow 1 - Data Preparation
Data # JKSE_from_1990_2022 <- tq_get("^JKSE", get = "stock.prices", from = "1900-01-01", to = "2022-12-31") # JKSE_from_1990_2022 %>% write_rds("JKSE_full") JKSE <- read_rds("JKSE_full") # JKSE <- JKSE_from_1990_2022[,6] # JKSE <- JKSE_from_1990_2022 jkse_tbl <- tk_tbl(JKSE) ## Warning in tk_tbl.data.frame(JKSE): Warning: No index to prese...
296 sym Python (17324 sym/40 pcs)
Forecasting IHSG with Timetk and Modeltime
Data getSymbols("^JKSE", src = "yahoo") ## Warning: ^JKSE contains missing values. Some functions will not work if objects ## contain missing values in the middle of the series. Consider using na.omit(), ## na.approx(), na.fill(), etc to remove or replace them. ## [1] "^JKSE" JKSE <- JKSE[,6] jkse_tbl <- tk_tbl(JKSE) ihsg_tbl <- jkse_tbl %>% ...
649 sym R (32982 sym/113 pcs) 1 img
Super Quadrant Stocks 16 Januari 2023
1 Data Prep 2 Super Quadrant Stocks 2.1 Full Super Quadrant Stocks 2.2 Full Super Quadrant Stocks - No Trend and Signal 2.2.1 3% Near The Line 2.2.2 Daily Change 2.2.3 Weekly Change 2.3 SQuadS : Consumer and Interest Rate Sector 2.3.1 Yesterday SQuadS : Consumer and Interest Rate Sector 2.3.2 Last Week SQuadS : Consumer and Intere...
3710 sym Python (738 sym/7 pcs) 67 img