Most Important MMD Chart Data Science
MM Dandytra, CFTe
Halo Investor dan Trader,
Halaman ini berisi Raw Information dari Technical Analysis ala Muhamad Makky Dandytra, CFTe untuk MMD Charts.
## [1] "Based on Trading Date 09 November 2022"Untuk informasi lengkap, silahkan kunjungi website https://www.mmdcharts.com.
Semoga berguna dan bermanfaat.
1 Graphic Stocks
1.1 Stocks with High Turnover (IDX Sector)
1.2 Stocks with High Turnover and Buy Signs
1.3 Stocks with Positive Change of Turnover
1.4 Stocks with Positive Change of Turnover (Trend)
1.5 Stocks with Trend and Momentum (Best Area only)
2 Graphic IDXSectors
2.1 IDXSector in Trend and Momentum
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Trend dan Stoch. Oscillator.
2.2 IDXIndustry in Trend and Momentum
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Trend dan Stoch. Oscillator.
2.3 IDXIndustry in Trend and Momentum (Best Area Only)
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Trend dan Stoch. Oscillator.
2.3.1 Table
2.4 IDXIndustry in Trend and Momentum (Infra Only)
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Trend dan Stoch. Oscillator.
2.5 IDXSector in Foreign Net Buy 5D Positive
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Net Buy Asing yang positif dalam 5 hari.
2.6 IDXIndustry in Foreign Net Buy 5D Positive
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Net Buy Asing yang positif dalam 5 hari.
2.7 IDXIndustry in Foreign Net Buy 5D Positive (Infra Only)
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Net Buy Asing yang positif dalam 5 hari.
2.8 IDXSector in 1D, 5D and 20D % Change
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
2.9 IDXIndustry in 1D, 5D and 20D % Change
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
2.10 IDXIndustry in 1D, 5D and 20D % Change (Infra Only)
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
2.11 IDXSector in Change Turnover 1D and 5D
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
2.12 IDXIndustry in Change Turnover 1D and 5D
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
2.13 IDXIndustry in Change Turnover 1D and 5D (Infra Only)
Grafik ini menampilkan Sektor dan Relasinya-nya dengan Rata - Rata Pergerakannya dalam 1D, 5D dan 20D.
3 SUPER QUADRANT STOCKS
3.1 SUPER QUADRANT STOCKS - Seluruh Sektor
Grafik Super Quadrant Stocks ini menampilkan Trend dan Sinyal dibagi per Sektor.
3.2 SUPER QUADRANT STOCKS - Seluruh Sektor dan sinyal Buy
Grafik Super Quadrant Stocks ini menampilkan Trend dan sinyal Buy dibagi per Sektor.
3.3 SUPER QUADRANT STOCKS - Outperform Trend dan Uptrend
Grafik Super Quadrant Stocks ini hanya menampilkan Saham dalam Outperform Trend dan Uptrend - dibagi berdasarkan Sektor.
3.4 SUPER QUADRANT STOCKS - Outperform Trend, Uptrend dan Sinyal Buy
Grafik Super Quadrant Stocks ini hanya menampilkan Saham dalam Outperform Trend dan Uptrend dengan sinyal Buy - dibagi berdasarkan Sektor.
3.5 SUPER QUADRANT STOCKS - Champion Quadrant dan Uptrend
Grafik Super Quadrant Stocks ini hanya menampilkan Saham dalam Champion Quadrant dan Uptrend - dibagi berdasarkan Sektor.
3.6 SUPER QUADRANT STOCKS - Champion Quadrant, Uptrend dan sinyal Buy
Grafik Super Quadrant Stocks ini hanya menampilkan Saham dalam Champion Quadrant dan Uptrend dengan sinyal Buy - dibagi berdasarkan Sektor.
3.7 SUPER QUADRANT STOCKS - TOP STOCKS FROM SUPER QUADRANT
4 Standard Graphic Stocks
4.1 1D and 5D Return Saham Outperform Trend dan Uptrend
4.2 5D and 20D Return Saham Outperform Trend dan Uptrend
4.3 5D and 20D Return TOP STOCKS FROM SUPER QUADRANT
4.4 YTDCh and Ch60D Return TOP STOCKS FROM SUPER QUADRANT
4.5 1D and 5D Return Saham dalam Champion Quadrant and Uptrend
4.6 1D Change of Price and Turnover dari Saham Outperform Trend dan Uptrend
5 Ch Turnover
5.1 Consumer Related
5.2 Infrastructure Related
5.3 Multiple Line Chart (draft)
# TPT.IDX
#
# TPT %>%
# filter(KairiMABB20 > 0, KairiMABB60 > 0, KairiMABB200 > 0) %>%
# arrange(DateTime, Ticker) %>%
# select(Ticker, Close, Ch1D, Ch5D, Ch20D, YTDRet) %>%
# ggplot(aes(x = DateTime))+
# geom_bar(aes(y = Ch1D))+
# facet_wrap(~ Ticker, ncol = 5)6 Cluster
6.1 1D, 5D, 20D & YTD Return
Table dan Grafik ini menggolongkan saham pada return 1, 5, 20 hari dan YTD (dari sejak awal tahun 2020).
6.1.1 Graph YTD - 5D
6.1.2 Graph YTD - 60D
6.1.3 Graph 60D - 20D
6.1.4 Graph 20D - 5D
6.1.5 Graph 1D - 5D
6.1.6 Table
7 Data Explorer (Draft)
head(TPT)## Ticker Close DateTime Trend SO SOSignal Ch1D YestCh Ch5D Ch20D
## 1 BFIN 1115 2022-11-09 Uptrend 55.87 Alert Buy 0.45 -1.77 0.90 4.21
## 2 BTPS 3180 2022-11-09 Uptrend 26.16 Still Sell -0.93 -4.46 -2.45 16.48
## 3 DMMX 1105 2022-11-09 Downtrend 67.25 Sell Now -2.64 1.34 0.00 6.76
## 4 TAPG 695 2022-11-09 Uptrend 62.50 Still Sell 0.00 -1.42 2.21 6.92
## 5 HMSP 930 2022-11-09 Downtrend 19.58 Buy Now 0.00 0.00 -5.10 3.33
## 6 MYOR 2370 2022-11-09 Uptrend 53.68 Alert Buy 0.42 -2.48 1.72 17.33
## Ch60D YTDRet Volume ChTurnover ChTurnover5D AvgCondition RCTrend
## 1 -6.30 -5.11 --- -0.52 -0.87 Low TO O/P Trend
## 2 5.30 -11.17 --- -0.52 -0.80 High TO O/P Trend
## 3 -13.33 -59.38 --- -0.70 -0.76 Low TO U/P Trend
## 4 0.00 13.93 --- -0.44 -0.73 Low TO O/P Trend
## 5 2.20 -3.63 --- -0.02 -0.73 Low TO U/P Trend
## 6 25.07 16.18 Above Previous 1.34 -0.72 Low TO O/P Trend
## RCMomentum RCTrendSignal RCMomentumSignal KairiMABB20 KairiMABB60
## 1 U/P Momentum --- --- 4.71 0.10
## 2 U/P Momentum --- --- 7.06 10.07
## 3 U/P Momentum --- U/P Momentum Signal -7.43 -11.12
## 4 U/P Momentum --- --- 0.53 0.62
## 5 U/P Momentum --- U/P Momentum Signal -4.26 1.31
## 6 U/P Momentum --- --- 0.48 17.72
## KairiMABB200 Prev.KairiMABB200 OP.Trend.Start BB Bandwidth F1D F2D
## 1 -7.11 -7.39 --- 78.99 19.79 0.90 0.66
## 2 1.74 2.92 --- 75.29 31.99 1.82 5.70
## 3 -33.75 -32.08 --- 26.66 27.74 -0.10 0.11
## 4 -0.05 0.29 U/P Trend Start 62.14 12.63 0.26 -1.95
## 5 -3.87 -3.65 --- 28.72 15.51 0.39 1.14
## 6 25.60 25.45 --- 57.13 20.41 -0.70 -1.75
## F1Wk F1Mo MTTrend WkESOValue WkESO PriceVolumeCond
## 1 -9.96 -12.34 Downtrend 72.46 Warning Sell Fake +
## 2 -13.40 -34.94 Uptrend 78.73 Still Sell fake -
## 3 0.56 0.56 Downtrend 30.89 Alert Buy fake -
## 4 5.66 28.70 Uptrend 58.73 Still Buy ---
## 5 2.97 16.70 New Downtrend 41.23 Alert Buy ---
## 6 -1.88 15.69 Uptrend 86.38 Still Sell Real +
## Last1DPVCond Last2DPVCond RC.JKSE MA.RC RC.BB PrevRC.BB Corr20D Corr60D
## 1 fake - fake - 157.71 150.61 79.32 79.48 0.76 0.75
## 2 Real - Real - 449.78 420.13 74.46 81.62 0.63 0.35
## 3 Real + Real + 156.29 168.85 23.88 34.85 -0.05 0.71
## 4 fake - fake - 98.30 97.78 55.69 60.46 0.76 0.62
## 5 --- --- 131.54 137.39 16.82 19.92 0.62 -0.32
## 6 fake - fake - 335.22 333.63 52.87 54.94 0.81 -0.57
## Corr5D KairiMA20 KairiMA60 KairiMA200 MA20Direction MA60Direction
## 1 0.78 5.43 -0.34 -6.20 2.25 -1.25
## 2 0.39 7.48 8.79 3.08 22.50 2.67
## 3 0.45 -6.92 -13.02 -48.42 3.50 -2.83
## 4 0.92 1.51 0.22 1.24 2.25 0.00
## 5 -0.63 -3.41 0.96 -2.58 1.50 0.33
## 6 0.57 1.43 14.85 21.49 17.50 7.92
## MA200Direction LTTrend MoESOValue MoESO MarketCap Turnover
## 1 -1.30 Uptrend 48.05 Alert Buy 17803.33 2.71
## 2 -1.25 New Uptrend 49.95 Buy Now 24252.79 14.16
## 3 -7.03 Downtrend 13.91 Buy Now 8500.00 0.80
## 4 0.57 Uptrend 54.70 Still Buy 13797.52 7.67
## 5 -0.20 Downtrend 33.33 Buy Now 107880.00 5.25
## 6 1.05 Uptrend 71.97 Warning Sell 52990.12 5.01
## AvgMOTurnover TurnoverRatio AvgMOTurnoverRatio IDXSector
## 1 21.24 0.02 NA Keuangan
## 2 20.26 0.06 0.09 Keuangan
## 3 5.09 0.01 0.05 Teknologi
## 4 15.87 0.06 0.12 Barang Konsumen Primer
## 5 36.50 0.00 0.03 Barang Konsumen Primer
## 6 24.45 0.01 0.05 Barang Konsumen Primer
## IDXIndustry Candle MA200
## 1 Pembiayaan Konsumen White Candle Below MA200
## 2 Bank Three Outside Down Above MA200
## 3 Jasa & Konsultan TI Harami - Bearish Below MA200
## 4 Perkebunan & Tanaman Pangan Hammer Above MA200
## 5 Rokok Hammer - Inverted Below MA200
## 6 Makanan Olahan Harami - Bullish Above MA200# value : SO, Ch1D, Ch5D, Ch20D, YTDRet, ChTurnover, KairiMABB20, KairiMABB60, KairiMABB200, BB, Bandwidth, F1D, F2D, F1Wk, F1Mo,KairiMA20, KairiMA60, KairiMA200, MA20Direction, MA60Direction, MA200Direction
# Discrete : Ticker, Trend, SOSignal, Volume, AvgCondition, RCTrend, RCMomentum, RCTrendSignal, RCMomentumSignal, PriceVolumeCond, MA200
TPT.value <- TPT %>%
select(c("SO", "Ch1D", "Ch5D", "Ch20D", "YTDRet", "ChTurnover", "KairiMABB20", "KairiMABB60", "KairiMABB200", "BB", "Bandwidth", "F1D", "F2D", "F1Wk", "F1Mo","KairiMA20", "KairiMA60", "KairiMA200", "MA20Direction", "MA60Direction", "MA200Direction"))
TPT.Discrete <- TPT %>%
select(c("Trend", "SOSignal", "Volume", "AvgCondition", "RCTrend", "RCMomentum", "RCTrendSignal", "RCMomentumSignal", "PriceVolumeCond", "MA200"))library(DataExplorer)##
## Attaching package: 'DataExplorer'## The following object is masked from 'package:expss':
##
## split_columnsplot_intro(TPT)
plot_intro(TPT.Discrete)
plot_intro(TPT.value)
plot_missing(TPT)
plot_missing(TPT.Discrete)
plot_missing(TPT.value)
plot_bar(TPT.Discrete)
plot_histogram(TPT.value)
8 Stock Insights
8.1 Select Stocks
single.stock <- "JSMR"8.2 Plot
TPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_time_series(DateTime, Close)## .date_var is missing. Using: DateTimeTPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_time_series(DateTime, Ch1D, .smooth = F)## .date_var is missing. Using: DateTime8.3 Seasonality
TPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_seasonal_diagnostics(DateTime, Ch1D, .feature_set = "wday.lbl")## .date_var is missing. Using: DateTime8.4 STL Decomposition
TPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_stl_diagnostics(DateTime, Close, .feature_set = c("observed", "season", "trend"))## .date_var is missing. Using: DateTime## frequency = 5 observations per 1 week## trend = 65 observations per 3 monthsTPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_stl_diagnostics(DateTime, Ch1D, .feature_set = c("observed", "season", "trend"))## .date_var is missing. Using: DateTime## frequency = 5 observations per 1 week## trend = 65 observations per 3 months8.5 ACF / PACF
TPT.IDX %>%
filter(Ticker == single.stock) %>%
arrange(DateTime) %>%
select(DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_acf_diagnostics(DateTime, Ch1D, .lags = 20)## .date_var is missing. Using: DateTime## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.8.6 Joining
TPT.IDX %>%
filter(Ticker %in% c("ASII", "TLKM")) %>%
arrange(Ticker, DateTime) %>%
select(Ticker, DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
plot_time_series(DateTime, Close, .facet_vars = Ticker)## .date_var is missing. Using: DateTimeTPT.IDX %>%
filter(Ticker %in% c("ASII", "TLKM")) %>%
arrange(Ticker, DateTime) %>%
select(Ticker, DateTime, Close, Ch1D) %>%
filter_by_time(.start_date = "2021-12-31") %>%
pivot_wider(names_from = Ticker, values_from = c(Close, Ch1D)) %>%
drop_na() %>%
mutate(across(Close_ASII:Ch1D_TLKM, .fns = standardize_vec)) %>%
pivot_longer(Close_ASII:Ch1D_TLKM) %>%
filter(name == c("Close_ASII", "Close_TLKM")) %>%
plot_time_series(DateTime, value, .color_var = name, .smooth = FALSE)## .date_var is missing. Using: DateTime## Standardization Parameters
## mean: 6519.125
## standard deviation: 542.299783005895## Standardization Parameters
## mean: 4367.45
## standard deviation: 194.546237931931## Standardization Parameters
## mean: 0.0807
## standard deviation: 1.88289590656915## Standardization Parameters
## mean: 0.0138
## standard deviation: 1.573777442075579 Draft Top Stocks
10 Super Quadrant Stocks - Stable Algorithm
# unique(dandy.rotation$IDXSector)
# head(dandy.rotation)
# selected.date <- print("2022-02-09")
# Date.Signs2 <- "Based on Trading Date 09 Februari 2022"
#
# TPT <- TPT.IDX %>%
# filter(DateTime == selected.date)
#
# dandy.rotation <- TPT %>%
# mutate(RCBB = RC.BB - PrevRC.BB,
# kairiRC = round(((RC.JKSE - MA.RC)/MA.RC*100),2))
#
# dandy.rotation %>%
# filter(Ticker == "ENRG")# library(gganimate)
#
# # head(airquality)
#
# TPT.IDX %>%
# na.omit() %>%
# filter(DateTime >= max(DateTime) %-time% "2 day") %>%
# arrange(DateTime) %>%
# filter(IDXSector == "Kesehatan") %>%
# ggplot(aes(x = KairiMABB200, y = KairiMABB20, label = Ticker, color = Trend))+
# # geom_text_repel(aes(size = ChTurnover))+
# geom_text_repel(max.overlaps = 20)+
# geom_point(aes(shape = SOSignal))+
# theme_bw() +
# theme(panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
# # facet_wrap(~ IDXSector, ncol = 2, scales = "free")+
# scale_shape_manual(values=SOSignal.shape,name = "Signal")+
# scale_size(range = c(2, 6), name = "%Ch in Turnover")+
# scale_y_continuous("Momentum Performance")+
# scale_x_continuous("Trend Performance")+
# scale_color_manual(values=Trend.color)+
# geom_vline(xintercept = 0, linetype="dotted")+
# geom_hline(yintercept = 0, linetype="dotted")+
# annotate("text", x = max(dandy.rotation.consumer.interest$KairiMABB200), y = max(dandy.rotation.consumer.interest$KairiMABB20), label = "Champion", hjust = "top")+
# annotate("text", x = min(dandy.rotation.consumer.interest$KairiMABB200), y = min(dandy.rotation.consumer.interest$KairiMABB20), label = "Loser", hjust = "bottom")+
# annotate("text", x = max(dandy.rotation.consumer.interest$KairiMABB200), y = min(dandy.rotation.consumer.interest$KairiMABB20), label = "Recharge", hjust = "top")+
# annotate("text", x = min(dandy.rotation.consumer.interest$KairiMABB200), y = max(dandy.rotation.consumer.interest$KairiMABB20), label = "Quick", hjust = "bottom")+
# # labs(title = {DateTime}, subtitle = Date.Signs2, caption = "IG : @superquadrant")+
# # watermark(show = TRUE, lab = "Super Quadrant Stocks")+
# ggtitle("Now showing {DateTime}")+
# # transition_time(DateTime)
# transition_states(DateTime, transition_length = 2, state_length = 2)10.1 Super Quadrant Stocks : Consumer and Interest Rate Sector
10.2 Super Quadrant Stocks : Commodity and Infrastructure Related Sector
10.3 Super Quadrant Stocks : Champion Quadrant & Uptrend
10.3.1 Cluster Stocks in Champion Quadrant and Uptrend
10.3.2 Table
10.3.3 Table (cluster)
10.4 Super Quadrant Stocks : Champion Quadrant (Short Term Return Graphic)
10.5 Super Quadrant Stocks : Champion Quadrant (Medium Term Return Graphic)
10.6 Super Quadrant Stocks : Champion Quadrant with Cluster (Short Term Return Graphic)
10.7 Super Quadrant Stocks : Champion Quadrant with Cluster (Medium Term Return Graphic)
10.8 New Outperform Trend
10.8.1 Draft
11 Line Graph
range(TPT.IDX$DateTime)## [1] "2022-01-14" "2022-11-09"min_max_norm <- function(x) {
(x - min(x)) / (max(x) - min(x))
}
TPT.IDX %>%
filter(Ticker %in% c("ASII", "TLKM", "JSMR", "BBCA", "GGRM")) %>%
filter(DateTime >= max(TPT.IDX$DateTime) %-time% "20 day") %>%
arrange(DateTime) %>%
select(DateTime, Ticker, Close) %>%
pivot_wider(names_from = Ticker, values_from = Close) %>%
# mutate(across(-DateTime, .fns = standardize_vec)) %>%
mutate(across(-DateTime, .fns = normalize_vec)) %>%
# mutate(across(-DateTime, .fns = min_max_norm)) %>%
pivot_longer(-DateTime) %>%
plot_time_series(.date_var = DateTime, .color_var = name, .value = value, .smooth = F)## Normalization Parameters
## min: 3300
## max: 3480## Normalization Parameters
## min: 6375
## max: 6700## Normalization Parameters
## min: 8500
## max: 8900## Normalization Parameters
## min: 4130
## max: 4450## Normalization Parameters
## min: 22200
## max: 25475