1 Data Prep

2 Super Quadrant Stocks - Stable Algorithm

2.1 Full Super Quadrant Stocks

2.2 Super Quadrant Stocks : Consumer and Interest Rate Sector

2.4 New Outperform Trend

2.5 Selected Super Quadrant Stocks

## [1] "Barang Konsumen Non-Primer" "Infrastruktur"

2.6 Super Quadrant Stocks : Champion Quadrant & Uptrend

2.7 Super Quadrant Stocks : Outperform Trend (Spring)

2.8 Super Quadrant Stocks : Outperform Momentum (Sunrise)

2.9 Super Quadrant Stocks : Outperform Momentum (Sunset)

2.10 Super Quadrant Stocks : Underperform Trend (Autumn)

2.11 Super Quadrant Stocks : Champion Quadrant (Short Term Return Graphic)

2.12 Super Quadrant Stocks : Champion Quadrant (Medium Term Return Graphic)

2.13 Cluster Stocks in Champion Quadrant and Uptrend

2.13.1 Table

2.13.2 Table (cluster)

2.13.2.1 Super Quadrant Stocks : Champion Quadrant with Cluster (Short Term Return Graphic)

2.13.2.2 Super Quadrant Stocks : Champion Quadrant with Cluster (Medium Term Return Graphic)

3 General Cluster

3.1 1D, 5D, 20D & YTD Return

Table dan Grafik ini menggolongkan saham pada return 1, 5, 20 hari dan YTD (dari sejak awal tahun 2020).

3.1.1 Graph YTD - 5D

3.1.2 Graph YTD - 60D

3.1.3 Graph 60D - 20D

3.1.4 Graph 20D - 5D

3.1.5 Graph 1D - 5D

3.1.6 Table

4 Ch Turnover

5 SuperQuadrant Yesterday

5.1 Super Quadrant Stocks : Consumer and Interest Rate Sector

5.3 Super Quadrant Stocks : Champion Quadrant & Uptrend

5.4 2 Days Comparison Champion Quadrant and Uptrend

5.5 2 Days Comparison Spring

5.6 2 Days Comparison Autumn

5.7 2 Days Comparison Sunrise

5.8 2 Days Comparison Sunset

5.9 Comparing Yesterdays

5.10 Yesterdays

6 Timetk

stock <- "TLKM"
stocks <- c("TLKM", "ISAT", "EXCL")

6.1 Plot Time Series

TPT.IDX %>% 
  filter(Ticker %in% stocks) %>%
  arrange(DateTime) %>% 
  filter_by_time(.start_date = "2022-10-03") %>%
  group_by(Ticker) %>% 
  plot_time_series(DateTime, Close, Ticker, .smooth = F)
## .date_var is missing. Using: DateTime

6.2 Seasonality

TPT.IDX %>% 
  # filter(Ticker == "TLKM") %>% 
  filter(Ticker %in% stock) %>% 
  arrange(DateTime) %>% 
  plot_seasonal_diagnostics(DateTime, Ch1D)

6.3 Seasonal Decomposition

TPT.IDX %>% 
  # filter(Ticker == "TLKM") %>% 
  filter(Ticker %in% stock) %>% 
  arrange(DateTime) %>% 
  plot_stl_diagnostics(DateTime, Ch1D, .feature_set = c("observed", "trend", "season",  "remainder"))
## frequency = 5 observations per 1 week
## trend = 65 observations per 3 months