HK ETF 3466 Daily

# Load data from star schema
etf_daily <- open_dataset(DAILY_PARQUET_DIR)

# Filter for stock_code 2800
etf_2800_all <- etf_daily %>%
  filter(stock_code == STOCK_CODE) %>%
  collect()

if (nrow(etf_2800_all) == 0) {
  stop("No data found for stock_code ", STOCK_CODE)
}

# Ensure trade_date is Date type
etf_2800_all$trade_date <- as.Date(etf_2800_all$trade_date)

# Sort by date (most recent first)
etf_2800_all <- etf_2800_all %>%
  arrange(desc(trade_date))

# Get last 5 days and last 30 days
etf_2800_recent <- etf_2800_all %>% slice_head(n = DAYS_TO_SHOW)
etf_2800_last30 <- etf_2800_all %>% slice_head(n = 30)

# Key variables
key_vars <- c("trade_date", "volume_cleaned", "turnover_cleaned", "aum_cleaned", 
              "closing_price", "nav", "day_high", "day_low", 
              "outstanding_units_cleaned", "premium_discount_percent")

# Calculate historical statistics (needed early for comparisons)
stats <- etf_2800_all %>%
  summarise(
    volume_median = median(volume_cleaned, na.rm = TRUE),
    volume_mean = mean(volume_cleaned, na.rm = TRUE),
    volume_min = min(volume_cleaned, na.rm = TRUE),
    volume_max = max(volume_cleaned, na.rm = TRUE),
    turnover_median = median(turnover_cleaned, na.rm = TRUE),
    turnover_mean = mean(turnover_cleaned, na.rm = TRUE),
    turnover_min = min(turnover_cleaned, na.rm = TRUE),
    turnover_max = max(turnover_cleaned, na.rm = TRUE),
    total_days = n()
  )

# Prepare comparison data
comparison <- etf_2800_recent %>%
  select(trade_date, volume_cleaned, turnover_cleaned) %>%
  mutate(
    volume_vs_median = ifelse(is.na(volume_cleaned), NA, volume_cleaned / stats$volume_median),
    volume_vs_mean = ifelse(is.na(volume_cleaned), NA, volume_cleaned / stats$volume_mean),
    turnover_vs_median = ifelse(is.na(turnover_cleaned), NA, turnover_cleaned / stats$turnover_median),
    turnover_vs_mean = ifelse(is.na(turnover_cleaned), NA, turnover_cleaned / stats$turnover_mean),
    volume_pct_rank = NA_real_,
    turnover_pct_rank = NA_real_
  )

# Calculate percentile ranks
for (i in 1:nrow(comparison)) {
  if (!is.na(comparison$volume_cleaned[i])) {
    comparison$volume_pct_rank[i] <- mean(etf_2800_all$volume_cleaned <= comparison$volume_cleaned[i], na.rm = TRUE) * 100
  }
  if (!is.na(comparison$turnover_cleaned[i])) {
    comparison$turnover_pct_rank[i] <- mean(etf_2800_all$turnover_cleaned <= comparison$turnover_cleaned[i], na.rm = TRUE) * 100
  }
}

Data Overview

cat("**Stock Code:**", STOCK_CODE, "\n\n")

## **Stock Code:** 3466

cat("**Total Records:**", nrow(etf_2800_all), "\n\n")

## **Total Records:** 65

cat("**Date Range:**", format(min(etf_2800_all$trade_date)), "to", format(max(etf_2800_all$trade_date)), "\n\n")

## **Date Range:** 2025-09-17 to 2025-12-19

cat("**Last Updated:**", format(Sys.Date()))

## **Last Updated:** 2025-12-20

---

Closing Price Trend

# Prepare data for chart - order by date ascending for line chart
chart_data <- etf_2800_all %>%
  select(trade_date, closing_price) %>%
  arrange(trade_date) %>%
  mutate(
    is_latest = trade_date == max(trade_date, na.rm = TRUE)
  )

# Get latest date for labeling
latest_date <- max(chart_data$trade_date, na.rm = TRUE)
latest_price <- chart_data$closing_price[chart_data$trade_date == latest_date][1]

ggplot(chart_data, aes(x = trade_date, y = closing_price)) +
  geom_line(color = "#007bff", size = 0.8) +
  geom_point(data = chart_data %>% filter(is_latest), 
             color = "red", size = 3, shape = 19) +
  geom_point(data = chart_data %>% filter(!is_latest), 
             color = "#007bff", size = 0.5, alpha = 0.6) +
  labs(
    title = paste("Closing Price Trend - ETF", STOCK_CODE),
    subtitle = paste("Latest:", format(latest_date), "- Price:", ifelse(is.na(latest_price), "NA", formatC(latest_price, format = "f", digits = 4))),
    x = "Date",
    y = "Closing Price (HKD)",
    caption = "Red dot indicates latest data point"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 11, color = "gray50"),
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 9),
    plot.caption = element_text(size = 9, color = "gray60", hjust = 0),
    panel.grid.minor = element_blank()
  ) +
  scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
  scale_y_continuous(labels = scales::number_format(accuracy = 0.01))

---

Latest Date Verification

latest_date <- max(etf_2800_all$trade_date)
dec_16 <- etf_2800_recent %>%
  filter(trade_date == latest_date)

if (nrow(dec_16) > 0) {
  # Create a summary table
  latest_summary <- data.frame(
    Metric = c("Date", "Volume", "Turnover", "AUM", "Closing Price", "NAV", "Suspension Flag"),
    Value = c(
      format(latest_date),
      ifelse(is.na(dec_16$volume_cleaned[1]), "NA (missing/suspended)", 
             formatC(dec_16$volume_cleaned[1], format = "d", big.mark = ",")),
      ifelse(is.na(dec_16$turnover_cleaned[1]), "NA (missing/suspended)",
             formatC(dec_16$turnover_cleaned[1], format = "f", digits = 2, big.mark = ",")),
      ifelse(is.na(dec_16$aum_cleaned[1]), "NA",
             formatC(dec_16$aum_cleaned[1], format = "f", digits = 0, big.mark = ",")),
      ifelse(is.na(dec_16$closing_price[1]), "NA",
             formatC(dec_16$closing_price[1], format = "f", digits = 4)),
      ifelse(is.na(dec_16$nav[1]), "NA",
             formatC(dec_16$nav[1], format = "f", digits = 4)),
      ifelse(is.na(dec_16$suspension_flag[1]), "NA", dec_16$suspension_flag[1])
    ),
    stringsAsFactors = FALSE
  )
  
  # Add comparison metrics if available
  if (!is.na(dec_16$volume_cleaned[1]) && !is.na(dec_16$turnover_cleaned[1])) {
    vol_idx <- which(comparison$trade_date == latest_date)
    comparison_metrics <- data.frame(
      Metric = c("Volume vs Median", "Volume vs Mean", 
                 "Turnover vs Median", "Turnover vs Mean"),
      Value = c(
        sprintf("%.2fx (%s percentile)", 
                dec_16$volume_cleaned[1] / stats$volume_median,
                sprintf("%.1f%%", comparison$volume_pct_rank[vol_idx])),
        sprintf("%.2fx", dec_16$volume_cleaned[1] / stats$volume_mean),
        sprintf("%.2fx (%s percentile)",
                dec_16$turnover_cleaned[1] / stats$turnover_median,
                sprintf("%.1f%%", comparison$turnover_pct_rank[vol_idx])),
        sprintf("%.2fx", dec_16$turnover_cleaned[1] / stats$turnover_mean)
      ),
      stringsAsFactors = FALSE
    )
    latest_summary <- rbind(latest_summary, comparison_metrics)
  }
  
  latest_summary %>%
    gt() %>%
    tab_header(
      title = "Latest Date Data Summary",
      subtitle = paste("ETF", STOCK_CODE, "-", format(latest_date))
    ) %>%
    cols_label(
      Metric = "Metric",
      Value = "Value"
    ) %>%
    tab_style(
      style = cell_text(weight = "bold"),
      locations = cells_column_labels()
    ) %>%
    tab_style(
      style = cell_text(align = "left"),
      locations = cells_body(columns = Metric)
    ) %>%
    tab_style(
      style = cell_text(align = "right"),
      locations = cells_body(columns = Value)
    ) %>%
    opt_table_font(font = "Arial") %>%
    tab_options(
      table.width = pct(60),
      column_labels.background.color = "#f8f9fa",
      table_body.hlines.color = "#e9ecef",
      heading.border.bottom.color = "#dee2e6"
    )
} else {
  cat("⚠ Warning: No data found for latest date\n")
}

Latest Date Data Summary
ETF 3466 - 2025-12-19
Metric	Value
Date	2025-12-19
Volume	2,259,083
Turnover	44,620,172.00
AUM	NA
Closing Price	19.7800
NAV	NA
Suspension Flag	No
Volume vs Median	0.59x (3.1% percentile)
Volume vs Mean	0.50x
Turnover vs Median	0.60x (6.2% percentile)
Turnover vs Mean	0.51x

---

Comparison: Recent vs Historical

Percentile Explanation: Percentile rank shows what percentage of historical values are less than or equal to the current value. For example, a 75th percentile means the current value is higher than 75% of all historical values (higher is better for volume/turnover). A 25th percentile means it’s higher than only 25% of historical values (lower activity).

comparison_display <- comparison %>%
  select(trade_date, volume_cleaned, volume_vs_median, volume_vs_mean, volume_pct_rank,
         turnover_cleaned, turnover_vs_median, turnover_vs_mean, turnover_pct_rank)

comparison_display %>%
  gt() %>%
  tab_header(
    title = "Recent Performance vs Historical Statistics",
    subtitle = paste("ETF", STOCK_CODE, "- Comparison of last 5 days")
  ) %>%
  fmt_date(columns = trade_date, date_style = "yMd") %>%
  fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = volume_vs_median, decimals = 2, pattern = "{x}x") %>%
  fmt_number(columns = volume_vs_mean, decimals = 2, pattern = "{x}x") %>%
  fmt_number(columns = volume_pct_rank, decimals = 1, pattern = "{x}%") %>%
  fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
  fmt_number(columns = turnover_vs_median, decimals = 2, pattern = "{x}x") %>%
  fmt_number(columns = turnover_vs_mean, decimals = 2, pattern = "{x}x") %>%
  fmt_number(columns = turnover_pct_rank, decimals = 1, pattern = "{x}%") %>%
  sub_missing(columns = everything(), missing_text = "NA") %>%
  cols_label(
    trade_date = "Date",
    volume_cleaned = "Volume",
    volume_vs_median = "vs Median",
    volume_vs_mean = "vs Mean",
    volume_pct_rank = "Percentile",
    turnover_cleaned = "Turnover",
    turnover_vs_median = "vs Median",
    turnover_vs_mean = "vs Mean",
    turnover_pct_rank = "Percentile"
  ) %>%
  tab_spanner(
    label = "Volume",
    columns = c(volume_cleaned, volume_vs_median, volume_vs_mean, volume_pct_rank)
  ) %>%
  tab_spanner(
    label = "Turnover",
    columns = c(turnover_cleaned, turnover_vs_median, turnover_vs_mean, turnover_pct_rank)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_body()
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", align = "center"),
    locations = cells_column_spanners()
  ) %>%
  opt_table_font(font = "Arial") %>%
  tab_options(
    table.width = pct(100),
    column_labels.background.color = "#f8f9fa",
    table_body.hlines.color = "#e9ecef",
    heading.border.bottom.color = "#dee2e6"
  )

Recent Performance vs Historical Statistics
ETF 3466 - Comparison of last 5 days
Date	Volume				Turnover
	Volume	vs Median	vs Mean	Percentile	Turnover	vs Median	vs Mean	Percentile
12/19/2025	2,259,083	0.59x	0.50x	3.1%	44,620,172.00	0.60x	0.51x	6.2%
12/18/2025	2,796,401	0.73x	0.61x	21.5%	55,171,311.00	0.74x	0.63x	20.0%
12/17/2025	2,883,051	0.75x	0.63x	23.1%	56,790,904.00	0.76x	0.65x	26.2%
12/16/2025	4,505,201	1.17x	0.99x	63.1%	88,448,319.00	1.18x	1.01x	61.5%
12/15/2025	2,262,897	0.59x	0.50x	4.6%	44,876,631.00	0.60x	0.51x	7.7%

---

Recent 5 Days

display_data <- etf_2800_recent %>%
  select(any_of(key_vars))

display_data %>%
  gt() %>%
  tab_header(
    title = "Recent 5 Days - Key Metrics",
    subtitle = paste("ETF", STOCK_CODE)
  ) %>%
  fmt_date(columns = trade_date, date_style = "yMd") %>%
  fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
  fmt_number(columns = aum_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = closing_price, decimals = 4) %>%
  fmt_number(columns = nav, decimals = 4) %>%
  fmt_number(columns = day_high, decimals = 4) %>%
  fmt_number(columns = day_low, decimals = 4) %>%
  fmt_number(columns = outstanding_units_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = premium_discount_percent, decimals = 2) %>%
  cols_label(
    trade_date = "Date",
    volume_cleaned = "Volume",
    turnover_cleaned = "Turnover",
    aum_cleaned = "AUM",
    closing_price = "Close Price",
    nav = "NAV",
    day_high = "Day High",
    day_low = "Day Low",
    outstanding_units_cleaned = "Outstanding Units",
    premium_discount_percent = "Premium/Discount %"
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_body()
  ) %>%
  opt_table_font(font = "Arial") %>%
  tab_options(
    table.width = pct(100),
    column_labels.background.color = "#f8f9fa",
    table_body.hlines.color = "#e9ecef",
    heading.border.bottom.color = "#dee2e6"
  )

Recent 5 Days - Key Metrics
ETF 3466
Date	Volume	Turnover	AUM	Close Price	NAV	Day High	Day Low	Outstanding Units	Premium/Discount %
12/19/2025	2,259,083	44,620,172.00	NA	19.7800	NA	19.8400	19.6800	281,600,000	NA
12/18/2025	2,796,401	55,171,311.00	NA	19.7300	NA	19.7800	19.6500	281,600,000	NA
12/17/2025	2,883,051	56,790,904.00	5,540,000,000	19.7400	19.7000	19.7600	19.6300	281,600,000	0.20
12/16/2025	4,505,201	88,448,319.00	5,520,000,000	19.7000	19.6200	19.8800	19.5700	281,600,000	0.40
12/15/2025	2,262,897	44,876,631.00	5,580,000,000	19.8800	19.8500	19.9000	19.6500	281,600,000	0.17

---

Recent 30 Days

table_30days <- etf_2800_last30 %>%
  select(any_of(key_vars)) %>%
  arrange(desc(trade_date))

table_30days %>%
  gt() %>%
  tab_header(
    title = "Recent 30 Days - Complete Data",
    subtitle = paste("ETF", STOCK_CODE, "- Most recent dates first")
  ) %>%
  fmt_date(columns = trade_date, date_style = "yMd") %>%
  fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
  fmt_number(columns = aum_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = closing_price, decimals = 4) %>%
  fmt_number(columns = nav, decimals = 4) %>%
  fmt_number(columns = day_high, decimals = 4) %>%
  fmt_number(columns = day_low, decimals = 4) %>%
  fmt_number(columns = outstanding_units_cleaned, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = premium_discount_percent, decimals = 2) %>%
  sub_missing(columns = everything(), missing_text = "NA") %>%
  cols_label(
    trade_date = "Date",
    volume_cleaned = "Volume",
    turnover_cleaned = "Turnover",
    aum_cleaned = "AUM",
    closing_price = "Close Price",
    nav = "NAV",
    day_high = "Day High",
    day_low = "Day Low",
    outstanding_units_cleaned = "Outstanding Units",
    premium_discount_percent = "Premium/Discount %"
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_body()
  ) %>%
  opt_table_font(font = "Arial") %>%
  tab_options(
    table.width = pct(100),
    column_labels.background.color = "#f8f9fa",
    table_body.hlines.color = "#e9ecef",
    heading.border.bottom.color = "#dee2e6",
    table.font.size = px(12)
  )

Recent 30 Days - Complete Data
ETF 3466 - Most recent dates first
Date	Volume	Turnover	AUM	Close Price	NAV	Day High	Day Low	Outstanding Units	Premium/Discount %
12/19/2025	2,259,083	44,620,172.00	NA	19.7800	NA	19.8400	19.6800	281,600,000	NA
12/18/2025	2,796,401	55,171,311.00	NA	19.7300	NA	19.7800	19.6500	281,600,000	NA
12/17/2025	2,883,051	56,790,904.00	5,540,000,000	19.7400	19.7000	19.7600	19.6300	281,600,000	0.20
12/16/2025	4,505,201	88,448,319.00	5,520,000,000	19.7000	19.6200	19.8800	19.5700	281,600,000	0.40
12/15/2025	2,262,897	44,876,631.00	5,580,000,000	19.8800	19.8500	19.9000	19.6500	281,600,000	0.17
12/12/2025	2,948,400	58,355,764.00	5,500,000,000	19.8100	19.7500	19.8500	19.7500	278,600,000	0.32
12/11/2025	2,386,100	47,115,514.00	5,470,000,000	19.7100	19.6400	19.8600	19.6700	278,600,000	0.35
12/10/2025	3,452,006	67,663,249.00	5,430,000,000	19.7200	19.7000	19.7200	19.5100	276,000,000	0.12
12/9/2025	3,499,589	68,889,000.00	5,420,000,000	19.6400	19.6400	19.8600	19.6100	276,000,000	−0.01
12/8/2025	3,082,200	61,022,458.00	5,450,000,000	19.7800	19.7500	19.9800	19.7500	276,000,000	0.15
12/5/2025	3,718,565	73,924,053.00	5,500,000,000	19.9100	19.9400	19.9500	19.8200	276,000,000	−0.15
12/4/2025	5,439,600	108,337,496.00	5,500,000,000	19.9500	19.9400	20.0400	19.8400	276,000,000	0.07
12/3/2025	2,787,600	55,583,808.00	5,480,000,000	19.9000	19.8600	20.0600	19.8300	276,000,000	0.18
12/2/2025	3,933,205	78,415,078.00	5,500,000,000	20.0400	20.0200	20.0400	19.7800	275,000,000	0.08
12/1/2025	3,233,202	63,833,537.00	5,430,000,000	19.7700	19.7600	19.8600	19.6500	275,000,000	0.04
11/28/2025	4,729,520	93,411,417.00	5,300,000,000	19.7800	19.7300	19.8400	19.7100	269,000,000	0.27
11/27/2025	2,634,023	52,234,065.00	5,320,000,000	19.8100	19.7900	19.9300	19.7300	269,000,000	0.10
11/26/2025	3,388,820	67,073,895.00	5,260,000,000	19.7700	19.7300	19.8800	19.7300	266,800,000	0.20
11/25/2025	3,443,998	68,061,257.00	5,190,000,000	19.7500	19.6900	19.8500	19.6800	264,000,000	0.32
11/24/2025	7,433,400	145,090,990.00	5,090,000,000	19.6200	19.6100	19.6400	19.4300	259,600,000	0.06
11/21/2025	11,953,470	233,555,215.00	4,830,000,000	19.4500	19.4200	19.8900	19.4300	249,000,000	0.18
11/20/2025	6,337,821	126,310,010.00	4,940,000,000	19.9000	19.8800	20.1200	19.7800	249,000,000	0.12
11/19/2025	4,099,410	82,033,944.00	4,600,000,000	20.0400	19.9900	20.1000	19.9000	230,600,000	0.27
11/18/2025	13,022,696	259,655,182.00	4,580,000,000	19.8900	19.8700	20.2800	19.8400	230,600,000	0.12
11/17/2025	5,738,800	116,356,056.00	4,680,000,000	20.3200	20.3000	20.4400	20.2000	230,600,000	0.08
11/14/2025	7,144,203	146,078,716.00	4,680,000,000	20.4200	20.3400	20.5800	20.3200	230,600,000	0.41
11/13/2025	3,467,005	71,337,958.00	4,750,000,000	20.6600	20.6200	20.6600	20.4800	230,600,000	0.20
11/12/2025	4,937,201	100,924,612.00	4,700,000,000	20.6000	20.5600	20.6000	20.1400	228,600,000	0.18
11/11/2025	2,600,799	52,168,146.00	4,590,000,000	20.1200	20.1200	20.1800	19.9500	228,600,000	−0.01
11/10/2025	4,457,410	88,765,361.00	4,580,000,000	20.0400	20.0400	20.0400	19.7500	228,600,000	0.02

---

Historical Statistics

Volume Statistics

volume_stats_df <- data.frame(
  Metric = c("Median", "Mean", "Minimum", "Maximum", "Total Days"),
  Value = c(
    stats$volume_median,
    stats$volume_mean,
    stats$volume_min,
    stats$volume_max,
    stats$total_days
  )
)

volume_stats_df %>%
  gt() %>%
  fmt_number(columns = Value, rows = 1:4, decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = Value, rows = 5, decimals = 0, use_seps = FALSE) %>%
  cols_label(
    Metric = "Metric",
    Value = "Value"
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_body(columns = Value)
  ) %>%
  opt_table_font(font = "Arial") %>%
  tab_options(
    table.width = pct(50),
    column_labels.background.color = "#f8f9fa",
    table_body.hlines.color = "#e9ecef"
  )

Metric	Value
Median	3,853,600
Mean	4,547,128
Minimum	2,226,400
Maximum	13,022,696
Total Days	65

Turnover Statistics

turnover_stats_df <- data.frame(
  Metric = c("Median", "Mean", "Minimum", "Maximum"),
  Value = c(
    stats$turnover_median,
    stats$turnover_mean,
    stats$turnover_min,
    stats$turnover_max
  )
)

turnover_stats_df %>%
  gt() %>%
  fmt_number(columns = Value, decimals = 2, use_seps = TRUE) %>%
  cols_label(
    Metric = "Metric",
    Value = "Value"
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_body(columns = Value)
  ) %>%
  opt_table_font(font = "Arial") %>%
  tab_options(
    table.width = pct(50),
    column_labels.background.color = "#f8f9fa",
    table_body.hlines.color = "#e9ecef"
  )

Metric	Value
Median	74,666,108.00
Mean	87,869,208.97
Minimum	41,939,244.00
Maximum	259,655,182.00

---

Report generated: 2025-12-20 08:23:36.162749