HK ETF 3416 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:** 3416

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 3416 - 2025-12-19
Metric	Value
Date	2025-12-19
Volume	21,491,781
Turnover	225,432,284.00
AUM	NA
Closing Price	10.5100
NAV	NA
Suspension Flag	No
Volume vs Median	1.03x (53.8% percentile)
Volume vs Mean	0.77x
Turnover vs Median	1.00x (50.8% percentile)
Turnover vs Mean	0.76x

---

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 3416 - Comparison of last 5 days
Date	Volume				Turnover
	Volume	vs Median	vs Mean	Percentile	Turnover	vs Median	vs Mean	Percentile
12/19/2025	21,491,781	1.03x	0.77x	53.8%	225,432,284.00	1.00x	0.76x	50.8%
12/18/2025	15,084,000	0.72x	0.54x	26.2%	156,737,040.00	0.70x	0.53x	26.2%
12/17/2025	26,402,332	1.26x	0.95x	72.3%	274,134,204.00	1.22x	0.93x	70.8%
12/16/2025	122,335,336	5.84x	4.40x	98.5%	1,266,818,309.00	5.62x	4.28x	98.5%
12/15/2025	25,327,000	1.21x	0.91x	69.2%	266,891,341.00	1.18x	0.90x	66.2%

---

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 3416
Date	Volume	Turnover	AUM	Close Price	NAV	Day High	Day Low	Outstanding Units	Premium/Discount %
12/19/2025	21,491,781	225,432,284.00	NA	10.5100	NA	10.5300	10.4500	1,455,600,000	NA
12/18/2025	15,084,000	156,737,040.00	NA	10.4300	NA	10.4500	10.3400	1,455,600,000	NA
12/17/2025	26,402,332	274,134,204.00	15,160,000,000	10.4300	10.4200	10.4600	10.3200	1,455,600,000	0.14
12/16/2025	122,335,336	1,266,818,309.00	15,020,000,000	10.3400	10.3200	10.5100	10.2900	1,455,600,000	0.16
12/15/2025	25,327,000	266,891,341.00	15,260,000,000	10.5200	10.4900	10.5800	10.5000	1,455,600,000	0.29

---

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 3416 - Most recent dates first
Date	Volume	Turnover	AUM	Close Price	NAV	Day High	Day Low	Outstanding Units	Premium/Discount %
12/19/2025	21,491,781	225,432,284.00	NA	10.5100	NA	10.5300	10.4500	1,455,600,000	NA
12/18/2025	15,084,000	156,737,040.00	NA	10.4300	NA	10.4500	10.3400	1,455,600,000	NA
12/17/2025	26,402,332	274,134,204.00	15,160,000,000	10.4300	10.4200	10.4600	10.3200	1,455,600,000	0.14
12/16/2025	122,335,336	1,266,818,309.00	15,020,000,000	10.3400	10.3200	10.5100	10.2900	1,455,600,000	0.16
12/15/2025	25,327,000	266,891,341.00	15,260,000,000	10.5200	10.4900	10.5800	10.5000	1,455,600,000	0.29
12/12/2025	17,530,560	185,803,580.00	15,460,000,000	10.6200	10.6200	10.6300	10.5500	1,455,600,000	−0.04
12/11/2025	15,438,368	162,480,255.00	14,090,000,000	10.5100	10.4900	10.5900	10.4800	1,342,800,000	0.14
12/10/2025	46,650,500	488,362,131.00	14,090,000,000	10.5100	10.5000	10.5200	10.4400	1,342,800,000	0.09
12/9/2025	71,009,278	747,503,369.00	13,730,000,000	10.5000	10.4800	10.6200	10.4900	1,311,200,000	0.22
12/8/2025	15,312,528	162,755,730.00	13,880,000,000	10.6200	10.5900	10.6800	10.6100	1,311,200,000	0.27
12/5/2025	22,245,938	236,101,903.00	13,970,000,000	10.6600	10.6600	10.6700	10.5800	1,311,200,000	0.02
12/4/2025	16,128,518	170,551,585.00	13,880,000,000	10.6100	10.5900	10.6200	10.5300	1,311,200,000	0.17
12/3/2025	35,074,000	370,596,547.00	13,600,000,000	10.5500	10.5200	10.6400	10.5300	1,292,400,000	0.24
12/2/2025	12,245,500	130,300,605.00	13,720,000,000	10.6300	10.6200	10.6700	10.6100	1,292,400,000	0.13
12/1/2025	20,729,794	220,044,845.00	13,540,000,000	10.6200	10.6000	10.6500	10.5900	1,278,000,000	0.18
11/28/2025	22,718,390	240,850,404.00	13,490,000,000	10.6000	10.5600	10.6400	10.5700	1,278,000,000	0.37
11/27/2025	39,594,518	426,384,701.00	13,510,000,000	10.7500	10.7200	10.8200	10.6900	1,260,000,000	0.25
11/26/2025	24,741,584	266,613,121.00	13,480,000,000	10.7500	10.7200	10.8100	10.7400	1,258,000,000	0.32
11/25/2025	20,141,813	216,048,053.00	13,360,000,000	10.7300	10.7100	10.7800	10.6600	1,248,000,000	0.23
11/24/2025	31,230,194	330,483,703.00	12,780,000,000	10.6300	10.6200	10.6600	10.5000	1,204,000,000	0.11
11/21/2025	141,122,152	1,481,646,629.00	11,460,000,000	10.4700	10.4400	10.6000	10.4500	1,098,400,000	0.33
11/20/2025	37,169,564	396,744,943.00	11,070,000,000	10.6900	10.6700	10.7600	10.6400	1,038,000,000	0.21
11/19/2025	26,785,293	286,517,485.00	11,060,000,000	10.7000	10.6600	10.7600	10.6500	1,038,000,000	0.33
11/18/2025	47,287,010	506,368,271.00	11,080,000,000	10.7000	10.6800	10.8000	10.6500	1,038,000,000	0.19
11/17/2025	21,292,600	230,057,686.00	11,200,000,000	10.8100	10.8000	10.8300	10.7700	1,038,000,000	0.11
11/14/2025	17,729,013	192,322,820.00	11,220,000,000	10.8300	10.8200	10.8800	10.8100	1,038,000,000	0.11
11/13/2025	16,059,956	174,711,566.00	11,320,000,000	10.8900	10.9100	10.9000	10.8600	1,038,000,000	−0.15
11/12/2025	20,933,503	227,500,352.00	11,290,000,000	10.8600	10.8800	10.8900	10.8400	1,038,000,000	−0.18
11/11/2025	11,238,827	121,641,154.00	11,250,000,000	10.8400	10.8400	10.8500	10.8000	1,038,000,000	0.01
11/10/2025	17,870,328	192,693,247.00	11,220,000,000	10.8200	10.8200	10.8200	10.7300	1,038,000,000	0.04

---

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	20,933,503
Mean	27,827,769
Minimum	6,257,086
Maximum	141,122,152
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	225,432,284.00
Mean	296,025,340.38
Minimum	67,710,473.00
Maximum	1,481,646,629.00

---

Report generated: 2025-12-20 08:23:33.196409