# 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:** 2828
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 2828 - 2025-12-19 |
| Metric |
Value |
| Date |
2025-12-19 |
| Volume |
199,781,264 |
| Turnover |
18,195,532,380.00 |
| AUM |
NA |
| Closing Price |
91.2200 |
| NAV |
NA |
| Suspension Flag |
No |
| Volume vs Median |
2.43x (96.9% percentile) |
| Volume vs Mean |
2.30x |
| Turnover vs Median |
2.35x (96.9% percentile) |
| Turnover vs Mean |
2.22x |
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 2828 - Comparison of last 5 days |
| Date |
Volume
|
Turnover
|
| Volume |
vs Median |
vs Mean |
Percentile |
Turnover |
vs Median |
vs Mean |
Percentile |
| 12/19/2025 |
199,781,264 |
2.43x |
2.30x |
96.9% |
18,195,532,380.00 |
2.35x |
2.22x |
96.9% |
| 12/18/2025 |
64,679,401 |
0.79x |
0.74x |
33.8% |
5,838,013,578.00 |
0.75x |
0.71x |
30.8% |
| 12/17/2025 |
88,519,751 |
1.08x |
1.02x |
55.4% |
7,990,394,067.00 |
1.03x |
0.97x |
55.4% |
| 12/16/2025 |
68,160,750 |
0.83x |
0.78x |
40.0% |
6,109,611,902.00 |
0.79x |
0.74x |
33.8% |
| 12/15/2025 |
84,179,901 |
1.02x |
0.97x |
53.8% |
7,738,989,247.00 |
1.00x |
0.94x |
50.8% |
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 2828 |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
199,781,264 |
18,195,532,380.00 |
NA |
91.2200 |
NA |
91.5000 |
90.6000 |
344,122,237 |
NA |
| 12/18/2025 |
64,679,401 |
5,838,013,578.00 |
NA |
90.6000 |
NA |
90.7600 |
89.7200 |
344,122,237 |
NA |
| 12/17/2025 |
88,519,751 |
7,990,394,067.00 |
31,190,000,000 |
90.6000 |
90.6600 |
90.9000 |
89.5800 |
344,122,237 |
−0.07 |
| 12/16/2025 |
68,160,750 |
6,109,611,902.00 |
27,990,000,000 |
89.7200 |
89.7800 |
91.2000 |
89.2600 |
311,835,420 |
−0.06 |
| 12/15/2025 |
84,179,901 |
7,738,989,247.00 |
28,730,000,000 |
91.4000 |
91.4200 |
92.3200 |
91.3200 |
314,324,858 |
−0.02 |
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 2828 - Most recent dates first |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
199,781,264 |
18,195,532,380.00 |
NA |
91.2200 |
NA |
91.5000 |
90.6000 |
344,122,237 |
NA |
| 12/18/2025 |
64,679,401 |
5,838,013,578.00 |
NA |
90.6000 |
NA |
90.7600 |
89.7200 |
344,122,237 |
NA |
| 12/17/2025 |
88,519,751 |
7,990,394,067.00 |
31,190,000,000 |
90.6000 |
90.6600 |
90.9000 |
89.5800 |
344,122,237 |
−0.07 |
| 12/16/2025 |
68,160,750 |
6,109,611,902.00 |
27,990,000,000 |
89.7200 |
89.7800 |
91.2000 |
89.2600 |
311,835,420 |
−0.06 |
| 12/15/2025 |
84,179,901 |
7,738,989,247.00 |
28,730,000,000 |
91.4000 |
91.4200 |
92.3200 |
91.3200 |
314,324,858 |
−0.02 |
| 12/12/2025 |
67,353,341 |
6,249,079,308.00 |
29,930,000,000 |
92.9600 |
93.0800 |
93.1600 |
92.0000 |
321,629,132 |
−0.13 |
| 12/11/2025 |
134,921,508 |
12,365,187,950.00 |
29,450,000,000 |
91.5800 |
91.5900 |
92.5400 |
91.3000 |
321,629,132 |
−0.01 |
| 12/10/2025 |
93,062,413 |
8,493,785,020.00 |
27,720,000,000 |
91.6800 |
91.8000 |
91.6800 |
90.8600 |
301,963,254 |
−0.13 |
| 12/9/2025 |
106,984,402 |
9,856,074,070.00 |
27,660,000,000 |
91.5000 |
91.6100 |
93.2400 |
91.4200 |
301,963,254 |
−0.12 |
| 12/8/2025 |
70,879,880 |
6,626,330,493.00 |
27,550,000,000 |
93.1600 |
93.1200 |
94.5200 |
93.1000 |
295,942,107 |
0.04 |
| 12/5/2025 |
93,438,204 |
8,741,936,420.00 |
27,910,000,000 |
94.2600 |
94.3100 |
94.5000 |
92.7800 |
295,942,107 |
−0.06 |
| 12/4/2025 |
84,003,100 |
7,798,499,002.00 |
29,260,000,000 |
93.3400 |
93.3400 |
93.6200 |
92.2400 |
313,538,605 |
0.00 |
| 12/3/2025 |
74,448,556 |
6,905,898,130.00 |
30,050,000,000 |
92.4000 |
92.4400 |
93.5200 |
92.2800 |
325,118,777 |
−0.04 |
| 12/2/2025 |
67,467,381 |
6,336,898,247.00 |
31,230,000,000 |
93.7600 |
93.8500 |
94.5600 |
93.4600 |
332,813,090 |
−0.09 |
| 12/1/2025 |
129,127,430 |
12,097,498,360.00 |
31,170,000,000 |
93.5800 |
93.6900 |
94.2600 |
93.2000 |
332,813,090 |
−0.11 |
| 11/28/2025 |
41,926,101 |
3,917,531,537.00 |
31,220,000,000 |
93.3600 |
93.2600 |
93.9800 |
93.0600 |
334,790,527 |
0.11 |
| 11/27/2025 |
45,548,373 |
4,269,061,470.00 |
29,690,000,000 |
93.6200 |
93.6100 |
94.3600 |
93.1200 |
317,214,299 |
0.01 |
| 11/26/2025 |
88,675,546 |
8,324,368,513.00 |
30,210,000,000 |
93.5400 |
93.5900 |
94.4400 |
93.5400 |
322,859,459 |
−0.05 |
| 11/25/2025 |
64,605,670 |
6,044,448,972.00 |
30,550,000,000 |
93.5000 |
93.5500 |
94.1200 |
92.9600 |
326,653,479 |
−0.05 |
| 11/24/2025 |
44,211,231 |
4,092,668,898.00 |
30,470,000,000 |
92.6400 |
92.7400 |
92.9600 |
91.4400 |
328,556,491 |
−0.11 |
| 11/21/2025 |
50,217,185 |
4,593,394,023.00 |
30,290,000,000 |
91.0200 |
91.1200 |
92.8000 |
90.9200 |
332,510,069 |
−0.11 |
| 11/20/2025 |
68,869,719 |
6,423,303,147.00 |
29,630,000,000 |
93.4400 |
93.4000 |
94.2400 |
92.7800 |
317,329,809 |
0.04 |
| 11/19/2025 |
53,967,000 |
5,046,922,432.00 |
29,050,000,000 |
93.5000 |
93.4800 |
94.3800 |
93.1400 |
310,848,487 |
0.02 |
| 11/18/2025 |
32,355,051 |
3,040,925,610.00 |
28,230,000,000 |
93.5200 |
93.7300 |
95.0800 |
93.3000 |
301,223,710 |
−0.22 |
| 11/17/2025 |
82,251,450 |
7,840,649,694.00 |
29,910,000,000 |
95.4400 |
95.3000 |
96.0000 |
94.8400 |
313,952,647 |
0.15 |
| 11/14/2025 |
101,149,726 |
9,771,092,800.00 |
34,650,000,000 |
96.0600 |
96.0100 |
97.2800 |
95.9000 |
360,942,728 |
0.05 |
| 11/13/2025 |
207,969,711 |
20,236,580,190.00 |
33,360,000,000 |
97.9400 |
98.0700 |
98.5400 |
96.6800 |
340,196,760 |
−0.13 |
| 11/12/2025 |
51,439,203 |
5,006,179,455.00 |
30,340,000,000 |
97.3400 |
97.4600 |
97.8600 |
96.8400 |
311,337,167 |
−0.12 |
| 11/11/2025 |
116,009,653 |
11,165,586,930.00 |
35,470,000,000 |
96.6600 |
96.6600 |
96.9600 |
95.8600 |
366,951,054 |
0.00 |
| 11/10/2025 |
16,747,067 |
1,605,425,818.00 |
30,000,000,000 |
96.3600 |
96.4800 |
96.5400 |
94.9200 |
311,012,504 |
−0.12 |
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 |
82,251,450 |
| Mean |
86,863,536 |
| Minimum |
16,747,067 |
| Maximum |
215,719,406 |
| 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 |
7,738,989,247.00 |
| Mean |
8,206,614,262.28 |
| Minimum |
1,605,425,818.00 |
| Maximum |
20,236,580,190.00 |
Report generated: 2025-12-20 08:23:31.177538