# 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:** 7226
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 7226 - 2025-12-19 |
| Metric |
Value |
| Date |
2025-12-19 |
| Volume |
161,906,136 |
| Turnover |
820,199,474.00 |
| AUM |
NA |
| Closing Price |
5.0700 |
| NAV |
NA |
| Suspension Flag |
No |
| Volume vs Median |
0.79x (29.2% percentile) |
| Volume vs Mean |
0.73x |
| Turnover vs Median |
0.65x (20.0% percentile) |
| Turnover vs Mean |
0.61x |
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 7226 - Comparison of last 5 days |
| Date |
Volume
|
Turnover
|
| Volume |
vs Median |
vs Mean |
Percentile |
Turnover |
vs Median |
vs Mean |
Percentile |
| 12/19/2025 |
161,906,136 |
0.79x |
0.73x |
29.2% |
820,199,474.00 |
0.65x |
0.61x |
20.0% |
| 12/18/2025 |
167,434,032 |
0.81x |
0.76x |
35.4% |
823,357,024.00 |
0.65x |
0.61x |
21.5% |
| 12/17/2025 |
184,412,291 |
0.90x |
0.83x |
47.7% |
918,625,378.00 |
0.73x |
0.68x |
29.2% |
| 12/16/2025 |
303,518,695 |
1.48x |
1.37x |
86.2% |
1,493,509,155.00 |
1.19x |
1.11x |
63.1% |
| 12/15/2025 |
157,629,159 |
0.77x |
0.71x |
24.6% |
817,598,869.00 |
0.65x |
0.61x |
18.5% |
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 7226 |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
161,906,136 |
820,199,474.00 |
NA |
5.0700 |
NA |
5.1200 |
4.9900 |
2,235,240,000 |
NA |
| 12/18/2025 |
167,434,032 |
823,357,024.00 |
NA |
4.9600 |
NA |
4.9900 |
4.8600 |
2,235,240,000 |
NA |
| 12/17/2025 |
184,412,291 |
918,625,378.00 |
11,250,000,000 |
5.0300 |
5.0300 |
5.0800 |
4.9000 |
2,235,240,000 |
−0.08 |
| 12/16/2025 |
303,518,695 |
1,493,509,155.00 |
10,960,000,000 |
4.9300 |
4.9400 |
5.0900 |
4.8500 |
2,221,240,000 |
−0.10 |
| 12/15/2025 |
157,629,159 |
817,598,869.00 |
11,360,000,000 |
5.1200 |
5.1200 |
5.2800 |
5.1100 |
2,221,240,000 |
0.14 |
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 7226 - Most recent dates first |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
161,906,136 |
820,199,474.00 |
NA |
5.0700 |
NA |
5.1200 |
4.9900 |
2,235,240,000 |
NA |
| 12/18/2025 |
167,434,032 |
823,357,024.00 |
NA |
4.9600 |
NA |
4.9900 |
4.8600 |
2,235,240,000 |
NA |
| 12/17/2025 |
184,412,291 |
918,625,378.00 |
11,250,000,000 |
5.0300 |
5.0300 |
5.0800 |
4.9000 |
2,235,240,000 |
−0.08 |
| 12/16/2025 |
303,518,695 |
1,493,509,155.00 |
10,960,000,000 |
4.9300 |
4.9400 |
5.0900 |
4.8500 |
2,221,240,000 |
−0.10 |
| 12/15/2025 |
157,629,159 |
817,598,869.00 |
11,360,000,000 |
5.1200 |
5.1200 |
5.2800 |
5.1100 |
2,221,240,000 |
0.14 |
| 12/12/2025 |
165,208,602 |
879,239,656.00 |
11,880,000,000 |
5.3700 |
5.3900 |
5.4000 |
5.2200 |
2,205,640,000 |
−0.31 |
| 12/11/2025 |
130,729,262 |
685,924,218.00 |
11,450,000,000 |
5.1800 |
5.1900 |
5.3600 |
5.1600 |
2,205,640,000 |
−0.17 |
| 12/10/2025 |
143,155,943 |
745,915,683.00 |
11,620,000,000 |
5.2800 |
5.2800 |
5.3000 |
5.1400 |
2,200,440,000 |
−0.16 |
| 12/9/2025 |
126,763,301 |
671,427,497.00 |
11,510,000,000 |
5.2200 |
5.2400 |
5.4600 |
5.2200 |
2,200,440,000 |
−0.20 |
| 12/8/2025 |
110,771,200 |
605,599,154.00 |
11,980,000,000 |
5.4300 |
5.4500 |
5.5300 |
5.4200 |
2,200,440,000 |
−0.20 |
| 12/5/2025 |
162,894,710 |
875,598,665.00 |
11,980,000,000 |
5.4400 |
5.4500 |
5.5000 |
5.2400 |
2,200,440,000 |
−0.15 |
| 12/4/2025 |
148,719,900 |
785,755,105.00 |
11,790,000,000 |
5.3600 |
5.3600 |
5.4200 |
5.1500 |
2,200,440,000 |
0.00 |
| 12/3/2025 |
131,209,000 |
688,116,802.00 |
11,440,000,000 |
5.2000 |
5.2100 |
5.3400 |
5.1800 |
2,196,840,000 |
−0.20 |
| 12/2/2025 |
146,740,402 |
794,777,103.00 |
11,820,000,000 |
5.3800 |
5.3800 |
5.5100 |
5.3300 |
2,196,840,000 |
−0.06 |
| 12/1/2025 |
129,544,760 |
699,628,970.00 |
11,920,000,000 |
5.4000 |
5.4300 |
5.4600 |
5.3000 |
2,196,840,000 |
−0.39 |
| 11/28/2025 |
98,927,500 |
528,769,651.00 |
11,730,000,000 |
5.3500 |
5.3400 |
5.4000 |
5.2800 |
2,196,840,000 |
0.19 |
| 11/27/2025 |
150,233,500 |
809,727,382.00 |
11,700,000,000 |
5.3400 |
5.3400 |
5.5000 |
5.3200 |
2,192,840,000 |
−0.08 |
| 11/26/2025 |
131,290,722 |
710,108,875.00 |
11,790,000,000 |
5.3700 |
5.3800 |
5.4700 |
5.3600 |
2,192,840,000 |
−0.18 |
| 11/25/2025 |
235,612,400 |
1,270,949,399.00 |
11,770,000,000 |
5.3600 |
5.3700 |
5.4800 |
5.2600 |
2,192,840,000 |
−0.07 |
| 11/24/2025 |
281,762,240 |
1,453,744,944.00 |
11,090,000,000 |
5.2300 |
5.2400 |
5.2800 |
5.0000 |
2,116,440,000 |
−0.28 |
| 11/21/2025 |
446,783,121 |
2,236,777,710.00 |
10,340,000,000 |
4.9500 |
4.9700 |
5.1200 |
4.9100 |
2,079,640,000 |
−0.39 |
| 11/20/2025 |
311,502,209 |
1,655,176,799.00 |
10,810,000,000 |
5.3200 |
5.3200 |
5.5000 |
5.2000 |
2,034,840,000 |
0.15 |
| 11/19/2025 |
163,445,910 |
885,729,349.00 |
10,620,000,000 |
5.3800 |
5.3800 |
5.5300 |
5.3300 |
1,974,040,000 |
−0.02 |
| 11/18/2025 |
279,903,500 |
1,537,232,428.00 |
10,440,000,000 |
5.4400 |
5.4600 |
5.6400 |
5.3900 |
1,913,240,000 |
−0.33 |
| 11/17/2025 |
179,161,860 |
1,021,735,627.00 |
10,640,000,000 |
5.6800 |
5.6800 |
5.8400 |
5.6200 |
1,874,840,000 |
−0.09 |
| 11/14/2025 |
205,442,900 |
1,205,120,020.00 |
10,860,000,000 |
5.8000 |
5.7900 |
5.9800 |
5.7600 |
1,874,840,000 |
0.11 |
| 11/13/2025 |
178,805,220 |
1,079,466,619.00 |
11,510,000,000 |
6.1300 |
6.1400 |
6.2300 |
5.9200 |
1,874,840,000 |
−0.18 |
| 11/12/2025 |
152,026,080 |
916,107,556.00 |
11,330,000,000 |
6.0400 |
6.0500 |
6.1300 |
5.9300 |
1,874,840,000 |
−0.20 |
| 11/11/2025 |
158,548,100 |
951,878,118.00 |
11,230,000,000 |
6.0200 |
6.0300 |
6.1200 |
5.8900 |
1,863,640,000 |
−0.24 |
| 11/10/2025 |
181,420,000 |
1,074,844,488.00 |
11,130,000,000 |
6.0000 |
6.0200 |
6.0300 |
5.7900 |
1,850,440,000 |
−0.18 |
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 |
205,442,900 |
| Mean |
221,110,460 |
| Minimum |
98,927,500 |
| Maximum |
568,560,803 |
| 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 |
1,258,555,039.00 |
| Mean |
1,350,869,833.54 |
| Minimum |
528,769,651.00 |
| Maximum |
3,680,095,107.00 |
Report generated: 2025-12-20 08:23:30.172627