TidyTuesday: Risk Exposure Analysis

TidyTuesday

Data Viz

Finance

Trading

Risk

Visualizing the relationship between Cash Reserve (PAT Balance) and Margin Exposure over time using ggplot2

Author

chokotto

Published

January 28, 2026

Overview

This week’s TidyTuesday visualizes the relationship between Cash Reserve (cumulative post-tax profit) and Margin Exposure (open margin positions) from my personal trading accounts.

Key metrics:

PAT Balance: Cumulative Profit After Tax - represents available cash reserve
Exposure: Total margin position value (JPY equivalent)
Exposure Ratio: Exposure / |PAT Balance| - measures leverage relative to cash reserve

Dataset

Source: Personal trading records from Rakuten Securities (JP/US) and SBI Securities (US)

Show code

library(tidyverse)
library(scales)
library(arrow)
library(patchwork)

# Load TidyTuesday helpers
project_root <- normalizePath(file.path(getwd(), "../../../../.."))
helpers_path <- file.path(project_root, "config", "R", "tidytuesday_helpers.R")
if (file.exists(helpers_path)) {
  source(helpers_path, local = FALSE)
} else {
  message("Helpers not found at: ", helpers_path)
}

Show code

# Load monthly balance data (pre-processed)
data_path <- file.path(getwd(), "data", "monthly_balance.parquet")

if (file.exists(data_path)) {
  monthly_balance <- read_parquet(data_path) %>%
    mutate(
      date = as.Date(date),
      year_month = format(date, "%Y-%m")
    )
  cat("Loaded", nrow(monthly_balance), "monthly balance records\n")
} else {
  stop("Data not found at: ", data_path)
}

Loaded 26 monthly balance records

Show code

# Calculate totals
total_by_month <- monthly_balance %>%
  group_by(year_month, date) %>%
  summarise(
    pat_balance = sum(pat_balance, na.rm = TRUE),
    exposure = sum(exposure, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  mutate(
    broker = "Total",
    exposure_ratio = if_else(
      pat_balance != 0,
      exposure / abs(pat_balance) * 100,
      NA_real_
    )
  )

# Add ratio to monthly_balance
monthly_balance <- monthly_balance %>%
  mutate(
    exposure_ratio = if_else(
      pat_balance != 0,
      exposure / abs(pat_balance) * 100,
      NA_real_
    )
  )

# Combine
all_data <- bind_rows(monthly_balance, total_by_month)

cat("Date range:", min(all_data$year_month), "to", max(all_data$year_month), "\n")

Date range: 2025-01 to 2026-01

Show code

cat("Brokers:", paste(unique(all_data$broker), collapse = ", "), "\n")

Brokers: Rakuten, SBI, Total

Chart 1: PAT Balance & Exposure Time Series

Show code

# Prepare data for dual-axis simulation
total_data <- all_data %>%
  filter(broker == "Total") %>%
  select(date, pat_balance, exposure) %>%
  pivot_longer(
    cols = c(pat_balance, exposure),
    names_to = "metric",
    values_to = "value"
  ) %>%
  mutate(
    metric = case_when(
      metric == "pat_balance" ~ "PAT Balance (Cash Reserve)",
      metric == "exposure" ~ "Exposure (Margin Position)"
    )
  )

p1 <- ggplot(total_data, aes(x = date, y = value, color = metric, fill = metric)) +
  geom_area(
    data = filter(total_data, metric == "PAT Balance (Cash Reserve)"),
    alpha = 0.3,
    show.legend = FALSE
  ) +
  geom_line(linewidth = 1.2) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", alpha = 0.7) +
  scale_y_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"),
    expand = expansion(mult = c(0.1, 0.1))
  ) +
  scale_x_date(
    date_breaks = "2 months",
    date_labels = "%Y-%m"
  ) +
  scale_color_manual(
    values = c("PAT Balance (Cash Reserve)" = "#22c55e", "Exposure (Margin Position)" = "#ef4444"),
    name = NULL
  ) +
  scale_fill_manual(
    values = c("PAT Balance (Cash Reserve)" = "#22c55e", "Exposure (Margin Position)" = "#ef4444")
  ) +
  labs(
    title = "Cash Reserve vs Margin Exposure",
    subtitle = "Monthly time series (Total across all brokers)",
    x = NULL,
    y = "Amount (JPY)",
    caption = tt_caption(source = "Personal trading records")
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "#64748b", size = 11),
    legend.position = "top",
    legend.justification = "center",
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank()
  )

p1

Chart 2: Exposure Ratio Time Series

Show code

ratio_data <- all_data %>%
  filter(broker == "Total", !is.na(exposure_ratio))

p2 <- ggplot(ratio_data, aes(x = date, y = exposure_ratio)) +
  geom_area(fill = "#ef4444", alpha = 0.2) +
  geom_line(color = "#ef4444", linewidth = 1.2) +
  geom_hline(
    yintercept = 100,
    linetype = "dashed",
    color = "#f59e0b",
    linewidth = 0.8
  ) +
  geom_hline(
    yintercept = 200,
    linetype = "dotted",
    color = "#dc2626",
    linewidth = 0.8
  ) +
  annotate(
    "text",
    x = max(ratio_data$date),
    y = 105,
    label = "100% (Exposure = |PAT|)",
    hjust = 1,
    vjust = -0.5,
    color = "#f59e0b",
    size = 3
  ) +
  annotate(
    "text",
    x = max(ratio_data$date),
    y = 205,
    label = "200% (High Risk)",
    hjust = 1,
    vjust = -0.5,
    color = "#dc2626",
    size = 3
  ) +
  scale_y_continuous(
    labels = label_percent(scale = 1),
    expand = expansion(mult = c(0, 0.15))
  ) +
  scale_x_date(
    date_breaks = "2 months",
    date_labels = "%Y-%m"
  ) +
  labs(
    title = "Exposure Ratio Over Time",
    subtitle = "Exposure / |PAT Balance| × 100 (Total)",
    x = NULL,
    y = "Exposure Ratio",
    caption = tt_caption(source = "Personal trading records")
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "#64748b", size = 11),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank()
  )

p2

Chart 3: Broker Breakdown

Show code

broker_data <- all_data %>%
  filter(broker != "Total")

broker_colors <- c("Rakuten" = "#3b82f6", "SBI" = "#8b5cf6")

# Exposure by broker
p3a <- ggplot(broker_data, aes(x = date, y = exposure, fill = broker)) +
  geom_area(alpha = 0.7, position = "stack") +
  scale_y_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"),
    expand = expansion(mult = c(0, 0.05))
  ) +
  scale_x_date(
    date_breaks = "2 months",
    date_labels = "%Y-%m"
  ) +
  scale_fill_manual(values = broker_colors, name = "Broker") +
  labs(
    title = "Margin Exposure by Broker",
    subtitle = "Stacked area chart showing broker contribution",
    x = NULL,
    y = "Exposure (JPY)"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "#64748b", size = 10),
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank()
  )

# PAT Balance by broker
p3b <- ggplot(broker_data, aes(x = date, y = pat_balance, fill = broker)) +
  geom_area(alpha = 0.7, position = "stack") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", alpha = 0.7) +
  scale_y_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"),
    expand = expansion(mult = c(0.1, 0.05))
  ) +
  scale_x_date(
    date_breaks = "2 months",
    date_labels = "%Y-%m"
  ) +
  scale_fill_manual(values = broker_colors, name = "Broker") +
  labs(
    title = "PAT Balance (Cash Reserve) by Broker",
    subtitle = "Cumulative post-tax profit by broker",
    x = NULL,
    y = "PAT Balance (JPY)",
    caption = tt_caption(source = "Personal trading records")
  ) +
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "#64748b", size = 10),
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank()
  )

# Combine with patchwork
p3a / p3b + plot_layout(guides = "collect") & theme(legend.position = "top")

Chart 4: Risk-Balance Matrix

Show code

scatter_data <- all_data %>%
  filter(broker == "Total") %>%
  mutate(
    time_index = row_number(),
    label = substr(year_month, 3, 7)  # YY-MM format
  )

# Calculate max for diagonal line
max_val <- max(c(max(scatter_data$exposure), max(abs(scatter_data$pat_balance))))
min_val <- min(c(min(scatter_data$pat_balance), 0))

p4 <- ggplot(scatter_data, aes(x = pat_balance, y = exposure)) +
  # Diagonal reference line (100%)
  geom_abline(
    slope = 1,
    intercept = 0,
    linetype = "dashed",
    color = "#f59e0b",
    alpha = 0.7
  ) +
  # Zero PAT line
  geom_vline(xintercept = 0, linetype = "dotted", color = "gray50", alpha = 0.5) +
  # Points with color gradient
  geom_point(
    aes(color = time_index),
    size = 4,
    alpha = 0.8
  ) +
  # Labels
  geom_text(
    aes(label = label),
    vjust = -1,
    size = 2.5,
    color = "#64748b"
  ) +
  # Color scale
  scale_color_viridis_c(
    name = "Time\n(older→newer)",
    option = "viridis"
  ) +
  scale_x_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥")
  ) +
  scale_y_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥")
  ) +
  # Annotations for risk zones
  annotate(
    "text",
    x = max(scatter_data$pat_balance) * 0.7,
    y = max(scatter_data$exposure) * 0.25,
    label = "Lower Risk Zone\n(High PAT, Low Exposure)",
    color = "#22c55e",
    size = 3.5,
    fontface = "bold"
  ) +
  annotate(
    "text",
    x = ifelse(min(scatter_data$pat_balance) < 0, min(scatter_data$pat_balance) * 0.5, max(scatter_data$pat_balance) * 0.2),
    y = max(scatter_data$exposure) * 0.85,
    label = "Higher Risk Zone\n(Low/Negative PAT,\nHigh Exposure)",
    color = "#ef4444",
    size = 3.5,
    fontface = "bold"
  ) +
  labs(
    title = "Risk-Balance Matrix",
    subtitle = "Each point = one month (color: time progression)",
    x = "PAT Balance (Cash Reserve) - JPY",
    y = "Margin Exposure - JPY",
    caption = tt_caption(source = "Personal trading records")
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "#64748b", size = 11),
    legend.position = "right",
    panel.grid.minor = element_blank()
  )

p4

Summary Statistics

Show code

total_summary <- all_data %>%
  filter(broker == "Total")

cat("=== Risk Exposure Summary ===\n\n")

=== Risk Exposure Summary ===

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cat("Analysis Period:", min(total_summary$year_month), "to", max(total_summary$year_month), "\n")

Analysis Period: 2025-01 to 2026-01

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cat("Total Months:", nrow(total_summary), "\n\n")

Total Months: 13

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cat("PAT Balance (Cash Reserve):\n")

PAT Balance (Cash Reserve):

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cat("  Current:", scales::number(tail(total_summary$pat_balance, 1), prefix = "¥", big.mark = ","), "\n")

  Current: ¥12,477,234

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cat("  Maximum:", scales::number(max(total_summary$pat_balance), prefix = "¥", big.mark = ","), "\n")

  Maximum: ¥12,477,234

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cat("  Minimum:", scales::number(min(total_summary$pat_balance), prefix = "¥", big.mark = ","), "\n\n")

  Minimum: -¥3,342,595

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cat("Exposure (Margin Position):\n")

Exposure (Margin Position):

Show code

cat("  Current:", scales::number(tail(total_summary$exposure, 1), prefix = "¥", big.mark = ","), "\n")

  Current: ¥35,691,977

Show code

cat("  Maximum:", scales::number(max(total_summary$exposure), prefix = "¥", big.mark = ","), "\n")

  Maximum: ¥35,691,977

Show code

cat("  Minimum:", scales::number(min(total_summary$exposure), prefix = "¥", big.mark = ","), "\n\n")

  Minimum: ¥0

Show code

valid_ratios <- total_summary$exposure_ratio[!is.na(total_summary$exposure_ratio)]
if (length(valid_ratios) > 0) {
  cat("Exposure Ratio:\n")
  cat("  Current:", sprintf("%.1f%%", tail(valid_ratios, 1)), "\n")
  cat("  Maximum:", sprintf("%.1f%%", max(valid_ratios)), "\n")
  cat("  Average:", sprintf("%.1f%%", mean(valid_ratios)), "\n")
}

Exposure Ratio:
  Current: 286.1% 
  Maximum: 117450.7% 
  Average: 20090.3%

Design Approach

ggplot2 + scales: Clean, publication-quality charts with proper number formatting for JPY.
Viridis Color Scale: Time progression in scatter plot uses colorblind-friendly Viridis palette.
patchwork: Combines multiple plots into cohesive layouts.
Reference Lines: 100% and 200% ratio lines provide quick risk assessment.
Dual Perspectives: Both absolute values and ratios shown for complete risk picture.

This post is part of the TidyTuesday weekly data visualization project.

Disclaimer

This analysis is for educational and practice purposes only. The figures shown are from personal trading records and should not be considered as investment advice. Margin trading involves significant risk of loss.

--- title: "TidyTuesday: Risk Exposure Analysis" description: "Visualizing the relationship between Cash Reserve (PAT Balance) and Margin Exposure over time using ggplot2" date: "2026-01-28" x-posted: true author: "chokotto" categories: ["TidyTuesday", "Data Viz", "R", "Finance", "Trading", "Risk"] image: "thumbnail.svg" engine: knitr freeze: false execute: warning: false message: false code-fold: true code-tools: true code-summary: "Show code" twitter-card: card-type: summary_large_image image: "thumbnail.png" title: "TidyTuesday: Risk Exposure Analysis" description: "Cash Reserve vs Margin Exposure time series with ggplot2" --- ## Overview This week's TidyTuesday visualizes the relationship between **Cash Reserve** (cumulative post-tax profit) and **Margin Exposure** (open margin positions) from my personal trading accounts. Key metrics: - **PAT Balance**: Cumulative Profit After Tax - represents available cash reserve - **Exposure**: Total margin position value (JPY equivalent) - **Exposure Ratio**: Exposure / \|PAT Balance\| - measures leverage relative to cash reserve ## Dataset **Source**: Personal trading records from Rakuten Securities (JP/US) and SBI Securities (US) ```{r} #| label: load-packages #| message: false #| warning: false library(tidyverse) library(scales) library(arrow) library(patchwork) # Load TidyTuesday helpers project_root <- normalizePath(file.path(getwd(), "../../../../..")) helpers_path <- file.path(project_root, "config", "R", "tidytuesday_helpers.R") if (file.exists(helpers_path)) { source(helpers_path, local = FALSE) } else { message("Helpers not found at: ", helpers_path) } ``` ```{r} #| label: load-data #| message: false # Load monthly balance data (pre-processed) data_path <- file.path(getwd(), "data", "monthly_balance.parquet") if (file.exists(data_path)) { monthly_balance <- read_parquet(data_path) %>% mutate( date = as.Date(date), year_month = format(date, "%Y-%m") ) cat("Loaded", nrow(monthly_balance), "monthly balance records\n") } else { stop("Data not found at: ", data_path) } # Calculate totals total_by_month <- monthly_balance %>% group_by(year_month, date) %>% summarise( pat_balance = sum(pat_balance, na.rm = TRUE), exposure = sum(exposure, na.rm = TRUE), .groups = "drop" ) %>% mutate( broker = "Total", exposure_ratio = if_else( pat_balance != 0, exposure / abs(pat_balance) * 100, NA_real_ ) ) # Add ratio to monthly_balance monthly_balance <- monthly_balance %>% mutate( exposure_ratio = if_else( pat_balance != 0, exposure / abs(pat_balance) * 100, NA_real_ ) ) # Combine all_data <- bind_rows(monthly_balance, total_by_month) cat("Date range:", min(all_data$year_month), "to", max(all_data$year_month), "\n") cat("Brokers:", paste(unique(all_data$broker), collapse = ", "), "\n") ``` ## Chart 1: PAT Balance & Exposure Time Series ```{r} #| label: chart-1-amounts #| fig-width: 12 #| fig-height: 6 # Prepare data for dual-axis simulation total_data <- all_data %>% filter(broker == "Total") %>% select(date, pat_balance, exposure) %>% pivot_longer( cols = c(pat_balance, exposure), names_to = "metric", values_to = "value" ) %>% mutate( metric = case_when( metric == "pat_balance" ~ "PAT Balance (Cash Reserve)", metric == "exposure" ~ "Exposure (Margin Position)" ) ) p1 <- ggplot(total_data, aes(x = date, y = value, color = metric, fill = metric)) + geom_area( data = filter(total_data, metric == "PAT Balance (Cash Reserve)"), alpha = 0.3, show.legend = FALSE ) + geom_line(linewidth = 1.2) + geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", alpha = 0.7) + scale_y_continuous( labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"), expand = expansion(mult = c(0.1, 0.1)) ) + scale_x_date( date_breaks = "2 months", date_labels = "%Y-%m" ) + scale_color_manual( values = c("PAT Balance (Cash Reserve)" = "#22c55e", "Exposure (Margin Position)" = "#ef4444"), name = NULL ) + scale_fill_manual( values = c("PAT Balance (Cash Reserve)" = "#22c55e", "Exposure (Margin Position)" = "#ef4444") ) + labs( title = "Cash Reserve vs Margin Exposure", subtitle = "Monthly time series (Total across all brokers)", x = NULL, y = "Amount (JPY)", caption = tt_caption(source = "Personal trading records") ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "#64748b", size = 11), legend.position = "top", legend.justification = "center", axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank() ) p1 ``` ## Chart 2: Exposure Ratio Time Series ```{r} #| label: chart-2-ratio #| fig-width: 12 #| fig-height: 5 ratio_data <- all_data %>% filter(broker == "Total", !is.na(exposure_ratio)) p2 <- ggplot(ratio_data, aes(x = date, y = exposure_ratio)) + geom_area(fill = "#ef4444", alpha = 0.2) + geom_line(color = "#ef4444", linewidth = 1.2) + geom_hline( yintercept = 100, linetype = "dashed", color = "#f59e0b", linewidth = 0.8 ) + geom_hline( yintercept = 200, linetype = "dotted", color = "#dc2626", linewidth = 0.8 ) + annotate( "text", x = max(ratio_data$date), y = 105, label = "100% (Exposure = |PAT|)", hjust = 1, vjust = -0.5, color = "#f59e0b", size = 3 ) + annotate( "text", x = max(ratio_data$date), y = 205, label = "200% (High Risk)", hjust = 1, vjust = -0.5, color = "#dc2626", size = 3 ) + scale_y_continuous( labels = label_percent(scale = 1), expand = expansion(mult = c(0, 0.15)) ) + scale_x_date( date_breaks = "2 months", date_labels = "%Y-%m" ) + labs( title = "Exposure Ratio Over Time", subtitle = "Exposure / |PAT Balance| × 100 (Total)", x = NULL, y = "Exposure Ratio", caption = tt_caption(source = "Personal trading records") ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "#64748b", size = 11), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank() ) p2 ``` ## Chart 3: Broker Breakdown ```{r} #| label: chart-3-broker #| fig-width: 12 #| fig-height: 8 broker_data <- all_data %>% filter(broker != "Total") broker_colors <- c("Rakuten" = "#3b82f6", "SBI" = "#8b5cf6") # Exposure by broker p3a <- ggplot(broker_data, aes(x = date, y = exposure, fill = broker)) + geom_area(alpha = 0.7, position = "stack") + scale_y_continuous( labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"), expand = expansion(mult = c(0, 0.05)) ) + scale_x_date( date_breaks = "2 months", date_labels = "%Y-%m" ) + scale_fill_manual(values = broker_colors, name = "Broker") + labs( title = "Margin Exposure by Broker", subtitle = "Stacked area chart showing broker contribution", x = NULL, y = "Exposure (JPY)" ) + theme_minimal(base_size = 11) + theme( plot.title = element_text(face = "bold", size = 14), plot.subtitle = element_text(color = "#64748b", size = 10), legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank() ) # PAT Balance by broker p3b <- ggplot(broker_data, aes(x = date, y = pat_balance, fill = broker)) + geom_area(alpha = 0.7, position = "stack") + geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", alpha = 0.7) + scale_y_continuous( labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥"), expand = expansion(mult = c(0.1, 0.05)) ) + scale_x_date( date_breaks = "2 months", date_labels = "%Y-%m" ) + scale_fill_manual(values = broker_colors, name = "Broker") + labs( title = "PAT Balance (Cash Reserve) by Broker", subtitle = "Cumulative post-tax profit by broker", x = NULL, y = "PAT Balance (JPY)", caption = tt_caption(source = "Personal trading records") ) + theme_minimal(base_size = 11) + theme( plot.title = element_text(face = "bold", size = 14), plot.subtitle = element_text(color = "#64748b", size = 10), legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank() ) # Combine with patchwork p3a / p3b + plot_layout(guides = "collect") & theme(legend.position = "top") ``` ## Chart 4: Risk-Balance Matrix ```{r} #| label: chart-4-scatter #| fig-width: 10 #| fig-height: 8 scatter_data <- all_data %>% filter(broker == "Total") %>% mutate( time_index = row_number(), label = substr(year_month, 3, 7) # YY-MM format ) # Calculate max for diagonal line max_val <- max(c(max(scatter_data$exposure), max(abs(scatter_data$pat_balance)))) min_val <- min(c(min(scatter_data$pat_balance), 0)) p4 <- ggplot(scatter_data, aes(x = pat_balance, y = exposure)) + # Diagonal reference line (100%) geom_abline( slope = 1, intercept = 0, linetype = "dashed", color = "#f59e0b", alpha = 0.7 ) + # Zero PAT line geom_vline(xintercept = 0, linetype = "dotted", color = "gray50", alpha = 0.5) + # Points with color gradient geom_point( aes(color = time_index), size = 4, alpha = 0.8 ) + # Labels geom_text( aes(label = label), vjust = -1, size = 2.5, color = "#64748b" ) + # Color scale scale_color_viridis_c( name = "Time\n(older→newer)", option = "viridis" ) + scale_x_continuous( labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥") ) + scale_y_continuous( labels = label_number(scale = 1e-6, suffix = "M", prefix = "¥") ) + # Annotations for risk zones annotate( "text", x = max(scatter_data$pat_balance) * 0.7, y = max(scatter_data$exposure) * 0.25, label = "Lower Risk Zone\n(High PAT, Low Exposure)", color = "#22c55e", size = 3.5, fontface = "bold" ) + annotate( "text", x = ifelse(min(scatter_data$pat_balance) < 0, min(scatter_data$pat_balance) * 0.5, max(scatter_data$pat_balance) * 0.2), y = max(scatter_data$exposure) * 0.85, label = "Higher Risk Zone\n(Low/Negative PAT,\nHigh Exposure)", color = "#ef4444", size = 3.5, fontface = "bold" ) + labs( title = "Risk-Balance Matrix", subtitle = "Each point = one month (color: time progression)", x = "PAT Balance (Cash Reserve) - JPY", y = "Margin Exposure - JPY", caption = tt_caption(source = "Personal trading records") ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "#64748b", size = 11), legend.position = "right", panel.grid.minor = element_blank() ) p4 ``` ## Summary Statistics ```{r} #| label: summary-stats total_summary <- all_data %>% filter(broker == "Total") cat("=== Risk Exposure Summary ===\n\n") cat("Analysis Period:", min(total_summary$year_month), "to", max(total_summary$year_month), "\n") cat("Total Months:", nrow(total_summary), "\n\n") cat("PAT Balance (Cash Reserve):\n") cat(" Current:", scales::number(tail(total_summary$pat_balance, 1), prefix = "¥", big.mark = ","), "\n") cat(" Maximum:", scales::number(max(total_summary$pat_balance), prefix = "¥", big.mark = ","), "\n") cat(" Minimum:", scales::number(min(total_summary$pat_balance), prefix = "¥", big.mark = ","), "\n\n") cat("Exposure (Margin Position):\n") cat(" Current:", scales::number(tail(total_summary$exposure, 1), prefix = "¥", big.mark = ","), "\n") cat(" Maximum:", scales::number(max(total_summary$exposure), prefix = "¥", big.mark = ","), "\n") cat(" Minimum:", scales::number(min(total_summary$exposure), prefix = "¥", big.mark = ","), "\n\n") valid_ratios <- total_summary$exposure_ratio[!is.na(total_summary$exposure_ratio)] if (length(valid_ratios) > 0) { cat("Exposure Ratio:\n") cat(" Current:", sprintf("%.1f%%", tail(valid_ratios, 1)), "\n") cat(" Maximum:", sprintf("%.1f%%", max(valid_ratios)), "\n") cat(" Average:", sprintf("%.1f%%", mean(valid_ratios)), "\n") } ``` ## Design Approach 1. **ggplot2 + scales**: Clean, publication-quality charts with proper number formatting for JPY. 2. **Viridis Color Scale**: Time progression in scatter plot uses colorblind-friendly Viridis palette. 3. **patchwork**: Combines multiple plots into cohesive layouts. 4. **Reference Lines**: 100% and 200% ratio lines provide quick risk assessment. 5. **Dual Perspectives**: Both absolute values and ratios shown for complete risk picture. ------------------------------------------------------------------------ *This post is part of the [TidyTuesday](https://github.com/rfordatascience/tidytuesday) weekly data visualization project.* :::: {.callout-caution collapse="false" appearance="minimal" icon="false"} ## Disclaimer ::: {style="font-size: 0.85em; color: #64748b; line-height: 1.6;"} This analysis is for educational and practice purposes only. The figures shown are from personal trading records and should not be considered as investment advice. Margin trading involves significant risk of loss. ::: ::::