Last observation: 16 sept. 2026 (N = 7)
First observation: 5 janv. 1983 (N = 2)
Last data update: 16 sept. 2026, 06:31
Last compile: 16 sept. 2026, 21:10
stocks_catalog recense les actions suivies sur investing.com : 39 952 sociétés de 91 pays (nom, ISIN, devise, identifiant). L’historique de cours quotidien (OHLCV) est constitué pour la France (stocks_FRA, 749 valeurs), les États-Unis (stocks_USA, 62 valeurs) et 26 grandes capitalisations mondiales (stocks, ci-dessous) — Europe hors France, Suisse, Japon, Corée, Taïwan, Hong Kong — absentes des deux panels précédents. Voir aussi stocks_FRA et stocks_USA.
Le catalogue : sociétés cotées suivies, par pays
Code
cat_df <- stocks_cat |>
mutate(pays = str_to_title(country)) |>
count(pays, name = "n") |>
slice_max(n, n = 26) |>
add_flag_color("pays") |>
mutate(slug = to_slug(pays),
flag = ifelse(is.na(slug), NA_character_,
file.path("..", "..", "icon", "flag", "round",
paste0(slug, ".png"))),
color = ifelse(is.na(color) | toupper(color) %in% c("#FFFFFF", "#FEFEFE"),
"grey65", color)) |>
arrange(n) |> mutate(rk = row_number())
ggplot(cat_df, aes(n, rk)) +
geom_col(aes(fill = color), width = 0.72, colour = "grey60", linewidth = 0.2,
orientation = "y") +
geom_text(aes(label = format(n, big.mark = " ")), hjust = -0.2, size = 2.8,
colour = "grey30") +
ggimage::geom_image(aes(x = -max(n) * 0.045, image = flag), size = 0.03,
asp = 1.1, na.rm = TRUE) +
scale_fill_identity() +
scale_y_continuous(breaks = cat_df$rk, labels = cat_df$pays,
expand = expansion(add = 0.8)) +
scale_x_continuous(expand = expansion(mult = c(0.10, 0.10))) +
theme_minimal() + xlab("Nombre de sociétés cotées suivies") + ylab("")
Ailleurs qu’en France et aux États-Unis, seule la fiche signalétique est disponible — pas encore l’historique de cours.
Géants de part et d’autre de l’Atlantique
Cours rebasés à 100 début 2005, échelle logarithmique. Couleur = couleur caractéristique du drapeau, icône du pays en fin de série.
Code
sel <- tribble(
~symbol, ~lab, ~src,
"AAPL", "Apple", "USA", "MSFT", "Microsoft", "USA",
"JPM", "JPMorgan", "USA", "KO", "Coca-Cola", "USA",
"LVMH", "LVMH", "FRA", "OREP", "L'Oréal", "FRA",
"SASY", "Sanofi", "FRA", "TOTF", "TotalEnergies", "FRA")
grab <- function(df, src_) df |>
semi_join(filter(sel, src == src_), "symbol") |> deglitch() |>
inner_join(filter(sel, src == src_), "symbol") |>
transmute(lab, src, Date, Close = as.numeric(Close))
px <- bind_rows(grab(stocks_USA, "USA"), grab(stocks_FRA, "FRA")) |>
filter(Date >= as.Date("2005-01-01"), !is.na(Close), Close > 0) |>
mutate(pays = ifelse(src == "USA", "United States", "France")) |>
group_by(lab) |> arrange(Date) |>
mutate(y = 100 * Close / first(Close)) |> ungroup()
pal <- qcol(unique(px$lab))
ends <- px |> group_by(lab, pays) |> slice_max(Date, n = 1) |> ungroup() |>
arrange(y) |> mutate(ly = log10(y))
gap <- 0.055 * diff(range(log10(px$y)))
for (i in seq_len(nrow(ends))[-1])
if (ends$ly[i] - ends$ly[i - 1] < gap) ends$ly[i] <- ends$ly[i - 1] + gap
ends <- ends |> mutate(ly = 10^(ly - mean(ly - log10(y))),
slug = to_slug(pays),
flag = file.path("..", "..", "icon", "flag", "round",
paste0(slug, ".png")))
ggplot(px, aes(Date, y, colour = lab, group = lab)) +
geom_line(linewidth = 0.6) +
ggimage::geom_image(data = ends, inherit.aes = FALSE,
aes(x = max(px$Date) + 250, y = ly, image = flag), size = 0.04, asp = 1.5) +
geom_text(data = ends, aes(x = max(px$Date) + 520, y = ly, label = lab,
colour = lab), hjust = 0, size = 3) +
scale_colour_manual(values = pal, guide = "none") +
scale_x_date(expand = expansion(mult = c(0.02, 0.24))) +
scale_y_log10() +
theme_minimal() + xlab("") + ylab("Cours rebasé (base 100 = janv. 2005)")
Grandes capitalisations mondiales (stocks)
Quelques poids lourds hors France / États-Unis, cours (devise locale) rebasés à 100 début 2015, échelle logarithmique.
Code
sel <- tribble(
~symbol, ~lab,
"ASML", "ASML", "NOVOb", "Novo Nordisk",
"NESN", "Nestlé", "SAP", "SAP",
"7203", "Toyota", "2330", "TSMC",
"0700", "Tencent", "005930","Samsung Elec.")
px <- stocks |>
semi_join(sel, "symbol") |> deglitch() |>
inner_join(sel, "symbol") |>
filter(Date >= as.Date("2015-01-01"), !is.na(Close), Close > 0) |>
group_by(lab) |> arrange(Date) |>
mutate(y = 100 * as.numeric(Close) / first(as.numeric(Close))) |> ungroup()
ends <- px |> group_by(lab) |> slice_max(Date, n = 1) |> ungroup() |>
arrange(y) |> mutate(ly = log10(y))
gap <- 0.05 * diff(range(log10(px$y)))
for (i in seq_len(nrow(ends))[-1])
if (ends$ly[i] - ends$ly[i - 1] < gap) ends$ly[i] <- ends$ly[i - 1] + gap
ends$ly <- 10^(ends$ly - mean(ends$ly - log10(ends$y)))
ggplot(px, aes(Date, y, colour = lab, group = lab)) +
geom_line(linewidth = 0.6) +
geom_text(data = ends, aes(x = max(px$Date), y = ly, label = lab), hjust = 0,
size = 3, nudge_x = as.numeric(diff(range(px$Date))) * 0.02) +
scale_colour_manual(values = qcol(unique(px$lab)), guide = "none") +
scale_x_date(expand = expansion(mult = c(0.02, 0.22))) +
scale_y_log10() +
theme_minimal() + xlab("") + ylab("Cours rebasé (base 100 = janv. 2015)")