Stock Stats

// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © peterdmv
//
// Thanks to ArmerSchlucker for the original version of this table indicator.
// Thanks to Qullamaggie / MikeC / TheScrutiniser and GlinckEastwoot for ADR% formula
//
// Release: v2 — uplifted to Pine v6 and refactored (button-style stat row).

//@version=6
indicator(title="Stock Stats", overlay=true)

// Helper functions
// Function to format large numbers into short displayable strings
f_num(value) =>
    str.tostring(math.round(value, 2))

f_num_large(value) =>
    if value >= 1e12
        str.tostring(value / 1e12, '0.00') + 'T'
    else if value >= 1e9
        str.tostring(value / 1e9, '0.00') + 'B'
    else if value >= 1e6
        str.tostring(value / 1e6, '0.00') + 'M'
    else if value >= 1e3
        str.tostring(value / 1e3, '0.00') + 'K'
    else
        str.tostring(value, '0.00')

f_num_percent(value) =>
    str.tostring(math.round(value, 2)) + "%"

// Market cap category label, using conventional breakpoints (Nano < $50M,
// Micro < $300M, Small < $2B, Mid < $10B, Large < $200B, else Mega)
f_cap_category(value) =>
    if na(value)
        "Mkt Cap"
    else if value < 50e6
        "Nano Cap"
    else if value < 300e6
        "Micro Cap"
    else if value < 2e9
        "Small Cap"
    else if value < 10e9
        "Mid Cap"
    else if value < 200e9
        "Large Cap"
    else
        "Mega Cap"

// Configuration
show_mkt_cap = input(true, title='Show Market Cap')
show_float = input(false, title='Show Shares Float')
show_adr = input(true, title="Show ADR%")
show_atr = input(false, title="Show ATR")
show_lod_dist = input(false, title="Show LoD dist.")
show_52wk_high = input(false, title="Show 52-Week High")
show_vol_buzz = input(true, title="Show Volume Buzz")
show_run_rate = input(true, title="Show Run Rate",
  tooltip="Projects today's full-session volume from the volume traded so far, shown " +
  "as % vs. the average daily volume. For regular US equities (09:30–16:00 ET " +
  "session), it corrects for the typical U-shaped intraday pattern (heavier volume " +
  "at the open/close, lighter at midday) and compares to the 100-day average. For " +
  "24/7 crypto tickers, it uses the full UTC day, assumes a flat intraday " +
  "distribution, and compares to the trailing average for the same day of week — " +
  "so a Saturday projects against recent Saturdays, capturing lower weekend volume.")

adr_len = input(20, title="ADR% Length",
  tooltip="The number of daily bars used in the calculation of the ADR%")
atr_len = input(14, title="ATR Length",
  tooltip="The number of daily bars used in the calculation of the ATR")

bg_col = input(#00000000, title="Background Color")
label_col = input(color.gray, title="Text Color (Labels)")
mkt_cap_col = input(color.gray, title="Text Color (Mkt Cap)")
float_col = input(color.gray, title="Text Color (Float)")
adr_col = input(color.gray, title="Text Color (ADR%)")
atr_col = input(color.gray, title="Text Color (ATR)")
lod_dist_col = input(color.gray, title="Text Color (LoD dist.)")
high_52wk_col = input(color.gray, title="Text Color (52-Week High)")
vol_buzz_col = input(color.gray, title="Text Color (Volume Buzz)")
run_rate_col = input(color.gray, title="Text Color (Run Rate)")
show_empty_row = input(true, title="Add an empty row above the stat row",
  tooltip="Serves the purpose of not having the pane icons overlap with the " +
  "text in the table if multiple panes are shown and the chart pane is hovered")

// Calculate indicators
mkt_cape = syminfo.shares_outstanding_total * close
mkt_caps = request.security(syminfo.tickerid, 'D', mkt_cape)

floate = syminfo.shares_outstanding_float * close
floats = request.security(syminfo.tickerid, 'D', floate)

adre = 100 * (ta.sma(high / low, adr_len) - 1)
adrs = request.security(syminfo.tickerid, 'D', adre)
//atr = request.security(syminfo.tickerid, 'D', ta.atr(atr_len))
atr_daily = request.security(syminfo.tickerid, 'D', ta.atr(atr_len))
atr_weekly = request.security(syminfo.tickerid, 'W', ta.atr(atr_len))
atr = timeframe.isweekly ? atr_weekly : atr_daily
lod_dist = request.security(syminfo.tickerid, 'D', 100 * (close - low) / atr)
high_52wk = request.security(syminfo.tickerid, 'W', ta.highest(high, 52))
vol_buzze = (volume / ta.sma(volume, 100) - 1) * 100
vol_buzzs = request.security(syminfo.tickerid, 'D', vol_buzze)

// Run Rate
// Projects today's full-session volume from the volume traded so far. The model
// used depends on the ticker: regular US equities have a fixed session and a well-
// documented open/close volume skew; crypto trades 24/7 with no such session and
// no equally well-established intraday shape, so it gets a separate, simpler model.
vol_today_e = volume
vol_today_s = request.security(syminfo.tickerid, 'D', vol_today_e)

// --- Equity/session model (regular US session, 09:30–16:00 ET) ---
// f_cum_frac models the typical cumulative % of a session's total volume realized
// by a given number of minutes elapsed since the open (390-minute session). This
// is an approximate, generally-typical U-shaped curve — heaviest in the first and
// last 30 minutes, thinnest around midday — not derived from this specific ticker.
f_cum_frac(elapsed) =>
    e = math.max(0.0, math.min(elapsed, 390.0))
    if e <= 30
        e / 30 * 0.21
    else if e <= 90
        0.21 + (e - 30) / 60 * (0.36 - 0.21)
    else if e <= 150
        0.36 + (e - 90) / 60 * (0.45 - 0.36)
    else if e <= 210
        0.45 + (e - 150) / 60 * (0.52 - 0.45)
    else if e <= 270
        0.52 + (e - 210) / 60 * (0.60 - 0.52)
    else if e <= 330
        0.60 + (e - 270) / 60 * (0.71 - 0.60)
    else if e <= 360
        0.71 + (e - 330) / 30 * (0.82 - 0.71)
    else
        0.82 + (e - 360) / 30 * (1.00 - 0.82)

avg_vol_e = ta.sma(volume, 100)
avg_vol_s = request.security(syminfo.tickerid, 'D', avg_vol_e)

// "Run rate" is a live projection, so it should only show a value while the
// regular session is actually open right now — otherwise there's nothing left to
// project (the latest daily bar is already a fully completed session) and it
// would just silently duplicate Volume Buzz's number under a misleading label.
// That requires the latest daily bar to actually be TODAY's (not a stale prior
// session carried over a weekend/holiday) AND the current ET clock time to fall
// within the regular session window.
last_bar_day_s = request.security(syminfo.tickerid, 'D', dayofmonth)
last_bar_month_s = request.security(syminfo.tickerid, 'D', month)
last_bar_year_s = request.security(syminfo.tickerid, 'D', year)
today_day_et = dayofmonth(timenow, "America/New_York")
today_month_et = month(timenow, "America/New_York")
today_year_et = year(timenow, "America/New_York")
is_equity_session_today = last_bar_day_s == today_day_et and last_bar_month_s == today_month_et and last_bar_year_s == today_year_et

now_min_et = hour(timenow, "America/New_York") * 60 + minute(timenow, "America/New_York")
equity_elapsed_min = now_min_et - (9 * 60 + 30)
is_equity_market_open = is_equity_session_today and equity_elapsed_min > 0 and equity_elapsed_min <= 390
equity_frac_elapsed = f_cum_frac(equity_elapsed_min)
equity_run_rate_pct = is_equity_market_open ? ((vol_today_s / equity_frac_elapsed) / avg_vol_s - 1) * 100 : na

// --- Crypto (24/7) model ---
// No session open/close, so "today" is the full UTC calendar day — the convention
// TradingView's own daily bars use for crypto — and elapsed time is treated as
// flat/uniform through the day: there's no well-established consensus intraday
// volume shape across crypto venues/pairs the way there is for US equities, so
// this deliberately doesn't guess at one.
//
// What IS well documented is that crypto volume drops on weekends (much of it is
// still tied to TradFi desks that are closed then). Rather than a blunt weekend-
// vs-weekday split, today is compared against the trailing average for the SAME
// day of week (e.g. a Saturday projects against recent Saturdays, a Tuesday
// against recent Tuesdays) — that captures the weekend effect naturally and is
// simpler and more robust to compute than filtering a rolling window by day type.
f_same_dow_avg(n_weeks) =>
    sum = 0.0
    for w = 1 to n_weeks
        sum += volume[w * 7]
    sum / n_weeks

same_dow_avg_e = f_same_dow_avg(8)
same_dow_avg_s = request.security(syminfo.tickerid, 'D', same_dow_avg_e)

now_min_utc = hour(timenow, "UTC") * 60 + minute(timenow, "UTC")
crypto_frac_elapsed = now_min_utc / 1440.0
crypto_run_rate_pct = crypto_frac_elapsed <= 0 or na(same_dow_avg_s) or same_dow_avg_s == 0 ? na : ((vol_today_s / crypto_frac_elapsed) / same_dow_avg_s - 1) * 100

run_rate_pct = syminfo.type == "crypto" ? crypto_run_rate_pct : equity_run_rate_pct

// Create table
// Each stat renders as a "button": a label cell stacked on a bold value cell,
// both sharing the table's background so the pair reads as one chip. Chips are
// packed left-to-right across a single row, skipping any stats toggled off.
n_cols = (show_mkt_cap ? 1 : 0) + (show_float ? 1 : 0) + (show_adr ? 1 : 0) +
  (show_atr ? 1 : 0) + (show_lod_dist ? 1 : 0) +
  (show_52wk_high ? 1 : 0) + (show_vol_buzz ? 1 : 0) + (show_run_rate ? 1 : 0)

row_label = show_empty_row ? 1 : 0
row_value = row_label + 1

table t = table.new(position.top_right, math.max(n_cols, 1), row_value + 1, bgcolor=bg_col)

f_chip(column, label, value, value_color) =>
    table.cell(t, column, row_label, label, text_color=label_col, text_size=size.small, text_halign=text.align_center)
    table.cell(t, column, row_value, value, text_color=value_color, text_size=size.normal, text_halign=text.align_center, text_formatting=text.format_bold)

if barstate.islast and timeframe.period != "W" and timeframe.period != "M"
    int col = 0
    if show_empty_row
        for c = 0 to n_cols - 1
            table.cell(t, c, 0, "")
    if show_mkt_cap
        f_chip(col, f_cap_category(mkt_caps), f_num_large(mkt_caps), mkt_cap_col)
        col += 1
    if show_float
        f_chip(col, "Float", f_num_large(floats), float_col)
        col += 1
    if show_adr
        f_chip(col, "ADR%", f_num_percent(adrs), adr_col)
        col += 1
    if show_atr
        f_chip(col, "ATR", f_num(atr), atr_col)
        col += 1
    if show_lod_dist
        f_chip(col, "LoDD", f_num_percent(lod_dist), lod_dist_col)
        col += 1
    if show_52wk_high
        f_chip(col, "52W High", f_num(high_52wk), high_52wk_col)
        col += 1
    if show_vol_buzz
        f_chip(col, "VolBuzz", f_num_percent(vol_buzzs), vol_buzz_col)
        col += 1
    if show_run_rate
        f_chip(col, "RunRate", na(run_rate_pct) ? "–" : f_num_percent(run_rate_pct), run_rate_col)
        col += 1