Market Pulse Dashboard

Parse a CSV price series, calculate its change, and draw a compact market chart. Demo mode is deterministic; the same app includes a cached HTTP path for live data.

Complete app

app.star

load("cache.star", "cache")
load("encoding/csv.star", "csv")
load("http.star", "http")
load("humanize.star", "humanize")
load("math.star", "math")
load("render.star", "render")
load("schema.star", "schema")

DEMO_MODE = True
DEMO_CSV = """minute,price
0,181.2
1,182.6
2,181.9
3,184.1
4,183.5
5,185.7
6,186.3
7,185.9
"""

def parse_prices(source):
    rows = csv.read_all(source, skip = 1)
    return [float(row[1]) for row in rows]

def fetch_prices(symbol):
    key = "market:%s" % symbol
    stored = cache.get(key)
    if stored != None:
        return stored

    response = http.get(
        "https://markets.example/prices.csv?symbol=%s" % symbol,
        ttl_seconds = 300,
    )
    if response.status_code != 200:
        fail("Market request failed: %d" % response.status_code)

    source = response.body()
    cache.set(key, source, ttl_seconds = 300)
    return source

def main(config):
    symbol = config.str("symbol", "SPX")
    source = DEMO_CSV if DEMO_MODE else fetch_prices(symbol)
    prices = parse_prices(source)
    points = [(index, value) for index, value in enumerate(prices)]
    change = ((prices[-1] - prices[0]) / prices[0]) * 100
    color = "#4ade80" if change >= 0 else "#fb7185"

    return render.Root(
        child = render.Stack(
            children = [
                render.Padding(
                    pad = (1, 2, 0, 1),
                    child = render.Column(
                        expanded = True,
                        main_align = "space_between",
                        children = [
                            render.Text(symbol, font = "6x10", color = "#e2e8f0"),
                            render.Column(
                                children = [
                                    render.Text(
                                        "$%s" % humanize.ftoa(prices[-1], 1),
                                        color = color,
                                    ),
                                    render.Text(
                                        "%s%%" % humanize.ftoa(math.floor(change * 10) / 10, 1),
                                        font = "CG-pixel-3x5-mono",
                                        color = color,
                                    ),
                                ],
                            ),
                        ],
                    ),
                ),
                render.Padding(
                    pad = (28, 6, 0, 0),
                    child = render.Plot(
                        data = points,
                        width = 36,
                        height = 26,
                        color = color,
                        fill = True,
                        fill_color = color + "55",
                        x_lim = (0, len(points) - 1),
                        y_lim = (180, 188),
                    ),
                ),
            ],
        ),
    )

def get_schema():
    return schema.Schema(
        version = "1",
        fields = [
            schema.Dropdown(
                id = "symbol",
                name = "Market",
                desc = "Market symbol to display.",
                icon = "money-bill-trend-up",
                default = "SPX",
                options = [
                    schema.Option(display = "S&P 500", value = "SPX"),
                    schema.Option(display = "Nasdaq", value = "IXIC"),
                ],
            ),
        ],
    )

Download this app.star.

How it works

1. csv.read_all() skips the header and the comprehension converts prices to floats.
2. The first and last values produce the percentage change and its gain/loss color.
3. humanize and math keep the two displayed numbers compact and predictable.
4. Plot receives (x, y) pairs and explicit limits sized for the 64×32 display.
5. fetch_prices() checks the shared cache before requesting and caching live CSV.

Connect live prices

Replace markets.example with a CSV endpoint whose second column is the price, then set DEMO_MODE to False. Include the selected symbol in the cache key so installations do not collide.