Trading blotters, order books and market data

Perspective was originally developed to handle Financial market data: wide tables, high update rates, keyed replacement of rows, and users who need to re-slice the data themselves while it is moving.

The building blocks

A blotter is an indexed table in a data grid. With index set to the order or trade id, each update() replaces that row in place; partial updates (only the changed fields) are supported, and remove() deletes by key.

const table = await worker.table(
    { id: "string", symbol: "string", side: "string", price: "float", qty: "integer", status: "string", time: "datetime" },
    { index: "id" },
);

table.update([{ id: "o-1841", status: "filled" }]);

An order book is a pivot of that same table: group_by price, split_by side, sum of quantity, filtered to open orders.

await viewer.restore({
    plugin: "X Bar",
    group_by: ["price"],
    split_by: ["side"],
    columns: ["qty"],
    filter: [["status", "==", "open"]],
});

Candlesticks are a pivot too: group_by a time bucket expression, with first, last, high and low aggregates over aliases of the price column, drawn by the Candlestick or OHLC plugin.

await viewer.restore({
    plugin: "Candlestick",
    group_by: ["bucket(\"time\", 'm')"],
    columns: ["open", "close", "high", "low"],
    expressions: {
        "bucket(\"time\", 'm')": "bucket(\"time\", 'm')",
        open: '"price"',
        close: '"price"',
        high: '"price"',
        low: '"price"',
    },
    aggregates: { open: "first", close: "last", high: "high", low: "low" },
});

Because every one of these is a View over one streaming Table, they stay mutually consistent tick by tick, and users can change any of them — regroup by sector, filter to a book, switch the blotter to a heatmap — without code.

Conditional formatting

The data grid supports per-column number formatting, positive/negative foreground and background colors, gradients and in-cell bars, all set from the column settings panel and captured in the saved configuration.

Deployment shapes

• Desktop containers and internal web apps — <perspective-viewer> is a standard Web Component with no framework dependency, and ships React bindings.
• Python services — host tables from Tornado, FastAPI/Starlette or aiohttp; ingest pandas, polars or pyarrow directly.
• ClickHouse, DuckDB, PostgreSQL, Polars — put the UI directly over the tick store with a virtual server.
• Research notebooks — the same widget in Jupyter.

Examples

• Market — blotter, order book chart and candlesticks over one simulated feed.
• Market — Orders