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The monthly trial balance every Taiwanese securities firm files with the Taiwan Stock Exchange: current and non-current assets and liabilities, share capital, and the income accounts a broker's quarter is made of - brokerage fees, securities lending, underwriting, dealing gains and futures commissions.

The securities arms of Yuanta, Fubon, KGI, SinoPac, Taishin and the other financial holding companies file monthly while their parents report quarterly, so the month's brokerage fee income is a firm-level read on Taiwanese trading commissions weeks before the group reports.

A different dataset from futures_broker_financials, which is the US futures regulator's capital return: this is the exchange's own ledger of what each securities firm holds and earns.

Available Data Points:

• Period Date: The period the value describes — what the number is about (the valid clock).
• Published At: When the value first became knowable — when the publisher released it (the knowledge clock). Filter on this for anything where look-ahead bias would matter.
• Value: The numeric value, where the series has one.
• Value Text: The textual value, for categorical series such as a trial's status or phase.
• Unit: Unit of the value, e.g. vehicles, percent, USD.
• Value Type: number, percent, currency, count or text.
• Frequency: How often the publisher releases the series.
• Market: The market the reading describes — an ISO-3166 alpha-2 code (DE), a sub-national one (US-CA), a supranational aggregate (EA, EU) or GLOBAL.
• Series Slug: Which series the reading belongs to. Unique within a publisher, not globally.
• Series Name: Human-readable name of the series.
• Source Slug: Publisher/feed identifier, e.g. kba_registrations.
• Source Name: Human-readable name of the publisher or feed.
• Publisher: Who publishes the underlying data.
• Entity Ref: The publisher's own identifier for what the reading is about — a manufacturer name, a securitisation trust, a reactor unit, a country code. Kept so any value can be traced back to the row it was parsed from.

Coverage

All 65 firms in the exchange's monthly file, about 45 days after the month closes. The exchange serves only the latest month and replaces it, so history begins with July 2026, the first month ORTEX captured. Ten top-level accounts and the month's brokerage fees are parsed from each file.

The filer is the securities firm rather than the listed group, and a firm is attributed to its listed parent only through a curated mapping. Foreign houses' Taiwan branches are a small part of their parents and are deliberately left unmapped.

What to look for

The income lines are year-to-date balances that reset each January, so compare the same month across years, or use brokerage_fee_income_month, the one monthly flow. Amounts are whole New Taiwan dollars, not thousands.

Read a firm's fee income against its peers' for the same month rather than on its own: Taiwanese turnover moves every broker together, and a firm growing faster than the rest is taking share. Dealing gains can be negative and swing with the market.

Two dates on every reading, and the difference is the edge

periodDate is what the number is about. publishedAt is when it first became knowable. The gap between them is the whole point of alternative data — and it is also the trap. A Form 10-D describing July is filed in mid-August, so a backtest that treats the period as the knowledge date reads a document six weeks before it existed.

from_date and to_date filter on periodDate, because that is the axis you plot against. Join on publishedAt when you are joining against returns.

Filtering: one market or all of them

Called with no filters, the endpoint returns every series in the dataset, for every market ORTEX covers it in. Each row names its own market, seriesSlug and sourceSlug, so a single request is enough to chart the whole dataset.

Narrow it with any combination of market (one market), series (one measure) and source (one publisher). Filter values that are each valid but select nothing together return 400 invalidSelection rather than a misleading empty result.

Raw readings here; comparable ones next door

This endpoint serves the publisher's numbers. Raw values are frequently not comparable — receivables of $10.9bn against $4.9bn say nothing without each book's size, and a 5.3% charge-off rate means something different on a subprime book than a prime one. The Alt Data signals endpoint serves the same series as percentiles and z-scores against a stated comparison set, which is the form a screen or a backtest wants.

Why a company you expect may be missing

Three reasons, and none of them is a bug:

• The dataset is a window, not a census: Several of these publishers release a rolling window rather than a complete history.
• The filer is not the listed company: Registers name a marque, a charter or a trust. ORTEX maps those to the listed parent where the link is defensible and leaves them unmapped where it is not — a wrong mapping silently attributes another company's data to your ticker.
• The population is genuinely narrow: Not every listed company appears in every register, and no amount of mapping changes that.

How much history there is

Several of these publishers overwrite their own pages and keep no archive. Where that is so, the series begins on the day ORTEX started capturing it and lengthens from there — nobody, including the publisher, can supply the days before that. Everything ORTEX has ever fetched is archived on capture, so the series only ever grows.

Try it out! You can test the API with the trial key TEST. Simply include it in your request headers; either here, or add the header "Ortex-Api-Key": "TEST" to your http request.

Trial key restrictions: Trial keys may be limited to a specific date range and set of regions. Requests outside the allowed regions or entirely outside the allowed date range are rejected; a date window that partially overlaps the allowed range is trimmed to it, reported in meta.restriction.

Get full access! Get a key at app.ortex.com/apis