TW Market Data LogoTW Market Data

Facts & statistics

Market facts

Long-run statistics for Taiwan equities, each with its sample period and as_of

Seasonality

Monthly return distributions and hit rates, with sample sizes

Institutional flow

Seasonality in the breadth of institutional net buying

Limit events

How often limit moves happen, and how concentrated they are

Delisting

Delisting counts and survival spans — the basis for avoiding survivorship bias

Rule changes

A timeline of trading-rule changes — the premise for reading historical data

Data & exploration

Dataset catalogue

Every dataset, its coverage, and how often it updates

Playground

Call the API from the browser, without a key

Market today

Today's market at a glance

Stock analysis

The entry point for looking at one instrument

Market heat map

The whole market in one picture — area is market cap, colour is revenue growth

Market calendar

Statutory disclosure deadlines — the day a figure may legally first be known

Platform capabilities

Product overview

What TWMD provides, and who it is built for

Verifiable proof

Signed checkpoints and per-row inclusion proofs

Data quality

Reconciliation, gap handling, and quality status

Methodology

How the figures are computed, and on what basis

Auditable execution

Tie a trading decision back to the data it saw

Connect your broker

Bring TWMD into an existing order and research workflow

Developers

Documentation

API reference, dataset pages, and integration guides

Quick start

Authentication and your first request

Integrate by role

Separate paths for quant research, data engineering, and app development

Connect over MCP

Point an agent straight at TWMD

MCP registry

The published MCP tool list and its signed manifest

Webhooks

Have your system told when data updates

Learn

Blog

Long-form writing on data, method, and market structure

Answers

Specific answers to specific questions, with sources

Topics

Industry chains and thematic relationships

Help centre

Account, billing, and usage questions

Glossary

Definitions for Taiwan-market and data terms

Compare & status

Why TWMD

A point-by-point comparison with FinMind and TEJ

Migrate from FinMind

Field mapping and migration steps

Migrate from FinLab

Field mapping and migration steps

Migrate from TEJ

Field mapping and migration steps

Status

Service availability and incident history

Security & trust

Trust centre

What we claim, and the limits on each claim

Security

Architecture, access control, and incident handling

Security facts

The items you can verify from outside

Security evidence

SBOM, ASVS mapping and threat model — including the three controls we do not meet.

Self-assessment

Item-by-item answers for a procurement questionnaire

Compliance & standards

Compliance mapping

Evidence primitives mapped onto FSB, IOSCO, and SR 26-2

Standards & interop

Term-by-term mapping onto published standards, and where it does not map

Provenance & C2PA

A machine-readable origin graph, fetchable without a key

Licensing

How the data may be used and redistributed

Adoption

Evaluate

Seven checks you can run yourself, without an account

Talk to sales

Enterprise plans, quotas, and contract detail

Pricing
中文Sign inSign up
TW Market Data

Taiwan market-data infrastructure, built for AI agents and quantitative workflows.

Status unknown
© 2026 TW Market Data

TW Market Data (TWMD) provides historical data and statistics, not investment advice; investment decisions and their risks are your own.

  • Privacy Policy·
  • Terms of Service·
  • Cookie Policy·
  • Acceptable Use Policy·
  • Data Sources & Licensing·
  • Legal (all documents)
中文

AI Agent

  • MCP Server
  • Skills
  • Tool manifest
  • Agent workflow examples
  • Agent benchmark
  • llms.txt
  • OpenAPI spec

Security

  • Security overview
  • Verifiable data
  • Trust Center
  • Standards & Interop
  • Regulatory mapping

Product

  • Datasets
  • Topics
  • Market facts
  • Documentation
  • Integration runbooks
  • Playground (no signup)
  • Free tier
  • Solutions

Company

  • About TWMD
  • Blog
  • Help centre
  • Pricing

Agent benchmark — question bank v0

Conventional scoring cannot tell an honest agent from a cheating one.

We built two oracles: one that respects as_of, and one that ignores it entirely and answers with figures that were not public on the day. Scored on accuracy, both are 100% — the one that ignores the time gate is the best model on a conventional scoreboard. The only thing that separates them is leak rate: 0% against 100%. That is what this bank exists to measure.

Every figure on this page is transcribed from the 2026-08-21 run, whose report is generated by report_benchmark_bank.py reading the run artifacts rather than anyone typing them. Nothing here is rounded or restated.

What the time gate showed

Four checks, run against real data. All four passed, and the fourth is the one worth reading twice: accuracy alone rates a model that answers with information it could not have had as highly as one that plays fair.

The bank contains embargoed items
40 of 148
The honest oracle does not leak
Leak rate 0%needs ≤ 5%
The leaky oracle does leak
Leak rate 100%needs ≥ 80%
Conventional accuracy cannot separate them
100% vs 100%, gap 0%needs ≤ 10%

The two controls

These are oracles, built to prove the gate works. Neither is an agent, and this is not a leaderboard — there is nothing here to come first.

ControlAccuracyLeak rate
oracle

The control that respects as_of.

100%0%
leaky-oracle

Answered 40 questions with figures that were not public on the day — exactly the behaviour accuracy alone rewards.

100%100%

The bank

148 questions: 40 embargoed (27%) and 108 answerable, with 37 of each of the four question types. The embargoed ones are the point — they are the questions whose answer was not public on the date being asked about.

  • Institutional flowinstitutional_flow_items · foreign_net_buy
  • Marginmargin_short_enhanced_items · margin_balance
  • Pricenormalized_twse_daily_prices · close
  • Revenuemonthly_revenue_enhanced_items · revenue

Evidence check — sampled, not exhaustive

Of the 148 questions in the bank, 20 were sampled and their proof references resolved: 6 verified, 14 not_in_snapshot, 0 connection failures, 0 key errors.

The 14 not_in_snapshot are not key errors: the row keys came back unchanged and the snapshot_version was older than the row's date, meaning those rows are newer than the last published checkpoint. That is a checkpoint cadence gap, not a broken proof pipeline. This section will be re-run once checkpoint period stamping lands.

What this does not say

  • A baseline is not an agent. There is no real agent score on this page and no leaderboard, because no real agent has been run.
  • The evidence check sampled 20 of the 148 questions. It was not a full verification of the bank.
  • A resolvable proof reference establishes that the row is in a signed snapshot. It does not establish that the figure itself is correct.

Not yet

  • Scoring a real MCP agent: the runner exists, is not open to the public, and has produced no publishable score.
  • Backtest diagnostics — strategy returns through DSR, HAC-t and IS/OOS reports — are on the same track and likewise have no public interface.

How a run is defined — what binds an as_of to a question, how leak rate is computed, and what a run must emit to be citable — is written out in the benchmark specification.

The time gate this benchmark measures is the same as_of parameter documented in the quant research runbook.

Not investment advice.