Tw Market Data

Showing 25 of 41 from the live probe.

• List datasets

  List available Taiwan-market datasets (discovery entry point). Returns id / 中文名 / category / tier / one-line description for each. Use this first to find the right data. Args: category: optional, e.g. 'chip'(籌碼) 'fundamental'(基本面) 'price'(行情) 'macro'(總經) 'relation'(關聯/產業鏈) 'derivatives'(期權) 'event'(事件) 'rag_text'(文本). tier: optional minimum plan: 'free' 'starter' 'pro' 'max' 'developer' 'enterprise'.

• Describe a dataset's semantics

  FULL semantics of one dataset: grain (what a row is), field meanings+units, ★TIME-CORRECTNESS rules (knowledge_time_field / point_in_time_safe — read before backtesting), relations for cross-table reasoning, agent_hints (when to use), quant_use (which factors). Args: dataset_id.

• Query a dataset (PIT-safe)

  Query rows with built-in look-ahead protection. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD) for backtesting/agent-learning. For non-point-in-time-safe datasets (fundamentals, monthly_revenue, dividend_policy…) rows are filtered by DISCLOSURE date <= as_of, so the agent only sees what was public at that moment. Omit as_of only for present-day lookups (warned). ★ IF A VALUE IS IN `coverage.missing`, IT IS NOT AVAILABLE. Say it is not available. **Never estimate it, interpolate

• Find related datasets and tickers

  Traverse the knowledge graph for cross-table / supply-chain reasoning. - dataset_id: returns join-able datasets (+why) to plan multi-table analysis. - ticker: returns its industry value-chain node + peers in the same node (supply-chain reasoning). Args: dataset_id (e.g. 'equity_daily_prices') and/or ticker (e.g. '2330').

• Search filings and announcements

  Semantic search over MOPS filings, financial-statement notes and company news. Answers questions a keyword filter cannot: "what risks did this company disclose this quarter?", "which companies mentioned CoWoS capacity expansion?" — matching on MEANING, so a paragraph that never uses your exact words still ranks. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). Chunks are filtered `published_at <= as_of` in SQL BEFORE ranking, so a backtest cannot retrieve a filing that did not e

• Explain where a value came from

  Where did this number come from, and when could anyone have known it? (WP-6) ★ THIS IS THE GROUNDING TOOL. Every other tool's answer is supposed to be expandable through this one: given a ticker, a field and a date, it names the dataset that serves that field, the knowledge column, and WHICH as_of rule applies on that date. Use it whenever you are about to state a number as fact. ★ IT DOES NOT OVERLAP WITH THE OTHER TWO PROVENANCE TOOLS, and they are not substitutes:

• Get an inclusion proof

  Prove a row was in the snapshot TWMD published — and check it yourself. Returns the Merkle sibling path, the signed root, and the checkpoint it belongs to. It returns the PATH rather than a yes/no on purpose: a service that answers "yes, it is included, trust me" is the opposite of verifiable. Recompute the root from the leaf and the path; the verifier is ~30 lines and is written out in docs/VERIFIABLE_DATA.md. ★ WHAT IT PROVES: integrity (the row was not altered after publ

• Cite a served number

  Produce a bibliographic citation for TWMD data — APA, BibTeX, and a re-verifiable token. ★ FOR PAPERS, REGULATORY FILINGS AND ANYTHING A REVIEWER WILL RE-CHECK LATER. A dataset is corrected, backfilled and re-run. Three years from now a reviewer opening our API sees different numbers than the paper, and nobody — author, reviewer, or us — can tell whether the data changed or the author mis-transcribed. So the citation carries `as_of`, the Merkle `checkpoint_root`, and a `

• Read the primary source text

  Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff. ★ NOT `search_filings`. That one ranks passages by similarity and hands you fragments; this hands you whole documents so you can read what was actually said and where it sat in the filing. Similarity is not importance, and a fragment cannot show you its own context. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). The cutoff is applied in SQL on the source's declared knowledge-time column

• Ask in natural language

  Answer a plain-language question in Taiwanese-market vocabulary, sentence by sourced sentence. ★ FOR BEGINNERS WHO DO NOT KNOW WHICH DATASET THEY WANT. Ask "PBR 是什麼" or "什麼叫漲跌停" in ordinary words; routing happens on our side. `describe_dataset` explains a table you already named, `search_filings` digs through company disclosures, and `query_dataset` returns rows — this one turns a beginner's wording into a sourced explanation instead. ★ EVERY SENTENCE CARRIES A CITATION OR

• Chart a series

  Turn rows you already fetched into a Vega-Lite drawing your chat window can render. ★ IT DRAWS; IT DOES NOT FETCH. Hand it the output of `query_dataset` — this tool never touches the database, so it cannot bypass the `as_of` filter those rows were selected under. A plotting tool that fetched its own numbers would be a second data path, and a second path eventually disagrees with the first about what was knowable when. ★ GAPS BREAK THE LINE INSTEAD OF BEING BRIDGED. A missin

• Screen the market

  Turn a spoken shortlist description into explicit numeric cut-offs, and show the cut-offs. ★ THE THRESHOLDS COME BACK WITH THE SHORTLIST. "低本益比" becomes `per < 15`, and that 15 is printed in `applied[]` so you can disagree with it. A filter that hands over thirty names without saying where it drew the line cannot be checked by anyone — and whether the line was 15 or 20 completely changes which thirty. ★ PHRASES IT CANNOT MAP COME BACK IN `unparsed[]`. It will not quietly in

• Query the macro regime

  The Taiwan business-cycle light (NDC monitoring indicator) as a monthly series. ★ THIS IS A REVISED FIGURE, NOT A POINT-IN-TIME ONE. Every response carries `revision_basis: as_revised`. Our source holds exactly one row per month — the CURRENT value, not the value as first published — and it records no publication date. ★ `as_of` IS REFUSED, AND THE REFUSAL IS THE POINT. There is no honest point-in-time answer here yet. Do NOT work around it by asking for a date range that e

• Upcoming and past events

  Sort corporate dates into what is still ahead and what has already passed. ★ TWO DATES, NOT ONE. What is "upcoming" is decided by the date the event HAPPENS; `as_of` filters on the date it was ANNOUNCED. An ex-dividend declared on 2026-08-01 for 2026-09-15 is both already known and still ahead on 2026-08-10. Collapsing the two fields either hides every future date or reports last month's ex-dividend as though it were coming. ★ ELAPSED DATES ARE SEPARATED, NOT DISCARDED. The

• Compare tickers

  Lay two to five named companies side by side on the same measures, gaps marked as gaps. ★ AN ABSENT FIGURE STAYS ABSENT, AND THE COMPANY STAYS ON THE TABLE. A blank cell is reported as `available: false`, never filled with a zero, a previous period, or by quietly dropping the column. Dropping is the worst of the three: it converts "we do not hold this measure for that company" into "you did not ask about that company". ★ EACH CELL NAMES ITS OWN SOURCE. Margins and instituti

• Get a code example

  Emit a copy-pasteable HTTP snippet wired to the real endpoint, header and parameter names. ★ FOR WRITING YOUR OWN CLIENT, NOT FOR GETTING DATA. Every other tool here answers a question; this one hands you source code so your program can ask it directly over HTTPS. Nothing is fetched and no rows come back. ★ THE CONSTANTS ARE READ OUT OF THE SERVER, NOT REMEMBERED. Base URL, the `X-API-Key` header spelling, and the route path all come from the code that serves them. The wors

• Run a research recipe

  Replay a saved multi-step routine over rows you fetched, with every step listed. ★ THREE ROUTINES: `momentum_scan` (rank by a return column), `earnings_surprise` (actual versus estimate), `dividend_capture` (which ex-dates are still ahead). ★ A SAVED ROUTINE IS NOT A TRADING VIEW. The names are conventional labels for well-known sequences; what runs is arithmetic over rows you supplied. `steps[]` spells out each operation so you can disagree with the routine rather than tru

• Try a free sample (no key)

  Hand an unregistered caller a short taste of an open dataset, plus where to unlock the rest. ★ WHAT AN ACCOUNTLESS CALLER GETS INSTEAD OF A BARE REFUSAL. Somebody arriving through a chat connector with no plan would otherwise meet a flat rejection, which the host model relays as "this service turned you down". ★ WHAT IS ACTUALLY FREE, STATED CONCRETELY. The reference resources read with no key at all, and the five sample tickers (2330, 2317, 2454, 0050, 2603) answer through

• Run a backtest

  Run a point-in-time backtest and return its run_id, metrics, sources and honesty checks. The run may only see data stamped on or before `as_of` — that is enforced structurally, not by convention. Results arrive with the data `query_ids` behind them and an anti-overfitting verdict (out-of-sample, deflated Sharpe, multiple-comparison, crash stress); a run that fails the gate is returned REJECTED with reasons rather than hidden. Args: strategy_i

• Get a backtest result

  Retrieve a previous backtest by run_id — the full record, including why it was rejected. Only runs in YOUR namespace are visible; a run_id belonging to someone else is simply not found. Args: run_id (the `twmd_bt_…` handle returned by run_backtest).

• Replay a backtest

  Re-run a stored backtest and report whether it still produces the same numbers. Same spec, same `as_of`, same data questions. If the numbers moved, either the engine version changed or the underlying data was restated — both are reported, neither is smoothed over. Args: run_id.

• List backtests

  Browse an INDEX of your past backtest runs — ids and headline metrics only, no re-execution. Use when you want to find a run whose id you have forgotten. It never re-computes anything: `run_backtest` executes a new one, `get_backtest` opens a single record in full, and `replay_backtest` re-derives one to check reproducibility. This is the catalogue, not any of those three. Args: optional strategy_id filter, limit.

• Save to agent memory

  Remember something, with its sources and its knowledge time. Nothing is ever overwritten: saving a `factor_def` or `watchlist` under an existing key SUPERSEDES the previous version (both rows survive, so "what did I believe in June?" stays answerable), and saving identical content twice is a no-op rather than a duplicate. Args: kind: 'query' | 'factor_def' | 'watchlist' | 'finding' | 'note'. content: the thing to remember, as an object.

• Search agent memory

  Recall your own memories — hybrid (semantic + exact-term), with provenance attached. Every result carries where it came from (`source_query_ids`, replayable), when it was believed (`valid_from`/`valid_to`) and what knowledge time it is about (`as_of`), plus a `recall` block stating which model and which filters produced the answer. Args: query: what you are looking for, in words. kinds: restrict to some of 'query' 'factor_def' 'watchlist'

• Get the watchlist

  Read the tickers on one named watchlist, as it stands right now. A curated roster you maintain — distinct from `memory_search`, which digs through everything you ever recorded, and from `list_alerts`, which is about price triggers rather than symbols you are following. Args: key — the roster's name, default 'default'.