Finance Pulse

Look up the code of a company, index, currency, commodity or organisation by name: "micron" finds MU. The query is matched against codes and registered names, so use the company's own name rather than a brand (Alphabet, not Google). Returns the matches mentioned within the plan's history, most mentioned first, each with its code (id), name and kind; an empty list means nothing there matches, and a list as long as `limit` may have more matches behind it. Use the code as `symbol` or `code` in the other tools.

List developments for a symbol, sector, theme or storyline. sort=velocity: most statements in the last 24 hours; size: most statements; recent: latest statement first. symbol: comma-separate several (OR). Topics with fewer than 3 statements are left out unless you lower min_statements, so an empty list can mean "only small ones": retry with min_statements=1. With storyline_id every topic of the storyline is listed by default (sort="oldest" gives them in order).

One storyline in full: its topics, oldest first (how the story unfolded), and its newest statements. If `topics` is larger than the `members` list, ask again with `members` at its maximum; for a storyline longer than that, page through all of them with find_topics(storyline_id=..., sort="oldest"). While the list is cut, `top_topics` names the storyline's largest topics. `more_statements` says whether it has more statements than shown. If you expect to need them, ask for `statements` at its maximum: only then is `statements_cursor` set, and search_statements(storyline_id=..., cursor=...) continues exactly after them (add symbol to keep only those naming the company). With fewer, the cursor is null and search_statements(storyline_id=...) starts again at the newest, repeating the ones shown here.

One company or other entity in a single call. Use first for "what is going on with X". Returns who it is (name, kind, exchange); its mentions, topics, distinct sources, sentiment counts and score for the last 24 hours (d), the last 7 days (w) and all the history the plan includes, with the change against the period before and the counts over statements that name only this entity (`mentions_single`, `score_single`); the sectors of its statements; its largest topics; its storylines with the most mentions lately; the entities it shares topics with (`related`, empty for a rarely mentioned symbol); and its newest statements. A period without any mention is left out of `periods`. `more_statements` says whether the symbol has more statements than shown. If you expect to need them, ask for `statements` at its maximum: only then is `statements_cursor` set, and search_statements(symbol=..., cursor=...) continues exactly after them. With fewer, the cursor is null and search_statements(symbol=...) starts again at the newest, repeating the ones shown here. Do not guess a code: a wrong one costs a request. Look it up with find_symbol.

One development in full: its counts, how many of its statements come from news, speculative analysis and reddit (source_mix), the storylines it belongs to, and its newest statements, each with its outlet and headline. For older statements call search_statements with topic_id and the returned statements_cursor as cursor (null when there are no more).

What the API holds right now: how far back the plan's history goes (window_days), counts of statements, topics, storylines and symbols, the themes with their ids, and the accepted sector, sentiment, importance, source-type and entity-kind values. One call per conversation is enough.

Rank symbols (or the GICS sectors) by news attention. Use for "which tickers are suddenly in the news". period: d = last 24 hours, w = last 7 days, window = all the history the plan includes; or pass since (and until) to count any interval. sort=change ranks by the rise in mentions against the period (or equally long interval) before. `change` is null when there is nothing before to compare with: for period=window, for an interval given only by `until`, and for an interval whose preceding interval reaches before the plan's history. Use sort=mentions then. Each row: mentions, topics, sources (distinct outlets), sentiment counts, score and change; symbol rows also carry name, kind and the counts over statements naming only that symbol (`mentions_single`, `score_single`). Only for rank=symbols: sector limits the screen to one sector; min_mentions drops rarely mentioned codes; entity_kinds narrows the kind of entity (`reference` lists the kinds). Without it the list mixes companies with rates, commodities and central banks; pass "equity,etf" for tradable tickers.

Search news statements, newest first. Use for "what is the news saying about X". symbol: ticker or entity code, e.g. NVDA; comma-separate several (OR), no spaces inside a code. importance_min=high keeps only key facts. source_type=news leaves out speculative analysis and reddit. storyline_id: every statement of that storyline's topics; add symbol for only those naming its company. since/until: ISO-8601 times on the article's publication time, e.g. 2026-10-01T00:00:00Z. Each statement has its source, the article's headline and its topic. Pass next_cursor back as cursor for the next page (null means no more).

News volume and sentiment over time for exactly one of symbol, sector, theme_id or topic_id. Each bucket: t (UTC start), count, bullish, bearish, neutral, sources (distinct outlets) and score = (bullish - bearish) / count. Buckets with no statements are omitted here. The last bucket is usually the current, still incomplete day or hour: do not read a low last bucket as a drop. interval=hour is for short ranges: pass since/until, and if the range is too long the error says the maximum.

One company's news grouped into continuing stories: an event, then its follow-ups, reactions and consequences. Use for "how did the X story unfold" or "what are the ongoing stories about X". By default only storylines with two or more topics are listed: an empty list means "none with several topics", so pass min_topics=1 to see the single-topic ones. Each storyline has a name (one of its topics' names), first_seen and last_seen, its number of topics, statements and mentions (statements naming the symbol), distinct sources, sentiment counts of the mentions, `top_topics` (its largest topics; that is all of them when `topics` is no larger than the list) and `related`: storylines of other symbols that share topics with it. Storylines belong to one symbol, so news about two companies appears under each. The grouping is automatic and can be wrong or incomplete, and one real story can be split over several storylines: a reading aid, not a count of events. Call get_storyline when you need every topic in order or the statements.

The standing subjects the news is grouped into, each with its number of developments and statements, 24-hour volume, distinct sources and sentiment counts. With symbol or sector: only the themes that have statements about it; the counts are still those of the whole theme, so use search_statements with theme_id and symbol for the symbol's own statements. Use the returned id as theme_id in the other tools.

The developments most outlets are reporting. Use for "what is moving / what is the big news right now". kind=live: the current list. kind=slot: the top stories of each recent time slot, newest slot first. Each item's `trending` holds its three newest labels (the live list or a slot's start, the rank and the number of sources there); `trending_labels` is how many labels it has in all. The slot list is long (`total`): narrow it with symbol or theme_id rather than asking for all of it. For a whole day rather than right now, use find_topics with sort=velocity (statements in the last 24 hours). Each item has its distinct source count, sentiment counts and newest statement. symbol: one code only.