search_news
Search news articles.
Returns a list of matching articles. Each article includes:
- title, source, date, link, category, rank, shares, summary
- bias_values: dict of per-dimension bias scores using plain-text keys (e.g. 'liberal conservative bias'),
same schema as get_bias_from_url and get_all_source_biases (when available)
- context: AI-generated contextual background for the article (when available)
- raw_data: additional raw metadata fields (when available)
Args:
query: Search keywords (required).
limit: Max results (1-100, default 20).
source: Filter by source name, e.g. 'CNN', 'Reuters'.
category: Filter by category. One of: 'trending', 'tech', 'markets', 'politics',
'business', 'science', 'memes'.
days_back: Only include articles from the last N days. 0 means no date filter. Default: 720 (2 years).
min_shares: Minimum total social shares.
sort: Sort order. One of: 'rank' (relevance, default), 'date' (newest), 'shares' (most shared).
get_ticker
Get comprehensive data for a stock, ETF, or crypto ticker.
Returns:
- ticker, name, type (e.g. 'stock', 'etf', 'crypto'), industry
- latest_price, page_url
- bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated)
- price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (model price forecast)
- future_uncertainty_urls: dict with image URLs for future_uncertainty, term_structure, volatility_surface, return_profile (when available)
- future_uncertainty_last_updated, term_structure_last_updated
- iv_rank_percentile (0-100, IV rank over past year)
- long_vol_call, long_vol_put, short_vol_call, short_vol_put: full option pack dicts (when available)
Throws an error if the ticker is not recognized.
Args:
ticker: Ticker symbol, e.g. 'AAPL', 'AMZN', 'BTC', 'ETH', 'SPY'.
get_source_bias
Get comprehensive bias analysis for a news source.
Returns:
- source_name, slug_name, page_url
- articles_analyzed: total articles in the bias database for this source
- avg_social_shares: average social shares per article
- emotionality_score (0-10): how emotional the writing is
- prescriptiveness_score (0-10): how much the source tells readers what to think/do
- bias_scores: dict of all measured bias dimensions with scores (-50 to +50 for bipolar,
0 to +50 for unipolar). WARNING: this endpoint returns emoji-prefixed display keys
(e.g. '🔵 Liberal <—> Conservative 🔴') rather than the plain-text keys used by
get_bias_from_url, get_all_source_biases, and search_news (e.g. 'liberal conservative bias').
Do not attempt to cross-reference bias_scores keys here with bias_values keys from other endpoints.
- bias_description: AI-generated overall bias summary narrative
- liberal_conservative_description: narrative on political leaning
- libertarian_authoritarian_description: narrative on authority stance
- signature_phrases: words/phrases uniquely overrepresented vs other sources
- signature_negative_phrases: uniquely negative/alarming phrases
- most_shared_phrases: phrases in their most viral articles
- most_emotional_phrases: phrases used in their most emotional articles
- pays_for_traffic_keywords: keywords this source buys ads for
- similar_sources: sources with the most similar bias profile
- most_different_sources: sources with the most different bias profile
- trends_graph_url: URL to a chart of this source's coverage volume over time
- bias_plot_urls: dict of 2D bias scatter plot image URLs (political_lib_auth, subjective_objective, informative_opinion, oversimplification_factful) — only present when available
- recent_articles: list of most recent articles with full article fields and per-article bias_values
Throws an error if the source is not found.
Args:
source: Source name (e.g. 'Fox News', 'CNN', 'Reuters') or domain (e.g. 'foxnews.com').
Slug-style input (e.g. 'fox-news') is NOT supported — use full name or domain only.
recent_articles: Number of recent articles to include (1-50, default 10).
get_all_source_biases
Get bias scores for every news source in the Helium database.
Returns a list of all sources (active within the last 36 days, with >100 articles analyzed),
sorted by avg_social_shares descending. Use this to compare sources, find the most credible
outlets, identify politically extreme sources, or build a ranked overview of the media landscape.
Each entry contains:
- source_name, slug_name, page_url
- articles_analyzed: total articles analyzed for this source
- avg_social_shares: average social shares per article (proxy for reach/influence)
- emotionality_score (0-10): average emotional intensity of the writing
- prescriptiveness_score (0-10): how much the source tells readers what to think/do
- bias_values: dict mapping classifier key → integer score (-50 to +50 for bipolar,
0 to +50 for unipolar). These keys are identical to what get_bias_from_url returns,
so you can compare article-level and source-level scores directly.
Political / ideological (bipolar: neg=left pole, pos=right pole):
'liberal conservative bias' neg=liberal, pos=conservative
'libertarian authoritarian bias' neg=libertarian, pos=authoritarian
'dovish hawkish bias' neg=dovish, pos=hawkish
'establishment bias' neg=anti-establishment, pos=pro-establishment
Credibility / quality (bipolar):
'overall credibility' neg=uncredible, pos=credible
'integrity bias' neg=low integrity, pos=high integrity
'article intelligence' neg=low intelligence, pos=high intelligence
'delusion bias' neg=truth-seeking, pos=delusional
'objective subjective bias' neg=objective, pos=subjective
'bearish bullish bias' neg=bearish, pos=bullish
'emotional bias' neg=negative tone, pos=positive tone
Unipolar bias dimensions (higher = more of that trait):
'objective sensational bias' sensationalism
'opinion bias' opinion vs informative
'descriptive prescriptive bias' prescriptive vs descriptive
'political bias' political content
'fearful bias' fear-based framing
'overconfidence bias' overconfidence
'gossip bias' gossip
'manipulation bias' manipulative framing
'ideological bias' ideological rigidity
'conspiracy bias' conspiracy content
'double standard bias' double standards
'virtue signal bias' virtue signaling
'oversimplification bias' oversimplification
'appeal to authority bias' appeal to authority
'begging the question bias' question-begging
'victimization bias' victimization framing
'terrorism bias' terrorism content
'scapegoat bias' scapegoating
'hypocrisy bias' hypocrisy
'suicidal empathy bias' suicidal-empathy framing
'cruelty bias' cruelty
'woke bias' woke framing
'written by AI' AI-written likelihood
'immature bias' immaturity
'circular reasoning bias' circular reasoning
'covering the response bias' covering-the-response tactic
'spam bias' spam-like content
Tip: use get_source_bias for full narrative descriptions and recent articles on a specific source.
Tip: bias_values keys here are identical to those in get_bias_from_url and search_news — compare them directly.
Warning: get_source_bias returns bias_scores with emoji-prefixed display keys (e.g. '🔵 Liberal <—> Conservative 🔴')
that are NOT interchangeable with the plain-text keys used here. Do not cross-reference them.
get_option_price
Get Helium's proprietary ML model-predicted price for a specific option contract.
Helium trains per-symbol regression models on historical options data. This tool
looks up the most recent available options chain for the symbol (today or up to
5 days back), finds the exact contract matching strike/expiration/type, and runs
it through that model to produce a predicted fair-value price.
Returns:
- symbol: the ticker
- strike: the strike price used
- expiration: the expiration date used
- option_type: 'call' or 'put'
- predicted_price: Helium's model-predicted option price in dollars
- prob_itm: probability of expiring in the money (0.0–1.0), or null if model unavailable
- options_data_date: the date of the options chain snapshot the model was run on
(so you know how fresh the underlying market data is)
Throws an error if no options chain data is available for the symbol within the past 5 days,
or if the exact contract (strike/expiration/type combination) does not exist in that chain.
Args:
symbol: Ticker symbol, e.g. 'AAPL', 'SPY'.
strike: Strike price as a number, e.g. 150.0.
expiration: Expiration date as 'YYYY-MM-DD', e.g. '2026-06-20'.
option_type: Must be 'call' or 'put'.
search_balanced_news
Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources.
Unlike search_news (which returns individual RSS articles), this returns Helium's own
synthesized stories: each one draws from multiple sources and includes an AI-written
summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers.
Returns a list of stories, each with:
- title, simple_title, date, category
- page_url: full URL to the story on heliumtrades.com
- image: story image URL (when available)
- summary: Helium's synthesized overview
- takeaway: key conclusion
- context: background context
- evidence: numbered evidence items
- potential_outcomes: forward-looking outcomes with probabilities
- relevant_tickers: related stock tickers
- num_sources: number of source articles synthesized
- rank: search relevance score
Args:
query: Search keywords (required).
limit: Max results (1-50, default 10).
category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'.
days_back: Only include stories from the last N days. 0 means no date filter.