marketdx 0.1.1

The MarketDX financial impact graph, in Python — news→market impact (why, who, direction, ripple) across stocks, commodities, FX, crypto, and private companies.

marketdx

The financial impact graph, in Python.
News → who it touches and why — the causal channel, the story's lean, and the ripple — across stocks, commodities, FX, crypto, and private companies.
A research, screening & feature layer — direction is the news's content lean, not a price forecast.

▶ Live playground · Docs & pricing · Sample dataset

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pip install marketdx          # add [pandas] for .to_df():  pip install "marketdx[pandas]"

from marketdx import MarketDX

mdx = MarketDX(api_key="avn_live_…")                     # get a key at marketdx.lab.ai
for s in mdx.news(megatrend="ai-power", impact="indirect"):
    print(s.title, [(e.name, e.impact.net_direction) for e in s.entities])

That's the whole graph: every news event, every affected entity, labeled with direction, relevance, the causal aspect (the why), and whether it's the epicenter or a ripple — across five asset classes, including private companies ticker feeds can't see.

No key yet? Explore everything with zero signup in the playground, then grab a free key at marketdx.lab.ai.

Why the SDK (not just requests)

• Typed graph — signal.entities[0].impact.aspects[0].direction with autocomplete, not raw dicts.
• Auto-pagination — for s in mdx.news(...) walks every page for you. Nothing to manage.
• Names, not ids — megatrend="ai-power" or "AI Power & Cooling" or 10040000 all work.
• .to_df() — the whole result as a pandas DataFrame, one row per (event × entity × aspect).
• Typed errors — AuthError, QuotaError, RateLimitError, BadRequestError, NotFoundError.

The graph, a few ways

# 1. Ripple: a themed event that also touches NON-thematic entities (our differentiator)
for s in mdx.news(megatrend="semiconductors", impact="indirect", max_items=50):
    ...

# 2. Beyond tickers: private companies in a trend (OpenAI, Anthropic, Ampere, ChangXin…)
for c in mdx.megatrends("semiconductors").off_coverage():
    print(c.name, c.type, "→", c.megatrend["node_name"])

# 3. Per-stock impact timeline + its news-derived rivals
tl    = mdx.stock("NVDA.US").news(aspect="competition")
peers = mdx.stock("NVDA.US").competitors()

# 4. News-driven screen — where the news leans positive on a theme (the model's read, for research)
positive_lean = mdx.stocks(megatrend="ai-power", direction="pos", country="US", order_by="news_count")

# 5. Semantic search — match news by MEANING, not keywords
hits = mdx.news_search("chip export controls to China")

Straight to pandas

.to_df() returns the same columns as the sample dataset (impact-signals.csv) — so anything you prototyped on the free CSV runs unchanged on the live graph:

df = mdx.news(megatrend="ai-power", impact="indirect").to_df()
# published_at · title · brief_text · entity_name · entity_ticker · entity_type · direction ·
# aspect · reason · relevance · impact · impact_score · node_name · entity_country · publisher · url

df.groupby(["entity_type", "direction"]).size()      # who the news lands on, +/− by asset class (the model's read)
df[df.aspect == "tariff"].entity_name.value_counts()  # who the tariff channel touches

Metering & errors

Every call carries X-Credits-Charged / X-RateLimit-*; check your balance any time (free, unmetered):

mdx.account()   # {'plan': …, 'credits': {'balance', 'daily_quota', 'resets_at', 'unlimited'}, 'rate_limit': …}

from marketdx.errors import QuotaError, RateLimitError

try:
    signals = mdx.news(megatrend="ai-power").to_list()
except RateLimitError as e:
    time.sleep(e.retry_after or 1)
except QuotaError:
    ...   # daily quota spent — resets 00:00 UTC

Enum values (aspect, direction, entity_type, …) are type hints for your editor — the API is the source of truth, so new values work without upgrading the SDK. The live list: mdx.enums().

Reference

news · news_search · news_types · megatrends (.stocks / .off_coverage) · gics (.stocks) · stocks (search + screener) · stock (.news / .competitors / .peers) · enums · account. Full API docs: marketdx.lab.ai.

License

MIT. Built by MarketDX — democratizing financial data.