Rumorz tracks 100s of sources across the web to extract investments insights and sentiment analytics. We index a large amount of web data in real-time and use AI Agents to save you time reading, staying up to date, and identifying trends and investment opportunities.

AI Agents read and analyze web data 24/7 and ingest 100s of pages of text a day into a knowledge graph, allowing social and semantic analysis at state of the art performance.

pip install rumorz

Sign up for an account on Rumorz and generate an API key

• Screener: a ranking of all entities in the Rumorz Graph by social metrics (mentions, sentiment, excitement, optimism, pessimism, fear, uncertainty, surprise')
• Real-time updates: Get real-time updates on the cryptomarket or specific entities
• Tick-level time-series data: get real-time amd historical sentiment data for all entities in the Graph
• Annotated news: Get news articles related to any entity with sentiment and AI annotations
• Search: search and find financial assets, companies or people in the Rumorz Graph

• AI Agents
• Market monitoring and alerts
• Sentiment based investment and trading strategies
• Financial research, analysis and alpha generation
• Data source for AI Agents and RAG based applications
• Social media bot development: Telegram, Discord, Twitter/X etc.
• Workflow automation: emails, PDFs, reports etc.

• Get a real-time AI update on most the positive/negative tokens by sentiment
• Plot the sentiment scores of Bitcoin over time

Go to rumorz.io and submit your email to get a key

We listen to 100s of news websites and sources from the web 24/7.

We are integrated with various LLM providers and use mostly GPT-4 and 3.5

Rumorz tracks financial assets (crypto only for now), organizations, companies and people on the web. For now we're only tracking the crypto ecosystem but we plan to add US Stocks in the future as well.

We use the latest Language Models specifically trained for financial data to generate sentiment scores.

Yes, any summary or update generated uses real-time data.

Yes, the data can be used for backtesting and other analysis. However, as we are in Public Beta, you should expect changes in data methodology in the next couple of months.

# Import required modules
import os
import datetime as dt
from rumorz.client import RumorzClient
from rumorz.enums import Lookback, EntityType

# Initialize the Rumorz client with your API key
rumorz = RumorzClient(api_key='<API_KEY>')

# Get screener data for people based on the last 24 hours
person_screener = rumorz.graph.get_screener(
    entity_type=EntityType.PERSON
)

# Get posts related to Bitcoin within the last week
posts = rumorz.graph.get_posts(
    type=EntityType.FINANCIAL_ASSET,
    name="Bitcoin",
    symbol="BTC",
    lookback=Lookback.ONE_WEEK,
)

# Get time series data for Ethereum within the last week, on page 1
timeseries = rumorz.graph.get_time_series(
    type=EntityType.FINANCIAL_ASSET,
    name="Ethereum",
    symbol="ETH",
    lookback=Lookback.ONE_WEEK,
    page=1,
    limit=100,
)

# Get a live market update
market_update = rumorz.agent.get_market_update()
print("Market Update:", market_update)

# Robust search to find entities in the graph
entities = rumorz.graph.get_entities(
    name_search="elop musk",  # Typo in the name
    type=EntityType.PERSON,
)
print("People matching 'elon musk':", entities)

# Get an update on Elon Musk
elon_musk_update = rumorz.agent.get_entity_update(
    name="Elon Musk",
    type=EntityType.PERSON,
    lookback=Lookback.ONE_DAY,
)
print("Latest about Elon Musk:", elon_musk_update)