We extract market commentary, stock ratings, ticker mentions, and author sentiment from TheStreet. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for News Articles objects from thestreet.com. All fields typed and schema-versioned.
"article_id": "ts-8472910", "url": "https://www.thestreet.com/investing/apple-stock-earnings-preview", "headline": "Apple Faces Key Test in China Ahead of Q3 Earnings", "author_name": "Martin Baccardax", "published_at": "2026-10-24T14:30:00Z", "tickers_mentioned": "['AAPL', 'MSFT']", "category": "Investing", "tags": "['Earnings', 'Technology', 'China']"
| # | article_id | url | headline | subheadline | author_name | published_at |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ticker Mentions objects from thestreet.com. All fields typed and schema-versioned.
"mention_id": "mnt-99281", "ticker": "AAPL", "exchange": "NASDAQ", "company_name": "Apple Inc.", "mention_context": "Apple shares dipped 1.2% following the supply chain report.", "article_url": "https://www.thestreet.com/investing/apple-stock-earnings-preview", "published_at": "2026-10-24T14:30:00Z", "sentiment_proxy": "negative"
| # | mention_id | ticker | exchange | company_name | mention_context | article_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Author Profiles objects from thestreet.com. All fields typed and schema-versioned.
"author_id": "auth-402", "name": "Martin Baccardax", "bio": "Lead Market Analyst covering macroeconomic trends and mega-cap tech.", "twitter_handle": "@MartinBaccardax", "article_count": 3402, "primary_sector": "Technology", "latest_article_url": "https://www.thestreet.com/investing/apple-stock-earnings-preview"
| # | author_id | name | bio | twitter_handle | article_count | primary_sector |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Market Commentary objects from thestreet.com. All fields typed and schema-versioned.
"commentary_id": "com-1029", "category": "Markets", "headline": "Pre-Market Movers: Tech Leads the Charge", "summary": "Nasdaq futures point to a higher open following strong semiconductor guidance.", "key_takeaways": "['Semiconductors rally', 'Yields remain flat', 'Retail earnings mixed']", "author_name": "Stephen Guilfoyle", "published_at": "2026-10-25T12:00:00Z", "related_tickers": "['NVDA', 'AMD']"
| # | commentary_id | category | headline | summary | key_takeaways | author_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Stock Ratings (Free) objects from thestreet.com. All fields typed and schema-versioned.
"rating_id": "rtg-5821", "ticker": "TSLA", "rating_grade": "Hold", "rating_date": "2026-10-20T09:15:00Z", "previous_grade": "Buy", "sector": "Consumer Discretionary", "price_at_rating": 214.5, "article_url": "https://www.thestreet.com/investing/tesla-downgrade"
| # | rating_id | ticker | rating_grade | rating_date | previous_grade | sector |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our TheStreet scraper transforms unstructured HTML articles into machine-readable JSON, extracting precise timestamps, ticker symbols, and author metadata for sentiment analysis.
Extract headlines, subheadlines, body copy, and bulleted takeaways from news articles and market commentary.
Isolate every stock ticker mentioned in the text, linking the narrative directly to the tradable asset.
Capture exact publication and update timestamps down to the second for accurate historical backtesting.
Monitor specific journalists and analysts to build sentiment profiles based on their historical coverage.
Target specific sections like Crypto, Investing, Personal Finance, or Technology to limit noise in your dataset.
Monitor the latest feed and push new articles via webhook within seconds of publication.
Scrape years of archived articles to build a comprehensive corpus for NLP model training.
Bypass rate limits and caching layers using residential proxies and intelligent request throttling.
Track changes to articles over time, capturing post-publication edits and headline modifications.
Brief in. Clean data out.
Provide target categories, author lists, or historical date ranges. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for thestreet.com.
Schema validation, null-rate checks, and timestamp verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Media sites rely on aggressive caching and dynamic content hydration. Here is how we extract clean data at scale.
Many financial media sites use JavaScript to load related tickers, live price widgets, and author metadata after the initial page load. We use Playwright to execute the JavaScript and capture the fully rendered DOM.
High-frequency scraping triggers Web Application Firewalls. We distribute requests across a large pool of US residential proxies, randomising user agents and request intervals to blend in with legitimate reader traffic.
Editorial content often contains inconsistent HTML, inline ads, and embedded widgets. Our extraction logic strips out the noise, returning clean, continuous text blocks suitable for NLP processing.
Timezones and date formats vary across sections. We parse and normalise all timestamps to UTC ISO 8601 format, ensuring your time-series data remains perfectly aligned.
We do not just rely on explicit ticker tags. Our parsers use regular expressions and DOM proximity checks to identify company mentions in the text and map them to their corresponding exchange tickers.
Quantitative funds ingest news text and timestamps to generate real-time sentiment scores for high-frequency trading models.
AI teams use historical financial journalism to fine-tune large language models for finance-specific vocabulary and context.
Analysts monitor coverage volume and tone around specific meme stocks or retail favourites to gauge market participation.
PR firms and investor relations teams track brand mentions, executive quotes, and overall narrative tone in financial media.
Traders map news publication timestamps against tick-level price data to measure market reaction times to earnings or macroeconomic news.
Corporate strategy teams monitor how competitors are covered by major financial outlets to inform their own communication strategies.
"Financial news is only actionable if you can map the narrative to a ticker symbol and a microsecond timestamp before the market reacts."
Extracting data from TheStreet requires handling aggressive caching layers, dynamic content hydration, and strict rate limits. DataFlirt manages the infrastructure so your quantitative analysts can focus on signal generation and backtesting, rather than maintaining fragile scraping scripts.
Everything supported by our thestreet.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles high-throughput URL discovery and scheduling, while Playwright executes JavaScript for accurate DOM extraction on complex article pages.
We route requests through rotating US residential proxies to avoid IP bans and ensure consistent access to the latest financial news.
Pipelines run on Kubernetes with Airflow managing dependencies and schedules, ensuring SLA compliance for real-time news delivery.
Data delivered to where your team already works — no new tooling required.
About thestreet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available news articles and market commentary is generally permissible. DataFlirt extracts only public, non-authenticated content. We do not bypass paywalls or extract premium subscription data like Action Alerts PLUS.
For real-time pipelines monitoring specific categories or RSS feeds, we can deliver parsed JSON via webhook within 30 to 60 seconds of publication on the site.
Yes. We can traverse category archives and author pages to backfill years of historical articles, providing a comprehensive dataset for backtesting models.
We capture all explicitly tagged tickers in the article metadata and use regex proximity rules to identify unlinked company mentions in the text, ensuring high recall for sentiment mapping.
No. We do not support scraping authenticated, paywalled content such as TheStreet Smarts or the Action Alerts PLUS portfolio.
We recommend JSON or Parquet. These formats preserve the nested structure of the data, keeping metadata like timestamps and authors cleanly separated from the raw body text.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical backfill for model training or a real-time feed for algorithmic trading, we build and operate the infrastructure. Contact us to define your schema.
