We extract earnings transcripts, Quant Ratings, analyst coverage, and ticker financials from Seeking Alpha. 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 Ticker Overview objects from seekingalpha.com. All fields typed and schema-versioned.
"ticker": "AAPL", "company_name": "Apple Inc.", "sector": "Information Technology", "market_cap": 2984000000000, "quant_rating": 3.48, "wall_street_rating": 4.12, "sa_author_rating": 3.85, "followers": 3104921
| # | ticker | company_name | sector | industry | market_cap | employees |
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Complete list of extractable fields for Earnings Transcripts objects from seekingalpha.com. All fields typed and schema-versioned.
"transcript_id": "4689210", "ticker": "MSFT", "quarter": "Q3", "year": 2025, "publish_date": "2025-04-24T21:30:00Z", "executives": "['Satya Nadella', 'Amy Hood']", "analysts": "['Keith Weiss', 'Mark Murphy']"
| # | transcript_id | ticker | quarter | year | publish_date | executives |
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Complete list of extractable fields for Quant & Factor Grades objects from seekingalpha.com. All fields typed and schema-versioned.
"ticker": "NVDA", "quant_score": 4.98, "valuation_grade": "F", "growth_grade": "A+", "profitability_grade": "A+", "momentum_grade": "A+", "revisions_grade": "A-", "date_updated": "2025-10-14T09:00:00Z"
| # | ticker | quant_score | valuation_grade | growth_grade | profitability_grade | momentum_grade |
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Complete list of extractable fields for Dividend Data objects from seekingalpha.com. All fields typed and schema-versioned.
"ticker": "JNJ", "dividend_yield": 3.12, "annual_payout": 4.96, "payout_ratio": 44.5, "consecutive_years": 62, "dividend_safety_grade": "A+", "ex_dividend_date": "2025-11-18"
| # | ticker | dividend_yield | annual_payout | payout_ratio | dividend_growth_5y | consecutive_years |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for SA Articles & Analysis objects from seekingalpha.com. All fields typed and schema-versioned.
"article_id": "5128394", "ticker": "TSLA", "title": "Tesla: Margins Continue To Erode", "author_name": "Stone Fox Capital", "publish_date": "2025-09-12T14:22:00Z", "rating_stance": "Sell", "comments_count": 412, "author_followers": 48291
| # | article_id | ticker | title | author_name | author_followers | publish_date |
|---|---|---|---|---|---|---|
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Our Seeking Alpha scraper extracts the core financial signals: transcripts, factor grades, and retail sentiment, bypassing heavy bot mitigation layers with complete JavaScript rendering.
Full text extraction of presentation and Q&A segments, properly attributed to specific executives and analysts for NLP parsing.
Capture the proprietary Seeking Alpha Quant Rating along with Valuation, Growth, Profitability, Momentum, and Revisions grades.
Extract aggregate Wall Street analyst ratings, price targets, and earnings estimates across current and forward quarters.
Track dividend yield, payout ratios, consecutive growth years, and dividend safety grades for income investing models.
Extract expense ratios, AUM, holding summaries, and asset allocation data for exchange-traded funds.
Monitor the breaking news feed and PR summaries tagged by ticker, capturing institutional updates as they hit the wire.
Extract structured comparison metrics across direct industry competitors as defined by Seeking Alpha algorithms.
Capture public article metadata, summary bullets, author stance (Buy/Sell/Hold), and comment volumes to gauge retail sentiment.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide ticker lists, sector filters, or specific data types like transcripts or quant ratings. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for seekingalpha.com.
Schema validation, null-rate checks, and transcript formatting tests before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Seeking Alpha invests heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Seeking Alpha relies on advanced bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with retail investor traffic.
Seeking Alpha is a complex React application. We run full Playwright browser sessions with JavaScript execution to ensure all dynamic financial tables and charts hydrate correctly before extraction.
Article feeds and news sections use infinite scroll and dynamic API calls. Our pipeline correctly triggers these loading events to capture complete historical data without missing records.
For large ticker universes, we maintain a hash index of last-seen ratings and articles. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, layout changes, and coverage drops, responding before you notice.
Hedge funds ingest Quant Ratings and Factor Grades as alternative signals to backtest and inform algorithmic trading strategies.
Data science teams parse earnings call transcripts and author summaries to measure executive tone and retail sentiment shifts.
Analysts aggregate Wall Street consensus, peer comparisons, and dividend safety scores to accelerate fundamental research.
Wealth managers monitor dividend growth histories, payout ratios, and ex-dividend dates across thousands of equities.
Corporate strategy teams track peer earnings transcripts and analyst ratings to benchmark performance and market perception.
Funds monitor article publication velocity, comment volumes, and author stances to gauge retail investor interest in specific tickers.
"Seeking Alpha aggregates the highest density of retail and institutional sentiment on the web, but extracting it requires bypassing aggressive anti-bot layers."
Financial models require structured data, not HTML. We handle the residential proxies, JavaScript execution, and pagination logic required to parse Seeking Alpha continuously. DataFlirt absorbs the extraction complexity so your quants can focus on backtesting and signal generation.
Everything supported by our seekingalpha.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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About seekingalpha.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated financial data, transcripts, and metadata. We do not extract paywalled Premium content, circumvent authentication walls, or violate GDPR. Clients should review Seeking Alpha ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 403 or CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. We parse the public transcripts, separating executive commentary from analyst Q&A, and structure the text blocks with correct speaker attribution for immediate NLP use.
News and article metadata can be streamed at sub-15-minute intervals. Full ticker universe updates for Quant Ratings and Factor Grades typically run on a daily cadence after market close.
Yes. We can execute a backfill run to extract years of historical earnings transcripts for a defined list of tickers, subject to public availability on the platform.
Our minimum engagement starts at a defined list of 500 tickers with weekly delivery. For larger universes like the Russell 3000 or custom schema requirements, we price based on volume and frequency.
Yes. We provide a sample run of up to 50 tickers or 20 transcripts as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off transcript corpus or a continuous feed of Quant Ratings across 5,000 tickers, we scope, build, and operate the pipeline. Tell us what you need.
