We extract Zacks Ranks, earnings estimates, ESP metrics, VGM style scores, and analyst ratings. 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 Stock Snapshot objects from zacks.com. All fields typed and schema-versioned.
"ticker": "AAPL", "company_name": "Apple Inc.", "zacks_rank": 3, "value_score": "D", "growth_score": "B", "momentum_score": "C", "vgm_score": "C", "industry_rank": "Top 34%"
| # | ticker | company_name | exchange | zacks_rank | value_score | growth_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Earnings Estimates objects from zacks.com. All fields typed and schema-versioned.
"ticker": "AAPL", "current_qtr_est": 1.54, "next_qtr_est": 1.32, "current_year_est": 6.58, "next_year_est": 7.12, "zacks_esp": 0.45, "earnings_date": "2026-04-30", "yoy_growth_est": 8.2
| # | ticker | current_qtr_est | next_qtr_est | current_year_est | next_year_est | zacks_esp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fundamental Metrics objects from zacks.com. All fields typed and schema-versioned.
"ticker": "AAPL", "market_cap": 2840000000000, "pe_ratio": 28.4, "peg_ratio": 2.1, "price_to_sales": 7.4, "dividend_yield": 0.52, "beta": 1.28, "roe": 145.2
| # | ticker | market_cap | pe_ratio | peg_ratio | price_to_sales | price_to_book |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Analyst Ratings objects from zacks.com. All fields typed and schema-versioned.
"ticker": "AAPL", "strong_buy_count": 18, "buy_count": 6, "hold_count": 9, "sell_count": 1, "average_broker_rating": 1.84, "rating_change_30d": 0, "rating_change_90d": -2
| # | ticker | strong_buy_count | buy_count | hold_count | sell_count | strong_sell_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Mutual Funds & ETFs objects from zacks.com. All fields typed and schema-versioned.
"ticker": "SPY", "fund_name": "SPDR S&P 500 ETF Trust", "asset_class": "Large Cap Blend", "zacks_mutual_fund_rank": 2, "expense_ratio": 0.09, "yield": 1.34, "net_assets": 492000000000, "inception_date": "1993-01-22"
| # | ticker | fund_name | asset_class | zacks_mutual_fund_rank | expense_ratio | yield |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Zacks scraper handles complex HTML table structures, dynamic charts, and financial data normalisation. We manage the proxy rotation and session limits to deliver clean quantitative data directly to your models.
Capture the proprietary 1 to 5 Zacks Rank for thousands of equities, updated daily before market open.
Extract the Expected Surprise Prediction percentage to identify stocks likely to beat or miss consensus estimates.
Scrape Value, Growth, and Momentum letter grades alongside the composite VGM score for granular factor modelling.
Track upward and downward estimate revisions across 7-day, 30-day, and 60-day windows to capture analyst sentiment shifts.
Extract the Zacks Industry Rank and Sector Rank to build top down rotation strategies.
Capture the exact breakdown of Strong Buy, Buy, Hold, Sell, and Strong Sell recommendations from covering analysts.
Scrape historical dividend payouts, current yields, and 5-year dividend growth rates for income portfolios.
Extract fund specific metrics including the Zacks Mutual Fund Rank, expense ratios, and asset allocation breakdowns.
Configure pipelines to run immediately after market close or just before market open to ensure data freshness for trading models.
Brief in. Clean data out.
Provide a universe of tickers, sectors, or asset classes. We map the required Zacks data fields to a strict schema.
We configure Scrapy extractors to parse Zacks' complex financial tables and handle required proxy rotation.
Schema validation, null-rate checks on critical fields like EPS estimates, and data type enforcement.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial portals deploy strict rate limits and complex DOM structures. Here is how we ensure reliable delivery of Zacks market data.
Financial websites aggressively throttle IPs that request too many ticker pages in sequence. We use US residential proxies and apply randomised delays between requests to blend in with retail investor traffic, ensuring complete universe coverage without blocks.
Much of Zacks data is embedded in dense, nested HTML tables rather than clean APIs. Our extraction logic maps specific table cells to strict schema fields, handling missing values and structural inconsistencies across different asset classes.
Stale financial data is useless. We orchestrate pipelines via Apache Airflow to trigger extraction jobs exactly when Zacks updates its daily ranks, guaranteeing your models ingest the latest estimate revisions before the opening bell.
Instead of processing the entire dataset daily, we can compute diffs against previous runs. This allows us to emit lightweight webhook payloads specifically when a ticker experiences a Zacks Rank upgrade or downgrade.
A string where a float should be breaks trading models. We enforce strict data types during the validation phase, stripping currency symbols, converting percentages to decimals, and handling 'N/A' values appropriately before delivery.
Systematic funds ingest daily Zacks Ranks and EPS estimate revisions as alpha factors in multi-factor equities models.
Analysts monitor the Zacks ESP metric combined with recent broker rating changes to position portfolios ahead of earnings calls.
Asset managers screen thousands of equities using VGM Style Scores to construct portfolios tilted towards Value or Momentum.
Fundamental analysts track long term EPS estimate trends and industry rank changes to validate qualitative investment theses.
Fintech platforms enrich their user facing ticker pages with consensus analyst ratings and dividend yield histories.
Risk teams monitor sudden downward estimate revisions or rank downgrades across portfolio holdings to trigger review alerts.
"Zacks holds the industry standard for earnings estimate revisions and surprise predictions, but extracting that alpha requires a resilient extraction pipeline."
Financial data decays rapidly. Relying on manual exports or fragile scripts means missing critical rating changes. DataFlirt builds production grade pipelines that handle Zacks rate limits and complex table structures, delivering clean market data directly to your quantitative models before the opening bell.
Everything supported by our zacks.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 concurrency crawl orchestration, mapping complex financial HTML tables into strict Python dataclasses for validation.
We route requests through US based residential proxies to bypass financial site rate limits and avoid datacenter IP blocks.
Pipelines are scheduled via Apache Airflow to execute precisely around market hours, ensuring data freshness for quantitative models.
Data delivered to where your team already works — no new tooling required.
About zacks.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available financial data is generally permissible under applicable law. DataFlirt extracts only public, non authenticated data such as the public Zacks Rank, ESP metrics, and consensus estimates. We do not bypass paywalls to access Zacks Premium or Ultimate content. Clients should review terms of service and consult legal counsel for specific trading use cases.
We typically schedule extraction pipelines to run daily, immediately after Zacks updates its ranks and estimates. This ensures your models have the latest data before the US market opens.
Yes. Every pipeline run produces a timestamped snapshot. Over time, this builds a time series dataset allowing you to backtest strategies based on Zacks Rank upgrades and downgrades.
Yes. Our pipelines can extract the Zacks Mutual Fund Rank, ETF profiles, expense ratios, and asset allocation data alongside standard equities.
Financial data often contains gaps. Our validation layer enforces strict typing. Missing numeric values are explicitly cast to null rather than left as string artifacts, ensuring downstream quantitative models do not break.
We typically start with a defined universe, such as the Russell 3000 or S&P 500, delivered daily. Pricing scales based on the number of tickers tracked and the frequency of extraction.
Yes. We offer a sample run for a subset of tickers during the scoping phase. This allows your quant team to validate the schema, data types, and completeness before committing to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop relying on manual exports. Tell us your target ticker universe and delivery cadence, and we will build the extraction pipeline.
