VOL. XCIV, NO. 247

★ FINANCIAL TOOLS & SERVICES DIRECTORY ★

PRICE: 5 CENTS

Sunday, October 5, 2025

View ETF Action View QuantRocket Back to Home

Head-to-head

ETF Action vs QuantRocket comparison

Compare pricing, supported platforms, categories, and standout capabilities to decide which tool fits your workflow.

Quick takeaways

ETF Action adds ETF Overview, ETF Performance, ETF Holdings, ETF Fundamentals, ETF Valuation, ETF Factors, ETF Overlap, Data Visualizations, Portfolio, Watchlist, 13F, Alerts, Blogs, and Videos coverage that QuantRocket skips.

QuantRocket includes Quant, Auto-Trading & Bots, Advanced Order Types, Paper Trading, and Broker Connectors categories that ETF Action omits.

ETF Action highlights: Global ETF ‘Channels’ taxonomy with category reports and dashboards (Size & Style US/Global, Region & Country, Sector & Industry, Thematic, Fixed Income, Non‑Traditional, Commodities, Crypto)., Look‑through analytics on fund holdings for factor/style, sector/industry, country, concentration; portfolio overlap analysis across funds or entire models., and Flows & trading dashboards (volumes/ADV); flows noted as T+1 timing in daily updates..

QuantRocket is known for: Includes survivorship-bias-free US minute-bar data (from 2007 onward) for Zipline backtests and live trading, with optional real-time feeds from brokers like IBKR and Alpaca., Supports point-in-time screening and ranking pipelines, and integrates with Alphalens and Pyfolio for in-notebook analysis inside Jupyter., and Global coverage through Interactive Brokers’ historical and real-time data across 60+ exchanges, plus optional feeds like EDI global EOD, Sharadar fundamentals, and Brain sentiment datasets..

ETF Action

etfaction.com

Independent ETF analytics platform for advisors and asset managers. Offers global ETF classification, look‑through holdings analytics, overlap analysis, flows/volumes with T+1 flow timing, live quotes (FMV) via Polygon.io, model portfolio tools, 13F screening (Advisor Pro), and enterprise options including API access. DIY/Advisor Pro plans emphasize self‑service research and Excel/CSV downloads; Enterprise adds collaboration, custom dashboards, and API. A separate Model Builder plan enables model uploads and ‘Bridge’ trading integration. API details are not publicly documented—confirm endpoints and auth with vendor.

Platforms

Web

API

Pricing

Free

Subscription

Quick highlights

Global ETF ‘Channels’ taxonomy with category reports and dashboards (Size & Style US/Global, Region & Country, Sector & Industry, Thematic, Fixed Income, Non‑Traditional, Commodities, Crypto).
Look‑through analytics on fund holdings for factor/style, sector/industry, country, concentration; portfolio overlap analysis across funds or entire models.
Flows & trading dashboards (volumes/ADV); flows noted as T+1 timing in daily updates.
Live pricing on platform pages via Polygon.io FMV quotes (real‑time-ish indicative).
Screeners for ETFs; watchlists; data exports to Excel/CSV (DIY 50 rows, Advisor Pro 500 rows).

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QuantRocket

quantrocket.com

A Docker-based research, backtesting, and live-trading platform built around Jupyter. The free tier is limited to research, while paid plans unlock live and paper trading along with bundled US minute-bar data. Broader global datasets are available via third-party providers. Its tight IBKR integration brings advanced order types, while real-time market data can be streamed from IBKR, Polygon, or Alpaca.

Platforms

Web

API

Pricing

Free

Subscription

Quick highlights

Includes survivorship-bias-free US minute-bar data (from 2007 onward) for Zipline backtests and live trading, with optional real-time feeds from brokers like IBKR and Alpaca.
Supports point-in-time screening and ranking pipelines, and integrates with Alphalens and Pyfolio for in-notebook analysis inside Jupyter.
Global coverage through Interactive Brokers’ historical and real-time data across 60+ exchanges, plus optional feeds like EDI global EOD, Sharadar fundamentals, and Brain sentiment datasets.
Deep IBKR integration enabling advanced order types such as algorithmic, parent-child, and bracket orders, as well as combos/spreads, margin 'what-if' checks, option greeks, and auction imbalance data.
Streams tick-level data into TimescaleDB with WebSocket access, and allows flexible bar aggregation.

Community votes (overall)

0% upvotes 0% downvotes

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Overlap

Shared focus areas

3 overlaps

Mutual strengths include Screeners, Backtesting, and Data APIs.

Where they differ

ETF Action

Distinct strengths include:

Global ETF ‘Channels’ taxonomy with category reports and dashboards (Size & Style US/Global, Region & Country, Sector & Industry, Thematic, Fixed Income, Non‑Traditional, Commodities, Crypto).
Look‑through analytics on fund holdings for factor/style, sector/industry, country, concentration; portfolio overlap analysis across funds or entire models.
Flows & trading dashboards (volumes/ADV); flows noted as T+1 timing in daily updates.
Live pricing on platform pages via Polygon.io FMV quotes (real‑time-ish indicative).

QuantRocket

Distinct strengths include:

Includes survivorship-bias-free US minute-bar data (from 2007 onward) for Zipline backtests and live trading, with optional real-time feeds from brokers like IBKR and Alpaca.
Supports point-in-time screening and ranking pipelines, and integrates with Alphalens and Pyfolio for in-notebook analysis inside Jupyter.
Global coverage through Interactive Brokers’ historical and real-time data across 60+ exchanges, plus optional feeds like EDI global EOD, Sharadar fundamentals, and Brain sentiment datasets.
Deep IBKR integration enabling advanced order types such as algorithmic, parent-child, and bracket orders, as well as combos/spreads, margin 'what-if' checks, option greeks, and auction imbalance data.

Feature-by-feature breakdown

Attribute	ETF Action	QuantRocket
Categories Which research workflows each platform targets	Shared: Screeners, Backtesting, Data APIs Unique: ETF Overview, ETF Performance, ETF Holdings, ETF Fundamentals, ETF Valuation, ETF Factors, ETF Overlap, Data Visualizations, Portfolio, Watchlist, 13F, Alerts, Blogs, Videos	Shared: Screeners, Backtesting, Data APIs Unique: Quant, Auto-Trading & Bots, Advanced Order Types, Paper Trading, Broker Connectors
Asset types Supported asset classes and universes	ETFs, Stocks	Stocks, ETFs, Futures, Currencies, Options
Experience levels Who each product is built for	Beginner, Intermediate, Advanced	Beginner, Intermediate, Advanced
Platforms Where you can access the product	Web, API	Web, API
Pricing High-level pricing models	Free, Subscription	Free, Subscription
Key features Core capabilities called out by each vendor	Unique Global ETF ‘Channels’ taxonomy with category reports and dashboards (Size & Style US/Global, Region & Country, Sector & Industry, Thematic, Fixed Income, Non‑Traditional, Commodities, Crypto). Look‑through analytics on fund holdings for factor/style, sector/industry, country, concentration; portfolio overlap analysis across funds or entire models. Flows & trading dashboards (volumes/ADV); flows noted as T+1 timing in daily updates. Live pricing on platform pages via Polygon.io FMV quotes (real‑time-ish indicative). Screeners for ETFs; watchlists; data exports to Excel/CSV (DIY 50 rows, Advisor Pro 500 rows). Model portfolio creation & back‑testing; private model marketplaces for advisors/strategists.	Unique Includes survivorship-bias-free US minute-bar data (from 2007 onward) for Zipline backtests and live trading, with optional real-time feeds from brokers like IBKR and Alpaca. Supports point-in-time screening and ranking pipelines, and integrates with Alphalens and Pyfolio for in-notebook analysis inside Jupyter. Global coverage through Interactive Brokers’ historical and real-time data across 60+ exchanges, plus optional feeds like EDI global EOD, Sharadar fundamentals, and Brain sentiment datasets. Deep IBKR integration enabling advanced order types such as algorithmic, parent-child, and bracket orders, as well as combos/spreads, margin 'what-if' checks, option greeks, and auction imbalance data. Streams tick-level data into TimescaleDB with WebSocket access, and allows flexible bar aggregation. REST API ('Houston') with Python client and CLI tools; endpoints return CSV or JSON for easy downstream use.
Tested Verified by hands-on testing inside Find My Moat	Not yet	Not yet
Editor pick Featured inside curated shortlists	Standard listing	Standard listing

Frequently Asked Questions

Which workflows do ETF Action and QuantRocket both support?

Both platforms cover Screeners, Backtesting, and Data APIs workflows, so you can research those use cases in either tool before digging into the feature differences below.

Do ETF Action and QuantRocket require subscriptions?

Both ETF Action and QuantRocket keep freemium access with optional paid upgrades, so you can trial each platform before committing.

How can you access ETF Action and QuantRocket?

Both ETF Action and QuantRocket prioritize web or desktop access. Investors wanting a mobile-first workflow may need to rely on responsive web views.

What unique strengths set the two platforms apart?

ETF Action differentiates itself with Global ETF ‘Channels’ taxonomy with category reports and dashboards (Size & Style US/Global, Region & Country, Sector & Industry, Thematic, Fixed Income, Non‑Traditional, Commodities, Crypto)., Look‑through analytics on fund holdings for factor/style, sector/industry, country, concentration; portfolio overlap analysis across funds or entire models., and Flows & trading dashboards (volumes/ADV); flows noted as T+1 timing in daily updates., whereas QuantRocket stands out for Includes survivorship-bias-free US minute-bar data (from 2007 onward) for Zipline backtests and live trading, with optional real-time feeds from brokers like IBKR and Alpaca., Supports point-in-time screening and ranking pipelines, and integrates with Alphalens and Pyfolio for in-notebook analysis inside Jupyter., and Global coverage through Interactive Brokers’ historical and real-time data across 60+ exchanges, plus optional feeds like EDI global EOD, Sharadar fundamentals, and Brain sentiment datasets..

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