VOL. XCIV, NO. 247

★ FINANCIAL TOOLS & SERVICES DIRECTORY ★

PRICE: 5 CENTS

Sunday, October 5, 2025

View PortfoliosLab View QuantRocket Back to Home

Head-to-head

PortfoliosLab vs QuantRocket comparison

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

Quick takeaways

PortfoliosLab adds ETF Screeners, Portfolio, Watchlist, Correlation, Stock Comparison, ETF Comparison, Risk Metrics (VaR/ES/Drawdown), Factor Exposure, Financials, and APIs & SDKs coverage that QuantRocket skips.

QuantRocket includes Auto-Trading & Bots, Advanced Order Types, Paper Trading, Data APIs, and Broker Connectors categories that PortfoliosLab omits.

PortfoliosLab highlights: Portfolio analytics and backtesting with benchmarking, monthly returns, and risk-adjusted ratios such as Sharpe, Sortino, Omega, Calmar, and Martin., Optimization models include Mean–Variance (MVO), Risk Parity, and Hierarchical Risk Parity (HRP), with the ability to backtest from a chosen optimization date., and Risk analytics cover drawdowns, Value at Risk (VaR), Expected Shortfall (CVaR), and multiple volatility estimators..

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..

PortfoliosLab

portfolioslab.com

Portfolio analytics platform with screeners, optimizers, and backtesting. The free tier includes 10 years of data and basic calculations. Plus extends coverage to 40+ years and 200 calculations per month, while Pro unlocks unlimited calculations, 500 holdings per portfolio, CSV import/export, and screener exports. Enterprise offers an API, data-feed integration, and white-labeling. Broker sync is not supported; CSV imports are recommended.

Platforms

Web

Pricing

Free

Subscription

Quick highlights

Portfolio analytics and backtesting with benchmarking, monthly returns, and risk-adjusted ratios such as Sharpe, Sortino, Omega, Calmar, and Martin.
Optimization models include Mean–Variance (MVO), Risk Parity, and Hierarchical Risk Parity (HRP), with the ability to backtest from a chosen optimization date.
Risk analytics cover drawdowns, Value at Risk (VaR), Expected Shortfall (CVaR), and multiple volatility estimators.
Comprehensive stock, ETF, and mutual fund screeners with sortable columns, filters, and risk-versus-return scatterplots. Screener results export is available on Pro.
Factor analysis tools for Alpha and Beta measurement.

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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.

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Overlap

Shared focus areas

3 overlaps

Mutual strengths include Screeners, Backtesting, and Quant.

Where they differ

PortfoliosLab

Distinct strengths include:

Risk Metrics (VaR/ES/Drawdown)

Factor Exposure

Portfolio analytics and backtesting with benchmarking, monthly returns, and risk-adjusted ratios such as Sharpe, Sortino, Omega, Calmar, and Martin.
Optimization models include Mean–Variance (MVO), Risk Parity, and Hierarchical Risk Parity (HRP), with the ability to backtest from a chosen optimization date.
Risk analytics cover drawdowns, Value at Risk (VaR), Expected Shortfall (CVaR), and multiple volatility estimators.
Comprehensive stock, ETF, and mutual fund screeners with sortable columns, filters, and risk-versus-return scatterplots. Screener results export is available on Pro.

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	PortfoliosLab	QuantRocket
Categories Which research workflows each platform targets	Shared: Screeners, Backtesting, Quant Unique: ETF Screeners, Portfolio, Watchlist, Correlation, Stock Comparison, ETF Comparison, Risk Metrics (VaR/ES/Drawdown), Factor Exposure, Financials, APIs & SDKs	Shared: Screeners, Backtesting, Quant Unique: Auto-Trading & Bots, Advanced Order Types, Paper Trading, Data APIs, Broker Connectors
Asset types Supported asset classes and universes	Stocks, ETFs, Mutual Funds, Cryptos	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	Web, API
Pricing High-level pricing models	Free, Subscription	Free, Subscription
Key features Core capabilities called out by each vendor	Unique Portfolio analytics and backtesting with benchmarking, monthly returns, and risk-adjusted ratios such as Sharpe, Sortino, Omega, Calmar, and Martin. Optimization models include Mean–Variance (MVO), Risk Parity, and Hierarchical Risk Parity (HRP), with the ability to backtest from a chosen optimization date. Risk analytics cover drawdowns, Value at Risk (VaR), Expected Shortfall (CVaR), and multiple volatility estimators. Comprehensive stock, ETF, and mutual fund screeners with sortable columns, filters, and risk-versus-return scatterplots. Screener results export is available on Pro. Factor analysis tools for Alpha and Beta measurement. Support for both static and transactional portfolios with calendar- or threshold-based rebalancing options.	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 PortfoliosLab and QuantRocket both support?

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

Do PortfoliosLab and QuantRocket require subscriptions?

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

How can you access PortfoliosLab and QuantRocket?

Both PortfoliosLab 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?

PortfoliosLab differentiates itself with Portfolio analytics and backtesting with benchmarking, monthly returns, and risk-adjusted ratios such as Sharpe, Sortino, Omega, Calmar, and Martin., Optimization models include Mean–Variance (MVO), Risk Parity, and Hierarchical Risk Parity (HRP), with the ability to backtest from a chosen optimization date., and Risk analytics cover drawdowns, Value at Risk (VaR), Expected Shortfall (CVaR), and multiple volatility estimators., 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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