VOL. XCIV, NO. 247
★ FINANCIAL TOOLS & SERVICES DIRECTORY ★
PRICE: 5 CENTS
Saturday, September 27, 2025
Investors comparing Portfolio Visualizer and QuantRocket will find that Both Portfolio Visualizer and QuantRocket concentrate on Quant, and Backtesting workflows, making them natural alternatives for similar investment research jobs. Portfolio Visualizer leans into Data Visualizations, Factor Exposure, and Risk Metrics (VaR/ES/Drawdown), which can be decisive for teams that need depth over breadth. QuantRocket stands out with Screeners, Auto-Trading & Bots, and Advanced Order Types that the competition lacks. Use the feature-by-feature table to inspect unique capabilities and confirm which roadmap best maps to your process.
Head-to-head
Portfolio Visualizer vs QuantRocket
Compare pricing, supported platforms, categories, and standout capabilities to decide which tool fits your workflow.
Quick takeaways
- Portfolio Visualizer adds Data Visualizations, Factor Exposure, Risk Metrics (VaR/ES/Drawdown), Monte Carlo, and Correlation coverage that QuantRocket skips.
- QuantRocket includes Screeners, Auto-Trading & Bots, Advanced Order Types, Paper Trading, Data APIs, and Broker Connectors categories that Portfolio Visualizer omits.
- Portfolio Visualizer highlights: Portfolio backtesting for mutual funds, ETFs, and stocks with configurable rebalancing rules; separate modules for asset-class backtesting., Monte Carlo simulations for portfolio growth, survival probabilities, and goal-based financial planning., and Optimization tools including efficient frontier modeling, mean–variance optimization, and the Black–Litterman model..
- 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..
Portfolio Visualizer
portfoliovisualizer.com
Web-based analytics suite for portfolio backtesting, optimization, and factor analysis. The free tier supports up to ~15 assets and limited history, while Basic and Pro tiers extend to ~150 assets with YTD results, model saving, and data export. Paid plans include a 14-day free trial.
Categories
Platforms
Pricing
Quick highlights
- Portfolio backtesting for mutual funds, ETFs, and stocks with configurable rebalancing rules; separate modules for asset-class backtesting.
- Monte Carlo simulations for portfolio growth, survival probabilities, and goal-based financial planning.
- Optimization tools including efficient frontier modeling, mean–variance optimization, and the Black–Litterman model.
- Factor analytics with multi-factor regressions and a risk-factor allocation optimizer.
- Correlation analysis at the asset or asset-class level via heatmaps and matrices.
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.
Categories
Platforms
Pricing
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.
Shared focus areas
Both platforms align on these research themes, so you can stay within one workflow when your use case involves them.
Where they differ
Portfolio Visualizer
Distinct strengths include:
- Portfolio backtesting for mutual funds, ETFs, and stocks with configurable rebalancing rules; separate modules for asset-class backtesting.
- Monte Carlo simulations for portfolio growth, survival probabilities, and goal-based financial planning.
- Optimization tools including efficient frontier modeling, mean–variance optimization, and the Black–Litterman model.
- Factor analytics with multi-factor regressions and a risk-factor allocation optimizer.
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 | Portfolio Visualizer | QuantRocket |
|---|---|---|
Categories Which research workflows each platform targets | Shared: Quant, Backtesting Unique: Data Visualizations, Factor Exposure, Risk Metrics (VaR/ES/Drawdown), Monte Carlo, Correlation | Shared: Quant, Backtesting Unique: Screeners, Auto-Trading & Bots, Advanced Order Types, Paper Trading, Data APIs, Broker Connectors |
Asset types Supported asset classes and universes | Stocks, ETFs, Mutual Funds, Bonds, Commodities | 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
| Unique
|
Tested Verified by hands-on testing inside Find My Moat | Yes | Not yet |
Editor pick Featured inside curated shortlists | Standard listing | Standard listing |
Frequently Asked Questions
Which workflows do Portfolio Visualizer and QuantRocket both support?
Both platforms cover Quant, and Backtesting workflows, so you can research those use cases in either tool before digging into the feature differences below.
Do Portfolio Visualizer and QuantRocket require subscriptions?
Both Portfolio Visualizer and QuantRocket keep freemium access with optional paid upgrades, so you can trial each platform before committing.
How can you access Portfolio Visualizer and QuantRocket?
Both Portfolio Visualizer 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?
Portfolio Visualizer differentiates itself with Portfolio backtesting for mutual funds, ETFs, and stocks with configurable rebalancing rules; separate modules for asset-class backtesting., Monte Carlo simulations for portfolio growth, survival probabilities, and goal-based financial planning., and Optimization tools including efficient frontier modeling, mean–variance optimization, and the Black–Litterman model., 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..
Curation & Accuracy
This directory blends AI‑assisted discovery with human curation. Entries are reviewed, edited, and organized with the goal of expanding coverage and sharpening quality over time. Your feedback helps steer improvements (because no single human can capture everything all at once).
Details change. Pricing, features, and availability may be incomplete or out of date. Treat listings as a starting point and verify on the provider’s site before making decisions. If you spot an error or a gap, send a quick note and I’ll adjust.