VOL. XCIV, NO. 247
★ FINANCIAL TOOLS & SERVICES DIRECTORY ★
PRICE: 5 CENTS
Saturday, September 27, 2025
Investors comparing Portfolio123 and QuantRocket will find that Both Portfolio123 and QuantRocket concentrate on Screeners, Quant, and Backtesting workflows, making them natural alternatives for similar investment research jobs. Portfolio123 leans into Portfolio, Correlation, and Calendar, which can be decisive for teams that need depth over breadth. QuantRocket stands out with Auto-Trading & Bots, Advanced Order Types, and Paper Trading 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
Portfolio123 vs QuantRocket
Compare pricing, supported platforms, categories, and standout capabilities to decide which tool fits your workflow.
Quick takeaways
- Portfolio123 adds Portfolio, Correlation, Calendar, APIs & SDKs, and Data Visualizations coverage that QuantRocket skips.
- QuantRocket includes Auto-Trading & Bots, Advanced Order Types, and Paper Trading categories that Portfolio123 omits.
- Portfolio123 highlights: Build custom multi-factor ranking systems and rank stocks by universe, sector, or industry., Spreadsheet-style screening with formulas (including Piotroski F-Score) across current and historical data., and Backtesting with realistic assumptions for slippage, commissions, buy/sell rules, position sizing, and hedging..
- 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..
Portfolio123
portfolio123.com
Quant research and live-deployment platform with point-in-time fundamentals and estimates. Users can screen, backtest, and simulate strategies, then deploy them live with broker integrations. Supports API access and a no-code desktop DataMiner. FactSet or S&P Compustat licenses are required for full historical fundamentals.
Categories
Platforms
Pricing
Quick highlights
- Build custom multi-factor ranking systems and rank stocks by universe, sector, or industry.
- Spreadsheet-style screening with formulas (including Piotroski F-Score) across current and historical data.
- Backtesting with realistic assumptions for slippage, commissions, buy/sell rules, position sizing, and hedging.
- ‘Books’ feature to combine multiple strategies and view correlations between them.
- Point-in-time fundamentals, estimates, and corporate actions with dividends handled on ex/pay dates (no survivorship bias or look-ahead).
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
Portfolio123
Distinct strengths include:
- Build custom multi-factor ranking systems and rank stocks by universe, sector, or industry.
- Spreadsheet-style screening with formulas (including Piotroski F-Score) across current and historical data.
- Backtesting with realistic assumptions for slippage, commissions, buy/sell rules, position sizing, and hedging.
- ‘Books’ feature to combine multiple strategies and view correlations between them.
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 | Portfolio123 | QuantRocket |
|---|---|---|
Categories Which research workflows each platform targets | Shared: Screeners, Quant, Backtesting, Data APIs, Broker Connectors Unique: Portfolio, Correlation, Calendar, APIs & SDKs, Data Visualizations | Shared: Screeners, Quant, Backtesting, Data APIs, Broker Connectors Unique: Auto-Trading & Bots, Advanced Order Types, Paper Trading |
Asset types Supported asset classes and universes | Stocks, ETFs | 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, Desktop, API | 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 | Highlighted | Standard listing |
Frequently Asked Questions
Which workflows do Portfolio123 and QuantRocket both support?
Both platforms cover Screeners, Quant, Backtesting, Data APIs, and Broker Connectors workflows, so you can research those use cases in either tool before digging into the feature differences below.
Do Portfolio123 and QuantRocket require subscriptions?
Both Portfolio123 and QuantRocket keep freemium access with optional paid upgrades, so you can trial each platform before committing.
How can you access Portfolio123 and QuantRocket?
Both Portfolio123 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?
Portfolio123 differentiates itself with Build custom multi-factor ranking systems and rank stocks by universe, sector, or industry., Spreadsheet-style screening with formulas (including Piotroski F-Score) across current and historical data., and Backtesting with realistic assumptions for slippage, commissions, buy/sell rules, position sizing, and hedging., 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.