VOL. XCIV, NO. 247
★ FINANCIAL TOOLS & SERVICES DIRECTORY ★
PRICE: 5 CENTS
Saturday, September 27, 2025
Investors comparing QuantConnect and QuantRocket will find that Both QuantConnect and QuantRocket concentrate on Quant, Backtesting, and Paper Trading workflows, making them natural alternatives for similar investment research jobs. QuantConnect leans into Options & Derivatives, and APIs & SDKs, which can be decisive for teams that need depth over breadth. QuantRocket stands out with Screeners, Advanced Order Types, and Data APIs 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
QuantConnect vs QuantRocket
Compare pricing, supported platforms, categories, and standout capabilities to decide which tool fits your workflow.
Quick takeaways
- QuantConnect adds Options & Derivatives, and APIs & SDKs coverage that QuantRocket skips.
- QuantRocket includes Screeners, Advanced Order Types, Data APIs, and Broker Connectors categories that QuantConnect omits.
- QuantConnect highlights: Seamless environment that connects research, backtests, and live trading on institutional-grade co-located servers., Powered by the open-source LEAN engine (Python 3.11 and C#), with the flexibility to run locally or in the cloud., and Multi-asset coverage across equities, ETFs, options, futures, FX, and crypto, with a long list of broker and data integrations including Interactive Brokers, Schwab, TradeStation, Tastytrade, Alpaca, OANDA, Binance, Coinbase, Kraken, Bybit, and Bloomberg EMSX..
- 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..
QuantConnect
quantconnect.com
QuantConnect runs on the open-source LEAN engine, giving quants and systematic traders a unified workflow from research to backtesting to live deployment. You can run everything locally or in the cloud. Historical data down to tick and second resolution is available on paid tiers, and live-trading notifications scale by plan (from a handful per hour to thousands). Some broker and data feeds—like Trading Technologies futures or premium vendors—are only unlocked at higher tiers.
Platforms
Pricing
Quick highlights
- Seamless environment that connects research, backtests, and live trading on institutional-grade co-located servers.
- Powered by the open-source LEAN engine (Python 3.11 and C#), with the flexibility to run locally or in the cloud.
- Multi-asset coverage across equities, ETFs, options, futures, FX, and crypto, with a long list of broker and data integrations including Interactive Brokers, Schwab, TradeStation, Tastytrade, Alpaca, OANDA, Binance, Coinbase, Kraken, Bybit, and Bloomberg EMSX.
- Robust options support with Greeks, implied volatility, and helpers for building multi-leg strategies such as iron condors.
- Generates detailed backtest reports you can download as PDFs and raw results exportable as CSV or JSON.
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
QuantConnect
Distinct strengths include:
- Seamless environment that connects research, backtests, and live trading on institutional-grade co-located servers.
- Powered by the open-source LEAN engine (Python 3.11 and C#), with the flexibility to run locally or in the cloud.
- Multi-asset coverage across equities, ETFs, options, futures, FX, and crypto, with a long list of broker and data integrations including Interactive Brokers, Schwab, TradeStation, Tastytrade, Alpaca, OANDA, Binance, Coinbase, Kraken, Bybit, and Bloomberg EMSX.
- Robust options support with Greeks, implied volatility, and helpers for building multi-leg strategies such as iron condors.
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 | QuantConnect | QuantRocket |
|---|---|---|
Categories Which research workflows each platform targets | Shared: Quant, Backtesting, Paper Trading, Auto-Trading & Bots Unique: Options & Derivatives, APIs & SDKs | Shared: Quant, Backtesting, Paper Trading, Auto-Trading & Bots Unique: Screeners, Advanced Order Types, Data APIs, Broker Connectors |
Asset types Supported asset classes and universes | Stocks, ETFs, Options, Futures, Currencies, 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, 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 | Not yet | Not yet |
Editor pick Featured inside curated shortlists | Standard listing | Standard listing |
Frequently Asked Questions
Which workflows do QuantConnect and QuantRocket both support?
Both platforms cover Quant, Backtesting, Paper Trading, and Auto-Trading & Bots workflows, so you can research those use cases in either tool before digging into the feature differences below.
Do QuantConnect and QuantRocket require subscriptions?
Both QuantConnect and QuantRocket keep freemium access with optional paid upgrades, so you can trial each platform before committing.
How can you access QuantConnect and QuantRocket?
Both QuantConnect 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?
QuantConnect differentiates itself with Seamless environment that connects research, backtests, and live trading on institutional-grade co-located servers., Powered by the open-source LEAN engine (Python 3.11 and C#), with the flexibility to run locally or in the cloud., and Multi-asset coverage across equities, ETFs, options, futures, FX, and crypto, with a long list of broker and data integrations including Interactive Brokers, Schwab, TradeStation, Tastytrade, Alpaca, OANDA, Binance, Coinbase, Kraken, Bybit, and Bloomberg EMSX., 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.