What QuantRocket does

QuantRocket is a privately held software company that builds a Docker-based quantitative trading platform for individual quants, data scientists, and small trading teams who code in Python. The platform serves researchers and active traders by combining multiple backtesting engines (Zipline, Pipeline, Alphalens, Moonshot, and MoonshotML), a unified securities master, and multi-provider market data — including Sharadar fundamentals, EDI global prices, Polygon.io real-time, Brain sentiment, and Interactive Brokers — under a single JupyterLab-driven environment that can be deployed locally or to the Oracle Cloud free tier. Live trading execution is supported through Interactive Brokers and Alpaca, with multi-strategy, multi-account order management, P&L tracking, and implementation shortfall analysis.

The technology stack emphasizes openness and self-hosting: Docker-based microservices run on Linux, Mac, or Windows with TimescaleDB as the time-series store, and the company maintains and modernizes Zipline (originally from Quantopian). A proprietary backtester called Moonshot — pandas-based and vectorized — extends into MoonshotML for walk-forward ML strategies, while integrations with Pyfolio and Moonchart provide post-backtest analysis. The product is sold via subscription-based software licenses with a generous free tier and a Professional paid tier; paid access unlocks complete, up-to-date data and live-trading capabilities. QuantRocket distributes exclusively through digital self-serve channels, relying on documentation, a public support forum, code libraries, and tutorials rather than traditional sales or marketing. No funding rounds, parent company, or material M&A are recorded, and operations appear to be run by founder Brian Stanley.

Name

QuantRocket

Legal name

QuantRocket LLC

Website

https://quantrocket.com

Company type

Private

Founded year

2020

Operating status

Operating

Headcount range

1–10 employees

Short description

QuantRocket provides a Docker-based Python platform for quantitative research, backtesting, and live trading across global markets. It serves individual quants, data scientists, and small trading teams through integrations with Interactive Brokers, Alpaca, and multiple data providers via a freemium subscription model.

Ownership category

akta.pro rank

Headquarters

HQ city

Asheville

HQ country

United States

HQ region

North America

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Operations, Marketing or Sales, Infrastructure

Revenue model

1. Software Licenses: QuantRocket offers paid software licenses (Professional tier mentioned) that provide access to complete, up-to-date data and live trading capabilities. The free tier provides access to research and backtesting tools with sample data.

Pricing tiers

Go-to-market motion2 records

Distribution channels3 records

Marketing channels5 records

Core offering

QuantRocket is a Docker-based quantitative trading platform that enables Python-using traders and data scientists to research, backtest, and live trade algorithmic strategies across global markets. The platform unifies multiple open-source backtesters (Zipline, Pipeline, Alphalens, Moonshot, MoonshotML) with multi-broker connectivity (Interactive Brokers, Alpaca) and integrated data providers (Sharadar, EDI, Polygon.io, Brain) for end-to-end quant workflows.

Product overview

QuantRocket is a comprehensive quantitative trading platform built as a unified product with multiple integrated components. The core platform centers on Python-based backtesting and live trading capabilities, featuring multiple backtesting engines: Zipline (event-driven, Pipeline-based), Moonshot (pandas-based vectorized), Alphalens (factor analysis), and Pipeline (large-scale security screening). Data management includes Securities Master for instrument metadata, Historical Price Data from multiple providers (US Stock, EDI, Sharadar, IBKR), and Real-Time Data streaming from IBKR, Alpaca, and Polygon.io. Live trading executes through Interactive Brokers and Alpaca with multi-strategy, multi-account support. The platform runs as Docker-based microservices with JupyterLab IDE, includes Data Browser for visual data exploration, and provides Performance Tracking with P&L and implementation shortfall analysis. Additional tools include Pyfolio and Moonchart for visualization, with optional sentiment data from Brain for alternative data strategies.

Differentiator

Problem solved

Functional benefit

Brands

• Moonshot: A pandas-based backtester for data scientists supporting daily or intraday strategies for equities, futures, and FX.
• MoonshotML
• Zipline
• Alphalens

Products and services

• QuantRocket Platform A comprehensive quantitative trading platform that enables Python-using traders and data scientists to research, backtest, and live trade algorithmic strategies in global markets, unifying multiple backtesting engines (Zipline, Moonshot, Pipeline, Alphalens, MoonshotML), multi-broker connectivity (Interactive Brokers, Alpaca), and integrated data providers (Sharadar, EDI, Polygon.io, Brain) into a Docker-based deployable platform.
• Zipline Backtester Event-driven backtesting and live trading engine originally powered by Quantopian, maintained and enhanced by QuantRocket. Supports equities and futures with comprehensive Pipeline API for screening and ranking large universes, daily and minute-level backtesting, live trading automation, and Pyfolio integration.
• Pipeline API Historical, point-in-time screening and ranking API for securities using factors, filters, and classifiers, designed for large datasets. Supports Sharadar fundamentals, IBKR borrow fees, shortable shares, Brain sentiment data, and custom databases, computing results before portfolio construction for efficient factor-based strategy research.
• Alphalens Open-source performance analysis library for evaluating the predictive value of alpha factors, generating tear sheets showing relative performance of stock baskets bucketed by factor values. Supports segmented analysis for large date ranges, subgroup analysis, overnight/intraday return analysis, and integration with Pipeline API.
• Moonshot Backtester QuantRocket's proprietary pandas-based, vectorized backtester designed for data scientists, supporting fast multi-strategy backtests and parameter scans for equities, futures, and FX. Features include daily or intraday strategies, segmented backtests, configurable commissions and slippage modeling, liquidity constraints, and Moonchart integration.
• MoonshotML Machine learning extension for Moonshot enabling walk-forward optimization with rolling and expanding windows. Supports scikit-learn, Keras + TensorFlow, and XGBoost. Features incremental/out-of-core learning for large datasets, feature standardization, one-hot encoding, and model customization for single-security and multi-security prediction strategies.
• Historical Price Data Service Historical market data collection and storage for equities, futures, FX, options, and CFDs from multiple data providers (US Stock, EDI global, Sharadar, Interactive Brokers, custom databases) with daily and intraday frequencies, stored in SQLite or TimescaleDB databases with sharding options for performance.
• Real-Time Data Streaming Real-time market data streaming from Interactive Brokers, Alpaca, and Polygon.io via WebSockets, with tick data collection, bar aggregation to any interval, and streaming to TimescaleDB for trading or analysis. Includes concurrent data collection and market data field reference for trades, quotes, options Greeks, and auction imbalance data.
• Live Trading Execution Multi-strategy, multi-account, multi-broker live trading execution via Interactive Brokers and Alpaca with fully automated or semi-manual order review workflows. Features include account allocations, order scheduling via cron, blotter for P&L tracking, implementation shortfall analysis, and Financial Advisor (FA) order support.
• Data Browser Graphical tool built with Plotly and Dash for browsing securities master database, price charts, fundamental metrics, Pipeline output, and strategy orders, accessed from JupyterLab Launcher for visual exploration without API queries.

Quantifiable outcome

• Backtests run up to 75x faster than QuantConnect for data-intensive strategies

Segments4 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration13 records

AI capability5 records

Feature5 records

Partnerships

Seven partnerships are on record, tiered core and supporting.

• Interactive Brokers (IBKR)coreTechnology or IntegrationInteractive Brokers is a primary broker and data provider for QuantRocket. Users need IBKR Pro accounts (not Lite) to connect. QuantRocket uses IB Gateway to route all communication for market data, order submission, and account management. Supports live trading, paper trading, and historical/real-time data collection through IBKR.
• AlpacacoreTechnology or IntegrationAlpaca provides broker connectivity and real-time market data. QuantRocket supports connecting to multiple Alpaca live and/or paper accounts for trading. Alpaca data feeds include IEX and SIP options. Also provides easy-to-borrow stock data.
• Polygon.iosupportingTechnology or IntegrationPolygon.io provides real-time market data for US stocks. QuantRocket supports collecting minute or second aggregate data from Polygon for large universes. Can use Polygon data with Zipline for live trading.
• BrainsupportingTechnology or IntegrationBrain provides alternative data including: Brain Sentiment Indicator (financial news sentiment for 5,000+ US stocks), Brain Language Metrics on Company Filings (10-K/10-Q sentiment for 6,000+ stocks), and Brain Language Metrics on Earnings Call Transcripts (4,500+ stocks).
• SharadarcoreTechnology or IntegrationSharadar provides corporate fundamentals, institutional ownership, insider holdings, SEC Form 8-K filings, S&P 500 constituents, and end-of-day prices for US stocks with over 20 years of history. Supports Zipline Pipeline integration and dedicated history databases.
• EDI (Exchange Data International)coreTechnology or IntegrationEDI provides end-of-day prices for every stock exchange in the world with history back to 2007, including delisted stocks. Used for global equity coverage beyond US markets.
• Nasdaq Data Link (Quandl)supportingTechnology or IntegrationNasdaq Data Link (formerly Quandl) is referenced as a data provider connection available in QuantRocket's license service for accessing alternative data.

Scale indicators6 records

Recent moves6 records

Expansion highlights5 records

Direct peers

• Backtrader: Open-source Python backtesting framework popular with retail and prosumer quants. Overlaps QuantRocket's Moonshot use case but lacks bundled data providers, broker connectivity, and managed infrastructure.
• AlgoTrader: Institutional-grade algorithmic trading platform supporting equities, FX, futures, and crypto with strategy backtesting, paper trading, and live execution. Comparable to QuantRocket in the quantitative strategy lifecycle but skewed toward institutional clients.
• QuantConnect: Cloud-based algorithmic trading platform supporting Python and C# with its own LEAN backtesting engine. The most direct competitor to QuantRocket, cited explicitly in QuantRocket's own 75x speed comparison.
• CloudQuant: Alternative-data-focused quant trading platform and marketplace with backtesting and live trading capabilities. Comparable to QuantRocket in serving data-driven systematic traders, though positioned more around alt-data signals than multi-engine backtesting.

Others

• Polygon.io: Real-time and historical US market data provider and a QuantRocket data partner. An infrastructure vendor in the same ecosystem rather than a horizontal competitor, but its own trading-platform ambitions partially overlap QuantRocket's real-time analytics layer.
• Alpaca: Commission-free US equities API-first broker and a QuantRocket broker partner. Sits adjacent rather than directly competitive — Alpaca exposes trading APIs while QuantRocket orchestrates execution and research across multiple brokers including Alpaca.

Broad incumbents

• Interactive Brokers: QuantRocket's primary broker partner and a global multi-asset brokerage with its own TWS API and Trader Workstation. Not a direct product competitor, but IBKR's own quant tooling (TWS, IB API) is the in-house alternative to QuantRocket's platform.
• TradingView: Web-based charting and social trading platform with Pine Script for strategy backtesting and broker connectivity. Touches QuantRocket on backtesting and trading workflows, but its center of gravity is charting and community rather than data infrastructure.
• NinjaTrader: Established futures and equities trading platform offering charting, backtesting, and automated strategy execution via NinjaScript. Overlaps QuantRocket on futures/equities automation but is C#-based and oriented to active traders rather than quants.
• MetaTrader (MQL5): Mass-market retail trading platform with built-in MQL scripting language for algorithmic strategies, deep broker integration, and global user base. A broader incumbent in the same end market as QuantRocket but oriented to retail traders rather than Python-literate quants.

Market position

Strengths4 records

Weaknesses5 records

Competitive moat4 records

Key risks6 records

Key highlights7 records

Customer concentration

• LinkedInlinkedin.com/company/quantrocket

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about QuantRocket