spb/tools-spboucher-ai
Public
1{2 "slug": "quantconnect",3 "name": "QuantConnect",4 "aliases": [5 "LEAN",6 "QuantConnect LEAN",7 "Lean Engine"8 ],9 "vendor": {10 "name": "QuantConnect Corporation",11 "slug": "quantconnect",12 "website": "https://www.quantconnect.com"13 },14 "homepage_url": "https://www.quantconnect.com/",15 "docs_url": "https://www.quantconnect.com/docs/v2/",16 "repo_url": "https://github.com/QuantConnect/Lean",17 "pricing_url": "https://www.quantconnect.com/pricing",18 "license_url": "https://github.com/QuantConnect/Lean/blob/master/LICENSE",19 "product_type": "platform",20 "product_status": "active",21 "summary": "Algorithmic trading platform built on the open-source LEAN engine, with cloud backtesting, research notebooks and brokerage-connected live trading",22 "description": "QuantConnect is an algorithmic trading platform from QuantConnect Corporation in the financial analysis and investing category. Its core is LEAN, an open-source, event-driven backtesting and live-trading engine written in C# that executes strategies written in Python or C#. The hosted platform wraps LEAN with curated historical data for equities, options, futures, forex, crypto and indexes, a browser IDE, Jupyter-style research notebooks and a cluster of backtest and live-trading nodes.\n\nStrategies are built as algorithm classes that subscribe to data, define a universe and place orders through a brokerage model; the same code runs in backtests, paper trading and live accounts. Live deployment connects to brokerages such as Interactive Brokers, Alpaca, Kraken, Coinbase and Charles Schwab, with higher-tier plans adding FIX and prime brokerage routes. The LEAN CLI lets users run the engine locally in Docker, sync projects with the cloud and work from VS Code.\n\nTypical users are independent quants, small funds and students who want a reproducible research-to-production path without maintaining their own data plumbing. Billing is per organization: the free plan covers backtesting with daily and minute data, while paper and live trading, more compute nodes, tick-resolution data and local coding require paid tiers.\n\nLimits to consider: the hosted data library is the main convenience, so bringing custom datasets takes extra work; backtests depend on QuantConnect's fill and slippage models; and the engine's C# core and large API surface have a learning curve compared with lightweight Python libraries.",23 "capabilities": [24 "Event-driven backtesting of Python and C# algorithms on the LEAN engine",25 "Hosted historical data across equities, options, futures, forex, crypto and indexes",26 "Research notebooks for exploratory analysis before coding a strategy",27 "Paper trading and live deployment to supported brokerages",28 "LEAN CLI for local execution in Docker and cloud project sync",29 "Universe selection, custom data readers and alternative-data add-ons",30 "Parameter optimization across cloud nodes"31 ],32 "workflows": [33 "Prototype a signal in a research notebook, port it to an algorithm and backtest it",34 "Run LEAN locally with the CLI and push the project to the cloud for live trading",35 "Deploy a strategy to a brokerage account through a live node"36 ],37 "advantages": [38 "Engine is open source under Apache-2.0 and can run without the cloud",39 "Same code path from backtest to live trading",40 "Broad asset-class coverage with curated data"41 ],42 "limitations": [43 "Live trading, tick data and local coding are gated behind paid tiers",44 "Custom datasets require adapters to the LEAN data model",45 "Results depend on the platform's fill, slippage and fee models",46 "C# core and broad API carry a learning curve"47 ],48 "prerequisites": [49 "QuantConnect account",50 "Python or C# knowledge",51 "Docker for local LEAN CLI use",52 "Brokerage account for live trading"53 ],54 "audiences": [55 "Independent quantitative traders",56 "Small systematic funds",57 "Finance students learning algorithmic trading"58 ],59 "alternatives": [60 "zipline-reloaded",61 "vectorbt",62 "alpaca",63 "interactive-brokers-api"64 ],65 "comparison_notes": "Zipline Reloaded is a Python-only backtester that users host themselves and feed with their own data bundles, whereas QuantConnect bundles data, compute and brokerage routing around the open-source LEAN engine. vectorbt favours fast vectorized parameter sweeps over event-driven simulation. Alpaca and the Interactive Brokers API are execution venues rather than research platforms; QuantConnect can route live orders to both.",66 "categories": [67 {68 "slug": "financial-analysis-investing",69 "primary": true70 },71 {72 "slug": "market-data-apis",73 "label": "Bundled historical data for backtesting"74 }75 ],76 "tags": [77 "backtesting",78 "algorithmic-trading",79 "python",80 "csharp",81 "open-source",82 "market-data",83 "cli"84 ],85 "use_cases": [86 "backtest-trading-strategies",87 "access-market-data",88 "automate-trading"89 ],90 "operating_systems": [91 "web",92 "macos",93 "linux",94 "windows"95 ],96 "execution_modes": [97 "cloud",98 "local"99 ],100 "interfaces": [101 "web",102 "cli",103 "api",104 "library"105 ],106 "deployment_models": [107 "saas",108 "on-premises"109 ],110 "integrations": [111 "Interactive Brokers",112 "Alpaca",113 "Kraken",114 "Coinbase",115 "Charles Schwab",116 "TradeStation",117 "tastytrade",118 "Binance",119 "VS Code",120 "Docker"121 ],122 "data_formats": [123 "CSV",124 "Custom LEAN data files"125 ],126 "languages": [127 "Python",128 "C#"129 ],130 "source_model": "mixed",131 "license_spdx": "Apache-2.0",132 "pricing_model": [133 "freemium",134 "paid",135 "enterprise"136 ],137 "pricing_checked_at": "2026-11-02",138 "self_hostable": true,139 "sources": [140 {141 "url": "https://www.quantconnect.com/",142 "type": "official_site",143 "supports": [144 "name",145 "summary",146 "product_type"147 ],148 "checked_at": "2026-11-02",149 "title": "QuantConnect"150 },151 {152 "url": "https://www.quantconnect.com/docs/v2/",153 "type": "official_docs",154 "supports": [155 "capabilities",156 "languages",157 "interfaces"158 ],159 "checked_at": "2026-11-02",160 "title": "QuantConnect Documentation"161 },162 {163 "url": "https://github.com/QuantConnect/Lean/blob/master/LICENSE",164 "type": "license_page",165 "supports": [166 "license_spdx",167 "source_model"168 ],169 "checked_at": "2026-11-02",170 "title": "LEAN LICENSE (Apache 2.0)"171 },172 {173 "url": "https://www.quantconnect.com/pricing",174 "type": "pricing_page",175 "supports": [176 "pricing_model",177 "limitations",178 "integrations"179 ],180 "checked_at": "2026-11-02",181 "title": "QuantConnect Pricing"182 }183 ],184 "confidence": "high",185 "depth": "deep",186 "featured": false,187 "discovery_method": "editorial-research",188 "editorial_status": "reviewed",189 "visibility": "public",190 "author": "batch-F"191}192