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The AI-powered quant trading platform for automated portfolio management without code.

Software for data-driven traders to research and trade quantitative strategies in global markets using Python.

QuantRocket is a comprehensive platform designed for quantitative traders and data scientists. It provides the infrastructure to research, backtest, and deploy quantitative trading strategies using Python. Key components include support for multiple backtesters like Zipline, Pipeline, Alphalens, and Moonshot, each catering to different strategy styles. The platform offers access to global market data, eliminating the need for extensive data wrangling. It supports machine learning workflows with walk-forward optimization and out-of-core learning capabilities. QuantRocket can be deployed on various environments, including local machines, cloud platforms, and VPNs, offering flexibility and control over compute resources. Integration with external trading applications allows for data querying and order submission, making it a versatile tool for both standalone and connected trading setups.
QuantRocket is a comprehensive platform designed for quantitative traders and data scientists.
Explore all tools that specialize in backtest trading strategies. This domain focus ensures QuantRocket delivers optimized results for this specific requirement.
Explore all tools that specialize in analyze market data. This domain focus ensures QuantRocket delivers optimized results for this specific requirement.
Explore all tools that specialize in backtesting. This domain focus ensures QuantRocket delivers optimized results for this specific requirement.
Supports Zipline, Pipeline, Alphalens, and Moonshot, allowing users to choose the appropriate tool for their strategy.
Provides access to a wide range of global market data, including equities, futures, and FX.
Offers tools for walk-forward optimization and out-of-core learning using scikit-learn, Keras, TensorFlow, and XGBoost.
Can be deployed on Linux, Mac, Windows, cloud platforms, or local workstations.
Allows integration with external trading applications for data querying and order submission.
Install QuantRocket via Docker.
Configure data sources and API keys.
Choose a backtesting library (e.g., Zipline, Moonshot) based on strategy requirements.
Load historical market data into the QuantRocket database.
Develop and backtest quantitative strategies using Python.
Implement risk management and order execution logic.
Deploy strategies to live trading environments.
All Set
Ready to go
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