--- hide-toc: true firstpage: lastpage: --- # QTrade QTrade is a simple, modular, and highly customizable trading interface capable of handling backtesting, reinforcement learning tasks. With features ranging from traditional signal-based strategies to reinforcement learning-driven approaches, QTrade allows traders to focus on developing and testing strategies without the burden of implementation details. ## Key Features - **Backtesting**: Efficient simulation of trading strategies on historical data. - **Reinforcement Learning**: Provides a highly customizable Gym environment for training and testing AI-driven trading agents. ```{toctree} :caption: User Guide :maxdepth: 1 :hidden: guide/getting_started guide/trading_environment guide/customize_environment ``` ```{toctree} :caption: API :hidden: api/core api/backtest api/env ``` ```{toctree} :caption: Development :hidden: GitHub kitchen-sink/index stability changelog Contribute to the Docs ``` --- ## How to Use 1. **Install QTrade** Follow the instructions in the [Installation Guide](#installing) to set up QTrade in your Python environment. 2. **Explore Tutorials** Learn to create strategies, backtest them, and use gym trading environment by following the [Getting Started](guide/getting_started.md) and the [Trading Environment](guide/trading_environment.md). 3. **API Reference** Dive deeper into QTrade's core components with the [API Reference](api/core.md). 4. **Get Involved** Contribute to the development or documentation through the links in the [Development Section](#development). --- ## Installing QTrade can be installed with [pip](https://pip.pypa.io): ```bash $ pip install qtrade-lib ``` Alternatively, you can obtain the latest source code from [GitHub](https://github.com/gguan/qtrade): ```bash $ git clone https://github.com/gguan/qtrade.git $ cd qtrade $ pip install . ``` To run the example code: ```bash $ pip install -r examples/requirements.txt $ python examples/simple_strategy.py ``` --- ## Usage The [User Guide](guide/getting_started.md) is the place to learn how to use the library and accomplish common tasks. For more advanced customization, refer to the [Customization Guide](customisation/index.md). The [Reference Documentation](reference/index.md) provides API-level documentation.