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Guide · Open source

How to use Qbot: install, run and what to expect

Updated · written with AI from the project's own documentation and checked against our catalogue data

Qbot is an open-source platform from the UFund-Me project for building and testing automated stock, fund, futures or crypto trading strategies. This guide is for Python users who want to set it up locally and understand what it covers. By the end you will know the prerequisites, the install commands from the project README, and what the platform is designed to do.

QbotUFund-Me/Qbot · ★ 18.8k · MIT

Quick start

  1. 1Confirm Python 3.8 or 3.9. Check that your Python version is 3.8 or 3.9, the only versions the README says were tested.
  2. 2Clone the repository. Run git clone with --depth 1 from your chosen workspace folder.
  3. 3Install requirements. Run pip install -r dev/requirements.txt inside the Qbot folder.
  4. 4Set PYTHONPATH. Export PYTHONPATH with the project root and backend/multi-fact/mfm_learner folder, as shown in the README.
  5. 5Run main.py. Start the platform with python main.py, or pythonw main.py on Mac.

What Qbot covers and who it is for

According to the README, Qbot is a free quantitative research platform that spans data acquisition, strategy development, backtesting, simulated trading and live trading. It places emphasis on AI strategies, including machine learning and reinforcement learning, combined with multi-factor models.

The README says some Python knowledge and basic trading experience help. It is not a no-code product.

  • Supported trading objects listed in the README: stocks, funds, futures and virtual currencies (crypto).
  • Strategy research is the main goal; the README describes online backtesting, simulated trading and automated live trading as ways to validate strategies.

Check the Python prerequisite before installing

The README states that Qbot was tested only on Python 3.8 and Python 3.9. Other versions were not tested, so use one of those two versions to avoid unknown issues.

  • Python 3.8 or Python 3.9 is required according to the README.
  • Use a fresh environment if you manage several Python versions on one machine.

Clone the repository and install dependencies

Open a terminal, move to the folder you want to use as a workspace, then clone the repository with a shallow copy. Change into the Qbot folder and install the requirements file from the dev directory.

  • The README's clone command uses --depth 1, which downloads only the latest commit history.
cd ~ # $HOME as workspace git clone https://github.com/UFund-Me/Qbot --depth 1 cd Qbot pip install -r dev/requirements.txt

Set PYTHONPATH and run main.py

Before starting, the README asks you to add the project root and the multi-factor learner folder to PYTHONPATH. Then run main.py from the Qbot directory. The README's command uses export syntax, which applies to Linux and shell environments.

  • On macOS the README says to use pythonw main.py instead of python main.py.
  • The README gives the export line for Linux and shell use; Windows users should adapt it to their own shell.
export PYTHONPATH=${PYTHONPATH}:$(pwd):$(pwd)/backend/multi-fact/mfm_learner python main.py #if run on Mac, please use 'pythonw main.py'

Read the documentation before trading

The project keeps an online documentation site and an install guide at docs/Install_guide.md in the repository. Use these for setup details beyond the README's quick start.

Backtesting and simulated trading let you test a strategy on historical or simulated data. The README does not promise any profit, and live trading involves real financial risk. Treat results as research output, not guarantees.

  • Online docs: https://ufund-me.github.io/Qbot
  • Install guide in the repo: docs/Install_guide.md

Compare Qbot with similar projects

The README lists comparable projects: qlib, backtrader, vnpy and quantstats. Qbot's README places these within its own architecture diagram, which groups quant AI strategies, backtesting and automated trading with visualisation tools.

Pick Qbot if you want an integrated platform with a GUI front end and AI strategy research. Pick a single-purpose library if you only need one part of the workflow.

What people use it for

  • Researching machine learning models for market prediction on historical data.
  • Backtesting a multi-factor stock strategy before any live use.
  • Learning how quantitative strategies are built and tested as a student.
  • Trying simulated trading to see how a strategy behaves without real money.
  • Comparing factor-based and reinforcement learning approaches inside one platform.

Frequently asked questions

Is Qbot free to use?

The README describes Qbot as a free, open-source quantitative research platform, and the repository is licensed under MIT. The MIT licence allows reuse under its terms; check the LICENSE file in the repo for the full text.

What Python version does Qbot need?

The README says Qbot was tested only on Python 3.8 and Python 3.9. Other versions were not tested by the project.

How do I install Qbot?

Clone the GitHub repository with git clone --depth 1, install dependencies with pip install -r dev/requirements.txt, set PYTHONPATH, then run python main.py. The steps are in the tutorial above.

Do I need programming knowledge to use Qbot?

Yes. The README says the project needs some Python knowledge and some trading experience. It is not a turnkey, no-code product.

Is Qbot a good alternative to qlib or backtrader?

The README lists qlib, backtrader, vnpy and quantstats as comparable projects. Qbot combines data, strategy, backtesting, simulated and live trading in one platform, while those tools each focus on narrower parts. Choose based on which parts of the workflow you need.

Will Qbot make me money?

The README makes no performance claims and does not guarantee a profitable strategy. Live trading carries financial risk, so test thoroughly before using real money.

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Our verdict

Qbot suits Python users, students and individual investors who want to research and backtest quantitative strategies, including machine learning ones. It is a poor fit for anyone who wants a no-code product, a guaranteed-profit strategy, or who cannot run Python 3.8 or 3.9.

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