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ufund-me/qbot

17,322Jupyter NotebookAudience · dataComplexity · 4/5Setup · hard

TLDR

Qbot is a free, self-hosted AI quantitative trading platform that lets you design, backtest, and automate stock and crypto trading bots using machine learning strategies, all from a single Python toolkit with a GUI.

Mindmap

mindmap
  root((qbot))
    What it does
      AI trading platform
      Backtesting engine
      Live order execution
    Tech Stack
      Python
      qlib
      backtrader
      vnpy
    Use Cases
      Stock backtesting
      Paper trading
      Live automation
    Audience
      Quant traders
      Data scientists
      Python coders
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Things people build with this

USE CASE 1

Backtest a stock-picking strategy on historical Chinese market data and review its Sharpe ratio and drawdown

USE CASE 2

Run a reinforcement learning agent that paper-trades futures in a simulated environment before going live

USE CASE 3

Combine multi-factor models with AI to rank and trade ETFs or funds automatically

USE CASE 4

Automate live order routing to a brokerage via the vnpy connector with a GUI-driven control panel

Tech stack

PythonJupyter Notebookqlibbacktradervnpyquantstats

Getting it running

Difficulty · hard Time to first run · 1day+

Requires Python 3.8 or 3.9 and multiple trading library integrations, live trading setup adds brokerage API configuration on top.

In plain English

Qbot is a Chinese-developed, free and open-source quantitative investment platform with a heavy emphasis on AI. In plain terms, it is a toolkit for building, testing, and running automated trading bots, programs that decide when to buy and sell financial instruments, and it focuses on using machine learning rather than only hand-written rules. The README's diagram summarises it as intelligent trading strategies plus a backtesting system plus automated trading, with visualisation tools on top. The way it works is split into modular layers: a data layer pulls in market data, a strategy layer is where the user writes or trains the trading logic, including AI strategies built with machine learning, reinforcement learning, or deep learning combined with multi-factor models, and a trading engine connects to the market to execute orders. The project stitches together several existing Python libraries: qlib for AI strategy research, backtrader and easyquant for backtests, vnpy and pytrader for live order routing, and quantstats for performance dashboards. It supports stocks, funds, futures, and cryptocurrencies, and ships with a GUI client so non-developers can drive it. Someone would use this if they want a fully local, self-hosted way to evaluate stock or fund ideas, run historical backtests, paper-trade in a simulated environment, and eventually flip a switch to automated live trading on a single platform. The author notes that a little Python knowledge and a little trading experience help. The code is Python in Jupyter notebooks and has been tested on Python 3.8 and 3.9. Documentation lives at ufund-me.github.io/Qbot. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Using Qbot and qlib, set up an AI-driven multi-factor strategy that scores Chinese A-share stocks and run a backtest with backtrader showing the equity curve.
Prompt 2
Help me configure Qbot's GUI client to paper-trade a moving-average crossover strategy on crypto data and display the quantstats performance dashboard.
Prompt 3
Using Qbot and vnpy, write a live trading bot that places market orders when a deep learning model predicts a 2% intraday price move.
Prompt 4
Show me how to evaluate a Qbot strategy using the built-in quantstats dashboard to review Sharpe ratio, max drawdown, and monthly returns.
Prompt 5
Walk me through connecting Qbot to real market data for Chinese A-shares and running an overnight mean-reversion backtest with transaction cost modeling.
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