AI驱动的自动化智能投研、智能投顾平台
Qbot is an AI-oriented automated quantitative investment platform, which aims to realize the potential,
empower AI technologies in quantitative investment.
🤖 Qbot = 智能交易策略 + 回测系统 + 自动化量化交易 (+ 可视化分析工具)
| | | |
| | | \_ quantstats (dashboard\online operate)
| | \______________ Qbot - vnpy, pytrader, pyfunds
| \____________________________ BackTest - backtrader, easyquant
\________________________________________ quant.ai - qlib, deep learning strategies
***不建议 fork 项目，本项目会持续更新，只 fork 看不到更新，建议 Star ⭐️ ~***
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git clone https://github.com/UFund-Me/Qbot.git
cd Qbot
pip install -r requirements.txt
python main.py #if run on Mac, please use 'pythonw main.py'
Install Guide | Online documents
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< Run ``./env_setup.sh`` to say hello >
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export USER_ID="admin" # replace your info
export PASSWORD="admin1234." # replace your info
pip install -r requirements.txt
cd pytrader
python test_backtrade.py
python test_trader.py
# visualization
python main.py
# if run on Mac, please use 'pythonw main.py'
需要 node 开发环境: npm、node，点击查看详细操作文档
运行命令
cd pyfunds/fund-strategies
npm install
npm start
体验下来，dagster是很适合金融数据采集、处理，还有机器学习的场景。当然这里的场景更偏向于“批处理”，“定时任务”的处理与编排。
dagster-daemon run &
dagit -h 0.0.0.0 -p 3000
部分未整理。。。
| 股票 | 基金 | 期货 |
| GBDT | RNN | Reinforcement Learning | 🔥 Transformer |
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Results and models are available in the model zoo. AI strategies is shown at here, local run "python pytrader/strategies/workflow_by_code.py", also provide
点击展开查看具体AI模型benchmark结果
| status | benchmark | framework | DGCNN | RegNetX | addition | arXiv | |
|---|---|---|---|---|---|---|---|
| GBDT | ✗ | ✗ | XGBoost | ✗ | ✗ | Tianqi Chen, et al. KDD 2016 | ✗ |
| GBDT | ✗ | ✗ | LightGBM | ✗ | ✓ | Guolin Ke, et al. NIPS 2017 | ✗ |
| GBDT | ✗ | ✗ | Catboost | ✗ | ✓ | Liudmila Prokhorenkova, et al. NIPS 2018 | ✗ |
| MLP | ✓ | ✓ | pytorch | ✗ | ✗ | -- | ✗ |
| LSTM | ✓ | ✓ | pytorch | ✗ | ✗ | Sepp Hochreiter, et al. Neural computation 1997 | ✗ |
| LightGBM | ✓ | ✓ | pytorch | ✗ | ✗ | -- | ✗ |
| GRU | ✓ | ✗ | pytorch | ✗ | ✗ | Kyunghyun Cho, et al. 2014 | ✗ |
| ALSTM | ✗ | ✗ | pytorch | ✗ | ✗ | Yao Qin, et al. IJCAI 2017 | ✗ |
| GATs | ✗ | ✓ | pytorch | ✗ | ✗ | Petar Velickovic, et al. 2017 | ✗ |
| SFM | ✓ | ✓ | pytorch | ✗ | ✗ | Liheng Zhang, et al. KDD 2017 | ✗ |
| TFT | ✓ | ✓ | tensorflow | ✗ | ✗ | Bryan Lim, et al. International Journal of Forecasting 2019 | ✗ |
| TabNet | ✓ | ✗ | pytorch | ✗ | ✗ | Sercan O. Arik, et al. AAAI 2019 | ✗ |
| DoubleEnsemble | ✓ | ✓ | LightGBM | ✗ | ✗ | Chuheng Zhang, et al. ICDM 2020 | ✗ |
| TCTS | ✓ | ✗ | pytorch | ✗ | ✗ | Xueqing Wu, et al. ICML 2021 | ✗ |
| Transformer | ✓ | ✗ | pytorch | ✗ | ✗ | Ashish Vaswani, et al. NeurIPS 2017 | ✗ |
| Localformer | ✓ | ✗ | pytorch | ✗ | ✗ | Juyong Jiang, et al. | ✗ |
| TRA | ✓ | ✗ | pytorch | ✗ | ✗ | Hengxu, Dong, et al. KDD 2021 | ✗ |
| TCN | ✓ | ✗ | pytorch | ✗ | ✗ | Shaojie Bai, et al. 2018 | ✗ |
| ADARNN | ✓ | ✗ | pytorch | ✗ | ✗ | YunTao Du, et al. 2021 | ✗ |
| ADD | ✓ | ✗ | pytorch | ✗ | ✗ | Hongshun Tang, et al.2020 | ✗ |
| IGMTF | ✓ | ✗ | pytorch | ✗ | ✗ | Wentao Xu, et al.2021 | ✗ |
| HIST | ✓ | ✗ | pytorch | ✗ | ✗ | Wentao Xu, et al.2021 | ✗ |
Note: All the about 300+ models, methods of 40+ papers in quant.ai supported by Model Zoo can be trained or used in this codebase.
在线文档 jupyter notebook
Click HERE to more detail.
声明：别轻易用于实盘，市场有风险，投资需谨慎。
Starting Portfolio Value: 10000.00
Startdate=datetime.datetime(2010, 1, 1),
Enddate=datetime.datetime(2020, 4, 21),
# 设置佣金为0.001, 除以100去掉%号
cerebro.broker.setcommission(commission=0.001)
A股回测MACD策略:
A股回测KDJ策略:
A股回测 KDJ+MACD 策略:
- 很多策略需要做回测验证；
- 本项目由前后端支持，有上位机app支持，但目前框架还比较乱，需要做调整；
- 各种策略需要抽象设计，支持统一调用；
- 增强数据获取的实时性，每秒数据，降低延迟；
- 在线文档的完善，目前主要几个部分：新手使用指引、经典策略原理和源码、智能策略原理和源码、常见问题等；
- 新的feature开发，欢迎在issues交流；
We appreciate all contributions to improve Qbot. Please refer to CONTRIBUTING.md for the contributing guideline.
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Github discussions 💬 or issues 💭
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WeChat Group ID: Yida_Zhang2
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Email: yidazhang1#gmail.com
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知乎：@Charmve
- 知识星球：AI量化投资 （加我微信，邀请）
交易策略和自动化工具只是提供便利，并不代表实际交易收益。该项目任何内容不构成任何投资建议。市场有风险，投资需谨慎。
If you like the project, you can become a sponsor at Open Collective or use GitHub Sponsors.
Thank you for supporting Qbot!
Last but not least, we're thankful to these open-source repo for sharing their services for free:
基于Backtrader、vnpy、qlib、tushare、backtest、easyquant等开源项目，感谢开发者。
感谢大家的支持与喜欢！
Code with ❤️ & ☕️ @Charmve 2022-2023