🤖 Qbot

Qbot website ^HOT Qbot platform ^{TRY IT OUT}

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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Quick Start

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'

USAGE ʕ •ᴥ•ʔ

Installation

Install Guide | Online documents

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< Run ``./env_setup.sh`` to say hello >
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Get Started

Local

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'

Web

需要 node 开发环境: npm、node

运行命令

cd pyfunds/fund-strategies

npm install
npm start

No-code operation (TODO)

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

Strategies

经典策略

股票	基金	期货
布林线均值回归 ('2022) 多因子选股 ('2023) 小市值 ('2021) 指数增强 ('2022) Alpha对冲 ('2022) 网络交易 ('2022) 拐点交易 ('2022)	4433法则 ('2022)	双均线策略 ('2022) 网络交易 ('2022)

智能策略

GBDT

RNN

Reinforcement Learning

🔥 Transformer

GBDT

BOOST

Line Regression ('2020)

CNN

RNN

TFT (IJoF'2019)

GATs (NIPS'2017)

SFM (KDD'2017)

Transformer (NeurIPS'2017)

Benchmark and Model zoo

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	✗