Zvt
modular quant framework.
Install / Use
/learn @zvtvz/ZvtREADME
The origin of ZVT
The Three Major Principles of Stock Trading
Declaration
This project does not currently guarantee any backward compatibility, so please upgrade with caution.
As the author's thoughts evolve, some things that were once considered important may become less so, and thus may not be maintained.
Whether the addition of some new elements will be useful to you needs to be assessed by yourself.
Read this in other languages: 中文.
Read the docs:https://zvt.readthedocs.io/en/latest/
Install
python3 -m pip install -U zvt
Main ui
Dash & Plotly UI
It's good for backtest and research, but it is not applicable for real-time market data and user interaction.
After the installation is complete, enter zvt on the command line
zvt
<p align="center"><img src='https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/zvt-factor.png'/></p> <p align="center"><img src='https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/zvt-trader.png'/></p>The example shown here relies on data, factor, trader, please read docs
The core concept of the system is visual, and the name of the interface corresponds to it one-to-one, so it is also uniform and extensible.
You can write and run the strategy in your favorite ide, and then view its related targets, factor, signal and performance on the UI.
Rest api and standalone UI
It is more flexible and more scalable, more suitable for handling real-time market data and user interaction. Combined with the dynamic tag system provided by ZVT, it offers a trading approach that combines AI with human intervention.
- Init tag system
run following scripts:
https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/init_tag_system.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/stock_pool_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_data_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_tick_runner.py
- Install uvicorn
pip install uvicorn
- Run zvt server
After the installation is complete, enter zvt_server on the command line
zvt_server
Or run it from source code: https://github.com/zvtvz/zvt/blob/master/src/zvt/zvt_server.py
- Check the api docs
open http://127.0.0.1:8090/docs
- Deploy the front end service
Front end source code: https://github.com/zvtvz/zvt_ui
Change the env file: https://github.com/zvtvz/zvt_ui/blob/main/.env
Set {your server IP} to zvt_server IP
NEXT_PUBLIC_SERVER = {your server IP}
Then refer to the frontend's README to start the frontend service.
open http://127.0.0.1:3000/trade
<p align="center"><img src='https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/big-picture.jpg'/></p>Behold, the power of zvt:
>>> from zvt.domain import Stock, Stock1dHfqKdata
>>> from zvt.ml import MaStockMLMachine
>>> Stock.record_data(provider="em")
>>> entity_ids = ["stock_sz_000001", "stock_sz_000338", "stock_sh_601318"]
>>> Stock1dHfqKdata.record_data(provider="em", entity_ids=entity_ids, sleeping_time=1)
>>> machine = MaStockMLMachine(entity_ids=["stock_sz_000001"], data_provider="em")
>>> machine.train()
>>> machine.predict()
>>> machine.draw_result(entity_id="stock_sz_000001")
The few lines of code above has done: data capture, persistence, incremental update, machine learning, prediction, and display results. Once you are familiar with the core concepts of the system, you can apply it to any target in the market.
Data
China stock
>>> from zvt.domain import *
>>> Stock.record_data(provider="em")
>>> df = Stock.query_data(provider="em", index='code')
>>> print(df)
id entity_id timestamp entity_type exchange code name list_date end_date
code
000001 stock_sz_000001 stock_sz_000001 1991-04-03 stock sz 000001 平安银行 1991-04-03 None
000002 stock_sz_000002 stock_sz_000002 1991-01-29 stock sz 000002 万 科Ａ 1991-01-29 None
000004 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None
000005 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None
000006 stock_sz_000006 stock_sz_000006 1992-04-27 stock sz 000006 深振业Ａ 1992-04-27 None
... ... ... ... ... ... ... ... ... ...
605507 stock_sh_605507 stock_sh_605507 2021-08-02 stock sh 605507 国邦医药 2021-08-02 None
605577 stock_sh_605577 stock_sh_605577 2021-08-24 stock sh 605577 龙版传媒 2021-08-24 None
605580 stock_sh_605580 stock_sh_605580 2021-08-19 stock sh 605580 恒盛能源 2021-08-19 None
605588 stock_sh_605588 stock_sh_605588 2021-08-12 stock sh 605588 冠石科技 2021-08-12 None
605589 stock_sh_605589 stock_sh_605589 2021-08-10 stock sh 605589 圣泉集团 2021-08-10 None
[4136 rows x 9 columns]
USA stock
>>> Stockus.record_data()
>>> df = Stockus.query_data(index='code')
>>> print(df)
id entity_id timestamp entity_type exchange code name list_date end_date
code
A stockus_nyse_A stockus_nyse_A NaT stockus nyse A 安捷伦 None None
AA stockus_nyse_AA stockus_nyse_AA NaT stockus nyse AA 美国铝业 None None
AAC stockus_nyse_AAC stockus_nyse_AAC NaT stockus nyse AAC Ares Acquisition Corp-A None None
AACG stockus_nasdaq_AACG stockus_nasdaq_AACG NaT stockus nasdaq AACG ATA Creativity Global ADR None None
AACG stockus_nyse_AACG stockus_nyse_AACG NaT stockus nyse AACG ATA Creativity Global ADR None None
... ... ... ... ... ... ... ... ... ...
ZWRK stockus_nasdaq_ZWRK stockus_nasdaq_ZWRK NaT stockus nasdaq ZWRK Z-Work Acquisition Corp-A None None
ZY stockus_nasdaq_ZY stockus_nasdaq_ZY NaT stockus nasdaq ZY Zymergen Inc None None
ZYME stockus_nyse_ZYME stockus_nyse_ZYME NaT stockus nyse ZYME Zymeworks Inc None None
ZYNE stockus_nasdaq_ZYNE stockus_nasdaq_ZYNE NaT stockus nasdaq ZYNE Zynerba Pharmaceuticals Inc None None
ZYXI stockus_nasdaq_ZYXI stockus_nasdaq_ZYXI NaT stockus nasdaq ZYXI Zynex Inc None None
[5826 rows x 9 columns]
>>> Stockus.query_data(code='AAPL')
id entity_id timestamp entity_type exchange code name list_date end_date
0 stockus_nasdaq_AAPL stockus_nasdaq_AAPL None stockus nasdaq AAPL 苹果 None None
Hong Kong stock
>>> Stockhk.record_data()
>>> df = Stockhk.query_data(index='code')
>>> print(df)
id entity_id timestamp entity_type exchange code name list_date end_date
code
00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None
00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None
00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None
00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None
00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None
... ... ... ... ... ... ... ... ... ...
09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None
09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None
09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None
09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None
80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None
[2597 rows x 9 columns]
>>> df[df.code=='00700']
id entity_id timestamp entity_type exchange code name list_date end_date
2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None
`
