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市场模型

ZVT 将市场抽象为如下的模型:

  • TradableEntity (交易标的)
  • ActorEntity (市场参与者)
  • EntityEvent (交易标的 和 市场参与者 发生的事件)

快速开始

安装

python3 -m pip install -U zvt

运行界面

安装完成后,在命令行下输入 zvt

zvt

打开 http://127.0.0.1:8050/

这里展示的例子依赖后面的下载历史数据,数据更新请参考后面文档

系统的核心概念是可视化的,界面的名称与其一一对应,因此也是统一可扩展的。

你可以在你喜欢的ide里编写和运行策略,然后运行界面查看其相关的标的,因子,信号和净值展示。

导入

>>> from zvt.domain import *

TradableEntity (交易标的)

A股交易标的

>>> Stock.record_data()
>>> df = Stock.query_data(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]

美股交易标的

>>> 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

港股交易标的

>>> 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

还有更多

>>> from zvt.contract import *
>>> zvt_context.tradable_schema_map

{'stockus': zvt.domain.meta.stockus_meta.Stockus,
 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk,
 'index': zvt.domain.meta.index_meta.Index,
 'etf': zvt.domain.meta.etf_meta.Etf,
 'stock': zvt.domain.meta.stock_meta.Stock,
 'block': zvt.domain.meta.block_meta.Block,
 'fund': zvt.domain.meta.fund_meta.Fund}

其中key为交易标的的类型,value为其schema,系统为schema提供了统一的 记录(record_data)查询(query_data) 方法。

>>> Index.record_data()
>>> df=Index.query_data(filters=[Index.category=='scope',Index.exchange='sh'])
>>> print(df)
                 id        entity_id  timestamp entity_type exchange    code    name  list_date end_date publisher category  base_point
0   index_sh_000001  index_sh_000001 1990-12-19       index       sh  000001    上证指数 1991-07-15     None   csindex    scope      100.00
1   index_sh_000002  index_sh_000002 1990-12-19       index       sh  000002    A股指数 1992-02-21     None   csindex    scope      100.00
2   index_sh_000003  index_sh_000003 1992-02-21       index       sh  000003    B股指数 1992-08-17     None   csindex    scope      100.00
3   index_sh_000010  index_sh_000010 2002-06-28       index       sh  000010   上证180 2002-07-01     None   csindex    scope     3299.06
4   index_sh_000016  index_sh_000016 2003-12-31       index       sh  000016    上证50 2004-01-02     None   csindex    scope     1000.00
..              ...              ...        ...         ...      ...     ...     ...        ...      ...       ...      ...         ...
25  index_sh_000020  index_sh_000020 2007-12-28       index       sh  000020    中型综指 2008-05-12     None   csindex    scope     1000.00
26  index_sh_000090  index_sh_000090 2009-12-31       index       sh  000090    上证流通 2010-12-02     None   csindex    scope     1000.00
27  index_sh_930903  index_sh_930903 2012-12-31       index       sh  930903    中证A股 2016-10-18     None   csindex    scope     1000.00
28  index_sh_000688  index_sh_000688 2019-12-31       index       sh  000688    科创50 2020-07-23     None   csindex    scope     1000.00
29  index_sh_931643  index_sh_931643 2019-12-31       index       sh  931643  科创创业50 2021-06-01     None   csindex    scope     1000.00

[30 rows x 12 columns]

EntityEvent (交易标的 发生的事件)

有了交易标的,才有交易标的 发生的事。

行情数据

交易标的 行情schema 遵从如下的规则:

{entity_shema}{level}{adjust_type}Kdata
  • entity_schema

就是前面说的TradableEntity,比如Stock,Stockus等。

  • level
>>> for level in IntervalLevel:
        print(level.value)
  • adjust type
>>> for adjust_type in AdjustType:
        print(adjust_type.value)

注意: 为了兼容历史数据,前复权是个例外,{adjust_type}不填

前复权

>>> Stock1dKdata.record_data(code='000338', provider='em')
>>> df = Stock1dKdata.query_data(code='000338', provider='em')
>>> print(df)

                              id        entity_id  timestamp provider    code  name level   open  close   high    low     volume      turnover  change_pct  turnover_rate
0     stock_sz_000338_2007-04-30  stock_sz_000338 2007-04-30     None  000338  潍柴动力    1d   2.33   2.00   2.40   1.87   207375.0  1.365189e+09      3.2472         0.1182
1     stock_sz_000338_2007-05-08  stock_sz_000338 2007-05-08     None  000338  潍柴动力    1d   2.11   1.94   2.20   1.87    86299.0  5.563198e+08     -0.0300         0.0492
2     stock_sz_000338_2007-05-09  stock_sz_000338 2007-05-09     None  000338  潍柴动力    1d   1.90   1.81   1.94   1.66    93823.0  5.782065e+08     -0.0670         0.0535
3     stock_sz_000338_2007-05-10  stock_sz_000338 2007-05-10     None  000338  潍柴动力    1d   1.78   1.85   1.98   1.75    47720.0  2.999226e+08      0.0221         0.0272
4     stock_sz_000338_2007-05-11  stock_sz_000338 2007-05-11     None  000338  潍柴动力    1d   1.81   1.73   1.81   1.66    39273.0  2.373126e+08     -0.0649         0.0224
...                          ...              ...        ...      ...     ...   ...   ...    ...    ...    ...    ...        ...           ...         ...            ...
3426  stock_sz_000338_2021-08-27  stock_sz_000338 2021-08-27     None  000338  潍柴动力    1d  19.39  20.30  20.30  19.25  1688497.0  3.370241e+09      0.0601         0.0398
3427  stock_sz_000338_2021-08-30  stock_sz_000338 2021-08-30     None  000338  潍柴动力    1d  20.30  20.09  20.31  19.78  1187601.0  2.377957e+09     -0.0103         0.0280
3428  stock_sz_000338_2021-08-31  stock_sz_000338 2021-08-31     None  000338  潍柴动力    1d  20.20  20.07  20.63  19.70  1143985.0  2.295195e+09     -0.0010         0.0270
3429  stock_sz_000338_2021-09-01  stock_sz_000338 2021-09-01     None  000338  潍柴动力    1d  19.98  19.68  19.98  19.15  1218697.0  2.383841e+09     -0.0194         0.0287
3430  stock_sz_000338_2021-09-02  stock_sz_000338 2021-09-02     None  000338  潍柴动力    1d  19.71  19.85  19.97  19.24  1023545.0  2.012006e+09      0.0086         0.0241

[3431 rows x 15 columns]

>>> Stockus1dKdata.record_data(code='AAPL', provider='em')
>>> df = Stockus1dKdata.query_data(code='AAPL', provider='em')
>>> print(df)

                                  id            entity_id  timestamp provider  code name level    open   close    high     low      volume      turnover  change_pct  turnover_rate
0     stockus_nasdaq_AAPL_1984-09-07  stockus_nasdaq_AAPL 1984-09-07     None  AAPL   苹果    1d   -5.59   -5.59   -5.58   -5.59   2981600.0  0.000000e+00      0.0000         0.0002
1     stockus_nasdaq_AAPL_1984-09-10  stockus_nasdaq_AAPL 1984-09-10     None  AAPL   苹果    1d   -5.59   -5.59   -5.58   -5.59   2346400.0  0.000000e+00      0.0000         0.0001
2     stockus_nasdaq_AAPL_1984-09-11  stockus_nasdaq_AAPL 1984-09-11     None  AAPL   苹果    1d   -5.58   -5.58   -5.58   -5.58   5444000.0  0.000000e+00      0.0018         0.0003
3     stockus_nasdaq_AAPL_1984-09-12  stockus_nasdaq_AAPL 1984-09-12     None  AAPL   苹果    1d   -5.58   -5.59   -5.58   -5.59   4773600.0  0.000000e+00     -0.0018         0.0003
4     stockus_nasdaq_AAPL_1984-09-13  stockus_nasdaq_AAPL 1984-09-13     None  AAPL   苹果    1d   -5.58   -5.58   -5.58   -5.58   7429600.0  0.000000e+00      0.0018         0.0004
...                              ...                  ...        ...      ...   ...  ...   ...     ...     ...     ...     ...         ...           ...         ...            ...
8765  stockus_nasdaq_AAPL_2021-08-27  stockus_nasdaq_AAPL 2021-08-27     None  AAPL   苹果    1d  147.48  148.60  148.75  146.83  55802388.0  8.265452e+09      0.0072         0.0034
8766  stockus_nasdaq_AAPL_2021-08-30  stockus_nasdaq_AAPL 2021-08-30     None  AAPL   苹果    1d  149.00  153.12  153.49  148.61  90956723.0  1.383762e+10      0.0304         0.0055
8767  stockus_nasdaq_AAPL_2021-08-31  stockus_nasdaq_AAPL 2021-08-31     None  AAPL   苹果    1d  152.66  151.83  152.80  151.29  86453117.0  1.314255e+10     -0.0084         0.0052
8768  stockus_nasdaq_AAPL_2021-09-01  stockus_nasdaq_AAPL 2021-09-01     None  AAPL   苹果    1d  152.83  152.51  154.98  152.34  80313711.0  1.235321e+10      0.0045         0.0049
8769  stockus_nasdaq_AAPL_2021-09-02  stockus_nasdaq_AAPL 2021-09-02     None  AAPL   苹果    1d  153.87  153.65  154.72  152.40  71171317.0  1.093251e+10      0.0075         0.0043

[8770 rows x 15 columns]

后复权

>>> Stock1dHfqKdata.record_data(code='000338', provider='em')
>>> df = Stock1dHfqKdata.query_data(code='000338', provider='em')
>>> print(df)

                              id        entity_id  timestamp provider    code  name level    open   close    high     low     volume      turnover  change_pct  turnover_rate
0     stock_sz_000338_2007-04-30  stock_sz_000338 2007-04-30     None  000338  潍柴动力    1d   70.00   64.93   71.00   62.88   207375.0  1.365189e+09      2.1720         0.1182
1     stock_sz_000338_2007-05-08  stock_sz_000338 2007-05-08     None  000338  潍柴动力    1d   66.60   64.00   68.00   62.88    86299.0  5.563198e+08     -0.0143         0.0492
2     stock_sz_000338_2007-05-09  stock_sz_000338 2007-05-09     None  000338  潍柴动力    1d   63.32   62.00   63.88   59.60    93823.0  5.782065e+08     -0.0313         0.0535
3     stock_sz_000338_2007-05-10  stock_sz_000338 2007-05-10     None  000338  潍柴动力    1d   61.50   62.49   64.48   61.01    47720.0  2.999226e+08      0.0079         0.0272
4     stock_sz_000338_2007-05-11  stock_sz_000338 2007-05-11     None  000338  潍柴动力    1d   61.90   60.65   61.90   59.70    39273.0  2.373126e+08     -0.0294         0.0224
...                          ...              ...        ...      ...     ...   ...   ...     ...     ...     ...     ...        ...           ...         ...            ...
3426  stock_sz_000338_2021-08-27  stock_sz_000338 2021-08-27     None  000338  潍柴动力    1d  331.97  345.95  345.95  329.82  1688497.0  3.370241e+09      0.0540         0.0398
3427  stock_sz_000338_2021-08-30  stock_sz_000338 2021-08-30     None  000338  潍柴动力    1d  345.95  342.72  346.10  337.96  1187601.0  2.377957e+09     -0.0093         0.0280
3428  stock_sz_000338_2021-08-31  stock_sz_000338 2021-08-31     None  000338  潍柴动力    1d  344.41  342.41  351.02  336.73  1143985.0  2.295195e+09     -0.0009         0.0270
3429  stock_sz_000338_2021-09-01  stock_sz_000338 2021-09-01     None  000338  潍柴动力    1d  341.03  336.42  341.03  328.28  1218697.0  2.383841e+09     -0.0175         0.0287
3430  stock_sz_000338_2021-09-02  stock_sz_000338 2021-09-02     None  000338  潍柴动力    1d  336.88  339.03  340.88  329.67  1023545.0  2.012006e+09      0.0078         0.0241

[3431 rows x 15 columns]

财务因子

>>> FinanceFactor.record_data(code='000338')
>>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp')

            basic_eps  total_op_income    net_profit  op_income_growth_yoy  net_profit_growth_yoy     roe    rota  gross_profit_margin  net_margin  timestamp
timestamp
2002-12-31        NaN     1.962000e+07  2.471000e+06                   NaN                    NaN     NaN     NaN               0.2068      0.1259 2002-12-31
2003-12-31       1.27     3.574000e+09  2.739000e+08              181.2022               109.8778  0.7729  0.1783               0.2551      0.0766 2003-12-31
2004-12-31       1.75     6.188000e+09  5.369000e+08                0.7313                 0.9598  0.3245  0.1474               0.2489      0.0868 2004-12-31
2005-12-31       0.93     5.283000e+09  3.065000e+08               -0.1463                -0.4291  0.1327  0.0603               0.2252      0.0583 2005-12-31
2006-03-31       0.33     1.859000e+09  1.079000e+08                   NaN                    NaN     NaN     NaN                  NaN      0.0598 2006-03-31
...               ...              ...           ...                   ...                    ...     ...     ...                  ...         ...        ...
2020-08-28       0.59     9.449000e+10  4.680000e+09                0.0400                -0.1148  0.0983  0.0229               0.1958      0.0603 2020-08-28
2020-10-31       0.90     1.474000e+11  7.106000e+09                0.1632                 0.0067  0.1502  0.0347               0.1949      0.0590 2020-10-31
2021-03-31       1.16     1.975000e+11  9.207000e+09                0.1327                 0.0112  0.1919  0.0444               0.1931      0.0571 2021-03-31
2021-04-30       0.42     6.547000e+10  3.344000e+09                0.6788                 0.6197  0.0622  0.0158               0.1916      0.0667 2021-04-30
2021-08-31       0.80     1.264000e+11  6.432000e+09                0.3375                 0.3742  0.1125  0.0287               0.1884      0.0653 2021-08-31

[66 rows x 10 columns]

财务三张表

#资产负债表
>>> BalanceSheet.record_data(code='000338')
#利润表
>>> IncomeStatement.record_data(code='000338')
#现金流量表
>>> CashFlowStatement.record_data(code='000338')

还有更多

>>> zvt_context.schemas
[zvt.domain.dividend_financing.DividendFinancing,
 zvt.domain.dividend_financing.DividendDetail,
 zvt.domain.dividend_financing.SpoDetail...]

zvt_context.schemas为系统支持的schema,schema即表结构,即数据,其字段含义的查看方式如下:

  • help

输入schema.按tab提示其包含的字段,或者.help()

>>> FinanceFactor.help()
  • 源码

domain里的文件为schema的定义,查看相应字段的注释即可。

通过以上的例子,你应该掌握了统一的记录数据的方法:

Schema.record_data(provider='your provider',codes='the codes')

注意可选参数provider,其代表数据提供商,一个schema可以有多个provider,这是系统稳定的基石。

查看已实现的provider

>>> Stock.provider_map_recorder
{'joinquant': zvt.recorders.joinquant.meta.jq_stock_meta_recorder.JqChinaStockRecorder,
 'exchange': zvt.recorders.exchange.exchange_stock_meta_recorder.ExchangeStockMetaRecorder,
 'em': zvt.recorders.em.meta.em_stock_meta_recorder.EMStockRecorder,
 'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder.EastmoneyChinaStockListRecorder}

你可以使用任意一个provider来获取数据,默认使用第一个。

再举个例子,股票板块数据获取:

>>> Block.provider_map_recorder
{'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_block_meta_recorder.EastmoneyChinaBlockRecorder,
 'sina': zvt.recorders.sina.meta.sina_block_recorder.SinaBlockRecorder}

>>> Block.record_data(provider='sina')
Block registered recorders:{'eastmoney': <class 'zvt.recorders.eastmoney.meta.china_stock_category_recorder.EastmoneyChinaBlockRecorder'>, 'sina': <class 'zvt.recorders.sina.meta.sina_china_stock_category_recorder.SinaChinaBlockRecorder'>}
2020-03-04 23:56:48,931  INFO  MainThread  finish record sina blocks:industry
2020-03-04 23:56:49,450  INFO  MainThread  finish record sina blocks:concept

再多了解一点record_data:

  • 参数code[单个],codes[多个]代表需要抓取的股票代码
  • 不传入code,codes则是全市场抓取
  • 该方法会把数据存储到本地并只做增量更新

定时任务的方式更新可参考东财数据定时更新

全市场选股

查询数据使用的是query_data方法,把全市场的数据记录下来后,就可以在本地快速查询需要的数据了。

一个例子:2018年年报 roe>8% 营收增长>8% 的前20个股

>>> df=FinanceFactor.query_data(filters=[FinanceFactor.roe>0.08,FinanceFactor.report_period=='year',FinanceFactor.op_income_growth_yoy>0.08],start_timestamp='2019-01-01',order=FinanceFactor.roe.desc(),limit=20,columns=["code"]+FinanceFactor.important_cols(),index='code')

          code  basic_eps  total_op_income    net_profit  op_income_growth_yoy  net_profit_growth_yoy     roe    rota  gross_profit_margin  net_margin  timestamp
code
000048  000048     2.7350     4.919000e+09  1.101000e+09                0.4311                 1.5168  0.7035  0.1988               0.5243      0.2355 2020-04-30
000912  000912     0.3500     4.405000e+09  3.516000e+08                0.1796                 1.2363  4.7847  0.0539               0.2175      0.0795 2019-03-20
002207  002207     0.2200     3.021000e+08  5.189000e+07                0.1600                 1.1526  1.1175  0.1182               0.1565      0.1718 2020-04-27
002234  002234     5.3300     3.276000e+09  1.610000e+09                0.8023                 3.2295  0.8361  0.5469               0.5968      0.4913 2020-04-21
002458  002458     3.7900     3.584000e+09  2.176000e+09                1.4326                 4.9973  0.8318  0.6754               0.6537      0.6080 2020-02-20
...        ...        ...              ...           ...                   ...                    ...     ...     ...                  ...         ...        ...
600701  600701    -3.6858     7.830000e+08 -3.814000e+09                1.3579                -0.0325  1.9498 -0.7012               0.4173     -4.9293 2020-04-29
600747  600747    -1.5600     3.467000e+08 -2.290000e+09                2.1489                -0.4633  3.1922 -1.5886               0.0378     -6.6093 2020-06-30
600793  600793     1.6568     1.293000e+09  1.745000e+08                0.1164                 0.8868  0.7490  0.0486               0.1622      0.1350 2019-04-30
600870  600870     0.0087     3.096000e+07  4.554000e+06                0.7773                 1.3702  0.7458  0.0724               0.2688      0.1675 2019-03-30
688169  688169    15.6600     4.205000e+09  7.829000e+08                0.3781                 1.5452  0.7172  0.4832               0.3612      0.1862 2020-04-28

[20 rows x 11 columns]

以上,你应该会回答如下的三个问题了:

  • 有什么数据?
  • 如何记录数据?
  • 如何查询数据?

更高级的用法以及扩展数据,可以参考详细文档里的数据部分。

写个策略

有了 交易标的交易标的发生的事,就可以写策略了。

所谓策略回测,无非就是,重复以下过程:

在某时间点,找到符合条件的标的,对其进行买卖,看其表现。

系统支持两种模式:

  • solo (随意的)

在 某个时间 根据发生的事件 计算条件 并买卖

  • formal (正式的)

系统设计的二维索引多标的计算模型

一个很随便的人(solo)

嗯,这个策略真的很随便,就像我们大部分时间做的那样。

报表出来的时,我看一下报表,机构加仓超过5%我就买入,机构减仓超过50%我就卖出。

代码如下:

# -*- coding: utf-8 -*-
import pandas as pd

from zvt.api import get_recent_report_date
from zvt.contract import ActorType, AdjustType
from zvt.domain import StockActorSummary, Stock1dKdata
from zvt.trader import StockTrader
from zvt.utils import pd_is_not_null, is_same_date, to_pd_timestamp


class FollowIITrader(StockTrader):
    finish_date = None

    def on_time(self, timestamp: pd.Timestamp):
        recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp))
        if self.finish_date and is_same_date(recent_report_date, self.finish_date):
            return
        filters = [StockActorSummary.actor_type == ActorType.raised_fund.value,
                   StockActorSummary.report_date == recent_report_date]

        if self.entity_ids:
            filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)]

        df = StockActorSummary.query_data(filters=filters)

        if pd_is_not_null(df):
            self.logger.info(f'{df}')
            self.finish_date = recent_report_date

        long_df = df[df['change_ratio'] > 0.05]
        short_df = df[df['change_ratio'] < -0.5]
        try:
            self.trade_the_targets(due_timestamp=timestamp, happen_timestamp=timestamp,
                                   long_selected=set(long_df['entity_id'].to_list()),
                                   short_selected=set(short_df['entity_id'].to_list()))
        except Exception as e:
            self.logger.error(e)


if __name__ == '__main__':
    entity_id = 'stock_sh_600519'
    Stock1dKdata.record_data(entity_id=entity_id, provider='em')
    StockActorSummary.record_data(entity_id=entity_id, provider='em')
    FollowIITrader(start_timestamp='2002-01-01', end_timestamp='2021-01-01', entity_ids=[entity_id],
                   provider='em', adjust_type=AdjustType.qfq, profit_threshold=None).run()

所以,写一个策略其实还是很简单的嘛。 你可以发挥想象力,社保重仓买买买,外资重仓买买买,董事长跟小姨子跑了卖卖卖......

然后,刷新一下http://127.0.0.1:8050/,看你运行策略的performance

更多可参考策略例子

严肃一点(formal)

简单的计算可以通过query_data来完成,这里说的是系统设计的二维索引多标的计算模型。

下面以技术因子为例对计算流程进行说明:

In [7]: from zvt.factors.technical_factor import *
In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer())

data_df

data_df为factor的原始数据,即通过query_data从数据库读取到的数据,为一个二维索引DataFrame

In [11]: factor.data_df
Out[11]:
                           level   high                          id        entity_id   open    low  timestamp  close
entity_id       timestamp
stock_sh_601318 2019-01-02    1d  54.91  stock_sh_601318_2019-01-02  stock_sh_601318  54.78  53.70 2019-01-02  53.94
                2019-01-03    1d  55.06  stock_sh_601318_2019-01-03  stock_sh_601318  53.91  53.82 2019-01-03  54.42
                2019-01-04    1d  55.71  stock_sh_601318_2019-01-04  stock_sh_601318  54.03  53.98 2019-01-04  55.31
                2019-01-07    1d  55.88  stock_sh_601318_2019-01-07  stock_sh_601318  55.80  54.64 2019-01-07  55.03
                2019-01-08    1d  54.83  stock_sh_601318_2019-01-08  stock_sh_601318  54.79  53.96 2019-01-08  54.54
...                          ...    ...                         ...              ...    ...    ...        ...    ...
stock_sz_000338 2019-06-03    1d  11.04  stock_sz_000338_2019-06-03  stock_sz_000338  10.93  10.74 2019-06-03  10.81
                2019-06-04    1d  10.85  stock_sz_000338_2019-06-04  stock_sz_000338  10.84  10.57 2019-06-04  10.73
                2019-06-05    1d  10.92  stock_sz_000338_2019-06-05  stock_sz_000338  10.87  10.59 2019-06-05  10.59
                2019-06-06    1d  10.71  stock_sz_000338_2019-06-06  stock_sz_000338  10.59  10.49 2019-06-06  10.65
                2019-06-10    1d  11.05  stock_sz_000338_2019-06-10  stock_sz_000338  10.73  10.71 2019-06-10  11.02

[208 rows x 8 columns]

factor_df

factor_df为transformer对data_df进行计算后得到的数据,设计因子即对transformer进行扩展,例子中用的是MacdTransformer()。

In [12]: factor.factor_df
Out[12]:
                           level   high                          id        entity_id   open    low  timestamp  close      diff       dea      macd
entity_id       timestamp
stock_sh_601318 2019-01-02    1d  54.91  stock_sh_601318_2019-01-02  stock_sh_601318  54.78  53.70 2019-01-02  53.94       NaN       NaN       NaN
                2019-01-03    1d  55.06  stock_sh_601318_2019-01-03  stock_sh_601318  53.91  53.82 2019-01-03  54.42       NaN       NaN       NaN
                2019-01-04    1d  55.71  stock_sh_601318_2019-01-04  stock_sh_601318  54.03  53.98 2019-01-04  55.31       NaN       NaN       NaN
                2019-01-07    1d  55.88  stock_sh_601318_2019-01-07  stock_sh_601318  55.80  54.64 2019-01-07  55.03       NaN       NaN       NaN
                2019-01-08    1d  54.83  stock_sh_601318_2019-01-08  stock_sh_601318  54.79  53.96 2019-01-08  54.54       NaN       NaN       NaN
...                          ...    ...                         ...              ...    ...    ...        ...    ...       ...       ...       ...
stock_sz_000338 2019-06-03    1d  11.04  stock_sz_000338_2019-06-03  stock_sz_000338  10.93  10.74 2019-06-03  10.81 -0.121336 -0.145444  0.048215
                2019-06-04    1d  10.85  stock_sz_000338_2019-06-04  stock_sz_000338  10.84  10.57 2019-06-04  10.73 -0.133829 -0.143121  0.018583
                2019-06-05    1d  10.92  stock_sz_000338_2019-06-05  stock_sz_000338  10.87  10.59 2019-06-05  10.59 -0.153260 -0.145149 -0.016223
                2019-06-06    1d  10.71  stock_sz_000338_2019-06-06  stock_sz_000338  10.59  10.49 2019-06-06  10.65 -0.161951 -0.148509 -0.026884
                2019-06-10    1d  11.05  stock_sz_000338_2019-06-10  stock_sz_000338  10.73  10.71 2019-06-10  11.02 -0.137399 -0.146287  0.017776

[208 rows x 11 columns]

result_df

result_df为可用于选股器的二维索引DataFrame,通过对data_df或factor_df计算来实现。 该例子在计算macd之后,利用factor_df,黄白线在0轴上为True,否则为False,具体代码

In [14]: factor.result_df
Out[14]:
                            score
entity_id       timestamp
stock_sh_601318 2019-01-02  False
                2019-01-03  False
                2019-01-04  False
                2019-01-07  False
                2019-01-08  False
...                           ...
stock_sz_000338 2019-06-03  False
                2019-06-04  False
                2019-06-05  False
                2019-06-06  False
                2019-06-10  False

[208 rows x 1 columns]

result_df的格式如下:

filter_result 为 True 或 False, score_result 取值为 0 到 1。

结合选股器和回测,整个流程如下:

环境设置(可选)

>>> from zvt import *
>>> zvt_env
{'zvt_home': '/Users/foolcage/zvt-home',
 'data_path': '/Users/foolcage/zvt-home/data',
 'tmp_path': '/Users/foolcage/zvt-home/tmp',
 'ui_path': '/Users/foolcage/zvt-home/ui',
 'log_path': '/Users/foolcage/zvt-home/logs'}

>>> zvt_config 
  • jq_username 聚宽数据用户名
  • jq_password 聚宽数据密码
  • smtp_host 邮件服务器host
  • smtp_port 邮件服务器端口
  • email_username smtp邮箱账户
  • email_password smtp邮箱密码
  • wechat_app_id
  • wechat_app_secrect
>>> init_config(current_config=zvt_config, jq_username='xxx', jq_password='yyy')

通用的配置方式为: init_config(current_config=zvt_config, **kv)

下载历史数据(可选)

百度网盘: https://pan.baidu.com/s/1kHAxGSxx8r5IBHe5I7MAmQ 提取码: yb6c

google drive: https://drive.google.com/drive/folders/17Bxijq-PHJYrLDpyvFAm5P6QyhKL-ahn?usp=sharing

里面包含joinquant的日/周线后复权数据,个股估值,基金及其持仓数据,eastmoney的财务等数据。

把下载的数据解压到正式环境的data_path(所有db文件放到该目录下,没有层级结构)

数据的更新是增量的,下载历史数据只是为了节省时间,全部自己更新也是可以的。

注册聚宽(可选)

项目数据支持多provider,在数据schema一致性的基础上,可根据需要进行选择和扩展,目前支持新浪,东财,交易所等免费数据。

数据的设计上是让provider来适配schema,而不是反过来,这样即使某provider不可用了,换一个即可,不会影响整个系统的使用。

但免费数据的缺点是显而易见的:不稳定,爬取清洗数据耗时耗力,维护代价巨大,且随时可能不可用。
个人建议:如果只是学习研究,可以使用免费数据;如果是真正有意投身量化,还是选一家可靠的数据提供商。

项目支持聚宽的数据,可戳以下链接申请使用(目前可免费使用一年)
https://www.joinquant.com/default/index/sdk?channelId=953cbf5d1b8683f81f0c40c9d4265c0d

项目中大部分的免费数据目前都是比较稳定的,且做过严格测试,特别是东财的数据,可放心使用

添加其他数据提供商, 请参考数据扩展教程

开发

clone代码

git clone https://github.com/zvtvz/zvt.git

设置项目的virtual env(python>=3.6),安装依赖

pip3 install -r requirements.txt
pip3 install pytest

测试案例

pycharm导入工程(推荐,你也可以使用其他ide),然后pytest跑测试案例

大部分功能使用都可以从tests里面参考

贡献

期待能有更多的开发者参与到 zvt 的开发中来,我会保证尽快 Reivew PR 并且及时回复。但提交 PR 请确保

先看一下1分钟代码规范

  1. 通过所有单元测试,如若是新功能,请为其新增单元测试
  2. 遵守开发规范
  3. 如若需要,请更新相对应的文档

也非常欢迎开发者能为 zvt 提供更多的示例,共同来完善文档。

请作者喝杯咖啡

如果你觉得项目对你有帮助,可以请作者喝杯咖啡
Alipay      Wechat

联系方式

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知乎专栏:
https://zhuanlan.zhihu.com/automoney

Thanks

jetbrains

MIT License Copyright (c) 2019 zvtvz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

简介

ZVT是对fooltrader重新思考后编写的量化项目,其包含可扩展的数据recorder,api,因子计算,选股,回测,交易,以及统一的可视化,定位为中低频 多级别 多因子 多标的 全市场分析和交易框架。 展开 收起
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