JQData

https://www.joinquant.com/help/api/doc
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JQData is a comprehensive financial data API from JoinQuant that provides access to market data...
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### Example: Get Industry Component Stocks

Source: https://www.joinquant.com/help/api/doc?id=9864&name=JQDatadoc

Demonstrates how to retrieve and print the first five stocks for the computer/internet industry.

```python
# 获取计算机/互联网行业的成分股
stocks = get_industry_stocks('I64')
print(stocks[:5])

# 输出
['000503.XSHE', '000606.XSHE', '000676.XSHE', '000835.XSHE', '002072.XSHE']
```

--------------------------------

### Get Concept Stocks Example

Source: https://www.joinquant.com/help/api/doc?id=9866&name=JQDatadoc

Example demonstrating how to retrieve and print the first five stock codes for the wind power concept sector.

```python
# 获取风力发电概念板块的成分股
stocks = get_concept_stocks('SC0084')
print(stocks[:5])
#输出
['000027.XSHE', '000039.XSHE', '000065.XSHE', '000099.XSHE', '000155.XSHE']
```

--------------------------------

### Query Options Database Example

Source: https://www.joinquant.com/help/api/doc?id=10523&name=JQDatadoc

Example of querying the options database for currently tradable contract information.

```python
# 查询opt库，当前可交易的50ETF期权合约信息
from jqdatasdk import query,opt
# 或from jqdatasdk import *
from datetime import datetime
opt.run_query(query(opt.OPT_CONTRACT_INFO).filter((opt.OPT_CONTRACT_INFO.underlying_symbol == "510050.XSHG") & (opt.OPT_CONTRACT_INFO.last_trade_date > datetime.now())))
```

--------------------------------

### Query Bond Database Example

Source: https://www.joinquant.com/help/api/doc?id=10525&name=JQDatadoc

Example of querying the bond database for historical repo daily price data.

```python
# 查询bond库, 国债逆回购日行情2019年到2021年的数据,数据量约1.3万条
from jqdatasdk import query,bond
# 或from jqdatasdk import *
q = query(bond.REPO_DAILY_PRICE).filter(bond.REPO_DAILY_PRICE.date>'2019-01-01',
                                                   bond.REPO_DAILY_PRICE.date<='2021-12-31'
                                                   )
df = jq.bond.run_offset_query(q)
```

--------------------------------

### Retrieve Historical Financial Data Example

Source: https://www.joinquant.com/help/api/doc?id=10326&name=JQDatadoc

Example demonstrating how to fetch financial data for multiple stocks over several quarters.

```python
from jqdatasdk import *
security = ['000001.XSHE', '600000.XSHG']
df = get_history_fundamentals(security, fields=[balance.cash_equivalents, 
        cash_flow.net_deposit_increase, income.total_operating_revenue], 
        watch_date=None, stat_date='2019q1', count=5, interval='1q', stat_by_year=False)
print(df)
print(df.groupby('code').mean())
```

--------------------------------

### Query multi-day financial data example

Source: https://www.joinquant.com/help/api/doc?id=9883&name=JQDatadoc

Example demonstrating how to construct a query object and retrieve multi-day valuation and EPS data for specific stocks.

```python
q = query(valuation.turnover_ratio,
              valuation.market_cap,
              indicator.eps
            ).filter(valuation.code.in_(['000001.XSHE', '600000.XSHG']))
df = get_fundamentals_continuously(q, end_date='2017-12-25', count=5,panel=False)[:5]
print(df)

# 输出
          day         code  turnover_ratio  market_cap   eps
0  2017-12-19  000001.XSHE          1.4174   2280.2307  0.38
1  2017-12-20  000001.XSHE          0.6539   2276.7964  0.38
2  2017-12-21  000001.XSHE          0.8779   2324.8738  0.38
3  2017-12-22  000001.XSHE          0.4391   2321.4397  0.38
4  2017-12-25  000001.XSHE          0.9372   2275.0796  0.38
```

--------------------------------

### Query Finance Database Example

Source: https://www.joinquant.com/help/api/doc?id=10523&name=JQDatadoc

Example of querying the finance database for stock price data with filtering and ordering.

```python
# 查询finance库，万科 AH 股价格的前10条数据
from jqdatasdk import query,finance
# 或from jqdatasdk import *
q=query(finance.STK_AH_PRICE_COMP
  ).filter(
        finance.STK_AH_PRICE_COMP.a_code=='000002.XSHE'
  ).order_by(
        finance.STK_AH_PRICE_COMP.day
  ).limit(10)
df=finance.run_query(q)
```

--------------------------------

### Fetch Industry List Example

Source: https://www.joinquant.com/help/api/doc?id=9863&name=JQDatadoc

Example demonstrating how to retrieve the CSRC industry list for a specific date.

```python
# 获取2020-05-07当天证监会行业行业列表
df=get_industries(name='zjw', date="2022-05-07")[:5]
print(df)

      name start_date
L72  商务服务业 1996-08-29
L71    租赁业 1997-01-30
G53  铁路运输业 1998-05-11
G57  管道运输业 1996-11-04
G56  航空运输业 1997-11-05
```

--------------------------------

### Example Usage of get_account_info

Source: https://www.joinquant.com/help/api/doc?id=10532&name=logon

Demonstrates calling the account info function and printing the resulting dictionary.

```python
#查询账号信息
infos = get_account_info()
print(infos)

>>>{'license': 1,
 'date_range_start': '2022-04-22 00:00:00',
 'query_count_limit': 500000,
 'date_range_end': '*',
 'expire_time': '2023-04-22 00:00:00',
 'mob': '199XXXXXXXX'}
```

--------------------------------

### Retrieve Industry Data Examples

Source: https://www.joinquant.com/help/api/doc?id=9861&name=JQDatadoc

Examples demonstrating how to fetch industry data for a single stock and multiple stocks with DataFrame formatting.

```python
#获取贵州茅台("600519.XSHG")的所属行业数据
d = get_industry("600519.XSHG",date="2018-06-01")
print(d)

# 输出
{'600519.XSHG': {'sw_l1': {'industry_code': '801120', 'industry_name': '食品饮料I'}, 'sw_l2': {'industry_code': '801123', 'industry_name': '饮料制造II'}, 'sw_l3': {'industry_code': '851231', 'industry_name': '白酒III'}, 'zjw': {'industry_code': 'C15', 'industry_name': '酒、饮料和精制茶制造业'}, 'jq_l2': {'industry_code': 'HY478', 'industry_name': '白酒与葡萄酒指数'}, 'jq_l1': {'industry_code': 'HY005', 'industry_name': '日常消费指数'}}}
```

```python
d = get_industry(security= ['000001.XSHE','000002.XSHE'], date="2022-06-01",df=True)
d[d.type=='zjw']

# 输出

           code type industry_code industry_name
1   000002.XSHE  zjw           K70          房地产业
11  000001.XSHE  zjw           J66        货币金融服务
```

--------------------------------

### Example Query for Trading Calendar Data

Source: https://www.joinquant.com/help/api/doc?id=9867&name=JQDatadoc

A practical example demonstrating how to filter by date and limit the number of returned records.

```python
q=query(finance.STK_EXCHANGE_LINK_CALENDAR).filter(finance.STK_EXCHANGE_LINK_CALENDAR.day>='2015-01-04').limit(4)
df=finance.run_query(q)
print(df)

        id         day  link_id link_name  type_id   type
0  1244833  2015-01-04   310001       沪股通   312003     休市
1      148  2015-01-04   310003    港股通(沪)   312003     休市
2  1244834  2015-01-05   310001       沪股通   312001  正常交易日
3      149  2015-01-05   310003    港股通(沪)   312001  正常交易日
```

--------------------------------

### Example: Query Remaining Data Allowance

Source: https://www.joinquant.com/help/api/doc?id=10492&name=logon

Demonstrates calling the account info function and printing the returned dictionary.

```python
#查询当日剩余可调用数据条数
infos = get_account_info()
print(infos)

>>>{'license': 1,
 'date_range_start': datetime.datetime(2021, 7, 14, 0, 0),
 'query_count_limit': 100000,
 'date_range_end': '*',
 'expire_time': datetime.datetime(2024, 3, 7, 0, 0),
 'mob': '199XXXXXXXX'}
```

--------------------------------

### Example: Fetching Option Tick Data

Source: https://www.joinquant.com/help/api/doc?id=9969&name=JQDatadoc

Example demonstrating how to retrieve tick data for a specific option contract using end_dt and count parameters.

```python
#获取PG2204-C-4300.XDCE期权合约在“2022-03-07”前8个时间单位的tick数据,start_dt和count只能有一个不为None值
df=get_ticks('PG2204-C-4300.XDCE',end_dt='2022-03-07',count=8)
print(df)

                     time  current   high    low  volume    money  position  \
0 2021-07-09 13:38:43.228    649.8  649.8  627.6     2.0  25548.0       1.0   
1 2021-07-16 13:52:32.956    583.2  583.2  583.2     1.0  11664.0       1.0   
2 2021-08-12 13:37:10.824    699.8  699.8  699.8     1.0  13996.0       2.0   
3 2021-08-24 13:35:48.156    550.6  550.6  550.6     1.0  11012.0       2.0   
4 2021-12-22 14:23:37.476    411.8  411.8  411.8     1.0   8236.0       1.0   

    a1_p  a1_v   b1_p  b1_v  
0  682.8   2.0  645.4   1.0  
1  603.2   1.0  570.4   2.0  
2  702.2   1.0  660.4   1.0  
3  587.8   1.0  556.4   1.0  
4  431.2   1.0  411.2   1.0  
```

--------------------------------

### Example: Fetching Alpha 101 Factors

Source: https://www.joinquant.com/help/api/doc?id=10534&name=JQDatadoc

Example usage of get_all_alpha_101 with pandas display settings configured.

```python
import pandas as pd
pd.set_option('display.max_rows', None)  
pd.set_option('display.max_columns', None)

get_all_alpha_101(code=None,date='2023-07-20',alpha=None)[:5]

             alpha_001  alpha_002  alpha_003  alpha_004  alpha_005  alpha_006  \
000001.XSHE  -0.397467  -0.547174  -0.441010  -0.888889  -0.372487  -0.348236   
000002.XSHE   0.232496  -0.895346   0.008940  -0.888889  -0.370538  -0.129153   
000004.XSHE  -0.004089   0.451901   0.242697  -0.666667  -0.335587  -0.298457   
000005.XSHE   0.232496   0.356102  -0.409565  -0.444444  -0.408287  -0.344957   
000006.XSHE   0.423200  -0.681307  -0.016132  -1.000000  -0.505174  -0.244237   

             alpha_007  alpha_008  alpha_009  alpha_010  alpha_011  alpha_012  \
000001.XSHE  -1.000000  -0.528328      -0.01   0.285572   0.076890      -0.01   
000002.XSHE  -0.033333  -0.791792       0.02   0.353422   0.644826      -0.02   
000004.XSHE  -0.950000  -0.959560      -0.48   0.079625   1.852289       0.48   
000005.XSHE   0.150000  -0.705506       0.00   0.304131   0.524998       0.00   
000006.XSHE  -0.350000  -0.725125      -0.03   0.243963   0.428006      -0.03   
....
....
```

--------------------------------

### Example Usage of get_index_stocks

Source: https://www.joinquant.com/help/api/doc?id=10763&name=JQDatadoc

Demonstrates how to call the function and print the first five stock codes from the returned list.

```python
JQ= get_index_stocks('JQ0001.SPI')
print(JQ[:5])

['000001.XSHE', '000002.XSHE', '000004.XSHE', '000006.XSHE', '000007.XSHE']
```

--------------------------------

### Retrieve style factor returns

Source: https://www.joinquant.com/help/api/doc?id=10318&name=JQDatadoc

Examples demonstrating how to fetch style factor returns for specific factors and industries.

```python
#返回市值、贝塔、动量和申万行业中电器设备Ⅰ的风格暴露收益率
from jqdatasdk import *
df = get_factor_style_returns(['size','beta','momentum','801730'] , end_date='2022-09-01',count=2)
print(df)
>>>            momentum      size      beta
2022-08-31 -0.006386  0.005105 -0.004319
2022-09-01  0.001502 -0.002413 -0.002344
```

```python
#返回各风格因子和行业的风格暴露收益率
factors=['size','beta','momentum']
industry=get_industries(name='sw_l1', date="2024-03-04").index.tolist()
factors+=industry
df = get_factor_style_returns(factors , end_date='2024-03-04',count=2)
print(df)
                size      beta  momentum    801740    801110    801160  \
2024-03-01 -0.000049  0.007306  0.001623  0.004321  0.005648 -0.005210   
2024-03-04  0.001993  0.002615  0.006406 -0.003117  0.010871  0.010889   

              801770    801010    801120    801750    801050    801890  \
2024-03-01  0.007611 -0.014258 -0.005887  0.013469 -0.001397  0.003204   
2024-03-04  0.004516 -0.004206 -0.005126 -0.000859  0.003369  0.000285   

              801950    801980    801970    801170    801710    801780  \
2024-03-01  0.011792 -0.011260  0.001416 -0.009285 -0.000874  0.002390   
2024-03-04  0.009799  0.001316 -0.001012 -0.000165 -0.006680 -0.008169   

              801960    801130    801180    801760    801200    801230  \
2024-03-01 -0.002266 -0.001771 -0.007426  0.006103 -0.000325 -0.002224   
2024-03-04  0.016863  0.003803 -0.017994  0.001779 -0.003024 -0.006256   

              801880    801140    801720    801080    801790    801030  \
2024-03-01 -0.004408 -0.007582 -0.005680  0.009736 -0.006065 -0.001757   
2024-03-04 -0.012024 -0.004254 -0.006216  0.005643 -0.016220 -0.003845   

              801210    801150    801730    801040  
2024-03-01 -0.005916 -0.007509  0.002144 -0.004160  
2024-03-04 -0.003953  0.014519  0.002164 -0.004327
```

--------------------------------

### Example Usage of get_factor_kanban_values

Source: https://www.joinquant.com/help/api/doc?id=10450&name=JQDatadoc

Demonstrates how to import the JQData SDK and retrieve factor kanban values for the沪深300 index.

```python
from jqdatasdk import *
df = get_factor_kanban_values(universe='hs300',bt_cycle='month_3',model='long_only',category=['quality','basics','emotion','growth','risk','pershare'],skip_paused=False,commision_slippage=0)
print(df.head(5))

>>>         date category    ...           ir   good_ic
0 2022-09-22   basics    ...    -0.174076  0.952381
1 2022-09-22   basics    ...    -0.114981  0.873016
2 2022-09-22   basics    ...    -0.052311  0.841270
3 2022-09-22   basics    ...    -0.041307  0.841270
4 2022-09-22   basics    ...    -0.015338  0.841270

[5 rows x 21 columns]
```

--------------------------------

### Retrieve multiple instruments as panel

Source: https://www.joinquant.com/help/api/doc?id=9986&name=JQDatadoc

Example of fetching data for multiple instruments using the panel parameter.

```python
#获取多支期权
 # panel=True时且pandas<0.25时返回[pandas.Panel]对象 , panel= False 或 pandas>=0.25时返回[pandas.DataFrame]对象
panel=get_price(['MO2306-P-7400.CCFX','MO2306-P-7600.CCFX'] ,count=5, panel=True,end_date='2022-07-27 14:00:00')
#返回[pandas.DataFrame]对象,行索引是[datetime.datetime]对象, 列索引是期权代号
df=panel['open']
print(df)
```

--------------------------------

### Example usage of get_order_future_bar

Source: https://www.joinquant.com/help/api/doc?id=10719&name=JQDatadoc

Demonstrates how to call the function and print the resulting DataFrame.

```python
df=get_order_future_bar(symbol='IF', future_type='4q',start_dt='2024-04-05', end_dt='2024-05-05', unit='5m', 
                     fields=('code','date','open','high','low','close','volume','money','open_interest'))
print(df[:2])

          code                date    open    high     low   close  volume  \
0  IF2409.CCFX 2024-04-08 09:35:00  3497.2  3497.6  3485.0  3485.2   890.0   
1  IF2409.CCFX 2024-04-08 09:40:00  3485.6  3485.6  3476.6  3482.0   609.0   

         money  open_interest  
0  932502900.0        51019.0  
1  635936940.0        50911.0  
```

--------------------------------

### Example Usage of Risk Factors

Source: https://www.joinquant.com/help/api/doc?id=10440&name=JQDatadoc

Demonstrates how to fetch specific risk factors for a stock and print the resulting data.

```python
from jqdatasdk import get_factor_values
factor_data = get_factor_values(securities=['000001.XSHE'], factors=['Variance20','Skewness20','Kurtosis20'],start_date='2022-01-01', end_date='2022-01-10')

# 查看因子值
print(factor_data['Kurtosis20'])
```

--------------------------------

### Retrieve Market Data as Panel

Source: https://www.joinquant.com/help/api/doc?id=9981&name=JQDatadoc

Example of fetching data for multiple instruments using the panel parameter.

```python
#获取多支期货
# panel=True时且pandas<0.25时返回[pandas.Panel]对象 , panel= False 或 pandas>=0.25时返回[pandas.DataFrame]对象
panel=get_price(['IF2206.CCFX', 'IF2206.CCFX'] ,count=2, panel=True,end_date='2021-12-19')
#返回[pandas.DataFrame]对象,行索引是[datetime.datetime]对象, 列索引是期货代号
df=panel['open']
print(df)

            IF2206.CCFX  IF2206.CCFX
2021-12-16       4981.8       4981.8
2021-12-17       5008.4       5008.4
```

--------------------------------

### Get CSI 300 Index Style Exposure

Source: https://www.joinquant.com/help/api/doc?id=10768&name=JQDatadoc

Example demonstrating how to retrieve the 'size' style factor exposure for the CSI 300 index over a specific date range.

```python
get_index_style_exposure('000300.XSHG', factors='size',start_date='2025-01-01', end_date='2025-01-31')

                size
2025-01-02    0.852575
2025-01-03    0.838789
2025-01-06    0.836143
2025-01-07    0.845130
2025-01-08    0.844728
2025-01-09    0.849589
2025-01-10    0.841113
2025-01-13    0.844195
2025-01-14    0.855145
2025-01-15    0.856006
2025-01-16    0.858106
2025-01-17    0.856345
2025-01-20    0.860688
2025-01-21    0.859198
```

--------------------------------

### Install JQData SDK

Source: https://www.joinquant.com/help/api/doc?id=9832&name=logon

Use pip to install the JQData SDK in your local Python environment.

```bash
pip install jqdatasdk
```

--------------------------------

### Install or Upgrade JQData SDK

Source: https://www.joinquant.com/help/api/doc?id=10748&name=JQDatadoc

Commands to install or update the jqdatasdk package in a Python environment.

```bash
pip install jqdatasdk
```

```bash
pip install -U jqdatasdk
```

```bash
C:\JQData>python.exe -m pip install jqdatasdk
```

```bash
pip install thriftpy2==0.4.20
```

--------------------------------

### Example: Fetching Factor Covariance

Source: https://www.joinquant.com/help/api/doc?id=10654&name=JQDatadoc

Demonstrates how to initialize the JQData SDK and retrieve the covariance matrix for a specific date range.

```python
from jqdatasdk import *
df = get_factor_cov("2024-03-01",'2024-03-10')
print(df.head(5))
```

--------------------------------

### Example: Fetching Call Auction Data

Source: https://www.joinquant.com/help/api/doc?id=9876&name=JQDatadoc

Example usage of get_call_auction to retrieve data for a specific stock over a defined date range.

```python
#获取平安银行2019-09-02至2019-09-05期间的集合竞价数据
df=get_call_auction('000001.XSHE','2022-09-02','2022-09-05')
print(df)

          code                time  current    volume      money   a1_p  \
0  000001.XSHE 2022-09-02 09:25:00    12.62  369458.0  4662559.0  12.63   
1  000001.XSHE 2022-09-05 09:25:00    12.46  394700.0  4917962.0  12.46   

     a1_v   a2_p     a2_v   a3_p     a3_v   a4_p    a4_v   a5_p     a5_v  \
0  8200.0  12.64   5000.0  12.65  12900.0  12.66  4500.0  12.67   4900.0   
1  9355.0  12.47  28000.0  12.48  58100.0  12.49  4600.0  12.50  13400.0   

    b1_p      b1_v   b2_p     b2_v   b3_p     b3_v   b4_p     b4_v   b5_p  \
0  12.62   95142.0  12.61  26200.0  12.60  83500.0  12.59  15500.0  12.58   
1  12.45  638300.0  12.44  95900.0  12.43  66000.0  12.42  59000.0  12.41   

       b5_v  
0  107600.0  
1  217500.0
```

--------------------------------

### Retrieve Market Data as DataFrame

Source: https://www.joinquant.com/help/api/doc?id=10760&name=JQDatadoc

Example showing how to fetch minute-level market data for multiple securities and return it as a pandas DataFrame.

```python
df =get_price(['JQ0001.SPI','JQ0002.SPI'], start_date= '2024-01-29 09:30:00',end_date='2024-01-29 09:32:00', frequency='1m', fields=['open','close','low','high','volume','money','high_limit','low_limit','avg','pre_close'])
print(df)

                 time        code       open      close        low       high  \
0 2024-01-29 09:31:00  JQ0001.SPI  2666.3434  2665.2402  2664.5472  2668.9695   
1 2024-01-29 09:32:00  JQ0001.SPI  2665.6406  2674.3320  2665.6406  2674.3320   
2 2024-01-29 09:31:00  JQ0002.SPI  3852.0857  3848.8644  3847.8467  3855.6814   
3 2024-01-29 09:32:00  JQ0002.SPI  3849.6311  3862.6013  3849.6311  3862.6072   

         volume         money  high_limit  low_limit        avg  pre_close  
0  3.670972e+09  3.482636e+10   2932.9777  2399.7091  2032.3485  2660.6427  
1  1.612269e+09  1.677370e+10   2932.9777  2399.7091  2670.9946  2665.2402  
2  1.842943e+09  1.532560e+10   4237.2943  3466.8771  2931.1753  3843.4875  
3  7.824228e+08  6.945955e+09   4237.2943  3466.8771  3857.7282  3848.8644  
```

--------------------------------

### Retrieve Index Valuation Data Example

Source: https://www.joinquant.com/help/api/doc?id=10716&name=JQDatadoc

Example usage of get_index_valuation to fetch specific valuation fields for an index over a date range.

```python
df=get_index_valuation('000001.XSHG', start_date='2024-05-02', end_date='2024-06-03', fields=['pe_ratio','turnover_ratio','pb_ratio','ps_ratio','pcf_ratio','capitalization','market_cap','circulating_cap','circulating_market_cap','pe_ratio_lyr','pcf_ratio2','dividend_ratio','free_cap','free_market_cap','a_cap','a_market_cap'])
df[:2]

          code         day  pe_ratio  turnover_ratio  pb_ratio  ps_ratio  \
0  000001.XSHG  2024-05-06   13.7636          0.9871    1.2518    1.1333   
1  000001.XSHG  2024-05-07   13.7963          0.8289    1.2548    1.1360   

   pcf_ratio  capitalization   market_cap  circulating_cap  \
0    53.3981    5.702561e+08  583216.0276     4.484397e+08   
1    53.5255    5.702543e+08  584603.7107     4.485195e+08   

   circulating_market_cap  pe_ratio_lyr  pcf_ratio2  dividend_ratio  \
0             447100.1502       13.6186      6.0075          2.9276   
1             448185.3737       13.6510      6.0217          2.9213   

       free_cap  free_market_cap         a_cap  a_market_cap  
0  1.573695e+08      174875.2534  4.813381e+08   479749.7052  
1  1.573688e+08      175177.0132  4.813348e+08   480818.7916  
```

--------------------------------

### 获取期货龙虎榜数据基础查询

Source: https://www.joinquant.com/help/api/doc?id=9910&name=JQDatadoc

使用 finance.run_query 执行基础的期货龙虎榜数据查询。

```python
from jqdatasdk import finance
finance.run_query(query(finance.FUT_MEMBER_POSITION_RANK).filter(finance.FUT_MEMBER_POSITION_RANK.code==code).limit(n))
```

--------------------------------

### Example: Retrieve CNE6 Style and Industry Factor Returns

Source: https://www.joinquant.com/help/api/doc?id=10659&name=JQDatadoc

Retrieves returns for all CNE6 style factors combined with industry factors.

```python
#返回各CNE6风格因子和行业的风格暴露收益率
factors=['btop', 'divyild', 'earnqlty', 'earnvar', 'earnyild', 'financial_leverage', 'invsqlty', 'liquidty', 'long_growth', 'ltrevrsl', 'market_beta', 'market_size', 'midcap', 'profit', 'relative_momentum', 'resvol']
industry=get_industries(name='sw_l1', date="2024-02-04").index.tolist()
factors+=industry
df = get_factor_style_returns(factors , end_date='2024-02-04',count=2)
print(df)
>>>             btop   divyild  earnqlty    ...       801150    801730    801040
2024-02-01 -0.000462 -0.000610 -0.000269    ...     0.003406  0.001192  0.001316
2024-02-02 -0.000113  0.000923 -0.000061    ...    -0.006946 -0.005291  0.005308

[2 rows x 47 columns]
```

--------------------------------

### Retrieve Index Data

Source: https://www.joinquant.com/help/api/doc?id=9989&name=JQDatadoc

Example of fetching daily bar data for a single index.

```python
#获取沪深300指数在“2020-9-16”前5个时间单位的数据

df = get_bars('000300.XSHG', 8, unit='1d',fields=['date','open','high','low','close'],include_now=False,end_dt='2020-9-16')
print(df)
         date     open     high      low    close
0  2020-09-04  4737.24  4778.20  4726.56  4770.22
1  2020-09-07  4759.52  4789.30  4655.56  4669.37
2  2020-09-08  4683.83  4707.90  4639.05  4694.39
3  2020-09-09  4634.25  4645.20  4558.03  4584.59
4  2020-09-10  4632.10  4647.75  4572.21  4581.98
5  2020-09-11  4572.26  4630.88  4563.11  4627.28
6  2020-09-14  4652.05  4666.99  4625.99  4651.05
7  2020-09-15  4649.45  4691.50  4630.54  4688.48
```

--------------------------------

### Retrieve Multiple Indices Data

Source: https://www.joinquant.com/help/api/doc?id=9989&name=JQDatadoc

Example of fetching 5-minute interval data for multiple indices.

```python
df = get_bars(['000300.XSHG','000001.XSHG'],4, unit='5m',fields=['date','open','high','low','close'],include_now=True,end_dt='2020-9-16 14:22:35')
print(df)


                             date     open     high      low    close
000300.XSHG 0 2020-09-16 14:10:00  4657.03  4657.93  4650.24  4650.24
            1 2020-09-16 14:15:00  4649.63  4649.63  4641.87  4643.58
            2 2020-09-16 14:20:00  4643.67  4646.66  4642.36  4642.36
            3 2020-09-16 14:22:00  4641.59  4641.64  4638.90  4638.90
000001.XSHG 0 2020-09-16 14:10:00  3282.83  3283.60  3279.32  3279.32
            1 2020-09-16 14:15:00  3278.67  3278.67  3273.17  3274.24
            2 2020-09-16 14:20:00  3274.25  3275.90  3273.28  3273.36
            3 2020-09-16 14:22:00  3272.78  3273.05  3271.11  3271.11
```

--------------------------------

### Example Usage of get_factor_values

Source: https://www.joinquant.com/help/api/doc?id=10436&name=JQDatadoc

Demonstrates how to fetch specific growth factors for a stock over a defined date range and print the results.

```python
from jqdatasdk import get_factor_values

factor_data = get_factor_values(securities=['000001.XSHE'], factors=['operating_revenue_growth_rate','total_asset_growth_rate','net_operate_cashflow_growth_rate'], 
                                start_date='2022-01-01', end_date='2022-01-10')
# 查看因子值
print(factor_data['net_operate_cashflow_growth_rate'])
```

--------------------------------

### 查询股东户数基础接口

Source: https://www.joinquant.com/help/api/doc?id=10015&name=JQDatadoc

使用 finance.run_query 获取股东户数数据，每次调用最多返回 5000 行。

```python
from jqdatasdk import finance
finance.run_query(query(finance.STK_HOLDER_NUM).filter(finance.STK_HOLDER_NUM.code==code).limit(n))
```

--------------------------------

### get_concepts()

Source: https://www.joinquant.com/help/api/doc?id=9865&name=JQDatadoc

Retrieves a list of all available concept sectors, including their codes, names, and start dates.

```APIDOC
## get_concepts()

### Description
Retrieves the list of concept sectors available in the JQData system. The data is updated daily at 8:00 AM.

### Returns
- **pandas.DataFrame**: A DataFrame containing the following columns:
  - **index**: Concept code
  - **name**: Concept name
  - **start_date**: The date the concept was introduced

### Example
```python
df = get_concepts()[:5]
print(df)
```
```

--------------------------------

### Get Index Valuation Function Signature

Source: https://www.joinquant.com/help/api/doc?id=10716&name=JQDatadoc

The function signature for retrieving index valuation data.

```python
get_index_valuation(security_list, start_date=None, end_date=None, fields=None, count=None)
```

--------------------------------

### Get Historical Financial Data Definition

Source: https://www.joinquant.com/help/api/doc?id=10326&name=JQDatadoc

Function signature for retrieving multi-period financial data.

```python
get_history_fundamentals(security, fields, watch_date=None, stat_date=None, count=1, interval='1q', stat_by_year=False)
```

--------------------------------

### opt.run_query(query(opt.OPT_DAILY_PREOPEN))

Source: https://www.joinquant.com/help/api/doc?id=9925&name=JQDatadoc

Retrieves daily pre-open static data for options. Users can filter by contract code and limit the number of returned records.

```APIDOC
## SDK Method: opt.run_query(query(opt.OPT_DAILY_PREOPEN))

### Description
Retrieves daily pre-open static data for options, supporting Shanghai and Shenzhen stock exchange options. Data is updated daily at 9:05 AM.

### Parameters
- **query(opt.OPT_DAILY_PREOPEN)**: Specifies the table to query. Can be combined with specific fields.
- **filter(opt.OPT_DAILY_PREOPEN.code==code)**: Filters results by specific contract code.
- **limit(n)**: Limits the number of returned records (max 5000 per call).

### Response
Returns a pandas DataFrame where each row represents a record and columns represent the queried fields.

### Example
```python
from jqdatasdk import *
q = query(opt.OPT_DAILY_PREOPEN.code, opt.OPT_DAILY_PREOPEN.trading_code).filter(opt.OPT_DAILY_PREOPEN.code=='10001313.XSHG').limit(10)
df = opt.run_query(q)
print(df)
```
```

--------------------------------

### Retrieve Multiple Assets Data

Source: https://www.joinquant.com/help/api/doc?id=9990&name=JQDatadoc

Example of retrieving data for multiple assets using the panel parameter.

```python
# panel=True时且pandas<0.25时返回[pandas.Panel]对象 , panel= False 或 pandas>=0.25时返回[pandas.DataFrame]对象

panel=get_price(['000300.XSHG', '000001.XSHE'] ,count=2, panel=True)
#返回[pandas.DataFrame]对象,行索引是[datetime.datetime]对象, 列索引是指数代号
df=panel['money']
print(df)

             000300.XSHG  000001.XSHE
2015-12-30  1.557441e+11  641277248.0
2015-12-31  1.450437e+11  591643584.0
```

--------------------------------

### Example: Normalize Exchange-Traded Fund Codes

Source: https://www.joinquant.com/help/api/doc?id=10254&name=JQDatadoc

Demonstrates converting a list of fund codes into their respective JoinQuant standard formats with market suffixes.

```python
#输入
normalize_code(['150008','561130'])
#输出
['150008.XSHE', '561130.XSHG']
```

--------------------------------

### Retrieve Multiple Options Data

Source: https://www.joinquant.com/help/api/doc?id=9987&name=JQDatadoc

Example of fetching data for multiple option contracts using a list.

```python
# panel=True时且pandas<0.25时返回[pandas.Panel]对象 , panel= False 或 pandas>=0.25时返回panel=get_price(['ZC107C740.XZCE','ZC107C750.XZCE'] ,count=2, panel=True,end_date='2021-04-05')
#返回[pandas.DataFrame]对象,行索引是[datetime.datetime]对象, 列索引是股票代号
df=panel['open']
print(df)

            ZC107C740.XZCE  ZC107C750.XZCE
2021-04-01            26.0            23.1
2021-04-02            27.6            22.6   
```

--------------------------------

### Example: Fetching Index Tick Data

Source: https://www.joinquant.com/help/api/doc?id=9971&name=JQDatadoc

Demonstrates how to retrieve tick data for the Shanghai Composite Index within a specific time range.

```python
# 获取上证指数2019-11-19 10:00:00 到 2019-11-19 10:01:00的tick数据
df=get_ticks('000001.XSHG','2019-11-19 10:00:00','2019-11-19 10:01:00')
print(df)
                      time    current       high       low        volume  \
0  2019-11-19 10:00:01.608  2913.4288  2914.6199  2902.855  2.795233e+09   
1  2019-11-19 10:00:06.600  2913.1890  2914.6199  2902.855  2.800322e+09   
2  2019-11-19 10:00:10.628  2913.0532  2914.6199  2902.855  2.807082e+09   
3  2019-11-19 10:00:16.608  2913.3964  2914.6199  2902.855  2.814624e+09   
4  2019-11-19 10:00:21.620  2913.2224  2914.6199  2902.855  2.818837e+09   
5  2019-11-19 10:00:25.620  2913.6741  2914.6199  2902.855  2.824412e+09   
6  2019-11-19 10:00:31.608  2912.6091  2914.6199  2902.855  2.830766e+09   
7  2019-11-19 10:00:36.588  2913.6492  2914.6199  2902.855  2.835326e+09   
8  2019-11-19 10:00:40.620  2913.5004  2914.6199  2902.855  2.840942e+09   
9  2019-11-19 10:00:46.580  2913.7269  2914.6199  2902.855  2.846658e+09   
10 2019-11-19 10:00:51.588  2913.7699  2914.6199  2902.855  2.850605e+09   
11 2019-11-19 10:00:55.608  2913.9637  2914.6199  2902.855  2.856479e+09   

           money  
0   3.171538e+10  
1   3.178079e+10  
2   3.185376e+10  
3   3.193569e+10  
4   3.198723e+10  
5   3.204712e+10  
6   3.212560e+10  
7   3.217697e+10  
8   3.224518e+10  
9   3.231385e+10  
10  3.236239e+10  
11  3.242965e+10  
```
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