### Get Security Start Date

Source: https://www.joinquant.com/help/api/doc?id=10244&name=JQDatadoc

Example of retrieving the listing date for a security.

```python
#获取000001.XSHE的上市时间
start_date = get_security_info('000001.XSHE').start_date
print(start_date)
>>>1991-04-03
```

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

### Example: Get All Financial Indicator Data for a Specific Day

Source: https://www.joinquant.com/help/api/doc?id=9885&name=JQDatadoc

This example demonstrates how to retrieve all available financial indicator data for a given day.

```python

```

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

### Example: Displaying JQData Characteristic Securities

Source: https://www.joinquant.com/help/api/doc?id=10759&name=JQDatadoc

This example demonstrates how to call the get_all_securities function with 'spi' type and then slices the result to display the first 5 entries. It shows the structure of the returned data, including display name, name, start date, end date, and type.

```python
get_all_securities(types=['spi'], date=None)[:5]
```

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

### Example: Display Account Information

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

This example demonstrates how to call `get_account_info()` and print the returned dictionary containing account details.

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

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

### Get Price Function - Panel Format Example

Source: https://www.joinquant.com/help/api/doc?id=9978&name=JQDatadoc

This comment indicates an example for retrieving multiple futures contracts and returning the data in a Panel format, which is relevant for older pandas versions (<0.25).

```python
#获取多支期货

```

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

### Example: Query Market Trading Calendar Data

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

This example demonstrates how to query the market trading calendar for a date range starting from '2015-01-04' and retrieves up to 4 records. It shows the output format including the day, market ID, market name, type ID, and type.

```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)
```

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

### Get Financing and Securities Lending Data for Multiple Funds

Source: https://www.joinquant.com/help/api/doc?id=10343&name=JQDatadoc

Fetch financing and securities lending data for multiple funds over a specified period. This example first retrieves a list of all fund codes available on a given date.

```python
fund=get_all_securities("fund",date='2023-02-01').index.tolist()
df=get_mtss(fund,'2023-02-07','2023-02-09',fields=['date','sec_code','fin_value','fin_buy_value','fin_refund_value','sec_value','sec_sell_value','sec_refund_value','fin_sec_value'])
```

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

### Install JQData

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

Instructions for installing the jqdatasdk package for Python users.

```APIDOC
## Install JQData

To install the JQData SDK for Python, open your terminal or command prompt and run:

```
pip install jqdatasdk
```

If you encounter any issues, please refer to the detailed JQData installation tutorial.
```

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

### Import JQData Library and Get Factor Values

Source: https://www.joinquant.com/help/api/doc?id=10635&name=JQDatadoc

Imports the JQData library and demonstrates the usage of the `get_factor_values` function to retrieve specific style factor data for a given security and date range. The example then prints the 'btop' factor values.

```python
# 导入函数库
from jqdatasdk import *
# 取值函数
get_factor_values(securities, factors, start_date, end_date, count)

```

```python
from jqdatasdk import get_factor_values
factor_data = get_factor_values(securities=['000001.XSHE'], factors=['btop','divyild','earnqlty'], 
                                start_date='2022-01-01', end_date='2022-01-10')
print(factor_data['btop'])
```

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

### Install JQData

Source: https://www.joinquant.com/help/api/doc?id=9823&name=logon

Instructions for installing or upgrading the jqdatasdk package for Python users.

```APIDOC
## Install JQData

### Description
Install the jqdatasdk package using pip.

### Method
pip install

### Endpoint
N/A

### Parameters
N/A

### Request Example
```bash
pip install jqdatasdk
```

### Response
N/A
```

```APIDOC
## Upgrade JQData

### Description
Upgrade the jqdatasdk package to the latest version.

### Method
pip install -U

### Endpoint
N/A

### Parameters
N/A

### Request Example
```bash
pip install -U jqdatasdk
```

### Response
N/A
```

```APIDOC
## Install Thriftpy2 (if needed)

### Description
Manually install a lower version of thriftpy2 if C++ components are required during installation and you wish to avoid installing them.

### Method
pip install

### Endpoint
N/A

### Parameters
N/A

### Request Example
```bash
pip install thriftpy2==0.4.20
```

### Response
N/A
```

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

### Install ThriftPy2 for Compatibility

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

If installation prompts for C++ components and you wish to avoid installing them, install a specific lower version of thriftpy2. This is a common workaround for certain dependency issues.

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

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

### Example: Query Latest Daily Pre-open Options Data

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

This example demonstrates how to query the latest daily pre-open static data for a specific option contract code. It selects specific fields, filters by contract code, orders by date in descending order, and limits the result to 10 entries.

```python
#查询"10001313.XSHG "最新的期权每日盘前静态数据。
from jqdatasdk import *
q=query(opt.OPT_DAILY_PREOPEN.code,
        opt.OPT_DAILY_PREOPEN.trading_code,
        opt.OPT_DAILY_PREOPEN.name,
opt.OPT_DAILY_PREOPEN.exercise_date,).filter(opt.OPT_DAILY_PREOPEN.code=='10001313.XSHG').order_by(opt.OPT_DAILY_PREOPEN.date.desc()).limit(10)
df=opt.run_query(q)
print(df)
```

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

### Example: Get Hengrui Medicine Status Changes

Source: https://www.joinquant.com/help/api/doc?id=10023&name=JQDatadoc

This example demonstrates how to retrieve the status change history for a specific stock, '600276.XSHG' (Hengrui Medicine), returning up to 10 records.

```python
# Specify the query object for Hengrui Medicine (600276.XSHG) company status changes
q=query(finance.STK_STATUS_CHANGE).filter(finance.STK_STATUS_CHANGE.code=='600276.XSHG').limit(10)
df=finance.run_query(q)
print(df)
```

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

### Example: Fetching Net Operating Cash Flow Growth Rate

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

Demonstrates how to fetch specific growth factor values for a given stock and date range. The example shows how to print the retrieved net operating cash flow growth rate.

```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'])
```

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

### Get Security Display Name

Source: https://www.joinquant.com/help/api/doc?id=10244&name=JQDatadoc

Example of retrieving the Chinese display name for a security on a specific date.

```python
# 获取000001.XSHE某一日期的中文名称
display_name = get_security_info('000001.XSHE',date='2017-08-31’).display_name
print(display_name)
>>>平安银行
```

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

### Get Security Information

Source: https://www.joinquant.com/help/api/doc?id=9840&name=JQDatadoc

Retrieves detailed information about a specific security. Use this to get attributes like display name, start date, end date, type, and parent fund for a given security code.

```python
get_security_info(code,date=None)
```

```python
# 获取000001.XSHE某一日期的中文名称
display_name = get_security_info('000001.XSHE',date='2017-08-31’).display_name
print(display_name)
>>>平安银行
```

```python
#获取000001.XSHE的上市时间
start_date = get_security_info('000001.XSHE').start_date
print(start_date)
>>>1991-04-03
```

```python
#获取150008.XSHE的母基金代码
parent = get_security_info('150008.XSHE').parent
print(parent)
```

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

### Example: Fetching and Printing AH Stock Price Data

Source: https://www.joinquant.com/help/api/doc?id=9868&name=JQDatadoc

This example demonstrates how to construct a query for the `STK_AH_PRICE_COMP` table, specifying a stock code and limiting the results. The fetched data is then printed to the console.

```python
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(4)
df=finance.run_query(q)
print(df)
```

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

### Get Convertible Bond Listing Date

Source: https://www.joinquant.com/help/api/doc?id=10341&name=JQDatadoc

Example of retrieving the listing date for a convertible bond using its security code.

```python
# 获取000001.XSHE的上市时间
start_date = get_security_info('110043.XSHG').start_date
print(start_date)
>>>2018-03-14
```

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

### Get Convertible Bond Chinese Name

Source: https://www.joinquant.com/help/api/doc?id=10341&name=JQDatadoc

Example of retrieving the Chinese display name for a convertible bond using its security code.

```python
# 获取110043.XSHG的中文名称
display_name = get_security_info('110043.XSHG').display_name
print(display_name)
>>>无锡转债
```

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

### Example: Query Latest Option Contract Data

Source: https://www.joinquant.com/help/api/doc?id=9918&name=JQDatadoc

This example demonstrates how to query the latest basic data for a specific option contract using its code. The result is returned as a pandas DataFrame.

```python
#查询"10001313.XSHG "最新的期权基本资料数据。
from jqdatasdk import *
q=query(opt.OPT_CONTRACT_INFO).filter(opt.OPT_CONTRACT_INFO.code=='10001313.XSHG')
df=opt.run_query(q)
print(df)
```

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

### Query Bond Database for Repo Daily Prices (Python)

Source: https://www.joinquant.com/help/api/doc?id=10524&name=JQDatadoc

Example showing how to query the bond database for daily repo prices within a specified date range using `run_offset_query`. It's recommended to filter by indexed fields like date or security code for faster queries.

```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 = bond.run_offset_query(q)

```

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

### Install or Upgrade JQData SDK

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

If the `get_account_info` interface is not defined, upgrade your jqdatasdk to the latest version using pip.

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

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

### Get Valuation Data for a Specific Date

Source: https://www.joinquant.com/help/api/doc?id=9884&name=JQDatadoc

Fetches valuation data for all stocks on a specified trading day. The example shows data for '2021-01-05' and truncates the output.

```python
get_fundamentals(query(valuation),date="2021-01-05")[:4]
```

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

### Get Historical Data for a Single Futures Contract

Source: https://www.joinquant.com/help/api/doc?id=9980&name=JQDatadoc

Retrieves historical bar data for a specified futures contract. This example fetches 5 bars of 120-minute data for the 'A0603.XDCE' contract ending before '2006-03-15'.

```APIDOC
## get_bars (Single Contract)

### Description
Retrieves historical bar data for a single futures contract.

### Method
Python Function Call

### Parameters
- **'A0603.XDCE'** (string) - Required - The symbol of the futures contract.
- **5** (integer) - Required - The number of bars to retrieve.
- **unit='120m'** (string) - Required - The time unit for the bars (e.g., '120m' for 120 minutes).
- **fields=['open','close','low','high','volume','money','open_interest']** (list of strings) - Required - The fields to retrieve.
- **include_now=False** (boolean) - Optional - Whether to include data from the current incomplete bar.
- **end_dt='2006-03-15'** (string) - Optional - The end date for the data retrieval.

### Request Example
```python
df = get_bars('A0603.XDCE', 5, unit='120m',fields=['open','close','low','high','volume','money','open_interest'],include_now=False,end_dt='2006-03-15')
print(df)
```

### Response
#### Success Response
- **DataFrame** - A pandas DataFrame containing the historical bar data with columns for date, open, close, low, high, volume, money, and open_interest.
```

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

### Example Usage of get_factor_kanban_values

Source: https://www.joinquant.com/help/api/doc?id=10656&name=JQDatadoc

Demonstrates how to call the get_factor_kanban_values function and print the head of the resulting DataFrame. This example fetches data for the 'hs300' universe with a 3-month backtest cycle using the 'long_only' model and 'style_pro' category.

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

        date   category         code universe bt_cycle  skip_paused  \
0 2024-03-22  style_pro       resvol    hs300  month_3        False   
1 2024-03-22  style_pro  market_beta    hs300  month_3        False   
2 2024-03-22  style_pro       midcap    hs300  month_3        False   
3 2024-03-22  style_pro       profit    hs300  month_3        False   
4 2024-03-22  style_pro     liquidty    hs300  month_3        False   

   commision_slippage  compound_return_1q  compound_return_5q  \
0                   0            0.082971            0.095416   
1                   0            0.132900            0.087422   
2                   0            0.115523            0.012262   
3                   0            0.096083            0.045657   
4                   0            0.091171            0.074147   

   annual_return_1q  annual_return_5q  max_drawdown_1q  max_drawdown_5q  \
0          0.418498          0.491396         0.028479         0.201936   
1          0.728554          0.444245         0.033119         0.197513   
2          0.615251          0.054910         0.046911         0.192985   
3          0.495380          0.216301         0.032075         0.100147   
4          0.575279          0.451410         0.028270         0.157285   

   sharpe_1q  sharpe_5q  turnover_mean_1q  turnover_mean_5q  annual_return_bm  \
0   2.965449   1.225099          0.015205          0.016667          0.324823   
1   4.462848   1.209714          0.011111          0.017836          0.324823   
2   3.804401   0.048918          0.552632          0.017544          0.324823   
3   3.318204   0.946735          0.000292          0.000000          0.324823   
4   3.551314   1.142525          0.010069          0.004861          0.324823   

    ic_mean        ir   good_ic  
0 -0.075242 -0.207315  0.982456  
1 -0.067486 -0.166541  0.964912  
2 -0.045928 -0.156631  0.982456  
3 -0.043151 -0.203651  0.929825  
4 -0.036663 -0.102687  0.958333  

```

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

### Fetch All Government Bond Reverse Repo Daily Market Data

Source: https://www.joinquant.com/help/api/doc?id=9948&name=JQDatadoc

This example retrieves all available daily market data for government bond reverse repos. It then sorts the results by 'deal_number' in descending order and displays the top 5 entries.

```python
from jqdatasdk import *
df=bond.run_query(query(bond.REPO_DAILY_PRICE))
print(df.sort_values(by='deal_number',ascending=False)[:5])
```

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

### Get Historical Industry Component Records for an Entire Industry

Source: https://www.joinquant.com/help/api/doc?id=10769&name=JQDatadoc

Fetch historical inclusion and exclusion records for all stocks within a specified industry. This example shows how to retrieve the first 5 records for the ZJW industry.

```python
get_history_industry('zjw')[:5]
```

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

### Get Historical Data for Multiple Futures Contracts

Source: https://www.joinquant.com/help/api/doc?id=9980&name=JQDatadoc

Retrieves historical bar data for multiple futures contracts simultaneously. This example fetches 5 bars of 120-minute data for 'A2109.XDCE' and 'A2111.XDCE' contracts ending before '2020-12-19'.

```APIDOC
## get_bars (Multiple Contracts)

### Description
Retrieves historical bar data for multiple futures contracts.

### Method
Python Function Call

### Parameters
- **['A2109.XDCE', 'A2111.XDCE']** (list of strings) - Required - The symbols of the futures contracts.
- **5** (integer) - Required - The number of bars to retrieve.
- **unit='120m'** (string) - Required - The time unit for the bars (e.g., '120m' for 120 minutes).
- **fields=['open','close','low','high','volume','money','open_interest']** (list of strings) - Required - The fields to retrieve.
- **include_now=False** (boolean) - Optional - Whether to include data from the current incomplete bar.
- **end_dt='2020-12-19'** (string) - Optional - The end date for the data retrieval.

### Request Example
```python
df = get_bars(['A2109.XDCE', 'A2111.XDCE'], 5, unit='120m',fields=['open','close','low','high','volume','money','open_interest'],include_now=False,end_dt='2020-12-19')
print(df)
```

### Response
#### Success Response
- **DataFrame** - A pandas DataFrame containing the historical bar data for multiple contracts, indexed by contract symbol and date, with columns for open, close, low, high, volume, money, and open_interest.
```

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

### Querying Bond Daily Prices with run_offset_query

Source: https://www.joinquant.com/help/api/doc?id=10528&name=JQDatadoc

Example showing how to retrieve daily price data for government reverse repos within a specified date range using `run_offset_query`. This function helps in fetching large volumes of historical bond 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)
```

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

### Get Consolidated Balance Sheet Data

Source: https://www.joinquant.com/help/api/doc?id=9902&name=JQDatadoc

This example demonstrates how to query the consolidated balance sheet data for a financial company, extracting specific financial metrics like cash equivalents, total assets, and total liabilities.

```APIDOC
## Get Consolidated Balance Sheet Data

### Description
Retrieves consolidated balance sheet data for financial companies.

### Method
`finance.run_query()`

### Parameters
- `query`: A query object specifying the desired fields and filters.
  - `finance.FINANCE_BALANCE_SHEET.company_name` (string)
  - `finance.FINANCE_BALANCE_SHEET.code` (string)
  - `finance.FINANCE_BALANCE_SHEET.pub_date` (string)
  - `finance.FINANCE_BALANCE_SHEET.start_date` (string)
  - `finance.FINANCE_BALANCE_SHEET.end_date` (string)
  - `finance.FINANCE_BALANCE_SHEET.cash_equivalents` (float)
  - `finance.FINANCE_BALANCE_SHEET.total_assets` (float)
  - `finance.FINANCE_BALANCE_SHEET.total_liability` (float)
- `filter`:
  - `finance.FINANCE_BALANCE_SHEET.code` (string) - e.g., '601318.XSHG'
  - `finance.FINANCE_BALANCE_SHEET.pub_date` (string) - e.g., '>=2015-01-01'
  - `finance.FINANCE_BALANCE_SHEET.report_type` (integer) - e.g., 0
- `limit`: Maximum number of results to return.

### Request Example
```python
from jqdatasdk import finance
q = query(finance.FINANCE_BALANCE_SHEET.company_name, \
        finance.FINANCE_BALANCE_SHEET.code, \
        finance.FINANCE_BALANCE_SHEET.pub_date, \
        finance.FINANCE_BALANCE_SHEET.start_date, \
        finance.FINANCE_BALANCE_SHEET.end_date, \
        finance.FINANCE_BALANCE_SHEET.cash_equivalents, \
        finance.FINANCE_BALANCE_SHEET.total_assets, \
        finance.FINANCE_BALANCE_SHEET.total_liability \
).filter(finance.FINANCE_BALANCE_SHEET.code=='601318.XSHG', \
           finance.FINANCE_BALANCE_SHEET.pub_date>='2015-01-01', \
           finance.FINANCE_BALANCE_SHEET.report_type==0).limit(8)
df = finance.run_query(q)
print(df)
```

### Response
#### Success Response (DataFrame)
Returns a pandas DataFrame containing the requested financial data.
```

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

### Get Bars for Multiple Securities

Source: https://www.joinquant.com/help/api/doc?id=9999&name=JQDatadoc

This example shows how to fetch historical bar data for multiple securities simultaneously. The function call is similar to fetching data for a single security, but the security code parameter accepts a list of identifiers.

```APIDOC
## Get Bars for Multiple Securities

### Description
Fetches historical bar data for multiple securities simultaneously.

### Method
`get_bars`

### Parameters
- **security_codes** (list of strings) - Required - A list of unique identifiers for the securities (e.g., ['150049.XSHE','150050.XSHE']).
- **count** (integer) - Required - The number of historical bars to retrieve.
- **unit** (string) - Required - The time unit for the bars (e.g., '120m' for 120 minutes).
- **fields** (list of strings) - Required - A list of fields to retrieve (e.g., ['date', 'open', 'close', 'high', 'low', 'volume', 'money', 'factor']).
- **include_now** (boolean) - Optional - Whether to include data from the current, incomplete bar.
- **end_dt** (string) - Optional - The end date for the data retrieval (YYYY-MM-DD).
- **fq_ref_date** (string) - Optional - The reference date for forward/backward factor adjustment (YYYY-MM-DD).

### Request Example
```python
df = get_bars(['150049.XSHE','150050.XSHE'], 5, unit='120m',fields=['date', 'open', 'close', 'high', 'low', 'volume', 'money','factor'],include_now=False,
              end_dt='2018-12-05',fq_ref_date='2000-01-01')
print(df)
```

### Response
#### Success Response (DataFrame)
- The response is a pandas DataFrame with a MultiIndex, where the first level is the security code and the second level is the index of the bar.
- **date** (datetime) - The timestamp of the bar.
- **open** (float) - The opening price of the bar.
- **close** (float) - The closing price of the bar.
- **high** (float) - The highest price during the bar.
- **low** (float) - The lowest price during the bar.
- **volume** (float) - The trading volume during the bar.
- **money** (float) - The trading value during the bar.
- **factor** (float) - The adjustment factor for the bar.
```