# Get Company Operational Efficiency

get_company_operational_efficiency(code, num=10, next_key=None, currency_code=None, financial_type=0)

  • Description

    Get company operational efficiency data for the specified stock, including employee count, revenue per capita, operating profit per capita, and net profit per capita.

  • Parameters

    Parameter Type Description
    code str Stock code
    num int Number of records per page
    next_key str Pagination key
    currency_code str Currency code
    financial_type F10Type Financial report period
  • F10Type Enum

    Value Description
    0 No filter
    1 Q1 (First quarter)
    2 Q2 (Second quarter)
    3 Q3 (Third quarter)
    4 Q4 (Fourth quarter)
    5 Q6 (Semi-annual)
    6 Q9 (Three-quarter)
    7 Annual
  • Return

    Parameter Type Description
    ret RET_CODE API call result
    data dict When ret == RET_OK, returns operational efficiency data dict
    str When ret != RET_OK, returns error description
    • Return dict fields:

      Field Type Description
      item_list list Operational efficiency list; each item is a dict, fields shown below
      next_key str Pagination key
      currency_code str Currency code
    • item_list sub-item fields:

      Field Type Description
      fiscal_year int Fiscal year
      financial_type F10Type Financial report type
      period_text str Report period
      end_date int Period end timestamp
      end_date_str str Period end date string
      employee_num int Number of employees
      employee_num_yoy float YoY change in employee count
      income_per_capita float Revenue per capita
      income_per_capita_yoy float YoY change in revenue per capita
      profit_per_capita float Operating profit per capita
      profit_per_capita_yoy float YoY change in operating profit per capita
      net_profit_per_capita float Net profit per capita
      net_profit_per_capita_yoy float YoY change in net profit per capita
  • Example

from futu import *
import pandas as pd
quote_ctx = OpenQuoteContext(host='127.0.0.1', port=11111)

ret, data = quote_ctx.get_company_operational_efficiency("HK.00700")
if ret == RET_OK:
    df = pd.DataFrame(data.get('item_list', []))
    print(df.to_string(index=False))
else:
    print('error:', data)
quote_ctx.close()
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  • Output
fiscal_year period_text   end_date end_date_str  employee_num  employee_num_yoy  income_per_capita  income_per_capita_yoy  profit_per_capita  profit_per_capita_yoy  net_profit_per_capita  net_profit_per_capita_yoy
        2025     2025/FY 1767110400   2025-12-31        115849            4.7857       6489188.5126                 8.6594       2085145.3184                10.7787           1983625.2362                    11.6246
        2024     2024/FY 1735574400   2024-12-31        110558            4.8768       5972041.8242                 3.3726       1882260.8947                23.9566           1777049.1506                    58.6906
        2023     2023/FY 1703952000   2023-12-31        105417           -2.7841       5777199.1234                12.9662       1518483.7360                48.5723           1119819.3839                   -35.6529
        2022     2022/FY 1672416000   2022-12-31        108436           -3.8440       5114094.9500                 2.9643       1022049.8727               -57.5666           1740279.9808                   -13.8522
        2021     2021/FY 1640880000   2021-12-31        112771           31.3459       4966862.0478               -11.5377       2408597.9551                12.2453           2020111.5535                     8.3170
        2020     2020/FY 1609344000   2020-12-31         85858           36.5317       5614666.0765                -6.4170       2145833.8186                13.6879           1864998.0199                    22.3097
        2019     2019/FY 1577721600   2019-12-31         62885           15.7911       5999666.0570                 4.2027       1887477.1408                 4.9760           1524815.1387                     3.5346
        2018     2018/FY 1546185600   2018-12-31         54309           21.2362       5757682.8886                 8.4796       1798007.6966               -10.8064           1472757.7381                    -8.9654
        2017     2017/FY 1514649600   2017-12-31         44796           15.5280       5307616.7514                35.4518       2015849.6294                39.2885           1617800.6964                    51.3504
        2016     2016/FY 1483113600   2016-12-31         38775           26.5461       3918452.6112                16.7235       1447246.9374                 9.1517           1068910.3803                    12.5205
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Restrictions

  • Maximum 30 requests per 30 seconds.
  • Supports equities and funds.