##Instruction:
Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Table: account (4 columns)
--------------------------
• account_id (integer): the id of the account
• district_id (integer): location of branch
• frequency (text): frequency of the acount
• date (date): the creation date of the account
  Values: in the form YYMMDD

Table: card (4 columns)
-----------------------
• card_id (integer): id number of credit card
• disp_id (integer): disposition id
• type (text): type of credit card
  Values: "junior": junior class of credit card; 
"classic": standard class of credit card; 
"gold": high-leve...
• issued (date): the date when the credit card issued
  Values: in the form YYMMDD

Table: client (4 columns)
-------------------------
• client_id (integer): the unique number
• gender (text)
  Values: F：female 
M：male
• birth_date (date): birth date
• district_id (integer): location of branch

Table: disp (4 columns)
-----------------------
• disp_id (integer): unique number of identifying this row of record
• client_id (integer): id number of client
• account_id (integer): id number of account
• type (text): type of disposition
  Values: "OWNER" : "USER" : "DISPONENT"
commonsense evidence:
the account can only have the right to issue pe...

Table: district (16 columns)
----------------------------
• district_id (integer): location of branch
• A2 (text): district_name
• A3 (text): region
• A4 (text)
• A5 (text): municipality < district < region
• A6 (text): municipality < district < region
• A7 (text): municipality < district < region
• A8 (integer): municipality < district < region
• A9 (integer)
  Values: not useful
• A10 (real): ratio of urban inhabitants
• A11 (integer): average salary
• A12 (real): unemployment rate 1995
• A13 (real): unemployment rate 1996
• A14 (integer): no. of entrepreneurs per 1000 inhabitants
• A15 (integer): no. of committed crimes 1995
• A16 (integer): no. of committed crimes 1996

Table: loan (7 columns)
-----------------------
• loan_id (integer): the id number identifying the loan data
• account_id (integer): the id number identifying the account
• date (date): the date when the loan is approved
• amount (integer): approved amount
  Values: unit：US dollar
• duration (integer): loan duration
  Values: unit：month
• payments (real): monthly payments
  Values: unit：month
• status (text): repayment status
  Values: 'A' stands for contract finished, no problems;
'B' stands for contract finished, loan not paid;
'C' ...

Table: order (6 columns)
------------------------
• order_id (integer): identifying the unique order
• account_id (integer): id number of account
• bank_to (text): bank of the recipient
• account_to (integer): account of the recipient
  Values: each bank has unique two-letter code
• amount (real): debited amount
• k_symbol (text): purpose of the payment
  Values: "POJISTNE" stands for insurance payment
"SIPO" stands for household payment
"LEASING" stands for lea...

Table: trans (10 columns)
-------------------------
• trans_id (integer): transaction id
• account_id (integer)
• date (date): date of transaction
• type (text): +/- transaction
  Values: "PRIJEM" stands for credit
"VYDAJ" stands for withdrawal
• operation (text): mode of transaction
  Values: "VYBER KARTOU": credit card withdrawal
"VKLAD": credit in cash
"PREVOD Z UCTU" :collection from anot...
• amount (integer): amount of money
  Values: Unit：USD
• balance (integer): balance after transaction
  Values: Unit：USD
• k_symbol (text)
  Values: "POJISTNE": stands for insurrance payment
"SLUZBY": stands for payment for statement
"UROK": stands ...
• bank (text)
  Values: each bank has unique two-letter code
• account (integer)

##Evidence:
Weekly statement issuance refers to frequency = 'POPLATEK TYDNE'; high-volume clients are those with total transaction amount > 10000

##Role Access Policy (Column-Level):
Role: DataOperator_3
Accessible Columns: account: account_id, date, district_id; card: disp_id, issued, type; client: ALL COLUMNS; disp: account_id, type; district: A10, A11, A14, A16, A2, A3, A4, A5, A6, A7, A8, A9, district_id; loan: account_id, amount, date, duration, loan_id, payments; order: amount, k_symbol; trans: ALL COLUMNS

Based on the above schema and your role's column-level access policy, generate a SQL query to answer the question. If your role does not have access to the required columns, respond with: "Sorry, I cannot answer."


Question: What are the key statistics for account owners with weekly statement issuance, including how many owners there are, their average number of transactions, how many have credit cards, how many are high-volume clients with over 10,000 in total transactions, the number of districts they're located in, and their average account balance?
Evidence: Weekly statement issuance refers to frequency = 'POPLATEK TYDNE'; high-volume clients are those with total transaction amount > 10000
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: card: card_id; disp: client_id, disp_id; account: frequency)
Gold SQL: WITH ClientTransactionStats AS (
    SELECT 
        T2.client_id,
        COUNT(DISTINCT T3.trans_id) AS transaction_count,
        SUM(T3.amount) AS total_transaction_amount,
        AVG(T3.balance) AS avg_balance
    FROM account AS T1
    JOIN disp AS T2 ON T2.account_id = T1.account_id
    JOIN trans AS T3 ON T3.account_id = T1.account_id
    WHERE T1.frequency = 'POPLATEK TYDNE'
      AND T2.type = 'OWNER'
    GROUP BY T2.client_id
),
ClientCards AS (
    SELECT 
        T2.client_id,
        COUNT(DISTINCT C.card_id) AS card_count,
        GROUP_CONCAT(DISTINCT C.type) AS card_types
    FROM account AS T1
    JOIN disp AS T2 ON T2.account_id = T1.account_id
    JOIN disp AS D ON D.client_id = T2.client_id
    LEFT JOIN card AS C ON C.disp_id = D.disp_id
    WHERE T1.frequency = 'POPLATEK TYDNE'
      AND T2.type = 'OWNER'
    GROUP BY T2.client_id
)
SELECT 
    COUNT(DISTINCT CTS.client_id) AS total_weekly_statement_owners,
    AVG(CTS.transaction_count) AS avg_transactions_per_client,
    SUM(CASE WHEN CC.card_count > 0 THEN 1 ELSE 0 END) AS clients_with_cards,
    SUM(CASE WHEN CTS.total_transaction_amount > 10000 THEN 1 ELSE 0 END) AS high_volume_clients,
    (SELECT COUNT(DISTINCT CL.district_id) 
     FROM client CL
     JOIN disp D ON D.client_id = CL.client_id
     JOIN account A ON A.account_id = D.account_id
     WHERE A.frequency = 'POPLATEK TYDNE' AND D.type = 'OWNER') AS districts_count,
    ROUND(AVG(CTS.avg_balance), 2) AS average_client_balance
FROM ClientTransactionStats CTS
LEFT JOIN ClientCards CC ON CC.client_id = CTS.client_id
WHERE CTS.transaction_count > 0
  OR CC.card_count > 0;