##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:
Litomerice is a district name; PRIJEM refers to deposits/credits, VYDAJ refers to withdrawals/debits; loan status 'A' indicates good loans, 'B' indicates bad loans

##Role Access Policy (Column-Level):
Role: ClientServices
Accessible Columns: client: client_id, gender, birth_date, district_id; account: account_id, frequency, date; card: card_id, type, issued; disp: disp_id, account_id, type; district: district_id, A2, A3, A4

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 comprehensive statistics for accounts opened in Litomerice in 1996, including client demographics, transaction activity, loan information, and quarterly distribution of account openings?
Evidence: Litomerice is a district name; PRIJEM refers to deposits/credits, VYDAJ refers to withdrawals/debits; loan status 'A' indicates good loans, 'B' indicates bad loans
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: account_id, amount, balance, trans_id; disp: client_id; account: district_id; loan: loan_id, status)
Gold SQL: WITH AccountsInLitomerice1996 AS (
    SELECT 
        T2.account_id,
        T2.date,
        T1.A2 AS district_name,
        STRFTIME('%m', T2.date) AS month_opened
    FROM 
        district AS T1 
    INNER JOIN 
        account AS T2 ON T1.district_id = T2.district_id 
    WHERE 
        STRFTIME('%Y', T2.date) = '1996' 
        AND T1.A2 = 'Litomerice'
),
ClientInfo AS (
    SELECT 
        d.account_id,
        COUNT(DISTINCT c.client_id) AS num_clients,
        SUM(CASE WHEN c.gender = 'M' THEN 1 ELSE 0 END) AS male_clients,
        SUM(CASE WHEN c.gender = 'F' THEN 1 ELSE 0 END) AS female_clients,
        AVG(CAST(STRFTIME('%Y', '1996-01-01') AS INTEGER) - CAST(STRFTIME('%Y', c.birth_date) AS INTEGER)) AS avg_client_age
    FROM 
        disp d
    JOIN 
        client c ON d.client_id = c.client_id
    GROUP BY 
        d.account_id
),
AccountActivity AS (
    SELECT 
        account_id,
        COUNT(trans_id) AS transaction_count,
        SUM(CASE WHEN type = 'PRIJEM' THEN amount ELSE 0 END) AS total_deposits,
        SUM(CASE WHEN type = 'VYDAJ' THEN amount ELSE 0 END) AS total_withdrawals,
        MAX(balance) AS max_balance
    FROM 
        trans
    WHERE 
        STRFTIME('%Y', date) = '1996'
    GROUP BY 
        account_id
),
LoanStatus AS (
    SELECT 
        account_id,
        COUNT(loan_id) AS loan_count,
        SUM(amount) AS total_loan_amount,
        SUM(CASE WHEN status = 'A' THEN 1 ELSE 0 END) AS good_loans,
        SUM(CASE WHEN status = 'B' THEN 1 ELSE 0 END) AS bad_loans
    FROM 
        loan
    GROUP BY 
        account_id
)
SELECT 
    COUNT(a.account_id) AS total_accounts,
    SUM(CASE WHEN ci.num_clients > 1 THEN 1 ELSE 0 END) AS accounts_with_multiple_clients,
    AVG(ci.avg_client_age) AS average_client_age,
    SUM(ci.male_clients) AS total_male_clients,
    SUM(ci.female_clients) AS total_female_clients,
    SUM(COALESCE(aa.transaction_count, 0)) AS total_transactions_in_1996,
    SUM(COALESCE(aa.total_deposits, 0)) AS total_deposits_in_1996,
    SUM(COALESCE(ls.loan_count, 0)) AS total_loans,
    AVG(COALESCE(ls.total_loan_amount, 0)) AS avg_loan_amount,
    COUNT(DISTINCT CASE WHEN ls.loan_count > 0 THEN a.account_id END) AS accounts_with_loans,
    ROUND(AVG(CASE WHEN month_opened BETWEEN '01' AND '03' THEN 1 ELSE 0 END) * 100, 2) AS percent_opened_q1,
    ROUND(AVG(CASE WHEN month_opened BETWEEN '04' AND '06' THEN 1 ELSE 0 END) * 100, 2) AS percent_opened_q2,
    ROUND(AVG(CASE WHEN month_opened BETWEEN '07' AND '09' THEN 1 ELSE 0 END) * 100, 2) AS percent_opened_q3,
    ROUND(AVG(CASE WHEN month_opened BETWEEN '10' AND '12' THEN 1 ELSE 0 END) * 100, 2) AS percent_opened_q4
FROM 
    AccountsInLitomerice1996 a
LEFT JOIN 
    ClientInfo ci ON a.account_id = ci.account_id
LEFT JOIN 
    AccountActivity aa ON a.account_id = aa.account_id
LEFT JOIN 
    LoanStatus ls ON a.account_id = ls.account_id