A/C WITH BANK

A/C WITH BANK是指在银行开设的账户。通过在银行开设账户，个人或企业可以存入资金、进行转账、支付账单、申请贷款等金融服务。在账户中，银行会记录存入和支出的资金流动，以及账户余额等信息。开设A/C WITH BANK通常需要提供个人或企业的身份证明和其他相关资料。

相关问题

c语言银行排队问题之单队列

单队列的解决方案如下：

1. 定义一个队列结构，包含以下信息：
- 队列元素数组
- 队列头
- 队列尾
- 队列长度

2. 定义一个结构体，包含每个顾客的信息：
- 姓名
- 操作类型（取钱或存钱）
- 金额

3. 根据输入的顾客信息，创建一个结构体对象，并将其放入队列的尾部。

4. 每次有顾客离开银行，从队列头部取出一个元素，并按照其操作类型分别进行相关操作。

5. 如果队列为空，则表示所有顾客都已经离开银行，结束程序。

代码实现示例：

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_QUEUE_SIZE 100

struct Customer {
    char name[20];
    char type[4];
    int amount;
};

struct Queue {
    struct Customer items[MAX_QUEUE_SIZE];
    int front, rear;
    int size;
};

struct Queue* createQueue() {
    struct Queue* q = (struct Queue*)malloc(sizeof(struct Queue));
    q->front = -1;
    q->rear = -1;
    q->size = 0;
    return q;
}

int isEmpty(struct Queue* q) {
    return q->size == 0;
}

int isFull(struct Queue* q) {
    return q->size == MAX_QUEUE_SIZE;
}

void enqueue(struct Queue* q, struct Customer c) {
    if (isFull(q)) {
        printf("Queue is full.\n");
        return;
    }
    if (isEmpty(q)) {
        q->front = 0;
        q->rear = 0;
    } else {
        q->rear = (q->rear + 1) % MAX_QUEUE_SIZE;
    }
    q->items[q->rear] = c;
    q->size++;
}

struct Customer dequeue(struct Queue* q) {
    if (isEmpty(q)) {
        printf("Queue is empty.\n");
        exit(1);
    }
    struct Customer item = q->items[q->front];
    if (q->front == q->rear) {
        q->front = -1;
        q->rear = -1;
    } else {
        q->front = (q->front + 1) % MAX_QUEUE_SIZE;
    }
    q->size--;
    return item;
}

void bankSimulator() {
    struct Queue* q = createQueue();

    int time = 0;
    int teller_busy = 0;
    struct Customer current_customer;

    // simulate customer arrival and departure
    while (1) {
        // check if customer arrives
        if (time % 5 == 0) { // every 5 minutes a customer arrives
            struct Customer c;
            printf("Enter customer name: ");
            scanf("%s", c.name);
            printf("Enter operation type (deposit/withdraw): ");
            scanf("%s", c.type);
            printf("Enter amount: ");
            scanf("%d", &c.amount);

            enqueue(q, c);
            printf("Customer %s joined the queue.\n", c.name);
        }

        // serve customer if teller is free and there is someone in the queue
        if (!isEmpty(q) && teller_busy == 0) {
            current_customer = dequeue(q);
            teller_busy = 1;
            printf("Teller serving customer %s for %s %d.\n", 
                current_customer.name, current_customer.type, current_customer.amount);
        }

        // process current customer
        if (teller_busy == 1) {
            if (strcmp(current_customer.type, "deposit") == 0) {
                printf("Depositing %d for customer %s.\n", current_customer.amount, current_customer.name);
            } else if (strcmp(current_customer.type, "withdraw") == 0) {
                printf("Withdrawing %d for customer %s.\n", current_customer.amount, current_customer.name);
            } else {
                printf("Invalid operation type.\n");
            }
            teller_busy = 0;
            printf("Customer %s has left the bank.\n", current_customer.name);
        }

        // check if all customers have left
        if (isEmpty(q) && teller_busy == 0) {
            break;
        }

        // increment time by 1 minute
        time++;
    }

    free(q);
}

int main() {
    bankSimulator();
    return 0;
}

Daily foreign exchange rates (spot rates) can be obtained from the Federal Reserve Bank in St Louis (FRED). The data are the noon buying rates in New York City certified by the Federal Reserve Bank of New York. Consider the exchange rates between the U.S. dollar and the Euro from January 4, 1999 to March 8, 2013. See the file d-exuseu.txt. (a) Compute the daily log return of the exchange rate. (b) Compute the sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of the log returns of the exchange rate. (c) Obtain a density plot of the daily long returns of Dollar-Euro exchange rate. (d) Test H0 : µ = 0 versus Ha : µ ̸= 0, where µ denotes the mean of the daily log return of Dollar-Euro exchange rate.

(a) The daily log return of the exchange rate can be calculated using the following formula:

log return = ln(price[t]) - ln(price[t-1])

where price[t] represents the exchange rate at time t and price[t-1] represents the exchange rate at time t-1.

Using the data in the file d-exuseu.txt, we can calculate the daily log returns as follows (assuming the data is stored in a variable called "exchange_rate"):

import numpy as np

log_returns = np.log(exchange_rate[1:]) - np.log(exchange_rate[:-1])

The first element of "exchange_rate" is excluded from the calculation because there is no previous price to compare it to.

(b) The sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of the log returns can be calculated using the following code:

mean = np.mean(log_returns)
std_dev = np.std(log_returns)
skewness = stats.skew(log_returns)
kurtosis = stats.kurtosis(log_returns, fisher=False)
minimum = np.min(log_returns)
maximum = np.max(log_returns)

print("Sample mean:", mean)
print("Standard deviation:", std_dev)
print("Skewness:", skewness)
print("Excess kurtosis:", kurtosis - 3) # convert to excess kurtosis
print("Minimum:", minimum)
print("Maximum:", maximum)

This code requires the "scipy.stats" module to be imported at the beginning of the script. The output will show the sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of the log returns.

(c) To obtain a density plot of the daily log returns, we can use the following code:

import matplotlib.pyplot as plt

plt.hist(log_returns, bins=50, density=True)
plt.xlabel("Daily log return")
plt.ylabel("Density")
plt.show()

This code will create a histogram of the log returns with 50 bins and normalize it to create a density plot. The output will show the density plot of the log returns.

(d) To test the hypothesis H0 : µ = 0 versus Ha : µ ̸= 0, where µ denotes the mean of the daily log return of Dollar-Euro exchange rate, we can use a t-test. The null hypothesis states that the mean log return is equal to zero, while the alternative hypothesis states that the mean log return is not equal to zero.

from scipy.stats import ttest_1samp

t_stat, p_value = ttest_1samp(log_returns, 0)

print("t-statistic:", t_stat)
print("p-value:", p_value)

This code uses the "ttest_1samp" function from the "scipy.stats" module to calculate the t-statistic and the p-value. The output will show the t-statistic and the p-value of the test. If the p-value is less than the significance level (e.g., 0.05), we can reject the null hypothesis and conclude that the mean log return is significantly different from zero.