09. 循环语句-股价涨跌幅提醒

# ==================== 示例1:遍历列表 ====================
stocks = ['中信证券', '国泰君安', '海通证券']
for stock in stocks:
    print(f'分析股票: {stock}')

# ==================== 示例2:遍历字符串 ====================
code = '600519.SH'
for char in code:
    print(char, end=' ')
print()

# ==================== 示例3:遍历范围 ====================
for i in range(5):
    print(f'第{i+1}次迭代')

# ==================== 示例4:遍历字典 ====================
stock_info = {'name': '中信证券', 'code': '600030.SH', 'price': 24.78}
for key in stock_info:
    print(f'{key}: {stock_info[key]}')

# 用法1:指定结束值
for i in range(5):
    print(f'i = {i}', end='  ')
print()

# 用法2:指定起始和结束值（遍历年份）
for year in range(2020, 2025):
    print(f'年份: {year}')

# 用法3:指定步长（偶数序列）
for i in range(0, 10, 2):
    print(f'偶数: {i}', end='  ')
print()

# 用法4:负步长（倒序）
for i in range(10, 0, -1):
    print(f'倒计时: {i}', end=' ')
print()

import numpy as np
dates = np.arange('2024-01-01', '2024-01-06', dtype='datetime64[D]')
print('2024年1月前5个工作日:')
for date in dates:
    print(f'  {date}')

count = 0
while count < 5:
    print(f'计数: {count}')
    count += 1  # 重要:更新循环变量,避免无限循环
# 循环执行5次后,count变为5,条件不再满足

print('价格监控模拟:')
price = 18.00   # 初始价格
target = 20.00  # 目标价格
days = 0        # 天数计数器

while price < target:
    price += 0.50  # 每天价格上涨0.50元
    days += 1
    print(f'第{days}天, 价格: {price:.2f}元')

    if days > 100:  # 安全措施:避免无限循环
        print('达到最大监控天数')
        break

print(f'\n达到目标价20元, 耗时{days}天')

# ⚠️ 平台原始代码 - 请原样输入至教学平台（注释除外），平台才会判定答案正确
r_list=[0.0192,-0.0001,0.006,0.0074,-0.0127,-0.0067,0.0095,-0.0095]  # 定义每日涨跌幅列表
for i in r_list:  # 逐日遍历涨跌幅数据
    if i<-0.01:  # 判断当日跌幅是否超过1%
        break  # 发现跌幅超限,立即跳出循环
print('涨跌幅数据',i)  # 输出触发跌幅预警时的涨跌幅值

returns = [0.0192, -0.0001, 0.006, 0.0074, -0.0127, 0.0095]

print('正收益交易日:')
count = 0
for r in returns:
    if r < 0:       # 如果收益率为负
        continue    # 跳过本次迭代,直接进入下一次
    count += 1
    print(f'  第{count}个正收益日: {r:.2%}')

print(f'\n总计正收益日: {count}天')

# 场景1:循环正常结束 → 执行else
prices = [10, 11, 12, 13, 14]
for price in prices:
    if price > 20:
        print('发现高价股票')
        break
else:
    print('所有股票价格都在合理范围')

print('=' * 50)

# 场景2:循环被break中断 → 不执行else
prices2 = [10, 11, 25, 13, 14]
for price in prices2:
    if price > 20:
        print('发现高价股票')
        break
else:
    print('所有股票价格都在合理范围')

prices = [24.78, 20.15, 14.03, 22.41, 16.17]

for price in prices:
    if price <= 0:
        print(f'发现异常价格: {price}')
        break
else:
    print('✓ 所有价格数据有效')
# 这种 for-else 模式在金融数据清洗中非常常用

import numpy as np

# 模拟一周股价数据
np.random.seed(42)
base_price = 20.0
price_changes = np.random.randn(5) * 0.5
prices = base_price + np.cumsum(price_changes)

print('=' * 60)
print('股价涨跌幅监控系统')
print('=' * 60)

alert_threshold = -0.01  # 跌幅超过-1%触发警报

for i, price in enumerate(prices, 1):
    if i == 1:
        daily_return = 0
    else:
        daily_return = (price - prices[i-2]) / prices[i-2]

    return_pct = daily_return * 100

    if daily_return < alert_threshold:
        status = '⚠️ 警告'; action = '考虑减仓'
    elif daily_return > 0.01:
        status = '✓ 上涨'; action = '继续持有'
    else:
        status = '正常'; action = '观望'

    print(f'\n第{i}天:')
    print(f'  收盘价: {price:.2f}元')
    print(f'  涨跌幅: {return_pct:+.2f}%')
    print(f'  状态: {status}  建议: {action}')

    if daily_return < alert_threshold:
        print('\n' + '=' * 60)
        print('触发风险预警,系统暂停监控')
        print('=' * 60)
        break
else:
    print('\n' + '=' * 60)
    print('一周监控结束,市场表现平稳')
    print('=' * 60)

returns = [
    [0.01, 0.02, -0.01, 0.03, 0.02],   # 股票A
    [0.02, -0.01, 0.03, 0.01, -0.02],   # 股票B
    [-0.01, 0.03, 0.02, -0.02, 0.01]    # 股票C
]
stock_names = ['股票A', '股票B', '股票C']

print('每日正收益股票统计:\n')
for day in range(5):
    positive_count = 0
    positive_stocks = []
    for stock_idx in range(3):
        if returns[stock_idx][day] > 0:
            positive_count += 1
            positive_stocks.append(stock_names[stock_idx])
    print(f'第{day+1}天: {positive_count}只上涨 → {", ".join(positive_stocks)}')

import time
import numpy as np

n = 100000
list1 = list(range(n))
list2 = list(range(n))

# 方法1:Python for循环
start = time.time()
result_loop = []
for i in range(n):
    result_loop.append(list1[i] + list2[i])
loop_time = time.time() - start

# 方法2:列表推导
start = time.time()
result_listcomp = [list1[i] + list2[i] for i in range(n)]
listcomp_time = time.time() - start

# 方法3:zip+列表推导
start = time.time()
result_zip = [a + b for a, b in zip(list1, list2)]
zip_time = time.time() - start

# 方法4:NumPy向量化
arr1 = np.array(list1)
arr2 = np.array(list2)
start = time.time()
result_numpy = arr1 + arr2
numpy_time = time.time() - start

print(f'Python循环:  {loop_time:.4f}秒')
print(f'列表推导:    {listcomp_time:.4f}秒')
print(f'zip方法:     {zip_time:.4f}秒')
print(f'NumPy向量化: {numpy_time:.4f}秒')
safe_numpy_time = numpy_time if numpy_time > 0 else 1e-12
print(f'\n最快方法比最慢快 {loop_time/safe_numpy_time:.1f}倍')
#print(f'\n最快方法比最慢快 {loop_time/numpy_time:.1f}倍')