44. Python内置函数

numbers = [1, 2, 3, 4, 5]
print(f'长度: {len(numbers)}')

numbers = [1, 2, 3, 4, 5]
print(f'求和: {sum(numbers)}')

numbers = [1, 2, 3, 4, 5]
print(f'降序排序: {sorted(numbers, reverse=True)}')

print(f'绝对值: {abs(-10)}')
print(f'四舍五入: {round(3.14159, 2)}')

print(round(2.5))  # 2（最近的偶数）
print(round(3.5))  # 4（最近的偶数）

# 传统四舍五入需要使用 decimal 模块
from decimal import Decimal, ROUND_HALF_UP
print(Decimal('2.5').quantize(Decimal('1'), rounding=ROUND_HALF_UP))  # 3

print(f'2的3次方: {pow(2, 3)}')
print(f'2的10次方对1000取模: {pow(2, 10, 1000)}')

# 收益率序列
returns = [0.05, -0.02, 0.03, 0.07, -0.01]

# 平均收益率
avg_return = sum(returns) / len(returns)
print(f'平均收益率: {avg_return:.2%}')

# 最大收益和最大亏损
print(f'最大收益: {max(returns):.2%}')
print(f'最大亏损: {min(returns):.2%}')

# 极差（Range）
range_value = max(returns) - min(returns)
print(f'收益极差: {range_value:.2%}')

# 将字符串价格列表转换为浮点数
prices = ['10.5', '20.3', '15.8']
prices_float = list(map(float, prices))
print(f'转换为浮点数: {prices_float}')

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
evens = list(filter(lambda x: x % 2 == 0, numbers))
print(f'偶数: {evens}')

names = ['张三', '李四', '王五']
ages = [25, 30, 35]

pairs = list(zip(names, ages))
print(f'配对: {pairs}')

# 直接构造字典
name_age_dict = dict(zip(names, ages))
print(f'字典: {name_age_dict}')

stocks = ['茅台', '五粮液', '招行']
for index, stock in enumerate(stocks, start=1):
    print(f'{index}. {stock}')

numbers = [1, 2, 3, 4, 5]

# map + lambda
squared_map = list(map(lambda x: x**2, numbers))

# 列表推导式（推荐）
squared_comp = [x**2 for x in numbers]

print(f'map方式: {squared_map}')
print(f'推导式: {squared_comp}')

# zip 配对
matrix = [(1, 'a'), (2, 'b'), (3, 'c')]

# zip(*) 解包
numbers, letters = zip(*matrix)
print(f'数字: {numbers}')
print(f'字母: {letters}')

# 原始数据：字符串格式的价格和数量
raw_data = [
    ('10.5', '100'),
    ('20.3', '200'),
    ('15.8', '150')
]

# 使用 map() 进行数据转换
prices = list(map(lambda x: float(x[0]), raw_data))
quantities = list(map(lambda x: int(x[1]), raw_data))

# 计算每笔交易金额
amounts = list(map(lambda p, q: p * q, prices, quantities))
print(f'交易金额: {amounts}')

# 过滤出金额大于2000的交易
large_trades = list(filter(lambda x: x > 2000, amounts))
print(f'大额交易: {large_trades}')

returns = [0.05, -0.02, 0.03, 0.07, -0.01]

cum_return = 1.0   # 初始累计收益
max_drawdown = 0    # 最大回撤
peak = 1.0          # 历史峰值

for r in returns:
    cum_return *= (1 + r)          # 更新累计收益
    if cum_return > peak:
        peak = cum_return          # 更新历史峰值
    drawdown = (peak - cum_return) / peak  # 计算当前回撤
    max_drawdown = max(max_drawdown, drawdown)

print(f'最大回撤: {max_drawdown:.2%}')

import math

returns = [0.05, -0.02, 0.03, 0.07, -0.01]
mean = sum(returns) / len(returns)

# 计算标准差
variance = sum((r - mean) ** 2 for r in returns) / len(returns)
std_dev = math.sqrt(variance)

# 假设无风险利率2%（年化），252个交易日
risk_free_rate = 0.02
sharpe_ratio = (mean * 252 - risk_free_rate) / (std_dev * math.sqrt(252))
print(f'年化夏普比率: {sharpe_ratio:.2f}')

returns = [0.05, -0.02, 0.03, 0.07, -0.01]

# 统计正收益天数
positive_count = len([r for r in returns if r > 0])
print(f'正收益次数: {positive_count}/{len(returns)}')

# 计算中位数
sorted_returns = sorted(returns)
n = len(sorted_returns)
if n % 2 == 0:
    median = (sorted_returns[n//2 - 1] + sorted_returns[n//2]) / 2
else:
    median = sorted_returns[n//2]
print(f'中位数收益率: {median:.2%}')

returns = [0.01, 0.02, 0.03, -0.01, 0.02]

print(f'所有收益为正: {all(r > 0 for r in returns)}')
print(f'存在负收益: {any(r < 0 for r in returns)}')

# 检查投资组合权重归一化
weights = [0.3, 0.4, 0.3]
print(f'权重之和为1: {abs(sum(weights) - 1.0) < 1e-10}')

# 陷阱：修改正在遍历的列表
lst = [1, 2, 3, 4, 5]
# ❌ 错误做法：for item in lst: lst.remove(item)
# ✅ 正确做法：创建新列表
lst_odd = [x for x in lst if x % 2 != 0]
print(f'过滤偶数后: {lst_odd}')

# 陷阱：可变默认参数
def foo(lst=None):
    if lst is None:
        lst = []
    lst.append(1)
    return lst

print(foo())  # [1]
print(foo())  # [1]（每次调用都是新列表）