06. Numpy-数组运算

# ⚠️ 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
import numpy as np  # 导入NumPy数值计算库
# 已知数据
stock_p=np.array([4.98,5.02,4.95,4.91,4.98,4.92,4.88,4.92,4.88,4.82,4.85,4.89,4.91,4.86,4.84,4.92,5.01,5.04])
# 创建NumPy数组stock_return
stock_return=np.array([-0.7968,1.4141,0.8147,-1.4056,1.2195,0.8197,-0.813,0.8197,1.2448,-0.6186,-0.818,-0.4073,1.0288,0.4132,-1.626,-1.7964,-0.5952,-1.5625])
# 1. 计算股票的最高价并输出
p_max=stock_p.max()
print(f"中国建筑8月最高股价为{p_max}")  # 输出中国建筑8月最高股价为
# 2. 计算股票的平均涨跌幅，使用round()函数保留三位小数并输出
return_avg=stock_return.mean().round(3)
print(f"平均涨跌幅为{return_avg}")  # 输出平均涨跌幅为
# 3. 计算股票涨跌幅的方差和标准差,使用round()函数并保留3位小数
return_var=stock_return.var().round(3)
return_std=stock_return.std().round(3)  # 计算标准差并四舍五入
print(f"涨跌幅的方差{return_var}，标准差{return_std}")  # 输出涨跌幅的方差

import numpy as np

# 3只股票 × 4个交易日的收益率矩阵
returns = np.array([
    [0.05, 0.03, -0.02, 0.04],  # 股票A
    [0.02, 0.01, 0.03, -0.01],  # 股票B
    [-0.01, 0.02, 0.01, 0.05]   # 股票C
])

# axis=0：跨行计算 → 每日市场平均收益率
daily_avg = returns.mean(axis=0)
print(f'每日平均收益率: {daily_avg}')

# axis=1：跨列计算 → 每只股票平均收益率
stock_avg = returns.mean(axis=1)
print(f'每只股票平均收益率: {stock_avg}')

# 沿axis=1方向计算累积和
cumulative_returns = returns.cumsum(axis=1)
print(f'累积收益率:\n{cumulative_returns}')
# 例如股票A第3列 = 0.05 + 0.03 + (-0.02) = 0.06

import numpy as np

# 5个交易日的股价数据
prices = np.array([10, 20, 30, 40, 50])

# 加法：价格调整
adjustment = np.array([1, 2, 3, 4, 5])
new_prices = prices + adjustment
print(f'调整后价格: {new_prices}')

# 减法：相邻日价差
price_diff = prices[1:] - prices[:-1]
print(f'价格变化: {price_diff}')

# 乘法：计算持仓市值
shares = np.array([100, 200, 150])
position_value = prices[:3] * shares
print(f'持仓价值: {position_value}')

# 除法：计算简单收益率
# 公式：return = (P_t - P_{t-1}) / P_{t-1}
simple_returns = (prices[1:] - prices[:-1]) / prices[:-1]
print(f'简单收益率: {simple_returns}')

# 计算1到5年的复利因子（年化收益率5%）
compound = (1 + 0.05) ** np.arange(1, 6)
print(f'5年复利因子: {compound}')

# 5个交易日的股价
prices = np.array([100, 105, 98, 102, 110])

# 对数收益率 = ln(P_t) - ln(P_{t-1})
log_returns = np.diff(np.log(prices))
print(f'对数收益率: {log_returns}')

# 4只股票的年化收益率
annual_returns = np.array([0.05, 0.06, 0.04, 0.07])

# 指数函数：连续复利计算5年累计收益率
cumulative_5yr = np.exp(annual_returns * 5) - 1
print(f'5年累计收益率: {cumulative_5yr}')

# 波动率计算
volatility = np.std(annual_returns)
variance = volatility ** 2
print(f'波动率: {volatility:.4f}')
print(f'方差: {variance:.4f}')

prices_float = np.array([100.56, 105.23, 98.87])

print(f'向下取整: {np.floor(prices_float)}')
print(f'向上取整: {np.ceil(prices_float)}')
print(f'四舍五入: {np.round(prices_float)}')

import numpy as np

# 中国建筑8月股价数据（18个交易日）
stock_p = np.array([4.98, 5.02, 4.95, 4.91, 4.98, 4.92, 4.88, 4.92,
                    4.88, 4.82, 4.85, 4.89, 4.91, 4.86, 4.84, 4.92,
                    5.01, 5.04])

# 计算日收益率
daily_returns = (stock_p[1:] - stock_p[:-1]) / stock_p[:-1]

# 基本统计量
mean_return = daily_returns.mean()
std_return = daily_returns.std()
# 年化波动率 = 日波动率 × √252
annualized_vol = std_return * np.sqrt(252)

print('=' * 50)
print('中国建筑8月风险收益分析')
print('=' * 50)
print(f'日平均收益率: {mean_return:.4%}')
print(f'日收益率标准差: {std_return:.4%}')
print(f'年化波动率: {annualized_vol:.4%}')

# 价格统计
max_price = stock_p.max()
min_price = stock_p.min()
price_range = max_price - min_price
print(f'最高价: {max_price:.2f}元')
print(f'最低价: {min_price:.2f}元')
print(f'价格区间: {price_range:.2f}元')

# 下行风险：只考虑下跌日
downside_returns = daily_returns[daily_returns < 0]
downside_risk = downside_returns.std() * np.sqrt(252)
print(f'下行波动率: {downside_risk:.4%}')

# 夏普比率（假设无风险利率3%）
sharpe_ratio = (mean_return * 252 - 0.03) / annualized_vol
print(f'夏普比率: {sharpe_ratio:.2f}')

import numpy as np

# 两个投资组合的持仓
portfolio_a = np.array(['600519.SH', '000858.SZ', '600036.SH', '000002.SZ'])
portfolio_b = np.array(['600036.SH', '601318.SH', '000858.SZ', '600000.SH'])

# 交集：共同持仓
common = np.intersect1d(portfolio_a, portfolio_b)
print(f'共同持仓: {common}')

# 并集：所有股票（去重）
all_stocks = np.union1d(portfolio_a, portfolio_b)
print(f'所有股票: {all_stocks}')

# 差集：独有持仓
unique_a = np.setdiff1d(portfolio_a, portfolio_b)
unique_b = np.setdiff1d(portfolio_b, portfolio_a)
print(f'A组合独有: {unique_a}')
print(f'B组合独有: {unique_b}')

import numpy as np

# 5只股票的价格
prices = np.array([10.5, 20.3, 15.8, 25.2, 18.6])

# 比较运算 → 返回布尔数组
above_20 = prices > 20
print(f'价格>20元: {above_20}')

# 布尔索引：提取满足条件的元素
expensive_stocks = prices[prices > 20]
print(f'高价股票: {expensive_stocks}')

# 筛选价格在15-25元之间的股票
filtered = prices[(prices >= 15) & (prices <= 25)]
print(f'价格在15-25元之间: {filtered}')

import numpy as np

# 6个交易日的收益率
returns = np.array([0.05, -0.02, 0.03, 0.07, -0.01, 0.04])

# np.sort()：返回排序后的新数组
sorted_returns = np.sort(returns)
print(f'排序后: {sorted_returns}')

# np.argsort()：返回排序索引
indices = np.argsort(returns)
print(f'排序索引: {indices}')
print(f'按索引重建: {returns[indices]}')

# 搜索极值位置
max_idx = np.argmax(returns)
min_idx = np.argmin(returns)
print(f'最大值位置: {max_idx}, 值: {returns[max_idx]}')
print(f'最小值位置: {min_idx}, 值: {returns[min_idx]}')

# np.where：条件替换（三目运算）
adjusted = np.where(returns > 0, returns, 0)
print(f'负收益调整为0: {adjusted}')

import numpy as np
import time

n = 1000000
arr1 = np.random.randn(n)
arr2 = np.random.randn(n)

# Python循环
start = time.time()
result_slow = []
for i in range(n):
    result_slow.append(arr1[i] + arr2[i])
slow_time = time.time() - start

# NumPy向量化
start = time.time()
result_fast = arr1 + arr2
fast_time = time.time() - start

print(f'循环时间: {slow_time:.4f}秒')
print(f'向量化时间: {fast_time:.4f}秒')
print(f'加速比: {slow_time/fast_time:.1f}倍')