import sys, os
sys.path.insert(0, os.path.abspath('.'))
import warnings; warnings.filterwarnings('ignore')
import numpy as np, pandas as pd
import matplotlib.pyplot as plt
import invest_data as D
FONT = D.setup_matplotlib()
np.random.seed(D.SEED)
print('中文字体:', FONT, '| 数据目录:', D.DATA_DIR)

中文字体: Noto Sans CJK SC | 数据目录: /data/docs/investing/long-term-investing/data

# 载入5个标的的真实收盘价，对齐时间、归一化
assets = {
    "贵州茅台": D.load_a_price("600519")["close"],
    "宁德时代": D.load_a_price("300750")["close"],
    "苹果AAPL": D.load_us_price("AAPL")["close"],
    "英伟达NVDA": D.load_us_price("NVDA")["close"],
    "沪深300": D.load_index("000300")["close"],
}
prices = pd.DataFrame(assets).dropna()
# 取最近约4年共同区间
prices = prices.iloc[-252*4:]
norm = prices / prices.iloc[0] * 100

fig, ax = plt.subplots(figsize=(11, 5.5))
for col in norm.columns:
    ax.plot(norm.index, norm[col], lw=1.4, label=col)
ax.axhline(100, color="gray", ls="--", lw=0.8)
ax.set_title("五个标的 真实净值走势（期初归一化=100，近4年）")
ax.set_xlabel("日期"); ax.set_ylabel("相对净值"); ax.legend()
plt.tight_layout(); plt.show()
print("共同区间:", prices.index[0].date(), "→", prices.index[-1].date())

共同区间: 2022-02-24 → 2026-06-18

rets = prices.pct_change().dropna()
corr = rets.corr()
fig, ax = plt.subplots(figsize=(7, 6))
im = ax.imshow(corr.values, cmap="RdYlGn_r", vmin=-1, vmax=1)
ax.set_xticks(range(len(corr))); ax.set_xticklabels(corr.columns, rotation=45, ha="right")
ax.set_yticks(range(len(corr))); ax.set_yticklabels(corr.columns)
for i in range(len(corr)):
    for j in range(len(corr)):
        ax.text(j, i, f"{corr.values[i,j]:.2f}", ha="center", va="center", fontsize=10)
ax.set_title("日收益率 相关性矩阵")
fig.colorbar(im, ax=ax, fraction=0.046)
plt.tight_layout(); plt.show()

w = pd.Series(0.2, index=prices.columns)   # 等权
port_norm = (norm * w).sum(axis=1)          # 等权组合净值（期初=100）

def stats(series):
    return D.cagr(series), D.max_drawdown(series)

rows = []
for col in norm.columns:
    c, m = stats(norm[col]); rows.append((col, c*100, m*100))
c, m = stats(port_norm); rows.append(("★等权组合", c*100, m*100))
tbl = pd.DataFrame(rows, columns=["标的", "年化收益%", "最大回撤%"]).set_index("标的").round(1)
print(tbl.to_string())

fig, ax = plt.subplots(figsize=(11, 5.5))
for col in norm.columns:
    ax.plot(norm.index, norm[col], lw=1, alpha=0.45, label=col)
ax.plot(port_norm.index, port_norm.values, lw=2.6, color="black", label="★等权组合")
ax.axhline(100, color="gray", ls="--", lw=0.8)
ax.set_title("等权组合 vs 单一标的（粗黑线为组合）")
ax.set_xlabel("日期"); ax.set_ylabel("相对净值"); ax.legend(fontsize=8)
plt.tight_layout(); plt.show()

         年化收益%  最大回撤%
标的                   
贵州茅台      -5.6  -34.5
宁德时代       9.8  -54.7
苹果AAPL    15.8  -33.5
英伟达NVDA   66.5  -61.6
沪深300      2.0  -31.6
★等权组合     27.5  -27.3

rets_p = prices.pct_change().fillna(0)
target = np.array([0.2]*5)

# 1) 买入持有：期初等权，之后随市值漂移
bh_val = (1 + rets_p).cumprod()
bh_port = (bh_val * target).sum(axis=1)

# 2) 每年再平衡：每年第一个交易日重置回等权
val = target.copy().astype(float)
reb_series = []
year_seen = set()
for d, r in rets_p.iterrows():
    val = val * (1 + r.values)            # 当日按各自收益增长
    if d.year not in year_seen and len(year_seen) > 0:
        total = val.sum(); val = total * target   # 再平衡回等权
    year_seen.add(d.year)
    reb_series.append((d, val.sum()))
reb_port = pd.Series(dict(reb_series))

fig, ax = plt.subplots(figsize=(11, 5))
ax.plot(bh_port.index, bh_port.values, label="买入持有(不再平衡)", color="#c0392b", lw=1.8)
ax.plot(reb_port.index, reb_port.values, label="每年再平衡", color="#16a085", lw=1.8)
ax.set_title("再平衡 vs 买入持有（等权组合，期初=1）")
ax.set_xlabel("日期"); ax.set_ylabel("净值"); ax.legend()
plt.tight_layout(); plt.show()

print(f"买入持有  最终净值 {bh_port.iloc[-1]:.2f}, 最大回撤 {D.max_drawdown(bh_port)*100:.1f}%")
print(f"每年再平衡 最终净值 {reb_port.iloc[-1]:.2f}, 最大回撤 {D.max_drawdown(reb_port)*100:.1f}%")

买入持有  最终净值 2.85, 最大回撤 -27.3%
每年再平衡 最终净值 2.46, 最大回撤 -27.2%

06 · 构建与跟踪组合¶

本章学什么 / 读完会什么¶

6.1 为什么要分散：别把鸡蛋放一个篮子¶

6.2 相关性：组合里资产「同不同步」的尺子¶

6.3 构造一个简单组合：等权 vs 单押¶

6.4 再平衡：自动「低买高卖」的纪律¶

6.5 仓位管理：你能承受多大波动¶

本章小结¶