35.上市公司净利润增长率的计算

fig, ax1 = plt.subplots(figsize=(10, 6))

# 左轴：营业利润
ax1.set_xlabel('年份', fontsize=12)
ax1.set_ylabel('营业利润(亿元)', fontsize=12, color='steelblue')
ax1.plot(demo_profit.index, demo_profit.values,
         color='steelblue', marker='o', linewidth=2.5, label='营业利润')
ax1.tick_params(axis='y', labelcolor='steelblue')
ax1.grid(True, alpha=0.3, linestyle='--')

# 右轴：增长率
ax2 = ax1.twinx()
ax2.set_ylabel('增长率(%)', fontsize=12, color='coral')
ax2.plot(growth_demo.index, growth_demo.values,
         color='coral', marker='s', linewidth=2.5, label='增长率')
ax2.tick_params(axis='y', labelcolor='coral')
ax2.axhline(y=0, color='gray', linestyle='--', linewidth=1, alpha=0.7)

# 标记负增长区间
ax2.fill_between(growth_demo.index, 0, growth_demo.values,
                 where=(growth_demo.values < 0),
                 color='red', alpha=0.2, label='负增长区间')

plt.title('营业利润与增长率趋势', fontsize=14, pad=15)
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
fig.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 1, figsize=(10, 8))

# 子图1：原始增长率 vs 移动平均
growth_ma = growth_demo.rolling(window=3, center=True).mean()
axes[0].plot(growth_demo.index, growth_demo.values,
             marker='o', linewidth=2, label='原始增长率', alpha=0.7)
axes[0].plot(growth_ma.index, growth_ma.values,
             linewidth=3, label='3年移动平均', color='red')
axes[0].axhline(y=0, color='gray', linestyle='--', linewidth=1)
axes[0].set_title('增长率趋势分解', fontsize=14)
axes[0].set_ylabel('增长率(%)')
axes[0].legend()
axes[0].grid(True, alpha=0.3)

# 子图2：利润趋势 + 二次拟合线
axes[1].plot(demo_profit.index, demo_profit.values,
             marker='s', linewidth=2.5, color='steelblue')
z = np.polyfit(demo_profit.index, demo_profit.values, 2)
p = np.poly1d(z)
axes[1].plot(demo_profit.index, p(demo_profit.index),
             '--', color='red', linewidth=2, label='趋势线(二次)')
axes[1].set_title('营业利润趋势', fontsize=14)
axes[1].set_xlabel('年份')
axes[1].set_ylabel('营业利润(亿元)')
axes[1].legend()
axes[1].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

forecast_years = 3
recent_growth_rate = (growth_demo.dropna().tail(3).mean()) / 100
last_value = demo_profit.iloc[-1]

# 预测未来3年
forecast_values = [last_value * (1 + recent_growth_rate) ** i
                   for i in range(1, forecast_years + 1)]
forecast_index = np.arange(demo_profit.index[-1] + 1,
                           demo_profit.index[-1] + forecast_years + 1)

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(demo_profit.index, demo_profit.values,
        marker='o', linewidth=2.5, label='历史数据', color='steelblue')
ax.plot(forecast_index, forecast_values,
        marker='s', linewidth=2.5, linestyle='--',
        label='预测数据', color='coral')

# 置信区间 (±20%)
upper = [v * 1.2 for v in forecast_values]
lower = [v * 0.8 for v in forecast_values]
ax.fill_between(forecast_index, lower, upper,
                color='coral', alpha=0.2, label='预测区间(±20%)')

ax.set_title('营业利润趋势外推预测', fontsize=14)
ax.set_xlabel('年份')
ax.set_ylabel('营业利润(亿元)')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

stock_price = 50   # 假设股价
current_eps = 2    # 假设每股收益
pe_ratio = stock_price / current_eps

scenarios = [5, 10, 15, 20, 25, 30]
peg_data = []
for g in scenarios:
    peg_val = pe_ratio / g
    status = '低估' if peg_val < 1 else ('合理' if peg_val < 1.2 else '高估')
    peg_data.append({'增长率(%)': g, 'PE': pe_ratio, 'PEG': round(peg_val, 2), '判断': status})

pd.DataFrame(peg_data)