请模拟以下代码：使用python，调用2016年1月至2023年4月的·wind数据库，然后计算量价信号结合表现，然后以图表的形式呈现出来

以下是一些代码示例，帮助您开始使用Python调用Wind数据库并计算量价信号并绘制图表：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import WindPy as wp

# 连接Wind
wp.start()

# 获取股票数据
start_date = "2016-01-01"
end_date = "2023-04-30"
data = wp.wsd("000001.SH", "open,high,low,close,volume", start_date, end_date)

# 转换数据为DataFrame格式
df = pd.DataFrame(np.array(data.Data).T, columns=data.Fields, index=data.Times)

# 计算量价信号
df['typical_price'] = (df['high'] + df['low'] + df['close']) / 3
df['money_flow'] = df['typical_price'] * df['volume']
df['money_flow_positive'] = np.where(df['typical_price'] > df['typical_price'].shift(1), df['money_flow'], 0)
df['money_flow_negative'] = np.where(df['typical_price'] < df['typical_price'].shift(1), df['money_flow'], 0)
df['money_flow_ratio'] = df['money_flow_positive'].rolling(window=10).sum() / df['money_flow_negative'].rolling(window=10).sum()

# 绘制图表
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(df.index, df['close'], label='Close')
ax.legend(loc='upper left')
ax2 = ax.twinx()
ax2.plot(df.index, df['money_flow_ratio'], color='red', label='Money Flow Ratio')
ax2.legend(loc='upper right')
plt.show()

这段代码将获取000001.SH指数的开盘价、最高价、最低价、收盘价和成交量数据，并计算了基于量价的指标——资金流量比率。最后，使用matplotlib库将指数收盘价和资金流量比率绘制在同一张图上。