[Python] 使用mplfinance和matplotlib绘制腾讯的MACD图表

from yahooquery import Ticker
import pandas as pd

# 要显示的数据数量
show_count = 250
# 要在x轴上显示的刻度数量
tick_count = 10

# 从yahooquery下载腾讯的股票数据
tencent = Ticker('0700.HK')
data = tencent.history(period="2y")
# 重置'symbol'和'date'的索引，并以'date'列设置新索引
data.reset_index(inplace=True)
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)

# 使用talib库计算移动平均线
data["sma5"] = talib.SMA(data.close, timeperiod=5)  # 5天移动平均线
data["sma20"] = talib.SMA(data.close, timeperiod=20) # 20天移动平均线
data["sma60"] = talib.SMA(data.close, timeperiod=60) # 60天移动平均线

# 计算MACD
data["macd"], data["macd_signal"], data["macd_hist"] = talib.MACD(data['close'])

# 只使用最后250个数据
data = data.tail(show_count)

import matplotlib.pyplot as plt
import mplfinance as mpf

# 设置图的整体大小
fig = mpf.figure(figsize=(12,10))

# 创建主图、MACD图和MACD振荡器图的子图
ax_main = fig.add_subplot(5, 1, (1, 3), facecolor='white') # 主图
ax_macd = fig.add_subplot(5, 1, (4, 4), facecolor='white') # MACD图
ax_hist = fig.add_subplot(5, 1, (5, 5), facecolor='white') # MACD振荡器图

# 调整子图之间的间距
fig.tight_layout()

added_plots = {
    'MA5': mpf.make_addplot(data.sma5, type='line', ax=ax_main, width=1.5),
    'MA20': mpf.make_addplot(data.sma20, type='line', ax=ax_main, width=1.5),
    'MA60': mpf.make_addplot(data.sma60, type='line', ax=ax_main, width=1.5),
}

mc = mpf.make_marketcolors(up="#BE452D", down="#4375E0", edge='black')

s = mpf.make_mpf_style(marketcolors=mc, gridcolor='#DDDDDD', gridstyle="--")

mpf.plot(data,
         style=s,
         type='candle',
         ax=ax_main,
         addplot=list(added_plots.values()),
         datetime_format='%Y-%m-%d',
         returnfig=True)

ax_main.set_ylabel('')

ax_macd.plot([0, len(index)-1],
                 [0, 0],
                 color='#DE0000',
                 linestyle='--',
                 linewidth=0.7)

ax_macd.plot(data.index,
                data['macd'],
                label='MACD')

ax_macd.plot(data.index,
                data['macd_signal'],
                label='MACD Signal')

histogram = data['macd_hist']
index = data.index.strftime('%Y-%m-%d')
ax_hist.bar(list(index), list(histogram.where(histogram > 0)), 0.7, color="#EC3019")
ax_hist.bar(list(index), list(histogram.where(histogram < 0)), 0.7, color="#4375E0")

# 为每个图表设置标题
ax_main.set_title('腾讯', fontsize=15, fontdict={'family': 'Noto Sans SC')
ax_macd.set_title('MACD', fontsize=15)
ax_hist.set_title('MACD Signal', fontsize=15)

# 为主图表设置图例
font = font_manager.FontProperties(family='Noto Sans SC', weight='normal')
ax_main.legend([None]*(len(added_plots)+2))
handles = ax_main.get_legend().legend_handles
ax_main.legend(handles=handles[2:], labels=list(added_plots.keys()), prop=font)

# 为MACD图表设置图例
ax_macd.legend(loc=2, prop=font)

# 设置x轴刻度标记
step = (len(data) - 1) / (tick_count - 1)
ticks = [0 + int(step * i) for i in range(tick_count)]
ax_main.xaxis.set_ticks(ticks)
ax_macd.xaxis.set_ticks(ticks)
ax_hist.xaxis.set_ticks(ticks)
ax_main.xaxis.set_ticklabels([])
ax_macd.xaxis.set_ticklabels([])

# 配置网格
ax_main.grid(color='#dddddd', linestyle='--', linewidth=0.5)
ax_macd.grid(color='#dddddd', linestyle='--', linewidth=0.5)
ax_hist.grid(color='#dddddd', linestyle='--', linewidth=0.5)

# 显示图表
plt.show()