Python绘制移动均线方法 含源代码

import pandas as pd
import datetime
from hs_udata import set_token,stock_quote_daily
from mpl_finance import candlestick_ohlc
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题

def GetData(stock_code,start,end):
    #stock_code：获取股票数据的股票代码
    #      start：开始日期
    #        end：结束日期
    date_start=datetime.datetime.strptime(start,'%Y-%m-%d')
    date_end  =datetime.datetime.strptime(end,'%Y-%m-%d')
    data = pd.DataFrame([])
    while date_start<date_end:
        # 获取日行情数据，接口说明见 https://udata.hs.net/datas/332/
        # adjust_way枚举值为：0-不复权，1-前复权，2-后复权，此处取前复权
        data_i = stock_quote_daily(en_prod_code=stock_code
                                   ,trading_date=date_start.strftime('%Y%m%d')
                                   ,adjust_way = 1)
        data=pd.concat([data,data_i],axis=0)      # 将行情数据按行拼接
        date_start+=datetime.timedelta(days=1)    # 日期变量自增
    # 返回行情数据
    return data
#1、获取行情数据

stock_code = "600570.SH"                        # 恒生电子 股票代码是600570.SH
start='2021-03-01'
end  ='2021-06-01'
set_token(token = 'xxxxxxxxxxxxxxxxxxxxxxxx')   # 注册恒有数之后，获取并替换token
data = GetData(stock_code,start,end)

#2、数据处理

data = data.loc[data.turnover_status=='交易']                            # 剔除非交易日
data_price = data[['trading_date','open_price','high_price','low_price'
                   ,'close_price','business_amount']]                    # 选取日期与高开低收价格
data_price.set_index('trading_date', inplace=True)                      # 将日期作为索引
data_price = data_price.astype(float)                                   # 将价格数据类型转为浮点数
# 将日期格式转为 candlestick_ohlc 可识别的数值
data_price['Date'] = list(map(lambda x:mdates.date2num(datetime.datetime.strptime(x,'%Y-%m-%d'))
                                ,data_price.index.tolist()))

data_price

#3、计算均值
data_price['MA5']=data_price['close_price'].rolling(window=5).mean()
data_price['MA10']=data_price['close_price'].rolling(window=10).mean()
data_price['MA20']=data_price['close_price'].rolling(window=20).mean()
data_price
 title=

#4、绘制图片
fig = plt.figure(figsize=(12,10))
grid = plt.GridSpec(12, 10, wspace=0.5, hspace=0.5)
#（1）绘制K线图

#K线数据

ohlc = data_price[['Date','open_price','high_price','low_price','close_price']]
ohlc.loc[:,'Date'] = range(len(ohlc))     # 重新赋值横轴数据，绘制K线图无间隔

#绘制K线
ax1 = fig.add_subplot(grid[0:8,0:12])   # 设置K线图的尺寸
candlestick_ohlc(ax1, ohlc.values.tolist(), width=.7
                 , colorup='red', colordown='green')
#（2）绘制均线

ax1.plot(range(len(data_price)), data_price['MA5']
         , color='red', lw=2, label='MA (5)')
ax1.plot(range(len(data_price)), data_price['MA10']
         , color='blue', lw=2, label='MA (10)')
ax1.plot(range(len(data_price)), data_price['MA20']
         , color='green', lw=2, label='MA (20)')
#设置标注
plt.title(stock_code,fontsize = 14)       # 设置图片标题
plt.ylabel('价 格（元）',fontsize = 14)   # 设置纵轴标题
plt.legend(loc='best')                    # 绘制图例
ax1.set_xticks([])                        # 日期标注在成交量中，故清空此处x轴刻度
ax1.set_xticklabels([])                   # 日期标注在成交量中，故清空此处x轴 

#（3）绘制成交量

#成交量数据
data_volume = data_price[['Date','close_price','open_price','business_amount']]
data_volume['color'] = data_volume.apply(lambda row: 1 if row['close_price'] >= row['open_price'] else 0, axis=1)        # 计算成交量柱状图对应的颜色，使之与K线颜色一致
data_volume.Date = ohlc.Date

 #绘制成交量
ax2 = fig.add_subplot(grid[8:10,0:12])  # 设置成交量图形尺寸
ax2.bar(data_volume.query('color==1')['Date']
        , data_volume.query('color==1')['business_amount']
        , color='r')                    # 绘制红色柱状图
ax2.bar(data_volume.query('color==0')['Date']
        , data_volume.query('color==0')['business_amount']
        , color='g')                    # 绘制绿色柱状图
plt.xticks(rotation=30) 
plt.xlabel('日 期',fontsize = 14)                               # 设置横轴标题

#修改横轴日期标注

date_list = ohlc.index.tolist()           # 获取日期列表
xticks_len = round(len(date_list)/(len(ax2.get_xticks())-1))      # 获取默认横轴标注的间隔
xticks_num = range(0,len(date_list),xticks_len)                   # 生成横轴标注位置列表
xticks_str = list(map(lambda x:date_list[int(x)],xticks_num))     # 生成正在标注日期列表
ax2.set_xticks(xticks_num)                                        # 设置横轴标注位置
ax2.set_xticklabels(xticks_str)                                   # 设置横轴标注日期
plt.show()