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量化交易吧 /  数理科学 帖子：3364690 新帖：11

常见的宏观指标获取方法

英雄就是我发表于：6 月 9 日 04：00回复(1) 
# https://www.joinquant.com/help/api/help?name=macroData
# PMI制造业采购经理指数 MAC_MANUFACTURING_PMI
from jqdata import macro
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)

start_year = '2017'
end_year = '2019'

q = query(macro.MAC_MANUFACTURING_PMI
    ).filter(macro.MAC_MANUFACTURING_PMI.stat_month >= start_year,
            macro.MAC_MANUFACTURING_PMI.stat_month <= end_year,
    ).order_by(macro.MAC_MANUFACTURING_PMI.stat_month.asc()
    ).with_entities(macro.MAC_MANUFACTURING_PMI.stat_month, 
                    macro.MAC_MANUFACTURING_PMI.pmi)
df = macro.run_query(q)
print(df)

plt.figure(figsize=(15,5),dpi=80)
plt.plot(df['stat_month'], df['pmi'], color='black', linestyle="-")
# plt.gca().xaxis.set_major_formatter(DateFormatter('%y-%m'))
plt.xticks(df['stat_month'], rotation=45)
pyplot.title('制造业PMI', loc='right')

   stat_month   pmi
0     2017-01  51.3
1     2017-02  51.6
2     2017-03  51.8
3     2017-04  51.2
4     2017-05  51.2
5     2017-06  51.7
6     2017-07  51.4
7     2017-08  51.7
8     2017-09  52.4
9     2017-10  51.6
10    2017-11  51.8
11    2017-12  51.6
12    2018-01  51.3
13    2018-02  50.3
14    2018-03  51.5
15    2018-04  51.4
16    2018-05  51.9
17    2018-06  51.5
18    2018-07  51.2
19    2018-08  51.3
20    2018-09  50.8
21    2018-10  50.2
22    2018-11  50.0
23    2018-12  49.4

Text(1.0, 1.0, '制造业PMI')
# GDP分地区国内生产总值表 MAC_AREA_GDP_QUARTER(季度) MAC_AREA_GDP_YEAR(年度) 含有全国各地址123产业GDP
from jqdata import macro
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)

start_year = '2000'
end_year = '2019'

q = query(macro.MAC_AREA_GDP_YEAR
    ).filter(macro.MAC_AREA_GDP_YEAR.stat_year >= start_year,
            macro.MAC_AREA_GDP_YEAR.stat_year <= end_year,
             macro.MAC_AREA_GDP_YEAR.area_code==156 
    ).order_by(macro.MAC_AREA_GDP_YEAR.stat_year.asc()
    ).with_entities(macro.MAC_AREA_GDP_YEAR.stat_year, 
                    macro.MAC_AREA_GDP_YEAR.gdp)
df = macro.run_query(q)
print(df)

plt.figure(figsize=(15,5),dpi=80)
plt.plot(df['stat_year'], df['gdp'], color='black',  linestyle="-")
plt.xticks(df['stat_year'], rotation=45)
pyplot.title('GDP', loc='right')

   stat_year          gdp
0       2000  100280.1393
1       2001  110863.1230
2       2002  121717.4247
3       2003  137422.0349
4       2004  161840.1609
5       2005  187318.9031
6       2006  219438.5000
7       2007  270232.3000
8       2008  319244.6000
9       2009  348517.7437
10      2010  412119.2558
11      2011  487940.1805
12      2012  538579.9535
13      2013  592963.2295
14      2014  641280.5743
15      2015  685992.9497
16      2016  740060.7967
17      2017  820754.2782
18      2018  900309.4780

Text(1.0, 1.0, 'GDP')
# CPI 分地区居民消费价格指数 MAC_AREA_CPI_MONTH
from jqdata import macro
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)

start_year = '2018'
end_year = '2019'

# q = query(macro.MAC_AREA_CPI_MONTH
#     ).filter(macro.MAC_AREA_CPI_MONTH.stat_month >= start_year,
#             macro.MAC_AREA_CPI_MONTH.stat_month <= end_year,
#              macro.MAC_AREA_CPI_MONTH.area_code==156 
#     ).order_by(macro.MAC_AREA_CPI_MONTH.stat_month.asc()
#     ).with_entities(macro.MAC_AREA_CPI_MONTH.stat_month, 
# #                     macro.MAC_AREA_CPI_MONTH.area_code, 
# #                     macro.MAC_AREA_CPI_MONTH.area_name, 
#                     macro.MAC_AREA_CPI_MONTH.item_name, 
#                     macro.MAC_AREA_CPI_MONTH.item_value)
# df = macro.run_query(q)
# print(df)

import tushare as ts
df = ts.get_cpi()
df = df.iloc[::-1]
df = df[df.month >= start_year]
# df = df[df.month <= end_year]


plt.figure(figsize=(15,5),dpi=80)
plt.plot(df['month'], df['cpi'], color='black',  linestyle="-")
plt.xticks(df['month'], rotation=45)
pyplot.title('CPI', loc='right')

Text(1.0, 1.0, 'CPI')
# PPI 工业品出厂价格指数 http://tushare.org/macro.html
from jqdata import macro
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)

start_year = '2018'
end_year = '2019'


import tushare as ts
df = ts.get_ppi()
df = df.iloc[::-1]
# print(df)
# month :统计月份
# ppiip :工业品出厂价格指数
# ppi :生产资料价格指数
# qm:采掘工业价格指数
# rmi:原材料工业价格指数
# pi:加工工业价格指数
# cg:生活资料价格指数
# food:食品类价格指数
# clothing:衣着类价格指数
# roeu:一般日用品价格指数
# dcg:耐用消费品价格指数
df = df[df.month >= start_year]


plt.figure(figsize=(15,5),dpi=80)
plt.plot(df['month'], df['ppiip'], color='black',  linestyle="-",label='ppiip工业品出厂价格指数')
plt.plot(df['month'], df['ppi'], color='red',  linestyle="-",label='ppi生产资料价格指数')
plt.xticks(df['month'], rotation=45)
pyplot.title('工业品出厂价格指数PPI', loc='right')
plt.legend()

<matplotlib.legend.Legend at 0x7f09254fe5c0>

       

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