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CEDApy
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add new functions

This commit is contained in:
TerenceLiu98 2021-05-27 17:06:23 +08:00
parent 96d9f18abb
commit 7259bd42ac
2 changed files with 330 additions and 9 deletions
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1
CEDA/economic/macro.py
View File

@ -1,5 +1,6 @@
import pandas as pd
import numpy as np
import re
import demjson
import requests
from fake_useragent import UserAgent

338
CEDA/economic/us.py
View File

@ -85,7 +85,7 @@ def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"):
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
return df
def unrate_monthly(startdate="1948-01-01", enddate="2021-01-01"):
def unrate(startdate="1948-01-01", enddate="2021-01-01"):
"""
Full Name: Unemployment Rate: Aged 15-64: All Persons for the United States
Description: Percent, Seasonally Adjusted, Monthly, Quarterly and Annually
@ -124,12 +124,56 @@ def unrate_monthly(startdate="1948-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "UR_Monthly", "UR_Quarterly", "UR_Annually"]
return df
def erate(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name: Employment Rate: Aged 25-54: All Persons for the United States
Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "LREM25TTUSM156S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "LREM25TTUSQ156S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "LREM25TTUSA156S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "ER_Monthly", "ER_Quarterly", "ER_Annually"]
def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"):
"""
Full Name: PCE
@ -188,7 +232,7 @@ def cpi(startdate="1960-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "CPI_Monthly", "CPI_Quarterly", "CPI_Annually"]
@ -203,6 +247,17 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "WM1NS",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "MANMM101USM657S",
"cosd": "{}".format(startdate),
@ -233,12 +288,44 @@ def m1(startdate="1960-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "M1_Monthly", "M1_Quarterly", "M1_Annually"]
df.columns = ["Date", "M1_weekly", "M1_Monthly", "M1_Quarterly", "M1_Annually"]
return df
def m2(startdate="1960-01-01", enddate="2021-01-01"):
"""
Full Name: M2 Money Stock
Description: Seasonally Adjusted, Weekly, Monthly, Quarterly and Annually, Seasonally Adjusted
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "WM2NS",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "M2SL",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
df.columns = ["Date", "M2_Weekly", "M2_Monthly"]
def m3(startdate="1960-01-01", enddate="2021-01-01"):
"""
@ -279,7 +366,7 @@ def m3(startdate="1960-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "M3_Monthly", "M3_Quarterly", "M3_Annually"]
@ -324,7 +411,7 @@ def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "ltgby_Monthly", "ltgby_Quarterly", "ltgby_Annually"]
@ -358,7 +445,240 @@ def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "gdp_ipd_Quarterly", "gdp_ipd_Annually"]
return df
def cci(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
Description: Normalised (Normal=100), Seasonally Adjusted, Monthly
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "CSCICP03USM665S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df.columns = ["Date", "CCI_Monthly"]
return df
def bci(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name: Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: OECD Indicator for the United States
Description: Normalised (Normal=100), Seasonally Adjusted, Monthly
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "BSCICP03USM665S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df.columns = ["Date", "BCI_Annually"]
return df
def ibr_3(startdate="1965-01-01", enddate="2021-01-01"):
"""
Full Name: 3-Month or 90-day Rates and Yields: Interbank Rates for the United States
Description: Percent, Not Seasonally Adjusted, Monthly and Quarterly
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "IR3TIB01USM156N",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "IR3TIB01USQ156N",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_quarterly, df_quarterly, on = "DATE", direction = "backward")
df.columns = ["Date", "ibr3_monthly", "ibr3_Quarterly"]
def gfcf_3(startdate="1965-01-01", enddate="2021-01-01"):
"""
Full Name: Gross Fixed Capital Formation in United States
Description: United States Dollars,Not Seasonally Adjusted, Quarterly and Annually
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USAGFCFQDSMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USAGFCFADSMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_quarterly, df_quarterly, on = "DATE", direction = "backward")
df.columns = ["Date", "ibr3_monthly", "ibr3_Annually"]
def pfce(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name: Employment Rate: Aged 25-54: All Persons for the United States
Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USAPFCEQDSMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USAPFCEADSMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Annually"]
def tlp(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name: Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the United States
Description: Growth Rate Previous Period,Seasonally Adjusted, Quarterly and YoY
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "ULQELP01USQ657S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "ULQELP01USQ659S",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_quarterly, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "PFCE_Quarterly", "PFCE_Quarterly_YoY"]
def rt(startdate="1955-01-01", enddate="2021-01-01"):
"""
Full Name:Total Retail Trade in United States
Description: Monthly and Anually
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USASARTMISMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
request_header = {"User-Agent": ua.random}
request_params = {
"id": "USASARTAISMEI",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_monthly, df_annually, on = "DATE", direction = "backward")
df.columns = ["Date", "RT_Quarterly", "RT_Annually"]
return df
def bir(startdate="2003-01-01", enddate="2021-01-01"):
"""
Full Name:Total Retail Trade in United States
Description: Monthly and Anually
Return: pd.DataFrame
"""
tmp_url = url["fred_econ"]
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params = {
"id": "T5YIE",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_5y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_5y["DATE"] = pd.to_datetime(df_5y["DATE"], format="%Y-%m-%d")
request_header = {"User-Agent": ua.random}
request_params = {
"id": "T10YIE",
"cosd": "{}".format(startdate),
"coed": "{}".format(enddate)
}
r = requests.get(tmp_url, params = request_params, headers = request_header)
data_text = r.content
df_10y = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
df_10y["DATE"] = pd.to_datetime(df_10y["DATE"], format="%Y-%m-%d")
df = pd.merge_asof(df_5y, df_10y, on = "DATE", direction = "backward")
df.columns = ["Date", "BIR_5y", "BIR_10y"]
return df