684 lines
27 KiB
Python
684 lines
27 KiB
Python
import pandas as pd
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import numpy as np
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import requests
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from fake_useragent import UserAgent
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import io
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# Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205
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url = {
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"fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?"
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}
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def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Gross Domestic Product
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Description: Billions of Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "GDP",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def gdpc1_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Real Gross Domestic Product
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Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "GDPC1",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def oecd_gdp_monthly(startdate="1947-01-01", enddate="2021-01-01"):
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"""
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Full Name: Real Gross Domestic Product
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Description: Billions of Chained 2012 Dollars, Quarterly, Seasonally Adjusted Annual Rate
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "USALORSGPNOSTSAM",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def payems_monthly(startdate="1939-01-01", enddate="2021-01-01"):
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"""
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Full Name: All Employees, Total Nonfarm
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Description: Thousands of Persons,Seasonally Adjusted, Monthly
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "PAYEMS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def unrate(startdate="1948-01-01", enddate="2021-01-01"):
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"""
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Full Name: Unemployment Rate: Aged 15-64: All Persons for the United States
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Description: Percent, Seasonally Adjusted, Monthly, Quarterly and Annually
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSM156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSQ156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LRUN64TTUSA156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
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df.columns = ["Date", "UR_Monthly", "UR_Quarterly", "UR_Annually"]
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return df
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def erate(startdate="1955-01-01", enddate="2021-01-01"):
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"""
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Full Name: Employment Rate: Aged 25-54: All Persons for the United States
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Description: Percent,Seasonally Adjusted, Monthly, Quarterly and Annually
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSM156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSQ156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "LREM25TTUSA156S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
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df.columns = ["Date", "ER_Monthly", "ER_Quarterly", "ER_Annually"]
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def pce_monthly(startdate="1959-01-01", enddate="2021-01-01"):
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"""
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Full Name: PCE
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Description: Percent, Monthly, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "PCE",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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return df
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def cpi(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: Total All Items for the United States
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Description: Percent, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USM661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USQ661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "CPALTT01USA661S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
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df.columns = ["Date", "CPI_Monthly", "CPI_Quarterly", "CPI_Annually"]
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return df
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def m1(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: M3 for the United States
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Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "WM1NS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USM657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USQ657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MANMM101USA657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_quarterly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
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df.columns = ["Date", "M1_weekly", "M1_Monthly", "M1_Quarterly", "M1_Annually"]
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return df
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def m2(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: M2 Money Stock
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Description: Seasonally Adjusted, Weekly, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "WM2NS",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_weekly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_weekly["DATE"] = pd.to_datetime(df_weekly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "M2SL",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_weekly, df_monthly, on = "DATE", direction = "backward")
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df.columns = ["Date", "M2_Weekly", "M2_Monthly"]
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def m3(startdate="1960-01-01", enddate="2021-01-01"):
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"""
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Full Name: Consumer Price Index: M3 for the United States
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Description: Growth Rate Previous Period, Monthly, Quarterly and Annually, Seasonally Adjusted
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USM657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USQ657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "MABMM301USA657S",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_annually = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_annually["DATE"] = pd.to_datetime(df_annually["DATE"], format="%Y-%m-%d")
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df = pd.merge_asof(df_monthly, df_quarterly, on = "DATE", direction = "backward")
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df = pd.merge_asof(df, df_annually, on = "DATE", direction = "backward")
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df.columns = ["Date", "M3_Monthly", "M3_Quarterly", "M3_Annually"]
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return df
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def ltgby(startdate="1955-01-01", enddate="2021-01-01"):
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"""
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Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
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Description: Percent,Not Seasonally Adjusted, Monthly, Quarterly and Annually
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Return: pd.DataFrame
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"""
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tmp_url = url["fred_econ"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "IRLTLT01USM156N",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_monthly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_monthly["DATE"] = pd.to_datetime(df_monthly["DATE"], format="%Y-%m-%d")
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params = {
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"id": "IRLTLT01USQ156N",
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"cosd": "{}".format(startdate),
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"coed": "{}".format(enddate)
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.content
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df_quarterly = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
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df_quarterly["DATE"] = pd.to_datetime(df_quarterly["DATE"], format="%Y-%m-%d")
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"id": "IRLTLT01USA156N",
|
|
"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", "ltgby_Monthly", "ltgby_Quarterly", "ltgby_Annually"]
|
|
return df
|
|
|
|
def gdp_ipd(startdate="1955-01-01", enddate="2021-01-01"):
|
|
"""
|
|
Full Name: Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States
|
|
Description: Percent,Not 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": "USAGDPDEFQISMEI",
|
|
"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": "USAGDPDEFAISMEI",
|
|
"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", "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 |