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data_process.py
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data_process.py
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import numpy as np
import pandas as pd
import io
import requests
import matplotlib.pyplot as plt
def find_problem_dates(t_bill, corporate_dates):
"""
This function checks if two list of dates match.
input variables: two lists containing string data
"""
in_t_not_cor = []
in_cor_not_t = []
for i in corporate_dates:
if i not in t_bill:
in_cor_not_t.append(i)
for i in t_bill:
if i not in corporate_dates:
in_t_not_cor.append(i)
return in_t_not_cor, in_cor_not_t
#in_t_not_cor, in_cor_not_t = find_problem_dates(list(t_bill_daily["DATE"]),
# list(corporate_daily["DATE"]))
"""Data Extraction"""
start_date = "1996-12-31"
end_date = "2019-01-22"
# below are urls of csv data
# the last line shows the overall range of provided data
# the date in the middle shows the part we extract
# be sure not to extract data that exceeds the range
# 1996-12-31 to 2019-01-21
# ----------------------------------------------------------------------------
# read url
# ----------------------------------------------------------------------------
tbill_10year_daily_url = "https://fred.stlouisfed.org/graph/fredgraph.csv?bg"\
"color=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23f"\
"fffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&t"\
"ts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&s"\
"how_tooltip=yes&id=DGS10&scale=left&cosd"\
"=" + start_date + "&coed=" + end_date + "&"\
"line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&"\
"mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Daily&fam=avg&fgst=lin&fg"\
"snd=2009-06-01&line_index=1&transformation=&vintage_date=2019-01-21&rev"\
"ision_date=2019-01-21&nd=1962-01-02"
corporate3A_effective_yield_url = "https://fred.stlouisfed.org/graph/fredgra"\
"ph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgc"\
"olor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%2344444"\
"4&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_ti"\
"tles=yes&show_tooltip=yes&id=BAMLC0A1CAAAEY&scale=left&cosd"\
"=" + start_date + "&coed=" + end_date + "&"\
"line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&"\
"mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Daily%2C%20Close&fam=avg&"\
"fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date="\
"2019-01-21&revision_date=2019-01-21&nd=1996-12-31"
vix_url = "https://raw.githubusercontent.com/israeldi/IAQF_Repo/master/VIX.c"\
"sv?token=Adz3eLTpsYEkd6i1sNXwIytcoMKygd-sks5ceYo-wA%3D%3D"
sp500_url = "https://raw.githubusercontent.com/israeldi/IAQF_Repo/master/SP5"\
"00.csv?token=Adz3eEwHk1Ida5DihqZQAnKa1uMaO77Jks5ceYqmwA%3D%3D"
tbill_10m2_url = "https://raw.githubusercontent.com/israeldi/IAQF_Repo/maste"\
"r/T10Y2Y.csv?token=Adz3eAowDIe2I2EJwGmywnScMyKygKkxks5cehM3wA%3D%3D"
skew_url = "https://raw.githubusercontent.com/israeldi/IAQF_Repo/master/skew"\
"dailyprices.csv?token=Adz3eCe-TW-ul1jvDK0YdfxxR-fggTvHks5cehNiwA%3D%3D"
swap_spread_url = "https://raw.githubusercontent.com/israeldi/IAQF_Repo/master"\
"/swap_spread.csv?token=Ad9pkVtJaR6En7AlPCcLvWJPD_7H0CPkks5cfW45wA%3D%3D"
# ----------------------------------------------------------------------------
# convert url into dataframe
# ----------------------------------------------------------------------------
tbill_string_file = requests.get(tbill_10year_daily_url).content
t_bill_daily = pd.read_csv(io.StringIO(tbill_string_file.decode('utf-8')))
cor_3A_string_file = requests.get(corporate3A_effective_yield_url).content
corporate_daily = pd.read_csv(io.StringIO(cor_3A_string_file.decode('utf-8')))
vix_string_file = requests.get(vix_url).content
vix_daily = pd.read_csv(io.StringIO(vix_string_file.decode('utf-8')))
sp500_string_file = requests.get(sp500_url).content
sp500_daily = pd.read_csv(io.StringIO(sp500_string_file.decode('utf-8')))
t10m2_string_file = requests.get(tbill_10m2_url).content
t10m2_daily = pd.read_csv(io.StringIO(t10m2_string_file.decode('utf-8')))
skew_string_file = requests.get(skew_url).content
skew_daily = pd.read_csv(io.StringIO(skew_string_file.decode('utf-8')))
swap_spread_string_file = requests.get(swap_spread_url).content
swap_spread_daily = pd.read_csv(io.StringIO(swap_spread_string_file.decode('utf-8')))
""" Data Cleaning """
# ----------------------------------------------------------------------------
# convert date into same standard
# ----------------------------------------------------------------------------
t_bill_daily["DATE"] = pd.to_datetime(t_bill_daily["DATE"])
corporate_daily["DATE"] = pd.to_datetime(corporate_daily["DATE"])
vix_daily["DATE"] = pd.to_datetime(vix_daily["Date"])
sp500_daily["DATE"] = pd.to_datetime(sp500_daily["Date"])
t10m2_daily["DATE"] = pd.to_datetime(t10m2_daily["DATE"])
skew_daily["DATE"] = pd.to_datetime(skew_daily["Date"])
swap_spread_daily["DATE"] = pd.to_datetime(swap_spread_daily["Dates"])
# ----------------------------------------------------------------------------
# merge dataframes
# ----------------------------------------------------------------------------
combined = pd.merge(corporate_daily, t_bill_daily,
how='inner', left_on=['DATE'], right_on=['DATE'])
combined = pd.merge(combined, vix_daily,
how = "inner", left_on = ["DATE"], right_on = ["DATE"])
combined = pd.merge(combined, sp500_daily,
how = "inner", left_on = ["DATE"], right_on = ["DATE"])
combined = pd.merge(combined, t10m2_daily,
how = "inner", left_on = ["DATE"], right_on = ["DATE"])
combined = pd.merge(combined, skew_daily,
how = "inner", left_on = ["DATE"], right_on = ["DATE"])
combined = pd.merge(combined, swap_spread_daily,
how = "inner", left_on = ["DATE"], right_on = ["DATE"])
# ----------------------------------------------------------------------------
# select features
# ----------------------------------------------------------------------------
col_names = ["DATE", "BAMLC0A1CAAAEY", "DGS10", "VIX", "Adj Close", "T10Y2Y",
"SKEW", "SWAPSPREAD"]
combined = combined[col_names]
# ----------------------------------------------------------------------------
# deal with missing values, displayed as "." or "NA"
# ----------------------------------------------------------------------------
combined = pd.DataFrame.replace(combined, to_replace=".", value=float("NaN"))
combined = combined.fillna(method="ffill")
# ----------------------------------------------------------------------------
# deal with data type problem, convert all str to float
# ----------------------------------------------------------------------------
combined.BAMLC0A1CAAAEY = combined.BAMLC0A1CAAAEY.astype(float)
combined.DGS10 = combined.DGS10.astype(float)
combined.T10Y2Y = combined.T10Y2Y.astype(float)
# ----------------------------------------------------------------------------
# generate spread, defined as (BAMLC0A1CAAAEY - DGS10)
# ----------------------------------------------------------------------------
combined["SPREAD"] = combined["BAMLC0A1CAAAEY"] - combined["DGS10"]
# ----------------------------------------------------------------------------
# generate feature difference data
# defined as DIFF_{t} = DATA_{t} - DATA_{t-1}
# ----------------------------------------------------------------------------
combined["SPREAD_DIFF1"] = combined["SPREAD"] - combined["SPREAD"].shift(1)
combined["DGS10_DIFF1"] = combined["DGS10"] - combined["DGS10"].shift(1)
combined["VIX_DIFF1"] = combined["VIX"] - combined["VIX"].shift(1)
combined["SLOPE"] = combined["T10Y2Y"] - combined["T10Y2Y"].shift(1)
combined["SKEW_DIFF1"] = combined["SKEW"] - combined["SKEW"].shift(1)
combined["SP500_R"] = (combined["Adj Close"] -
combined["Adj Close"].shift(1)) / combined["Adj Close"].shift(1)
combined["SPREAD_DIFF1_TOMORROW"] = combined["SPREAD_DIFF1"].shift(-1)
features = ["DGS10_DIFF1", "VIX_DIFF1", "SLOPE", "SKEW_DIFF1", "SP500_R", "SWAPSPREAD"]
others = ["DATE", "SPREAD_DIFF1", "SPREAD_DIFF1_TOMORROW", "SPREAD"]
combined = combined[features+others].dropna()
# ----------------------------------------------------------------------------
# convert to monthly frequency
# ----------------------------------------------------------------------------
"""
combined["y_and_m"] = combined["DATE"].map(lambda x: 100 * x.year + x.month)
my_agg_functions = {"DGS10_DIFF1":"sum", "VIX_DIFF1":"sum", "SLOPE":"sum",
"SKEW_DIFF1":"sum", "SP500_R":"sum", "SPREAD_DIFF1":"sum"}
combined_monthly = combined.groupby(by="y_and_m").agg(my_agg_functions)
combined_monthly["SPREAD_M_DIFF1"] = combined_monthly["SPREAD_DIFF1"].shift(-1)
combined_monthly = combined_monthly[features+["SPREAD_M_DIFF1"]+["SPREAD_DIFF1"]]
combined_monthly["SPREAD_M_AR1"] = combined_monthly["SPREAD_DIFF1"]
combined_monthly["SPREAD_M_AR2"] = combined_monthly["SPREAD_DIFF1"].shift(1)
combined_monthly["VIX_AR1"] = combined_monthly["VIX_DIFF1"].shift(1)
#features = features + ["SPREAD_M_AR1", "SPREAD_M_AR2", "VIX_AR1"]
combined_monthly = combined_monthly.dropna()
"""