Remove FRED, and other minor adjustments

Author: gusamarante

README.md

@@ -41,8 +41,6 @@ but I found an earlier version of the paper on SSRN where the authors go deeper
 - To get daily / real-time estimates, the factor loadings estimated from the monthly frquency are used to transform the daily data
 
 
-
-
 # Usage
 The tricky part of using this model is getting the correct data format:
 - The model works with annualized log-yields for zero-coupon bonds

example_br.py

@@ -1,5 +1,3 @@
-# TODO Turn this into a jupyter notebook
-
 from pyacm import NominalACM
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates

pyacm/utils.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pandas as pd
 
 
 def vec(mat):
@@ -42,63 +41,3 @@ def commutation_matrix(shape):
     w = np.arange(m * n).reshape((m, n), order="F").T.ravel(order="F")
     k = np.eye(m * n)[w, :]
     return k
-
-
-class FRED(object):
-    """
-    Wrapper for the data API of the FRED
-    """
-
-    def fetch(self, series_id):
-        """
-        Grabs series from the FRED website and returns them in a pandas
-         dataframe
-
-        Parameters
-        ----------
-        series_id: str, list, dict
-            string with series ID, list of strings of the series ID or
-            dict with series ID as keys and their desired names as values
-        """
-
-        if type(series_id) is list:
-
-            df = pd.DataFrame()
-
-            for cod in series_id:
-                single_series = self._fetch_single_code(cod)
-                df = pd.concat([df, single_series], axis=1)
-
-            df.sort_index(inplace=True)
-
-        elif type(series_id) is dict:
-
-            df = pd.DataFrame()
-
-            for cod in series_id.keys():
-                single_series = self._fetch_single_code(cod)
-                df = pd.concat([df, single_series], axis=1)
-
-            df.columns = series_id.values()
-
-        else:
-
-            df = self._fetch_single_code(series_id)
-
-        return df
-
-    @staticmethod
-    def _fetch_single_code(series_id):
-
-        url = r'https://fred.stlouisfed.org/data/' + series_id + '.txt'
-        df = pd.read_csv(url, sep='\t')
-        series_start = df[df[df.columns[0]].str.contains(r'DATE\s+VALUE')].index[0] + 1
-        df = df.loc[series_start:]
-        df = df[df.columns[0]].str.split(r'\s+', expand=True)
-        df = df[~(df[1] == '.')]
-        df = pd.DataFrame(data=df[1].values.astype(float),
-                          index=pd.to_datetime(df[0]),
-                          columns=[series_id])
-        df.index.rename('Date', inplace=True)
-
-        return df

setup.py

@@ -22,15 +22,15 @@
     long_description=long_description,
     packages=find_packages(),
     install_requires=[
+        'matplotlib',
+        'numpy',
         'pandas',
         'scikit-learn',
-        'numpy',
-        'matplotlib',
         'tqdm',
     ],
     keywords=[
         'asset pricing',
         'yield curve',
         'term premium',
     ],
-)
+)