Add method to grab FRED data

Author: gusamarante

pit_estimates.py

@@ -1,5 +1,8 @@
 """
-Generate point-in-time estimates
+Generate point-in-time estimates.
+
+For each date, rerun the model and save the latest estimate of expected return
+and term premium
 """
 from tqdm import tqdm
 import pandas as pd

pyacm/utils.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pandas as pd
 
 
 def vec(mat):
@@ -41,3 +42,63 @@ 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('DATE\s+VALUE')].index[0] + 1
+        df = df.loc[series_start:]
+        df = df[df.columns[0]].str.split('\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

@@ -26,6 +26,7 @@
         'scikit-learn',
         'numpy',
         'matplotlib',
+        'tqdm',
     ],
     keywords=[
         'asset pricing',