ENH: Adjust OHLC when loading bars to remove split/dividend noise

The adjustment is optional so that previous unadjusted values
can be compared to adjusted.

zipline/utils/factory.py

@@ -377,18 +377,86 @@ def _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
     return data
 
 
-def load_from_yahoo(indexes=None, stocks=None, start=None, end=None):
+def load_from_yahoo(indexes=None,
+                    stocks=None,
+                    start=None,
+                    end=None,
+                    adjusted=True):
+    """
+    Loads price data from Yahoo into a dataframe for each of the indicated
+    securities.  By default, 'price' is taken from Yahoo's 'Adjusted Close',
+    which removes the impact of splits and dividends. If the argument
+    'adjusted' is False, then the non-adjusted 'close' field is used instead.
+
+    :Arguments:
+        indexes : dict (Default: {'SPX': '^GSPC'})
+            Financial indexes to load.
+        stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
+                                 'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
+            Stock closing prices to load.
+        start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices from start date on.
+        end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices until end date.
+        adjusted : bool (Default: True)
+            Adjust the price for splits and dividends.
+
+    """
     data = _load_raw_yahoo_data(indexes, stocks, start, end)
-    df = pd.DataFrame({key: d['Adj Close'] for key, d in data.iteritems()})
+    if adjusted:
+        close_key = 'Adj Close'
+    else:
+        close_key = 'Close'
+    df = pd.DataFrame({key: d[close_key] for key, d in data.iteritems()})
     df.index = df.index.tz_localize(pytz.utc)
     return df
 
 
-def load_bars_from_yahoo(indexes=None, stocks=None, start=None, end=None):
+def load_bars_from_yahoo(indexes=None,
+                         stocks=None,
+                         start=None,
+                         end=None,
+                         adjusted=True):
+    """
+    Loads data from Yahoo into a panel with the following
+    column names for each indicated security:
+        - open
+        - high
+        - low
+        - close
+        - volume
+        - price
+
+    Note that 'price' is Yahoo's 'Adjusted Close', which removes the
+    impact of splits and dividends. If the argument 'adjusted' is True, then
+    the open, high, low, and close values are adjusted as well.
+
+    :Arguments:
+        indexes : dict (Default: {'SPX': '^GSPC'})
+            Financial indexes to load.
+        stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
+                                 'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
+            Stock closing prices to load.
+        start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices from start date on.
+        end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices until end date.
+        adjusted : bool (Default: True)
+            Adjust open/high/low/close for splits and dividends.  The 'price'
+            field is always adjusted.
+
+    """
     data = _load_raw_yahoo_data(indexes, stocks, start, end)
     panel = pd.Panel(data)
     # Rename columns
     panel.minor_axis = ['open', 'high', 'low', 'close', 'volume', 'price']
     panel.major_axis = panel.major_axis.tz_localize(pytz.utc)
-
+    # Adjust data
+    if adjusted:
+        adj_cols = ['open', 'high', 'low', 'close']
+        for ticker in panel.items:
+            ratio = (panel[ticker]['price'] / panel[ticker]['close'])
+            ratio_filtered = ratio.fillna(0).values
+            for col in adj_cols:
+                panel[ticker][col] *= ratio_filtered
     return panel