Merge pull request #465 from addisonlynch/dev

Added IEX Daily Reader (Historical)

docs/source/remote_data.rst

@@ -29,6 +29,7 @@ extract data from various Internet sources into a pandas DataFrame.
 Currently the following sources are supported:
 
     - :ref:`Google Finance<remote_data.google>`
+    - :ref:`IEX<remote_data.iex>`
     - :ref:`Enigma<remote_data.enigma>`
     - :ref:`Quandl<remote_data.quandl>`
     - :ref:`St.Louis FED (FRED)<remote_data.fred>`
@@ -64,6 +65,25 @@ Google Finance
     f = web.DataReader('F', 'google', start, end)
     f.ix['2010-01-04']
 
+.. _remote_data.iex:
+
+IEX
+===
+
+Historical stock prices from `IEX <https://iextrading.com/developer/>`__, 
+
+.. ipython:: python
+
+    import pandas_datareader.data as web
+    from datetime import datetime
+    start = datetime(2015, 2, 9)
+    end = datetime(2017, 5, 24)
+    f = web.DataReader('F', 'iex', start, end)
+    f.loc['2015-02-09']
+
+
+Prices are available up for the past 5 years.
+
 .. _remote_data.enigma:
 
 Enigma

pandas_datareader/data.py

@@ -15,6 +15,7 @@
 from pandas_datareader.google.daily import GoogleDailyReader
 from pandas_datareader.google.options import Options as GoogleOptions
 from pandas_datareader.google.quotes import GoogleQuotesReader
+from pandas_datareader.iex.daily import IEXDailyReader
 from pandas_datareader.iex.deep import Deep as IEXDeep
 from pandas_datareader.iex.tops import LastReader as IEXLasts
 from pandas_datareader.iex.tops import TopsReader as IEXTops
@@ -286,6 +287,13 @@ def DataReader(name, data_source=None, start=None, end=None,
                                  chunksize=25,
                                  retry_count=retry_count, pause=pause,
                                  session=session).read()
+
+    elif data_source == "iex":
+        return IEXDailyReader(symbols=name, start=start, end=end,
+                              chunksize=25,
+                              retry_count=retry_count, pause=pause,
+                              session=session).read()
+
     elif data_source == "iex-tops":
         return IEXTops(symbols=name, start=start, end=end,
                        retry_count=retry_count, pause=pause,

pandas_datareader/iex/daily.py

@@ -0,0 +1,113 @@
+import datetime
+import json
+
+import pandas as pd
+
+from dateutil.relativedelta import relativedelta
+from pandas_datareader.base import _DailyBaseReader
+
+# Data provided for free by IEX
+# Data is furnished in compliance with the guidelines promulgated in the IEX
+# API terms of service and manual
+# See https://iextrading.com/api-exhibit-a/ for additional information
+# and conditions of use
+
+
+class IEXDailyReader(_DailyBaseReader):
+
+    """
+    Returns DataFrame/Panel of historical stock prices from symbols, over date
+    range, start to end. To avoid being penalized by Google Finance servers,
+    pauses between downloading 'chunks' of symbols can be specified.
+
+    Parameters
+    ----------
+    symbols : string, array-like object (list, tuple, Series), or DataFrame
+        Single stock symbol (ticker), array-like object of symbols or
+        DataFrame with index containing stock symbols.
+    start : string, (defaults to '1/1/2010')
+        Starting date, timestamp. Parses many different kind of date
+        representations (e.g., 'JAN-01-2010', '1/1/10', 'Jan, 1, 1980')
+    end : string, (defaults to today)
+        Ending date, timestamp. Same format as starting date.
+    retry_count : int, default 3
+        Number of times to retry query request.
+    pause : int, default 0
+        Time, in seconds, to pause between consecutive queries of chunks. If
+        single value given for symbol, represents the pause between retries.
+    chunksize : int, default 25
+        Number of symbols to download consecutively before intiating pause.
+    session : Session, default None
+        requests.sessions.Session instance to be used
+    """
+
+    def __init__(self, symbols=None, start=None, end=None, retry_count=3,
+                 pause=0.35, session=None, chunksize=25):
+        super(IEXDailyReader, self).__init__(symbols=symbols, start=start,
+                                             end=end, retry_count=retry_count,
+                                             pause=pause, session=session,
+                                             chunksize=chunksize)
+
+    @property
+    def url(self):
+        return 'https://api.iextrading.com/1.0/stock/market/batch'
+
+    @property
+    def endpoint(self):
+        return "chart"
+
+    def _get_params(self, symbol):
+        chart_range = self._range_string_from_date()
+        print(chart_range)
+        if isinstance(symbol, list):
+            symbolList = ','.join(symbol)
+        else:
+            symbolList = symbol
+        params = {
+            "symbols": symbolList,
+            "types": self.endpoint,
+            "range": chart_range,
+        }
+        return params
+
+    def _range_string_from_date(self):
+        delta = relativedelta(self.start, datetime.datetime.now())
+        if 2 <= (delta.years * -1) <= 5:
+            return "5y"
+        elif 1 <= (delta.years * -1) <= 2:
+            return "2y"
+        elif 0 <= (delta.years * -1) < 1:
+            return "1y"
+        else:
+            raise ValueError(
+                "Invalid date specified. Must be within past 5 years.")
+
+    def read(self):
+        """read data"""
+        try:
+            return self._read_one_data(self.url,
+                                       self._get_params(self.symbols))
+        finally:
+            self.close()
+
+    def _read_lines(self, out):
+        data = out.read()
+        json_data = json.loads(data)
+        result = {}
+        if type(self.symbols) is str:
+            syms = [self.symbols]
+        else:
+            syms = self.symbols
+        for symbol in syms:
+            d = json_data.pop(symbol)["chart"]
+            df = pd.DataFrame(d)
+            df.set_index("date", inplace=True)
+            values = ["open", "high", "low", "close", "volume"]
+            df = df[values]
+            sstart = self.start.strftime('%Y-%m-%d')
+            send = self.end.strftime('%Y-%m-%d')
+            df = df.loc[sstart:send]
+            result.update({symbol: df})
+        if len(result) > 1:
+            return result
+        return result[self.symbols]

pandas_datareader/tests/test_iex_daily.py

@@ -0,0 +1,78 @@
+from datetime import datetime
+
+import pytest
+
+import pandas_datareader.data as web
+
+
+class TestIEXDaily(object):
+
+    @classmethod
+    def setup_class(cls):
+        pytest.importorskip("lxml")
+
+    @property
+    def start(self):
+        return datetime(2015, 2, 9)
+
+    @property
+    def end(self):
+        return datetime(2017, 5, 24)
+
+    def test_iex_bad_symbol(self):
+        with pytest.raises(Exception):
+            web.DataReader("BADTICKER", "iex,", self.start, self.end)
+
+    def test_iex_bad_symbol_list(self):
+        with pytest.raises(Exception):
+            web.DataReader(["AAPL", "BADTICKER"], "iex", self.start, self.end)
+
+    def test_daily_invalid_date(self):
+        start = datetime(2010, 1, 5)
+        end = datetime(2017, 5, 24)
+        with pytest.raises(Exception):
+            web.DataReader(["AAPL", "TSLA"], "iex", start, end)
+
+    def test_single_symbol(self):
+        df = web.DataReader("AAPL", "iex", self.start, self.end)
+        assert list(df) == ["open", "high", "low", "close", "volume"]
+        assert len(df) == 578
+        assert df["volume"][-1] == 19219154
+
+    def test_multiple_symbols(self):
+        syms = ["AAPL", "MSFT", "TSLA"]
+        df = web.DataReader(syms, "iex", self.start, self.end)
+        assert sorted(list(df)) == syms
+        for sym in syms:
+            assert len(df[sym] == 578)
+
+    def test_multiple_symbols_2(self):
+        syms = ["AAPL", "MSFT", "TSLA"]
+        good_start = datetime(2017, 2, 9)
+        good_end = datetime(2017, 5, 24)
+        df = web.DataReader(syms, "iex", good_start, good_end)
+        assert isinstance(df, dict)
+        assert len(df) == 3
+        assert sorted(list(df)) == syms
+
+        a = df["AAPL"]
+        t = df["TSLA"]
+
+        assert len(a) == 73
+        assert len(t) == 73
+
+        expected1 = a.loc["2017-02-09"]
+        assert expected1["close"] == 132.42
+        assert expected1["high"] == 132.445
+
+        expected2 = a.loc["2017-05-24"]
+        assert expected2["close"] == 153.34
+        assert expected2["high"] == 154.17
+
+        expected3 = t.loc["2017-02-09"]
+        assert expected3["close"] == 269.20
+        assert expected3["high"] == 271.18
+
+        expected4 = t.loc["2017-05-24"]
+        assert expected4["close"] == 310.22
+        assert expected4["high"] == 311.0