add BarFeed support for pandas, generic sequences

pyalgotrade/barfeed/listfeed.py

@@ -0,0 +1,177 @@
+# PyAlgoTrade
+#
+# Copyright 2011-2015 Gabriel Martin Becedillas Ruiz
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+.. moduleauthor:: Alex McFarlane <[email protected]>, Tyler Kontra <[email protected]>
+"""
+
+from pyalgotrade.utils import dt
+from pyalgotrade.barfeed import membf
+from pyalgotrade.barfeed import csvfeed
+from pyalgotrade import bar
+
+import datetime
+
+
+class BarFeed(membf.BarFeed):
+    """Base class for Iterable[Dict] based :class:`pyalgotrade.barfeed.BarFeed`.
+
+    .. note::
+        This is a base class and should not be used directly.
+    """
+
+    def __init__(self, frequency, maxLen=None):
+        super(BarFeed, self).__init__(frequency, maxLen)
+
+        self.__barFilter = None
+        self.__dailyTime = datetime.time(0, 0, 0)
+
+    def getDailyBarTime(self):
+        return self.__dailyTime
+
+    def setDailyBarTime(self, time):
+        self.__dailyTime = time
+
+    def getBarFilter(self):
+        return self.__barFilter
+
+    def setBarFilter(self, barFilter):
+        self.__barFilter = barFilter
+
+    def _addBarsFromListofDicts(self, instrument, iterable, rowParser):
+        loadedBars = map(rowParser.parseBar, iterable)
+        loadedBars = filter(
+            lambda bar_: (bar_ is not None) and
+                (self.__barFilter is None or self.__barFilter.includeBar(bar_)),
+            loadedBars
+        )
+        self.addBarsFromSequence(instrument, loadedBars)
+
+
+class Feed(BarFeed):
+    """A BarFeed that loads bars from a custom feed that has the following columns:
+    ::
+
+                  Date Time     Open    Close     High      Low    Volume  Adj Close
+        2015-08-14 09:06:00  0.00690  0.00690  0.00690  0.00690  1.346117       9567
+
+
+    :param frequency: The frequency of the bars. Check :class:`pyalgotrade.bar.Frequency`.
+    :param timezone: The default timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
+    :type timezone: A pytz timezone.
+    :param maxLen: The maximum number of values that the :class:`pyalgotrade.dataseries.bards.BarDataSeries` will hold.
+        Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the
+        opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.
+    :type maxLen: int.
+
+    .. note::
+        * The data should be sampled across regular time points, you can
+            regularlise (e.g. for 5min intervals) as::
+
+                df = df.set_index('Date Time').resample('s').interpolate().resample('5T').asfreq()
+                df = df.dropna().reset_index()
+            which is described in a SO [post](https://stackoverflow.com/a/39730730/4013571)
+        * It is ok if the **Adj Close** column is empty.
+        * When working with multiple instruments:
+
+         * If all the instruments loaded are in the same timezone, then the timezone parameter may not be specified.
+         * If any of the instruments loaded are in different timezones, then the timezone parameter should be set.
+    """
+
+    def __init__(self, frequency, timezone=None, maxLen=None):
+        super(Feed, self).__init__(frequency, maxLen)
+
+        self.__timezone = timezone
+        # Assume bars don't have adjusted close. This will be set to True after
+        # loading the first file if the adj_close column is there.
+        self.__haveAdjClose = False
+
+        self.__barClass = bar.BasicBar
+
+        self.__dateTimeFormat = "%Y-%m-%d %H:%M:%S"
+        self.__columnNames = {
+            "datetime": "Date Time",
+            "open": "Open",
+            "high": "High",
+            "low": "Low",
+            "close": "Close",
+            "volume": "Volume",
+            "adj_close": "Adj Close",
+        }
+        # self.__dateTimeFormat expects time to be set so there is no need to
+        # fix time.
+        self.setDailyBarTime(None)
+
+    def barsHaveAdjClose(self):
+        return self.__haveAdjClose
+
+    def setNoAdjClose(self):
+        self.__columnNames["adj_close"] = None
+        self.__haveAdjClose = False
+
+    def setColumnName(self, col, name):
+        self.__columnNames[col] = name
+
+    def setDateTimeFormat(self, dateTimeFormat):
+        self.__dateTimeFormat = dateTimeFormat
+
+    def setBarClass(self, barClass):
+        self.__barClass = barClass
+
+    def addBarsFromListofDicts(self, instrument, list_of_dicts, timezone=None):
+        """Loads bars for a given instrument from a list of dictionaries.
+        The instrument gets registered in the bar feed.
+
+        :param instrument: Instrument identifier.
+        :type instrument: string.
+        :param list_of_dicts: A list of dicts. First item should contain
+            columns.
+        :type list_of_dicts: list
+        :param timezone: The timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
+        :type timezone: A pytz timezone.
+        """
+
+        if timezone is None:
+            timezone = self.__timezones
+
+        if not isinstance(list_of_dicts, (list, tuple)):
+            raise ValueError('This function only supports types: {list, tuple}')
+        if not isinstance(list_of_dicts[0], dict):
+            raise ValueError('List should only contain dicts')
+
+        rowParser = csvfeed.GenericRowParser(
+            self.__columnNames,
+            self.__dateTimeFormat,
+            self.getDailyBarTime(),
+            self.getFrequency(),
+            timezone,
+            self.__barClass
+        )
+
+        missing_columns = [
+            col for col in self.__columnNames.values()
+            if col not in list_of_dicts[0].keys()
+        ]
+        if missing_columns:
+            raise ValueError('Missing required columns: {}'.format(repr(missing_columns)))
+
+        super(Feed, self)._addBarsFromListofDicts(
+            instrument, list_of_dicts, rowParser)
+
+        if rowParser.barsHaveAdjClose():
+            self.__haveAdjClose = True
+        elif self.__haveAdjClose:
+            raise Exception("Previous bars had adjusted close and these ones don't have.")

pyalgotrade/barfeed/pandasfeed.py

@@ -0,0 +1,173 @@
+# PyAlgoTrade
+#
+# Copyright 2011-2015 Gabriel Martin Becedillas Ruiz
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+.. moduleauthor:: Alex McFarlane <[email protected]>, Tyler Kontra <[email protected]>
+"""
+
+from pyalgotrade.utils import dt
+from pyalgotrade.barfeed import membf
+from pyalgotrade.barfeed import csvfeed
+from pyalgotrade import bar
+
+import datetime
+
+
+class BarFeed(membf.BarFeed):
+    """Base class for Pandas DataFrame based :class:`pyalgotrade.barfeed.BarFeed`.
+
+    .. note::
+        This is a base class and should not be used directly.
+    """
+
+    def __init__(self, frequency, maxLen=None):
+        super(BarFeed, self).__init__(frequency, maxLen)
+
+        self.__barFilter = None
+        self.__dailyTime = datetime.time(0, 0, 0)
+
+    def getDailyBarTime(self):
+        return self.__dailyTime
+
+    def setDailyBarTime(self, time):
+        self.__dailyTime = time
+
+    def getBarFilter(self):
+        return self.__barFilter
+
+    def setBarFilter(self, barFilter):
+        self.__barFilter = barFilter
+
+
+class Feed(BarFeed):
+    """A BarFeed that loads bars from a custom feed that has the following columns:
+    ::
+
+                  Date Time     Open    Close     High      Low    Volume  Adj Close
+        2015-08-14 09:06:00  0.00690  0.00690  0.00690  0.00690  1.346117       9567
+
+
+    :param frequency: The frequency of the bars. Check :class:`pyalgotrade.bar.Frequency`.
+    :param timezone: The default timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
+    :type timezone: A pytz timezone.
+    :param maxLen: The maximum number of values that the :class:`pyalgotrade.dataseries.bards.BarDataSeries` will hold.
+        Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the
+        opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.
+    :type maxLen: int.
+
+    .. note::
+        * The data should be sampled across regular time points, you can
+            regularlise (e.g. for 5min intervals) as::
+
+                df = df.set_index('Date Time').resample('s').interpolate().resample('5T').asfreq()
+                df = df.dropna().reset_index()
+            which is described in a SO [post](https://stackoverflow.com/a/39730730/4013571)
+        * It is ok if the **Adj Close** column is empty.
+        * When working with multiple instruments:
+
+         * If all the instruments loaded are in the same timezone, then the timezone parameter may not be specified.
+         * If any of the instruments loaded are in different timezones, then the timezone parameter should be set.
+    """
+
+    def __init__(self, frequency, timezone=None, maxLen=None):
+        super(Feed, self).__init__(frequency, maxLen)
+
+        self.__timezone = timezone
+        # Assume bars don't have adjusted close. This will be set to True after
+        # loading the first file if the adj_close column is there.
+        self.__haveAdjClose = False
+
+        self.__barClass = bar.BasicBar
+
+        self.__dateTimeFormat = "%Y-%m-%d %H:%M:%S"
+        self.__columnNames = {
+            "datetime": "Date Time",
+            "open": "Open",
+            "high": "High",
+            "low": "Low",
+            "close": "Close",
+            "volume": "Volume",
+            "adj_close": "Adj Close",
+        }
+        # self.__dateTimeFormat expects time to be set so there is no need to
+        # fix time.
+        self.setDailyBarTime(None)
+
+    def barsHaveAdjClose(self):
+        return self.__haveAdjClose
+
+    def setNoAdjClose(self):
+        self.__columnNames["adj_close"] = None
+        self.__haveAdjClose = False
+
+    def setColumnName(self, col, name):
+        self.__columnNames[col] = name
+
+    def setDateTimeFormat(self, dateTimeFormat):
+        self.__dateTimeFormat = dateTimeFormat
+
+    def setBarClass(self, barClass):
+        self.__barClass = barClass
+
+    def addBarsFromDataFrame(self, instrument, df, timezone=None):
+        """Loads bars for a given instrument from a Pandas DataFrame.
+        The instrument gets registered in the bar feed.
+
+        :param instrument: Instrument identifier.
+        :type instrument: string.
+        :param df: The pandas DataFrame
+        :type df: pd.DataFrame
+        :param timezone: The timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
+        :type timezone: A pytz timezone.
+        """
+
+        if timezone is None:
+            timezone = self.__timezone
+
+        df_has_adj_close = self.__columnNames['adj_close'] in df.columns
+
+        missing_columns = [
+            col for col in self.__columnNames.values()
+            if col not in df.columns
+        ]
+        if missing_columns:
+            raise ValueError('Missing required columns: {}'.format(repr(missing_columns)))
+
+        # Convert DataFrame row(s) to Bar(s)
+        loadedBars = df.apply(
+          lambda row: bar.BasicBar(
+            row[self.__columnNames['datetime']],
+            row[self.__columnNames['open']],
+            row[self.__columnNames['close']],
+            row[self.__columnNames['high']],
+            row[self.__columnNames['low']],
+            row[self.__columnNames['volume']],
+            row[self.__columnNames['adj_close']],
+            self.getFrequency(),
+            extra = row[set(row.columns).difference(self.__columnNames.values())] 
+          )
+        )
+        loadedBars = filter(
+            lambda bar_: (bar_ is not None) and
+                (self.__barFilter is None or self.__barFilter.includeBar(bar_)),
+            loadedBars
+        )
+        self.addBarsFromSequence(instrument, loadedBars)
+
+        if df_has_adj_close:
+            self.__haveAdjClose = True
+        elif self.__haveAdjClose:
+            raise Exception("Previous bars had adjusted close and these ones don't have.")