def run(self, source=None, start=None, end=None):
        """Run the algorithm.

        :Arguments:
            source : can be either:
                     - pandas.Panel
                     - pandas.DataFrame
                     - zipline source
                     - list of zipline sources

               If pandas.DataFrame is provided, it must have the
               following structure:
               * column names must consist of ints representing the
                 different sids
               * index must be DatetimeIndex
               * array contents should be price info.

        :Returns:
            daily_stats : pandas.DataFrame
              Daily performance metrics such as returns, alpha etc.

        """
        if source is None:
            assert(self.sources)
        else:
            self.set_sources(source, start, end)

        # Create transforms by wrapping them into StatefulTransforms
        self.transforms = []
        for namestring, trans_descr in self.registered_transforms.iteritems():
            sf = StatefulTransform(
                trans_descr['class'],
                *trans_descr['args'],
                **trans_descr['kwargs']
            )
            sf.namestring = namestring

            self.transforms.append(sf)

        environment = create_trading_environment(
            start=self.start_datetime,
            end=self.end_datetime,
            capital_base=self.capital_base
        )

        # create transforms and zipline
        self.gen = self._create_generator(environment)

        # loop through simulated_trading, each iteration returns a
        # perf ndict
        perfs = list(self.gen)

        # convert perf ndict to pandas dataframe
        daily_stats = self._create_daily_stats(perfs)

        return daily_stats

    def run(self, source, sim_params=None, benchmark_return_source=None):
        """Run the algorithm.

        :Arguments:
            source : can be either:
                     - pandas.DataFrame
                     - zipline source
                     - list of zipline sources

               If pandas.DataFrame is provided, it must have the
               following structure:
               * column names must consist of ints representing the
                 different sids
               * index must be DatetimeIndex
               * array contents should be price info.

        :Returns:
            daily_stats : pandas.DataFrame
              Daily performance metrics such as returns, alpha etc.

        """
        if isinstance(source, (list, tuple)):
            assert self.sim_params is not None or sim_params is not None, \
                """When providing a list of sources, \
                sim_params have to be specified as a parameter
                or in the constructor."""
        elif isinstance(source, pd.DataFrame):
            # if DataFrame provided, wrap in DataFrameSource
            source = DataFrameSource(source)
        elif isinstance(source, pd.Panel):
            source = DataPanelSource(source)

        if not isinstance(source, (list, tuple)):
            self.sources = [source]
        else:
            self.sources = source

        # Check for override of sim_params.
        # If it isn't passed to this function,
        # use the default params set with the algorithm.
        # Else, we create simulation parameters using the start and end of the
        # source provided.
        if not sim_params:
            if not self.sim_params:
                start = source.start
                end = source.end

                sim_params = create_simulation_parameters(
                    start=start,
                    end=end,
                    capital_base=self.capital_base
                )
            else:
                sim_params = self.sim_params

        # Create transforms by wrapping them into StatefulTransforms
        self.transforms = []
        for namestring, trans_descr in self.registered_transforms.iteritems():
            sf = StatefulTransform(
                trans_descr['class'],
                *trans_descr['args'],
                **trans_descr['kwargs']
            )
            sf.namestring = namestring

            self.transforms.append(sf)

        # create transforms and zipline
        self.gen = self._create_generator(sim_params)

        # loop through simulated_trading, each iteration returns a
        # perf dictionary
        perfs = []
        for perf in self.gen:
            perfs.append(perf)

        # convert perf dict to pandas dataframe
        daily_stats = self._create_daily_stats(perfs)

        return daily_stats

    def run(self, source, start=None, end=None):
        """Run the algorithm.

        :Arguments:
            source : can be either:
                     - pandas.DataFrame
                     - zipline source
                     - list of zipline sources

               If pandas.DataFrame is provided, it must have the
               following structure:
               * column names must consist of ints representing the
                 different sids
               * index must be DatetimeIndex
               * array contents should be price info.

        :Returns:
            daily_stats : pandas.DataFrame
              Daily performance metrics such as returns, alpha etc.

        """
        if isinstance(source, (list, tuple)):
            assert start is not None and end is not None, \
                """When providing a list of sources, \
                start and end date have to be specified."""
        elif isinstance(source, pd.DataFrame):
            # if DataFrame provided, wrap in DataFrameSource
            source = DataFrameSource(source)
        elif isinstance(source, pd.Panel):
            source = DataPanelSource(source)

        # If values not set, try to extract from source.
        if start is None:
            start = source.start
        if end is None:
            end = source.end

        if not isinstance(source, (list, tuple)):
            self.sources = [source]
        else:
            self.sources = source

        # Create transforms by wrapping them into StatefulTransforms
        self.transforms = []
        for namestring, trans_descr in self.registered_transforms.iteritems():
            sf = StatefulTransform(
                trans_descr['class'],
                *trans_descr['args'],
                **trans_descr['kwargs']
            )
            sf.namestring = namestring

            self.transforms.append(sf)

        environment = create_trading_environment(
            start=start,
            end=end,
            capital_base=self.capital_base
        )

        # create transforms and zipline
        self.gen = self._create_generator(environment)

        # loop through simulated_trading, each iteration returns a
        # perf ndict
        perfs = list(self.gen)

        # convert perf ndict to pandas dataframe
        daily_stats = self._create_daily_stats(perfs)

        return daily_stats

    def run(self, source, overwrite_sim_params=True,
            benchmark_return_source=None):
        """Run the algorithm.

        :Arguments:
            source : can be either:
                     - pandas.DataFrame
                     - zipline source
                     - list of sources

               If pandas.DataFrame is provided, it must have the
               following structure:
               * column names must consist of ints representing the
                 different sids
               * index must be DatetimeIndex
               * array contents should be price info.

        :Returns:
            daily_stats : pandas.DataFrame
              Daily performance metrics such as returns, alpha etc.

        """
        if isinstance(source, list):
            if overwrite_sim_params:
                warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
 dates. Make sure to set the correct fields in sim_params passed to
 __init__().""", UserWarning)
                overwrite_sim_params = False
        elif isinstance(source, pd.DataFrame):
            # if DataFrame provided, wrap in DataFrameSource
            source = DataFrameSource(source)
        elif isinstance(source, pd.Panel):
            source = DataPanelSource(source)

        if isinstance(source, list):
            self.set_sources(source)
        else:
            self.set_sources([source])

        # Override sim_params if params are provided by the source.
        if overwrite_sim_params:
            if hasattr(source, 'start'):
                self.sim_params.period_start = source.start
            if hasattr(source, 'end'):
                self.sim_params.period_end = source.end
            all_sids = [sid for s in self.sources for sid in s.sids]
            self.sim_params.sids = set(all_sids)
            # Changing period_start and period_close might require updating
            # of first_open and last_close.
            self.sim_params._update_internal()

        # Create history containers
        if len(self.history_specs) != 0:
            self.history_container = HistoryContainer(
                self.history_specs,
                self.sim_params.sids,
                self.sim_params.first_open)

        # Create transforms by wrapping them into StatefulTransforms
        self.transforms = []
        for namestring, trans_descr in iteritems(self.registered_transforms):
            sf = StatefulTransform(
                trans_descr['class'],
                *trans_descr['args'],
                **trans_descr['kwargs']
            )
            sf.namestring = namestring

            self.transforms.append(sf)

        # force a reset of the performance tracker, in case
        # this is a repeat run of the algorithm.
        self.perf_tracker = None

        # create transforms and zipline
        self.gen = self._create_generator(self.sim_params)

        with ZiplineAPI(self):
            # loop through simulated_trading, each iteration returns a
            # perf dictionary
            perfs = []
            for perf in self.gen:
                perfs.append(perf)

            # convert perf dict to pandas dataframe
            daily_stats = self._create_daily_stats(perfs)

        self.analyze(daily_stats)

        return daily_stats