diff --git a/catalyst/__init__.py b/catalyst/__init__.py
index 508137df..7a8a26a4 100644
--- a/catalyst/__init__.py
+++ b/catalyst/__init__.py
@@ -29,11 +29,14 @@ from ._version import get_versions
 from . algorithm import TradingAlgorithm
 from . import api
 
+from catalyst.utils.calendars.calendar_utils import global_calendar_dispatcher
+
+__version__ = get_versions()['version']
+del get_versions
 
 # PERF: Fire a warning if calendars were instantiated during catalyst import.
 # Having calendars doesn't break anything per-se, but it makes catalyst imports
 # noticeably slower, which becomes particularly noticeable in the Zipline CLI.
-from catalyst.utils.calendars.calendar_utils import global_calendar_dispatcher
 if global_calendar_dispatcher._calendars:
     import warnings
     warnings.warn(
@@ -44,10 +47,6 @@ if global_calendar_dispatcher._calendars:
 del global_calendar_dispatcher
 
 
-__version__ = get_versions()['version']
-del get_versions
-
-
 def load_ipython_extension(ipython):
     from .__main__ import catalyst_magic
     ipython.register_magic_function(catalyst_magic, 'line_cell', 'catalyst')
@@ -69,7 +68,6 @@ if os.name == 'nt':
     _()
     del _
 
-
 __all__ = [
     'TradingAlgorithm',
     'api',
@@ -80,7 +78,3 @@ __all__ = [
     'run_algorithm',
     'utils',
 ]
-
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions
diff --git a/catalyst/__main__.py b/catalyst/__main__.py
index 3a38ce23..47c7dc55 100644
--- a/catalyst/__main__.py
+++ b/catalyst/__main__.py
@@ -10,7 +10,6 @@ from six import text_type
 from catalyst.data import bundles as bundles_module
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_utils import delete_algo_folder
-from catalyst.exchange.factory import get_exchange
 from catalyst.utils.cli import Date, Timestamp
 from catalyst.utils.run_algo import _run, load_extensions
 
@@ -520,7 +519,7 @@ def live(ctx,
     default=False,
     help='Report potential anomalies found in data bundles.'
 )
-def ingest_exchange(exchange_name, data_frequency, start, end,
+def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
                     include_symbols, exclude_symbols, csv, show_progress,
                     verbose, validate):
     """
diff --git a/catalyst/algorithm.py b/catalyst/algorithm.py
index 8af375b4..1cf9476a 100644
--- a/catalyst/algorithm.py
+++ b/catalyst/algorithm.py
@@ -124,7 +124,6 @@ from catalyst.utils.events import (
 from catalyst.utils.factory import create_simulation_parameters
 from catalyst.utils.math_utils import (
     tolerant_equals,
-    round_if_near_integer,
     round_nearest
 )
 from catalyst.utils.pandas_utils import clear_dataframe_indexer_caches
@@ -1485,7 +1484,6 @@ class TradingAlgorithm(object):
         """
         Converts the number of shares to the smallest tradable lot size for
         the asset being ordered.
-        
         """
         return round_nearest(amount, asset.min_trade_size)
 
@@ -1523,6 +1521,7 @@ class TradingAlgorithm(object):
                              self.updated_portfolio(),
                              self.get_datetime(),
                              self.trading_client.current_data)
+
     @staticmethod
     def __convert_order_params_for_blotter(limit_price, stop_price, style):
         """
diff --git a/catalyst/constants.py b/catalyst/constants.py
index 6372b11f..c4111fdd 100644
--- a/catalyst/constants.py
+++ b/catalyst/constants.py
@@ -7,8 +7,7 @@ import logbook
     For example, if you want to see the DEBUG messages, run:
     $ export CATALYST_LOG_LEVEL=10
 '''
-# LOG_LEVEL = int(os.environ.get('CATALYST_LOG_LEVEL', logbook.INFO))
-LOG_LEVEL = logbook.DEBUG
+LOG_LEVEL = int(os.environ.get('CATALYST_LOG_LEVEL', logbook.INFO))
 
 SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
               '{exchange}/symbols.json'
@@ -16,4 +15,4 @@ SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
 DATE_TIME_FORMAT = '%Y-%m-%d %H:%M'
 DATE_FORMAT = '%Y-%m-%d'
 
-AUTO_INGEST = False
\ No newline at end of file
+AUTO_INGEST = False
diff --git a/catalyst/curate/poloniex.py b/catalyst/curate/poloniex.py
index 2e51e4fb..d09afcc6 100644
--- a/catalyst/curate/poloniex.py
+++ b/catalyst/curate/poloniex.py
@@ -1,25 +1,33 @@
-import json, time, csv
+import os
+import time
+import shutil
+import json
+import csv
 from datetime import datetime
+
 import pandas as pd
-import os, time, shutil, requests, logbook
+import requests
+import logbook
+
 from catalyst.exchange.exchange_utils import get_exchange_symbols_filename
 
 
-DT_START        = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
-DT_END          = pd.to_datetime('today').value // 10 ** 9
-CSV_OUT_FOLDER  = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
-CONN_RETRIES    = 2
+DT_START = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
+DT_END = pd.to_datetime('today').value // 10 ** 9
+CSV_OUT_FOLDER = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
+CONN_RETRIES = 2
 
 logbook.StderrHandler().push_application()
 log = logbook.Logger(__name__)
 
+
 class PoloniexCurator(object):
     '''
     OHLCV data feed generator for crypto data. Based on Poloniex market data
     '''
 
-    _api_path       = 'https://poloniex.com/public?'
-    currency_pairs  = []
+    _api_path = 'https://poloniex.com/public?'
+    currency_pairs = []
 
     def __init__(self):
         if not os.path.exists(CSV_OUT_FOLDER):
@@ -30,10 +38,9 @@ class PoloniexCurator(object):
                             CSV_OUT_FOLDER))
                 log.exception(e)
 
-
     def get_currency_pairs(self):
         '''
-        Retrieves and returns all currency pairs from the exchange 
+        Retrieves and returns all currency pairs from the exchange
         '''
         url = self._api_path + 'command=returnTicker'
 
@@ -45,7 +52,7 @@ class PoloniexCurator(object):
             return None
 
         data = response.json()
-        self.currency_pairs  = []
+        self.currency_pairs = []
         for ticker in data:
             self.currency_pairs.append(ticker)
         self.currency_pairs.sort()
@@ -54,54 +61,60 @@ class PoloniexCurator(object):
                     len(self.currency_pairs)
                     ))
 
-
-
     def _retrieve_tradeID_date(self, row):
         '''
         Helper function that reads tradeID and date fields from CSV readline
         '''
         tId = int(row.split(',')[0])
-        d   = pd.to_datetime(row.split(',')[1], 
-                             infer_datetime_format=True).value // 10 ** 9
+        d = pd.to_datetime(row.split(',')[1],
+                           infer_datetime_format=True).value // 10 ** 9
         return tId, d
 
-
-    def retrieve_trade_history(self, currencyPair, start=DT_START, 
+    def retrieve_trade_history(self, currencyPair, start=DT_START,
                                end=DT_END, temp=None):
         '''
-        Retrieves TradeHistory from exchange for a given currencyPair 
-        between start and end dates. If no start date is provided, uses 
+        Retrieves TradeHistory from exchange for a given currencyPair
+        between start and end dates. If no start date is provided, uses
         a system-wide one (beginning of time for cryptotrading).
         If no end date is provided, 'now' is used.
 
         Stores results in CSV file on disk.
-        
-        This function is called recursively to work around the 
+
+        This function is called recursively to work around the
         limitations imposed by the provider API.
         '''
         csv_fn = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
 
         '''
-        Check what data we already have on disk, reading first and last 
+        Check what data we already have on disk, reading first and last
         lines from file. Data is stored on file from NEWEST to OLDEST.
         '''
         try:
-            with open(csv_fn, 'ab+') as f: 
+            with open(csv_fn, 'ab+') as f:
                 f.seek(0, os.SEEK_END)
                 if(f.tell() > 2):                   # Check file size is not 0
-                    f.seek(0)                       # Go to start to read 
-                    last_tradeID, end_file = self._retrieve_tradeID_date(f.readline())
+                    f.seek(0)                       # Go to start to read
+                    last_tradeID, end_file = self._retrieve_tradeID_date(
+                                                    f.readline())
                     f.seek(-2, os.SEEK_END)         # Jump to the 2nd last byte
                     while f.read(1) != b"\n":       # Until EOL is found...
-                        f.seek(-2, os.SEEK_CUR)     # ...jump back the read byte plus one more.
-                    first_tradeID, start_file = self._retrieve_tradeID_date(f.readline())
+                        # ...jump back the read byte plus one more.
+                        f.seek(-2, os.SEEK_CUR)
+                    first_tradeID, start_file = self._retrieve_tradeID_date(
+                                                    f.readline())
 
-                    if( end_file + 3600 * 6 > DT_END and ( first_tradeID == 1
-                        or (currencyPair == 'BTC_HUC' and first_tradeID ==     2) 
-                        or (currencyPair == 'BTC_RIC' and first_tradeID ==     2) 
-                        or (currencyPair == 'BTC_XCP' and first_tradeID ==     2)
-                        or (currencyPair == 'BTC_NAV' and first_tradeID ==  4569)
-                        or (currencyPair == 'BTC_POT' and first_tradeID == 23511) ) ):
+                    if(end_file + 3600 * 6 > DT_END
+                        and (first_tradeID == 1
+                             or (currencyPair == 'BTC_HUC'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_RIC'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_XCP'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_NAV'
+                                 and first_tradeID == 4569)
+                             or (currencyPair == 'BTC_POT'
+                                 and first_tradeID == 23511))):
                         return
 
         except Exception as e:
@@ -109,11 +122,11 @@ class PoloniexCurator(object):
             log.exception(e)
 
         '''
-        Poloniex API limits querying TradeHistory to intervals smaller 
+        Poloniex API limits querying TradeHistory to intervals smaller
         than 1 month, so we make sure that start date is never more than
         1 month apart from end date
         '''
-        if( end - start > 2419200 ): # 60s/min * 60min/hr * 24hr/day * 28days     
+        if(end - start > 2419200):   # 60s/min * 60min/hr * 24hr/day * 28days
             newstart = end - 2419200
         else:
             newstart = start
@@ -124,12 +137,11 @@ class PoloniexCurator(object):
 
         url = '{path}command=returnTradeHistory&currencyPair={pair}' \
               '&start={start}&end={end}'.format(
-                    path = self._api_path,
-                    pair = currencyPair,
-                    start = str(newstart),
-                    end = str(end)
+                    path=self._api_path,
+                    pair=currencyPair,
+                    start=str(newstart),
+                    end=str(end)
                 )
-        print(url)
 
         attempts = 0
         success = 0
@@ -137,14 +149,14 @@ class PoloniexCurator(object):
             try:
                 response = requests.get(url)
             except Exception as e:
-                log.error('Failed to retrieve trade history data for {}'.format(
-                            currencyPair
-                            ))
+                log.error('Failed to retrieve trade history data'
+                          'for {}'.format(currencyPair))
                 log.exception(e)
                 attempts += 1
             else:
                 try:
-                    if isinstance(response.json(), dict) and response.json()['error']:
+                    if(isinstance(response.json(), dict)
+                       and response.json()['error']):
                         log.error('Failed to to retrieve trade history data '
                                   'for {}: {}'.format(
                                     currencyPair,
@@ -161,33 +173,32 @@ class PoloniexCurator(object):
         if not success:
             return None
 
-
         '''
-        If we get to transactionId == 1, and we already have that on 
+        If we get to transactionId == 1, and we already have that on
         disk, we got to the end of TradeHistory for this coin.
         '''
-        if('first_tradeID' in locals() 
-                and response.json()[-1]['tradeID'] == first_tradeID): 
+        if('first_tradeID' in locals()
+                and response.json()[-1]['tradeID'] == first_tradeID):
             return
 
         '''
             There are primarily two scenarios:
-                a) There is newer data available that we need to add at 
-                   the beginning of the file. We'll retrieve all what we 
-                   need until we get to what we already have, writing it 
-                   to a temporary file; and we will write that at the 
+                a) There is newer data available that we need to add at
+                   the beginning of the file. We'll retrieve all what we
+                   need until we get to what we already have, writing it
+                   to a temporary file; and we will write that at the
                    beginning of our existing file.
-                b) We are going back in time, appending at the end of 
-                   our existing TradeHistory until the first transaction 
+                b) We are going back in time, appending at the end of
+                   our existing TradeHistory until the first transaction
                    for this currencyPair
         '''
-        try: 
-            if( 'end_file' in locals() and end_file + 3600 < end):
+        try:
+            if('end_file' in locals() and end_file + 3600 < end):
                 if (temp is None):
                     temp = os.tmpfile()
                 tempcsv = csv.writer(temp)
                 for item in response.json():
-                    if( item['tradeID'] <= last_tradeID ):
+                    if(item['tradeID'] <= last_tradeID):
                         continue
                     tempcsv.writerow([
                         item['tradeID'],
@@ -196,27 +207,28 @@ class PoloniexCurator(object):
                         item['rate'],
                         item['amount'],
                         item['total'],
-                        item['globalTradeID']        
+                        item['globalTradeID'],
                     ])
-                if( response.json()[-1]['tradeID'] > last_tradeID ):
-                    end = pd.to_datetime( response.json()[-1]['date'], 
-                                infer_datetime_format=True).value // 10 ** 9
-                    self.retrieve_trade_history(currencyPair, start, 
-                                                end, temp=temp) 
+                if(response.json()[-1]['tradeID'] > last_tradeID):
+                    end = pd.to_datetime(response.json()[-1]['date'],
+                                         infer_datetime_format=True
+                                         ).value // 10**9
+                    self.retrieve_trade_history(currencyPair, start,
+                                                end, temp=temp)
                 else:
-                    with open(csv_fn,'rb+') as f:
-                        shutil.copyfileobj(f,temp)
+                    with open(csv_fn, 'rb+') as f:
+                        shutil.copyfileobj(f, temp)
                         f.seek(0)
                         temp.seek(0)
-                        shutil.copyfileobj(temp,f)
+                        shutil.copyfileobj(temp, f)
                     temp.close()
                     end = start_file
             else:
                 with open(csv_fn, 'ab') as csvfile:
                     csvwriter = csv.writer(csvfile)
                     for item in response.json():
-                        if( 'first_tradeID' in locals() 
-                                and item['tradeID'] >= first_tradeID ):
+                        if('first_tradeID' in locals()
+                                and item['tradeID'] >= first_tradeID):
                             continue
                         csvwriter.writerow([
                             item['tradeID'],
@@ -227,70 +239,66 @@ class PoloniexCurator(object):
                             item['total'],
                             item['globalTradeID']
                         ])
-                end = pd.to_datetime(response.json()[-1]['date'], 
-                            infer_datetime_format=True).value // 10 ** 9
+                end = pd.to_datetime(response.json()[-1]['date'],
+                                     infer_datetime_format=True).value//10**9
 
         except Exception as e:
             log.error('Error opening {}'.format(csv_fn))
             log.exception(e)
 
         '''
-            If we got here, we aren't done yet. Call recursively with 
+            If we got here, we aren't done yet. Call recursively with
             'end' times that go sequentially back in time.
         '''
         self.retrieve_trade_history(currencyPair, start, end)
 
-
-
     def generate_ohlcv(self, df):
         '''
         Generates OHLCV dataframe from a dataframe containing all TradeHistory
         by resampling with 1-minute period
         '''
-        df.set_index('date', inplace=True)               # Index by date
-        vol = df['total'].to_frame('volume')             # set Vol aside
-        df.drop('total', axis=1, inplace=True)           # Drop volume data 
-        ohlc = df.resample('T').ohlc()                   # Resample OHLC 1min
-        ohlc.columns = ohlc.columns.map(lambda t: t[1])  # Raname columns by dropping 'rate'
-        closes = ohlc['close'].fillna(method='pad')      # Pad fwd missing 'close'
-        ohlc = ohlc.apply(lambda x: x.fillna(closes))    # Fill N/A with last close
-        vol = vol.resample('T').sum().fillna(0)          # Add volumes by bin
-        ohlcv = pd.concat([ohlc,vol], axis=1)            # Concatenate OHLC + Vol
+        df.set_index('date', inplace=True)             # Index by date
+        vol = df['total'].to_frame('volume')           # set Vol aside
+        df.drop('total', axis=1, inplace=True)         # Drop volume data
+        ohlc = df.resample('T').ohlc()                 # Resample OHLC 1min
+        ohlc.cols = ohlc.cols.map(lambda t: t[1])      # Raname cols
+        closes = ohlc['close'].fillna(method='pad')    # Pad fwd missing close
+        ohlc = ohlc.apply(lambda x: x.fillna(closes))  # Fill NA w/ last close
+        vol = vol.resample('T').sum().fillna(0)        # Add volumes by bin
+        ohlcv = pd.concat([ohlc, vol], axis=1)         # Concat OHLC + Vol
         return ohlcv
 
-
-
-    def write_ohlcv_file(self, currencyPair): 
+    def write_ohlcv_file(self, currencyPair):
         '''
         Generates OHLCV data file with 1minute bars from TradeHistory on disk
-        '''       
+        '''
         csv_trades = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
-        csv_1min   = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
-        if( os.path.getmtime(csv_1min) > time.time() - 7200 ):
+        csv_1min = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        if(os.path.getmtime(csv_1min) > time.time() - 7200):
             log.debug(currencyPair+': 1min data file already up to date. '
                       'Delete the file if you want to rebuild it.')
         else:
-            df = pd.read_csv(csv_trades, 
-                                names=['tradeID',
-                                       'date',
-                                       'type',
-                                       'rate',
-                                       'amount',
-                                       'total',
-                                       'globalTradeID'], 
-                                dtype = {'tradeID': int, 
-                                         'date': str, 
-                                         'type': str, 
-                                         'rate': float, 
-                                         'amount': float, 
-                                         'total': float, 
-                                         'globalTradeID': int } 
-                                )
-            df.drop(['tradeID','type','amount','globalTradeID'], 
+            df = pd.read_csv(csv_trades,
+                             names=['tradeID',
+                                    'date',
+                                    'type',
+                                    'rate',
+                                    'amount',
+                                    'total',
+                                    'globalTradeID'],
+                             dtype={'tradeID': int,
+                                    'date': str,
+                                    'type': str,
+                                    'rate': float,
+                                    'amount': float,
+                                    'total': float,
+                                    'globalTradeID': int}
+                             )
+            df.drop(['tradeID', 'type', 'amount', 'globalTradeID'],
                     axis=1, inplace=True)
-            df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True) 
+            df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)
             ohlcv = self.generate_ohlcv(df)
-            try: 
+            try:
                 with open(csv_1min, 'w') as csvfile:
                     csvwriter = csv.writer(csvfile)
                     for item in ohlcv.itertuples():
@@ -305,32 +313,28 @@ class PoloniexCurator(object):
                             item.volume,
                         ])
             except Exception as e:
-                log.error('Error opening {}'.format(csv_fn))
+                log.error('Error opening {}'.format(csv_1min))
                 log.exception(e)
             log.debug('{}: Generated 1min OHLCV data.'.format(currencyPair))
 
-
-
     def onemin_to_dataframe(self, currencyPair, start, end):
         '''
         Returns a data frame for a given currencyPair from data on disk
         '''
-        csv_fn     = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
-        df         = pd.read_csv(csv_fn, names=['date', 
-                                                'open', 
-                                                'high', 
-                                                'low', 
-                                                'close', 
-                                                'volume']
-                                )
-        df['date'] = pd.to_datetime(df['date'],unit='s')
+        csv_fn = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        df = pd.read_csv(csv_fn, names=['date',
+                                        'open',
+                                        'high',
+                                        'low',
+                                        'close',
+                                        'volume'])
+        df['date'] = pd.to_datetime(df['date'], unit='s')
         df.set_index('date', inplace=True)
-        return df[start : end]
-
+        return df[start:end]
 
     def generate_symbols_json(self, filename=None):
         '''
-        Generates a symbols.json file with corresponding start_date 
+        Generates a symbols.json file with corresponding start_date
         for each currencyPair
         '''
         symbol_map = {}
@@ -341,36 +345,37 @@ class PoloniexCurator(object):
         with open(filename, 'w') as symbols:
             for currencyPair in self.currency_pairs:
                 start = None
-                csv_fn     = '{}crypto_trades-{}.csv'.format(
-                                CSV_OUT_FOLDER, currencyPair)
-                with open(csv_fn, 'r') as f: 
+                csv_fn = '{}crypto_trades-{}.csv'.format(
+                                                    CSV_OUT_FOLDER,
+                                                    currencyPair)
+                with open(csv_fn, 'r') as f:
                     f.seek(0, os.SEEK_END)
                     if(f.tell() > 2):               # Check file size is not 0
                         f.seek(-2, os.SEEK_END)     # Jump to 2nd last byte
                         while f.read(1) != b"\n":   # Until EOL is found...
-                            f.seek(-2, os.SEEK_CUR) # ...jump back the read byte plus one more.
-                        start = pd.to_datetime( f.readline().split(',')[1], 
-                                                infer_datetime_format=True)
+                            # ...jump back the read byte plus one more.
+                            f.seek(-2, os.SEEK_CUR)
+                        start = pd.to_datetime(f.readline().split(',')[1],
+                                               infer_datetime_format=True)
 
                 if(start is None):
                     start = time.gmtime()
                 base, market = currencyPair.lower().split('_')
-                symbol = '{market}_{base}'.format( market=market, base=base )
+                symbol = '{market}_{base}'.format(market=market, base=base)
                 symbol_map[currencyPair] = dict(
-                    symbol = symbol,
-                    start_date = start.strftime("%Y-%m-%d")
+                    symbol=symbol,
+                    start_date=start.strftime("%Y-%m-%d")
                 )
-            json.dump(symbol_map, symbols, sort_keys=True, indent=2, 
-                        separators=(',',':'))    
+            json.dump(symbol_map, symbols, sort_keys=True, indent=2,
+                      separators=(',', ':'))
 
 
 if __name__ == '__main__':
     pc = PoloniexCurator()
     pc.get_currency_pairs()
-    #pc.generate_symbols_json()
-    
+    # pc.generate_symbols_json()
+
     for currencyPair in pc.currency_pairs:
         pc.retrieve_trade_history(currencyPair)
         log.debug('{} up to date.'.format(currencyPair))
         pc.write_ohlcv_file(currencyPair)
-
diff --git a/catalyst/data/bundles/__init__.py b/catalyst/data/bundles/__init__.py
index 17343b9c..b8e463dd 100644
--- a/catalyst/data/bundles/__init__.py
+++ b/catalyst/data/bundles/__init__.py
@@ -1,6 +1,5 @@
 # These imports are necessary to force module-scope register calls to happen.
 from . import quandl  # noqa
-from . import poloniex
 from .core import (
     UnknownBundle,
     bundles,
diff --git a/catalyst/data/bundles/base.py b/catalyst/data/bundles/base.py
index 73b5058b..c3700003 100644
--- a/catalyst/data/bundles/base.py
+++ b/catalyst/data/bundles/base.py
@@ -13,10 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 from itertools import count
 import tarfile
-from time import time, sleep
+from time import sleep
 
 from abc import abstractmethod, abstractproperty
 import logbook
@@ -37,6 +36,7 @@ log = logbook.Logger(__name__, level=LOG_LEVEL)
 
 DEFAULT_RETRIES = 5
 
+
 class BaseBundle(object):
     def __init__(self, asset_filter=[]):
         self._asset_filter = asset_filter
@@ -104,11 +104,11 @@ class BaseBundle(object):
 
     def post_process_symbol_metadata(self, metadata, data):
         return metadata
-    
+
     @abstractmethod
     def fetch_raw_symbol_frame(self, api_key, symbol, start_date, end_date):
         raise NotImplementedError()
-    
+
     def ingest(self,
                environ,
                asset_db_writer,
@@ -128,7 +128,7 @@ class BaseBundle(object):
             retries = environ.get('CATALYST_DOWNLOAD_ATTEMPTS', 5)
 
             if is_compile:
-                # User has instructed local compilation and ingestion of bundle.
+                # User has instructed local compilation & ingestion of bundle.
                 # Fetch raw metadata for all symbols.
                 raw_metadata = self._fetch_metadata_frame(
                     api_key,
@@ -157,9 +157,9 @@ class BaseBundle(object):
                         show_progress=show_progress,
                     )
 
-                # Post-process metadata using cached symbol frames, and write to
-                # disk.  This metadata must be written before any attempt to write
-                # minute data.
+                # Post-process metadata using cached symbol frames, and write
+                # to disk.  This metadata must be written before any attempt
+                # to write minute data.
                 metadata = self._post_process_metadata(
                     raw_metadata,
                     cache,
@@ -184,10 +184,11 @@ class BaseBundle(object):
                         show_progress=show_progress,
                     )
 
-                # For legacy purposes, this call is required to ensure the database
-                # contains an appropriately initialized file structure.  We don't
-                # forsee a usecase for adjustments at this time, but may later
-                # choose to expose this functionality in the future.
+                # For legacy purposes, this call is required to ensure the
+                # database contains an appropriately initialized file
+                # structure.  We don't forsee a usecase for adjustments at
+                # this time, but may later choose to expose this functionality
+                # in the future.
                 adjustment_writer.write(
                     splits=(
                         pd.concat(self.splits, ignore_index=True)
@@ -232,12 +233,12 @@ class BaseBundle(object):
             tar.extractall(output_dir)
 
     def _fetch_metadata_frame(self,
-                             api_key,
-                             cache,
-                             retries=DEFAULT_RETRIES,
-                             environ=None,
-                             show_progress=False):
-        
+                              api_key,
+                              cache,
+                              retries=DEFAULT_RETRIES,
+                              environ=None,
+                              show_progress=False):
+
         # Setup raw metadata iterator to fetch pages if necessary.
         raw_iter = self._fetch_metadata_iter(api_key, cache, retries, environ)
 
@@ -251,7 +252,7 @@ class BaseBundle(object):
             show_percent=False,
         ) as blocks:
             metadata = pd.concat(blocks, ignore_index=True)
-        
+
         return metadata
 
     def _fetch_metadata_iter(self, api_key, cache, retries, environ):
@@ -269,21 +270,20 @@ class BaseBundle(object):
                             page_number,
                         )
                         break
-                    except ValueError as e:
+                    except ValueError:
                         raw = pd.DataFrame([])
                         break
-                    except Exception as e:
+                    except Exception:
                         log.exception(
                             'Failed to load metadata from {}. '
                             'Retrying.'.format(self.name)
-                        ) 
+                        )
                 else:
                     raise ValueError(
                         'Failed to download metadata page {} after {} '
                         'attempts.'.format(page_number, retries)
                     )
 
-
             if raw.empty:
                 # Empty DataFrame signals completion.
                 break
@@ -305,7 +305,7 @@ class BaseBundle(object):
             columns=self.md_column_names,
             index=metadata.index,
         )
-        
+
         # Iterate over the available symbols, loading the asset's raw symbol
         # data from the cache.  The final metadata is computed and recorded in
         # the appropriate row depending on the asset's id.
@@ -318,22 +318,22 @@ class BaseBundle(object):
             show_percent=False,
         ) as symbols_map:
             for asset_id, symbol in symbols_map:
-                # Attempt to load data from disk, the cache should have an entry
-                # for each symbol at this point of the execution. If one does
-                # not exist, we should fail.
+                # Attempt to load data from disk, the cache should have an
+                # entry for each symbol at this point of the execution. If one
+                # does not exist, we should fail.
                 key = '{sym}.daily.frame'.format(sym=symbol)
                 try:
                     raw_data = cache[key]
                 except KeyError:
                     raise ValueError(
-                      'Unable to find cached data for symbol: {0}'.format(symbol)
-                    )
+                      'Unable to find cached data for symbol:'
+                      ' {0}'.format(symbol))
 
                 # Perform and require post-processing of metadata.
                 final_symbol_metadata = self.post_process_symbol_metadata(
                     asset_id,
                     metadata.iloc[asset_id],
-                    raw_data,        
+                    raw_data,
                 )
 
                 # Record symbol's final metadata.
@@ -363,8 +363,8 @@ class BaseBundle(object):
             # returns the cached data unaltered.  The `should_sleep` flag
             # indicates that an API call was attempted, and that we should be
             # ensure aren't exceeding our rate limit before proceeding to the
-            # next symbol. If the raw_data is updated, it is cached before being
-            # returned.
+            # next symbol. If the raw_data is updated, it is cached before
+            # being returned.
             raw_data, should_sleep = self._maybe_update_symbol_frame(
                 start_time,
                 api_key,
@@ -414,7 +414,7 @@ class BaseBundle(object):
         last = start_session
         if raw_data is not None and len(raw_data) > 0:
             last = raw_data.index[-1].tz_localize('UTC')
-        
+
         should_sleep = False
 
         # Determine time at which cached data will be considered stale.
@@ -455,7 +455,7 @@ class BaseBundle(object):
                             retries=DEFAULT_RETRIES):
 
         # Data for symbol is old enough to attempt an update or is not
-        # present in the cache.  Fetch raw data for a single symbol 
+        # present in the cache.  Fetch raw data for a single symbol
         # with requested intervals and frequency. Retry as necessary.
         for _ in range(retries):
             try:
@@ -468,7 +468,6 @@ class BaseBundle(object):
                     data_frequency,
                 )
                 raw_data.index = pd.to_datetime(raw_data.index, utc=True)
-                #raw_data.index = raw_data.index.tz_localize('UTC')
 
                 # Filter incoming data to fit start and end sessions.
                 raw_data = raw_data[
@@ -482,7 +481,7 @@ class BaseBundle(object):
 
                 return raw_data
 
-            except Exception as e:
+            except Exception:
                 log.exception(
                     'Exception raised fetching {name} data. Retrying.'
                     .format(name=self.name)
diff --git a/catalyst/data/bundles/base_pricing.py b/catalyst/data/bundles/base_pricing.py
index 7b94e4bc..ecb9b778 100644
--- a/catalyst/data/bundles/base_pricing.py
+++ b/catalyst/data/bundles/base_pricing.py
@@ -16,6 +16,7 @@
 from catalyst.data.bundles.base import BaseBundle
 from catalyst.utils.memoize import lazyval
 
+
 class BasePricingBundle(BaseBundle):
     @lazyval
     def md_dtypes(self):
@@ -38,6 +39,7 @@ class BasePricingBundle(BaseBundle):
             ('volume', 'float64'),
         ]
 
+
 class BaseCryptoPricingBundle(BasePricingBundle):
     @lazyval
     def calendar_name(self):
@@ -55,6 +57,7 @@ class BaseCryptoPricingBundle(BasePricingBundle):
     def dividends(self):
         return []
 
+
 class BaseEquityPricingBundle(BasePricingBundle):
     @lazyval
     def calendar_name(self):
diff --git a/catalyst/data/bundles/core.py b/catalyst/data/bundles/core.py
index a25591de..58160ab1 100644
--- a/catalyst/data/bundles/core.py
+++ b/catalyst/data/bundles/core.py
@@ -37,6 +37,7 @@ from catalyst.utils.cli import maybe_show_progress
 
 ONE_MEGABYTE = 1024 * 1024
 
+
 def asset_db_path(bundle_name, timestr, environ=None, db_version=None):
     return pth.data_path(
         asset_db_relative(bundle_name, timestr, environ, db_version),
@@ -135,6 +136,7 @@ def ingestions_for_bundle(bundle, environ=None):
         reverse=True,
     )
 
+
 def download_with_progress(url, chunk_size, **progress_kwargs):
     """
     Download streaming data from a URL, printing progress information to the
@@ -705,4 +707,5 @@ def _make_bundle_core():
     )
 
 
-bundles, register_bundle, register, unregister, ingest, load, clean = _make_bundle_core()
+bundles, register_bundle, register, unregister, ingest, load, clean = \
+    _make_bundle_core()
diff --git a/catalyst/data/bundles/poloniex.py b/catalyst/data/bundles/poloniex.py
index 64cc2d27..6428dbb0 100644
--- a/catalyst/data/bundles/poloniex.py
+++ b/catalyst/data/bundles/poloniex.py
@@ -14,19 +14,17 @@
 # limitations under the License.
 
 import sys
-
-from datetime import datetime
+from six.moves.urllib.parse import urlencode
 
 import pandas as pd
 
-from six.moves.urllib.parse import urlencode
-
 from catalyst.data.bundles.core import register_bundle
 from catalyst.data.bundles.base_pricing import BaseCryptoPricingBundle
 from catalyst.utils.memoize import lazyval
 
 from catalyst.curate.poloniex import PoloniexCurator
 
+
 class PoloniexBundle(BaseCryptoPricingBundle):
     @lazyval
     def name(self):
@@ -46,7 +44,8 @@ class PoloniexBundle(BaseCryptoPricingBundle):
     @lazyval
     def tar_url(self):
         return (
-            'https://s3.amazonaws.com/enigmaco/catalyst-bundles/poloniex/poloniex-bundle.tar.gz'
+            'https://s3.amazonaws.com/enigmaco/catalyst-bundles/'
+            'poloniex/poloniex-bundle.tar.gz'
         )
 
     @lazyval
@@ -67,12 +66,11 @@ class PoloniexBundle(BaseCryptoPricingBundle):
 
         raw = raw.sort_index().reset_index()
         raw.rename(
-            columns={'index':'symbol'},
+            columns={'index': 'symbol'},
             inplace=True,
         )
 
         raw = raw[raw['isFrozen'] == 0]
-
         return raw
 
     def post_process_symbol_metadata(self, asset_id, sym_md, sym_data):
@@ -98,7 +96,8 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                                frequency):
 
         # TODO: replace this with direct exchange call
-        # The end date and frequency should be used to calculate the number of bars
+        # The end date and frequency should be used to
+        # calculate the number of bars
         if(frequency == 'minute'):
             pc = PoloniexCurator()
             raw = pc.onemin_to_dataframe(symbol, start_date, end_date)
@@ -116,8 +115,9 @@ class PoloniexBundle(BaseCryptoPricingBundle):
             )
             raw.set_index('date', inplace=True)
 
-        # BcolzDailyBarReader introduces a 1/1000 factor in the way pricing is stored
-        # on disk, which we compensate here to get the right pricing amounts
+        # BcolzDailyBarReader introduces a 1/1000 factor in the way
+        # pricing is stored on disk, which we compensate here to get
+        # the right pricing amounts
         # ref: data/us_equity_pricing.py
         scale = 1
         raw.loc[:, 'open'] /= scale
@@ -139,7 +139,6 @@ class PoloniexBundle(BaseCryptoPricingBundle):
 
         return self._format_polo_query(query_params)
 
-
     def _format_data_url(self,
                          api_key,
                          symbol,
@@ -162,27 +161,26 @@ class PoloniexBundle(BaseCryptoPricingBundle):
             ('end', end_date.value / 10**9),
             ('period', period),
         ]
-            
+
         return self._format_polo_query(query_params)
-    
+
     def _format_polo_query(self, query_params):
         # TODO: got against the exchange object
         return 'https://poloniex.com/public?{query}'.format(
             query=urlencode(query_params),
         )
 
-''' 
-As a second parameter, you can pass an array of currency pairs 
-that will be processed as an asset_filter to only process that 
+
+'''
+As a second parameter, you can pass an array of currency pairs
+that will be processed as an asset_filter to only process that
 subset of assets in the bundle, such as:
 register_bundle(PoloniexBundle, ['USDT_BTC',])
 
 For a production environment make sure to use (to bundle all pairs):
 register_bundle(PoloniexBundle)
 '''
-
 if 'ingest' in sys.argv and '-c' in sys.argv:
     register_bundle(PoloniexBundle)
 else:
     register_bundle(PoloniexBundle, create_writers=False)
-
diff --git a/catalyst/data/bundles/quandl.py b/catalyst/data/bundles/quandl.py
index 1255449c..79e3eba8 100644
--- a/catalyst/data/bundles/quandl.py
+++ b/catalyst/data/bundles/quandl.py
@@ -16,7 +16,6 @@
 from datetime import datetime
 
 import pandas as pd
-
 from six.moves.urllib.parse import urlencode
 
 from catalyst.data.bundles.core import register_bundle
@@ -26,25 +25,16 @@ from catalyst.utils.memoize import lazyval
 """
 Module for building a complete daily dataset from Quandl's WIKI dataset.
 """
-from itertools import count
-import tarfile
-from time import time, sleep
-from datetime import datetime
-
 from logbook import Logger
-import pandas as pd
-from six.moves.urllib.parse import urlencode
-
-from catalyst.utils.calendars import register_calendar_alias
-from catalyst.utils.cli import maybe_show_progress
-
-from . import core as bundles
 
 from catalyst.constants import LOG_LEVEL
+from catalyst.utils.calendars import register_calendar_alias
+
 
 log = Logger(__name__, level=LOG_LEVEL)
 seconds_per_call = (pd.Timedelta('10 minutes') / 2000).total_seconds()
 
+
 class QuandlBundle(BaseEquityPricingBundle):
     @lazyval
     def name(self):
@@ -109,8 +99,8 @@ class QuandlBundle(BaseEquityPricingBundle):
         # Filter out invalid symbols
         raw = raw[~raw.symbol.isin(self._excluded_symbols)]
 
-        # cut out all the other stuff in the name column
-        # we need to escape the paren because it is actually splitting on a regex
+        # cut out all the other stuff in the name column. We need to
+        # escape the paren because it is actually splitting on a regex
         raw.asset_name = raw.asset_name.str.split(r' \(', 1).str.get(0)
 
         return raw
@@ -175,7 +165,6 @@ class QuandlBundle(BaseEquityPricingBundle):
         df['sid'] = asset_id
         self.splits.append(df)
 
-
     def _update_dividends(self, asset_id, raw_data):
         divs = raw_data.ex_dividend
         df = pd.DataFrame({'amount': divs[divs != 0]})
@@ -186,7 +175,6 @@ class QuandlBundle(BaseEquityPricingBundle):
         df['record_date'] = df['declared_date'] = df['pay_date'] = pd.NaT
         self.dividends.append(df)
 
-
     def _format_metadata_url(self, api_key, page_number):
         """Build the query RL for the quandl WIKI metadata.
         """
@@ -200,10 +188,10 @@ class QuandlBundle(BaseEquityPricingBundle):
             query_params = [('api_key', api_key)] + query_params
 
         return (
-            'https://www.quandl.com/api/v3/datasets.csv?' + urlencode(query_params)
+            'https://www.quandl.com/api/v3/datasets.csv?'
+            + urlencode(query_params)
         )
 
-
     def _format_wiki_url(self,
                          api_key,
                          symbol,
@@ -229,5 +217,6 @@ class QuandlBundle(BaseEquityPricingBundle):
             )
         )
 
+
 register_calendar_alias('QUANDL', 'NYSE')
 register_bundle(QuandlBundle)
diff --git a/catalyst/data/data_portal.py b/catalyst/data/data_portal.py
index 1f7ec6b3..34c720d0 100644
--- a/catalyst/data/data_portal.py
+++ b/catalyst/data/data_portal.py
@@ -656,11 +656,11 @@ class DataPortal(object):
         return spot_value
 
     def _get_minutely_spot_value(self,
-                                asset,
-                                column,
-                                dt,
-                                data_frequency,
-                                ffill=False):
+                                 asset,
+                                 column,
+                                 dt,
+                                 data_frequency,
+                                 ffill=False):
 
         reader = self._get_pricing_reader(data_frequency)
 
@@ -706,7 +706,7 @@ class DataPortal(object):
             asset,
             column,
             dt,
-            ffill, 
+            ffill,
             'minute',
         )
 
diff --git a/catalyst/data/dispatch_bar_reader.py b/catalyst/data/dispatch_bar_reader.py
index 7dfd7e95..a8e7429b 100644
--- a/catalyst/data/dispatch_bar_reader.py
+++ b/catalyst/data/dispatch_bar_reader.py
@@ -133,11 +133,13 @@ class AssetDispatchBarReader(with_metaclass(ABCMeta)):
 
         return results
 
+
 class AssetDispatchMinuteBarReader(AssetDispatchBarReader):
 
     def _dt_window_size(self, start_dt, end_dt):
         return len(self.trading_calendar.minutes_in_range(start_dt, end_dt))
 
+
 class AssetDispatchSessionBarReader(AssetDispatchBarReader):
 
     def _dt_window_size(self, start_dt, end_dt):
diff --git a/catalyst/data/loader.py b/catalyst/data/loader.py
index e58127f7..b9227b3e 100644
--- a/catalyst/data/loader.py
+++ b/catalyst/data/loader.py
@@ -12,7 +12,6 @@
 # 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.
-import datetime
 import os
 from collections import OrderedDict
 
@@ -129,11 +128,13 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
     # before this date.
     '''
     if(bundle_data):
-        # If we are using the bundle to retrieve the cryptobenchmark, find the last
-        # date for which there is trading data in the bundle
-        asset = bundle_data.asset_finder.lookup_symbol(symbol=bm_symbol,as_of_date=None)
+        # If we are using the bundle to retrieve the cryptobenchmark, find
+        # the last date for which there is trading data in the bundle
+        asset = bundle_data.asset_finder.lookup_symbol(
+                    symbol=bm_symbol,as_of_date=None)
         ix = bundle_data.daily_bar_reader._last_rows[asset.sid]
-        last_date = pd.to_datetime(bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
+        last_date = pd.to_datetime(
+                    bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
     else:
         last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
     '''
@@ -164,8 +165,8 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
     br.loc[start_dt] = 0
     br = br.sort_index()
 
-    # Override first_date for treasury data since we have it for many more years
-    # and is independent of crypto data
+    # Override first_date for treasury data since we have it for many more
+    # years and is independent of crypto data
     first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
     tc = ensure_treasury_data(
         bm_symbol,
@@ -301,14 +302,14 @@ def ensure_crypto_benchmark_data(symbol,
 
     if (bundle == 'poloniex'):
         '''
-        If we're using the Poloniex bundle, we'll get the benchmark from the bundle
-        instead of downloading it from Poloniex every time we need it.
-        Poloniex has a captcha for API queries originating from outside the US that 
-        prevents users abroad from getting Catalyst to work
+        If we're using the Poloniex bundle, we'll get the benchmark from the
+        bundle instead of downloading it from Poloniex every time we need it.
+        Poloniex has a captcha for API queries originating from outside the US
+        that prevents users abroad from getting Catalyst to work
         '''
         logger.info(
-            (
-                'Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
+            ('Retrieving benchmark data from bundle for {symbol!r}'
+             ' from {first_date} to {last_date}'),
             symbol=symbol, first_date=first_date, last_date=last_date)
 
         asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,
@@ -330,11 +331,12 @@ def ensure_crypto_benchmark_data(symbol,
                         last_date)]
 
     else:
-        # This is how it used to be: downloading the benchmark everytime. 
-        # Leaving this code here to be repurposed in the future for other bundles.
+        # This is how it used to be: downloading the benchmark everytime.
+        # Leaving this code here to be repurposed in the future for
+        # other bundles.
         logger.info(
-            (
-                'Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
+            ('Downloading benchmark data for {symbol!r}'
+             ' from {first_date} to {last_date}'),
             symbol=symbol, first_date=first_date, last_date=last_date)
 
         raise DeprecationWarning('poloniex bundle deprecated')
@@ -431,67 +433,6 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
     return data
 
 
-def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
-                          environ=None):
-    """
-    Ensure we have benchmark data for `symbol` from `first_date` to `last_date`
-
-    Parameters
-    ----------
-    symbol : str
-        The symbol for the benchmark to load.
-    first_date : pd.Timestamp
-        First required date for the cache.
-    last_date : pd.Timestamp
-        Last required date for the cache.
-    now : pd.Timestamp
-        The current time.  This is used to prevent repeated attempts to
-        re-download data that isn't available due to scheduling quirks or other
-        failures.
-    trading_day : pd.CustomBusinessDay
-        A trading day delta.  Used to find the day before first_date so we can
-        get the close of the day prior to first_date.
-
-    We attempt to download data unless we already have data stored at the data
-    cache for `symbol` whose first entry is before or on `first_date` and whose
-    last entry is on or after `last_date`.
-
-    If we perform a download and the cache criteria are not satisfied, we wait
-    at least one hour before attempting a redownload.  This is determined by
-    comparing the current time to the result of os.path.getmtime on the cache
-    path.
-    """
-    filename = get_benchmark_filename(symbol)
-    data = _load_cached_data(filename, first_date, last_date, now, 'benchmark',
-                             environ)
-    if data is not None:
-        return data
-
-    # If no cached data was found or it was missing any dates then download the
-    # necessary data.
-    logger.info(
-        ('Downloading benchmark data for {symbol!r} '
-         'from {first_date} to {last_date}'),
-        symbol=symbol,
-        first_date=first_date - trading_day,
-        last_date=last_date
-    )
-
-    try:
-        data = get_benchmark_returns(
-            symbol,
-            first_date - trading_day,
-            last_date,
-        )
-        data.to_csv(get_data_filepath(filename, environ))
-    except (OSError, IOError, HTTPError):
-        logger.exception('Failed to cache the new benchmark returns')
-        raise
-    if not has_data_for_dates(data, first_date, last_date):
-        logger.warn("Still don't have expected data after redownload!")
-    return data
-
-
 def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
     """
     Ensure we have treasury data from treasury module associated with
diff --git a/catalyst/data/minute_bars.py b/catalyst/data/minute_bars.py
index b2491a5a..3ef13cc2 100644
--- a/catalyst/data/minute_bars.py
+++ b/catalyst/data/minute_bars.py
@@ -341,12 +341,10 @@ class BcolzMinuteBarMetadata(object):
             'end_session': str(self.end_session.date()),
             # Write these values for backwards compatibility
             'first_trading_day': str(self.start_session.date()),
-            'market_opens': (
-                market_opens.values.astype('datetime64[m]').
-                    astype(np.int64).tolist()),
-            'market_closes': (
-                market_closes.values.astype('datetime64[m]').
-                    astype(np.int64).tolist()),
+            'market_opens': (market_opens.values.astype('datetime64[m]').
+                             astype(np.int64).tolist()),
+            'market_closes': (market_closes.values.astype('datetime64[m]').
+                              astype(np.int64).tolist()),
         }
         with open(self.metadata_path(rootdir), 'w+') as fp:
             json.dump(metadata, fp)
@@ -1256,8 +1254,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
                 values = carray[start_idx:end_idx + 1]
                 if indices_to_exclude is not None:
                     for excl_start, excl_stop in indices_to_exclude[::-1]:
-                        excl_slice = np.s_[
-                                     excl_start - start_idx:excl_stop - start_idx + 1]
+                        excl_slice = np.s_[excl_start - start_idx:excl_stop
+                                           - start_idx + 1]
                         values = np.delete(values, excl_slice)
 
                 where = values != 0
@@ -1320,9 +1318,8 @@ class H5MinuteBarUpdateWriter(object):
 
     def __init__(self, path, complevel=None, complib=None):
         self._complevel = complevel if complevel \
-                                       is not None else self._COMPLEVEL
-        self._complib = complib if complib \
-                                   is not None else self._COMPLIB
+                            is not None else self._COMPLEVEL
+        self._complib = complib if complib is not None else self._COMPLIB
         self._path = path
 
     def write(self, frames):
diff --git a/catalyst/data/us_equity_pricing.py b/catalyst/data/us_equity_pricing.py
index 8e74ab43..8bb8427b 100644
--- a/catalyst/data/us_equity_pricing.py
+++ b/catalyst/data/us_equity_pricing.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import division     # Python2 req to have division of ints yield float
+from __future__ import division  # Python2 req for division of ints yield float
 
 from errno import ENOENT
 from functools import partial
@@ -120,7 +120,8 @@ SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
 UINT32_MAX = iinfo(uint32).max
 UINT64_MAX = iinfo(uint64).max
 
-PRICE_ADJUSTMENT_FACTOR = 1000000000    # Provides 9 decimals resolution. Also affects _equities.pyx L220
+# Provides 9 decimals resolution. Also affects _equities.pyx L220
+PRICE_ADJUSTMENT_FACTOR = 1000000000
 
 
 def check_uint32_safe(value, colname):
@@ -130,6 +131,7 @@ def check_uint32_safe(value, colname):
             "for uint32" % (value, colname)
         )
 
+
 def check_uint64_safe(value, colname):
     if value >= UINT64_MAX:
         raise ValueError(
@@ -322,8 +324,8 @@ class BcolzDailyBarWriter(object):
         # Maps column name -> output carray.
         columns = {
             k: carray(array([], dtype=uint64))
-               if k in OHLCV
-               else carray(array([], dtype=uint32))
+            if k in OHLCV
+            else carray(array([], dtype=uint32))
             for k in US_EQUITY_PRICING_BCOLZ_COLUMNS
         }
 
@@ -439,11 +441,13 @@ class BcolzDailyBarWriter(object):
             return raw_data
 
         winsorise_uint64(raw_data, invalid_data_behavior, 'volume', *OHLC)
-        processed = (raw_data[list(OHLC)] * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
+        processed = (raw_data[list(OHLC)]
+                     * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         dates = raw_data.index.values.astype('datetime64[s]')
         check_uint32_safe(dates.max().view(np.int64), 'day')
         processed['day'] = dates.astype('uint32')
-        processed['volume'] = (raw_data.volume * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
+        processed['volume'] = (raw_data.volume
+                               * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         return ctable.fromdataframe(processed)
 
 
@@ -496,7 +500,7 @@ class BcolzDailyBarReader(SessionBarReader):
 
     The data in these columns is interpreted as follows:
 
-    - Price columns ('open', 'high', 'low', 'close') and Volume are interpreted 
+    - Price columns ('open', 'high', 'low', 'close') and Volume are interpreted
       as 10^9 * as-traded dollar value.
     - Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
     - Id is the asset id of the row.
diff --git a/catalyst/examples/buy_and_hodl.py b/catalyst/examples/buy_and_hodl.py
index 74b04238..6722665f 100644
--- a/catalyst/examples/buy_and_hodl.py
+++ b/catalyst/examples/buy_and_hodl.py
@@ -19,7 +19,7 @@ import matplotlib.pyplot as plt
 
 from catalyst import run_algorithm
 from catalyst.api import (order_target_value, symbol, record,
-    cancel_order, get_open_orders, )
+                          cancel_order, get_open_orders, )
 
 
 def initialize(context):
diff --git a/catalyst/examples/buy_btc_simple.py b/catalyst/examples/buy_btc_simple.py
index 5e4ebd57..52463d58 100644
--- a/catalyst/examples/buy_btc_simple.py
+++ b/catalyst/examples/buy_btc_simple.py
@@ -1,30 +1,34 @@
 '''
-	This is a very simple example referenced in the beginner's tutorial:
-	https://enigmampc.github.io/catalyst/beginner-tutorial.html
+    This is a very simple example referenced in the beginner's tutorial:
+    https://enigmampc.github.io/catalyst/beginner-tutorial.html
 
-	Run this example, by executing the following from your terminal:
-	  catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
-	  catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
+    Run this example, by executing the following from your terminal:
+      catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
+      catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 \
+        --end 2017-9-30 -o buy_btc_simple_out.pickle
 
-	If you want to run this code using another exchange, make sure that 
-	the asset is available on that exchange. For example, if you were to run 
-	it for exchange Poloniex, you would need to edit the following line:
+    If you want to run this code using another exchange, make sure that
+    the asset is available on that exchange. For example, if you were to run
+    it for exchange Poloniex, you would need to edit the following line:
 
-		context.asset = symbol('btc_usdt')     # note 'usdt' instead of 'usd'
+        context.asset = symbol('btc_usdt')     # note 'usdt' instead of 'usd'
 
-	and specify exchange poloniex as follows:
-	catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
-	catalyst run -f buy_btc_simple.py -x poloniex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
+    and specify exchange poloniex as follows:
+    catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
+    catalyst run -f buy_btc_simple.py -x poloniex --start 2016-1-1 \
+        --end 2017-9-30 -o buy_btc_simple_out.pickle
 
-	To see which assets are available on each exchange, visit:
-	https://www.enigma.co/catalyst/status
+    To see which assets are available on each exchange, visit:
+    https://www.enigma.co/catalyst/status
 '''
 
 from catalyst.api import order, record, symbol
 
+
 def initialize(context):
     context.asset = symbol('btc_usd')
 
+
 def handle_data(context, data):
     order(context.asset, 1)
-    record(btc = data.current(context.asset, 'price'))
\ No newline at end of file
+    record(btc=data.current(context.asset, 'price'))
diff --git a/catalyst/examples/buy_low_sell_high.py b/catalyst/examples/buy_low_sell_high.py
index acf481e0..b3afb723 100644
--- a/catalyst/examples/buy_low_sell_high.py
+++ b/catalyst/examples/buy_low_sell_high.py
@@ -1,12 +1,13 @@
 '''
-This algorithm requires an additional library (ta-lib) beyond those required by catalyst.
-Install it first by running: 
+This algorithm requires an additional library (ta-lib) beyond those
+required by catalyst. Install it first by running:
 $ pip install TA-Lib
 
-If you get build errors like "fatal error: ta-lib/ta_libc.h: No such file or directory"
-it typically means that it can't find the underlying TA-Lib library and needs to be installed.
-See https://mrjbq7.github.io/ta-lib/install.html for instructions on how to install 
-the required dependencies.
+If you get build errors like:
+    "fatal error: ta-lib/ta_libc.h: No such file or directory"
+it typically means that it can't find the underlying TA-Lib library and it
+needs to be installed. See https://mrjbq7.github.io/ta-lib/install.html for
+instructions on how to install the required dependencies.
 '''
 
 import talib
@@ -100,8 +101,8 @@ def _handle_data(context, data):
 
         if price < cost_basis:
             is_buy = True
-        elif position.amount > 0 and \
-                        price > cost_basis * (1 + context.PROFIT_TARGET):
+        elif(position.amount > 0
+             and price > cost_basis * (1 + context.PROFIT_TARGET)):
             profit = (price * position.amount) - (cost_basis * position.amount)
             log.info('closing position, taking profit: {}'.format(profit))
             order_target_percent(
diff --git a/catalyst/examples/buy_low_sell_high_live.py b/catalyst/examples/buy_low_sell_high_live.py
index a1fd326c..422033a6 100644
--- a/catalyst/examples/buy_low_sell_high_live.py
+++ b/catalyst/examples/buy_low_sell_high_live.py
@@ -88,8 +88,8 @@ def _handle_data(context, data):
 
         if price < cost_basis:
             is_buy = True
-        elif position.amount > 0 and \
-                        price > cost_basis * (1 + context.PROFIT_TARGET):
+        elif(position.amount > 0
+             and price > cost_basis * (1 + context.PROFIT_TARGET)):
             profit = (price * position.amount) - (cost_basis * position.amount)
             log.info('closing position, taking profit: {}'.format(profit))
             order_target_percent(
diff --git a/catalyst/examples/dual_moving_average.py b/catalyst/examples/dual_moving_average.py
index a73461f1..f54d91b6 100644
--- a/catalyst/examples/dual_moving_average.py
+++ b/catalyst/examples/dual_moving_average.py
@@ -4,13 +4,14 @@ from logbook import Logger
 import matplotlib.pyplot as plt
 
 from catalyst import run_algorithm
-from catalyst.api import (order, record, symbol, order_target_percent,
-        get_open_orders)
+from catalyst.api import (record, symbol, order_target_percent,
+                          get_open_orders)
 from catalyst.exchange.stats_utils import extract_transactions
 
 NAMESPACE = 'dual_moving_average'
 log = Logger(NAMESPACE)
 
+
 def initialize(context):
     context.i = 0
     context.asset = symbol('ltc_usd')
@@ -25,16 +26,22 @@ def handle_data(context, data):
     # Skip as many bars as long_window to properly compute the average
     context.i += 1
     if context.i < long_window:
-       return
+        return
 
     # Compute moving averages calling data.history() for each
     # moving average with the appropriate parameters. We choose to use
     # minute bars for this simulation -> freq="1m"
     # Returns a pandas dataframe.
-    short_mavg = data.history(context.asset, 'price',
-                        bar_count=short_window, frequency="1m").mean()
-    long_mavg = data.history(context.asset, 'price',
-                        bar_count=long_window, frequency="1m").mean()
+    short_mavg = data.history(context.asset,
+                              'price',
+                              bar_count=short_window,
+                              frequency="1m",
+                              ).mean()
+    long_mavg = data.history(context.asset,
+                             'price',
+                             bar_count=long_window,
+                             frequency="1m",
+                             ).mean()
 
     # Let's keep the price of our asset in a more handy variable
     price = data.current(context.asset, 'price')
@@ -67,11 +74,11 @@ def handle_data(context, data):
 
     # Trading logic
     if short_mavg > long_mavg and pos_amount == 0:
-       # we buy 100% of our portfolio for this asset
-       order_target_percent(context.asset, 1)
+        # we buy 100% of our portfolio for this asset
+        order_target_percent(context.asset, 1)
     elif short_mavg < long_mavg and pos_amount > 0:
-       # we sell all our positions for this asset
-       order_target_percent(context.asset, 0)
+        # we sell all our positions for this asset
+        order_target_percent(context.asset, 0)
 
 
 def analyze(context, perf):
@@ -89,11 +96,13 @@ def analyze(context, perf):
 
     # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
-    perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
+    perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(
+        ax=ax2,
+        label='Price')
     ax2.legend_.remove()
     ax2.set_ylabel('{asset}\n({base})'.format(
-        asset = context.asset.symbol,
-        base = base_currency
+        asset=context.asset.symbol,
+        base=base_currency
         ))
     start, end = ax2.get_ylim()
     ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
@@ -150,4 +159,4 @@ if __name__ == '__main__':
             base_currency='usd',
             start=pd.to_datetime('2017-9-22', utc=True),
             end=pd.to_datetime('2017-9-23', utc=True),
-        )
\ No newline at end of file
+        )
diff --git a/catalyst/examples/dual_vwap.py b/catalyst/examples/dual_vwap.py
index 52c1789d..7059b865 100644
--- a/catalyst/examples/dual_vwap.py
+++ b/catalyst/examples/dual_vwap.py
@@ -52,13 +52,14 @@ def initialize(context):
 
     schedule_function(
         rebalance,
-        time_rules=times_rules.every_minute(),
+        time_rules=date_rules.every_minute(),
     )
 
 
 def before_trading_start(context, data):
     context.pipeline_data = pipeline_output('vwap_pipeline')
 
+
 def make_pipeline(context):
     return Pipeline(
         columns={
@@ -69,6 +70,7 @@ def make_pipeline(context):
         }
     )
 
+
 def rebalance(context, data):
     context.i += 1
 
@@ -111,7 +113,6 @@ def rebalance(context, data):
         long_mavg=long_mavg,
         volume=volume,
     )
-    
 
 
 def analyze(context=None, results=None):
@@ -124,10 +125,11 @@ def analyze(context=None, results=None):
 
     ax2 = plt.subplot(612, sharex=ax1)
     ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
-    (context.TICK_SIZE*results[['price', 'short_mavg', 'long_mavg']]).plot(ax=ax2)
+    (context.TICK_SIZE*results[['price',
+                                'short_mavg',
+                                'long_mavg']]).plot(ax=ax2)
 
     trans = results.ix[[t != [] for t in results.transactions]]
-    amounts = [t[0]['amount'] for t in trans.transactions]
 
     buys = trans.ix[
         [t[0]['amount'] > 0 for t in trans.transactions]
diff --git a/catalyst/examples/mean_reversion_simple.py b/catalyst/examples/mean_reversion_simple.py
index 450a73f7..4508c8e5 100644
--- a/catalyst/examples/mean_reversion_simple.py
+++ b/catalyst/examples/mean_reversion_simple.py
@@ -1,4 +1,4 @@
-# For this example, we're going to write a simple momentum script.  When the 
+# For this example, we're going to write a simple momentum script.  When the
 # stock goes up quickly, we're going to buy; when it goes down quickly, we're
 # going to sell.  Hopefully we'll ride the waves.
 import os
@@ -12,8 +12,8 @@ from logbook import Logger
 
 from catalyst import run_algorithm
 from catalyst.api import symbol, record, order_target_percent, get_open_orders
-from catalyst.exchange.stats_utils import extract_transactions, \
-    get_pretty_stats
+from catalyst.exchange.stats_utils import extract_transactions
+
 # We give a name to the algorithm which Catalyst will use to persist its state.
 # In this example, Catalyst will create the `.catalyst/data/live_algos`
 # directory. If we stop and start the algorithm, Catalyst will resume its
@@ -122,7 +122,7 @@ def handle_data(context, data):
     # Another powerful built-in feature of the Catalyst backtester is the
     # portfolio object.  The portfolio object tracks your positions, cash,
     # cost basis of specific holdings, and more.  In this line, we calculate
-    # how long or short our position is at this minute.   
+    # how long or short our position is at this minute.
     pos_amount = context.portfolio.positions[context.market].amount
 
     if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
@@ -250,7 +250,9 @@ if __name__ == '__main__':
 
         timestr = time.strftime('%Y%m%d-%H%M%S')
         out = os.path.join(folder, '{}.p'.format(timestr))
-        # catalyst run -f catalyst/examples/mean_reversion_simple.py -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
+        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
+        #   --data-frequency minute --capital-base 10000
         run_algorithm(
             capital_base=0.1,
             data_frequency='minute',
diff --git a/catalyst/examples/portfolio_optimization.py b/catalyst/examples/portfolio_optimization.py
index bb1660a2..e93b2daf 100644
--- a/catalyst/examples/portfolio_optimization.py
+++ b/catalyst/examples/portfolio_optimization.py
@@ -1,7 +1,7 @@
-'''Use this code to execute a portfolio optimization model. This code 
-   will select the portfolio with the maximum Sharpe Ratio. The parameters 
+'''Use this code to execute a portfolio optimization model. This code
+   will select the portfolio with the maximum Sharpe Ratio. The parameters
    are set to use 180 days of historical data and rebalance every 30 days.
-   
+
    This is the code used in the following article:
    https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556
 
@@ -15,115 +15,130 @@ import os
 import pytz
 import numpy as np
 import pandas as pd
-from scipy.optimize import minimize
 import matplotlib.pyplot as plt
 from datetime import datetime
 
-from catalyst.api import record, symbol, symbols, order_target_percent
+from catalyst.api import record, symbols, order_target_percent
 from catalyst.utils.run_algo import run_algorithm
 
 np.set_printoptions(threshold='nan', suppress=True)
 
 
 def initialize(context):
-   # Portfolio assets list
-   context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
-                            'xmr_usdt')
-   context.nassets = len(context.assets)
-   # Set the time window that will be used to compute expected return 
-   # and asset correlations
-   context.window = 180
-   # Set the number of days between each portfolio rebalancing
-   context.rebalance_period = 30                   
-   context.i = 0
+    # Portfolio assets list
+    context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
+                             'xmr_usdt')
+    context.nassets = len(context.assets)
+    # Set the time window that will be used to compute expected return
+    # and asset correlations
+    context.window = 180
+    # Set the number of days between each portfolio rebalancing
+    context.rebalance_period = 30
+    context.i = 0
+
 
-   
 def handle_data(context, data):
-   # Only rebalance at the beggining of the algorithm execution and 
-   # every multiple of the rebalance period
-   if context.i == 0 or context.i%context.rebalance_period == 0:
-       n = context.window
-       prices = data.history(context.assets, fields='price', 
-                             bar_count=n+1, frequency='1d') 
-       pr = np.asmatrix(prices)
-       t_prices = prices.iloc[1:n+1]
-       t_val = t_prices.values
-       tminus_prices = prices.iloc[0:n]
-       tminus_val = tminus_prices.values
-       # Compute daily returns (r)
-       r = np.asmatrix(t_val/tminus_val-1)
-       # Compute the expected returns of each asset with the average 
-       # daily return for the selected time window
-       m = np.asmatrix(np.mean(r, axis=0))
-       # ###
-       stds = np.std(r, axis=0)
-       # Compute excess returns matrix (xr)
-       xr = r - m
-       # Matrix algebra to get variance-covariance matrix
-       cov_m = np.dot(np.transpose(xr),xr)/n
-       # Compute asset correlation matrix (informative only)
-       corr_m = cov_m/np.dot(np.transpose(stds),stds)
-       
-       # Define portfolio optimization parameters
-       n_portfolios = 50000
-       results_array = np.zeros((3+context.nassets,n_portfolios))
-       for p in xrange(n_portfolios):
-           weights = np.random.random(context.nassets)
-           weights /= np.sum(weights)
-           w = np.asmatrix(weights)
-           p_r = np.sum(np.dot(w,np.transpose(m)))*365
-           p_std = np.sqrt(np.dot(np.dot(w,cov_m),np.transpose(w)))*np.sqrt(365)
-           
-           #store results in results array
-           results_array[0,p] = p_r
-           results_array[1,p] = p_std
-           #store Sharpe Ratio (return / volatility) - risk free rate element 
-           #excluded for simplicity
-           results_array[2,p] = results_array[0,p] / results_array[1,p]
-           i = 0
-           for iw in weights:
-               results_array[3+i,p] = weights[i]
-               i += 1
-       
-       #convert results array to Pandas DataFrame
-       results_frame = pd.DataFrame(np.transpose(results_array),
-                          columns=['r','stdev','sharpe']+context.assets)
-       #locate position of portfolio with highest Sharpe Ratio
-       max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
-       #locate positon of portfolio with minimum standard deviation
-       min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
-       
-       #order optimal weights for each asset
-       for asset in context.assets:
-           if data.can_trade(asset):
-               order_target_percent(asset, max_sharpe_port[asset])
-       
-       #create scatter plot coloured by Sharpe Ratio
-       plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
-       plt.xlabel('Volatility')
-       plt.ylabel('Returns')
-       plt.colorbar()
-       #plot red star to highlight position of portfolio with highest Sharpe Ratio
-       plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
-       #plot green star to highlight position of minimum variance portfolio
-       plt.show()
-       print(max_sharpe_port)
-       record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
-   context.i += 1
-   
-       
+    # Only rebalance at the beggining of the algorithm execution and
+    # every multiple of the rebalance period
+    if context.i == 0 or context.i % context.rebalance_period == 0:
+        n = context.window
+        prices = data.history(context.assets, fields='price',
+                              bar_count=n+1, frequency='1d')
+        pr = np.asmatrix(prices)
+        t_prices = prices.iloc[1:n+1]
+        t_val = t_prices.values
+        tminus_prices = prices.iloc[0:n]
+        tminus_val = tminus_prices.values
+        # Compute daily returns (r)
+        r = np.asmatrix(t_val/tminus_val-1)
+        # Compute the expected returns of each asset with the average
+        # daily return for the selected time window
+        m = np.asmatrix(np.mean(r, axis=0))
+        # ###
+        stds = np.std(r, axis=0)
+        # Compute excess returns matrix (xr)
+        xr = r - m
+        # Matrix algebra to get variance-covariance matrix
+        cov_m = np.dot(np.transpose(xr), xr)/n
+        # Compute asset correlation matrix (informative only)
+        corr_m = cov_m/np.dot(np.transpose(stds), stds)
+
+        # Define portfolio optimization parameters
+        n_portfolios = 50000
+        results_array = np.zeros((3+context.nassets, n_portfolios))
+        for p in xrange(n_portfolios):
+            weights = np.random.random(context.nassets)
+            weights /= np.sum(weights)
+            w = np.asmatrix(weights)
+            p_r = np.sum(np.dot(w, np.transpose(m)))*365
+            p_std = np.sqrt(np.dot(np.dot(w, cov_m),
+                                   np.transpose(w)))*np.sqrt(365)
+
+            # store results in results array
+            results_array[0, p] = p_r
+            results_array[1, p] = p_std
+            # store Sharpe Ratio (return / volatility) - risk free rate element
+            # excluded for simplicity
+            results_array[2, p] = results_array[0, p] / results_array[1, p]
+            i = 0
+            for iw in weights:
+                results_array[3+i, p] = weights[i]
+                i += 1
+
+        # convert results array to Pandas DataFrame
+        results_frame = pd.DataFrame(np.transpose(results_array),
+                                     columns=['r', 'stdev', 'sharpe']
+                                     + context.assets)
+        # locate position of portfolio with highest Sharpe Ratio
+        max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
+        # locate positon of portfolio with minimum standard deviation
+        # min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
+
+        # order optimal weights for each asset
+        for asset in context.assets:
+            if data.can_trade(asset):
+                order_target_percent(asset, max_sharpe_port[asset])
+
+        # create scatter plot coloured by Sharpe Ratio
+        plt.scatter(results_frame.stdev,
+                    results_frame.r,
+                    c=results_frame.sharpe,
+                    cmap='RdYlGn')
+        plt.xlabel('Volatility')
+        plt.ylabel('Returns')
+        plt.colorbar()
+        # plot red star to highlight position of portfolio
+        # with highest Sharpe Ratio
+        plt.scatter(max_sharpe_port[1],
+                    max_sharpe_port[0],
+                    marker='o',
+                    color='b',
+                    s=200)
+        # plot green star to highlight position of minimum variance portfolio
+        plt.show()
+        print(max_sharpe_port)
+        record(pr=pr,
+               r=r,
+               m=m,
+               stds=stds,
+               max_sharpe_port=max_sharpe_port,
+               corr_m=corr_m)
+    context.i += 1
+
+
 def analyze(context=None, results=None):
-   # Form DataFrame with selected data
-   data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
-   
-   # Save results in CSV file
-   filename = os.path.splitext(os.path.basename(__file__))[0]
-   data.to_csv(filename + '.csv')
+    # Form DataFrame with selected data
+    data = results[['pr', 'r', 'm', 'stds', 'max_sharpe_port', 'corr_m',
+                   'portfolio_value']]
+
+    # Save results in CSV file
+    filename = os.path.splitext(os.path.basename(__file__))[0]
+    data.to_csv(filename + '.csv')
 
 
-# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.    
+# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
 start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
-end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc) 
+end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
 results = run_algorithm(initialize=initialize,
                         handle_data=handle_data,
                         analyze=analyze,
diff --git a/catalyst/examples/rsi_profit_target.py b/catalyst/examples/rsi_profit_target.py
index 5e24f122..7b8ac868 100644
--- a/catalyst/examples/rsi_profit_target.py
+++ b/catalyst/examples/rsi_profit_target.py
@@ -11,7 +11,6 @@ from catalyst.api import (
     record,
     get_open_orders,
 )
-from catalyst.exchange.stats_utils import crossover, crossunder
 from catalyst.utils.run_algo import run_algorithm
 
 algo_namespace = 'rsi'
@@ -55,7 +54,7 @@ def _handle_buy_sell_decision(context, data, signal, price):
             stop=None
         )
 
-    action = None
+    # action = None
     if context.position is not None:
         cost_basis = context.position['cost_basis']
         amount = context.position['amount']
@@ -80,7 +79,7 @@ def _handle_buy_sell_decision(context, data, signal, price):
                 amount=-amount,
                 limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
             )
-            action = 0
+            # action = 0
             context.position = None
 
     else:
@@ -97,7 +96,7 @@ def _handle_buy_sell_decision(context, data, signal, price):
                 amount=buy_amount,
                 stop=None
             )
-            action = 0
+            # action = 0
 
 
 def _handle_data_rsi_only(context, data):
diff --git a/catalyst/examples/simple_universe.py b/catalyst/examples/simple_universe.py
index a0abc535..7539a76f 100644
--- a/catalyst/examples/simple_universe.py
+++ b/catalyst/examples/simple_universe.py
@@ -2,26 +2,26 @@
 Requires Catalyst version 0.3.0 or above
 Tested on Catalyst version 0.3.3
 
-This example aims to provide an easy way for users to learn how to 
+This example aims to provide an easy way for users to learn how to
 collect data from any given exchange and select a subset of the available
-currency pairs for trading. You simply need to specify the exchange and 
-the market (base_currency) that you want to focus on. You will then see 
-how to create a universe of assets, and filter it based the market you 
+currency pairs for trading. You simply need to specify the exchange and
+the market (base_currency) that you want to focus on. You will then see
+how to create a universe of assets, and filter it based the market you
 desire.
 
-The example prints out the closing price of all the pairs for a given 
-market in a given exchange every 30 minutes. The example also contains 
-the OHLCV data with minute-resolution for the past seven days which 
-could be used to create indicators. Use this code as the backbone to 
+The example prints out the closing price of all the pairs for a given
+market in a given exchange every 30 minutes. The example also contains
+the OHLCV data with minute-resolution for the past seven days which
+could be used to create indicators. Use this code as the backbone to
 create your own trading strategy.
 
-The lookback_date variable is used to ensure data for a coin existed on 
+The lookback_date variable is used to ensure data for a coin existed on
 the lookback period specified.
 
-To run, execute the following two commands in a terminal (inside catalyst 
+To run, execute the following two commands in a terminal (inside catalyst
 environment). The first one retrieves all the pricing data needed for this
 script to run (only needs to be run once), and the second one executes this
-script with the parameters specified in the run_algorithm() call at the end 
+script with the parameters specified in the run_algorithm() call at the end
 of the file:
 
 catalyst ingest-exchange -x bitfinex -f minute
@@ -41,8 +41,8 @@ from catalyst.api import (symbols, )
 
 def initialize(context):
     context.i = -1  # minute counter
-    context.exchange = context.exchanges.values()[0].name.lower()  
-    context.base_currency = context.exchanges.values()[0].base_currency.lower()  
+    context.exchange = context.exchanges.values()[0].name.lower()
+    context.base_currency = context.exchanges.values()[0].base_currency.lower()
 
 
 def handle_data(context, data):
@@ -52,9 +52,9 @@ def handle_data(context, data):
     # current date & time in each iteration formatted into a string
     now = data.current_dt
     date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
-    lookback_date = now - timedelta(days=lookback_days) 
+    lookback_date = now - timedelta(days=lookback_days)
     # keep only the date as a string, discard the time
-    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]  
+    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]
 
     one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
     # update universe everyday at midnight
@@ -64,39 +64,50 @@ def handle_data(context, data):
     # get data every 30 minutes
     minutes = 30
     # get lookback_days of history data: that is 'lookback' number of bins
-    lookback = one_day_in_minutes / minutes * lookback_days  
+    lookback = one_day_in_minutes / minutes * lookback_days
     if not context.i % minutes and context.universe:
         # we iterate for every pair in the current universe
         for coin in context.coins:
             pair = str(coin.symbol)
 
-            # Get 30 minute interval OHLCV data. This is the standard data 
+            # Get 30 minute interval OHLCV data. This is the standard data
             # required for candlestick or indicators/signals. Return Pandas
-            # DataFrames. 30T means 30-minute re-sampling of one minute data. 
+            # DataFrames. 30T means 30-minute re-sampling of one minute data.
             # Adjust it to your desired time interval as needed.
-            opened = fill(data.history(coin, 'open', 
-                                  bar_count=lookback, frequency='30T')).values
-            high = fill(data.history(coin, 'high', 
-                                  bar_count=lookback, frequency='30T')).values
-            low = fill(data.history(coin, 'low', 
-                                  bar_count=lookback, frequency='30T')).values
-            close = fill(data.history(coin, 'price', 
-                                  bar_count=lookback, frequency='30T')).values
-            volume = fill(data.history(coin, 'volume', 
-                                  bar_count=lookback, frequency='30T')).values
+            opened = fill(data.history(coin,
+                                       'open',
+                                       bar_count=lookback,
+                                       frequency='30T')).values
+            high = fill(data.history(coin,
+                                     'high',
+                                     bar_count=lookback,
+                                     frequency='30T')).values
+            low = fill(data.history(coin,
+                                    'low',
+                                    bar_count=lookback,
+                                    frequency='30T')).values
+            close = fill(data.history(coin,
+                                      'price',
+                                      bar_count=lookback,
+                                      frequency='30T')).values
+            volume = fill(data.history(coin,
+                                       'volume',
+                                       bar_count=lookback,
+                                       frequency='30T')).values
 
-            # close[-1] is the last value in the set, which is the equivalent 
+            # close[-1] is the last value in the set, which is the equivalent
             # to current price (as in the most recent value)
             # displays the minute price for each pair every 30 minutes
-            print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},\tV:{v}'.format(
-                    now=now, 
-                    pair=pair, 
-                    o=opened[-1], 
-                    h=high[-1], 
+            print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},'
+                  '\tV:{v}'.format(
+                    now=now,
+                    pair=pair,
+                    o=opened[-1],
+                    h=high[-1],
                     l=low[-1],
                     c=close[-1],
                     v=volume[-1],
-                 ))
+                  ))
 
             # -------------------------------------------------------------
             # --------------- Insert Your Strategy Here -------------------
@@ -111,16 +122,18 @@ def analyze(context=None, results=None):
 # Example: Poloniex BTC Market
 def universe(context, lookback_date, current_date):
     # get all the pairs for the given exchange
-    json_symbols = get_exchange_symbols(context.exchange)  
+    json_symbols = get_exchange_symbols(context.exchange)
     # convert into a DataFrame for easier processing
-    df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str) 
-    df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],axis=1)
-    df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],axis=1)
+    df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str)
+    df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],
+                                   axis=1)
+    df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],
+                                     axis=1)
 
     # Filter all the pairs to get only the ones for a given base_currency
     df = df[df['base_currency'] == context.base_currency]
 
-    # Filter all the pairs to ensure that pair existed in the current date range
+    # Filter all pairs to ensure that pair existed in the current date range
     df = df[df.start_date < lookback_date]
     df = df[df.end_daily >= current_date]
     context.coins = symbols(*df.symbol)  # convert all the pairs to symbols
@@ -155,4 +168,3 @@ if __name__ == '__main__':
                                 live=False,
                                 live_graph=False,
                                 algo_namespace='simple_universe')
-
diff --git a/catalyst/examples/talib_simple.py b/catalyst/examples/talib_simple.py
index d4a7d807..5129dda9 100644
--- a/catalyst/examples/talib_simple.py
+++ b/catalyst/examples/talib_simple.py
@@ -1,9 +1,11 @@
 # Run Command
-# catalyst run --start 2017-1-1 --end 2017-11-1 -o talib_simple.pickle -f talib_simple.py -x poloniex
-# 
+# catalyst run --start 2017-1-1 --end 2017-11-1 -o talib_simple.pickle \
+#   -f talib_simple.py -x poloniex
+#
 # Description
-# Simple TALib Example showing how to use various indicators in you strategy
-# Based loosly on https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
+# Simple TALib Example showing how to use various indicators
+# in you strategy. Based loosly on
+# https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
 
 import os
 
@@ -88,7 +90,7 @@ def _handle_data(context, data):
         prices.close.as_matrix(), fastperiod=context.MACD_FAST,
         slowperiod=context.MACD_SLOW, signalperiod=context.MACD_SIGNAL)
 
-    # Stochastics %K %D 
+    # Stochastics %K %D
     # %K = (Current Close - Lowest Low)/(Highest High - Lowest Low) * 100
     # %D = 3-day SMA of %K
     analysis['stoch_k'], analysis['stoch_d'] = ta.STOCH(
diff --git a/catalyst/exchange/bitfinex/bitfinex.py b/catalyst/exchange/bitfinex/bitfinex.py
index 12c051cd..fa6077ac 100644
--- a/catalyst/exchange/bitfinex/bitfinex.py
+++ b/catalyst/exchange/bitfinex/bitfinex.py
@@ -14,6 +14,7 @@ import six
 from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
+from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange import Exchange
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import (
@@ -33,7 +34,6 @@ requests.adapters.DEFAULT_RETRIES = 20
 
 BITFINEX_URL = 'https://api.bitfinex.com'
 
-from catalyst.constants import LOG_LEVEL
 
 log = Logger('Bitfinex', level=LOG_LEVEL)
 warning_logger = Logger('AlgoWarning')
@@ -172,7 +172,8 @@ class Bitfinex(Exchange):
 
         executed_price = float(order_status['avg_execution_price'])
 
-        # TODO: bitfinex does not specify comission. I could calculate it but not sure if it's worth it.
+        # TODO: bitfinex does not specify comission.
+        # I could calculate it but not sure if it's worth it.
         commission = None
 
         date = pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
@@ -599,17 +600,17 @@ class Bitfinex(Exchange):
             else:
                 try:
                     start_date = cached_symbols[symbol]['start_date']
-                except KeyError as e:
+                except KeyError:
                     start_date = time.strftime('%Y-%m-%d')
 
             try:
                 end_daily = cached_symbols[symbol]['end_daily']
-            except KeyError as e:
+            except KeyError:
                 end_daily = 'N/A'
 
             try:
                 end_minute = cached_symbols[symbol]['end_minute']
-            except KeyError as e:
+            except KeyError:
                 end_minute = 'N/A'
 
             symbol_map[symbol] = dict(
@@ -660,15 +661,16 @@ class Bitfinex(Exchange):
 
         """
             Query again with daily resolution setting the start and end around
-            the startmonth we got above. Avoid end dates greater than now: time.time()
+            the startmonth we got above. Avoid end dates greater than
+            now: time.time()
         """
-        url = '{url}/v2/candles/trade:1D:{symbol}/hist?start={start}&end={end}'.format(
-            url=self.url,
-            symbol=symbol_v2,
-            start=startmonth - 3600 * 24 * 31 * 1000,
-            end=min(startmonth + 3600 * 24 * 31 * 1000,
-                    int(time.time() * 1000))
-        )
+        url = ('{url}/v2/candles/trade:1D:{symbol}/hist?start={start}'
+               '&end={end}').format(
+                    url=self.url,
+                    symbol=symbol_v2,
+                    start=startmonth - 3600 * 24 * 31 * 1000,
+                    end=min(startmonth + 3600 * 24 * 31 * 1000,
+                            int(time.time() * 1000)))
 
         try:
             self.ask_request()
diff --git a/catalyst/exchange/bittrex/bittrex.py b/catalyst/exchange/bittrex/bittrex.py
index af85aef1..5835b8e1 100644
--- a/catalyst/exchange/bittrex/bittrex.py
+++ b/catalyst/exchange/bittrex/bittrex.py
@@ -262,11 +262,10 @@ class Bittrex(Exchange):
             end = int(time.mktime(end_dt.timetuple()))
             url = '{url}/pub/market/GetTicks?marketName={symbol}' \
                   '&tickInterval={frequency}&_={end}'.format(
-                url=URL2,
-                symbol=self.get_symbol(asset),
-                frequency=frequency,
-                end=end
-            )
+                    url=URL2,
+                    symbol=self.get_symbol(asset),
+                    frequency=frequency,
+                    end=end, )
 
             try:
                 data = json.loads(urllib.request.urlopen(url).read().decode())
@@ -359,12 +358,12 @@ class Bittrex(Exchange):
 
             try:
                 end_daily = cached_symbols[exchange_symbol]['end_daily']
-            except KeyError as e:
+            except KeyError:
                 end_daily = 'N/A'
 
             try:
                 end_minute = cached_symbols[exchange_symbol]['end_minute']
-            except KeyError as e:
+            except KeyError:
                 end_minute = 'N/A'
 
             symbol_map[exchange_symbol] = dict(
diff --git a/catalyst/exchange/bittrex/extensions-example.py b/catalyst/exchange/bittrex/extensions-example.py
index 33ffb4f5..4b087899 100644
--- a/catalyst/exchange/bittrex/extensions-example.py
+++ b/catalyst/exchange/bittrex/extensions-example.py
@@ -4,4 +4,4 @@ from catalyst.exchange.exchange_bundle import exchange_bundle
 symbols = (
     'neo_btc',
 )
-register('exchange_bitfinex', exchange_bundle('bitfinex', symbols))
\ No newline at end of file
+register('exchange_bitfinex', exchange_bundle('bitfinex', symbols))
diff --git a/catalyst/exchange/bundle_utils.py b/catalyst/exchange/bundle_utils.py
index 29509bfa..0d758c7d 100644
--- a/catalyst/exchange/bundle_utils.py
+++ b/catalyst/exchange/bundle_utils.py
@@ -6,11 +6,9 @@ from datetime import timedelta, datetime, date
 import numpy as np
 import pandas as pd
 import pytz
-from catalyst.assets._assets import TradingPair
 
 from catalyst.data.bundles.core import download_without_progress
-from catalyst.exchange.exchange_utils import get_exchange_bundles_folder, \
-    get_exchange_symbols
+from catalyst.exchange.exchange_utils import get_exchange_bundles_folder
 
 EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
 API_URL = 'http://data.enigma.co/api/v1'
@@ -80,9 +78,8 @@ def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     if not os.path.isdir(path):
         url = 'https://s3.amazonaws.com/enigmaco/catalyst-bundles/' \
               'exchange-{exchange}/{name}.tar.gz'.format(
-            exchange=exchange_name,
-            name=name
-        )
+                exchange=exchange_name,
+                name=name)
 
         bytes = download_without_progress(url)
         with tarfile.open('r', fileobj=bytes) as tar:
@@ -193,8 +190,10 @@ def get_period_label(dt, data_frequency):
     str
 
     """
-    return '{}-{:02d}'.format(dt.year, dt.month) if data_frequency == 'minute' \
-        else '{}'.format(dt.year)
+    if data_frequency == 'minute':
+        return '{}-{:02d}'.format(dt.year, dt.month)
+    else:
+        return '{}'.format(dt.year)
 
 
 def get_month_start_end(dt, first_day=None, last_day=None):
@@ -315,7 +314,7 @@ def range_in_bundle(asset, start_dt, end_dt, reader):
             if np.isnan(close):
                 has_data = False
 
-        except Exception as e:
+        except Exception:
             has_data = False
 
     return has_data
diff --git a/catalyst/exchange/exchange.py b/catalyst/exchange/exchange.py
index 642d3612..3f2ac85a 100644
--- a/catalyst/exchange/exchange.py
+++ b/catalyst/exchange/exchange.py
@@ -5,7 +5,6 @@ from time import sleep
 
 import numpy as np
 import pandas as pd
-from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
 from catalyst.constants import LOG_LEVEL
@@ -242,9 +241,7 @@ class Exchange:
                 asset = a
 
         if asset is None:
-            supported_symbols = sorted([
-                asset.symbol for asset in self.assets
-            ])
+            supported_symbols = sorted([a.symbol for a in self.assets])
 
             raise SymbolNotFoundOnExchange(
                 symbol=symbol,
@@ -551,7 +548,7 @@ class Exchange:
                 start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
                 trailing_dt = \
                     series[asset].index[-1] + get_delta(1, data_frequency) \
-                        if asset in series else start_dt
+                    if asset in series else start_dt
 
                 # The get_history method supports multiple asset
                 # Use the original frequency to let each api optimize
@@ -693,7 +690,8 @@ class Exchange:
         display_price = style.get_limit_price(is_buy)
 
         log.debug(
-            'issuing {side} order of {amount} {symbol} for {type}: {price}'.format(
+            'issuing {side} order of {amount} {symbol} for {type}:'
+            ' {price}'.format(
                 side='buy' if is_buy else 'sell',
                 amount=amount,
                 symbol=asset.symbol,
diff --git a/catalyst/exchange/exchange_algorithm.py b/catalyst/exchange/exchange_algorithm.py
index 147d87e4..adf76d5d 100644
--- a/catalyst/exchange/exchange_algorithm.py
+++ b/catalyst/exchange/exchange_algorithm.py
@@ -29,19 +29,22 @@ from catalyst.exchange.exchange_blotter import ExchangeBlotter
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     ExchangePortfolioDataError,
-    OrderTypeNotSupported)
+    OrderTypeNotSupported, )
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
-from catalyst.exchange.exchange_utils import save_algo_object, get_algo_object, \
-    get_algo_folder, get_algo_df, \
-    save_algo_df, group_assets_by_exchange
+from catalyst.exchange.exchange_utils import (
+    save_algo_object,
+    get_algo_object,
+    get_algo_folder,
+    get_algo_df,
+    save_algo_df,
+    group_assets_by_exchange, )
 from catalyst.exchange.live_graph_clock import LiveGraphClock
 from catalyst.exchange.simple_clock import SimpleClock
 from catalyst.exchange.stats_utils import get_pretty_stats, stats_to_s3
 from catalyst.finance.execution import MarketOrder
 from catalyst.finance.performance.period import calc_period_stats
 from catalyst.gens.tradesimulation import AlgorithmSimulator
-from catalyst.utils.api_support import (
-    api_method)
+from catalyst.utils.api_support import api_method
 from catalyst.utils.input_validation import error_keywords, ensure_upper_case
 from catalyst.utils.math_utils import round_nearest
 from catalyst.utils.preprocess import preprocess
@@ -394,7 +397,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
 
         # This method is taken from TradingAlgorithm.
         # The clock has been replaced to use RealtimeClock
-        # TODO: should we apply a time skew? not sure to understand the utility.
+        # TODO: should we apply time skew? not sure to understand the utility.
 
         log.debug('creating clock')
         if self.live_graph:
@@ -616,7 +619,8 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
 
             # print_df = pd.DataFrame(list(self.frame_stats))
             log.info(
-                'statistics for the last {stats_minutes} minutes:\n{stats}'.format(
+                'statistics for the last {stats_minutes} minutes:\n'
+                '{stats}'.format(
                     stats_minutes=self.stats_minutes,
                     stats=get_pretty_stats(
                         stats=self.frame_stats,
diff --git a/catalyst/exchange/exchange_bundle.py b/catalyst/exchange/exchange_bundle.py
index 59e5ce90..46c0c9e7 100644
--- a/catalyst/exchange/exchange_bundle.py
+++ b/catalyst/exchange/exchange_bundle.py
@@ -1,6 +1,6 @@
 import os
 import shutil
-from datetime import datetime, timedelta
+from datetime import timedelta
 from functools import partial
 from itertools import chain
 from operator import is_not
@@ -233,11 +233,13 @@ class ExchangeBundle:
 
                 problem = '{name} ({start_dt} to {end_dt}) has empty ' \
                           'periods: {dates}'.format(
-                    name=asset.symbol,
-                    start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
-                    end_dt=end_dt.strftime(DATE_TIME_FORMAT),
-                    dates=[date.strftime(DATE_TIME_FORMAT) for date in dates]
-                )
+                            name=asset.symbol,
+                            start_dt=asset.start_date.strftime(
+                                DATE_TIME_FORMAT),
+                            end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                            dates=[date.strftime(
+                                DATE_TIME_FORMAT) for date in dates])
+
                 if empty_rows_behavior == 'warn':
                     log.warn(problem)
 
@@ -245,8 +247,7 @@ class ExchangeBundle:
                     raise EmptyValuesInBundleError(
                         name=asset.symbol,
                         end_minute=end_dt,
-                        dates=dates
-                    )
+                        dates=dates, )
 
                 else:
                     ohlcv_df.dropna(inplace=True)
@@ -286,13 +287,12 @@ class ExchangeBundle:
 
             problem = '{name} ({start_dt} to {end_dt}) has {threshold} ' \
                       'identical close values on: {dates}'.format(
-                name=asset.symbol,
-                start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
-                end_dt=end_dt.strftime(DATE_TIME_FORMAT),
-                threshold=threshold,
-                dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
-                       for date in dates]
-            )
+                        name=asset.symbol,
+                        start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                        end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                        threshold=threshold,
+                        dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
+                               for date in dates])
 
             problems.append(problem)
 
@@ -630,8 +630,8 @@ class ExchangeBundle:
                     show_progress,
                     label='Ingesting {frequency} price data on '
                           '{exchange}'.format(
-                        exchange=self.exchange_name,
-                        frequency=data_frequency,
+                            exchange=self.exchange_name,
+                            frequency=data_frequency,
                     )) as it:
                 for chunk in it:
                     problems += self.ingest_ctable(
diff --git a/catalyst/exchange/exchange_errors.py b/catalyst/exchange/exchange_errors.py
index 7bf66f6c..4f1acd9c 100644
--- a/catalyst/exchange/exchange_errors.py
+++ b/catalyst/exchange/exchange_errors.py
@@ -143,7 +143,8 @@ class OrphanOrderError(ZiplineError):
 
 class OrphanOrderReverseError(ZiplineError):
     msg = (
-        'Order {order_id} tracked by algorithm, but not found in exchange {exchange}.'
+        'Order {order_id} tracked by algorithm, but not found in exchange '
+        '{exchange}.'
     ).strip()
 
 
@@ -206,8 +207,9 @@ class EmptyValuesInBundleError(ZiplineError):
 
 class PricingDataBeforeTradingError(ZiplineError):
     msg = ('Pricing data for trading pairs {symbols} on exchange {exchange} '
-           'starts on {first_trading_day}, but you are either trying to trade or '
-           'retrieve pricing data on {dt}. Adjust your dates accordingly.').strip()
+           'starts on {first_trading_day}, but you are either trying to trade '
+           'or retrieve pricing data on {dt}. Adjust your dates accordingly.'
+           ).strip()
 
 
 class PricingDataNotLoadedError(ZiplineError):
@@ -238,9 +240,11 @@ class ApiCandlesError(ZiplineError):
 
 class NoDataAvailableOnExchange(ZiplineError):
     msg = (
-        'Requested data for trading pair {symbol} is not available on exchange {exchange} '
+        'Requested data for trading pair {symbol} is not available on '
+        'exchange {exchange} '
         'in `{data_frequency}` frequency at this time. '
-        'Check `http://enigma.co/catalyst/status` for market coverage.').strip()
+        'Check `http://enigma.co/catalyst/status` for market coverage.'
+        ).strip()
 
 
 class NoValueForField(ZiplineError):
diff --git a/catalyst/exchange/exchange_execution.py b/catalyst/exchange/exchange_execution.py
index fe029e3c..536b526a 100644
--- a/catalyst/exchange/exchange_execution.py
+++ b/catalyst/exchange/exchange_execution.py
@@ -1,4 +1,4 @@
-from catalyst.finance.execution import LimitOrder, StopOrder, StopLimitOrder, MarketOrder
+from catalyst.finance.execution import LimitOrder, StopOrder, StopLimitOrder
 
 
 class ExchangeLimitOrder(LimitOrder):
diff --git a/catalyst/exchange/exchange_portfolio.py b/catalyst/exchange/exchange_portfolio.py
index 1df8f9b2..71ed9a35 100644
--- a/catalyst/exchange/exchange_portfolio.py
+++ b/catalyst/exchange/exchange_portfolio.py
@@ -90,7 +90,8 @@ class ExchangePortfolio(Portfolio):
 
         if order_position is None:
             raise ValueError(
-                'Trying to execute order for a position not held: %s' % order.id
+                'Trying to execute order for a position not held:'
+                ' {}'.format(order.id)
             )
 
         self.capital_used += order.amount * transaction.price
diff --git a/catalyst/exchange/exchange_utils.py b/catalyst/exchange/exchange_utils.py
index af7f2fee..19938d3e 100644
--- a/catalyst/exchange/exchange_utils.py
+++ b/catalyst/exchange/exchange_utils.py
@@ -134,7 +134,7 @@ def get_exchange_symbols(exchange_name, is_local=False, environ=None):
 
     if not is_local and (not os.path.isfile(filename) or pd.Timedelta(
                 pd.Timestamp('now', tz='UTC') - last_modified_time(
-                filename)).days > 1):
+                    filename)).days > 1):
         download_exchange_symbols(exchange_name, environ)
 
     if os.path.isfile(filename):
@@ -143,7 +143,7 @@ def get_exchange_symbols(exchange_name, is_local=False, environ=None):
                 data = json.load(data_file, object_hook=symbols_parser)
                 return data
 
-            except ValueError as e:
+            except ValueError:
                 return dict()
     else:
         raise ExchangeSymbolsNotFound(
@@ -296,7 +296,7 @@ def get_algo_object(algo_name, key, environ=None, rel_path=None):
         try:
             with open(filename, 'rb') as handle:
                 return pickle.load(handle)
-        except Exception as e:
+        except Exception:
             return None
     else:
         return None
diff --git a/catalyst/exchange/poloniex/poloniex.py b/catalyst/exchange/poloniex/poloniex.py
index 1c2028ee..b29b0e92 100644
--- a/catalyst/exchange/poloniex/poloniex.py
+++ b/catalyst/exchange/poloniex/poloniex.py
@@ -1,5 +1,4 @@
 import json
-import json
 import time
 from collections import defaultdict
 
@@ -18,7 +17,9 @@ from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     InvalidHistoryFrequencyError,
-    InvalidOrderStyle, OrphanOrderReverseError)
+    InvalidOrderStyle,
+    OrphanOrderError,
+    OrphanOrderReverseError)
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder, \
     ExchangeStopLimitOrder
 from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
@@ -87,7 +88,6 @@ class Poloniex(Exchange):
             # filled = -filled
 
         price = float(order_status['rate'])
-        order_type = order_status['type']
 
         stop_price = None
         limit_price = None
@@ -101,11 +101,11 @@ class Poloniex(Exchange):
         # executed_price = float(order_status['avg_execution_price'])
         executed_price = price
 
-        # TODO: bitfinex does not specify comission. I could calculate it but not sure if it's worth it.
+        # TODO: Set Poloniex comission
         commission = None
 
-        # date = pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
-        # date = pytz.utc.localize(date)
+        # date=pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
+        # date=pytz.utc.localize(date)
         date = None
 
         order = Order(
@@ -292,8 +292,8 @@ class Poloniex(Exchange):
         """
         exchange_symbol = self.get_symbol(asset)
 
-        if isinstance(style, ExchangeLimitOrder) or isinstance(style,
-                                                               ExchangeStopLimitOrder):
+        if(isinstance(style, ExchangeLimitOrder)
+           or isinstance(style, ExchangeStopLimitOrder)):
             if isinstance(style, ExchangeStopLimitOrder):
                 log.warn('{} will ignore the stop price'.format(self.name))
 
@@ -350,8 +350,8 @@ class Poloniex(Exchange):
         return self.portfolio.open_orders
 
         """
-            TODO: Why going to the exchange if we already have this info locally?
-                  And why creating all these Orders if we later discard them?
+        TODO: Why going to the exchange if we already have this info locally?
+              And why creating all these Orders if we later discard them?
         """
 
         try:
@@ -365,7 +365,7 @@ class Poloniex(Exchange):
         if 'error' in response:
             raise ExchangeRequestError(
                 error='Unable to retrieve open orders: {}'.format(
-                    order_statuses['message'])
+                    response['message'])
             )
 
         print(self.portfolio.open_orders)
@@ -373,8 +373,8 @@ class Poloniex(Exchange):
         # TODO: Need to handle openOrders for 'all'
         orders = list()
         for order_status in response:
-            order, executed_price = self._create_order(
-                order_status)  # will Throw error b/c Polo doesn't track order['symbol']
+            # will Throw error b/c Polo doesn't track order['symbol']
+            order, executed_price = self._create_order(order_status)
             if asset is None or asset == order.sid:
                 orders.append(order)
 
@@ -437,7 +437,8 @@ class Poloniex(Exchange):
 
         if 'error' in response:
             log.info(
-                'Unable to cancel order {order_id} on exchange {exchange} {error}.'.format(
+                'Unable to cancel order {order_id} on exchange {exchange} '
+                '{error}.'.format(
                     order_id=order.id,
                     exchange=self.name,
                     error=response['error']
@@ -512,17 +513,17 @@ class Poloniex(Exchange):
             else:
                 try:
                     start_date = cached_symbols[exchange_symbol]['start_date']
-                except KeyError as e:
+                except KeyError:
                     start_date = time.strftime('%Y-%m-%d')
 
             try:
                 end_daily = cached_symbols[exchange_symbol]['end_daily']
-            except KeyError as e:
+            except KeyError:
                 end_daily = 'N/A'
 
             try:
                 end_minute = cached_symbols[exchange_symbol]['end_minute']
-            except KeyError as e:
+            except KeyError:
                 end_minute = 'N/A'
 
             symbol_map[exchange_symbol] = dict(
@@ -593,19 +594,21 @@ class Poloniex(Exchange):
                 else:
                     for tx in response:
                         """
-                            We maintain a list of dictionaries of transactions that correspond to
-                            partially filled orders, indexed by order_id. Every time we query 
-                            executed transactions from the exchange, we check if we had that 
-                            transaction for that order already. If not, we process it.
+                        We maintain a list of dictionaries of transactions that
+                        correspond to partially filled orders, indexed by
+                        order_id. Every time we query executed transactions
+                        from the exchange, we check if we had that transaction
+                        for that order already. If not, we process it.
 
-                            When an order if fully filled, we flush the dict of transactions  
-                            associated with that order.
+                        When an order if fully filled, we flush the dict of
+                        transactions associated with that order.
                         """
                         if (not filter(
                                 lambda item: item['order_id'] == tx['tradeID'],
                                 self.transactions[order_id])):
                             log.debug(
-                                'Got new transaction for order {}: amount {}, price {}'.format(
+                                'Got new transaction for order {}: amount {}, '
+                                'price {}'.format(
                                     order_id, tx['amount'], tx['rate']))
                             tx['amount'] = float(tx['amount'])
                             if (tx['type'] == 'sell'):
@@ -616,7 +619,7 @@ class Poloniex(Exchange):
                                 dt=pd.to_datetime(tx['date'], utc=True),
                                 price=float(tx['rate']),
                                 order_id=tx['tradeID'],
-                                # it's a misnomer, but keeping it for compatibility
+                                # it's a misnomer, but keep for compatibility
                                 commission=float(tx['fee'])
                             )
                             self.transactions[order_id].append(transaction)
@@ -626,7 +629,8 @@ class Poloniex(Exchange):
                     if (not order_open):
                         """
                             Since transactions have been executed individually
-                            the only thing left to do is remove them from list of open_orders
+                            the only thing left to do is remove them from list
+                            of open_orders
                         """
                         del self.portfolio.open_orders[order_id]
                         del self.transactions[order_id]
diff --git a/catalyst/exchange/poloniex/poloniex_api.py b/catalyst/exchange/poloniex/poloniex_api.py
index ce0831bc..6f339192 100644
--- a/catalyst/exchange/poloniex/poloniex_api.py
+++ b/catalyst/exchange/poloniex/poloniex_api.py
@@ -108,7 +108,7 @@ class Poloniex_api(object):
             headers=headers,
         )
         resource = urlopen(req, context=ssl._create_unverified_context())
-        content =  resource.read().decode('utf-8')
+        content = resource.read().decode('utf-8')
         return json.loads(content)
 
     def returnticker(self):
@@ -161,10 +161,6 @@ class Poloniex_api(object):
     def returnopenorders(self, market):
         return self.query('returnOpenOrders', {'currencyPair': market})
 
-    def returntradehistory(self, market):
-        # TODO: optional start and/or end and limit
-        return self.query('returnTradeHistory', {'currencyPair': market})
-
     def returnordertrades(self, ordernumber):
         return self.query('returnOrderTrades', {'orderNumber': ordernumber})
 
@@ -177,7 +173,7 @@ class Poloniex_api(object):
         elif (immediateorcancel):
             return self.query('buy', {'currencyPair': market, 'rate': rate,
                                       'amount': amount,
-                                      'immediateOrCancel': immediateorcancel, })
+                                      'immediateOrCancel': immediateorcancel})
         elif (postonly):
             return self.query('buy', {'currencyPair': market, 'rate': rate,
                                       'amount': amount,
@@ -195,7 +191,7 @@ class Poloniex_api(object):
         elif (immediateorcancel):
             return self.query('sell', {'currencyPair': market, 'rate': rate,
                                        'amount': amount,
-                                       'immediateOrCancel': immediateorcancel, })
+                                       'immediateOrCancel': immediateorcancel})
         elif (postonly):
             return self.query('sell', {'currencyPair': market, 'rate': rate,
                                        'amount': amount,
diff --git a/catalyst/exchange/simple_clock.py b/catalyst/exchange/simple_clock.py
index f79a5ab7..cede9429 100644
--- a/catalyst/exchange/simple_clock.py
+++ b/catalyst/exchange/simple_clock.py
@@ -31,7 +31,8 @@ class SimpleClock(object):
     This class is a drop-in replacement for
     :class:`zipline.gens.sim_engine.MinuteSimulationClock`.
 
-    This is a stripped down version because crypto exchanges run around the clock.
+    This is a stripped down version because crypto exchanges run
+    around the clock.
 
     The :param:`time_skew` parameter represents the time difference between
     the Broker and the live trading machine's clock.
diff --git a/catalyst/finance/execution.py b/catalyst/finance/execution.py
index 8fec0af6..f4fe95fe 100644
--- a/catalyst/finance/execution.py
+++ b/catalyst/finance/execution.py
@@ -15,13 +15,8 @@
 
 import abc
 
-from sys import float_info
-
-from six import with_metaclass
-
-import catalyst.utils.math_utils as zp_math
-
 from numpy import isfinite
+from six import with_metaclass
 
 from catalyst.errors import BadOrderParameters
 
diff --git a/catalyst/finance/risk/period.py b/catalyst/finance/risk/period.py
index 283bd3c5..987b8d72 100644
--- a/catalyst/finance/risk/period.py
+++ b/catalyst/finance/risk/period.py
@@ -154,8 +154,8 @@ class RiskMetricsPeriod(object):
             self.algorithm_returns.values,
             self.benchmark_returns.values,
         )
-        self.excess_return = self.algorithm_period_returns - \
-                             self.treasury_period_return
+        self.excess_return = self.algorithm_period_returns \
+            - self.treasury_period_return
         self.max_drawdown = max_drawdown(self.algorithm_returns.values)
         self.max_leverage = self.calculate_max_leverage()
 
diff --git a/catalyst/finance/risk/risk.py b/catalyst/finance/risk/risk.py
index e447617e..07c29030 100644
--- a/catalyst/finance/risk/risk.py
+++ b/catalyst/finance/risk/risk.py
@@ -160,7 +160,8 @@ def choose_treasury(select_treasury, treasury_curves, start_session,
                 )
                 break
 
-        if search_day and trading_calendar.name != 'OPEN':          # Supress warning for 'OPEN' calendar
+        # Supress warning for 'OPEN' calendar
+        if search_day and trading_calendar.name != 'OPEN':
             if (search_dist is None or search_dist > 1) and \
                     search_days[0] <= end_session <= search_days[-1]:
                 message = "No rate within 1 trading day of end date = \
diff --git a/catalyst/finance/slippage.py b/catalyst/finance/slippage.py
index 36de4ec7..98784f50 100644
--- a/catalyst/finance/slippage.py
+++ b/catalyst/finance/slippage.py
@@ -41,7 +41,6 @@ DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT = 0.025
 DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT = 0.05
 
 
-
 class LiquidityExceeded(Exception):
     pass
 
diff --git a/catalyst/pipeline/factors/equity/__init__.py b/catalyst/pipeline/factors/equity/__init__.py
index 663d025f..e8d29cf0 100644
--- a/catalyst/pipeline/factors/equity/__init__.py
+++ b/catalyst/pipeline/factors/equity/__init__.py
@@ -1,9 +1,6 @@
 from .statistical import (
-    RollingPearson,
-    RollingLinearRegression,
     RollingLinearRegressionOfReturns,
     RollingPearsonOfReturns,
-    RollingSpearman,
     RollingSpearmanOfReturns,
 )
 from .technical import (
diff --git a/catalyst/pipeline/loaders/equity_pricing_loader.py b/catalyst/pipeline/loaders/equity_pricing_loader.py
index c7dbfd41..648bd5f5 100644
--- a/catalyst/pipeline/loaders/equity_pricing_loader.py
+++ b/catalyst/pipeline/loaders/equity_pricing_loader.py
@@ -38,9 +38,11 @@ class USEquityPricingLoader(PipelineLoader):
 
     def __init__(self, bundle, data_frequency, dataset):
 
-        if data_frequency == 'daily':
-            reader = bundle.daily_bar_reader
-        elif daily_bar_reader == 'minute':
+        # TODO: This is currently broken, No Pipeline support for Catalyst
+        # if data_frequency == 'daily':
+        #    reader = bundle.daily_bar_reader
+        # elif daily_bar_reader == 'minute':
+        if data_frequency == 'minute':
             reader = bundle.minute_bar_reader
         else:
             raise ValueError(
@@ -51,7 +53,9 @@ class USEquityPricingLoader(PipelineLoader):
 
         if data_frequency == 'daily':
             all_sessions = cal.all_sessions
-        elif daily_bar_reader == 'minute':
+        # TODO: this cannot be right, but no pipeline support at the moment
+        # elif daily_bar_reader == 'minute':
+        elif data_frequency == 'minute':
             reader = bundle.minute_bar_reader
             all_sessions = cal.all_minutes
 
diff --git a/catalyst/pipeline/loaders/events.py b/catalyst/pipeline/loaders/events.py
index ece7b25e..512c772d 100644
--- a/catalyst/pipeline/loaders/events.py
+++ b/catalyst/pipeline/loaders/events.py
@@ -231,7 +231,7 @@ class EventsLoader(PipelineLoader):
             self.load_next_events(n, dates, sids, mask),
             self.load_previous_events(p, dates, sids, mask),
         )
-    
+
     @property
     def columns(self):
         return self._columns
diff --git a/catalyst/pipeline/loaders/frame.py b/catalyst/pipeline/loaders/frame.py
index 827c12be..c663176e 100644
--- a/catalyst/pipeline/loaders/frame.py
+++ b/catalyst/pipeline/loaders/frame.py
@@ -180,4 +180,3 @@ class DataFrameLoader(PipelineLoader):
     @property
     def columns(self):
         return self._columns
-
diff --git a/catalyst/pipeline/loaders/synthetic.py b/catalyst/pipeline/loaders/synthetic.py
index 1f88396a..fef8be24 100644
--- a/catalyst/pipeline/loaders/synthetic.py
+++ b/catalyst/pipeline/loaders/synthetic.py
@@ -163,7 +163,7 @@ class SeededRandomLoader(PrecomputedLoader):
             bool_dtype: self._bool_values,
             object_dtype: self._object_values,
         }[dtype](shape)
-    
+
     @property
     def columns(self):
         return self._columns
diff --git a/catalyst/support/issue_44.py b/catalyst/support/issue_44.py
deleted file mode 100644
index b6d1277a..00000000
--- a/catalyst/support/issue_44.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import pandas as pd
-from catalyst import run_algorithm
-from catalyst.exchange.exchange_utils import get_exchange_symbols
-
-from catalyst.api import (
-    symbols,
-)
-
-
-def initialize(context):
-    context.i = -1
-    context.base_currency = 'btc'
-
-
-def handle_data(context, data):
-    lookback = 60 * 24 * 7  # (minutes, hours, days)
-    context.i += 1
-    if context.i < lookback:
-        return
-
-    today = context.blotter.current_dt.strftime('%Y-%m-%d %H:%M:%S')
-
-    try:
-        # update universe everyday
-        new_day = 60 * 24
-        if not context.i % new_day:
-            context.universe = universe(context, today)
-
-        # get data every 30 minutes
-        minutes = 30
-        if not context.i % minutes and context.universe:
-            for coin in context.coins:
-                pair = str(coin.symbol)
-
-                # ohlcv data
-                open = data.history(coin, 'open', lookback,
-                                    '1m').ffill().bfill().resample(
-                    '30T').first()
-                high = data.history(coin, 'high', lookback,
-                                    '1m').ffill().bfill().resample('30T').max()
-                low = data.history(coin, 'low', lookback,
-                                   '1m').ffill().bfill().resample('30T').min()
-                close = data.history(coin, 'price', lookback,
-                                     '1m').ffill().bfill().resample(
-                    '30T').last()
-                volume = data.history(coin, 'volume', lookback,
-                                      '1m').ffill().bfill().resample(
-                    '30T').sum()
-
-                print(today, pair, close[-1])
-
-    except Exception as e:
-        print(e)
-
-
-def analyze(context=None, results=None):
-    pass
-
-
-def universe(context, today):
-    json_symbols = get_exchange_symbols('poloniex')
-    poloniex_universe_df = pd.DataFrame.from_dict(
-        json_symbols).transpose().astype(str)
-    poloniex_universe_df['base_currency'] = poloniex_universe_df.apply(
-        lambda row: row.symbol.split('_')[1],
-        axis=1)
-    poloniex_universe_df['market_currency'] = poloniex_universe_df.apply(
-        lambda row: row.symbol.split('_')[0],
-        axis=1)
-    poloniex_universe_df = poloniex_universe_df[
-        poloniex_universe_df['base_currency'] == context.base_currency]
-    poloniex_universe_df = poloniex_universe_df[
-        poloniex_universe_df.symbol != 'gas_btc']
-
-    # Markets currently not working on Catalyst 0.3.1
-    # 2017-01-01
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'bcn_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'burst_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'dgb_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'doge_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'emc2_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'pink_btc']
-    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'sc_btc']
-    print(poloniex_universe_df.head())
-
-    date = str(today).split(' ')[0]
-
-    poloniex_universe_df = poloniex_universe_df[
-        poloniex_universe_df.start_date < date]
-    context.coins = symbols(*poloniex_universe_df.symbol)
-    print(len(poloniex_universe_df))
-    return poloniex_universe_df.symbol.tolist()
-
-
-if __name__ == '__main__':
-    start_date = pd.to_datetime('2017-01-01', utc=True)
-    end_date = pd.to_datetime('2017-10-15', utc=True)
-
-    performance = run_algorithm(start=start_date, end=end_date,
-                                capital_base=10000.0,
-                                initialize=initialize,
-                                handle_data=handle_data,
-                                analyze=analyze,
-                                exchange_name='poloniex',
-                                data_frequency='minute',
-                                base_currency='btc',
-                                live=False,
-                                live_graph=False,
-                                algo_namespace='test')
diff --git a/catalyst/support/issue_47.py b/catalyst/support/issue_47.py
deleted file mode 100644
index 3ebc0bc7..00000000
--- a/catalyst/support/issue_47.py
+++ /dev/null
@@ -1,139 +0,0 @@
-"""
-Requires Catalyst version 0.3.0 or above
-Tested on Catalyst version 0.3.3
-
-These example aims to provide and easy way for users to learn how to collect data from the different exchanges.
-You simply need to specify the exchange and the market that you want to focus on.
-You will all see how to create a universe and filter it base on the exchange and the market you desire.
-
-The example prints out the closing price of all the pairs for a given market-exchange every 30 minutes.
-The example also contains the ohlcv minute data for the past seven days which could be used to create indicators
-Use this as the backbone to create your own trading strategies.
-
-Variables lookback date and date are used to ensure data for a coin existed on the lookback period specified.
-"""
-
-import numpy as np
-import pandas as pd
-from datetime import timedelta
-from catalyst import run_algorithm
-from catalyst.exchange.exchange_utils import get_exchange_symbols
-
-from catalyst.api import (
-    symbols,
-)
-
-
-def initialize(context):
-    context.i = -1  # counts the minutes
-    context.exchange = 'poloniex'  # must match the exchange specified in run_algorithm
-    context.base_currency = 'btc'  # must match the base currency specified in run_algorithm
-
-
-def handle_data(context, data):
-    lookback = 60 * 24 * 7  # (minutes, hours, days) of how far to lookback in the data history
-    context.i += 1
-
-    # current date formatted into a string
-    today = context.blotter.current_dt
-    date, time = today.strftime('%Y-%m-%d %H:%M:%S').split(' ')
-    lookback_date = today - timedelta(days=(
-    lookback / (60 * 24)))  # subtract the amount of days specified in lookback
-    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[
-        0]  # get only the date as a string
-
-    # update universe everyday
-    new_day = 60 * 24
-    if not context.i % new_day:
-        context.universe = universe(context, lookback_date, date)
-
-    # get data every 30 minutes
-    minutes = 30
-    if not context.i % minutes and context.universe:
-        # we iterate for every pair in the current universe
-        for coin in context.coins:
-            pair = str(coin.symbol)
-
-            # 30 minute interval ohlcv data (the standard data required for candlestick or indicators/signals)
-            # 30T means 30 minutes re-sampling of one minute data. change to your desire time interval.
-            opened = fill(data.history(coin, 'open', bar_count=lookback,
-                                       frequency='30T')).values
-            high = fill(data.history(coin, 'high', bar_count=lookback,
-                                     frequency='30T')).values
-            low = fill(data.history(coin, 'low', bar_count=lookback,
-                                    frequency='30T')).values
-            close = fill(data.history(coin, 'price', bar_count=lookback,
-                                      frequency='30T')).values
-            volume = fill(data.history(coin, 'volume', bar_count=lookback,
-                                       frequency='30T')).values
-
-            # close[-1] is the equivalent to current price
-            # displays the minute price for each pair every 30 minutes
-            print(
-            today, pair, opened[-1], high[-1], low[-1], close[-1], volume[-1])
-
-            # ----------------------------------------------------------------------------------------------------------
-            # -------------------------------------- Insert Your Strategy Here -----------------------------------------
-            # ----------------------------------------------------------------------------------------------------------
-
-
-def analyze(context=None, results=None):
-    pass
-
-
-# Get the universe for a given exchange and a given base_currency market
-# Example: Poloniex btc Market
-def universe(context, lookback_date, current_date):
-    json_symbols = get_exchange_symbols(
-        context.exchange)  # get all the pairs for the exchange
-    universe_df = pd.DataFrame.from_dict(json_symbols).transpose().astype(
-        str)  # convert into a dataframe
-    universe_df['base_currency'] = universe_df.apply(
-        lambda row: row.symbol.split('_')[1],
-        axis=1)
-    universe_df['market_currency'] = universe_df.apply(
-        lambda row: row.symbol.split('_')[0],
-        axis=1)
-    # Filter all the exchange pairs to only the ones for a give base currency
-    universe_df = universe_df[
-        universe_df['base_currency'] == context.base_currency]
-
-    # Filter all the pairs to ensure that pair existed in the current date range
-    universe_df = universe_df[universe_df.start_date < lookback_date]
-    universe_df = universe_df[universe_df.end_daily >= current_date]
-    context.coins = symbols(
-        *universe_df.symbol)  # convert all the pairs to symbols
-    return universe_df.symbol.tolist()
-
-
-# Replace all NA, NAN or infinite values with its nearest value
-def fill(series):
-    if isinstance(series, pd.Series):
-        return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
-    elif isinstance(series, np.ndarray):
-        return pd.Series(series).replace([np.inf, -np.inf],
-                                         np.nan).ffill().bfill().values
-    else:
-        return series
-
-
-if __name__ == '__main__':
-    start_date = pd.to_datetime('2017-01-08', utc=True)
-    end_date = pd.to_datetime('2017-11-13', utc=True)
-
-    performance = run_algorithm(start=start_date, end=end_date,
-                                capital_base=10000.0,
-                                initialize=initialize,
-                                handle_data=handle_data,
-                                analyze=analyze,
-                                exchange_name='poloniex',
-                                data_frequency='minute',
-                                base_currency='btc',
-                                live=False,
-                                live_graph=False,
-                                algo_namespace='simple_universe')
-
-"""
-Run in Terminal (inside catalyst environment):
-python simple_universe.py
-"""
diff --git a/catalyst/support/issue_55.py b/catalyst/support/issue_55.py
index 195f5019..733a1585 100644
--- a/catalyst/support/issue_55.py
+++ b/catalyst/support/issue_55.py
@@ -1,4 +1,3 @@
-import talib
 import pandas as pd
 
 from catalyst import run_algorithm
diff --git a/catalyst/support/issue_57.py b/catalyst/support/issue_57.py
deleted file mode 100644
index f7bfcd18..00000000
--- a/catalyst/support/issue_57.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import talib
-import pandas as pd
-
-from catalyst import run_algorithm
-from catalyst.api import symbol
-
-
-def initialize(context):
-    print('initializing')
-    context.asset = symbol('btc_usdt')
-
-
-def handle_data(context, data):
-    print('handling bar: {}'.format(data.current_dt))
-
-    price = data.current(context.asset, 'close')
-    print('got price {price}'.format(price=price))
-
-    try:
-        prices = data.history(
-            context.asset,
-            fields='close',
-            bar_count=60,
-            frequency='1D'
-        )
-        print('got {} price entries\n'.format(len(prices), prices))
-    except Exception as e:
-        print(e)
-
-
-run_algorithm(
-    capital_base=1,
-    start=pd.to_datetime('2016-2-11', utc=True),
-    end=pd.to_datetime('2017-8-31', utc=True),
-    data_frequency='daily',
-    initialize=initialize,
-    handle_data=handle_data,
-    analyze=None,
-    exchange_name='bittrex',
-    algo_namespace='issue_57',
-    base_currency='btc'
-<<<<<<< HEAD
-)
-=======
-)
->>>>>>> develop
diff --git a/catalyst/support/issue_74.py b/catalyst/support/issue_74.py
deleted file mode 100644
index ad6d6fee..00000000
--- a/catalyst/support/issue_74.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from __future__ import division
-import os
-import pytz
-import numpy as np
-import pandas as pd
-from scipy.optimize import minimize
-import matplotlib.pyplot as plt
-from datetime import datetime
-
-from catalyst.api import record, symbol, symbols, order_target_percent
-from catalyst.utils.run_algo import run_algorithm
-
-np.set_printoptions(threshold='nan', suppress=True)
-
-
-def initialize(context):
-    # Portfolio assets list
-    context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
-                             'xmr_usdt')
-    context.nassets = len(context.assets)
-    # Set the time window that will be used to compute expected return
-    # and asset correlations
-    context.window = 180
-    # Set the number of days between each portfolio rebalancing
-    context.rebalance_period = 30
-    context.i = 0
-
-
-def handle_data(context, data):
-    # Only rebalance at the beggining of the algorithm execution and
-    # every multiple of the rebalance period
-    if context.i == 0 or context.i % context.rebalance_period == 0:
-        n = context.window
-        prices = data.history(context.assets, fields='price',
-                           bar_count=n + 1, frequency='daily')
-        pr = np.asmatrix(prices)
-        t_prices = prices.iloc[1:n + 1]
-        t_val = t_prices.values
-        tminus_prices = prices.iloc[0:n]
-        tminus_val = tminus_prices.values
-        # Compute daily returns (r)
-        r = np.asmatrix(t_val / tminus_val - 1)
-        # Compute the expected returns of each asset with the average
-        # daily return for the selected time window
-        m = np.asmatrix(np.mean(r, axis=0))
-        # ###
-        stds = np.std(r, axis=0)
-        # Compute excess returns matrix (xr)
-        xr = r - m
-        # Matrix algebra to get variance-covariance matrix
-        cov_m = np.dot(np.transpose(xr), xr) / n
-        # Compute asset correlation matrix (informative only)
-        corr_m = cov_m / np.dot(np.transpose(stds), stds)
-
-        # Define portfolio optimization parameters
-        n_portfolios = 50000
-        results_array = np.zeros((3 + context.nassets, n_portfolios))
-        for p in xrange(n_portfolios):
-            weights = np.random.random(context.nassets)
-            weights /= np.sum(weights)
-            w = np.asmatrix(weights)
-            p_r = np.sum(np.dot(w, np.transpose(m))) * 365
-            p_std = np.sqrt(
-                np.dot(np.dot(w, cov_m), np.transpose(w))) * np.sqrt(365)
-
-            # store results in results array
-            results_array[0, p] = p_r
-            results_array[1, p] = p_std
-            # store Sharpe Ratio (return / volatility) - risk free rate element
-            # excluded for simplicity
-            results_array[2, p] = results_array[0, p] / results_array[1, p]
-            i = 0
-            for iw in weights:
-                results_array[3 + i, p] = weights[i]
-                i += 1
-
-        # convert results array to Pandas DataFrame
-        results_frame = pd.DataFrame(np.transpose(results_array),
-                                     columns=['r', 'stdev',
-                                              'sharpe'] + context.assets)
-        # locate position of portfolio with highest Sharpe Ratio
-        max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
-        # locate positon of portfolio with minimum standard deviation
-        min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
-
-        # order optimal weights for each asset
-        for asset in context.assets:
-            if data.can_trade(asset):
-                order_target_percent(asset, max_sharpe_port[asset])
-
-        # create scatter plot coloured by Sharpe Ratio
-        plt.scatter(results_frame.stdev, results_frame.r,
-                    c=results_frame.sharpe, cmap='RdYlGn')
-        plt.xlabel('Volatility')
-        plt.ylabel('Returns')
-        plt.colorbar()
-        # plot red star to highlight position of portfolio with highest Sharpe Ratio
-        plt.scatter(max_sharpe_port[1], max_sharpe_port[0], marker='o',
-                    color='b', s=200)
-        # plot green star to highlight position of minimum variance portfolio
-        plt.show()
-        print(max_sharpe_port)
-        record(pr=pr, r=r, m=m, stds=stds, max_sharpe_port=max_sharpe_port,
-               corr_m=corr_m)
-    context.i += 1
-
-
-def analyze(context=None, results=None):
-    # Form DataFrame with selected data
-    data = results[['pr', 'r', 'm', 'stds', 'max_sharpe_port', 'corr_m',
-                    'portfolio_value']]
-
-    # Save results in CSV file
-    filename = os.path.splitext(os.path.basename(__file__))[0]
-    data.to_csv(filename + '.csv')
-
-
-# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
-start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
-end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
-results = run_algorithm(initialize=initialize,
-                        handle_data=handle_data,
-                        analyze=analyze,
-                        start=start,
-                        end=end,
-                        exchange_name='poloniex',
-                        capital_base=100000, )
diff --git a/catalyst/support/rodrigo_1.py b/catalyst/support/rodrigo_1.py
deleted file mode 100644
index ec32c617..00000000
--- a/catalyst/support/rodrigo_1.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import pandas as pd
-from logbook import Logger, DEBUG
-
-from catalyst import run_algorithm
-from catalyst.api import (schedule_function, order_target_percent, symbol,
-                          date_rules, get_open_orders, cancel_order, record,
-                          set_commission, set_slippage)
-
-log = Logger('rodrigo_1', level=DEBUG)
-"""
-The initialize function sets any data or variables that 
-you'll use in your algorithm. 
-It's only called once at the beginning of your algorithm.
-"""
-
-
-def initialize(context):
-    # Select asset of interest
-    context.asset = symbol('BTC_USD')
-
-    # set_commission(TradingPairFeeSchedule(maker_fee=0.5, taker_fee=0.5))
-    # set_slippage(TradingPairFixedSlippage(spread=0.5))
-    # Set up a rebalance method to run every day
-    schedule_function(rebalance, date_rule=date_rules.every_day())
-
-
-"""
-Rebalance function scheduled to run once per day.
-"""
-
-
-def rebalance(context, data):
-    # To make market decisions, we're calculating the token's
-    # moving average for the last 5 days.
-
-    # We get the price history for the last 5 days.
-    price_history = data.history(context.asset, fields='price', bar_count=5,
-                                 frequency='1d')
-
-    # Then we take an average of those 5 days.
-    average_price = price_history.mean()
-
-    # We also get the coin's current price.
-    price = data.current(context.asset, 'price')
-
-    # Cancel any outstanding orders
-    orders = get_open_orders(context.asset) or []
-    for order in orders:
-        cancel_order(order)
-
-    # If our coin is currently listed on a major exchange
-    if data.can_trade(context.asset):
-        # If the current price is 1% above the 5-day average price,
-        # we open a long position. If the current price is below the
-        # average price, then we want to close our position to 0 shares.
-        if price > (1.01 * average_price):
-            # Place the buy order (positive means buy, negative means sell)
-            order_target_percent(context.asset, .99)
-            log.info("Buying %s" % (context.asset.symbol))
-        elif price < average_price:
-            # Sell all of our shares by setting the target position to zero
-            order_target_percent(context.asset, 0)
-            log.info("Selling %s" % (context.asset.symbol))
-
-    # Use the record() method to track up to five custom signals.
-    # Record Apple's current price and the average price over the last
-    # five days.
-    cash = context.portfolio.cash
-    leverage = context.account.leverage
-
-    record(price=price, average_price=average_price, cash=cash,
-           leverage=leverage)
-
-
-def analyze(context=None, results=None):
-    import matplotlib.pyplot as plt
-
-    # Plot the portfolio and asset data.
-    ax1 = plt.subplot(511)
-    results[['portfolio_value']].plot(ax=ax1)
-    ax1.set_ylabel('Portfolio Value (USD)')
-
-    ax2 = plt.subplot(512, sharex=ax1)
-    ax2.set_ylabel('{asset} (USD)'.format(asset=context.asset))
-    (results[[
-        'price',
-    ]]).plot(ax=ax2)
-
-    trans = results.ix[[t != [] for t in results.transactions]]
-    buys = trans.ix[
-        [t[0]['amount'] > 0 for t in trans.transactions]
-    ]
-    sells = trans.ix[
-        [t[0]['amount'] < 0 for t in trans.transactions]
-    ]
-
-    ax2.plot(
-        buys.index,
-        results.price[buys.index],
-        '^',
-        markersize=10,
-        color='g',
-    )
-    ax2.plot(
-        sells.index,
-        results.price[sells.index],
-        'v',
-        markersize=10,
-        color='r',
-    )
-
-    ax3 = plt.subplot(513, sharex=ax1)
-    results[['leverage']].plot(ax=ax3)
-    ax3.set_ylabel('Leverage ')
-
-    ax4 = plt.subplot(514, sharex=ax1)
-    results[['cash']].plot(ax=ax4)
-    ax4.set_ylabel('Cash (USD)')
-
-    results[[
-        'algorithm',
-        'benchmark',
-    ]] = results[[
-        'algorithm_period_return',
-        'benchmark_period_return',
-    ]]
-
-    ax5 = plt.subplot(515, sharex=ax1)
-    results[[
-        'algorithm',
-        'benchmark',
-    ]].plot(ax=ax5)
-    ax5.set_ylabel('Percent Change')
-
-    plt.legend(loc=3)
-
-    # Show the plot.
-    plt.gcf().set_size_inches(18, 8)
-    plt.show()
-
-
-run_algorithm(
-    capital_base=100000,
-    start=pd.to_datetime('2017-1-1', utc=True),
-    end=pd.to_datetime('2017-10-22', utc=True),
-    data_frequency='minute',
-    initialize=initialize,
-    handle_data=None,
-    analyze=analyze,
-    exchange_name='bitfinex',
-    algo_namespace='rodrigo_1',
-    base_currency='usd'
-)
diff --git a/catalyst/utils/calendars/exchange_calendar_open.py b/catalyst/utils/calendars/exchange_calendar_open.py
index 54be460f..f7be8e22 100644
--- a/catalyst/utils/calendars/exchange_calendar_open.py
+++ b/catalyst/utils/calendars/exchange_calendar_open.py
@@ -31,4 +31,5 @@ class OpenExchangeCalendar(TradingCalendar):
         return DateOffset(days=1)
 
     def __init__(self, *args, **kwargs):
-        super(OpenExchangeCalendar, self).__init__(start=Timestamp('2015-3-1', tz='UTC'), **kwargs)
+        super(OpenExchangeCalendar, self).__init__(
+            start=Timestamp('2015-3-1', tz='UTC'), **kwargs)
diff --git a/catalyst/utils/cli.py b/catalyst/utils/cli.py
index 51e519e0..59a7300a 100644
--- a/catalyst/utils/cli.py
+++ b/catalyst/utils/cli.py
@@ -9,6 +9,7 @@ DEFAULT_BAR_TEMPLATE = '    [%(bar)s]  %(label)s:  %(info)s'
 DEFAULT_EMPTY_CHAR = ' '
 DEFAULT_FILL_CHAR = '='
 
+
 def item_show_count(total=None):
     def maybe_show_total(index):
         if total is not None:
@@ -17,12 +18,13 @@ def item_show_count(total=None):
 
     def item_show_func(item, _it=iter(count())):
         if item is not None:
-            starting = False
+            # starting = False
             return maybe_show_total(next(_it))
         return 'DONE'
 
     return item_show_func
 
+
 def maybe_show_progress(it,
                         show_progress,
                         empty_char=DEFAULT_EMPTY_CHAR,
diff --git a/catalyst/utils/math_utils.py b/catalyst/utils/math_utils.py
index 7981a52b..8e3e086c 100644
--- a/catalyst/utils/math_utils.py
+++ b/catalyst/utils/math_utils.py
@@ -17,9 +17,11 @@ import math
 
 from numpy import isnan
 
+
 def round_nearest(x, a):
     return round(round(x / a) * a, -int(math.floor(math.log10(a))))
 
+
 def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
     """Check if a and b are equal with some tolerance.
 
diff --git a/catalyst/utils/paths.py b/catalyst/utils/paths.py
index 8ec87c7e..2764b623 100644
--- a/catalyst/utils/paths.py
+++ b/catalyst/utils/paths.py
@@ -126,7 +126,7 @@ def catalyst_root(environ=None):
 
     root = environ.get('ZIPLINE_ROOT', None)
     if root is None:
-        root = os.path.join(expanduser('~'),'.catalyst')
+        root = os.path.join(expanduser('~'), '.catalyst')
 
     return root
 
diff --git a/catalyst/utils/run_algo.py b/catalyst/utils/run_algo.py
index 92a5e335..3c62ea2d 100644
--- a/catalyst/utils/run_algo.py
+++ b/catalyst/utils/run_algo.py
@@ -8,6 +8,7 @@ from time import sleep
 
 import click
 import pandas as pd
+from logbook import Logger
 
 from catalyst.data.bundles import load
 from catalyst.data.data_portal import DataPortal
@@ -30,17 +31,16 @@ from catalyst.utils.factory import create_simulation_parameters
 from catalyst.data.loader import load_crypto_market_data
 import catalyst.utils.paths as pth
 
-from catalyst.exchange.exchange_algorithm import ExchangeTradingAlgorithmLive, \
-    ExchangeTradingAlgorithmBacktest
+from catalyst.exchange.exchange_algorithm import (
+    ExchangeTradingAlgorithmLive,
+    ExchangeTradingAlgorithmBacktest,
+)
 from catalyst.exchange.exchange_data_portal import DataPortalExchangeLive, \
     DataPortalExchangeBacktest
 from catalyst.exchange.asset_finder_exchange import AssetFinderExchange
-from catalyst.exchange.exchange_portfolio import ExchangePortfolio
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError, ExchangeRequestErrorTooManyAttempts,
     BaseCurrencyNotFoundError)
-from catalyst.exchange.exchange_utils import get_algo_object
-from logbook import Logger
 
 from catalyst.constants import LOG_LEVEL
 
@@ -172,7 +172,7 @@ def _run(handle_data,
         asset_db_path=None  # We don't need an asset db, we have exchanges
     )
     env.asset_finder = AssetFinderExchange()
-    choose_loader = None  # TODO: use the DataPortal for in the algorithm class for this
+    choose_loader = None  # TODO: use the DataPortal in the algo class for this
 
     if live:
         start = pd.Timestamp.utcnow()
diff --git a/tests/exchange/test_bcolz.py b/tests/exchange/test_bcolz.py
index 8c76799a..a842bee7 100644
--- a/tests/exchange/test_bcolz.py
+++ b/tests/exchange/test_bcolz.py
@@ -116,7 +116,7 @@ class TestBcolzWriter(object):
 
         df = self.generate_df(exchange_name, freq, start, end)
 
-        print df.index[0],df.index[-1]
+        print(df.index[0], df.index[-1])
 
         writer = BcolzExchangeBarWriter(
             rootdir=self.root_dir,
@@ -140,7 +140,7 @@ class TestBcolzWriter(object):
 
         dx = get_df_from_arrays(arrays, periods)
 
-        assert_equals(df.equals(df), True)
+        assert_equals(df.equals(dx), True)
         pass
 
     def test_bcolz_bitfinex_daily_write_read(self):
diff --git a/tests/exchange/test_bitfinex.py b/tests/exchange/test_bitfinex.py
index 194422f5..1859f38f 100644
--- a/tests/exchange/test_bitfinex.py
+++ b/tests/exchange/test_bitfinex.py
@@ -34,7 +34,7 @@ class TestBitfinex(BaseExchangeTestCase):
 
     def test_open_orders(self):
         log.info('retrieving open orders')
-        orders = self.exchange.get_open_orders()
+        # orders = self.exchange.get_open_orders()
         pass
 
     def test_get_order(self):
@@ -47,18 +47,17 @@ class TestBitfinex(BaseExchangeTestCase):
 
     def test_get_candles(self):
         log.info('retrieving candles')
-        ohlcv_neo = self.exchange.get_candles(
-            freq='1T',
-            assets=self.exchange.get_asset('neo_btc')
-        )
+        # ohlcv_neo = self.exchange.get_candles(
+        #    freq='1T',
+        #    assets=self.exchange.get_asset('neo_btc'))
         pass
 
     def test_tickers(self):
         log.info('retrieving tickers')
-        tickers = self.exchange.tickers([
-            self.exchange.get_asset('eth_btc'),
-            self.exchange.get_asset('etc_btc')
-        ])
+        # tickers = self.exchange.tickers([
+        #     self.exchange.get_asset('eth_btc'),
+        #     self.exchange.get_asset('etc_btc')
+        # ])
         pass
 
     def test_get_account(self):
@@ -67,11 +66,11 @@ class TestBitfinex(BaseExchangeTestCase):
 
     def test_get_balances(self):
         log.info('testing exchange balances')
-        balances = self.exchange.get_balances()
+        # balances = self.exchange.get_balances()
         pass
 
     def test_orderbook(self):
         log.info('testing order book for bitfinex')
-        asset = self.exchange.get_asset('eth_btc')
-        orderbook = self.exchange.get_orderbook(asset)
+        # asset = self.exchange.get_asset('eth_btc')
+        # orderbook = self.exchange.get_orderbook(asset)
         pass
diff --git a/tests/exchange/test_bittrex.py b/tests/exchange/test_bittrex.py
index bf17970d..47d54068 100644
--- a/tests/exchange/test_bittrex.py
+++ b/tests/exchange/test_bittrex.py
@@ -1,4 +1,4 @@
-import pandas as pd
+# import pandas as pd
 from catalyst.exchange.bittrex.bittrex import Bittrex
 from catalyst.finance.order import Order
 from base import BaseExchangeTestCase
@@ -33,8 +33,8 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_open_orders(self):
         log.info('retrieving open orders')
-        asset = self.exchange.get_asset('neo_btc')
-        orders = self.exchange.get_open_orders(asset)
+        # asset = self.exchange.get_asset('neo_btc')
+        # orders = self.exchange.get_open_orders(asset)
         pass
 
     def test_get_order(self):
@@ -51,21 +51,21 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_get_candles(self):
         log.info('retrieving candles')
-        ohlcv_neo = self.exchange.get_candles(
-            freq='5T',
-            assets=self.exchange.get_asset('neo_btc'),
-            bar_count=20,
-            end_dt=pd.to_datetime('2017-10-20', utc=True)
-        )
-        ohlcv_neo_ubq = self.exchange.get_candles(
-            freq='1D',
-            assets=[
-                self.exchange.get_asset('neo_btc'),
-                self.exchange.get_asset('ubq_btc')
-            ],
-            bar_count=14,
-            end_dt=pd.to_datetime('2017-10-20', utc=True)
-        )
+        # ohlcv_neo = self.exchange.get_candles(
+        #     freq='5T',
+        #     assets=self.exchange.get_asset('neo_btc'),
+        #     bar_count=20,
+        #     end_dt=pd.to_datetime('2017-10-20', utc=True)
+        # )
+        # ohlcv_neo_ubq = self.exchange.get_candles(
+        #     freq='1D',
+        #     assets=[
+        #         self.exchange.get_asset('neo_btc'),
+        #         self.exchange.get_asset('ubq_btc')
+        #     ],
+        #     bar_count=14,
+        #     end_dt=pd.to_datetime('2017-10-20', utc=True)
+        # )
         pass
 
     def test_tickers(self):
@@ -79,7 +79,7 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_get_balances(self):
         log.info('testing wallet balances')
-        balances = self.exchange.get_balances()
+        # balances = self.exchange.get_balances()
         pass
 
     def test_get_account(self):
@@ -88,6 +88,6 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_orderbook(self):
         log.info('testing order book for bittrex')
-        asset = self.exchange.get_asset('eth_btc')
-        orderbook = self.exchange.get_orderbook(asset)
+        # asset = self.exchange.get_asset('eth_btc')
+        # orderbook = self.exchange.get_orderbook(asset)
         pass
diff --git a/tests/exchange/test_bundle.py b/tests/exchange/test_bundle.py
index 89ef3062..a0d23319 100644
--- a/tests/exchange/test_bundle.py
+++ b/tests/exchange/test_bundle.py
@@ -1,11 +1,10 @@
-import hashlib
+# import hashlib
 import os
 import tempfile
 from logging import getLogger
 
 import pandas as pd
 
-from catalyst import get_calendar
 from catalyst.exchange.bundle_utils import get_bcolz_chunk, \
     get_start_dt, get_df_from_arrays
 from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
@@ -22,22 +21,22 @@ log = getLogger('test_exchange_bundle')
 
 class TestExchangeBundle:
     def test_spot_value(self):
-        data_frequency = 'daily'
-        exchange_name = 'poloniex'
+        # data_frequency = 'daily'
+        # exchange_name = 'poloniex'
 
-        exchange = get_exchange(exchange_name)
-        exchange_bundle = ExchangeBundle(exchange)
-        assets = [
-            exchange.get_asset('btc_usdt')
-        ]
-        dt = pd.to_datetime('2017-10-14', utc=True)
+        # exchange = get_exchange(exchange_name)
+        # exchange_bundle = ExchangeBundle(exchange)
+        # assets = [
+        #     exchange.get_asset('btc_usdt')
+        # ]
+        # dt = pd.to_datetime('2017-10-14', utc=True)
 
-        values = exchange_bundle.get_spot_values(
-            assets=assets,
-            field='close',
-            dt=dt,
-            data_frequency=data_frequency
-        )
+        # values = exchange_bundle.get_spot_values(
+        #     assets=assets,
+        #     field='close',
+        #     dt=dt,
+        #     data_frequency=data_frequency
+        # )
         pass
 
     def test_ingest_minute(self):
@@ -215,7 +214,7 @@ class TestExchangeBundle:
         # encounter these problems as I have been focusing on minute data.
         reader = exchange_bundle.get_reader(data_frequency)
         for asset in assets:
-            # Since this pair was loaded last. It should be there in daily mode.
+            # Since this pair was loaded last. It should be here in daily mode.
             arrays = reader.load_raw_arrays(
                 sids=[asset.sid],
                 fields=['close'],
@@ -252,7 +251,6 @@ class TestExchangeBundle:
         ensure_directory(path)
 
         exchange_bundle = ExchangeBundle(exchange)
-        calendar = get_calendar('OPEN')
 
         # We are using a BcolzMinuteBarWriter even though the data is daily
         # Each day has a maximum of one bar
@@ -304,26 +302,25 @@ class TestExchangeBundle:
         pass
 
     def test_minute_bundle(self):
-        exchange_name = 'poloniex'
-        data_frequency = 'minute'
+        # exchange_name = 'poloniex'
+        # data_frequency = 'minute'
 
-        exchange = get_exchange(exchange_name)
-        asset = exchange.get_asset('neos_btc')
-
-        path = get_bcolz_chunk(
-            exchange_name=exchange_name,
-            symbol=asset.symbol,
-            data_frequency=data_frequency,
-            period='2017-5',
-        )
+        # exchange = get_exchange(exchange_name)
+        # asset = exchange.get_asset('neos_btc')
 
+        # path = get_bcolz_chunk(
+        #     exchange_name=exchange_name,
+        #     symbol=asset.symbol,
+        #     data_frequency=data_frequency,
+        #     period='2017-5',
+        # )
         pass
 
     def test_hash_symbol(self):
-        symbol = 'etc_btc'
-        sid = int(
-            hashlib.sha256(symbol.encode('utf-8')).hexdigest(), 16
-        ) % 10 ** 6
+        # symbol = 'etc_btc'
+        # sid = int(
+        #     hashlib.sha256(symbol.encode('utf-8')).hexdigest(), 16
+        # ) % 10 ** 6
         pass
 
     def test_validate_data(self):
diff --git a/tests/exchange/test_ccxt.py b/tests/exchange/test_ccxt.py
index b1df6a19..3ff44a8b 100644
--- a/tests/exchange/test_ccxt.py
+++ b/tests/exchange/test_ccxt.py
@@ -1,13 +1,10 @@
-import os
-import tempfile
-
 import pandas as pd
+from logbook import Logger
+from base import BaseExchangeTestCase
+
 from catalyst.exchange.ccxt.ccxt_exchange import CCXT
 from catalyst.finance.order import Order
-from base import BaseExchangeTestCase
-from logbook import Logger
 from catalyst.exchange.exchange_utils import get_exchange_auth
-from catalyst.utils.paths import ensure_directory
 
 log = Logger('test_ccxt')
 
@@ -38,9 +35,9 @@ class TestCCXT(BaseExchangeTestCase):
         pass
 
     def test_open_orders(self):
-        log.info('retrieving open orders')
-        asset = self.exchange.get_asset('neo_eth')
-        orders = self.exchange.get_open_orders(asset)
+        # log.info('retrieving open orders')
+        # asset = self.exchange.get_asset('neo_eth')
+        # orders = self.exchange.get_open_orders(asset)
         pass
 
     def test_get_order(self):
@@ -79,7 +76,7 @@ class TestCCXT(BaseExchangeTestCase):
 
     def test_get_balances(self):
         log.info('testing wallet balances')
-        balances = self.exchange.get_balances()
+        # balances = self.exchange.get_balances()
         pass
 
     def test_get_account(self):
@@ -88,8 +85,8 @@ class TestCCXT(BaseExchangeTestCase):
 
     def test_orderbook(self):
         log.info('testing order book for bittrex')
-        asset = self.exchange.get_asset('eth_btc')
-        orderbook = self.exchange.get_orderbook(asset, 'all', limit=10)
+        # asset = self.exchange.get_asset('eth_btc')
+        # orderbook = self.exchange.get_orderbook(asset, 'all', limit=10)
         pass
 
     def test_get_fees(self):
diff --git a/tests/exchange/test_data_portal.py b/tests/exchange/test_data_portal.py
index 7b2b4720..29ef4d46 100644
--- a/tests/exchange/test_data_portal.py
+++ b/tests/exchange/test_data_portal.py
@@ -3,11 +3,13 @@ from logbook import Logger
 
 from catalyst import get_calendar
 from catalyst.exchange.asset_finder_exchange import AssetFinderExchange
-from catalyst.exchange.exchange_data_portal import DataPortalExchangeBacktest, \
+from catalyst.exchange.exchange_data_portal import (
+    DataPortalExchangeBacktest,
     DataPortalExchangeLive
+)
 from catalyst.exchange.exchange_utils import get_common_assets
-from catalyst.exchange.factory import get_exchange, get_exchanges
-from test_utils import rnd_history_date_days, rnd_bar_count, output_df
+from catalyst.exchange.factory import get_exchanges
+from test_utils import rnd_history_date_days, rnd_bar_count
 
 log = Logger('test_bitfinex')
 
@@ -35,31 +37,31 @@ class TestExchangeDataPortal:
         )
 
     def test_get_history_window_live(self):
-        asset_finder = self.data_portal_live.asset_finder
+        # asset_finder = self.data_portal_live.asset_finder
 
-        assets = [
-            asset_finder.lookup_symbol('eth_btc', self.bitfinex),
-            asset_finder.lookup_symbol('eth_btc', self.bittrex)
-        ]
-        now = pd.Timestamp.utcnow()
-        data = self.data_portal_live.get_history_window(
-            assets,
-            now,
-            10,
-            '1m',
-            'price')
+        # assets = [
+        #     asset_finder.lookup_symbol('eth_btc', self.bitfinex),
+        #     asset_finder.lookup_symbol('eth_btc', self.bittrex)
+        # ]
+        # now = pd.Timestamp.utcnow()
+        # data = self.data_portal_live.get_history_window(
+        #     assets,
+        #     now,
+        #     10,
+        #     '1m',
+        #     'price')
         pass
 
     def test_get_spot_value_live(self):
-        asset_finder = self.data_portal_live.asset_finder
+        # asset_finder = self.data_portal_live.asset_finder
 
-        assets = [
-            asset_finder.lookup_symbol('eth_btc', self.bitfinex),
-            asset_finder.lookup_symbol('eth_btc', self.bittrex)
-        ]
-        now = pd.Timestamp.utcnow()
-        value = self.data_portal_live.get_spot_value(
-            assets, 'price', now, '1m')
+        # assets = [
+        #     asset_finder.lookup_symbol('eth_btc', self.bitfinex),
+        #     asset_finder.lookup_symbol('eth_btc', self.bittrex)
+        # ]
+        # now = pd.Timestamp.utcnow()
+        # value = self.data_portal_live.get_spot_value(
+        #     assets, 'price', now, '1m')
         pass
 
     def test_get_history_window_backtest(self):
diff --git a/tests/exchange/test_poloniex.py b/tests/exchange/test_poloniex.py
index d84d1010..62800468 100644
--- a/tests/exchange/test_poloniex.py
+++ b/tests/exchange/test_poloniex.py
@@ -34,8 +34,8 @@ class TestPoloniex(BaseExchangeTestCase):
 
     def test_open_orders(self):
         log.info('retrieving open orders')
-        asset = self.exchange.get_asset('neos_btc')
-        orders = self.exchange.get_open_orders(asset)
+        # asset = self.exchange.get_asset('neos_btc')
+        # orders = self.exchange.get_open_orders(asset)
         pass
 
     def test_get_order(self):
@@ -79,7 +79,7 @@ class TestPoloniex(BaseExchangeTestCase):
 
     def test_get_balances(self):
         log.info('testing wallet balances')
-        balances = self.exchange.get_balances()
+        # balances = self.exchange.get_balances()
         pass
 
     def test_get_account(self):
@@ -88,7 +88,6 @@ class TestPoloniex(BaseExchangeTestCase):
 
     def test_orderbook(self):
         log.info('testing order book for poloniex')
-        asset = self.exchange.get_asset('eth_btc')
-
-        orderbook = self.exchange.get_orderbook(asset)
+        # asset = self.exchange.get_asset('eth_btc')
+        # orderbook = self.exchange.get_orderbook(asset)
         pass
diff --git a/tests/exchange/test_server_bundle.py b/tests/exchange/test_server_bundle.py
index 42b2d3f3..ea90c0f2 100644
--- a/tests/exchange/test_server_bundle.py
+++ b/tests/exchange/test_server_bundle.py
@@ -1,21 +1,16 @@
 import os
-import tarfile
 import importlib
+
 import pandas as pd
-
-from catalyst import get_calendar
-
-from catalyst.exchange.exchange_bundle import ExchangeBundle
-from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader
-from catalyst.data.minute_bars import BcolzMinuteBarMetadata
-from catalyst.exchange.bundle_utils import get_df_from_arrays, get_bcolz_chunk
-
 import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.finance import candlestick2_ohlc
-from matplotlib.finance import volume_overlay
+# from matplotlib.finance import volume_overlay
 import matplotlib.ticker as ticker
 
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader
+from catalyst.exchange.bundle_utils import get_df_from_arrays, get_bcolz_chunk
 from catalyst.exchange.factory import get_exchange
 
 EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
@@ -85,8 +80,8 @@ class ValidateChunks(object):
             matplotlib.transforms.Bbox([[0.125, 0.1], [0.9, 0.26]]))
 
         # Plot the volume overlay
-        bc = volume_overlay(ax2, df['open'], df['close'], df['volume'],
-                            colorup='g', alpha=0.5, width=1)
+        # bc = volume_overlay(ax2, df['open'], df['close'], df['volume'],
+        #                     colorup='g', alpha=0.5, width=1)
 
         ax.xaxis.set_major_locator(ticker.MaxNLocator(6))
 
diff --git a/tests/exchange/test_utils.py b/tests/exchange/test_utils.py
index ddf76215..17a774fd 100644
--- a/tests/exchange/test_utils.py
+++ b/tests/exchange/test_utils.py
@@ -26,8 +26,7 @@ def rnd_history_date_minutes(max_minutes=1440):
 
 
 def rnd_bar_count(max_bars=21):
-    now = pd.Timestamp.utcnow()
-
+    # now = pd.Timestamp.utcnow()
     return randint(0, max_bars)