merged from develop

Example: Simple Universe

README.rst

@@ -1,3 +1,72 @@
-All the documentation for `Catalyst <https://github.com/enigmampc/catalyst>`_
+.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
-can be found in the 
+    :target: https://enigmampc.github.io/catalyst
-`documentation website <https://enigmampc.github.io/catalyst>`_.
+    :align: center
+    :alt: Enigma | Catalyst
+
+|version tag|
+|version status|
+|discord|
+|twitter|
+
+|
+
+Catalyst is an algorithmic trading library for crypto-assets written in Python.
+It allows trading strategies to be easily expressed and backtested against 
+historical data (with daily and minute resolution), providing analytics and 
+insights regarding a particular strategy's performance. Catalyst also supports
+live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex, 
+and Poloniex) with more being added over time. Catalyst empowers users to share 
+and curate data and build profitable, data-driven investment strategies. Please 
+visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or 
+refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for 
+further technical details.
+
+Catalyst builds on top of the well-established 
+`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to 
+minimize structural changes to the general API to maximize compatibility with 
+existing trading algorithms, developer knowledge, and tutorials. Join us on 
+`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel 
+for questions around Catalyst, algorithmic trading and technical support.
+
+Overview
+========
+
+-  Ease of use: Catalyst tries to get out of your way so that you can 
+   focus on algorithm development. See 
+   `examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_ 
+   provided.
+-  Support for several of the top crypto-exchanges by trading volume:
+   `Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
+   and `Poloniex <https://www.poloniex.com>`_. 
+-  Secure: You and only you have access to each exchange API keys for your accounts.
+-  Input of historical pricing data of all crypto-assets by exchange, 
+   with daily and minute resolution. See 
+   `Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
+-  Backtesting and live-trading functionality, with a seamless transition
+   between the two modes.
+-  Output of performance statistics are based on Pandas DataFrames to 
+   integrate nicely into the existing PyData eco-system.
+-  Statistic and machine learning libraries like matplotlib, scipy, 
+   statsmodels, and sklearn support development, analysis, and 
+   visualization of state-of-the-art trading systems.
+-  Addition of Bitcoin price (btc_usdt) as a benchmark for comparing 
+   performance across trading algorithms.
+
+Go to our `Documentation Website <https://enigmampc.github.io/catalyst/>`_.
+
+
+
+
+.. |version tag| image:: https://img.shields.io/pypi/v/enigma-catalyst.svg
+   :target: https://pypi.python.org/pypi/enigma-catalyst
+
+.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
+   :target: https://pypi.python.org/pypi/enigma-catalyst
+
+.. |discord| image:: https://img.shields.io/badge/discord-join%20chat-green.svg
+   :target: https://discordapp.com/invite/SJK32GY
+
+.. |twitter| image:: https://img.shields.io/twitter/follow/enigmampc.svg?style=social&label=Follow&style=flat-square
+   :target: https://twitter.com/enigmampc
+
+

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

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
 
@@ -194,9 +193,7 @@ def ipython_only(option):
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the targeted exchange.',
-    help='The name of the targeted exchange (supported: bitfinex,'
-         ' bittrex, poloniex).',
 )
 @click.option(
     '-n',
@@ -258,8 +255,9 @@ def run(ctx,
         ctx.fail("must specify a base currency with '-c' in backtest mode")
 
     if capital_base is None:
-        ctx.fail("must specify a capital base with '--capital-base'"
+        ctx.fail("must specify a capital base with '--capital-base'")
-                 " in backtest mode")
+
+    click.echo('Running in backtesting mode.')
 
     perf = _run(
         initialize=None,
@@ -284,7 +282,9 @@ def run(ctx,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
-        live_graph=False
+        live_graph=False,
+        simulate_orders=True,
+        stats_output=None,
     )
 
     if output == '-':
@@ -312,11 +312,11 @@ def catalyst_magic(line, cell=None):
                 '--algotext', cell,
                 '--output', os.devnull,  # don't write the results by default
             ] + ([
-                     # these options are set when running in line magic mode
+                # these options are set when running in line magic mode
-                     # set a non None algo text to use the ipython user_ns
+                # set a non None algo text to use the ipython user_ns
-                     '--algotext', '',
+                '--algotext', '',
-                     '--local-namespace',
+                '--local-namespace',
-                 ] if cell is None else []) + line.split(),
+            ] if cell is None else []) + line.split(),
             '%s%%catalyst' % ((cell or '') and '%'),
             # don't use system exit and propogate errors to the caller
             standalone_mode=False,
@@ -336,6 +336,12 @@ def catalyst_magic(line, cell=None):
     type=click.File('r'),
     help='The file that contains the algorithm to run.',
 )
+@click.option(
+    '--capital-base',
+    type=float,
+    show_default=True,
+    help='The amount of capital (in base_currency) allocated to trading.',
+)
 @click.option(
     '-t',
     '--algotext',
@@ -374,9 +380,7 @@ def catalyst_magic(line, cell=None):
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the targeted exchange.',
-    help='The name of the targeted exchange (supported: bitfinex,'
-         ' bittrex, poloniex).',
 )
 @click.option(
     '-n',
@@ -395,9 +399,17 @@ def catalyst_magic(line, cell=None):
     default=False,
     help='Display live graph.',
 )
+@click.option(
+    '--simulate-orders/--no-simulate-orders',
+    is_flag=True,
+    default=True,
+    help='Simulating orders enable the paper trading mode. No orders will be '
+         'sent to the exchange unless set to false.',
+)
 @click.pass_context
 def live(ctx,
          algofile,
+         capital_base,
          algotext,
          define,
          output,
@@ -406,7 +418,8 @@ def live(ctx,
          exchange_name,
          algo_namespace,
          base_currency,
-         live_graph):
+         live_graph,
+         simulate_orders):
     """Trade live with the given algorithm.
     """
     if (algotext is not None) == (algofile is not None):
@@ -417,11 +430,22 @@ def live(ctx,
 
     if exchange_name is None:
         ctx.fail("must specify an exchange name '-x'")
+
     if algo_namespace is None:
         ctx.fail("must specify an algorithm name '-n' in live execution mode")
+
     if base_currency is None:
         ctx.fail("must specify a base currency '-c' in live execution mode")
 
+    if capital_base is None:
+        ctx.fail("must specify a capital base with '--capital-base'")
+
+    if simulate_orders:
+        click.echo('Running in paper trading mode.')
+
+    else:
+        click.echo('Running in live trading mode.')
+
     perf = _run(
         initialize=None,
         handle_data=None,
@@ -431,7 +455,7 @@ def live(ctx,
         algotext=algotext,
         defines=define,
         data_frequency=None,
-        capital_base=None,
+        capital_base=capital_base,
         data=None,
         bundle=None,
         bundle_timestamp=None,
@@ -445,7 +469,9 @@ def live(ctx,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
-        live_graph=live_graph
+        live_graph=live_graph,
+        simulate_orders=simulate_orders,
+        stats_output=None,
     )
 
     if output == '-':
@@ -460,9 +486,7 @@ def live(ctx,
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest.',
-    help='The name of the exchange bundle to ingest (supported: bitfinex,'
-         ' bittrex, poloniex).',
 )
 @click.option(
     '-f',
@@ -520,7 +544,8 @@ def live(ctx,
     default=False,
     help='Report potential anomalies found in data bundles.'
 )
-def ingest_exchange(exchange_name, data_frequency, start, end,
+@click.pass_context
+def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
                     include_symbols, exclude_symbols, csv, show_progress,
                     verbose, validate):
     """
@@ -565,9 +590,7 @@ def clean_algo(ctx, algo_namespace):
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest.',
-    help='The name of the exchange bundle to ingest (supported: bitfinex,'
-         ' bittrex, poloniex).',
 )
 @click.option(
     '-f',
@@ -606,9 +629,7 @@ def clean_exchange(ctx, exchange_name, data_frequency):
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest.',
-    help='The name of the exchange bundle to ingest (supported: bitfinex,'
-         ' bittrex, poloniex).',
 )
 @click.option(
     '-c',

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):
         """

catalyst/assets/_assets.pyx

@@ -405,6 +405,9 @@ cdef class TradingPair(Asset):
     cdef readonly float taker
     cdef readonly int trading_state
     cdef readonly object data_source
+    cdef readonly float max_trade_size
+    cdef readonly float lot
+    cdef readonly int decimals
 
     _kwargnames = frozenset({
         'sid',
@@ -423,10 +426,13 @@ cdef class TradingPair(Asset):
         'end_minute',
         'exchange_symbol',
         'min_trade_size',
+        'max_trade_size',
+        'lot',
         'maker',
         'taker',
         'trading_state',
-        'data_source'
+        'data_source',
+        'decimals'
     })
     def __init__(self,
                  object symbol,
@@ -443,8 +449,11 @@ cdef class TradingPair(Asset):
                  object auto_close_date=None,
                  object exchange_full=None,
                  float min_trade_size=0.0001,
+                 float max_trade_size=1000000,
                  float maker=0.0015,
                  float taker=0.0025,
+                 float lot=0,
+                 int decimals = 8,
                  int trading_state=0,
                  object data_source='catalyst'):
         """
@@ -509,9 +518,12 @@ cdef class TradingPair(Asset):
         :param auto_close_date:
         :param exchange_full:
         :param min_trade_size:
+        :param max_trade_size:
         :param maker:
         :param taker:
         :param data_source
+        :param decimals
+        :param lot
         """
 
         symbol = symbol.lower()
@@ -535,6 +547,9 @@ cdef class TradingPair(Asset):
         if end_date is None:
             end_date = pd.Timestamp.utcnow() + timedelta(days=365)
 
+        if lot == 0 and min_trade_size > 0:
+            lot = min_trade_size
+
         super().__init__(
             sid,
             exchange,
@@ -556,6 +571,9 @@ cdef class TradingPair(Asset):
         self.exchange_symbol = exchange_symbol
         self.trading_state = trading_state
         self.data_source = data_source
+        self.max_trade_size = max_trade_size
+        self.lot = lot
+        self.decimals = decimals
 
     def __repr__(self):
         return 'Trading Pair {symbol}({sid}) Exchange: {exchange}, ' \
@@ -582,6 +600,7 @@ cdef class TradingPair(Asset):
         """
         Convert to a python dict.
         """
+        #TODO: missing fields
         super_dict = super(TradingPair, self).to_dict()
         super_dict['end_daily'] = self.end_daily
         super_dict['end_minute'] = self.end_minute
@@ -610,6 +629,7 @@ cdef class TradingPair(Asset):
         and whose second element is a tuple of all the attributes that should
         be serialized/deserialized during pickling.
         """
+        #TODO: make sure that all fields set there
         return (self.__class__, (self.symbol,
                                  self.exchange,
                                  self.start_date,
@@ -620,7 +640,12 @@ cdef class TradingPair(Asset):
                                  self.first_traded,
                                  self.auto_close_date,
                                  self.exchange_full,
-                                 self.min_trade_size))
+                                 self.min_trade_size,
+                                 self.max_trade_size,
+                                 self.lot,
+                                 self.decimals,
+                                 self.taker,
+                                 self.maker))
 
 def make_asset_array(int size, Asset asset):
     cdef np.ndarray out = np.empty([size], dtype=object)

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 = int(os.environ.get('CATALYST_LOG_LEVEL', logbook.INFO))
-LOG_LEVEL = logbook.DEBUG
 
 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
+AUTO_INGEST = False

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_START = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
-DT_END          = pd.to_datetime('today').value // 10 ** 9
+DT_END = pd.to_datetime('today').value // 10 ** 9
-CSV_OUT_FOLDER  = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
+CSV_OUT_FOLDER = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
-CONN_RETRIES    = 2
+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?'
+    _api_path = 'https://poloniex.com/public?'
-    currency_pairs  = []
+    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], 
+        d = pd.to_datetime(row.split(',')[1],
-                             infer_datetime_format=True).value // 10 ** 9
+                           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 
+        Retrieves TradeHistory from exchange for a given currencyPair
-        between start and end dates. If no start date is provided, uses 
+        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 
+                    f.seek(0)                       # Go to start to read
-                    last_tradeID, end_file = self._retrieve_tradeID_date(f.readline())
+                    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.
+                        # ...jump back the read byte plus one more.
-                    first_tradeID, start_file = self._retrieve_tradeID_date(f.readline())
+                        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
+                    if(end_file + 3600 * 6 > DT_END
-                        or (currencyPair == 'BTC_HUC' and first_tradeID ==     2) 
+                        and (first_tradeID == 1
-                        or (currencyPair == 'BTC_RIC' and first_tradeID ==     2) 
+                             or (currencyPair == 'BTC_HUC'
-                        or (currencyPair == 'BTC_XCP' and first_tradeID ==     2)
+                                 and first_tradeID == 2)
-                        or (currencyPair == 'BTC_NAV' and first_tradeID ==  4569)
+                             or (currencyPair == 'BTC_RIC'
-                        or (currencyPair == 'BTC_POT' and first_tradeID == 23511) ) ):
+                                 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,
+                    path=self._api_path,
-                    pair = currencyPair,
+                    pair=currencyPair,
-                    start = str(newstart),
+                    start=str(newstart),
-                    end = str(end)
+                    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(
+                log.error('Failed to retrieve trade history data'
-                            currencyPair
+                          '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() 
+        if('first_tradeID' in locals()
-                and response.json()[-1]['tradeID'] == first_tradeID): 
+                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 
+                a) There is newer data available that we need to add at
-                   the beginning of the file. We'll retrieve all what we 
+                   the beginning of the file. We'll retrieve all what we
-                   need until we get to what we already have, writing it 
+                   need until we get to what we already have, writing it
-                   to a temporary file; and we will write that at the 
+                   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 
+                b) We are going back in time, appending at the end of
-                   our existing TradeHistory until the first transaction 
+                   our existing TradeHistory until the first transaction
                    for this currencyPair
         '''
-        try: 
+        try:
-            if( 'end_file' in locals() and end_file + 3600 < end):
+            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 ):
+                if(response.json()[-1]['tradeID'] > last_tradeID):
-                    end = pd.to_datetime( response.json()[-1]['date'], 
+                    end = pd.to_datetime(response.json()[-1]['date'],
-                                infer_datetime_format=True).value // 10 ** 9
+                                         infer_datetime_format=True
-                    self.retrieve_trade_history(currencyPair, start, 
+                                         ).value // 10**9
-                                                end, temp=temp) 
+                    self.retrieve_trade_history(currencyPair, start,
+                                                end, temp=temp)
                 else:
-                    with open(csv_fn,'rb+') as f:
+                    with open(csv_fn, 'rb+') as f:
-                        shutil.copyfileobj(f,temp)
+                        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() 
+                        if('first_tradeID' in locals()
-                                and item['tradeID'] >= first_tradeID ):
+                                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'], 
+                end = pd.to_datetime(response.json()[-1]['date'],
-                            infer_datetime_format=True).value // 10 ** 9
+                                     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
+        df.set_index('date', inplace=True)             # Index by date
-        vol = df['total'].to_frame('volume')             # set Vol aside
+        vol = df['total'].to_frame('volume')           # set Vol aside
-        df.drop('total', axis=1, inplace=True)           # Drop volume data 
+        df.drop('total', axis=1, inplace=True)         # Drop volume data
-        ohlc = df.resample('T').ohlc()                   # Resample OHLC 1min
+        ohlc = df.resample('T').ohlc()                 # Resample OHLC 1min
-        ohlc.columns = ohlc.columns.map(lambda t: t[1])  # Raname columns by dropping 'rate'
+        ohlc.cols = ohlc.cols.map(lambda t: t[1])      # Raname cols
-        closes = ohlc['close'].fillna(method='pad')      # Pad fwd missing 'close'
+        closes = ohlc['close'].fillna(method='pad')    # Pad fwd missing close
-        ohlc = ohlc.apply(lambda x: x.fillna(closes))    # Fill N/A with last 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
+        vol = vol.resample('T').sum().fillna(0)        # Add volumes by bin
-        ohlcv = pd.concat([ohlc,vol], axis=1)            # Concatenate OHLC + Vol
+        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'
+        csv_1min = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
-        if( os.path.getmtime(csv_1min) > time.time() - 7200 ):
+        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, 
+            df = pd.read_csv(csv_trades,
-                                names=['tradeID',
+                             names=['tradeID',
-                                       'date',
+                                    'date',
-                                       'type',
+                                    'type',
-                                       'rate',
+                                    'rate',
-                                       'amount',
+                                    'amount',
-                                       'total',
+                                    'total',
-                                       'globalTradeID'], 
+                                    'globalTradeID'],
-                                dtype = {'tradeID': int, 
+                             dtype={'tradeID': int,
-                                         'date': str, 
+                                    'date': str,
-                                         'type': str, 
+                                    'type': str,
-                                         'rate': float, 
+                                    'rate': float,
-                                         'amount': float, 
+                                    'amount': float,
-                                         'total': float, 
+                                    'total': float,
-                                         'globalTradeID': int } 
+                                    'globalTradeID': int}
-                                )
+                             )
-            df.drop(['tradeID','type','amount','globalTradeID'], 
+            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'
+        csv_fn = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
-        df         = pd.read_csv(csv_fn, names=['date', 
+        df = pd.read_csv(csv_fn, names=['date',
-                                                'open', 
+                                        'open',
-                                                'high', 
+                                        'high',
-                                                'low', 
+                                        'low',
-                                                'close', 
+                                        'close',
-                                                'volume']
+                                        'volume'])
-                                )
+        df['date'] = pd.to_datetime(df['date'], unit='s')
-        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_fn = '{}crypto_trades-{}.csv'.format(
-                                CSV_OUT_FOLDER, currencyPair)
+                                                    CSV_OUT_FOLDER,
-                with open(csv_fn, 'r') as f: 
+                                                    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.
+                            # ...jump back the read byte plus one more.
-                        start = pd.to_datetime( f.readline().split(',')[1], 
+                            f.seek(-2, os.SEEK_CUR)
-                                                infer_datetime_format=True)
+                        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,
+                    symbol=symbol,
-                    start_date = start.strftime("%Y-%m-%d")
+                    start_date=start.strftime("%Y-%m-%d")
                 )
-            json.dump(symbol_map, symbols, sort_keys=True, indent=2, 
+            json.dump(symbol_map, symbols, sort_keys=True, indent=2,
-                        separators=(',',':'))    
+                      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)
-

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,

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
+                # Post-process metadata using cached symbol frames, and write
-                # disk.  This metadata must be written before any attempt to write
+                # to disk.  This metadata must be written before any attempt
-                # minute data.
+                # 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
+                # For legacy purposes, this call is required to ensure the
-                # contains an appropriately initialized file structure.  We don't
+                # database contains an appropriately initialized file
-                # forsee a usecase for adjustments at this time, but may later
+                # structure.  We don't forsee a usecase for adjustments at
-                # choose to expose this functionality in the future.
+                # 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,
+                              api_key,
-                             cache,
+                              cache,
-                             retries=DEFAULT_RETRIES,
+                              retries=DEFAULT_RETRIES,
-                             environ=None,
+                              environ=None,
-                             show_progress=False):
+                              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
+                # Attempt to load data from disk, the cache should have an
-                # for each symbol at this point of the execution. If one does
+                # entry for each symbol at this point of the execution. If one
-                # not exist, we should fail.
+                # 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
+            # next symbol. If the raw_data is updated, it is cached before
-            # returned.
+            # 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)

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):

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()

catalyst/data/bundles/poloniex.py

@@ -14,19 +14,17 @@
 # limitations under the License.
 
 import sys
-
+from six.moves.urllib.parse import urlencode
-from datetime import datetime
 
 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
+        # BcolzDailyBarReader introduces a 1/1000 factor in the way
-        # on disk, which we compensate here to get the right pricing amounts
+        # 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)
-

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
+        # cut out all the other stuff in the name column. We need to
-        # we need to escape the paren because it is actually splitting on a regex
+        # 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)

catalyst/data/data_portal.py

@@ -656,11 +656,11 @@ class DataPortal(object):
         return spot_value
 
     def _get_minutely_spot_value(self,
-                                asset,
+                                 asset,
-                                column,
+                                 column,
-                                dt,
+                                 dt,
-                                data_frequency,
+                                 data_frequency,
-                                ffill=False):
+                                 ffill=False):
 
         reader = self._get_pricing_reader(data_frequency)
 
@@ -706,7 +706,7 @@ class DataPortal(object):
             asset,
             column,
             dt,
-            ffill, 
+            ffill,
             'minute',
         )
 

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):

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
+        # If we are using the bundle to retrieve the cryptobenchmark, find
-        # date for which there is trading data in the bundle
+        # 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)
+        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
+    # Override first_date for treasury data since we have it for many more
-    # and is independent of crypto data
+    # 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
+        If we're using the Poloniex bundle, we'll get the benchmark from the
-        instead of downloading it from Poloniex every time we need it.
+        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 
+        Poloniex has a captcha for API queries originating from outside the US
-        prevents users abroad from getting Catalyst to work
+        that prevents users abroad from getting Catalyst to work
         '''
         logger.info(
-            (
+            ('Retrieving benchmark data from bundle for {symbol!r}'
-                'Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
+             ' 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. 
+        # This is how it used to be: downloading the benchmark everytime.
-        # Leaving this code here to be repurposed in the future for other bundles.
+        # Leaving this code here to be repurposed in the future for
+        # other bundles.
         logger.info(
-            (
+            ('Downloading benchmark data for {symbol!r}'
-                'Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
+             ' 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

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': (market_opens.values.astype('datetime64[m]').
-                market_opens.values.astype('datetime64[m]').
+                             astype(np.int64).tolist()),
-                    astype(np.int64).tolist()),
+            'market_closes': (market_closes.values.astype('datetime64[m]').
-            'market_closes': (
+                              astype(np.int64).tolist()),
-                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_slice = np.s_[excl_start - start_idx:excl_stop
-                                     excl_start - start_idx:excl_stop - start_idx + 1]
+                                           - 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
+                            is not None else self._COMPLEVEL
-        self._complib = complib if complib \
+        self._complib = complib if complib is not None else self._COMPLIB
-                                   is not None else self._COMPLIB
         self._path = path
 
     def write(self, frames):

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
+            if k in OHLCV
-               else carray(array([], dtype=uint32))
+            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.

catalyst/examples/arbitrage_with_interface.py

@@ -263,13 +263,20 @@ def analyze(context, stats):
     pass
 
 
-run_algorithm(
+if __name__ == '__main__':
-    initialize=initialize,
+    # The execution mode: backtest or live
-    handle_data=handle_data,
+    MODE = 'live'
-    analyze=analyze,
+    if MODE == 'live':
-    exchange_name='poloniex,bitfinex',
+        run_algorithm(
-    live=True,
+            capital_base=0.1,
-    algo_namespace=algo_namespace,
+            initialize=initialize,
-    quote_currency='btc',
+            handle_data=handle_data,
-    live_graph=False
+            analyze=analyze,
-)
+            exchange_name='poloniex,bitfinex',
+            live=True,
+            algo_namespace=algo_namespace,
+            base_currency='btc',
+            live_graph=False,
+            simulate_orders=True,
+            stats_output=None,
+        )

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):
@@ -61,7 +61,6 @@ def handle_data(context, data):
             context.asset,
             target_hodl_value,
             limit_price=price * 1.1,
-            stop_price=price * 0.9,
         )
 
     record(

catalyst/examples/buy_btc_simple.py

@@ -1,30 +1,49 @@
 '''
-	This is a very simple example referenced in the beginner's tutorial:
+    This is a very simple example referenced in the beginner's tutorial:
-	https://enigmampc.github.io/catalyst/beginner-tutorial.html
+    https://enigmampc.github.io/catalyst/beginner-tutorial.html
 
-	Run this example, by executing the following from your terminal:
+    Run this example, by executing the following from your terminal:
-	  catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
+      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
+      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 
+    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 
+    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:
+    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:
+    and specify exchange poloniex as follows:
-	catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
+    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
+    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:
+    To see which assets are available on each exchange, visit:
-	https://www.enigma.co/catalyst/status
+    https://www.enigma.co/catalyst/status
 '''
-
+from catalyst import run_algorithm
 from catalyst.api import order, record, symbol
+import pandas as pd
+
 
 def initialize(context):
     context.asset = symbol('btc_usd')
 
+
 def handle_data(context, data):
     order(context.asset, 1)
-    record(btc = data.current(context.asset, 'price'))
+    record(btc=data.current(context.asset, 'price'))
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=10000,
+        data_frequency='daily',
+        initialize=initialize,
+        handle_data=handle_data,
+        exchange_name='bitfinex',
+        algo_namespace='buy_and_hodl',
+        base_currency='usd',
+        start=pd.to_datetime('2015-03-01', utc=True),
+        end=pd.to_datetime('2017-10-31', utc=True),
+    )

catalyst/examples/buy_low_sell_high.py

@@ -1,17 +1,19 @@
 '''
-This algorithm requires an additional library (ta-lib) beyond those required by catalyst.
+This algorithm requires an additional library (ta-lib) beyond those
-Install it first by running: 
+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"
+If you get build errors like:
-it typically means that it can't find the underlying TA-Lib library and needs to be installed.
+    "fatal error: ta-lib/ta_libc.h: No such file or directory"
-See https://mrjbq7.github.io/ta-lib/install.html for instructions on how to install 
+it typically means that it can't find the underlying TA-Lib library and it
-the required dependencies.
+needs to be installed. See https://mrjbq7.github.io/ta-lib/install.html for
+instructions on how to install the required dependencies.
 '''
 
 import talib
 from logbook import Logger
 
+from catalyst import run_algorithm
 from catalyst.api import (
     order,
     order_target_percent,
@@ -20,6 +22,7 @@ from catalyst.api import (
     get_open_orders,
 )
 from catalyst.exchange.stats_utils import get_pretty_stats
+import pandas as pd
 
 algo_namespace = 'buy_low_sell_high_xrp'
 log = Logger(algo_namespace)
@@ -100,8 +103,8 @@ def _handle_data(context, data):
 
         if price < cost_basis:
             is_buy = True
-        elif position.amount > 0 and \
+        elif (position.amount > 0
-                        price > cost_basis * (1 + context.PROFIT_TARGET):
+              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(
@@ -156,3 +159,18 @@ def handle_data(context, data):
 def analyze(context, stats):
     log.info('the daily stats:\n{}'.format(get_pretty_stats(stats)))
     pass
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=10000,
+        data_frequency='daily',
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='poloniex',
+        algo_namespace='buy_and_hodl',
+        base_currency='usd',
+        start=pd.to_datetime('2015-03-01', utc=True),
+        end=pd.to_datetime('2017-10-31', utc=True),
+    )

catalyst/examples/buy_low_sell_high_live.py

@@ -41,7 +41,7 @@ def _handle_data(context, data):
         context.asset,
         fields='price',
         bar_count=20,
-        frequency='1d'
+        frequency='1D'
     )
     rsi = talib.RSI(prices.values, timeperiod=14)[-1]
     log.info('got rsi: {}'.format(rsi))
@@ -88,8 +88,8 @@ def _handle_data(context, data):
 
         if price < cost_basis:
             is_buy = True
-        elif position.amount > 0 and \
+        elif (position.amount > 0
-                        price > cost_basis * (1 + context.PROFIT_TARGET):
+              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(
@@ -146,23 +146,15 @@ def analyze(context, stats):
     pass
 
 
-run_algorithm(
+if __name__ == '__main__':
-    capital_base=100000,
+    run_algorithm(
-    initialize=initialize,
+        capital_base=0.001,
-    handle_data=handle_data,
+        initialize=initialize,
-    analyze=analyze,
+        handle_data=handle_data,
-    exchange_name='poloniex',
+        analyze=analyze,
-    start=pd.to_datetime('2017-5-01', utc=True),
+        exchange_name='binance',
-    end=pd.to_datetime('2017-10-16', utc=True),
+        live=True,
-    base_currency='usdt',
+        algo_namespace=algo_namespace,
-    data_frequency='daily'
+        base_currency='btc',
-)
+        simulate_orders=True,
-# run_algorithm(
+    )
-#     initialize=initialize,
-#     handle_data=handle_data,
-#     analyze=analyze,
-#     exchange_name='poloniex',
-#     live=True,
-#     algo_namespace=algo_namespace,
-#     base_currency='btc'
-# )

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,
+from catalyst.api import (record, symbol, order_target_percent,
-        get_open_orders)
+                          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',
+    short_mavg = data.history(context.asset,
-                        bar_count=short_window, frequency="1m").mean()
+                              'price',
-    long_mavg = data.history(context.asset, 'price',
+                              bar_count=short_window,
-                        bar_count=long_window, frequency="1m").mean()
+                              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
+        # we buy 100% of our portfolio for this asset
-       order_target_percent(context.asset, 1)
+        order_target_percent(context.asset, 1)
     elif short_mavg < long_mavg and pos_amount > 0:
-       # we sell all our positions for this asset
+        # we sell all our positions for this asset
-       order_target_percent(context.asset, 0)
+        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,
+        asset=context.asset.symbol,
-        base = base_currency
+        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),
-        )
+        )

catalyst/examples/dual_vwap.py

@@ -1,188 +0,0 @@
-#!/usr/bin/env python
-#
-# Copyright 2017 Enigma MPC, Inc.
-# Copyright 2014 Quantopian, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from catalyst.api import (
-    order_target_percent,
-    record,
-    symbol,
-    get_open_orders,
-    set_max_leverage,
-    schedule_function,
-    date_rules,
-    attach_pipeline,
-    pipeline_output,
-)
-
-from catalyst.pipeline import Pipeline
-from catalyst.pipeline.data import CryptoPricing
-from catalyst.pipeline.factors.crypto import VWAP
-
-
-def initialize(context):
-    context.ASSET_NAME = 'USDT_BTC'
-    context.TARGET_INVESTMENT_RATIO = 0.8
-    context.SHORT_WINDOW = 30
-    context.LONG_WINDOW = 100
-
-    # For all trading pairs in the poloniex bundle, the default denomination
-    # currently supported by Catalyst is 1/1000th of a full coin. Use this
-    # constant to scale the price of up to that of a full coin if desired.
-    context.TICK_SIZE = 1000.0
-
-    context.i = 0
-    context.asset = symbol(context.ASSET_NAME)
-
-    set_max_leverage(1.0)
-
-    attach_pipeline(make_pipeline(context), 'vwap_pipeline')
-
-    schedule_function(
-        rebalance,
-        time_rules=times_rules.every_minute(),
-    )
-
-
-def before_trading_start(context, data):
-    context.pipeline_data = pipeline_output('vwap_pipeline')
-
-def make_pipeline(context):
-    return Pipeline(
-        columns={
-            'price': CryptoPricing.open.latest,
-            'volume': CryptoPricing.volume.latest,
-            'short_mavg': VWAP(window_length=context.SHORT_WINDOW),
-            'long_mavg': VWAP(window_length=context.LONG_WINDOW),
-        }
-    )
-
-def rebalance(context, data):
-    context.i += 1
-
-    # skip first LONG_WINDOW bars to fill windows
-    if context.i < context.LONG_WINDOW:
-        return
-
-    # get pipeline data for asset of interest
-    pipeline_data = context.pipeline_data
-    pipeline_data = pipeline_data[pipeline_data.index == context.asset].iloc[0]
-
-    # retrieve long and short moving averages from pipeline
-    short_mavg = pipeline_data.short_mavg
-    long_mavg = pipeline_data.long_mavg
-    price = pipeline_data.price
-    volume = pipeline_data.volume
-
-    # check that order has not already been placed
-    open_orders = get_open_orders()
-    if context.asset not in open_orders:
-        # check that the asset of interest can currently be traded
-        if data.can_trade(context.asset):
-            # adjust portfolio based on comparison of long and short vwap
-            if short_mavg > long_mavg:
-                order_target_percent(
-                    context.asset,
-                    context.TARGET_INVESTMENT_RATIO,
-                )
-            elif short_mavg < long_mavg:
-                order_target_percent(
-                    context.asset,
-                    0.0,
-                )
-
-    record(
-        price=price,
-        cash=context.portfolio.cash,
-        leverage=context.account.leverage,
-        short_mavg=short_mavg,
-        long_mavg=long_mavg,
-        volume=volume,
-    )
-    
-
-
-def analyze(context=None, results=None):
-    import matplotlib.pyplot as plt
-
-    # Plot the portfolio and asset data.
-    ax1 = plt.subplot(611)
-    results[['portfolio_value']].plot(ax=ax1)
-    ax1.set_ylabel('Portfolio value (USD)')
-
-    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)
-
-    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]
-    ]
-    sells = trans.ix[
-        [t[0]['amount'] < 0 for t in trans.transactions]
-    ]
-
-    ax2.plot(
-        buys.index,
-        context.TICK_SIZE * results.price[buys.index],
-        '^',
-        markersize=10,
-        color='g',
-    )
-    ax2.plot(
-        sells.index,
-        context.TICK_SIZE * results.price[sells.index],
-        'v',
-        markersize=10,
-        color='r',
-    )
-
-    ax3 = plt.subplot(613, sharex=ax1)
-    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
-    ax3.set_ylabel('Leverage (USD)')
-
-    ax4 = plt.subplot(614, sharex=ax1)
-    results[['cash']].plot(ax=ax4)
-    ax4.set_ylabel('Cash (USD)')
-
-    results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
-    ]] = results[[
-        'treasury_period_return',
-        'algorithm_period_return',
-        'benchmark_period_return',
-    ]]
-
-    ax5 = plt.subplot(615, sharex=ax1)
-    results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
-    ]].plot(ax=ax5)
-    ax5.set_ylabel('Percent Change')
-
-    ax6 = plt.subplot(616, sharex=ax1)
-    results[['volume']].plot(ax=ax6)
-    ax6.set_ylabel('Volume (mBTC/day)')
-
-    plt.legend(loc=3)
-
-    # Show the plot.
-    plt.gcf().set_size_inches(18, 8)
-    plt.show()

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
@@ -13,6 +13,7 @@ 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
+
 # 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
@@ -33,16 +34,19 @@ def initialize(context):
     # parameters or values you're going to use.
 
     # In our example, we're looking at Neo in Ether.
-    context.neo_eth = symbol('neo_eth')
+    context.market = symbol('neo_eth')
     context.base_price = None
     context.current_day = None
 
-    context.RSI_OVERSOLD = 55
+    context.RSI_OVERSOLD = 30
-    context.RSI_OVERBOUGHT = 82
+    context.RSI_OVERBOUGHT = 80
     context.CANDLE_SIZE = '5T'
 
     context.start_time = time.time()
 
+    # context.set_commission(maker=0.1, taker=0.2)
+    context.set_slippage(spread=0.0001)
+
 
 def handle_data(context, data):
     # This handle_data function is where the real work is done.  Our data is
@@ -59,14 +63,14 @@ def handle_data(context, data):
         context.current_day = today
 
     # We're computing the volume-weighted-average-price of the security
-    # defined above, in the context.neo_eth variable.  For this example, we're 
+    # defined above, in the context.market variable.  For this example, we're
     # using three bars on the 15 min bars.
 
     # The frequency attribute determine the bar size. We use this convention
     # for the frequency alias:
     # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
     prices = data.history(
-        context.neo_eth,
+        context.market,
         fields='close',
         bar_count=50,
         frequency=context.CANDLE_SIZE
@@ -81,7 +85,7 @@ def handle_data(context, data):
     # We need a variable for the current price of the security to compare to
     # the average. Since we are requesting two fields, data.current()
     # returns a DataFrame with
-    current = data.current(context.neo_eth, fields=['close', 'volume'])
+    current = data.current(context.market, fields=['close', 'volume'])
     price = current['close']
 
     # If base_price is not set, we use the current value. This is the
@@ -95,34 +99,36 @@ def handle_data(context, data):
     # Now that we've collected all current data for this frame, we use
     # the record() method to save it. This data will be available as
     # a parameter of the analyze() function for further analysis.
+
     record(
-        price=price,
         volume=current['volume'],
+        price=price,
         price_change=price_change,
         rsi=rsi[-1],
         cash=cash
     )
-
     # We are trying to avoid over-trading by limiting our trades to
     # one per day.
     if context.traded_today:
         return
 
+    # TODO: retest with open orders
     # Since we are using limit orders, some orders may not execute immediately
     # we wait until all orders are executed before considering more trades.
-    orders = get_open_orders(context.neo_eth)
+    orders = get_open_orders(context.market)
     if len(orders) > 0:
+        log.info('exiting because orders are open: {}'.format(orders))
         return
 
     # Exit if we cannot trade
-    if not data.can_trade(context.neo_eth):
+    if not data.can_trade(context.market):
         return
 
     # 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.neo_eth].amount
+    pos_amount = context.portfolio.positions[context.market].amount
 
     if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
         log.info(
@@ -133,7 +139,7 @@ def handle_data(context, data):
         # Set a style for limit orders,
         limit_price = price * 1.005
         order_target_percent(
-            context.neo_eth, 1, limit_price=limit_price
+            context.market, 1, limit_price=limit_price
         )
         context.traded_today = True
 
@@ -145,7 +151,7 @@ def handle_data(context, data):
         )
         limit_price = price * 0.995
         order_target_percent(
-            context.neo_eth, 0, limit_price=limit_price
+            context.market, 0, limit_price=limit_price
         )
         context.traded_today = True
 
@@ -168,7 +174,7 @@ def analyze(context=None, perf=None):
     perf.loc[:, 'price'].plot(ax=ax2, label='Price')
 
     ax2.set_ylabel('{asset}\n({base})'.format(
-        asset=context.neo_eth.symbol, base=base_currency
+        asset=context.market.symbol, base=base_currency
     ))
 
     transaction_df = extract_transactions(perf)
@@ -229,7 +235,7 @@ def analyze(context=None, perf=None):
         )
     plt.legend(loc=3)
     start, end = ax6.get_ylim()
-    ax6.yaxis.set_ticks(np.arange(0, end, end/5))
+    ax6.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     # Show the plot.
     plt.gcf().set_size_inches(18, 8)
@@ -239,7 +245,7 @@ def analyze(context=None, perf=None):
 
 if __name__ == '__main__':
     # The execution mode: backtest or live
-    MODE = 'live'
+    MODE = 'backtest'
 
     if MODE == 'backtest':
         folder = os.path.join(
@@ -249,7 +255,9 @@ 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',
@@ -267,13 +275,15 @@ if __name__ == '__main__':
 
     elif MODE == 'live':
         run_algorithm(
-            capital_base=0.1,
+            capital_base=0.05,
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='bittrex',
+            exchange_name='binance',
             live=True,
             algo_namespace=NAMESPACE,
             base_currency='eth',
-            live_graph=False
+            live_graph=False,
+            simulate_orders=True,
+            stats_output=None
         )

catalyst/examples/portfolio_optimization.py

@@ -1,7 +1,7 @@
-'''Use this code to execute a portfolio optimization model. This code 
+'''Use this code to execute a portfolio optimization model. This code
-   will select the portfolio with the maximum Sharpe Ratio. The parameters 
+   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
 
@@ -15,119 +15,135 @@ 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
+    # Portfolio assets list
-   context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
+    context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
-                            'xmr_usdt')
+                             'xmr_usdt')
-   context.nassets = len(context.assets)
+    context.nassets = len(context.assets)
-   # Set the time window that will be used to compute expected return 
+    # Set the time window that will be used to compute expected return
-   # and asset correlations
+    # and asset correlations
-   context.window = 180
+    context.window = 180
-   # Set the number of days between each portfolio rebalancing
+    # Set the number of days between each portfolio rebalancing
-   context.rebalance_period = 30                   
+    context.rebalance_period = 30
-   context.i = 0
+    context.i = 0
+
 
-   
 def handle_data(context, data):
-   # Only rebalance at the beggining of the algorithm execution and 
+    # Only rebalance at the beggining of the algorithm execution and
-   # every multiple of the rebalance period
+    # every multiple of the rebalance period
-   if context.i == 0 or context.i%context.rebalance_period == 0:
+    if context.i == 0 or context.i % context.rebalance_period == 0:
-       n = context.window
+        n = context.window
-       prices = data.history(context.assets, fields='price', 
+        prices = data.history(context.assets, fields='price',
-                             bar_count=n+1, frequency='1d') 
+                              bar_count=n + 1, frequency='1d')
-       pr = np.asmatrix(prices)
+        pr = np.asmatrix(prices)
-       t_prices = prices.iloc[1:n+1]
+        t_prices = prices.iloc[1:n + 1]
-       t_val = t_prices.values
+        t_val = t_prices.values
-       tminus_prices = prices.iloc[0:n]
+        tminus_prices = prices.iloc[0:n]
-       tminus_val = tminus_prices.values
+        tminus_val = tminus_prices.values
-       # Compute daily returns (r)
+        # Compute daily returns (r)
-       r = np.asmatrix(t_val/tminus_val-1)
+        r = np.asmatrix(t_val / tminus_val - 1)
-       # Compute the expected returns of each asset with the average 
+        # Compute the expected returns of each asset with the average
-       # daily return for the selected time window
+        # daily return for the selected time window
-       m = np.asmatrix(np.mean(r, axis=0))
+        m = np.asmatrix(np.mean(r, axis=0))
-       # ###
+        # ###
-       stds = np.std(r, axis=0)
+        stds = np.std(r, axis=0)
-       # Compute excess returns matrix (xr)
+        # Compute excess returns matrix (xr)
-       xr = r - m
+        xr = r - m
-       # Matrix algebra to get variance-covariance matrix
+        # Matrix algebra to get variance-covariance matrix
-       cov_m = np.dot(np.transpose(xr),xr)/n
+        cov_m = np.dot(np.transpose(xr), xr) / n
-       # Compute asset correlation matrix (informative only)
+        # Compute asset correlation matrix (informative only)
-       corr_m = cov_m/np.dot(np.transpose(stds),stds)
+        corr_m = cov_m / np.dot(np.transpose(stds), stds)
-       
+
-       # Define portfolio optimization parameters
+        # Define portfolio optimization parameters
-       n_portfolios = 50000
+        n_portfolios = 50000
-       results_array = np.zeros((3+context.nassets,n_portfolios))
+        results_array = np.zeros((3 + context.nassets, n_portfolios))
-       for p in xrange(n_portfolios):
+        for p in xrange(n_portfolios):
-           weights = np.random.random(context.nassets)
+            weights = np.random.random(context.nassets)
-           weights /= np.sum(weights)
+            weights /= np.sum(weights)
-           w = np.asmatrix(weights)
+            w = np.asmatrix(weights)
-           p_r = np.sum(np.dot(w,np.transpose(m)))*365
+            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)
+            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
+            # store results in results array
-           results_array[1,p] = p_std
+            results_array[0, p] = p_r
-           #store Sharpe Ratio (return / volatility) - risk free rate element 
+            results_array[1, p] = p_std
-           #excluded for simplicity
+            # store Sharpe Ratio (return / volatility) - risk free rate element
-           results_array[2,p] = results_array[0,p] / results_array[1,p]
+            # excluded for simplicity
-           i = 0
+            results_array[2, p] = results_array[0, p] / results_array[1, p]
-           for iw in weights:
+            i = 0
-               results_array[3+i,p] = weights[i]
+            for iw in weights:
-               i += 1
+                results_array[3 + i, p] = weights[i]
-       
+                i += 1
-       #convert results array to Pandas DataFrame
+
-       results_frame = pd.DataFrame(np.transpose(results_array),
+        # convert results array to Pandas DataFrame
-                          columns=['r','stdev','sharpe']+context.assets)
+        results_frame = pd.DataFrame(np.transpose(results_array),
-       #locate position of portfolio with highest Sharpe Ratio
+                                     columns=['r', 'stdev', 'sharpe']
-       max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
+                                             + context.assets)
-       #locate positon of portfolio with minimum standard deviation
+        # locate position of portfolio with highest Sharpe Ratio
-       min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
+        max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
-       
+        # locate positon of portfolio with minimum standard deviation
-       #order optimal weights for each asset
+        # min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
-       for asset in context.assets:
+
-           if data.can_trade(asset):
+        # order optimal weights for each asset
-               order_target_percent(asset, max_sharpe_port[asset])
+        for asset in context.assets:
-       
+            if data.can_trade(asset):
-       #create scatter plot coloured by Sharpe Ratio
+                order_target_percent(asset, max_sharpe_port[asset])
-       plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
+
-       plt.xlabel('Volatility')
+        # create scatter plot coloured by Sharpe Ratio
-       plt.ylabel('Returns')
+        plt.scatter(results_frame.stdev,
-       plt.colorbar()
+                    results_frame.r,
-       #plot red star to highlight position of portfolio with highest Sharpe Ratio
+                    c=results_frame.sharpe,
-       plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
+                    cmap='RdYlGn')
-       #plot green star to highlight position of minimum variance portfolio
+        plt.xlabel('Volatility')
-       plt.show()
+        plt.ylabel('Returns')
-       print(max_sharpe_port)
+        plt.colorbar()
-       record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
+        # plot red star to highlight position of portfolio
-   context.i += 1
+        # 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
+    # Form DataFrame with selected data
-   data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
+    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]
+    # Save results in CSV file
-   data.to_csv(filename + '.csv')
+    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.    
+if __name__ == '__main__':
-start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
+    # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
-end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc) 
+    start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
-results = run_algorithm(initialize=initialize,
+    end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
-                        handle_data=handle_data,
+    results = run_algorithm(initialize=initialize,
-                        analyze=analyze,
+                            handle_data=handle_data,
-                        start=start,
+                            analyze=analyze,
-                        end=end,
+                            start=start,
-                        exchange_name='poloniex',
+                            end=end,
-                        capital_base=100000, )
+                            exchange_name='poloniex',
+                            capital_base=100000, )

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):
@@ -115,7 +114,7 @@ def _handle_data_rsi_only(context, data):
         prices = data.history(
             context.asset,
             fields='price',
-            bar_count=17,
+            bar_count=20,
             frequency='30T'
         )
     except Exception as e:
@@ -157,7 +156,7 @@ def handle_data(context, data):
     dt = data.current_dt
 
     if context.last_bar is None or (
-                context.last_bar + timedelta(minutes=15)) <= dt:
+            context.last_bar + timedelta(minutes=15)) <= dt:
         context.last_bar = dt
     else:
         return
@@ -250,27 +249,17 @@ def analyze(context=None, results=None):
     pass
 
 
-# run_algorithm(
+if __name__ == '__main__':
-#     initialize=initialize,
+    # Backtest
-#     handle_data=handle_data,
+    run_algorithm(
-#     analyze=analyze,
+        capital_base=0.5,
-#     exchange_name='bittrex',
+        data_frequency='minute',
-#     live=True,
+        initialize=initialize,
-#     algo_namespace=algo_namespace,
+        handle_data=handle_data,
-#     base_currency='btc',
+        analyze=analyze,
-#     live_graph=False
+        exchange_name='poloniex',
-# )
+        algo_namespace=algo_namespace,
-
+        base_currency='btc',
-# Backtest
+        start=pd.to_datetime('2017-9-1', utc=True),
-run_algorithm(
+        end=pd.to_datetime('2017-10-1', utc=True),
-    capital_base=0.5,
+    )
-    data_frequency='minute',
-    initialize=initialize,
-    handle_data=handle_data,
-    analyze=analyze,
-    exchange_name='poloniex',
-    algo_namespace=algo_namespace,
-    base_currency='btc',
-    start=pd.to_datetime('2017-9-1', utc=True),
-    end=pd.to_datetime('2017-10-1', utc=True),
-)

catalyst/examples/simple_loop.py

@@ -9,7 +9,7 @@ from catalyst.exchange.stats_utils import get_pretty_stats, \
 
 def initialize(context):
     print('initializing')
-    context.asset = symbol('neo_eth')
+    context.asset = symbol('eth_btc')
     context.base_price = None
 
 
@@ -23,8 +23,11 @@ def handle_data(context, data):
         context.asset,
         fields='price',
         bar_count=20,
-        frequency='15T'
+        frequency='30T'
     )
+    last_traded = prices.index[-1]
+    print('last candle date: {}'.format(last_traded))
+
     rsi = talib.RSI(prices.values, timeperiod=14)[-1]
     print('got rsi: {}'.format(rsi))
 
@@ -107,25 +110,16 @@ def analyze(context, perf):
     pass
 
 
-run_algorithm(
+if __name__ == '__main__':
-    capital_base=250,
+    run_algorithm(
-    start=pd.to_datetime('2017-11-1 0:00', utc=True),
+        capital_base=1,
-    end=pd.to_datetime('2017-11-10 23:59', utc=True),
+        initialize=initialize,
-    data_frequency='daily',
+        handle_data=handle_data,
-    initialize=initialize,
+        analyze=None,
-    handle_data=handle_data,
+        exchange_name='poloniex',
-    analyze=analyze,
+        live=True,
-    exchange_name='bitfinex',
+        algo_namespace='simple_loop',
-    algo_namespace='simple_loop',
+        base_currency='eth',
-    base_currency='usd'
+        live_graph=False,
-)
+        simulate_orders=True
-# run_algorithm(
+    )
-#     initialize=initialize,
-#     handle_data=handle_data,
-#     analyze=None,
-#     exchange_name='binance',
-#     live=True,
-#     algo_namespace='simple_loop',
-#     base_currency='eth',
-#     live_graph=False,
-# )

catalyst/examples/simple_universe.py

@@ -2,73 +2,117 @@
 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.
+This example aims to provide an easy way for users to learn how to
-You simply need to specify the exchange and the market that you want to focus on.
+collect data from any given exchange and select a subset of the available
-You will all see how to create a universe and filter it base on the exchange and the market you desire.
+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-exchange every 30 minutes.
+The example prints out the closing price of all the pairs for a given
-The example also contains the ohlcv minute data for the past seven days which could be used to create indicators
+market in a given exchange every 30 minutes. The example also contains
-Use this as the backbone to create your own trading strategies.
+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 period specified.
+
+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
+of the file:
+
+catalyst ingest-exchange -x bitfinex -f minute
+
+python simple_universe.py
 
-Variables lookback date and date are used to ensure data for a coin existed on the lookback period specified.
 """
+from datetime import timedelta
 
 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, )
-from catalyst.api import (
-    symbols,
-)
 
 
 def initialize(context):
-    context.i = -1  # counts the minutes
+    context.i = -1  # minute counter
-    context.exchange = context.exchanges.values()[0].name.lower()  # exchange name
+    context.exchange = context.exchanges.values()[0].name.lower()
-    context.base_currency = context.exchanges.values()[0].base_currency.lower()  # market base currency
+    context.base_currency = context.exchanges.values()[0].base_currency.lower()
 
 
 def handle_data(context, data):
     context.i += 1
     lookback_days = 7  # 7 days
 
-    # current date formatted into a string
+    # current date & time in each iteration formatted into a string
-    today = data.current_dt
+    now = data.current_dt
-    date, time = today.strftime('%Y-%m-%d %H:%M:%S').split(' ')
+    date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
-    lookback_date = today - timedelta(days=lookback_days)  # subtract the amount of days specified in lookback
+    lookback_date = now - timedelta(days=lookback_days)
-    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]  # get only the date as a string
+    # keep only the date as a string, discard the time
+    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]
 
-    # update universe everyday
+    one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
-    new_day = 60 * 24  # assuming data_frequency='minute'
+    # update universe everyday at midnight
-    if not context.i % new_day:
+    if not context.i % one_day_in_minutes:
         context.universe = universe(context, lookback_date, date)
 
     # get data every 30 minutes
     minutes = 30
-    one_day_in_minutes = 1440  # 1440 assumes data_frequency='minute'
+
-    lookback = one_day_in_minutes / minutes * lookback_days  # get N lookback_days of history data
+    # get lookback_days of history data: that is 'lookback' number of bins
+    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)
 
-            # 30 minute interval ohlcv data (the standard data required for candlestick or indicators/signals)
+            # Get 30 minute interval OHLCV data. This is the standard data
-            # 30T means 30 minutes re-sampling of one minute data. change to your desire time interval.
+            # required for candlestick or indicators/signals. Return Pandas
-            opened = fill(data.history(coin, 'open', bar_count=lookback, frequency='30T')).values
+            # DataFrames. 30T means 30-minute re-sampling of one minute data.
-            high = fill(data.history(coin, 'high', bar_count=lookback, frequency='30T')).values
+            # Adjust it to your desired time interval as needed.
-            low = fill(data.history(coin, 'low', bar_count=lookback, frequency='30T')).values
+            opened = fill(data.history(coin,
-            close = fill(data.history(coin, 'price', bar_count=lookback, frequency='30T')).values
+                                       'open',
-            volume = fill(data.history(coin, 'volume', bar_count=lookback, frequency='30T')).values
+                                       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
+            # 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(today, pair, opened[-1], high[-1], low[-1], close[-1], volume[-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 -----------------------------------------
+            # --------------- Insert Your Strategy Here -------------------
-            # ----------------------------------------------------------------------------------------------------------
+            # -------------------------------------------------------------
 
 
 def analyze(context=None, results=None):
@@ -78,23 +122,24 @@ def analyze(context=None, results=None):
 # 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
+    # get all the pairs for the given exchange
-    universe_df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str)  # convert into a dataframe
+    json_symbols = get_exchange_symbols(context.exchange)
-    universe_df['base_currency'] = universe_df.apply(lambda row: row.symbol.split('_')[1],
+    # convert into a DataFrame for easier processing
-                                                                       axis=1)
+    df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str)
-    universe_df['market_currency'] = universe_df.apply(lambda row: row.symbol.split('_')[0],
+    df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],
-                                                                         axis=1)
+                                   axis=1)
+    df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],
+                                     axis=1)
 
-    # Filter all the exchange pairs to only the ones for a give base currency
+    # Filter all the pairs to get only the ones for a given base_currency
-    universe_df = universe_df[universe_df['base_currency'] == context.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
-    universe_df = universe_df[universe_df.start_date < lookback_date]
+    df = df[df.start_date < lookback_date]
-    universe_df = universe_df[universe_df.end_daily >= current_date]
+    df = df[df.end_daily >= current_date]
-    context.coins = symbols(*universe_df.symbol)  # convert all the pairs to symbols
+    context.coins = symbols(*df.symbol)  # convert all the pairs to symbols
 
-    # print(universe_df.symbol.tolist())
+    return df.symbol.tolist()
-    return universe_df.symbol.tolist()
 
 
 # Replace all NA, NAN or infinite values with its nearest value
@@ -102,7 +147,9 @@ 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
+        return pd.Series(series).replace(
+                     [np.inf, -np.inf], np.nan
+                    ).ffill().bfill().values
     else:
         return series
 
@@ -112,18 +159,13 @@ if __name__ == '__main__':
     end_date = pd.to_datetime('2017-11-13', utc=True)
 
     performance = run_algorithm(start=start_date, end=end_date,
-                                capital_base=100.0,  # amount of base_currency, not always in dollars unless usd
+                                capital_base=100.0,  # amount of base_currency
                                 initialize=initialize,
                                 handle_data=handle_data,
                                 analyze=analyze,
-                                exchange_name='bitfinex',
+                                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
-"""

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
+# Simple TALib Example showing how to use various indicators
-# Based loosly on https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
+# 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(

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 (
@@ -29,16 +30,17 @@ from catalyst.protocol import Account
 
 # Trying to account for REST api instability
 # https://stackoverflow.com/questions/15431044/can-i-set-max-retries-for-requests-request
+from catalyst.utils.deprecate import deprecated
+
 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')
 
 
+@deprecated
 class Bitfinex(Exchange):
     def __init__(self, key, secret, base_currency, portfolio=None):
         self.url = BITFINEX_URL
@@ -172,7 +174,8 @@ 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 +602,17 @@ 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 +663,16 @@ 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 = ('{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))
+                    int(time.time() * 1000)))
-        )
 
         try:
             self.ask_request()

catalyst/exchange/bittrex/bittrex.py

@@ -19,12 +19,14 @@ from catalyst.finance.execution import LimitOrder, StopLimitOrder
 from catalyst.finance.order import Order, ORDER_STATUS
 
 # TODO: consider using this: https://github.com/mondeja/bittrex_v2
+from catalyst.utils.deprecate import deprecated
 
 log = Logger('Bittrex', level=LOG_LEVEL)
 
 URL2 = 'https://bittrex.com/Api/v2.0'
 
 
+@deprecated
 class Bittrex(Exchange):
     def __init__(self, key, secret, base_currency, portfolio=None):
         self.api = Bittrex_api(key=key, secret=secret)
@@ -262,11 +264,10 @@ class Bittrex(Exchange):
             end = int(time.mktime(end_dt.timetuple()))
             url = '{url}/pub/market/GetTicks?marketName={symbol}' \
                   '&tickInterval={frequency}&_={end}'.format(
-                url=URL2,
+                    url=URL2,
-                symbol=self.get_symbol(asset),
+                    symbol=self.get_symbol(asset),
-                frequency=frequency,
+                    frequency=frequency,
-                end=end
+                    end=end, )
-            )
 
             try:
                 data = json.loads(urllib.request.urlopen(url).read().decode())
@@ -359,12 +360,12 @@ 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(

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))
+register('exchange_bitfinex', exchange_bundle('bitfinex', symbols))

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, \
+from catalyst.exchange.exchange_utils import get_exchange_bundles_folder
-    get_exchange_symbols
 
 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,
+                exchange=exchange_name,
-            name=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' \
+    if data_frequency == 'minute':
-        else '{}'.format(dt.year)
+        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

catalyst/exchange/ccxt/ccxt_exchange.py

@@ -3,23 +3,23 @@ from collections import defaultdict
 
 import ccxt
 import pandas as pd
-from ccxt import ExchangeNotAvailable
+import six
+from ccxt import ExchangeNotAvailable, InvalidOrder
+from logbook import Logger
 from six import string_types
 
-from catalyst.finance.order import Order, ORDER_STATUS
-
 from catalyst.algorithm import MarketOrder
 from catalyst.assets._assets import TradingPair
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
-from catalyst.exchange.exchange import Exchange, ExchangeLimitOrder
+from catalyst.exchange.exchange import Exchange
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
     ExchangeSymbolsNotFound, ExchangeRequestError, InvalidOrderStyle, \
-    ExchangeNotFoundError
+    ExchangeNotFoundError, CreateOrderError
+from catalyst.exchange.exchange_execution import ExchangeLimitOrder
 from catalyst.exchange.exchange_utils import mixin_market_params, \
-    from_ms_timestamp
+    from_ms_timestamp, get_epoch
+from catalyst.finance.order import Order, ORDER_STATUS
 
 log = Logger('CCXT', level=LOG_LEVEL)
 
@@ -34,8 +34,7 @@ SUPPORTED_EXCHANGES = dict(
 
 
 class CCXT(Exchange):
-    def __init__(self, exchange_name, key, secret, base_currency,
+    def __init__(self, exchange_name, key, secret, base_currency):
-                 portfolio=None):
         log.debug(
             'finding {} in CCXT exchanges:\n{}'.format(
                 exchange_name, ccxt.exchanges
@@ -59,8 +58,12 @@ class CCXT(Exchange):
 
         self._symbol_maps = [None, None]
 
-        markets_symbols = self.api.load_markets()
+        try:
-        log.debug('the markets:\n{}'.format(markets_symbols))
+            markets_symbols = self.api.load_markets()
+            log.debug('the markets:\n{}'.format(markets_symbols))
+
+        except ExchangeNotAvailable as e:
+            raise ExchangeRequestError(error=e)
 
         self.name = exchange_name
 
@@ -68,7 +71,6 @@ class CCXT(Exchange):
         self.load_assets()
 
         self.base_currency = base_currency
-        self._portfolio = portfolio
         self.transactions = defaultdict(list)
 
         self.num_candles_limit = 2000
@@ -182,33 +184,50 @@ class CCXT(Exchange):
 
     def get_candles(self, freq, assets, bar_count=None, start_dt=None,
                     end_dt=None):
+        is_single = (isinstance(assets, TradingPair))
+        if is_single:
+            assets = [assets]
+
         symbols = self.get_symbols(assets)
         timeframe = self.get_timeframe(freq)
-        delta = start_dt - pd.to_datetime('1970-1-1', utc=True)
+
-        ms = int(delta.total_seconds()) * 1000
+        ms = None
+        if start_dt is not None:
+            delta = start_dt - get_epoch()
+            ms = int(delta.total_seconds()) * 1000
 
         candles = dict()
         for asset in assets:
-            ohlcvs = self.api.fetch_ohlcv(
+            try:
-                symbol=symbols[0],
+                ohlcvs = self.api.fetch_ohlcv(
-                timeframe=timeframe,
+                    symbol=symbols[0],
-                since=ms,
+                    timeframe=timeframe,
-                limit=bar_count,
+                    since=ms,
-                params={}
+                    limit=bar_count,
-            )
+                    params={}
+                )
 
-            candles[asset] = []
+                candles[asset] = []
-            for ohlcv in ohlcvs:
+                for ohlcv in ohlcvs:
-                candles[asset].append(dict(
+                    candles[asset].append(dict(
-                    last_traded=pd.to_datetime(ohlcv[0], unit='ms', utc=True),
+                        last_traded=pd.to_datetime(
-                    open=ohlcv[1],
+                            ohlcv[0], unit='ms', utc=True
-                    high=ohlcv[2],
+                        ),
-                    low=ohlcv[3],
+                        open=ohlcv[1],
-                    close=ohlcv[4],
+                        high=ohlcv[2],
-                    volume=ohlcv[5]
+                        low=ohlcv[3],
-                ))
+                        close=ohlcv[4],
+                        volume=ohlcv[5]
+                    ))
 
-        return candles
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+        if is_single:
+            return six.next(six.itervalues(candles))
+
+        else:
+            return candles
 
     def _fetch_symbol_map(self, is_local):
         try:
@@ -274,7 +293,7 @@ class CCXT(Exchange):
         else:
             return None
 
-    def create_trading_pair(self, market, asset_def, is_local):
+    def create_trading_pair(self, market, asset_def=None, is_local=False):
         """
         Creating a TradingPair from market and asset data.
 
@@ -332,6 +351,7 @@ class CCXT(Exchange):
         for market in self.markets:
             asset_defs = self.get_asset_defs(market)
 
+            asset = None
             for asset_def in asset_defs:
                 if asset_def[0] is not None or not asset_defs[1]:
                     try:
@@ -342,8 +362,12 @@ class CCXT(Exchange):
                         )
                         self.assets.append(asset)
 
-                    except TypeError:
+                    except TypeError as e:
-                        pass
+                        log.warn('unable to add asset: {}'.format(e))
+
+            if asset is None:
+                asset = self.create_trading_pair(market=market)
+                self.assets.append(asset)
 
     def get_balances(self):
         try:
@@ -446,26 +470,45 @@ class CCXT(Exchange):
 
         side = 'buy' if amount > 0 else 'sell'
 
+        if hasattr(self.api, 'amount_to_lots'):
+            adj_amount = self.api.amount_to_lots(
+                symbol=symbol,
+                amount=abs(amount),
+            )
+            if adj_amount != abs(amount):
+                log.info(
+                    'adjusted order amount {} to {} based on lot size'.format(
+                        abs(amount), adj_amount,
+                    )
+                )
+        else:
+            adj_amount = abs(amount)
+
         try:
             result = self.api.create_order(
                 symbol=symbol,
                 type=order_type,
                 side=side,
-                amount=abs(amount),
+                amount=adj_amount,
                 price=price
             )
         except ExchangeNotAvailable as e:
             log.debug('unable to create order: {}'.format(e))
             raise ExchangeRequestError(error=e)
 
+        except InvalidOrder as e:
+            log.warn('the exchange rejected the order: {}'.format(e))
+            raise CreateOrderError(exchange=self.name, error=e)
+
         if 'info' not in result:
             raise ValueError('cannot use order without info attribute')
 
+        final_amount = adj_amount if side == 'buy' else -adj_amount
         order_id = result['id']
         order = Order(
             dt=pd.Timestamp.utcnow(),
             asset=asset,
-            amount=amount,
+            amount=final_amount,
             stop=style.get_stop_price(is_buy),
             limit=style.get_limit_price(is_buy),
             id=order_id
@@ -492,18 +535,7 @@ class CCXT(Exchange):
 
         return orders
 
-    def _get_asset_from_order(self, order_id):
-        open_orders = self.portfolio.open_orders
-        order = next(
-            (open_orders[id] for id in open_orders if id == order_id),
-            None
-        )  # type: Order
-        return order.asset if order is not None else None
-
     def get_order(self, order_id, asset_or_symbol=None):
-        if asset_or_symbol is None and self.portfolio is not None:
-            asset_or_symbol = self._get_asset_from_order(order_id)
-
         if asset_or_symbol is None:
             log.debug(
                 'order not found in memory, the request might fail '
@@ -524,9 +556,6 @@ class CCXT(Exchange):
         order_id = order_param.id \
             if isinstance(order_param, Order) else order_param
 
-        if asset_or_symbol is None and self.portfolio is not None:
-            asset_or_symbol = self._get_asset_from_order(order_id)
-
         if asset_or_symbol is None:
             log.debug(
                 'order not found in memory, cancelling order might fail '
@@ -555,20 +584,29 @@ class CCXT(Exchange):
         """
         tickers = dict()
         for asset in assets:
-            ccxt_symbol = self.get_symbol(asset)
+            try:
-            ticker = self.api.fetch_ticker(ccxt_symbol)
+                ccxt_symbol = self.get_symbol(asset)
+                ticker = self.api.fetch_ticker(ccxt_symbol)
 
-            ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
+                ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
 
-            if 'last_price' not in ticker:
+                if 'last_price' not in ticker:
-                # TODO: any more exceptions?
+                    # TODO: any more exceptions?
-                ticker['last_price'] = ticker['last']
+                    ticker['last_price'] = ticker['last']
 
-            # Using the volume represented in the base currency
+                # Using the volume represented in the base currency
-            ticker['volume'] = ticker['baseVolume'] \
+                ticker['volume'] = ticker['baseVolume'] \
-                if 'baseVolume' in ticker else 0
+                    if 'baseVolume' in ticker else 0
 
-            tickers[asset] = ticker
+                tickers[asset] = ticker
+
+            except ExchangeNotAvailable as e:
+                log.warn(
+                    'unable to fetch ticker: {} {}'.format(
+                        self.name, asset.symbol
+                    )
+                )
+                raise ExchangeRequestError(error=e)
 
         return tickers
 

catalyst/exchange/exchange.py

@@ -5,10 +5,8 @@ from time import sleep
 
 import numpy as np
 import pandas as pd
-from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
-from catalyst.algorithm import MarketOrder
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.data_portal import BASE_FIELDS
 from catalyst.exchange.bundle_utils import get_start_dt, \
@@ -17,14 +15,9 @@ from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import MismatchingBaseCurrencies, \
     BaseCurrencyNotFoundError, SymbolNotFoundOnExchange, \
     PricingDataNotLoadedError, \
-    NoDataAvailableOnExchange, NoValueForField
+    NoDataAvailableOnExchange, NoValueForField, LastCandleTooEarlyError
-from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
-    ExchangeLimitOrder, ExchangeStopOrder
-from catalyst.exchange.exchange_portfolio import ExchangePortfolio
 from catalyst.exchange.exchange_utils import get_exchange_symbols, \
     get_frequency, resample_history_df
-from catalyst.finance.order import ORDER_STATUS
-from catalyst.finance.transaction import Transaction
 
 log = Logger('Exchange', level=LOG_LEVEL)
 
@@ -36,7 +29,6 @@ class Exchange:
         self.name = None
         self.assets = []
         self._symbol_maps = [None, None]
-        self._portfolio = None
         self.minute_writer = None
         self.minute_reader = None
         self.base_currency = None
@@ -46,27 +38,6 @@ class Exchange:
         self.request_cpt = None
         self.bundle = ExchangeBundle(self.name)
 
-    @property
-    def positions(self):
-        return self.portfolio.positions
-
-    @property
-    def portfolio(self):
-        """
-        The exchange portfolio
-
-        Returns
-        -------
-        ExchangePortfolio
-        """
-        if self._portfolio is None:
-            self._portfolio = ExchangePortfolio(
-                start_date=pd.Timestamp.utcnow()
-            )
-            self.synchronize_portfolio()
-
-        return self._portfolio
-
     @abstractproperty
     def account(self):
         pass
@@ -205,10 +176,18 @@ class Exchange:
 
         assets = []
         for symbol in symbols:
-            asset = self.get_asset(
+            try:
-                symbol, data_frequency, is_exchange_symbol, is_local
+                asset = self.get_asset(
-            )
+                    symbol, data_frequency, is_exchange_symbol, is_local
-            assets.append(asset)
+                )
+                assets.append(asset)
+
+            except SymbolNotFoundOnExchange:
+                log.debug(
+                    'skipping non-existent market {} {}'.format(
+                        self.name, symbol
+                    )
+                )
         return assets
 
     def get_asset(self, symbol, data_frequency=None, is_exchange_symbol=False,
@@ -256,8 +235,10 @@ class Exchange:
 
             elif data_frequency is not None:
                 applies = (
-                    (data_frequency == 'minute' and a.end_minute is not None)
+                        (
-                    or (data_frequency == 'daily' and a.end_daily is not None)
+                                data_frequency == 'minute' and a.end_minute is not None)
+                        or (
+                                data_frequency == 'daily' and a.end_daily is not None)
                 )
 
             else:
@@ -270,9 +251,7 @@ class Exchange:
                 asset = a
 
         if asset is None:
-            supported_symbols = sorted([
+            supported_symbols = sorted([a.symbol for a in self.assets])
-                asset.symbol for asset in self.assets
-            ])
 
             raise SymbolNotFoundOnExchange(
                 symbol=symbol,
@@ -313,54 +292,6 @@ class Exchange:
         """
         pass
 
-    def check_open_orders(self):
-        """
-        Loop through the list of open orders in the Portfolio object.
-        For each executed order found, create a transaction and apply to the
-        Portfolio.
-
-        Returns
-        -------
-        list[Transaction]
-
-        """
-        transactions = list()
-        if self.portfolio.open_orders:
-            for order_id in list(self.portfolio.open_orders):
-                log.debug('found open order: {}'.format(order_id))
-
-                order, executed_price = self.get_order(order_id)
-                log.debug(
-                    'got updated order {} {}'.format(
-                        order, executed_price
-                    )
-                )
-                if order.status == ORDER_STATUS.FILLED:
-                    transaction = Transaction(
-                        asset=order.asset,
-                        amount=order.amount,
-                        dt=pd.Timestamp.utcnow(),
-                        price=executed_price,
-                        order_id=order.id,
-                        commission=order.commission
-                    )
-                    transactions.append(transaction)
-
-                    self.portfolio.execute_order(order, transaction)
-
-                elif order.status == ORDER_STATUS.CANCELLED:
-                    self.portfolio.remove_order(order)
-
-                else:
-                    delta = pd.Timestamp.utcnow() - order.dt
-                    log.info(
-                        'order {order_id} still open after {delta}'.format(
-                            order_id=order_id,
-                            delta=delta
-                        )
-                    )
-        return transactions
-
     def get_spot_value(self, assets, field, dt=None, data_frequency='minute'):
         """
         Public API method that returns a scalar value representing the value
@@ -478,7 +409,7 @@ class Exchange:
             method='ffill',
             fill_value=previous_value,
         )
-
+        series.sort_index(inplace=True)
         return series
 
     def get_history_window(self,
@@ -488,7 +419,7 @@ class Exchange:
                            frequency,
                            field,
                            data_frequency=None,
-                           ffill=True):
+                           is_current=False):
 
         """
         Public API method that returns a dataframe containing the requested
@@ -515,10 +446,15 @@ class Exchange:
             The frequency of the data to query; i.e. whether the data is
             'daily' or 'minute' bars.
 
-        # TODO: fill how?
+        is_current: bool
-        ffill: boolean
+            Skip date filters when current data is requested (last few bars
-            Forward-fill missing values. Only has effect if field
+            until now).
-            is 'price'.
+
+        Notes
+        -----
+        Catalysts requires an end data with bar count both CCXT wants a
+        start data with bar count. Since we have to make calculations here,
+        we ensure that the last candle match the end_dt parameter.
 
         Returns
         -------
@@ -530,6 +466,7 @@ class Exchange:
             frequency, data_frequency
         )
         adj_bar_count = candle_size * bar_count
+
         start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
 
         # The get_history method supports multiple asset
@@ -537,8 +474,8 @@ class Exchange:
             freq=freq,
             assets=assets,
             bar_count=bar_count,
-            start_dt=start_dt,
+            start_dt=start_dt if not is_current else None,
-            end_dt=end_dt
+            end_dt=end_dt if not is_current else None,
         )
 
         series = dict()
@@ -550,6 +487,17 @@ class Exchange:
                 data_frequency=frequency,
                 field=field,
             )
+            if end_dt is not None:
+                delta = get_delta(candle_size, data_frequency)
+                adj_end_dt = end_dt - delta
+                last_traded = asset_series.index[-1]
+
+                if last_traded < adj_end_dt:
+                    raise LastCandleTooEarlyError(
+                        last_traded=last_traded,
+                        end_dt=adj_end_dt,
+                        exchange=self.name,
+                    )
             series[asset] = asset_series
 
         df = pd.DataFrame(series)
@@ -607,6 +555,7 @@ class Exchange:
             frequency, data_frequency
         )
         adj_bar_count = candle_size * bar_count
+
         try:
             series = self.bundle.get_history_window_series_and_load(
                 assets=assets,
@@ -616,6 +565,7 @@ class Exchange:
                 data_frequency=data_frequency,
                 force_auto_ingest=force_auto_ingest
             )
+
         except (PricingDataNotLoadedError, NoDataAvailableOnExchange):
             series = dict()
 
@@ -669,51 +619,48 @@ class Exchange:
 
         return df
 
-    def synchronize_portfolio(self):
+    def calculate_totals(self, check_cash=False, positions=None):
         """
         Update the portfolio cash and position balances based on the
         latest ticker prices.
 
         """
         log.debug('synchronizing portfolio with exchange {}'.format(self.name))
-        balances = self.get_balances()
 
-        base_position_available = balances[self.base_currency]['free'] \
+        cash = None
-            if self.base_currency in balances else None
+        if check_cash:
+            balances = self.get_balances()
 
-        if base_position_available is None:
+            cash = balances[self.base_currency]['free'] \
-            raise BaseCurrencyNotFoundError(
+                if self.base_currency in balances else None
-                base_currency=self.base_currency,
-                exchange=self.name.title()
-            )
 
-        portfolio = self._portfolio
+            if cash is None:
-        portfolio.cash = base_position_available
+                raise BaseCurrencyNotFoundError(
-        log.debug('found base currency balance: {}'.format(portfolio.cash))
+                    base_currency=self.base_currency,
+                    exchange=self.name
+                )
+            log.debug('found base currency balance: {}'.format(cash))
 
-        if portfolio.starting_cash is None:
+        positions_value = 0.0
-            portfolio.starting_cash = portfolio.cash
+        if positions:
-
+            assets = set([position.asset for position in positions])
-        if portfolio.positions:
-            assets = list(portfolio.positions.keys())
             tickers = self.tickers(assets)
+            log.debug('got tickers for positions: {}'.format(tickers))
 
-            portfolio.positions_value = 0.0
             for asset in tickers:
-                # TODO: convert if the position is not in the base currency
                 ticker = tickers[asset]
-                position = portfolio.positions[asset]
+                positions = [p for p in positions if p.asset == asset]
 
-                position.last_sale_price = ticker['last_price']
+                for position in positions:
-                position.last_sale_date = ticker['last_traded']
+                    position.last_sale_price = ticker['last_price']
+                    position.last_sale_date = ticker['last_traded']
 
-                portfolio.positions_value += \
+                    positions_value += \
-                    position.amount * position.last_sale_price
+                        position.amount * position.last_sale_price
-                portfolio.portfolio_value = \
-                    portfolio.positions_value + portfolio.cash
 
-    def order(self, asset, amount, limit_price=None, stop_price=None,
+        return cash, positions_value
-              style=None):
+
+    def order(self, asset, amount, style):
         """Place an order.
 
         Parameters
@@ -772,24 +719,11 @@ class Exchange:
             )
 
         is_buy = (amount > 0)
+        display_price = style.get_limit_price(is_buy)
 
-        if limit_price is not None and stop_price is not None:
-            style = ExchangeStopLimitOrder(
-                limit_price, stop_price, exchange=self.name
-            )
-
-        elif limit_price is not None:
-            style = ExchangeLimitOrder(limit_price, exchange=self.name)
-
-        elif stop_price is not None:
-            style = ExchangeStopOrder(stop_price, exchange=self.name)
-
-        else:
-            style = MarketOrder(exchange=self.name)
-
-        display_price = limit_price if limit_price is not None else stop_price
         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,
@@ -798,12 +732,7 @@ class Exchange:
             )
         )
 
-        order = self.create_order(asset, amount, is_buy, style)
+        return self.create_order(asset, amount, is_buy, style)
-        if order:
-            self._portfolio.create_order(order)
-            return order.id
-        else:
-            return None
 
     # The methods below must be implemented for each exchange.
     @abstractmethod

catalyst/exchange/exchange_algorithm.py

@@ -13,7 +13,6 @@
 import pickle
 import signal
 import sys
-from collections import deque
 from datetime import timedelta
 from os import listdir
 from os.path import isfile, join
@@ -21,34 +20,32 @@ from time import sleep
 
 import logbook
 import pandas as pd
-from catalyst.assets._assets import TradingPair
 
 import catalyst.protocol as zp
 from catalyst.algorithm import TradingAlgorithm
 from catalyst.constants import LOG_LEVEL
-from catalyst.errors import OrderInBeforeTradingStart
 from catalyst.exchange.exchange_blotter import ExchangeBlotter
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     ExchangePortfolioDataError,
-    ExchangeTransactionError,
+    OrderTypeNotSupported, )
-    OrphanOrderError)
+from catalyst.exchange.exchange_execution import ExchangeLimitOrder
-from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
+from catalyst.exchange.exchange_utils import (
-    ExchangeLimitOrder, ExchangeStopOrder
+    save_algo_object,
-from catalyst.exchange.exchange_utils import save_algo_object, get_algo_object, \
+    get_algo_object,
-    get_algo_folder, get_algo_df, \
+    get_algo_folder,
-    save_algo_df
+    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
+from catalyst.exchange.stats_utils import get_pretty_stats, stats_to_s3, \
+    stats_to_algo_folder
 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 (
+from catalyst.utils.api_support import api_method
-    api_method,
+from catalyst.utils.input_validation import error_keywords, ensure_upper_case
-    disallowed_in_before_trading_start)
-from catalyst.utils.input_validation import error_keywords, ensure_upper_case, \
-    expect_types
 from catalyst.utils.math_utils import round_nearest
 from catalyst.utils.preprocess import preprocess
 
@@ -63,9 +60,90 @@ class ExchangeAlgorithmExecutor(AlgorithmSimulator):
 class ExchangeTradingAlgorithmBase(TradingAlgorithm):
     def __init__(self, *args, **kwargs):
         self.exchanges = kwargs.pop('exchanges', None)
+        self.simulate_orders = kwargs.pop('simulate_orders', None)
 
         super(ExchangeTradingAlgorithmBase, self).__init__(*args, **kwargs)
 
+        self.current_day = None
+
+        if self.simulate_orders is None \
+                and self.sim_params.arena == 'backtest':
+            self.simulate_orders = True
+
+        self.blotter = ExchangeBlotter(
+            data_frequency=self.data_frequency,
+            # Default to NeverCancel in catalyst
+            cancel_policy=self.cancel_policy,
+            simulate_orders=self.simulate_orders,
+            exchanges=self.exchanges
+        )
+
+    @staticmethod
+    def __convert_order_params_for_blotter(limit_price, stop_price, style):
+        """
+        Helper method for converting deprecated limit_price and stop_price
+        arguments into ExecutionStyle instances.
+
+        This function assumes that either style == None or (limit_price,
+        stop_price) == (None, None).
+        """
+        if stop_price:
+            raise OrderTypeNotSupported(order_type='stop')
+
+        if style:
+            if limit_price is not None:
+                raise ValueError(
+                    'An order style and a limit price was included in the '
+                    'order. Please pick one to avoid any possible conflict.'
+                )
+
+            # Currently limiting order types or limit and market to
+            # be in-line with CXXT and many exchanges. We'll consider
+            # adding more order types in the future.
+            if not isinstance(style, ExchangeLimitOrder) or \
+                    not isinstance(style, MarketOrder):
+                raise OrderTypeNotSupported(
+                    order_type=style.__class__.__name__
+                )
+
+            return style
+
+        if limit_price:
+            return ExchangeLimitOrder(limit_price)
+        else:
+            return MarketOrder()
+
+    @api_method
+    def set_commission(self, maker=None, taker=None):
+        key = self.blotter.commission_models.keys()[0]
+        if maker is not None:
+            self.blotter.commission_models[key].maker = maker
+
+        if taker is not None:
+            self.blotter.commission_models[key].taker = taker
+
+    @api_method
+    def set_slippage(self, spread=None):
+        key = self.blotter.slippage_models.keys()[0]
+        if spread is not None:
+            self.blotter.slippage_models[key].spread = spread
+
+    def _calculate_order(self, asset, amount,
+                         limit_price=None, stop_price=None, style=None):
+        # Raises a ZiplineError if invalid parameters are detected.
+        self.validate_order_params(asset,
+                                   amount,
+                                   limit_price,
+                                   stop_price,
+                                   style)
+
+        # Convert deprecated limit_price and stop_price parameters to use
+        # ExecutionStyle objects.
+        style = self.__convert_order_params_for_blotter(limit_price,
+                                                        stop_price,
+                                                        style)
+        return amount, style
+
     def round_order(self, amount, asset):
         """
         We need fractions with cryptocurrencies
@@ -204,50 +282,8 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
         super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
 
         self.frame_stats = list()
-        self.blotter = ExchangeBlotter(
-            data_frequency=self.data_frequency,
-            # Default to NeverCancel in catalyst
-            cancel_policy=self.cancel_policy,
-        )
         log.info('initialized trading algorithm in backtest mode')
 
-    def _calculate_order(self, asset, amount,
-                         limit_price=None, stop_price=None, style=None):
-        # Raises a ZiplineError if invalid parameters are detected.
-        self.validate_order_params(asset,
-                                   amount,
-                                   limit_price,
-                                   stop_price,
-                                   style)
-
-        # Convert deprecated limit_price and stop_price parameters to use
-        # ExecutionStyle objects.
-        style = self.__convert_order_params_for_blotter(limit_price,
-                                                        stop_price,
-                                                        style)
-        return amount, style
-
-    @staticmethod
-    def __convert_order_params_for_blotter(limit_price, stop_price, style):
-        """
-        Helper method for converting deprecated limit_price and stop_price
-        arguments into ExecutionStyle instances.
-
-        This function assumes that either style == None or (limit_price,
-        stop_price) == (None, None).
-        """
-        if style:
-            assert (limit_price, stop_price) == (None, None)
-            return style
-        if limit_price and stop_price:
-            return ExchangeStopLimitOrder(limit_price, stop_price)
-        if limit_price:
-            return ExchangeLimitOrder(limit_price)
-        if stop_price:
-            return ExchangeStopOrder(stop_price)
-        else:
-            return MarketOrder()
-
     def is_last_frame_of_day(self, data):
         # TODO: adjust here to support more intervals
         next_frame_dt = data.current_dt + timedelta(minutes=1)
@@ -265,6 +301,8 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
             )
             self.frame_stats.append(frame_stats)
 
+        self.current_day = data.current_dt.floor('1D')
+
     def _create_stats_df(self):
         stats = pd.DataFrame(self.frame_stats)
         stats.set_index('period_close', inplace=True, drop=False)
@@ -289,10 +327,10 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
     def __init__(self, *args, **kwargs):
         self.algo_namespace = kwargs.pop('algo_namespace', None)
         self.live_graph = kwargs.pop('live_graph', None)
-        self.simulate_orders = kwargs.pop('simulate_orders', None)
+        self.stats_output = kwargs.pop('stats_output', None)
 
         self._clock = None
-        self.frame_stats = deque(maxlen=60)
+        self.frame_stats = list()
 
         self.pnl_stats = get_algo_df(self.algo_namespace, 'pnl_stats')
 
@@ -310,7 +348,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.retry_order = 2
         self.retry_delay = 5
 
-        self.stats_minutes = 5
+        self.stats_minutes = 10
 
         super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
 
@@ -378,7 +416,7 @@ 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:
@@ -416,47 +454,83 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         return self.trading_client.transform()
 
     def updated_portfolio(self):
-        """
-        We skip the entire performance tracker business and update the
-        portfolio directly.
-
-        Returns
-        -------
-        ExchangePortfolio
-
-        """
-        # TODO: build cumulative portfolio
         return self.perf_tracker.get_portfolio(False)
 
     def updated_account(self):
         return self.perf_tracker.get_account(False)
 
-    def _synchronize_portfolio(self, attempt_index=0):
+    def synchronize_portfolio(self, attempt_index=0):
+        """
+        Synchronizes the portfolio tracked by the algorithm to refresh
+        its current value.
+
+        This includes updating the last_sale_price of all tracked
+        positions, returning the available cash, and raising error
+        if the data goes out of sync.
+
+        Parameters
+        ----------
+        attempt_index: int
+
+        Returns
+        -------
+        float
+            The amount of base currency available for trading.
+
+        float
+            The total value of all tracked positions.
+
+        """
+        tracker = self.perf_tracker.position_tracker
+        total_cash = 0.0
+        total_positions_value = 0.0
+
         try:
+            # Position keys correspond to assets
+            positions = self.portfolio.positions
+            assets = list(positions)
+            exchange_assets = group_assets_by_exchange(assets)
             for exchange_name in self.exchanges:
-                exchange = self.exchanges[exchange_name]
+                assets = exchange_assets[exchange_name] \
+                    if exchange_name in exchange_assets else []
 
-                exchange.synchronize_portfolio()
+                exchange_positions = \
+                    [positions[asset] for asset in assets]
 
-                # Applying the updated last_sales_price to the positions
+                check_cash = (not self.simulate_orders)
-                # in the performance tracker. This seems a bit redundant
+
-                # but it will make sense when we have multiple exchange portfolios
+                exchange = self.exchanges[exchange_name]  # Type: Exchange
-                # feeding into the same performance tracker.
+                cash, positions_value = exchange.calculate_totals(
-                tracker = self.perf_tracker.todays_performance.position_tracker
+                    positions=exchange_positions,
-                for asset in exchange.portfolio.positions:
+                    check_cash=check_cash,
-                    position = exchange.portfolio.positions[asset]
+                )
+                total_positions_value += positions_value
+
+                if cash is not None:
+                    total_cash += cash
+
+                for position in exchange_positions:
                     tracker.update_position(
-                        asset=asset,
+                        asset=position.asset,
                         last_sale_date=position.last_sale_date,
                         last_sale_price=position.last_sale_price
                     )
+
+            if cash is None:
+                total_cash = self.portfolio.cash
+
+            elif total_cash < self.portfolio.cash:
+                raise ValueError('Cash on exchanges is lower than the algo.')
+
+            return total_cash, total_positions_value
+
         except ExchangeRequestError as e:
             log.warn(
                 'update portfolio attempt {}: {}'.format(attempt_index, e)
             )
             if attempt_index < self.retry_synchronize_portfolio:
                 sleep(self.retry_delay)
-                self._synchronize_portfolio(attempt_index + 1)
+                return self.synchronize_portfolio(attempt_index + 1)
             else:
                 raise ExchangePortfolioDataError(
                     data_type='update-portfolio',
@@ -464,30 +538,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
                     error=e
                 )
 
-    def _check_open_orders(self, attempt_index=0):
-        try:
-            orders = list()
-            for exchange_name in self.exchanges:
-                exchange = self.exchanges[exchange_name]
-                exchange_orders = exchange.check_open_orders()
-
-                orders += exchange_orders
-
-            return orders
-        except ExchangeRequestError as e:
-            log.warn(
-                'check open orders attempt {}: {}'.format(attempt_index, e)
-            )
-            if attempt_index < self.retry_check_open_orders:
-                sleep(self.retry_delay)
-                return self._check_open_orders(attempt_index + 1)
-            else:
-                raise ExchangePortfolioDataError(
-                    data_type='order-status',
-                    attempts=attempt_index,
-                    error=e
-                )
-
     def add_pnl_stats(self, period_stats):
         """
         Save p&l stats.
@@ -577,15 +627,23 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         if not self.is_running:
             return
 
-        self._synchronize_portfolio()
+        # Resetting the frame stats every day to minimize memory footprint
+        today = data.current_dt.floor('1D')
+        if self.current_day is not None and today > self.current_day:
+            self.frame_stats = list()
 
-        transactions = self._check_open_orders()
+        new_transactions, new_commissions, closed_orders = \
-        if len(transactions) > 0:
+            self.blotter.get_transactions(data)
-            for transaction in transactions:
-                self.perf_tracker.process_transaction(transaction)
 
+        if len(new_transactions) > 0:
             self.perf_tracker.update_performance()
 
+        cash, positions_value = self.synchronize_portfolio()
+        log.info(
+            'got totals from exchanges, cash: {} positions: {}'.format(
+                cash, positions_value
+            )
+        )
         if self._handle_data:
             self._handle_data(self, data)
 
@@ -595,48 +653,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.validate_account_controls()
 
         try:
-            # Since the clock runs 24/7, I trying to disable the daily
+            self._save_stats_csv(self._process_stats(data))
-            # Performance tracker and keep only minute and cumulative
-            self.perf_tracker.update_performance()
-
-            frame_stats = self.prepare_period_stats(
-                data.current_dt, data.current_dt + timedelta(minutes=1))
-
-            # Saving the last hour in memory
-            self.frame_stats.append(frame_stats)
-
-            self.add_pnl_stats(frame_stats)
-            if self.recorded_vars:
-                self.add_custom_signals_stats(frame_stats)
-                recorded_cols = list(self.recorded_vars.keys())
-            else:
-                recorded_cols = None
-
-            self.add_exposure_stats(frame_stats)
-
-            print_df = pd.DataFrame(list(self.frame_stats))
-            log.info(
-                'statistics for the last {stats_minutes} minutes:\n{stats}'.format(
-                    stats_minutes=self.stats_minutes,
-                    stats=get_pretty_stats(
-                        stats_df=print_df,
-                        recorded_cols=recorded_cols,
-                        num_rows=self.stats_minutes
-                    )
-                ))
-
-            today = pd.to_datetime('today', utc=True)
-            daily_stats = self.prepare_period_stats(
-                start_dt=today,
-                end_dt=pd.Timestamp.utcnow()
-            )
-            save_algo_object(
-                algo_name=self.algo_namespace,
-                key=today.strftime('%Y-%m-%d'),
-                obj=daily_stats,
-                rel_path='daily_perf'
-            )
-
         except Exception as e:
             log.warn('unable to calculate performance: {}'.format(e))
 
@@ -650,93 +667,85 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         except Exception as e:
             log.warn('unable to save minute perfs to disk: {}'.format(e))
 
-        try:
+        self.current_day = data.current_dt.floor('1D')
-            for exchange_name in self.exchanges:
-                exchange = self.exchanges[exchange_name]
-                save_algo_object(
-                    algo_name=self.algo_namespace,
-                    key='portfolio_{}'.format(exchange_name),
-                    obj=exchange.portfolio
-                )
-        except Exception as e:
-            log.warn('unable to save portfolio to disk: {}'.format(e))
 
-    def _order(self,
+    def _process_stats(self, data):
-               asset,
+        today = data.current_dt.floor('1D')
-               amount,
-               limit_price=None,
-               stop_price=None,
-               style=None,
-               attempt_index=0):
-        try:
-            exchange = self.exchanges[asset.exchange]
-            return exchange.order(asset, amount, limit_price,
-                                  stop_price,
-                                  style)
-        except ExchangeRequestError as e:
-            log.warn(
-                'order attempt {}: {}'.format(attempt_index, e)
-            )
-            if attempt_index < self.retry_order:
-                sleep(self.retry_delay)
-                return self._order(
-                    asset, amount, limit_price, stop_price, style,
-                    attempt_index + 1)
-            else:
-                raise ExchangeTransactionError(
-                    transaction_type='order',
-                    attempts=attempt_index,
-                    error=e
-                )
 
-    @api_method
+        # Since the clock runs 24/7, I trying to disable the daily
-    @disallowed_in_before_trading_start(OrderInBeforeTradingStart())
+        # Performance tracker and keep only minute and cumulative
-    @expect_types(asset=TradingPair)
+        self.perf_tracker.update_performance()
-    def order(self,
-              asset,
-              amount,
-              limit_price=None,
-              stop_price=None,
-              style=None):
-        """
-        We use the exchange specific portfolio to place orders.
-        The cumulative portfolio does not contain open orders but exchange
-        portfolios do.
 
-        Parameters
+        frame_stats = self.prepare_period_stats(
-        ----------
+            data.current_dt, data.current_dt + timedelta(minutes=1))
-        asset: TradingPair
-        amount: float
-        limit_price: float
-        stop_price: float
-        style: Style
-        order: Order
-            The catalyst order object or None
-        """
-        amount, style = self._calculate_order(asset, amount,
-                                              limit_price, stop_price,
-                                              style)
 
-        order_id = self._order(asset, amount, limit_price, stop_price, style)
+        # Saving the last hour in memory
+        self.frame_stats.append(frame_stats)
 
-        exchange = self.exchanges[asset.exchange]
+        self.add_pnl_stats(frame_stats)
-        exchange_portfolio = exchange.portfolio
+        if self.recorded_vars:
-        if order_id is not None:
+            self.add_custom_signals_stats(frame_stats)
+            recorded_cols = list(self.recorded_vars.keys())
 
-            if order_id in exchange_portfolio.open_orders:
-                order = exchange_portfolio.open_orders[order_id]
-                self.perf_tracker.process_order(order)
-                return order
-
-            else:
-                raise OrphanOrderError(
-                    order_id=order_id,
-                    exchange=exchange.name
-                )
         else:
-            log.warn('unable to order {} {} on exchange {}'.format(
+            recorded_cols = None
-                amount, asset.symbol, asset.exchange))
+
-            return None
+        self.add_exposure_stats(frame_stats)
+
+        log.info(
+            'statistics for the last {stats_minutes} minutes:\n'
+            '{stats}'.format(
+                stats_minutes=self.stats_minutes,
+                stats=get_pretty_stats(
+                    stats=self.frame_stats,
+                    recorded_cols=recorded_cols,
+                    num_rows=self.stats_minutes
+                )
+            ))
+
+        # Saving the daily stats in a format usable for performance
+        # analysis.
+        daily_stats = self.prepare_period_stats(
+            start_dt=today,
+            end_dt=data.current_dt
+        )
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key=today.strftime('%Y-%m-%d'),
+            obj=daily_stats,
+            rel_path='daily_perf'
+        )
+
+        return recorded_cols
+
+    def _save_stats_csv(self, recorded_cols):
+        # Writing the stats output
+        csv_bytes = None
+        try:
+            csv_bytes = stats_to_algo_folder(
+                stats=self.frame_stats,
+                algo_namespace=self.algo_namespace,
+                recorded_cols=recorded_cols,
+            )
+        except Exception as e:
+            log.warn('unable save stats locally: {}'.format(e))
+
+        try:
+            if self.stats_output is not None:
+                if 's3://' in self.stats_output:
+                    stats_to_s3(
+                        uri=self.stats_output,
+                        stats=self.frame_stats,
+                        algo_namespace=self.algo_namespace,
+                        recorded_cols=recorded_cols,
+                        bytes_to_write=csv_bytes
+                    )
+                else:
+                    raise ValueError(
+                        'Only S3 stats output is supported for now.'
+                    )
+        except Exception as e:
+            log.warn('unable save stats externally: {}'.format(e))
 
     @api_method
     def batch_market_order(self, share_counts):

catalyst/exchange/exchange_blotter.py

@@ -1,21 +1,21 @@
+from time import sleep
+
+import pandas as pd
 from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
 from catalyst.constants import LOG_LEVEL
+from catalyst.exchange.exchange_errors import ExchangeRequestError, \
+    ExchangePortfolioDataError, ExchangeTransactionError
 from catalyst.finance.blotter import Blotter
 from catalyst.finance.commission import CommissionModel
+from catalyst.finance.order import ORDER_STATUS, Order
 from catalyst.finance.slippage import SlippageModel
-from catalyst.finance.transaction import create_transaction
+from catalyst.finance.transaction import create_transaction, Transaction
+from catalyst.utils.input_validation import expect_types
 
 log = Logger('exchange_blotter', level=LOG_LEVEL)
 
-# It seems like we need to accept greater slippage risk in cryptos
-# Orders won't often close at Equity levels.
-# TODO: should work with set_commission and set_slippage
-DEFAULT_SLIPPAGE_SPREAD = 0.0001
-DEFAULT_MAKER_FEE = 0.0015
-DEFAULT_TAKER_FEE = 0.0025
-
 
 class TradingPairFeeSchedule(CommissionModel):
     """
@@ -23,23 +23,24 @@ class TradingPairFeeSchedule(CommissionModel):
 
     Parameters
     ----------
-    fee : float, optional
+    maker : float, optional
-        The percentage fee.
+        The percentage maker fee.
+
+    taker: float, optional
+        The percentage taker fee.
     """
 
-    def __init__(self,
+    def __init__(self, maker=None, taker=None):
-                 maker_fee=DEFAULT_MAKER_FEE,
+        self.maker = maker
-                 taker_fee=DEFAULT_TAKER_FEE):
+        self.taker = taker
-        self.maker_fee = maker_fee
-        self.taker_fee = taker_fee
 
     def __repr__(self):
         return (
-            '{class_name}(maker_fee={maker_fee}, '
+            '{class_name}(maker={maker}, '
-            'taker_fee={taker_fee})'.format(
+            'taker={taker})'.format(
                 class_name=self.__class__.__name__,
-                maker_fee=self.maker_fee,
+                maker=self.maker,
-                taker_fee=self.taker_fee,
+                taker=self.taker,
             )
         )
 
@@ -47,16 +48,25 @@ class TradingPairFeeSchedule(CommissionModel):
         """
         Calculate the final fee based on the order parameters.
 
-        :param order:
+        :param order: Order
-        :param transaction:
+        :param transaction: Transaction
 
         :return float:
             The total commission.
         """
         cost = abs(transaction.amount) * transaction.price
 
+        asset = order.asset
+        maker = self.maker if self.maker is not None else asset.maker
+        taker = self.taker if self.taker is not None else asset.taker
+
+        multiplier = maker \
+            if ((order.amount > 0 and order.limit < transaction.price)
+                or (order.amount < 0 and order.limit > transaction.price)) \
+               and order.limit_reached else taker
+
         # Assuming just the taker fee for now
-        fee = cost * self.taker_fee
+        fee = cost * multiplier
         return fee
 
 
@@ -70,7 +80,7 @@ class TradingPairFixedSlippage(SlippageModel):
         spread / 2 will be added to buys and subtracted from sells.
     """
 
-    def __init__(self, spread=DEFAULT_SLIPPAGE_SPREAD):
+    def __init__(self, spread=0.0001):
         super(TradingPairFixedSlippage, self).__init__()
         self.spread = spread
 
@@ -121,6 +131,14 @@ class TradingPairFixedSlippage(SlippageModel):
 
 class ExchangeBlotter(Blotter):
     def __init__(self, *args, **kwargs):
+        self.simulate_orders = kwargs.pop('simulate_orders', False)
+
+        self.exchanges = kwargs.pop('exchanges', None)
+        if not self.exchanges:
+            raise ValueError(
+                'ExchangeBlotter must have an `exchanges` attribute.'
+            )
+
         super(ExchangeBlotter, self).__init__(*args, **kwargs)
 
         # Using the equity models for now
@@ -132,3 +150,148 @@ class ExchangeBlotter(Blotter):
         self.commission_models = {
             TradingPair: TradingPairFeeSchedule()
         }
+
+        self.retry_delay = 5
+        self.retry_check_open_orders = 5
+
+    def exchange_order(self, asset, amount, style=None, attempt_index=0):
+        try:
+            exchange = self.exchanges[asset.exchange]
+            return exchange.order(
+                asset, amount, style
+            )
+        except ExchangeRequestError as e:
+            log.warn(
+                'order attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_order:
+                sleep(self.retry_delay)
+
+                return self.exchange_order(
+                    asset, amount, style, attempt_index + 1
+                )
+            else:
+                raise ExchangeTransactionError(
+                    transaction_type='order',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    @expect_types(asset=TradingPair)
+    def order(self, asset, amount, style, order_id=None):
+        log.debug('ordering {} {}'.format(amount, asset.symbol))
+        if amount == 0:
+            log.warn('skipping 0 amount orders')
+            return None
+
+        if self.simulate_orders:
+            return super(ExchangeBlotter, self).order(
+                asset, amount, style, order_id
+            )
+
+        else:
+            order = self.exchange_order(
+                asset, amount, style
+            )
+
+            self.open_orders[order.asset].append(order)
+            self.orders[order.id] = order
+            self.new_orders.append(order)
+
+            return order.id
+
+    def check_open_orders(self):
+        """
+        Loop through the list of open orders in the Portfolio object.
+        For each executed order found, create a transaction and apply to the
+        Portfolio.
+
+        Returns
+        -------
+        list[Transaction]
+
+        """
+        for asset in self.open_orders:
+            exchange = self.exchanges[asset.exchange]
+
+            for order in self.open_orders[asset]:
+                log.debug('found open order: {}'.format(order.id))
+
+                new_order, executed_price = exchange.get_order(order.id, asset)
+                log.debug(
+                    'got updated order {} {}'.format(
+                        new_order, executed_price
+                    )
+                )
+                order.status = new_order.status
+
+                if order.status == ORDER_STATUS.FILLED:
+                    order.commission = new_order.commission
+                    if order.amount != new_order.amount:
+                        log.warn(
+                            'executed order amount {} differs '
+                            'from original'.format(
+                                new_order.amount, order.amount
+                            )
+                        )
+                        order.amount = new_order.amount
+
+                    transaction = Transaction(
+                        asset=order.asset,
+                        amount=order.amount,
+                        dt=pd.Timestamp.utcnow(),
+                        price=executed_price,
+                        order_id=order.id,
+                        commission=order.commission
+                    )
+                    yield order, transaction
+
+                elif order.status == ORDER_STATUS.CANCELLED:
+                    yield order, None
+
+                else:
+                    delta = pd.Timestamp.utcnow() - order.dt
+                    log.info(
+                        'order {order_id} still open after {delta}'.format(
+                            order_id=order.id,
+                            delta=delta
+                        )
+                    )
+
+    def get_exchange_transactions(self, attempt_index=0):
+        closed_orders = []
+        transactions = []
+        commissions = []
+
+        try:
+            for order, txn in self.check_open_orders():
+                order.dt = txn.dt
+
+                transactions.append(txn)
+
+                if not order.open:
+                    closed_orders.append(order)
+
+            return transactions, commissions, closed_orders
+
+        except ExchangeRequestError as e:
+            log.warn(
+                'check open orders attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_check_open_orders:
+                sleep(self.retry_delay)
+                return self.get_exchange_transactions(attempt_index + 1)
+
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='order-status',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def get_transactions(self, bar_data):
+        if self.simulate_orders:
+            return super(ExchangeBlotter, self).get_transactions(bar_data)
+
+        else:
+            return self.get_exchange_transactions()

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,
+                            name=asset.symbol,
-                    start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                            start_dt=asset.start_date.strftime(
-                    end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                                DATE_TIME_FORMAT),
-                    dates=[date.strftime(DATE_TIME_FORMAT) for date in dates]
+                            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,
+                        name=asset.symbol,
-                start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                        start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
-                end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                        end_dt=end_dt.strftime(DATE_TIME_FORMAT),
-                threshold=threshold,
+                        threshold=threshold,
-                dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
+                        dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
-                       for date in dates]
+                               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,
+                            exchange=self.exchange_name,
-                        frequency=data_frequency,
+                            frequency=data_frequency,
                     )) as it:
                 for chunk in it:
                     problems += self.ingest_ctable(

catalyst/exchange/exchange_data_portal.py

@@ -13,7 +13,8 @@ from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     ExchangeBarDataError,
     PricingDataNotLoadedError)
-from catalyst.exchange.exchange_utils import get_frequency, resample_history_df
+from catalyst.exchange.exchange_utils import get_frequency, \
+    resample_history_df, group_assets_by_exchange
 
 log = Logger('DataPortalExchange', level=LOG_LEVEL)
 
@@ -38,13 +39,7 @@ class DataPortalExchangeBase(DataPortal):
                             ffill=True,
                             attempt_index=0):
         try:
-            exchange_assets = dict()
+            exchange_assets = group_assets_by_exchange(assets)
-            for asset in assets:
-                if asset.exchange not in exchange_assets:
-                    exchange_assets[asset.exchange] = list()
-
-                exchange_assets[asset.exchange].append(asset)
-
             if len(exchange_assets) > 1:
                 df_list = []
                 for exchange_name in exchange_assets:
@@ -242,6 +237,7 @@ class DataPortalExchangeLive(DataPortalExchangeBase):
 
         """
         exchange = self.exchanges[exchange_name]
+
         df = exchange.get_history_window(
             assets,
             end_dt,
@@ -249,7 +245,7 @@ class DataPortalExchangeLive(DataPortalExchangeBase):
             frequency,
             field,
             data_frequency,
-            ffill)
+            False)
         return df
 
     def get_exchange_spot_value(self, exchange_name, assets, field, dt,

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 '
+           'starts on {first_trading_day}, but you are either trying to trade '
-           'retrieve pricing data on {dt}. Adjust your dates accordingly.').strip()
+           'or retrieve pricing data on {dt}. Adjust your dates accordingly.'
+           ).strip()
 
 
 class PricingDataNotLoadedError(ZiplineError):
@@ -217,6 +219,7 @@ class PricingDataNotLoadedError(ZiplineError):
            '{data_frequency} -i {symbol_list}`. See catalyst documentation '
            'for details.').strip()
 
+
 class PricingDataValueError(ZiplineError):
     msg = ('Unable to retrieve pricing data for {exchange} {symbol} '
            '[{start_dt} - {end_dt}]: {error}').strip()
@@ -237,10 +240,32 @@ 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):
     msg = ('Value not found for field: {field}.').strip()
+
+
+class OrderTypeNotSupported(ZiplineError):
+    msg = (
+        'Order type `{order_type}` not currencly supported by Catalyst. '
+        'Please use `limit` or `market` orders only.').strip()
+
+
+class NotEnoughCapitalError(ZiplineError):
+    msg = (
+        'Not enough capital on exchange {exchange} for trading. Each '
+        'exchange should contain at least as much {base_currency} '
+        'as the specified `capital_base`. The current balance {balance} is '
+        'lower than the `capital_base`: {capital_base}').strip()
+
+class LastCandleTooEarlyError(ZiplineError):
+    msg = (
+        'The trade date of the last candle {last_traded} is before the '
+        'specified end date minus one candle {end_dt}. Please verify how '
+        '{exchange} calculates the start date of OHLCV candles.').strip()

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):

catalyst/exchange/exchange_portfolio.py

@@ -40,7 +40,13 @@ class ExchangePortfolio(Portfolio):
 
         """
         log.debug('creating order {}'.format(order.id))
-        self.open_orders[order.id] = order
+
+        open_orders = self.open_orders[order.asset] \
+            if order.asset is self.open_orders else []
+
+        open_orders.append(order)
+
+        self.open_orders[order.asset] = open_orders
 
         order_position = self.positions[order.asset] \
             if order.asset in self.positions else None
@@ -52,6 +58,17 @@ class ExchangePortfolio(Portfolio):
         order_position.amount += order.amount
         log.debug('open order added to portfolio')
 
+    def _remove_open_order(self, order):
+        try:
+            open_orders = self.open_orders[order.asset]
+            if order in open_orders:
+                open_orders.remove(order)
+
+        except Exception:
+            raise ValueError(
+                'unable to clear order not found in open order list.'
+            )
+
     def execute_order(self, order, transaction):
         """
         Update the open orders and positions to apply an executed order.
@@ -66,14 +83,15 @@ class ExchangePortfolio(Portfolio):
 
         """
         log.debug('executing order {}'.format(order.id))
-        del self.open_orders[order.id]
+        self._remove_open_order(order)
 
         order_position = self.positions[order.asset] \
             if order.asset in self.positions else None
 
         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
@@ -99,7 +117,7 @@ class ExchangePortfolio(Portfolio):
 
         """
         log.info('removing cancelled order {}'.format(order.id))
-        del self.open_orders[order.id]
+        self._remove_open_order(order)
 
         order_position = self.positions[order.asset] \
             if order.asset in self.positions else None

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
@@ -604,6 +604,7 @@ def mixin_market_params(exchange_name, params, market):
     # TODO: make this more externalized / configurable
     if 'lot' in market:
         params['min_trade_size'] = market['lot']
+        params['lot'] = market['lot']
 
     if exchange_name == 'bitfinex':
         params['maker'] = 0.001
@@ -624,6 +625,24 @@ def mixin_market_params(exchange_name, params, market):
         if 'minimum_order_size' in info:
             params['min_trade_size'] = float(info['minimum_order_size'])
 
+            if 'lot' not in params:
+                params['lot'] = params['min_trade_size']
+
 
 def from_ms_timestamp(ms):
     return pd.to_datetime(ms, unit='ms', utc=True)
+
+
+def get_epoch():
+    return pd.to_datetime('1970-1-1', utc=True)
+
+
+def group_assets_by_exchange(assets):
+    exchange_assets = dict()
+    for asset in assets:
+        if asset.exchange not in exchange_assets:
+            exchange_assets[asset.exchange] = list()
+
+        exchange_assets[asset.exchange].append(asset)
+
+    return exchange_assets

catalyst/exchange/factory.py

@@ -6,8 +6,7 @@ from catalyst.exchange.exchange_utils import get_exchange_auth, \
     get_exchange_folder
 
 
-def get_exchange(exchange_name, base_currency=None, portfolio=None,
+def get_exchange(exchange_name, base_currency=None, must_authenticate=False):
-                 must_authenticate=False):
     exchange_auth = get_exchange_auth(exchange_name)
 
     has_auth = (exchange_auth['key'] != '' and exchange_auth['secret'] != '')
@@ -24,7 +23,6 @@ def get_exchange(exchange_name, base_currency=None, portfolio=None,
         key=exchange_auth['key'],
         secret=exchange_auth['secret'],
         base_currency=base_currency,
-        portfolio=portfolio
     )
 
 

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, \
@@ -27,10 +28,12 @@ from catalyst.exchange.poloniex.poloniex_api import Poloniex_api
 from catalyst.finance.order import Order, ORDER_STATUS
 from catalyst.finance.transaction import Transaction
 from catalyst.protocol import Account
+from catalyst.utils.deprecate import deprecated
 
 log = Logger('Poloniex', level=LOG_LEVEL)
 
 
+@deprecated
 class Poloniex(Exchange):
     def __init__(self, key, secret, base_currency, portfolio=None):
         self.api = Poloniex_api(key=key, secret=secret)
@@ -87,7 +90,6 @@ class Poloniex(Exchange):
             # filled = -filled
 
         price = float(order_status['rate'])
-        order_type = order_status['type']
 
         stop_price = None
         limit_price = None
@@ -101,11 +103,11 @@ 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=pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
-        # date = pytz.utc.localize(date)
+        # date=pytz.utc.localize(date)
         date = None
 
         order = Order(
@@ -292,8 +294,8 @@ class Poloniex(Exchange):
         """
         exchange_symbol = self.get_symbol(asset)
 
-        if isinstance(style, ExchangeLimitOrder) or isinstance(style,
+        if (isinstance(style, ExchangeLimitOrder)
-                                                               ExchangeStopLimitOrder):
+                or isinstance(style, ExchangeStopLimitOrder)):
             if isinstance(style, ExchangeStopLimitOrder):
                 log.warn('{} will ignore the stop price'.format(self.name))
 
@@ -350,8 +352,8 @@ class Poloniex(Exchange):
         return self.portfolio.open_orders
 
         """
-            TODO: Why going to the exchange if we already have this info locally?
+        TODO: Why going to the exchange if we already have this info locally?
-                  And why creating all these Orders if we later discard them?
+              And why creating all these Orders if we later discard them?
         """
 
         try:
@@ -365,7 +367,7 @@ 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 +375,8 @@ class Poloniex(Exchange):
         # TODO: Need to handle openOrders for 'all'
         orders = list()
         for order_status in response:
-            order, executed_price = self._create_order(
+            # will Throw error b/c Polo doesn't track order['symbol']
-                order_status)  # 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 +439,8 @@ 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 +515,17 @@ 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 +596,21 @@ class Poloniex(Exchange):
                 else:
                     for tx in response:
                         """
-                            We maintain a list of dictionaries of transactions that correspond to
+                        We maintain a list of dictionaries of transactions that
-                            partially filled orders, indexed by order_id. Every time we query 
+                        correspond to partially filled orders, indexed by
-                            executed transactions from the exchange, we check if we had that 
+                        order_id. Every time we query executed transactions
-                            transaction for that order already. If not, we process it.
+                        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  
+                        When an order if fully filled, we flush the dict of
-                            associated with that order.
+                        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 +621,7 @@ 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 +631,8 @@ 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]

catalyst/exchange/poloniex/poloniex_api.py

@@ -107,8 +107,9 @@ class Poloniex_api(object):
             data=post_data,
             headers=headers,
         )
-        return json.loads(
+        resource = urlopen(req, context=ssl._create_unverified_context())
-            urlopen(req, context=ssl._create_unverified_context()).read())
+        content = resource.read().decode('utf-8')
+        return json.loads(content)
 
     def returnticker(self):
         return self.query('returnTicker', {})
@@ -160,10 +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})
 
@@ -176,7 +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,
@@ -194,7 +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,

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.

catalyst/exchange/stats_utils.py

@@ -1,7 +1,18 @@
+import csv
 import numbers
 
+import copy
 import numpy as np
+import os
 import pandas as pd
+import boto3
+import time
+
+from catalyst.assets._assets import TradingPair
+
+from catalyst.exchange.exchange_utils import get_algo_folder
+
+s3 = boto3.resource('s3')
 
 
 def trend_direction(series):
@@ -119,62 +130,256 @@ def vwap(df):
     return ret
 
 
-def get_pretty_stats(stats_df, recorded_cols=None, num_rows=10):
+def set_position_row(row, asset, asset_values=list()):
+    """
+    Apply the position data as individual columns.
+
+    Parameters
+    ----------
+    row: dict[str, Object]
+    asset: TradingPair
+    asset_values: list[str]
+        If a recorded_col contains a tuple which first value is an asset
+        matching a position, its value will be displayed with the
+        position and not in the index.
+
+    Returns
+    -------
+
+    """
+    asset_cols = ['symbol']
+    row['symbol'] = asset.symbol
+
+    position = next((p for p in row['positions'] if p['sid'] == asset), None)
+
+    columns = ['amount', 'cost_basis', 'last_sale_price']
+    for column in columns:
+        if position is not None:
+            row[column] = position[column]
+
+        else:
+            row[column] = 0
+
+        asset_cols.append(column)
+
+    values = asset_values[asset] if asset in asset_values else list()
+    for column in values:
+        row[column] = values[column]
+
+        asset_cols.append(column)
+
+    return asset_cols
+
+
+def prepare_stats(stats, recorded_cols=list()):
+    """
+    Prepare the stats DataFrame for user-friendly output.
+
+    Parameters
+    ----------
+    stats: list[Object]
+    recorded_cols: list[str]
+
+    Returns
+    -------
+
+    """
+    asset_cols = list()
+
+    stats = copy.deepcopy(stats)
+    # Using a copy since we are adding rows inside the loop.
+    for row_index, row_data in enumerate(list(stats)):
+        assets = [p['sid'] for p in row_data['positions']]
+
+        asset_values = dict()
+        if recorded_cols is not None:
+            for column in recorded_cols[:]:
+                value = row_data[column]
+                if type(value) is dict:
+                    for asset in value:
+                        if not isinstance(asset, TradingPair):
+                            break
+
+                        if asset not in assets:
+                            assets.append(asset)
+
+                        if asset not in asset_values:
+                            asset_values[asset] = dict()
+
+                        asset_values[asset][column] = value[asset]
+
+        if len(assets) == 1:
+            row = stats[row_index]
+            asset_cols = set_position_row(row, assets[0], asset_values)
+
+        elif len(assets) > 1:
+            for asset_index, asset in enumerate(assets):
+                if asset_index > 0:
+                    row = copy.deepcopy(row_data)
+                    stats.append(row)
+
+                else:
+                    row = stats[row_index]
+
+                asset_cols = set_position_row(row, assets[asset_index],
+                                              asset_values)
+
+    df = pd.DataFrame(stats)
+
+    index_cols = [
+        'period_close', 'starting_cash', 'ending_cash', 'portfolio_value',
+        'pnl', 'long_exposure', 'short_exposure', 'orders', 'transactions',
+    ]
+
+    # Removing the asset specific entries
+    if recorded_cols is not None:
+        recorded_cols = [x for x in recorded_cols if x not in asset_cols]
+        for column in recorded_cols:
+            index_cols.append(column)
+
+    df['orders'] = df['orders'].apply(lambda orders: len(orders))
+    df['transactions'] = df['transactions'].apply(
+        lambda transactions: len(transactions)
+    )
+
+    if asset_cols:
+        columns = asset_cols
+        df.set_index(index_cols, drop=True, inplace=True)
+
+    else:
+        columns = index_cols
+        columns.remove('period_close')
+        df.set_index('period_close', drop=False, inplace=True)
+
+    df.dropna(axis=1, how='all', inplace=True)
+    df.sort_index(axis=0, level=0, inplace=True)
+
+    return df, columns
+
+
+def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
     """
     Format and print the last few rows of a statistics DataFrame.
     See the pyfolio project for the data structure.
 
     Parameters
     ----------
-    stats_df: DataFrame
+    stats: list[Object]
+        An array of statistics for the period.
+
     num_rows: int
+        The number of rows to display on the screen.
 
     Returns
     -------
     str
 
     """
-    stats_df.set_index('period_close', drop=True, inplace=True)
+    if isinstance(stats, pd.DataFrame):
-    stats_df.dropna(axis=1, how='all', inplace=True)
+        stats = stats.T.to_dict().values()
+
+    df, columns = prepare_stats(stats, recorded_cols=recorded_cols)
 
     pd.set_option('display.expand_frame_repr', False)
-    pd.set_option('precision', 3)
+    pd.set_option('precision', 8)
     pd.set_option('display.width', 1000)
     pd.set_option('display.max_colwidth', 1000)
 
-    columns = ['starting_cash', 'ending_cash', 'portfolio_value',
-               'pnl', 'long_exposure', 'short_exposure', 'orders',
-               'transactions', 'positions']
-
-    if recorded_cols is not None:
-        for column in recorded_cols:
-            columns.append(column)
-
-    def format_positions(positions):
-        parts = []
-        for position in positions:
-            msg = '{amount:.2f}{base} cost basis {cost_basis:.4f}{quote}'.format(
-                amount=position['amount'],
-                base=position['sid'].base_currency,
-                cost_basis=position['cost_basis'],
-                quote=position['sid'].quote_currency
-            )
-            parts.append(msg)
-        return ', '.join(parts)
-
     formatters = {
-        'orders': lambda orders: len(orders),
-        'transactions': lambda transactions: len(transactions),
         'returns': lambda returns: "{0:.4f}".format(returns),
-        'positions': format_positions
     }
 
-    return stats_df.tail(num_rows).to_string(
+    return df.tail(num_rows).to_string(
         columns=columns,
         formatters=formatters
     )
 
 
+def get_csv_stats(stats, recorded_cols=None):
+    """
+    Create a CSV buffer from the stats DataFrame.
+
+    Parameters
+    ----------
+    path: str
+    stats: list[Object]
+    recorded_cols: list[str]
+
+    Returns
+    -------
+
+    """
+    df, columns = prepare_stats(stats, recorded_cols=recorded_cols)
+
+    return df.to_csv(
+        None,
+        columns=columns,
+        # encoding='utf-8',
+        quoting=csv.QUOTE_NONNUMERIC
+    ).encode()
+
+
+def stats_to_s3(uri, stats, algo_namespace, recorded_cols=None,
+                folder='catalyst/stats', bytes_to_write=None):
+    """
+    Uploads the performance stats to a S3 bucket.
+
+    Parameters
+    ----------
+    uri: str
+    stats: list[Object]
+    algo_namespace: str
+    recorded_cols: list[str]
+    folder: str
+    bytes_to_write: str
+        Option to reuse bytes instead of re-computing the csv
+
+    Returns
+    -------
+
+    """
+    if bytes_to_write is None:
+        bytes_to_write = get_csv_stats(stats, recorded_cols=recorded_cols)
+
+    now = pd.Timestamp.utcnow()
+    timestr = now.strftime('%Y%m%d')
+    pid = os.getpid()
+
+    parts = uri.split('//')
+    obj = s3.Object(parts[1], '{}/{}-{}-{}.csv'.format(
+        folder, timestr, algo_namespace, pid
+    ))
+    obj.put(Body=bytes_to_write)
+
+
+def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
+    """
+    Saves the performance stats to the algo local folder.
+
+    Parameters
+    ----------
+    stats: list[Object]
+    algo_namespace: str
+    recorded_cols: list[str]
+
+    Returns
+    -------
+    str
+
+    """
+    bytes_to_write = get_csv_stats(stats, recorded_cols=recorded_cols)
+
+    timestr = time.strftime('%Y%m%d')
+    folder = get_algo_folder(algo_namespace)
+
+    filename = os.path.join(folder, '{}-{}.csv'.format(timestr, 'frames'))
+
+    with open(filename, 'wb') as handle:
+        handle.write(bytes_to_write)
+
+    return bytes_to_write
+
+
 def df_to_string(df):
     """
     Create a formatted str representation of the DataFrame.

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
 

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.excess_return = self.algorithm_period_returns \
-                             self.treasury_period_return
+            - self.treasury_period_return
         self.max_drawdown = max_drawdown(self.algorithm_returns.values)
         self.max_leverage = self.calculate_max_leverage()
 

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 = \

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
 

catalyst/pipeline/factors/equity/__init__.py

@@ -1,9 +1,6 @@
 from .statistical import (
-    RollingPearson,
-    RollingLinearRegression,
     RollingLinearRegressionOfReturns,
     RollingPearsonOfReturns,
-    RollingSpearman,
     RollingSpearmanOfReturns,
 )
 from .technical import (

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':
+        # TODO: This is currently broken, No Pipeline support for Catalyst
-            reader = bundle.daily_bar_reader
+        # if data_frequency == 'daily':
-        elif daily_bar_reader == 'minute':
+        #    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
 

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

catalyst/pipeline/loaders/frame.py

@@ -180,4 +180,3 @@ class DataFrameLoader(PipelineLoader):
     @property
     def columns(self):
         return self._columns
-

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

catalyst/support/issue_44.py

@@ -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')

catalyst/support/issue_47.py

@@ -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
-"""

catalyst/support/issue_55.py

@@ -1,4 +1,3 @@
-import talib
 import pandas as pd
 
 from catalyst import run_algorithm

catalyst/support/issue_57.py

@@ -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

catalyst/support/issue_74.py

@@ -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, )

catalyst/support/rodrigo_1.py

@@ -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'
-)

catalyst/testing/__init__.py

@@ -55,4 +55,4 @@ from .core import (  # noqa
     write_bcolz_minute_data,
     write_compressed,
 )
-from .fixtures import CatalystTestCase  # noqa
+from .fixtures import ZiplineTestCase  # noqa

catalyst/testing/core.py

@@ -643,7 +643,7 @@ def create_data_portal_from_trade_history(asset_finder, trading_calendar,
         return DataPortal(
             asset_finder, trading_calendar,
             first_trading_day=equity_daily_reader.first_trading_day,
-            daily_reader=equity_daily_reader,
+            equity_daily_reader=equity_daily_reader,
         )
     else:
         minutes = trading_calendar.minutes_in_range(

catalyst/testing/fixtures.py

@@ -62,7 +62,7 @@ from catalyst.utils.paths import ensure_directory
 catalyst_dir = os.path.dirname(catalyst.__file__)
 
 
-class CatalystTestCase(with_metaclass(FinalMeta, TestCase)):
+class ZiplineTestCase(with_metaclass(FinalMeta, TestCase)):
     """
     Shared extensions to core unittest.TestCase.
 
@@ -92,7 +92,7 @@ class CatalystTestCase(with_metaclass(FinalMeta, TestCase)):
             cls._base_init_fixtures_was_called = False
             cls.init_class_fixtures()
             assert cls._base_init_fixtures_was_called, (
-                "CatalystTestCase.init_class_fixtures() was not called.\n"
+                "ZiplineTestCase.init_class_fixtures() was not called.\n"
                 "This probably means that you overrode init_class_fixtures"
                 " without calling super()."
             )
@@ -170,7 +170,7 @@ class CatalystTestCase(with_metaclass(FinalMeta, TestCase)):
             self._init_instance_fixtures_was_called = False
             self.init_instance_fixtures()
             assert self._init_instance_fixtures_was_called, (
-                "CatalystTestCase.init_instance_fixtures() was not"
+                "ZiplineTestCase.init_instance_fixtures() was not"
                 " called.\n"
                 "This probably means that you overrode"
                 " init_instance_fixtures without calling super()."
@@ -251,7 +251,7 @@ def alias(attr_name):
 
 class WithDefaultDateBounds(object):
     """
-    CatalystTestCase mixin which makes it possible to synchronize date bounds
+    ZiplineTestCase mixin which makes it possible to synchronize date bounds
     across fixtures.
 
     This fixture should always be the last fixture in bases of any fixture or
@@ -264,13 +264,13 @@ class WithDefaultDateBounds(object):
         The date bounds to be used for fixtures that want to have consistent
         dates.
     """
-    START_DATE = pd.Timestamp('2016-01-03', tz='utc')
+    START_DATE = pd.Timestamp('2006-01-03', tz='utc')
-    END_DATE = pd.Timestamp('2016-12-29', tz='utc')
+    END_DATE = pd.Timestamp('2006-12-29', tz='utc')
 
 
 class WithLogger(object):
     """
-    CatalystTestCase mixin providing cls.log_handler as an instance-level
+    ZiplineTestCase mixin providing cls.log_handler as an instance-level
     fixture.
 
     After init_instance_fixtures has been called `self.log_handler` will be a
@@ -295,7 +295,7 @@ class WithLogger(object):
 
 class WithAssetFinder(WithDefaultDateBounds):
     """
-    CatalystTestCase mixin providing cls.asset_finder as a class-level fixture.
+    ZiplineTestCase mixin providing cls.asset_finder as a class-level fixture.
 
     After init_class_fixtures has been called, `cls.asset_finder` is populated
     with an AssetFinder.
@@ -402,7 +402,7 @@ class WithAssetFinder(WithDefaultDateBounds):
 
 class WithTradingCalendars(object):
     """
-    CatalystTestCase mixin providing cls.trading_calendar,
+    ZiplineTestCase mixin providing cls.trading_calendar,
     cls.all_trading_calendars, cls.trading_calendar_for_asset_type as a
     class-level fixture.
 
@@ -423,7 +423,7 @@ class WithTradingCalendars(object):
         with that asset type.
     """
     TRADING_CALENDAR_STRS = ('NYSE',)
-    TRADING_CALENDAR_FOR_ASSET_TYPE = {Equity: 'NYSE', Future: 'us_futures', }
+    TRADING_CALENDAR_FOR_ASSET_TYPE = {Equity: 'NYSE', Future: 'us_futures'}
     TRADING_CALENDAR_FOR_EXCHANGE = {}
     # For backwards compatibility, exisitng tests and fixtures refer to
     # `trading_calendar` with the assumption that the value is the NYSE
@@ -460,7 +460,7 @@ class WithTradingEnvironment(WithAssetFinder,
                              WithTradingCalendars,
                              WithDefaultDateBounds):
     """
-    CatalystTestCase mixin providing cls.env as a class-level fixture.
+    ZiplineTestCase mixin providing cls.env as a class-level fixture.
 
     After ``init_class_fixtures`` has been called, `cls.env` is populated
     with a trading environment whose `asset_finder` is the result of
@@ -560,7 +560,7 @@ class WithTradingEnvironment(WithAssetFinder,
 
 class WithSimParams(WithTradingEnvironment):
     """
-    CatalystTestCase mixin providing cls.sim_params as a class level fixture.
+    ZiplineTestCase mixin providing cls.sim_params as a class level fixture.
 
     The arguments used to construct the trading environment may be overridded
     by putting ``SIM_PARAMS_{argname}`` in the class dict except for the
@@ -615,7 +615,7 @@ class WithSimParams(WithTradingEnvironment):
 
 class WithTradingSessions(WithTradingCalendars, WithDefaultDateBounds):
     """
-    CatalystTestCase mixin providing cls.trading_days, cls.all_trading_sessions
+    ZiplineTestCase mixin providing cls.trading_days, cls.all_trading_sessions
     as a class-level fixture.
 
     After init_class_fixtures has been called, `cls.all_trading_sessions`
@@ -668,7 +668,7 @@ class WithTradingSessions(WithTradingCalendars, WithDefaultDateBounds):
 
 class WithTmpDir(object):
     """
-    CatalystTestCase mixing providing cls.tmpdir as a class-level fixture.
+    ZiplineTestCase mixing providing cls.tmpdir as a class-level fixture.
 
     After init_class_fixtures has been called, `cls.tmpdir` is populated with
     a `testfixtures.TempDirectory` object whose path is `cls.TMP_DIR_PATH`.
@@ -691,7 +691,7 @@ class WithTmpDir(object):
 
 class WithInstanceTmpDir(object):
     """
-    CatalystTestCase mixing providing self.tmpdir as an instance-level fixture.
+    ZiplineTestCase mixing providing self.tmpdir as an instance-level fixture.
 
     After init_instance_fixtures has been called, `self.tmpdir` is populated
     with a `testfixtures.TempDirectory` object whose path is
@@ -714,7 +714,7 @@ class WithInstanceTmpDir(object):
 
 class WithEquityDailyBarData(WithTradingEnvironment):
     """
-    CatalystTestCase mixin providing cls.make_equity_daily_bar_data.
+    ZiplineTestCase mixin providing cls.make_equity_daily_bar_data.
 
     Attributes
     ----------
@@ -810,7 +810,7 @@ class WithEquityDailyBarData(WithTradingEnvironment):
 
 class WithBcolzEquityDailyBarReader(WithEquityDailyBarData, WithTmpDir):
     """
-    CatalystTestCase mixin providing cls.bcolz_daily_bar_path,
+    ZiplineTestCase mixin providing cls.bcolz_daily_bar_path,
     cls.bcolz_daily_bar_ctable, and cls.bcolz_equity_daily_bar_reader
     class level fixtures.
 
@@ -895,7 +895,7 @@ class WithBcolzEquityDailyBarReader(WithEquityDailyBarData, WithTmpDir):
 
 class WithBcolzEquityDailyBarReaderFromCSVs(WithBcolzEquityDailyBarReader):
     """
-    CatalystTestCase mixin that provides
+    ZiplineTestCase mixin that provides
     cls.bcolz_equity_daily_bar_reader from a mapping of sids to CSV
     file paths.
     """
@@ -925,7 +925,7 @@ class _WithMinuteBarDataBase(WithTradingEnvironment):
 
 class WithEquityMinuteBarData(_WithMinuteBarDataBase):
     """
-    CatalystTestCase mixin providing cls.equity_minute_bar_days.
+    ZiplineTestCase mixin providing cls.equity_minute_bar_days.
 
     After init_class_fixtures has been called:
     - `cls.equity_minute_bar_days` has the range over which data has been
@@ -984,7 +984,7 @@ class WithEquityMinuteBarData(_WithMinuteBarDataBase):
 
 class WithFutureMinuteBarData(_WithMinuteBarDataBase):
     """
-    CatalystTestCase mixin providing cls.future_minute_bar_days.
+    ZiplineTestCase mixin providing cls.future_minute_bar_days.
 
     After init_class_fixtures has been called:
     - `cls.future_minute_bar_days` has the range over which data has been
@@ -1044,7 +1044,7 @@ class WithFutureMinuteBarData(_WithMinuteBarDataBase):
 
 class WithBcolzEquityMinuteBarReader(WithEquityMinuteBarData, WithTmpDir):
     """
-    CatalystTestCase mixin providing cls.bcolz_minute_bar_path,
+    ZiplineTestCase mixin providing cls.bcolz_minute_bar_path,
     cls.bcolz_minute_bar_ctable, and cls.bcolz_equity_minute_bar_reader
     class level fixtures.
 
@@ -1103,7 +1103,7 @@ class WithBcolzEquityMinuteBarReader(WithEquityMinuteBarData, WithTmpDir):
 
 class WithBcolzFutureMinuteBarReader(WithFutureMinuteBarData, WithTmpDir):
     """
-    CatalystTestCase mixin providing cls.bcolz_minute_bar_path,
+    ZiplineTestCase mixin providing cls.bcolz_minute_bar_path,
     cls.bcolz_minute_bar_ctable, and cls.bcolz_equity_minute_bar_reader
     class level fixtures.
 
@@ -1227,7 +1227,7 @@ class WithConstantFutureMinuteBarData(WithFutureMinuteBarData):
 
 class WithAdjustmentReader(WithBcolzEquityDailyBarReader):
     """
-    CatalystTestCase mixin providing cls.adjustment_reader as a class level
+    ZiplineTestCase mixin providing cls.adjustment_reader as a class level
     fixture.
 
     After init_class_fixtures has been called, `cls.adjustment_reader` will be
@@ -1359,7 +1359,7 @@ class WithEquityPricingPipelineEngine(WithAdjustmentReader,
 
 class WithSeededRandomPipelineEngine(WithTradingSessions, WithAssetFinder):
     """
-    CatalystTestCase mixin providing class-level fixtures for running pipelines
+    ZiplineTestCase mixin providing class-level fixtures for running pipelines
     against deterministically-generated random data.
 
     Attributes
@@ -1434,7 +1434,7 @@ class WithDataPortal(WithAdjustmentReader,
                      WithBcolzEquityMinuteBarReader,
                      WithBcolzFutureMinuteBarReader):
     """
-    CatalystTestCase mixin providing self.data_portal as an instance level
+    ZiplineTestCase mixin providing self.data_portal as an instance level
     fixture.
 
     After init_instance_fixtures has been called, `self.data_portal` will be
@@ -1485,12 +1485,12 @@ class WithDataPortal(WithAdjustmentReader,
             self.env.asset_finder,
             self.trading_calendar,
             first_trading_day=self.DATA_PORTAL_FIRST_TRADING_DAY,
-            daily_reader=(
+            equity_daily_reader=(
                 self.bcolz_equity_daily_bar_reader
                 if self.DATA_PORTAL_USE_DAILY_DATA else
                 None
             ),
-            minute_reader=(
+            equity_minute_reader=(
                 self.bcolz_equity_minute_bar_reader
                 if self.DATA_PORTAL_USE_MINUTE_DATA else
                 None
@@ -1526,7 +1526,7 @@ class WithDataPortal(WithAdjustmentReader,
 
 class WithResponses(object):
     """
-    CatalystTestCase mixin that provides self.responses as an instance
+    ZiplineTestCase mixin that provides self.responses as an instance
     fixture.
 
     After init_instance_fixtures has been called, `self.responses` will be

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)

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,

catalyst/utils/factory.py

@@ -37,7 +37,7 @@ from catalyst.utils.input_validation import expect_types
 __all__ = ['load_from_yahoo', 'load_bars_from_yahoo']
 
 
-def create_simulation_parameters(year=2016, start=None, end=None,
+def create_simulation_parameters(year=2006, start=None, end=None,
                                  capital_base=float("1.0e5"),
                                  num_days=None,
                                  data_frequency='daily',

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.
 

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
 

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, \
+from catalyst.exchange.exchange_algorithm import (
-    ExchangeTradingAlgorithmBacktest
+    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)
+    BaseCurrencyNotFoundError, NotEnoughCapitalError)
-from catalyst.exchange.exchange_utils import get_algo_object
-from logbook import Logger
 
 from catalyst.constants import LOG_LEVEL
 
@@ -91,7 +91,8 @@ def _run(handle_data,
          algo_namespace,
          base_currency,
          live_graph,
-         simulate_orders):
+         simulate_orders,
+         stats_output):
     """Run a backtest for the given algorithm.
 
     This is shared between the cli and :func:`catalyst.run_algo`.
@@ -140,7 +141,8 @@ def _run(handle_data,
         else:
             click.echo(algotext)
 
-    mode = 'live' if live else 'backtest'
+    mode = 'paper-trading' if simulate_orders else 'live-trading' \
+        if live else 'backtest'
     log.info('running algo in {mode} mode'.format(mode=mode))
 
     exchange_name = exchange
@@ -151,24 +153,10 @@ def _run(handle_data,
 
     exchanges = dict()
     for exchange_name in exchange_list:
-
-        # Looking for the portfolio from the cache first
-        portfolio = get_algo_object(
-            algo_name=algo_namespace,
-            key='portfolio_{}'.format(exchange_name),
-            environ=environ
-        )
-
-        if portfolio is None:
-            portfolio = ExchangePortfolio(
-                start if start is not None else pd.Timestamp.utcnow()
-            )
-
         exchanges[exchange_name] = get_exchange(
             exchange_name=exchange_name,
             base_currency=base_currency,
-            portfolio=portfolio,
+            must_authenticate=(live and not simulate_orders),
-            must_authenticate=live,
         )
 
     open_calendar = get_calendar('OPEN')
@@ -185,7 +173,7 @@ 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()
@@ -240,28 +228,25 @@ def _run(handle_data,
                     )
                 )
 
-                if capital_base is not None \
+                return base_currency_available
-                        and capital_base < base_currency_available:
-                    log.info(
-                        'using capital base limit: {} {}'.format(
-                            capital_base, base_currency
-                        )
-                    )
-                    amount = capital_base
-                else:
-                    amount = base_currency_available
-
-                return amount
             else:
                 raise BaseCurrencyNotFoundError(
                     base_currency=base_currency,
                     exchange=exchange_name
                 )
 
-        combined_capital_base = 0
+        if not simulate_orders:
-        for exchange_name in exchanges:
+            for exchange_name in exchanges:
-            exchange = exchanges[exchange_name]
+                exchange = exchanges[exchange_name]
-            combined_capital_base += fetch_capital_base(exchange)
+                balance = fetch_capital_base(exchange)
+
+                if balance < capital_base:
+                    raise NotEnoughCapitalError(
+                        exchange=exchange_name,
+                        base_currency=base_currency,
+                        balance=balance,
+                        capital_base=capital_base,
+                    )
 
         sim_params = create_simulation_parameters(
             start=start,
@@ -279,7 +264,8 @@ def _run(handle_data,
             exchanges=exchanges,
             algo_namespace=algo_namespace,
             live_graph=live_graph,
-            simulate_orders=simulate_orders
+            simulate_orders=simulate_orders,
+            stats_output=stats_output,
         )
     elif exchanges:
         # Removed the existing Poloniex fork to keep things simple
@@ -442,6 +428,7 @@ def run_algorithm(initialize,
                   algo_namespace=None,
                   live_graph=False,
                   simulate_orders=True,
+                  stats_output=None,
                   output=os.devnull):
     """Run a trading algorithm.
 
@@ -516,6 +503,14 @@ def run_algorithm(initialize,
         default_extension, extensions, strict_extensions, environ
     )
 
+    if capital_base is None:
+        raise ValueError(
+            'Please specify a `capital_base` parameter which is the maximum '
+            'amount of base currency available for trading. For example, '
+            'if the `capital_base` is 5ETH, the '
+            '`order_target_percent(asset, 1)` command will order 5ETH worth '
+            'of the specified asset.'
+        )
     # I'm not sure that we need this since the modified DataPortal
     # does not require extensions to be explicitly loaded.
 
@@ -564,5 +559,6 @@ def run_algorithm(initialize,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
         live_graph=live_graph,
-        simulate_orders=simulate_orders
+        simulate_orders=simulate_orders,
+        stats_output=stats_output
     )

docs/source/example-algos.rst

@@ -31,6 +31,11 @@ Overview
   `two-part video tutorial <videos.html#backtesting-a-strategy>`_ to show how 
   to get started in backtesting and live trading with Catalyst.
 
+- :ref:`Simple Universe <simple_universe>`: This code provides the 'universe' 
+  of available trading pairs on a given exchange on any given day. You can use 
+  this code to dynamically select which currency pairs you want to trade each 
+  day of your strategy. This example does not make any trades. 
+
 - :ref:`Portfolio Optimization <portfolio_optimization>`: Use this code to 
   execute a portfolio optimization model. This strategy will select the 
   portfolio with the maximum Sharpe Ratio. The parameters are set to use 180 
@@ -753,6 +758,177 @@ implemented after the video was recorded, which executes the orders at slighlty
 different prices, but resulting in significant changes in performance of our 
 strategy.
 
+.. _simple_universe:
+
+Simple Universe
+~~~~~~~~~~~~~~~
+
+Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_
+
+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 
+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 
+create your own trading strategy.
+
+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 
+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 
+of the file:
+
+.. code-block:: bash
+  
+  catalyst ingest-exchange -x bitfinex -f minute
+
+.. code-block:: bash
+
+  python simple_universe.py
+
+Credits: This code was originally submitted by `Abner Ayala-Acevedo 
+<https://github.com/abnera>`_. Thank you!
+
+.. code-block:: python
+
+  from datetime import timedelta
+
+  import numpy as np
+  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  # minute counter
+      context.exchange = context.exchanges.values()[0].name.lower()  
+      context.base_currency = context.exchanges.values()[0].base_currency.lower()  
+
+
+  def handle_data(context, data):
+      context.i += 1
+      lookback_days = 7  # 7 days
+
+      # 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) 
+      # keep only the date as a string, discard the time
+      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
+      if not context.i % one_day_in_minutes:
+          context.universe = universe(context, lookback_date, date)
+
+      # 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  
+      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 
+              # required for candlestick or indicators/signals. Return Pandas
+              # 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
+
+              # 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], 
+                      l=low[-1],
+                      c=close[-1],
+                      v=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):
+      # get all the pairs for the given 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)
+
+      # 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
+      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
+
+      return 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('2017-11-10', utc=True)
+      end_date = pd.to_datetime('2017-11-13', utc=True)
+
+      performance = run_algorithm(start=start_date, end=end_date,
+                                  capital_base=100.0,  # amount of base_currency
+                                  initialize=initialize,
+                                  handle_data=handle_data,
+                                  analyze=analyze,
+                                  exchange_name='bitfinex',
+                                  data_frequency='minute',
+                                  base_currency='btc',
+                                  live=False,
+                                  live_graph=False,
+                                  algo_namespace='simple_universe')
+
+
+
 .. _portfolio_optimization:
 
 Portfolio Optimization

docs/source/features.rst

@@ -1,5 +1,61 @@
+Features
+========
+
+This page describes the features that Catalyst provides in the current version,
+and what is planned for future releases.
+
+Current Functionality
+~~~~~~~~~~~~~~~~~~~~~
+
+* Backtesting and live-trading modes to run your trading algorithms, with a 
+  seamless transition between the two.
+* Paper trading simulates order in live-trading mode.
+* Support for 3 exchanges: Bitfinex, Bittrex and Poloniex in both modes 
+  (backtesting and live-trading). Historical data for backtesting is provided 
+  with daily resolution for all three exchanges, and minute resolution for 
+  Bitfinex and Poloniex. No minute-resolution data is currently available for 
+  Bittrex. Refer to 
+  `Catalyst Market Coverage <https://www.enigma.co/catalyst/status>`_ for 
+  details.
+* Interface with over 90 exchanges available in live and paper trading modes.
+* Granular commission models which closely simulates each exchange fee
+  structure in backtesting and paper trading.
+* Standardized naming convention for all asset pairs trading on any exchange in 
+  the form ``{market_currency}_{base_currency}``. See 
+  :ref:`naming`.
+* Output of performance statistics based on Pandas DataFrames to integrate 
+  nicely into the existing PyData ecosystem.
+* Support for accessing multiple exchanges per algorithm, which opens the door
+  to cross-exchange arbitrage opportunities.
+* Support for running multiple algorithms on the same exchange independently of
+  one another. Catalyst performance tracker stores just enough data to allow 
+  algorithms to run independently while still sharing critical data through 
+  exchanges.
+* Benchmark defaults to Bitcoin price (btc_usdt in Poloniex exchange) for the 
+  purpose of comparing performance across trading algorithms. A custom benchmark
+  can be specified through ``set_benchmark()`` (but see 
+  `issue #86 <https://github.com/enigmampc/catalyst/issues/86>`_). 
+* Support for MacOS, Linux and Windows installations.
+* Support for Python2 and Python3.
+
+For additional details on the functionality added on recent releases, see the
+:doc:`Release Notes<releases>`.
+
+Upcoming features
+~~~~~~~~~~~~~~~~~
+
+* Additional datasets beyond pricing data (Dec. 2017)
+* API documentation (Jan. 2017)
+* Support for decentralized exchanges (Jan. 2017)
+* Support for data ingestion of community-contributed data sets (Jan. 2017)
+* Pipeline support (Jan. 2018)
+* Web UI (Q2 2018)
+
+
+ .. _naming:
+
 Naming Convention
-=================
+~~~~~~~~~~~~~~~~~
 
 Catalyst introduces a standardized naming convention for all asset pairs 
 trading on any exchange in the following form:

docs/source/index.rst

@@ -1,4 +1,4 @@
-.. include:: welcome.rst
+.. include:: ../../README.rst
 |
 |
 Table of Contents
@@ -9,9 +9,8 @@ Table of Contents
 
    install
    beginner-tutorial
-   jupyter
    live-trading
-   naming-convention
+   features
    example-algos
    utilities
    videos
@@ -19,7 +18,6 @@ Table of Contents
    development-guidelines
    releases
 ..   bundles
-..   development-guidelines
 ..   appendix
 ..   release-process
 

docs/source/live-trading.rst

@@ -106,6 +106,10 @@ What differs are the arguments provided to the catalyst client or
 Here is the breakdown of the new arguments:
 
 - ``live``: Boolean flag which enables live trading.
+- ``capital_base``: The amount of base_currency assigned to the strategy.
+  It has to be lower or equal to the amount of base currency available for
+  trading on the exchange. For illustration, order_target_percent(asset, 1)
+  will order the capital_base amount specified here of the specified asset.
 - ``exchange_name``: The name of the targeted exchange
   (supported values: *bitfinex*, *bittrex*).
 - ``algo_namespace``: A arbitrary label assigned to your algorithm for
@@ -113,6 +117,8 @@ Here is the breakdown of the new arguments:
 - ``base_currency``: The base currency used to calculate the
   statistics of your algorithm. Currently, the base currency of all
   trading pairs of your algorithm must match this value.
+- ``simulate_orders``: Enables the paper trading mode, in which orders are
+  simulated in Catalyst instead of processed on the exchange.
 
 Here is a complete algorithm for reference:
 `Buy Low and Sell High <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_low_sell_high_live.py>`_

docs/source/releases.rst

@@ -2,6 +2,15 @@
 Release Notes
 =============
 
+Version 0.3.10
+^^^^^^^^^^^^^
+**Release Date**: 2017-12-12
+
+Bug Fixes
+~~~~~~~~~
+
+- Fixed issue with fetching assets with daily frequency
+
 Version 0.3.10
 ^^^^^^^^^^^^^
 **Release Date**: 2017-11-28
@@ -10,6 +19,16 @@ Bug Fixes
 ~~~~~~~~~
 
 - Fixed issue with fetching assets with daily frequency
+- Changed Poloniex interface (should solve :issue:`95` and :issue:`94`)
+- Solved issue with overriding commission and slippage (:issue:`87`)
+- Fixed inefficiency with Bittrex current prices (:issue:`76`)
+
+Build
+~~~~~
+- Integrated with CCXT
+- Added paper trading capability (`simulate_orders=True` param in live mode)
+- More granular commissions (:issue:`82`)
+- Added market orders in live mode (:issue:`81`)
 
 Version 0.3.9
 ^^^^^^^^^^^^^

docs/source/welcome.rst

@@ -1,43 +0,0 @@
-.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
-|
-Catalyst is an algorithmic trading library for crypto-assets written in Python.
-It allows trading strategies to be easily expressed and backtested against 
-historical data (with daily and minute resolution), providing analytics and 
-insights regarding a particular strategy's performance. Catalyst also supports
-live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex, 
-and Poloniex) with more being added over time. Catalyst empowers users to share 
-and curate data and build profitable, data-driven investment strategies. Please 
-visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or 
-refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for 
-further technical details.
-
-Catalyst builds on top of the well-established 
-`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to 
-minimize structural changes to the general API to maximize compatibility with 
-existing trading algorithms, developer knowledge, and tutorials. Join us on 
-`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel 
-for questions around Catalyst, algorithmic trading and technical support.
-
-Features
-========
-
--  Ease of use: Catalyst tries to get out of your way so that you can 
-   focus on algorithm development. See 
-   `examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_ 
-   provided.
--  Support for several of the top crypto-exchanges by trading volume:
-   `Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
-   and `Poloniex <https://www.poloniex.com>`_. 
--  Secure: You and only you have access to each exchange API keys for your accounts.
--  Input of historical pricing data of all crypto-assets by exchange, 
-   with daily and minute resolution. See 
-   `Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
--  Backtesting and live-trading functionality, with a seamless transition
-   between the two modes.
--  Output of performance statistics are based on Pandas DataFrames to 
-   integrate nicely into the existing PyData eco-system.
--  Statistic and machine learning libraries like matplotlib, scipy, 
-   statsmodels, and sklearn support development, analysis, and 
-   visualization of state-of-the-art trading systems.
--  Addition of Bitcoin price (btc_usdt) as a benchmark for comparing 
-   performance across trading algorithms.

docs/source/whatsnew/0.9.0.txt

@@ -105,7 +105,7 @@ None
 Miscellaneous
 ~~~~~~~~~~~~~
 
-* Adds :class:`~zipline.testing.fixtures.CatalystTestCase` which provides hooks
+* Adds :class:`~zipline.testing.fixtures.ZiplineTestCase` which provides hooks
   to consume test fixtures. Fixtures are things like:
   :class:`~zipline.testing.fixtures.WithAssetFinder` which will make
   ``self.asset_finder`` available to your test with some mock data

etc/python2.7-environment.yml

@@ -20,6 +20,7 @@ dependencies:
   - bcolz==0.12.1
   - bottleneck==1.2.1
   - chardet==3.0.4
+  - ccxt==1.10.319
   - click==6.7
   - contextlib2==0.5.5
   - cycler==0.10.0

etc/requirements.txt

@@ -81,5 +81,5 @@ empyrical==0.2.1
 tables==3.3.0
 
 #Catalyst dependencies
-ccxt==1.10.251
+ccxt==1.10.283
-
+boto3==1.4.8

tests/calendars/test_calendar_dispatcher.py

@@ -6,12 +6,12 @@ from catalyst.errors import (
     CyclicCalendarAlias,
     InvalidCalendarName,
 )
-from catalyst.testing import CatalystTestCase
+from catalyst.testing import ZiplineTestCase
 from catalyst.utils.calendars.calendar_utils import TradingCalendarDispatcher
 from catalyst.utils.calendars.exchange_calendar_ice import ICEExchangeCalendar
 
 
-class CalendarAliasTestCase(CatalystTestCase):
+class CalendarAliasTestCase(ZiplineTestCase):
 
     @classmethod
     def init_class_fixtures(cls):

tests/data/bundles/test_core.py

@@ -22,7 +22,7 @@ from catalyst.testing import (
     subtest,
     str_to_seconds,
 )
-from catalyst.testing.fixtures import WithInstanceTmpDir, CatalystTestCase, \
+from catalyst.testing.fixtures import WithInstanceTmpDir, ZiplineTestCase, \
     WithDefaultDateBounds
 from catalyst.testing.predicates import (
     assert_equal,
@@ -45,7 +45,7 @@ _1_ns = pd.Timedelta(1, unit='ns')
 
 class BundleCoreTestCase(WithInstanceTmpDir,
                          WithDefaultDateBounds,
-                         CatalystTestCase):
+                         ZiplineTestCase):
 
     START_DATE = pd.Timestamp('2014-01-06', tz='utc')
     END_DATE = pd.Timestamp('2014-01-10', tz='utc')

tests/data/bundles/test_quandl.py

@@ -17,14 +17,14 @@ from catalyst.testing import (
     tmp_dir,
     patch_read_csv,
 )
-from catalyst.testing.fixtures import CatalystTestCase
+from catalyst.testing.fixtures import ZiplineTestCase
 from catalyst.testing.predicates import (
     assert_equal,
 )
 from catalyst.utils.functional import apply
 
 
-class QuandlBundleTestCase(CatalystTestCase):
+class QuandlBundleTestCase(ZiplineTestCase):
     symbols = 'AAPL', 'BRK_A', 'MSFT', 'ZEN'
     asset_start = pd.Timestamp('2014-01', tz='utc')
     asset_end = pd.Timestamp('2015-01', tz='utc')

tests/data/bundles/test_yahoo.py

@@ -10,12 +10,12 @@ from catalyst.data.bundles.core import _make_bundle_core
 from catalyst.data.bundles import yahoo_equities
 from catalyst.lib.adjustment import Float64Multiply
 from catalyst.testing import test_resource_path, tmp_dir, read_compressed
-from catalyst.testing.fixtures import WithResponses, CatalystTestCase
+from catalyst.testing.fixtures import WithResponses, ZiplineTestCase
 from catalyst.testing.predicates import assert_equal
 from catalyst.utils.calendars import get_calendar
 
 
-class YahooBundleTestCase(WithResponses, CatalystTestCase):
+class YahooBundleTestCase(WithResponses, ZiplineTestCase):
     symbols = 'AAPL', 'IBM', 'MSFT'
     columns = 'open', 'high', 'low', 'close', 'volume'
     asset_start = pd.Timestamp('2014-01-02', tz='utc')

tests/data/test_dispatch_bar_reader.py

@@ -11,12 +11,6 @@
 # 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.
-
-
-'''
-#   ZIPLINE legacy test: Catalyst does not use DispatchBarReader, and thus
-#   this test suite is irrelevant, and is commented out in its entirety
-
 from numpy import array, nan
 from numpy.testing import assert_almost_equal
 from pandas import DataFrame, Timestamp
@@ -37,7 +31,7 @@ from catalyst.testing.fixtures import (
     WithBcolzEquityDailyBarReader,
     WithBcolzFutureMinuteBarReader,
     WithTradingSessions,
-    CatalystTestCase,
+    ZiplineTestCase,
 )
 
 OHLC = ['open', 'high', 'low', 'close']
@@ -46,7 +40,7 @@ OHLC = ['open', 'high', 'low', 'close']
 class AssetDispatchSessionBarTestCase(WithBcolzEquityDailyBarReader,
                                       WithBcolzFutureMinuteBarReader,
                                       WithTradingSessions,
-                                      CatalystTestCase):
+                                      ZiplineTestCase):
 
     TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
     TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
@@ -175,7 +169,7 @@ class AssetDispatchSessionBarTestCase(WithBcolzEquityDailyBarReader,
 
 class AssetDispatchMinuteBarTestCase(WithBcolzEquityMinuteBarReader,
                                      WithBcolzFutureMinuteBarReader,
-                                     CatalystTestCase):
+                                     ZiplineTestCase):
 
     TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
     TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
@@ -336,4 +330,3 @@ class AssetDispatchMinuteBarTestCase(WithBcolzEquityMinuteBarReader,
         for i, (sid, expected, msg) in enumerate(expected_per_sid):
             for j, result in enumerate(results):
                 assert_almost_equal(result[:, i], expected[j], err_msg=msg)
-'''

tests/data/test_minute_bars.py

@@ -38,8 +38,8 @@ from pandas import (
 from catalyst.data.bar_reader import NoDataOnDate
 from catalyst.data.minute_bars import (
     BcolzMinuteBarMetadata,
-#    BcolzMinuteBarWriter,
+    BcolzMinuteBarWriter,
-#    BcolzMinuteBarReader,
+    BcolzMinuteBarReader,
     BcolzMinuteOverlappingData,
     US_EQUITIES_MINUTES_PER_DAY,
     BcolzMinuteWriterColumnMismatch,
@@ -47,29 +47,24 @@ from catalyst.data.minute_bars import (
     H5MinuteBarUpdateReader,
 )
 
-from catalyst.exchange.exchange_bcolz import (
-    BcolzExchangeBarWriter,
-    BcolzExchangeBarReader,
-)
-
 from catalyst.testing.fixtures import (
     WithAssetFinder,
     WithInstanceTmpDir,
     WithTradingCalendars,
-    CatalystTestCase,
+    ZiplineTestCase,
 )
 
 # Calendar is set to cover several half days, to check a case where half
 # days would be read out of order in cases of windows which spanned over
 # multiple half days.
-TEST_CALENDAR_START = Timestamp('2015-06-02', tz='UTC')
+TEST_CALENDAR_START = Timestamp('2014-06-02', tz='UTC')
-TEST_CALENDAR_STOP = Timestamp('2016-12-31', tz='UTC')
+TEST_CALENDAR_STOP = Timestamp('2015-12-31', tz='UTC')
 
 
 class BcolzMinuteBarTestCase(WithTradingCalendars,
                              WithAssetFinder,
                              WithInstanceTmpDir,
-                             CatalystTestCase):
+                             ZiplineTestCase):
 
     ASSET_FINDER_EQUITY_SIDS = 1, 2
 
@@ -92,14 +87,14 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
 
         self.dest = self.instance_tmpdir.getpath('minute_bars')
         os.makedirs(self.dest)
-        self.writer = BcolzExchangeBarWriter(
+        self.writer = BcolzMinuteBarWriter(
-            rootdir=self.dest,
+            self.dest,
-            calendar=self.trading_calendar,
+            self.trading_calendar,
-            start_session=TEST_CALENDAR_START,
+            TEST_CALENDAR_START,
-            end_session=TEST_CALENDAR_STOP,
+            TEST_CALENDAR_STOP,
-            data_frequency='minute',
+            US_EQUITIES_MINUTES_PER_DAY,
         )
-        self.reader = BcolzExchangeBarReader(self.dest)
+        self.reader = BcolzMinuteBarReader(self.dest)
 
     def test_version(self):
         metadata = self.reader._get_metadata()
@@ -157,7 +152,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
         )
 
         # Create a new writer with `ohlc_ratios_per_sid` defined.
-        writer_with_ratios = BcolzExchangeBarWriter(
+        writer_with_ratios = BcolzMinuteBarWriter(
             self.dest,
             self.trading_calendar,
             TEST_CALENDAR_START,
@@ -166,7 +161,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
             ohlc_ratios_per_sid={sid: 25},
         )
         writer_with_ratios.write_sid(sid, data)
-        reader = BcolzExchangeBarReader(self.dest)
+        reader = BcolzMinuteBarReader(self.dest)
 
         open_price = reader.get_value(sid, minute, 'open')
         self.assertEquals(10.0, open_price)
@@ -454,7 +449,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
         # of appending new days will be writing to an existing directory.
         cday = self.trading_calendar.schedule.index.freq
         new_end_session = TEST_CALENDAR_STOP + cday
-        writer = BcolzExchangeBarWriter.open(self.dest, new_end_session)
+        writer = BcolzMinuteBarWriter.open(self.dest, new_end_session)
         next_day_minute = dt + cday
         new_data = DataFrame(
             data=ohlcv,
@@ -462,7 +457,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
         writer.write_sid(sid, new_data)
 
         # Get a new reader to test updated calendar.
-        reader = BcolzExchangeBarReader(self.dest)
+        reader = BcolzMinuteBarReader(self.dest)
 
         second_minute = dt + Timedelta(minutes=1)
 
@@ -807,7 +802,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
             index=minutes)
         self.writer.write_sid(sids[1], data_2)
 
-        reader = BcolzExchangeBarReader(self.dest)
+        reader = BcolzMinuteBarReader(self.dest)
 
         columns = ['open', 'high', 'low', 'close', 'volume']
         sids = [sids[0], sids[1]]
@@ -859,7 +854,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
             index=minutes)
         self.writer.write_sid(sids[1], data_2)
 
-        reader = BcolzExchangeBarReader(self.dest)
+        reader = BcolzMinuteBarReader(self.dest)
 
         columns = ['open', 'high', 'low', 'close', 'volume']
         sids = [sids[0], sids[1]]
@@ -882,7 +877,6 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
                 assert_almost_equal(data[sid].loc[minutes, col],
                                     arrays[i][j][minute_locs])
 
-    '''
     def test_adjust_non_trading_minutes(self):
         start_day = Timestamp('2015-06-01', tz='UTC')
         end_day = Timestamp('2015-06-02', tz='UTC')
@@ -928,9 +922,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
                 Timestamp('2015-06-02 20:01:00', tz='UTC'),
                 'open'
             )
-    '''
 
-    '''
     def test_adjust_non_trading_minutes_half_days(self):
         # half day
         start_day = Timestamp('2015-11-27', tz='UTC')
@@ -986,7 +978,6 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
                 Timestamp('2015-11-30 21:01:00', tz='UTC'),
                 'open'
             )
-    '''
 
     def test_set_sid_attrs(self):
         """Confirm that we can set the attributes of a sid's file correctly.
@@ -1032,13 +1023,13 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
 
         # Open a new writer to cover `open` method, also truncating only
         # applies to an existing directory.
-        writer = BcolzExchangeBarWriter.open(self.dest)
+        writer = BcolzMinuteBarWriter.open(self.dest)
 
         # Truncate to first day with data.
         writer.truncate(days[0])
 
         # Refresh the reader since truncate update the metadata.
-        self.reader = BcolzExchangeBarReader(self.dest)
+        self.reader = BcolzMinuteBarReader(self.dest)
 
         self.assertEqual(self.writer.last_date_in_output_for_sid(sid), days[0])
 
@@ -1096,7 +1087,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
         self.writer.truncate(self.test_calendar_start)
 
         # Refresh the reader since truncate update the metadata.
-        self.reader = BcolzExchangeBarReader(self.dest)
+        self.reader = BcolzMinuteBarReader(self.dest)
 
         self.assertEqual(
             self.writer.last_date_in_output_for_sid(sid),
@@ -1207,7 +1198,7 @@ class BcolzMinuteBarTestCase(WithTradingCalendars,
         self.writer.write(update_reader.read(minutes, sids))
 
         # Refresh the reader since truncate update the metadata.
-        reader = BcolzExchangeBarReader(self.dest)
+        reader = BcolzMinuteBarReader(self.dest)
 
         columns = ['open', 'high', 'low', 'close', 'volume']
         sids = [sids[0], sids[1]]

tests/data/test_resample.py

@@ -35,7 +35,7 @@ from catalyst.testing.fixtures import (
     WithBcolzEquityMinuteBarReader,
     WithBcolzEquityDailyBarReader,
     WithBcolzFutureMinuteBarReader,
-    CatalystTestCase,
+    ZiplineTestCase,
 )
 
 OHLC = ['open', 'high', 'low', 'close']
@@ -254,7 +254,7 @@ EXPECTED_SESSIONS = {
 
 class MinuteToDailyAggregationTestCase(WithBcolzEquityMinuteBarReader,
                                        WithBcolzFutureMinuteBarReader,
-                                       CatalystTestCase):
+                                       ZiplineTestCase):
 
     #    March 2016
     # Su Mo Tu We Th Fr Sa
@@ -525,7 +525,7 @@ class MinuteToDailyAggregationTestCase(WithBcolzEquityMinuteBarReader,
 
 
 class TestMinuteToSession(WithEquityMinuteBarData,
-                          CatalystTestCase):
+                          ZiplineTestCase):
 
     #    March 2016
     # Su Mo Tu We Th Fr Sa
@@ -565,7 +565,7 @@ class TestMinuteToSession(WithEquityMinuteBarData,
 
 
 class TestResampleSessionBars(WithBcolzFutureMinuteBarReader,
-                              CatalystTestCase):
+                              ZiplineTestCase):
 
     TRADING_CALENDAR_STRS = ('us_futures',)
     TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
@@ -667,7 +667,7 @@ class TestResampleSessionBars(WithBcolzFutureMinuteBarReader,
 
 
 class TestReindexMinuteBars(WithBcolzEquityMinuteBarReader,
-                            CatalystTestCase):
+                            ZiplineTestCase):
 
     TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
     TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
@@ -736,7 +736,7 @@ class TestReindexMinuteBars(WithBcolzEquityMinuteBarReader,
 
 
 class TestReindexSessionBars(WithBcolzEquityDailyBarReader,
-                             CatalystTestCase):
+                             ZiplineTestCase):
 
     TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
     TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'

tests/data/test_us_equity_pricing.py

@@ -50,7 +50,7 @@ from catalyst.testing.fixtures import (
     WithBcolzEquityDailyBarReader,
     WithTmpDir,
     WithTradingCalendars,
-    CatalystTestCase,
+    ZiplineTestCase,
 )
 from catalyst.utils.calendars import get_calendar
 
@@ -86,7 +86,7 @@ EQUITY_INFO['symbol'] = [chr(ord('A') + n) for n in range(len(EQUITY_INFO))]
 TEST_QUERY_ASSETS = EQUITY_INFO.index
 
 
-class BcolzDailyBarTestCase(WithBcolzEquityDailyBarReader, CatalystTestCase):
+class BcolzDailyBarTestCase(WithBcolzEquityDailyBarReader, ZiplineTestCase):
     EQUITY_DAILY_BAR_START_DATE = TEST_CALENDAR_START
     EQUITY_DAILY_BAR_END_DATE = TEST_CALENDAR_STOP
 
@@ -372,7 +372,7 @@ class BcolzDailyBarNeverReadAllTestCase(BcolzDailyBarTestCase):
 class BcolzDailyBarWriterMissingDataTestCase(WithAssetFinder,
                                              WithTmpDir,
                                              WithTradingCalendars,
-                                             CatalystTestCase):
+                                             ZiplineTestCase):
     # Sid 3 is active from 2015-06-02 to 2015-06-30.
     MISSING_DATA_SID = 3
     # Leave out data for a day in the middle of the query range.

tests/events/test_events_cme.py

@@ -12,11 +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.
-
-'''
-#   ZIPLINE legacy test: Catalyst only uses OPEN calendar, and thus 
-#   this test suite is irrelevant, and is commented out in its entirety
-
 from unittest import TestCase
 import pandas as pd
 
@@ -46,5 +41,3 @@ class TestStatelessRulesCME(StatelessRulesTests, TestCase):
 
 class TestStatefulRulesCME(StatefulRulesTests, TestCase):
     CALENDAR_STRING = "CME"
-
-'''

tests/events/test_events_nyse.py

@@ -12,11 +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.
-
-'''
-#   ZIPLINE legacy test: Catalyst only uses OPEN calendar, and thus 
-#   this test suite is irrelevant, and is commented out in its entirety
-
 from unittest import TestCase
 from datetime import timedelta
 import pandas as pd
@@ -167,5 +162,3 @@ class TestStatelessRulesNYSE(StatelessRulesTests, TestCase):
 
 class TestStatefulRulesNYSE(StatefulRulesTests, TestCase):
     CALENDAR_STRING = "NYSE"
-
-'''

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):

tests/exchange/test_bitfinex.py

@@ -4,10 +4,12 @@ from base import BaseExchangeTestCase
 from catalyst.exchange.bitfinex.bitfinex import Bitfinex
 from catalyst.exchange.exchange_utils import get_exchange_auth
 from catalyst.finance.execution import (LimitOrder)
+from catalyst.utils.deprecate import deprecated
 
 log = Logger('test_bitfinex')
 
 
+@deprecated
 class TestBitfinex(BaseExchangeTestCase):
     @classmethod
     def setup(self):
@@ -34,7 +36,7 @@ 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 +49,17 @@ class TestBitfinex(BaseExchangeTestCase):
 
     def test_get_candles(self):
         log.info('retrieving candles')
-        ohlcv_neo = self.exchange.get_candles(
+        # ohlcv_neo = self.exchange.get_candles(
-            freq='1T',
+        #    freq='1T',
-            assets=self.exchange.get_asset('neo_btc')
+        #    assets=self.exchange.get_asset('neo_btc'))
-        )
         pass
 
     def test_tickers(self):
         log.info('retrieving tickers')
-        tickers = self.exchange.tickers([
+        # tickers = self.exchange.tickers([
-            self.exchange.get_asset('eth_btc'),
+        #     self.exchange.get_asset('eth_btc'),
-            self.exchange.get_asset('etc_btc')
+        #     self.exchange.get_asset('etc_btc')
-        ])
+        # ])
         pass
 
     def test_get_account(self):
@@ -67,11 +68,11 @@ 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')
+        # asset = self.exchange.get_asset('eth_btc')
-        orderbook = self.exchange.get_orderbook(asset)
+        # orderbook = self.exchange.get_orderbook(asset)
         pass

tests/exchange/test_bittrex.py

@@ -1,13 +1,15 @@
-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
 from logbook import Logger
 from catalyst.exchange.exchange_utils import get_exchange_auth
+from catalyst.utils.deprecate import deprecated
 
 log = Logger('test_bittrex')
 
 
+@deprecated
 class TestBittrex(BaseExchangeTestCase):
     @classmethod
     def setup(self):
@@ -33,8 +35,8 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_open_orders(self):
         log.info('retrieving open orders')
-        asset = self.exchange.get_asset('neo_btc')
+        # asset = self.exchange.get_asset('neo_btc')
-        orders = self.exchange.get_open_orders(asset)
+        # orders = self.exchange.get_open_orders(asset)
         pass
 
     def test_get_order(self):
@@ -51,21 +53,21 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_get_candles(self):
         log.info('retrieving candles')
-        ohlcv_neo = self.exchange.get_candles(
+        # ohlcv_neo = self.exchange.get_candles(
-            freq='5T',
+        #     freq='5T',
-            assets=self.exchange.get_asset('neo_btc'),
+        #     assets=self.exchange.get_asset('neo_btc'),
-            bar_count=20,
+        #     bar_count=20,
-            end_dt=pd.to_datetime('2017-10-20', utc=True)
+        #     end_dt=pd.to_datetime('2017-10-20', utc=True)
-        )
+        # )
-        ohlcv_neo_ubq = self.exchange.get_candles(
+        # ohlcv_neo_ubq = self.exchange.get_candles(
-            freq='1D',
+        #     freq='1D',
-            assets=[
+        #     assets=[
-                self.exchange.get_asset('neo_btc'),
+        #         self.exchange.get_asset('neo_btc'),
-                self.exchange.get_asset('ubq_btc')
+        #         self.exchange.get_asset('ubq_btc')
-            ],
+        #     ],
-            bar_count=14,
+        #     bar_count=14,
-            end_dt=pd.to_datetime('2017-10-20', utc=True)
+        #     end_dt=pd.to_datetime('2017-10-20', utc=True)
-        )
+        # )
         pass
 
     def test_tickers(self):
@@ -79,7 +81,7 @@ 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 +90,6 @@ class TestBittrex(BaseExchangeTestCase):
 
     def test_orderbook(self):
         log.info('testing order book for bittrex')
-        asset = self.exchange.get_asset('eth_btc')
+        # asset = self.exchange.get_asset('eth_btc')
-        orderbook = self.exchange.get_orderbook(asset)
+        # orderbook = self.exchange.get_orderbook(asset)
         pass

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'
+        # data_frequency = 'daily'
-        exchange_name = 'poloniex'
+        # exchange_name = 'poloniex'
 
-        exchange = get_exchange(exchange_name)
+        # exchange = get_exchange(exchange_name)
-        exchange_bundle = ExchangeBundle(exchange)
+        # exchange_bundle = ExchangeBundle(exchange)
-        assets = [
+        # assets = [
-            exchange.get_asset('btc_usdt')
+        #     exchange.get_asset('btc_usdt')
-        ]
+        # ]
-        dt = pd.to_datetime('2017-10-14', utc=True)
+        # dt = pd.to_datetime('2017-10-14', utc=True)
 
-        values = exchange_bundle.get_spot_values(
+        # values = exchange_bundle.get_spot_values(
-            assets=assets,
+        #     assets=assets,
-            field='close',
+        #     field='close',
-            dt=dt,
+        #     dt=dt,
-            data_frequency=data_frequency
+        #     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'
+        # exchange_name = 'poloniex'
-        data_frequency = 'minute'
+        # data_frequency = 'minute'
 
-        exchange = get_exchange(exchange_name)
+        # exchange = get_exchange(exchange_name)
-        asset = exchange.get_asset('neos_btc')
+        # asset = exchange.get_asset('neos_btc')
-
-        path = get_bcolz_chunk(
-            exchange_name=exchange_name,
-            symbol=asset.symbol,
-            data_frequency=data_frequency,
-            period='2017-5',
-        )
 
+        # 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'
+        # symbol = 'etc_btc'
-        sid = int(
+        # sid = int(
-            hashlib.sha256(symbol.encode('utf-8')).hexdigest(), 16
+        #     hashlib.sha256(symbol.encode('utf-8')).hexdigest(), 16
-        ) % 10 ** 6
+        # ) % 10 ** 6
         pass
 
     def test_validate_data(self):