dual_vwap: scale volume down by 1000

README.rst

@@ -1 +1,196 @@
-All the documentation for `Catalyst <https://github.com/enigmampc/catalyst>`_ can be found in the `catalyst-docs wiki <https://github.com/enigmampc/catalyst-docs/wiki>`_.
+========
+Catalyst
+========
+
+|version status|
+
+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, providing analytics and insights regarding a particular strategy's performance.
+Catalyst will be expanded to support live-trading of crypto-assets in the coming months.
+Please visit `<enigma.co>`_ to learn 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.
+For now, please refer to the `Zipline API Docs <http://zipline.io>`_ as a general reference and bring any other questions you have to our #dev channel on `Slack <https://join.slack.com/enigmacatalyst/shared_invite/MTkzMjQ0MTg1NTczLTE0OTY3MjE3MDEtZGZmMTI5YzI3ZA>`_.
+
+Our primary contributions include the:
+
+- Introduction of an open trading calendar that permits simulation to allow trades on weekends, holidays, and outside of normal business hours.
+- Curation of OHLCV data bundle from `Poloniex's API <https://poloniex.com/support/api/>`_, which contains data in five-minute intervals as early as 2/19/2015.
+- Support for backtesting of daily trading strategies, support for five-minute backtesting is in development.
+- Addition of Bitcoin price (USDT_BTC) as a benchmark asset for comparing performance.
+
+Interested in getting involved?
+`Join us on Slack! <https://join.slack.com/enigmacatalyst/shared_invite/MTkzMjQ0MTg1NTczLTE0OTY3MjE3MDEtZGZmMTI5YzI3ZA>`_
+
+
+Installation
+============
+
+At the moment, Catalyst has some fairly specific and strict depedency requirements.
+We recommend the use of Python virtual environments if you wish to simplify the installation process, or otherwise isolate Catalyst's dependencies from your other projects.
+If you don't have ``virtualenv`` installed, see our later section on Virtual Environments.
+
+.. code-block:: bash
+
+    $ virtualenv catalyst-venv
+    $ source ./catalyst-venv/bin/activate
+    $ pip install enigma-catalyst
+
+**Note:** A successful installation will require several minutes in order to compile dependencies that expose C APIs.
+
+Dependencies
+------------
+
+Catalyst's depedencies can be found in the ``etc/requirements.txt`` file.
+If you need to install them outside of a typical ``pip install``, this is done using:
+
+.. code-block:: bash
+
+    $ pip install -r etc/requirements.txt
+
+Though not required by Catalyst directly, our example algorithms use matplotlib to visually display backtest results.
+If you wish to run any examples or use matplotlib during development, it can be installed using:
+
+.. code-block:: bash
+
+    $ pip install matplotlib
+
+**Note:** If you plan to use matplotlib and virtualenv on Mac OS X, see our later section for additional setup instructions.
+
+Getting Started
+===============
+
+The following code implements a simple buy and hold algorithm. The full source can be found in ``catalyst/examples/buy_and_hodl.py``.
+
+.. code:: python
+
+    import numpy as np
+    
+    from catalyst.api import (
+        order_target_value,
+        symbol,
+        record,
+        cancel_order,
+        get_open_orders,
+    )
+    
+    ASSET = 'USDT_BTC'
+    
+    TARGET_HODL_RATIO = 0.8
+    RESERVE_RATIO = 1.0 - TARGET_HODL_RATIO
+
+    def initialize(context):
+        context.is_buying = True
+        context.asset = symbol(ASSET)
+
+    def handle_data(context, data):
+        cash = context.portfolio.cash
+        target_hodl_value = TARGET_HODL_RATIO * context.portfolio.starting_cash
+        reserve_value = RESERVE_RATIO * context.portfolio.starting_cash
+        
+        # Cancel any outstanding orders from the previous day
+        orders = get_open_orders(context.asset) or []
+        for order in orders:
+            cancel_order(order)
+        
+        # Stop buying after passing reserve threshold
+        if cash <= reserve_value:
+            context.is_buying = False
+        
+        # Retrieve current price from pricing data
+        price = data[context.asset].price
+        
+        # Check if still buying and could (approximately) afford another purchase                    
+        if context.is_buying and cash > price:
+            # Place order to make position in asset equal to target_hodl_value
+            order_target_value(
+                context.asset,
+                target_hodl_value,
+                limit_price=1.1 * price,
+                stop_price=0.9 * price,
+            )
+        
+        # Record any state for later analysis
+        record(
+            price=price,
+            cash=context.portfolio.cash,
+            leverage=context.account.leverage,
+        )
+
+
+You can then run this algorithm using the Catalyst CLI. From the command
+line, run:
+
+.. code:: bash
+
+    $ catalyst ingest
+    $ catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-6-28 --capital-base 100000 -o bah.pickle
+
+This will download the crypto-asset price data from a poloniex bundle
+curated by Enigma in the specified time range and stream it through
+the algorithm and plot the resulting performance using matplotlib.
+
+You can find other examples in the ``catalyst/examples`` directory.
+
+Limitations
+-----------
+
+This project is currently in a pre-alpha state and has some limitations we'd like to address:
+
+- *Minimum Denomination:* The smallest tradable unit in Catalyst is equal to 1/1000th of a full coin. We plan to enable more granular increments, but have capped it at 1/1000th for the time being.
+- *Supported Assets:* Currently the poloniex bundle comes prepopulated with data for all 90 registered trading pairs. However, due to limitations in how portfolios are currently modeled, we recommend sticking to ``USDT_*`` trading pairs. USDT is an independent currency listed on Poloniex whose price is pegged to the US dollar. Currently, this list includes: ``USDT_BTC``, ``USDT_DASH``, ``USDT_ETC``, ``USDT_ETH``, ``USDT_LTC``, ``USDT_NXT``, ``USDT_REP``, ``USDT_STR``, ``USDT_XMR``, ``USDT_XRP``, and ``USDT_ZEC``. We plan to add support for basing your portfolio in arbitrary currencies and provide native support for modeling ForEx trades in the near future!
+
+Virtual Environments
+====================
+
+Here we will provide a brief tutorial for installing ``virtualenv`` and its basic usage.
+For more information regarding ``virtualenv``, please refer to this `virtualenv guide <http://python-guide-pt-br.readthedocs.io/en/latest/dev/virtualenvs/>`_.
+
+The ``virtualenv`` command can be installed using:
+
+.. code-block:: bash
+
+    $ pip install virtualenv
+
+To create a new virtual environment, choose a directory, e.g. ``/path/to/venv-dir``, where project-specific packages and files will be stored.  The environment is created by running:
+
+.. code-block:: bash
+
+    $ virtualenv /path/to/venv-dir
+
+To enter an environment, run the ``bin/activate`` script located in ``/path/to/venv-dir`` using:
+
+.. code-block:: bash
+
+    $ source /path/to/venv-dir/bin/activate
+
+Exiting an environment is accomplished using ``deactivate``, and removing it entirely is done by deleting ``/path/to/venv-dir``.
+
+OS X + virtualenv + matplotlib
+-------------------------------------
+
+A note about using matplotlib in virtual enviroments on OS X: it may be necessary to run
+
+.. code-block:: python
+
+    echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+
+in order to override the default ``macosx`` backend for your system, which may not be accessible from inside the virtual environment.
+This will allow Catalyst to open matplotlib charts from within a virtual environment, which is useful for displaying the performance of your backtests.  To learn more about matplotlib backends, please refer to the
+`matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
+
+Disclaimer
+==========
+
+Keep in mind that this project is still under active development, and is not recommended for production use in its current state.
+We are deeply committed to improving the overall user experience, reliability, and feature-set offered by Catalyst.
+If you have any suggestions, feedback, or general improvements regarding any of these topics, please let us know!
+
+Hello World,
+
+The Enigma Team
+
+.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
+   :target: https://pypi.python.org/pypi/enigma-catalyst

catalyst/algorithm.py

@@ -742,14 +742,15 @@ class TradingAlgorithm(object):
             for perf in self.get_generator():
                 perfs.append(perf)
 
+          
             # convert perf dict to pandas dataframe
-            daily_stats = self._create_daily_stats(perfs)
+            stats = self._create_daily_stats(perfs)
 
-            self.analyze(daily_stats)
+            self.analyze(stats)
         finally:
             self.data_portal = None
 
-        return daily_stats
+        return stats
 
     def _write_and_map_id_index_to_sids(self, identifiers, as_of_date):
         # Build new Assets for identifiers that can't be resolved as
@@ -1140,14 +1141,12 @@ class TradingAlgorithm(object):
 
         date_rule = date_rule or date_rules.every_day()
         if freq is 'daily':
-            # ignore time rule in daily mode
+            # Ignore any time rules in daily mode.
+            # every_minute in daily mode does nothing.
             time_rule = time_rules.every_minute()
         else:
-            # use provided time rule or default to every minute or 5 minutes
+            # use provided time rule or default to every minute
-            # based on desired data frequency.
+            time_rule = time_rule or time_rules.every_minute()
-            time_rule = time_rule or (time_rules.every_5_minutes()
-                                      if freq is '5-minute' else
-                                      time_rules.every_minute())
 
         # Check the type of the algorithm's schedule before pulling calendar
         # Note that the ExchangeTradingSchedule is currently the only
@@ -1171,7 +1170,13 @@ class TradingAlgorithm(object):
             )
 
         self.add_event(
-            make_eventrule(date_rule, time_rule, cal, half_days),
+            make_eventrule(
+                date_rule,
+                time_rule,
+                cal,
+                half_days=half_days,
+                data_frequency=self.data_frequency,
+            ),
             func,
         )
 
@@ -1703,12 +1708,12 @@ class TradingAlgorithm(object):
         return dt
 
     @api_method
-    def set_slippage(self, us_equities=None, us_futures=None):
+    def set_slippage(self, equities=None, us_futures=None):
         """Set the slippage models for the simulation.
 
         Parameters
         ----------
-        us_equities : EquitySlippageModel
+        equities : EquitySlippageModel
             The slippage model to use for trading US equities.
         us_futures : FutureSlippageModel
             The slippage model to use for trading US futures.
@@ -1720,14 +1725,14 @@ class TradingAlgorithm(object):
         if self.initialized:
             raise SetSlippagePostInit()
 
-        if us_equities is not None:
+        if equities is not None:
-            if Equity not in us_equities.allowed_asset_types:
+            if Equity not in equities.allowed_asset_types:
                 raise IncompatibleSlippageModel(
                     asset_type='equities',
-                    given_model=us_equities,
+                    given_model=equities,
-                    supported_asset_types=us_equities.allowed_asset_types,
+                    supported_asset_types=equities.allowed_asset_types,
                 )
-            self.blotter.slippage_models[Equity] = us_equities
+            self.blotter.slippage_models[Equity] = equities
 
         if us_futures is not None:
             if Future not in us_futures.allowed_asset_types:
@@ -1739,12 +1744,12 @@ class TradingAlgorithm(object):
             self.blotter.slippage_models[Future] = us_futures
 
     @api_method
-    def set_commission(self, us_equities=None, us_futures=None):
+    def set_commission(self, equities=None, us_futures=None):
         """Sets the commission models for the simulation.
 
         Parameters
         ----------
-        us_equities : EquityCommissionModel
+        equities : EquityCommissionModel
             The commission model to use for trading US equities.
         us_futures : FutureCommissionModel
             The commission model to use for trading US futures.
@@ -1758,14 +1763,14 @@ class TradingAlgorithm(object):
         if self.initialized:
             raise SetCommissionPostInit()
 
-        if us_equities is not None:
+        if equities is not None:
-            if Equity not in us_equities.allowed_asset_types:
+            if Equity not in equities.allowed_asset_types:
                 raise IncompatibleCommissionModel(
                     asset_type='equities',
-                    given_model=us_equities,
+                    given_model=equities,
-                    supported_asset_types=us_equities.allowed_asset_types,
+                    supported_asset_types=equities.allowed_asset_types,
                 )
-            self.blotter.commission_models[Equity] = us_equities
+            self.blotter.commission_models[Equity] = equities
 
         if us_futures is not None:
             if Future not in us_futures.allowed_asset_types:

catalyst/data/_minute_bar_internal.pyx

@@ -44,7 +44,7 @@ def five_minute_value(ndarray[long_t, ndim=1] market_opens,
     q = cython.cdiv(pos, five_minutes_per_day)
     r = cython.cmod(pos, five_minutes_per_day)
 
-    return market_opens[q] + r
+    return market_opens[q] + 5 * r
 
 def find_position_of_minute(ndarray[long_t, ndim=1] market_opens,
                             ndarray[long_t, ndim=1] market_closes,
@@ -112,10 +112,14 @@ def find_position_of_five_minute(ndarray[long_t, ndim=1] market_opens,
     market_open = market_opens[market_open_loc]
     market_close = market_closes[market_open_loc]
 
-    if not forward_fill and ((five_minute_val - market_open) >= five_minutes_per_day):
+    val_open_offset = (five_minute_val - market_open)/5
+    close_open_offset = (market_close - market_open)/5
+
+    if not forward_fill and val_open_offset >= five_minutes_per_day:
         raise ValueError("Given five minutes is not between an open and a close")
 
-    delta = int_min(five_minute_val - market_open, market_close - market_open)
+    # clamp offset to close index
+    delta = int_min(val_open_offset, close_open_offset)
 
     return (market_open_loc * five_minutes_per_day) + delta
 

catalyst/data/bundles/base.py

@@ -172,7 +172,6 @@ class BaseBundle(object):
 
                 # Compile 5-minute symbol data if bundle supports 5-minute mode and
                 # persist the dataset to disk.
-                '''
                 if '5-minute' in self.frequencies:
                     five_minute_bar_writer.write(
                         self._fetch_symbol_iter(
@@ -188,7 +187,6 @@ class BaseBundle(object):
                         length=len(symbol_map),
                         show_progress=show_progress,
                     )
-                '''
 
                 # Compile minute symbol data if bundle supports minute mode and
                 # persist the dataset to disk.
@@ -298,12 +296,14 @@ class BaseBundle(object):
                     except Exception as e:
                         log.exception(
                             'Failed to load metadata from {}. '
-                            'Retrying.'.format(self.name)
+                            'Retrying.'.format(
+                                name=self.name,
+                            )
                         ) 
                 else:
                     raise ValueError(
-                        'Failed to download metadata page {} after {} '
+                        'Failed to download metadata page %d after %d '
-                        'attempts.'.format(page_number, retries)
+                        'attempts.'.format(page_number, retries),
                     )
 
 
@@ -313,8 +313,7 @@ class BaseBundle(object):
 
             # Apply selective asset filtering, useful for benchmark
             # ingestion.
-            if self._asset_filter:
+            raw = raw[raw.symbol.isin(self._asset_filter)]
-                raw = raw[raw.symbol.isin(self._asset_filter)]
 
             # Update cached value for key.
             cache[key] = raw

catalyst/data/bundles/poloniex.py

@@ -36,7 +36,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
     def frequencies(self):
         return set((
             'daily',
-            #'5-minute',
+            '5-minute',
         ))
 
     @lazyval
@@ -103,7 +103,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         )
         raw.set_index('date', inplace=True)
 
-        scale = 1
+        scale = 1000.0
         raw.loc[:, 'open'] /= scale
         raw.loc[:, 'high'] /= scale
         raw.loc[:, 'low'] /= scale
@@ -132,7 +132,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                          data_frequency):
         period_map = {
             'daily': 86400,
-#            '5-minute': 300,
+            '5-minute': 300,
         }
 
         try:
@@ -155,13 +155,4 @@ class PoloniexBundle(BaseCryptoPricingBundle):
             query=urlencode(query_params),
         )
 
-''' 
+register_bundle(PoloniexBundle, ['USDT_BTC'])
-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)
-'''
-register_bundle(PoloniexBundle)

catalyst/data/data_portal.py

@@ -289,7 +289,7 @@ class DataPortal(object):
 
         self._daily_aggregator = DailyHistoryAggregator(
             self.trading_calendar.schedule.market_open,
-            _dispatch_minute_reader,
+            _dispatch_session_reader,
             self.trading_calendar
         )
         self._history_loader = DailyHistoryLoader(

catalyst/data/five_minute_bars.py

@@ -60,7 +60,7 @@ OPEN_FIVE_MINUTES_PER_DAY = 288
 
 DEFAULT_EXPECTEDLEN_CRYPTO = OPEN_FIVE_MINUTES_PER_DAY * 366 * 15
 
-OHLC_RATIO = 1000000
+OHLC_RATIO = 1000
 
 OHLC = frozenset(['open', 'high', 'low', 'close'])
 OHLCV = frozenset(['open', 'high', 'low', 'close', 'volume'])
@@ -1151,6 +1151,7 @@ class BcolzFiveMinuteBarReader(FiveMinuteBarReader):
 
         if field != 'volume':
             value *= self._ohlc_ratio_inverse_for_sid(sid)
+        #print 'minute pos: {}, {}: {}'.format(minute_pos, field, value)
         return value
 
     def get_last_traded_dt(self, asset, dt):
@@ -1161,8 +1162,8 @@ class BcolzFiveMinuteBarReader(FiveMinuteBarReader):
 
     def _find_last_traded_five_minute_position(self, asset, dt):
         volumes = self._open_minute_file('volume', asset)
-        start_date_minute = asset.start_date.value / NANOS_IN_FIVE_MINUTE
+        start_date_minute = asset.start_date.value / NANOS_IN_MINUTE
-        dt_minute = dt.value / NANOS_IN_FIVE_MINUTE
+        dt_minute = dt.value / NANOS_IN_MINUTE
 
         try:
             # if we know of a dt before which this asset has no volume,
@@ -1227,7 +1228,7 @@ class BcolzFiveMinuteBarReader(FiveMinuteBarReader):
         return find_position_of_five_minute(
             self._market_open_values,
             self._market_close_values,
-            minute_dt.value / NANOS_IN_FIVE_MINUTE,
+            minute_dt.value / NANOS_IN_MINUTE,
             self._five_minutes_per_day,
             False,
         )
@@ -1252,11 +1253,19 @@ class BcolzFiveMinuteBarReader(FiveMinuteBarReader):
             (minutes in range, sids) with a dtype of float64, containing the
             values for the respective field over start and end dt range.
         """
+        print 'start_dt:', start_dt
+        print 'end_dt:', end_dt
+
         start_idx = self._find_position_of_five_minute(start_dt)
         end_idx = self._find_position_of_five_minute(end_dt)
 
+        print 'start_idx:', start_idx
+        print 'end_idex:', end_idx
+
         num_minutes = (end_idx - start_idx + 1)
 
+        print 'num_minutes:', num_minutes
+
         results = []
 
         indices_to_exclude = self._exclusion_indices_for_range(
@@ -1293,6 +1302,8 @@ class BcolzFiveMinuteBarReader(FiveMinuteBarReader):
                     out[:len(where), i][where] = values[where]
 
             results.append(out)
+
+        print 'results:', results
         return results
 
 

catalyst/data/loader.py

@@ -93,8 +93,8 @@ def has_data_for_dates(series_or_df, first_date, last_date):
     dts = series_or_df.index
     if not isinstance(dts, pd.DatetimeIndex):
         raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts))
-    first, last = dts[[0, -1]].tz_localize(None)
+    first, last = dts[[0, -1]]
-    return (first <= first_date.tz_localize(None)) and (last >= last_date.tz_localize(None))
+    return (first <= first_date) and (last >= last_date)
 
 def load_crypto_market_data(trading_day=None,
                             trading_days=None,
@@ -134,19 +134,17 @@ def load_crypto_market_data(trading_day=None,
         trading_day,
         environ,
     )
-    # Override first_date for treasury data since we have it for many more years
-    # and is independent of crypto data
-    first_date_treasury = pd.Timestamp('1990-01-01', tz='UTC') 
     tc = ensure_treasury_data(
         bm_symbol,
-        first_date_treasury,
+        first_date,
         last_date,
         now,
         environ,
     )
     benchmark_returns = br[br.index.slice_indexer(first_date, last_date)]
-    treasury_curves = tc[tc.index.slice_indexer(first_date_treasury, last_date)]
+    treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
     return benchmark_returns, treasury_curves
+    
 
 
 def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY',
@@ -234,7 +232,6 @@ def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY',
     treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
     return benchmark_returns, treasury_curves
 
-
 def ensure_crypto_benchmark_data(symbol,
                                  first_date,
                                  last_date,
@@ -282,7 +279,7 @@ def ensure_crypto_benchmark_data(symbol,
             None,
             symbol,
             get_calendar(bundle.calendar_name),
-            first_date - trading_day,
+            first_date,
             last_date,
             'daily',
         )
@@ -367,7 +364,6 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
         logger.warn("Still don't have expected data after redownload!")
     return data
 
-
 def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
                           environ=None):
     """
@@ -482,6 +478,11 @@ def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
 
 def _load_cached_data(filename, first_date, last_date, now, resource_name,
                       environ=None):
+    if resource_name == 'benchmark':
+        from_csv = pd.Series.from_csv
+    else:
+        from_csv = pd.DataFrame.from_csv
+
     # Path for the cache.
     path = get_data_filepath(filename, environ)
 
@@ -489,10 +490,8 @@ def _load_cached_data(filename, first_date, last_date, now, resource_name,
     # yet, so don't try to read from 'path'.
     if os.path.exists(path):
         try:
-            data = pd.DataFrame.from_csv(path)
+            data = from_csv(path)
-            if data.empty:
+            data.index = pd.to_datetime(data.index).tz_localize('UTC')
-                raise ValueError("File is empty.")
-            data.index = pd.to_datetime(data.index, infer_datetime_format=True, errors='coerce' ).tz_localize('UTC')
             if has_data_for_dates(data, first_date, last_date):
                 return data
 

catalyst/data/us_equity_pricing.py

@@ -763,7 +763,7 @@ class BcolzDailyBarReader(SessionBarReader):
             if price == 0:
                 return nan
             else:
-                return price * 0.001
+                return price * 0.000001
         else:
             return price
 

catalyst/examples/buy_and_hodl.5min.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+#
+# Copyright 2017 Enigma MPC, Inc.
+# Copyright 2015 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.finance.slippage import VolumeShareSlippage
+
+from catalyst.api import (
+    order_target_value,
+    symbol,
+    record,
+    cancel_order,
+    get_open_orders,
+    set_slippage,
+)
+
+
+def initialize(context):
+    context.ASSET_NAME = 'USDT_BTC'
+    context.TARGET_HODL_RATIO = 0.8
+    context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
+
+    # 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.is_buying = True
+    context.asset = symbol(context.ASSET_NAME)
+
+    context.i = 0
+    
+    set_slippage(equities=VolumeShareSlippage(volume_limit=0.1))
+
+def handle_data(context, data):
+    context.i += 1
+
+    starting_cash = context.portfolio.starting_cash
+    target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
+    reserve_value = context.RESERVE_RATIO * starting_cash
+
+    # Cancel any outstanding orders
+    orders = get_open_orders(context.asset) or []
+    for order in orders:
+        cancel_order(order)
+    
+    # Stop buying after passing the reserve threshold
+    cash = context.portfolio.cash
+    if cash <= reserve_value:
+        context.is_buying = False
+
+    # Retrieve current asset price from pricing data
+    price = data[context.asset].price
+
+    # Check if still buying and could (approximately) afford another purchase
+    if context.is_buying and cash > price:
+        # Place order to make position in asset equal to target_hodl_value
+        order_target_value(
+            context.asset,
+            target_hodl_value,
+            limit_price=price*1.1,
+            stop_price=price*0.9,
+        )
+
+    record(
+        price=price,
+        volume=data[context.asset].volume,
+        cash=cash,
+        starting_cash=context.portfolio.starting_cash,
+        leverage=context.account.leverage,
+    )
+
+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']]).plot(ax=ax2)
+
+    trans = results.ix[[t != [] for t in results.transactions]]
+    buys = 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',
+    )
+
+    ax3 = plt.subplot(613, sharex=ax1)
+    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
+    ax3.set_ylabel('Leverage ')
+
+    ax4 = plt.subplot(614, sharex=ax1)
+    results[['starting_cash', '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']] / context.TICK_SIZE).plot(ax=ax6)
+    ax6.set_ylabel('Volume (mCoins/5min)')
+
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()

catalyst/examples/buy_and_hodl.py

@@ -15,6 +15,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from catalyst.finance.slippage import VolumeShareSlippage
+
 from catalyst.api import (
     order_target_value,
     symbol,
@@ -23,7 +25,6 @@ from catalyst.api import (
     get_open_orders,
 )
 
-
 def initialize(context):
     context.ASSET_NAME = 'USDT_BTC'
     context.TARGET_HODL_RATIO = 0.8
@@ -42,8 +43,6 @@ def initialize(context):
 def handle_data(context, data):
     context.i += 1
 
-    print 'i:', context.i
-
     starting_cash = context.portfolio.starting_cash
     target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
     reserve_value = context.RESERVE_RATIO * starting_cash
@@ -73,6 +72,7 @@ def handle_data(context, data):
 
     record(
         price=price,
+        volume=data[context.asset].volume,
         cash=cash,
         starting_cash=context.portfolio.starting_cash,
         leverage=context.account.leverage,
@@ -80,12 +80,13 @@ def handle_data(context, data):
 
 def analyze(context=None, results=None):
     import matplotlib.pyplot as plt
+
     # Plot the portfolio and asset data.
-    ax1 = plt.subplot(511)
+    ax1 = plt.subplot(611)
     results[['portfolio_value']].plot(ax=ax1)
     ax1.set_ylabel('Portfolio Value (USD)')
 
-    ax2 = plt.subplot(512, sharex=ax1)
+    ax2 = plt.subplot(612, sharex=ax1)
     ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
     (context.TICK_SIZE * results[['price']]).plot(ax=ax2)
 
@@ -101,11 +102,11 @@ def analyze(context=None, results=None):
         color='g',
     )
 
-    ax3 = plt.subplot(513, sharex=ax1)
+    ax3 = plt.subplot(613, sharex=ax1)
     results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
     ax3.set_ylabel('Leverage ')
 
-    ax4 = plt.subplot(514, sharex=ax1)
+    ax4 = plt.subplot(614, sharex=ax1)
     results[['starting_cash', 'cash']].plot(ax=ax4)
     ax4.set_ylabel('Cash (USD)')
 
@@ -119,7 +120,7 @@ def analyze(context=None, results=None):
         'benchmark_period_return',
     ]]
 
-    ax5 = plt.subplot(515, sharex=ax1)
+    ax5 = plt.subplot(615, sharex=ax1)
     results[[
         'treasury',
         'algorithm',
@@ -127,6 +128,10 @@ def analyze(context=None, results=None):
     ]].plot(ax=ax5)
     ax5.set_ylabel('Percent Change')
 
+    ax6 = plt.subplot(616, sharex=ax1)
+    (results[['volume']] / context.TICK_SIZE).plot(ax=ax6)
+    ax6.set_ylabel('Volume (mCoins/5min)')
+
     plt.legend(loc=3)
 
     # Show the plot.

catalyst/examples/dual_vwap.5min.py

@@ -0,0 +1,189 @@
+#!/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,
+    time_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 * 288
+    context.LONG_WINDOW = 100 * 288
+
+    # 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_rule=time_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']] / context.TICK_SIZE).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/dual_vwap.py

@@ -52,7 +52,7 @@ def initialize(context):
 
     schedule_function(
         rebalance,
-        time_rules=times_rules.every_minute(),
+        date_rule=date_rules.every_day(),
     )
 
 
@@ -178,7 +178,7 @@ def analyze(context=None, results=None):
     ax5.set_ylabel('Percent Change')
 
     ax6 = plt.subplot(616, sharex=ax1)
-    results[['volume']].plot(ax=ax6)
+    (results[['volume']] / context.TICK_SIZE).plot(ax=ax6)
     ax6.set_ylabel('Volume (mBTC/day)')
 
     plt.legend(loc=3)

catalyst/finance/performance/tracker.py

@@ -189,14 +189,14 @@ class PerformanceTracker(object):
 
     @property
     def progress(self):
-        if self.emission_rate == 'minute':
+        if self.emission_rate in set(('minute', '5-minute')):
             # Fake a value
             return 1.0
         elif self.emission_rate == 'daily':
             return self.session_count / self.total_session_count
 
     def set_date(self, date):
-        if self.emission_rate == 'minute':
+        if self.emission_rate in set(('minute', '5-minute')):
             self.saved_dt = date
             self.todays_performance.period_close = self.saved_dt
 
@@ -370,7 +370,9 @@ class PerformanceTracker(object):
                                             bench_since_open,
                                             account.leverage)
 
+        assert self.emission_rate in set(('minute', '5-minute'))
         minute_packet = self.to_dict(emission_type='minute')
+
         return minute_packet
 
     def handle_market_close(self, dt, data_portal):

catalyst/finance/risk/risk.py

@@ -158,7 +158,7 @@ def choose_treasury(select_treasury, treasury_curves, start_session,
                 )
                 break
 
-        if search_day and trading_calendar.name != 'OPEN':          # Supress warning for 'OPEN' calendar
+        if search_day:
             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/pipeline/loaders/crypto_pricing_loader.py

@@ -45,7 +45,7 @@ class CryptoPricingLoader(PipelineLoader):
             reader = bundle.five_minute_bar_reader
             all_sessions = cal.all_five_minutes
 
-        elif data_frequency == 'minute':
+        elif daily_bar_reader == 'minute':
             reader = bundle.minute_bar_reader
             all_sessions = cal.all_minutes
 
@@ -57,6 +57,7 @@ class CryptoPricingLoader(PipelineLoader):
         self.raw_price_loader = reader
         self._columns = dataset.columns
         self._all_sessions = all_sessions
+        self._data_frequency = data_frequency
 
     @classmethod
     def from_files(cls, pricing_path):
@@ -106,7 +107,6 @@ class CryptoPricingLoader(PipelineLoader):
 
 
 def _shift_dates(dates, start_date, end_date, shift):
-
     try:
         start = dates.get_loc(start_date)
     except KeyError:

catalyst/sources/benchmark_source.py

@@ -51,7 +51,10 @@ class BenchmarkSource(object):
         elif benchmark_returns is not None:
             daily_series = benchmark_returns[sessions[0]:sessions[-1]]
 
+            print 'BENCHMARK_RETURNS'
+
             if self.emission_rate == "minute":
+                print 'BENCHMARK_RETURNS minute'
                 # we need to take the env's benchmark returns, which are daily,
                 # and resample them to minute
                 minutes = trading_calendar.minutes_for_sessions_in_range(
@@ -66,6 +69,7 @@ class BenchmarkSource(object):
 
                 self._precalculated_series = minute_series
             elif self.emission_rate == '5-minute':
+                print 'BENCHMARK_RETURNS 5-minute'
                 five_minutes = \
                     trading_calendar.five_minutes_for_sessions_in_range(
                         sessions[0],
@@ -79,6 +83,7 @@ class BenchmarkSource(object):
                 
                 self._precalculated_series = five_minute_series
             else:
+                print 'BENCHMARK_RETURNS daily'
                 self._precalculated_series = daily_series
         else:
             raise Exception("Must provide either benchmark_asset or "
@@ -185,6 +190,7 @@ class BenchmarkSource(object):
 
             return benchmark_series.pct_change()[1:]
         else:
+            print '----------------------------------------'
             start_date = asset.start_date
             if start_date < trading_days[0]:
                 # get the window of close prices for benchmark_asset from the

catalyst/utils/calendars/exchange_calendar_open.py

@@ -1,7 +1,6 @@
 from datetime import time
 from pytz import timezone
 
-from pandas import Timestamp
 from pandas.tseries.offsets import DateOffset
 
 from catalyst.utils.memoize import lazyval
@@ -29,6 +28,3 @@ class OpenExchangeCalendar(TradingCalendar):
     @lazyval
     def day(self):
         return DateOffset(days=1)
-
-    def __init__(self, *args, **kwargs):
-        super(OpenExchangeCalendar, self).__init__(start=Timestamp('2015-03-01', tz='UTC'), **kwargs)

catalyst/utils/events.py

@@ -47,6 +47,8 @@ __all__ = [
     'NDaysBeforeLastTradingDayOfMonth',
     'StatefulRule',
     'OncePerDay',
+    'OncePerFiveMinutes',
+    'OncePerMinute',
 
     # Factory API
     'date_rules',
@@ -552,15 +554,18 @@ class StatefulRule(EventRule):
         """
         self.should_trigger = callable_
 
-
+class OncePerInterval(StatefulRule):
-class OncePerDay(StatefulRule):
     def __init__(self, rule=None):
         self.triggered = False
 
         self.date = None
         self.next_date = None
 
-        super(OncePerDay, self).__init__(rule)
+        super(OncePerInterval, self).__init__(rule)
+
+    @lazyval
+    def interval(self):
+        raise NotImplementedError
 
     def should_trigger(self, dt):
         if self.date is None or dt >= self.next_date:
@@ -570,11 +575,28 @@ class OncePerDay(StatefulRule):
 
             # record the timestamp for the next day, so that we can use it
             # to know if we've moved to the next day
-            self.next_date = dt + pd.Timedelta(1, unit="d")
+            self.next_date = dt + self.interval
 
         if not self.triggered and self.rule.should_trigger(dt):
             self.triggered = True
             return True
+        
+
+
+class OncePerDay(OncePerInterval):
+    @lazyval
+    def interval(self):
+        return pd.Timedelta(1, unit='d')
+
+class OncePerFiveMinutes(OncePerInterval):
+    @lazyval
+    def interval(self):
+        return pd.Timedelta(5, unit='m')
+
+class OncePerMinute(OncePerInterval):
+    @lazyval
+    def interval(self):
+        return pd.Timedelta(1, unit='m')
 
 
 # Factory API
@@ -612,7 +634,11 @@ class calendars(object):
     US_FUTURES = sentinel('US_FUTURES')
 
 
-def make_eventrule(date_rule, time_rule, cal, half_days=True):
+def make_eventrule(date_rule,
+                   time_rule,
+                   cal,
+                   half_days=True,
+                   data_frequency=None):
     """
     Constructs an event rule from the factory api.
     """
@@ -628,4 +654,15 @@ def make_eventrule(date_rule, time_rule, cal, half_days=True):
         nhd_rule.cal = cal
         inner_rule = date_rule & time_rule & nhd_rule
 
-    return OncePerDay(rule=inner_rule)
+    if data_frequency == 'daily':
+        return OncePerDay(rule=inner_rule)
+    elif data_frequency == '5-minute':
+        return OncePerFiveMinutes(rule=inner_rule)
+    elif data_frequency == 'minute':
+        return OncePerMinute(rule=inner_rule)
+    else:
+        raise ValueError(
+            'Cannot make event rule for data frequency: {}'.format(
+                data_frequency,
+            )
+        )