Merge pull request #1822 from quantopian/var-and-cvar

Add portfolio weights method to algorithms

tests/test_algorithm.py

@@ -123,6 +123,7 @@ from zipline.test_algorithms import (
     TestOrderPercentAlgorithm,
     TestOrderStyleForwardingAlgorithm,
     TestOrderValueAlgorithm,
+    TestPositionWeightsAlgorithm,
     TestRegisterTransformAlgorithm,
     TestTargetAlgorithm,
     TestTargetPercentAlgorithm,
@@ -1094,11 +1095,64 @@ class TestPositions(WithLogger,
                     ZiplineTestCase):
     START_DATE = pd.Timestamp('2006-01-03', tz='utc')
     END_DATE = pd.Timestamp('2006-01-06', tz='utc')
+    SIM_PARAMS_CAPITAL_BASE = 1000
 
-    sids = ASSET_FINDER_EQUITY_SIDS = [1, 133]
+    ASSET_FINDER_EQUITY_SIDS = (1, 133)
+
+    @classmethod
+    def make_equity_daily_bar_data(cls):
+        frame = pd.DataFrame(
+            {
+                'open': [90, 95, 100, 105],
+                'high': [90, 95, 100, 105],
+                'low': [90, 95, 100, 105],
+                'close': [90, 95, 100, 105],
+                'volume': 100,
+            },
+            index=cls.equity_daily_bar_days,
+        )
+        return ((sid, frame) for sid in cls.asset_finder.equities_sids)
+
+    @classmethod
+    def make_futures_info(cls):
+        return pd.DataFrame.from_dict(
+            {
+                1000: {
+                    'symbol': 'CLF06',
+                    'root_symbol': 'CL',
+                    'start_date': cls.START_DATE,
+                    'end_date': cls.END_DATE,
+                    'auto_close_date': cls.END_DATE + cls.trading_calendar.day,
+                    'exchange': 'CME',
+                    'multiplier': 100,
+                },
+            },
+            orient='index',
+        )
+
+    @classmethod
+    def make_future_minute_bar_data(cls):
+        trading_calendar = cls.trading_calendars[Future]
+
+        sids = cls.asset_finder.futures_sids
+        minutes = trading_calendar.minutes_for_sessions_in_range(
+            cls.future_minute_bar_days[0],
+            cls.future_minute_bar_days[-1],
+        )
+        frame = pd.DataFrame(
+            {
+                'open': 2.0,
+                'high': 2.0,
+                'low': 2.0,
+                'close': 2.0,
+                'volume': 100,
+            },
+            index=minutes,
+        )
+        return ((sid, frame) for sid in sids)
 
     def test_empty_portfolio(self):
-        algo = EmptyPositionsAlgorithm(self.sids,
+        algo = EmptyPositionsAlgorithm(self.asset_finder.equities_sids,
                                        sim_params=self.sim_params,
                                        env=self.env)
         daily_stats = algo.run(self.data_portal)
@@ -1124,6 +1178,50 @@ class TestPositions(WithLogger,
         empty_positions = daily_stats.positions.map(lambda x: len(x) == 0)
         self.assertTrue(empty_positions.all())
 
+    def test_position_weights(self):
+        sids = (1, 133, 1000)
+        equity_1, equity_133, future_1000 = \
+            self.asset_finder.retrieve_all(sids)
+
+        algo = TestPositionWeightsAlgorithm(
+            sids_and_amounts=zip(sids, [2, -1, 1]),
+            sim_params=self.sim_params,
+            env=self.env,
+        )
+        daily_stats = algo.run(self.data_portal)
+
+        expected_position_weights = [
+            # No positions held on the first day.
+            pd.Series({}),
+            # Each equity's position value is its price times the number of
+            # shares held. In this example, we hold a long position in 2 shares
+            # of equity_1 so its weight is (95.0 * 2) = 190.0 divided by the
+            # total portfolio value. The total portfolio value is the sum of
+            # cash ($905.00) plus the value of all equity positions.
+            #
+            # For a futures contract, its weight is the unit price times number
+            # of shares held times the multiplier. For future_1000, this is
+            # (2.0 * 1 * 100) = 200.0 divided by total portfolio value.
+            pd.Series({
+                equity_1: 190.0 / (190.0 - 95.0 + 905.0),
+                equity_133: -95.0 / (190.0 - 95.0 + 905.0),
+                future_1000: 200.0 / (190.0 - 95.0 + 905.0),
+            }),
+            pd.Series({
+                equity_1: 200.0 / (200.0 - 100.0 + 905.0),
+                equity_133: -100.0 / (200.0 - 100.0 + 905.0),
+                future_1000: 200.0 / (200.0 - 100.0 + 905.0),
+            }),
+            pd.Series({
+                equity_1: 210.0 / (210.0 - 105.0 + 905.0),
+                equity_133: -105.0 / (210.0 - 105.0 + 905.0),
+                future_1000: 200.0 / (210.0 - 105.0 + 905.0),
+            }),
+        ]
+
+        for i, expected in enumerate(expected_position_weights):
+            assert_equal(daily_stats.iloc[i]['position_weights'], expected)
+
 
 class TestBeforeTradingStart(WithDataPortal,
                              WithSimParams,

zipline/protocol.py

@@ -16,7 +16,7 @@ from warnings import warn
 
 import pandas as pd
 
-from zipline.assets import Asset
+from zipline.assets import Asset, Future
 from zipline.utils.input_validation import expect_types
 from .utils.enum import enum
 from zipline._protocol import BarData  # noqa
@@ -136,6 +136,10 @@ class Order(Event):
     )
 
 
+def asset_multiplier(asset):
+    return asset.multiplier if isinstance(asset, Future) else 1
+
+
 class Portfolio(object):
 
     def __init__(self):
@@ -169,6 +173,26 @@ class Portfolio(object):
         },
     )
 
+    @property
+    def current_portfolio_weights(self):
+        """
+        Compute each asset's weight in the portfolio by calculating its held
+        value divided by the total value of all positions.
+
+        Each equity's value is its price times the number of shares held. Each
+        futures contract's value is its unit price times number of shares held
+        times the multiplier.
+        """
+        position_values = pd.Series({
+            asset: (
+                position.last_sale_price *
+                position.amount *
+                asset_multiplier(asset)
+            )
+            for asset, position in self.positions.items()
+        })
+        return position_values / self.portfolio_value
+
 
 class Account(object):
     '''

zipline/test_algorithms.py

@@ -88,7 +88,7 @@ from zipline.api import (
 )
 from zipline.errors import UnsupportedOrderParameters
 from zipline.assets import Future, Equity
-from zipline.finance.commission import PerShare
+from zipline.finance.commission import PerShare, PerTrade
 from zipline.finance.execution import (
     LimitOrder,
     MarketOrder,
@@ -691,6 +691,27 @@ class EmptyPositionsAlgorithm(TradingAlgorithm):
         self.record(num_positions=len(self.portfolio.positions))
 
 
+class TestPositionWeightsAlgorithm(TradingAlgorithm):
+    """
+    An algorithm that records the weights of its portfolio holdings each day.
+    """
+    def initialize(self, sids_and_amounts, *args, **kwargs):
+        self.ordered = False
+        self.sids_and_amounts = sids_and_amounts
+        self.set_commission(us_equities=PerTrade(0), us_futures=PerTrade(0))
+        self.set_slippage(
+            us_equities=FixedSlippage(0), us_futures=FixedSlippage(0),
+        )
+
+    def handle_data(self, data):
+        if not self.ordered:
+            for s, amount in self.sids_and_amounts:
+                self.order(self.sid(s), amount)
+            self.ordered = True
+
+        self.record(position_weights=self.portfolio.current_portfolio_weights)
+
+
 class InvalidOrderAlgorithm(TradingAlgorithm):
     """
     An algorithm that tries to make various invalid order calls, verifying that