Merge pull request #1337 from quantopian/margin_changes

Capital Changes Refactoring

tests/test_algorithm.py

@@ -2040,14 +2040,18 @@ class TestCapitalChanges(WithLogger,
             index=pd.DatetimeIndex(days),
         )
 
-    def test_capital_changes_daily_mode(self):
+    @parameterized.expand([
+        ('target', 153000.0), ('delta', 50000.0)
+    ])
+    def test_capital_changes_daily_mode(self, change_type, value):
         sim_params = factory.create_simulation_parameters(
             start=pd.Timestamp('2006-01-03', tz='UTC'),
             end=pd.Timestamp('2006-01-09', tz='UTC')
         )
 
         capital_changes = {
-            pd.Timestamp('2006-01-06', tz='UTC'): 50000
+            pd.Timestamp('2006-01-06', tz='UTC'):
+                {'type': change_type, 'value': value}
         }
 
         algocode = """
@@ -2174,8 +2178,22 @@ def order_stuff(context, data):
                 expected_cumulative[stat]
             )
 
-    @parameterized.expand([('interday',), ('intraday',)])
-    def test_capital_changes_minute_mode_daily_emission(self, change):
+        self.assertEqual(
+            algo.capital_change_deltas,
+            {pd.Timestamp('2006-01-06', tz='UTC'): 50000.0}
+        )
+
+    @parameterized.expand([
+        ('interday_target', [('2006-01-04', 2388.0)]),
+        ('interday_delta', [('2006-01-04', 1000.0)]),
+        ('intraday_target', [('2006-01-04 17:00', 2186.0),
+                             ('2006-01-04 18:00', 2806.0)]),
+        ('intraday_delta', [('2006-01-04 17:00', 500.0),
+                            ('2006-01-04 18:00', 500.0)]),
+    ])
+    def test_capital_changes_minute_mode_daily_emission(self, change, values):
+        change_loc, change_type = change.split('_')
+
         sim_params = factory.create_simulation_parameters(
             start=pd.Timestamp('2006-01-03', tz='UTC'),
             end=pd.Timestamp('2006-01-05', tz='UTC'),
@@ -2183,13 +2201,8 @@ def order_stuff(context, data):
             capital_base=1000.0
         )
 
-        if change == 'intraday':
-            capital_changes = {
-                pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
-                pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0,
-            }
-        else:
-            capital_changes = {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
+        capital_changes = {pd.Timestamp(val[0], tz='UTC'): {
+            'type': change_type, 'value': val[1]} for val in values}
 
         algocode = """
 from zipline.api import set_slippage, set_commission, slippage, commission, \
@@ -2231,7 +2244,7 @@ def order_stuff(context, data):
             0.0, 1000.0, 0.0
         ])
 
-        if change == 'intraday':
+        if change_loc == 'intraday':
             # Fills at 491, +500 capital change comes at 638 (17:00) and
             # 698 (18:00), ends day at 879
             day2_return = (1388.0 + 149.0 + 147.0)/1388.0 * \
@@ -2268,7 +2281,7 @@ def order_stuff(context, data):
             expected_daily['ending_cash'] - \
             expected_daily['capital_used']
 
-        if change == 'intraday':
+        if change_loc == 'intraday':
             # Capital changes come after day start
             expected_daily['starting_cash'] -= expected_capital_changes
 
@@ -2313,8 +2326,29 @@ def order_stuff(context, data):
                 expected_cumulative[stat]
             )
 
-    @parameterized.expand([('interday',), ('intraday',)])
-    def test_capital_changes_minute_mode_minute_emission(self, change):
+        if change_loc == 'interday':
+            self.assertEqual(
+                algo.capital_change_deltas,
+                {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
+            )
+        else:
+            self.assertEqual(
+                algo.capital_change_deltas,
+                {pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
+                 pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0}
+            )
+
+    @parameterized.expand([
+        ('interday_target', [('2006-01-04', 2388.0)]),
+        ('interday_delta', [('2006-01-04', 1000.0)]),
+        ('intraday_target', [('2006-01-04 17:00', 2186.0),
+                             ('2006-01-04 18:00', 2806.0)]),
+        ('intraday_delta', [('2006-01-04 17:00', 500.0),
+                            ('2006-01-04 18:00', 500.0)]),
+    ])
+    def test_capital_changes_minute_mode_minute_emission(self, change, values):
+        change_loc, change_type = change.split('_')
+
         sim_params = factory.create_simulation_parameters(
             start=pd.Timestamp('2006-01-03', tz='UTC'),
             end=pd.Timestamp('2006-01-05', tz='UTC'),
@@ -2323,13 +2357,8 @@ def order_stuff(context, data):
             capital_base=1000.0
         )
 
-        if change == 'intraday':
-            capital_changes = {
-                pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
-                pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0,
-            }
-        else:
-            capital_changes = {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
+        capital_changes = {pd.Timestamp(val[0], tz='UTC'): {
+            'type': change_type, 'value': val[1]} for val in values}
 
         algocode = """
 from zipline.api import set_slippage, set_commission, slippage, commission, \
@@ -2370,7 +2399,7 @@ def order_stuff(context, data):
         expected_minute = {}
 
         capital_changes_after_start = np.array([0.0] * 1170)
-        if change == 'intraday':
+        if change_loc == 'intraday':
             capital_changes_after_start[539:599] = 500.0
             capital_changes_after_start[599:780] = 1000.0
 
@@ -2390,7 +2419,7 @@ def order_stuff(context, data):
         ))
 
         # +1000 capital changes comes before the day start if interday
-        day2adj = 0.0 if change == 'intraday' else 1000.0
+        day2adj = 0.0 if change_loc == 'intraday' else 1000.0
 
         expected_minute['starting_cash'] = np.concatenate((
             [1000.0] * 390,
@@ -2429,7 +2458,7 @@ def order_stuff(context, data):
         # the pnl, starting_value and starting_cash. If the change is intraday,
         # the returns after the change have to be calculated from two
         # subperiods
-        if change == 'intraday':
+        if change_loc == 'intraday':
             # The last packet (at 1/04 16:59) before the first capital change
             prev_subperiod_return = expected_minute['returns'][538]
 
@@ -2527,6 +2556,18 @@ def order_stuff(context, data):
                 expected_cumulative[stat]
             )
 
+        if change_loc == 'interday':
+            self.assertEqual(
+                algo.capital_change_deltas,
+                {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
+            )
+        else:
+            self.assertEqual(
+                algo.capital_change_deltas,
+                {pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
+                 pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0}
+            )
+
 
 class TestGetDatetime(WithLogger,
                       WithSimParams,

zipline/algorithm.py

@@ -409,9 +409,13 @@ class TradingAlgorithm(object):
 
         self.benchmark_sid = kwargs.pop('benchmark_sid', None)
 
-        # A dictionary of capital change values keyed by timestamp
+        # A dictionary of capital changes, keyed by timestamp, indicating the
+        # target/delta of the capital changes, along with values
         self.capital_changes = kwargs.pop('capital_changes', {})
 
+        # A dictionary of the actual capital change deltas, keyed by timestamp
+        self.capital_change_deltas = {}
+
     def init_engine(self, get_loader):
         """
         Construct and store a PipelineEngine from loader.
@@ -786,6 +790,70 @@ class TradingAlgorithm(object):
 
         return daily_stats
 
+    def calculate_capital_changes(self, dt, emission_rate, is_interday):
+        """
+        If there is a capital change for a given dt, this means the the change
+        occurs before `handle_data` on the given dt. In the case of the
+        change being a target value, the change will be computed on the
+        portfolio value according to prices at the given dt
+        """
+        try:
+            capital_change = self.capital_changes[dt]
+        except KeyError:
+            return
+
+        if emission_rate == 'daily':
+            # If we are running daily emission, prices won't
+            # necessarily be synced at the end of every minute, and we
+            # need the up-to-date prices for capital change
+            # calculations. We want to sync the prices as of the
+            # last market minute, and this is okay from a data portal
+            # perspective as we have technically not "advanced" to the
+            # current dt yet.
+            self.perf_tracker.position_tracker.sync_last_sale_prices(
+                self.trading_calendar.previous_minute(
+                    dt
+                ),
+                False,
+                self.data_portal
+            )
+
+        # Calculate performance before we sync prices price for the current dt
+        self.perf_tracker.cumulative_performance.calculate_performance()
+        self.perf_tracker.todays_performance.calculate_performance()
+
+        if capital_change['type'] == 'target':
+            # Get an updated portfolio value as of this dt, but do it in a way
+            # so that the performance is not recalculated. This is done so
+            # that `process_capital_change` can find the performance values
+            # for the end of the subperiod, which is the previous dt
+            self.perf_tracker.position_tracker.sync_last_sale_prices(
+                dt,
+                self._in_before_trading_start,
+                self.data_portal
+            )
+            portfolio_value = \
+                self.perf_tracker.position_tracker.stats().net_value + \
+                self.perf_tracker.cumulative_performance.ending_cash
+
+            capital_change_amount = capital_change['value'] - portfolio_value
+
+            log.info('Processing capital change to target %s at %s. Capital '
+                     'change delta is %s' % (capital_change['value'], dt,
+                                             capital_change_amount))
+        elif capital_change['type'] == 'delta':
+            capital_change_amount = capital_change['value']
+            log.info('Processing capital change of delta %s at %s'
+                     % (capital_change_amount, dt))
+        else:
+            log.error("Capital change %s does not indicate a valid type "
+                      "('target' or 'delta')" % capital_change)
+            return
+
+        self.capital_change_deltas.update({dt: capital_change_amount})
+        self.perf_tracker.process_capital_change(capital_change_amount,
+                                                 is_interday)
+
     @api_method
     def get_environment(self, field='platform'):
         """Query the execution environment.

zipline/finance/performance/period.py

@@ -242,8 +242,6 @@ class PerformancePeriod(object):
                 del self._payout_last_sale_prices[asset]
 
     def subdivide_period(self, capital_change):
-        self.calculate_performance()
-
         # Apply the capital change to the ending cash
         self.ending_cash += capital_change
 
@@ -550,8 +548,8 @@ class PerformancePeriod(object):
             getattr(self, 'day_trades_remaining', float('inf'))
         account.leverage = getattr(self, 'leverage',
                                    period_stats.gross_leverage)
-        account.net_leverage = period_stats.net_leverage
-
+        account.net_leverage = getattr(self, 'net_leverage',
+                                       period_stats.net_leverage)
         account.net_liquidation = getattr(self, 'net_liquidation',
                                           period_stats.net_liquidation)
         return account

zipline/finance/performance/tracker.py

@@ -238,16 +238,16 @@ class PerformanceTracker(object):
 
         return _dict
 
-    def process_capital_changes(self, capital_change, is_interday):
-        self.cumulative_performance.subdivide_period(capital_change)
+    def process_capital_change(self, capital_change_amount, is_interday):
+        self.cumulative_performance.subdivide_period(capital_change_amount)
 
         if is_interday:
             # Change comes between days
             self.todays_performance.adjust_period_starting_capital(
-                capital_change)
+                capital_change_amount)
         else:
             # Change comes in the middle of day
-            self.todays_performance.subdivide_period(capital_change)
+            self.todays_performance.subdivide_period(capital_change_amount)
 
     def process_transaction(self, transaction):
         self.txn_count += 1

zipline/gens/tradesimulation.py

@@ -95,13 +95,13 @@ class AlgorithmSimulator(object):
         Main generator work loop.
         """
         algo = self.algo
+        emission_rate = algo.perf_tracker.emission_rate
 
         def every_bar(dt_to_use, current_data=self.current_data,
                       handle_data=algo.event_manager.handle_data):
             # called every tick (minute or day).
 
-            if dt_to_use in algo.capital_changes:
-                process_minute_capital_changes(dt_to_use)
+            calculate_minute_capital_changes(dt_to_use)
 
             self.simulation_dt = dt_to_use
             algo.on_dt_changed(dt_to_use)
@@ -149,12 +149,9 @@ class AlgorithmSimulator(object):
 
             perf_tracker = algo.perf_tracker
 
-            if midnight_dt in algo.capital_changes:
-                # process any capital changes that came overnight
-                change = algo.capital_changes[midnight_dt]
-                log.info('Processing capital change of %s at %s' %
-                         (change, midnight_dt))
-                perf_tracker.process_capital_changes(change, is_interday=True)
+            # process any capital changes that came overnight
+            algo.calculate_capital_changes(
+                midnight_dt, emission_rate=emission_rate, is_interday=True)
 
             # Get the positions before updating the date so that prices are
             # fetched for trading close instead of midnight
@@ -204,34 +201,16 @@ class AlgorithmSimulator(object):
                 def execute_order_cancellation_policy():
                     algo.blotter.execute_cancel_policy(DAY_END)
 
-                def process_minute_capital_changes(dt):
-                    # If we are running daily emission, prices won't
-                    # necessarily be synced at the end of every minute, and we
-                    # need the up-to-date prices for capital change
-                    # calculations. We want to sync the prices as of the
-                    # last market minute, and this is okay from a data portal
-                    # perspective as we have technically not "advanced" to the
-                    # current dt yet.
-                    algo.perf_tracker.position_tracker.sync_last_sale_prices(
-                        self.algo.trading_calendar.previous_minute(dt),
-                        False,
-                        self.data_portal
-                    )
-
+                def calculate_minute_capital_changes(dt):
                     # process any capital changes that came between the last
                     # and current minutes
-                    change = algo.capital_changes[dt]
-                    log.info('Processing capital change of %s at %s' %
-                             (change, dt))
-                    algo.perf_tracker.process_capital_changes(
-                        change,
-                        is_interday=False
-                    )
+                    algo.calculate_capital_changes(
+                        dt, emission_rate=emission_rate, is_interday=False)
             else:
                 def execute_order_cancellation_policy():
                     pass
 
-                def process_minute_capital_changes(dt):
+                def calculate_minute_capital_changes(dt):
                     pass
 
             for dt, action in self.clock:
@@ -241,7 +220,7 @@ class AlgorithmSimulator(object):
                     once_a_day(dt)
                 elif action == DAY_END:
                     # End of the day.
-                    if algo.perf_tracker.emission_rate == 'daily':
+                    if emission_rate == 'daily':
                         handle_benchmark(normalize_date(dt))
                     execute_order_cancellation_policy()
 

zipline/protocol.py

@@ -126,6 +126,7 @@ class Account(object):
         self.buying_power = float('inf')
         self.equity_with_loan = 0.0
         self.total_positions_value = 0.0
+        self.total_positions_exposure = 0.0
         self.regt_equity = 0.0
         self.regt_margin = float('inf')
         self.initial_margin_requirement = 0.0