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https://github.com/wassname/catalyst.git
synced 2026-07-14 11:15:09 +08:00
ENH: Change simulation loop to use benchmarks as simulation 'clock'.
Refactor PerformanceTracker, Blotter, and AlgorithmSimulator to work with handling the end of a bar at the AlgorithmSimulator level instead of within PerformanceTracker. - PerforamnceTracker and Blotter are longer generators, both provide functions to process events instead. - AlgorithmSimulator calls each from within the loop running over the data generator. - Change test_perf_tracker utility to be compatible with change away from PerformanceTracker as a generator. Has the effect of: - Fixing the timing of order emission. - Allow minutely emission of benchmarks, which was prevented by the extra grouping previously caused by Blotter. Minutely emission also depends on work for streaming benchmarks through performance and risk at a minute granularity.
This commit is contained in:
fawce
+53
-57
@@ -22,7 +22,6 @@ from nose_parameterized import parameterized
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import datetime
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import pytz
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import itertools
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from operator import attrgetter
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import zipline.utils.factory as factory
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import zipline.finance.performance as perf
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@@ -63,23 +62,26 @@ def calculate_results(host, events):
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perf_tracker = perf.PerformanceTracker(host.sim_params)
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all_events = (msg[1] for msg in heapq.merge(
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all_events = heapq.merge(
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((event.dt, event) for event in events),
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((event.dt, event) for event in host.benchmark_events)))
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((event.dt, event) for event in host.benchmark_events))
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transformed_events = list(perf_tracker.transform(
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itertools.groupby(all_events, attrgetter('dt'))))
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#flatten the list of events
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filtered_events = [(date, filt_event) for (date, filt_event)
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in all_events if date <= events[-1].dt]
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filtered_events.sort(key=lambda x: x[0])
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grouped_events = itertools.groupby(filtered_events, lambda x: x[0])
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results = []
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for te in transformed_events:
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for event in te[1]:
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for message in event.perf_messages:
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results.append(message)
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perf_messages, risk = perf_tracker.handle_simulation_end()
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results.append(perf_messages[0])
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bm_updated = False
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for date, group in grouped_events:
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for _, event in group:
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perf_tracker.process_event(event)
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if event.type == DATASOURCE_TYPE.BENCHMARK:
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bm_updated = True
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if bm_updated:
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msg = perf_tracker.handle_market_close()
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results.append(msg)
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bm_updated = False
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return results
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@@ -239,9 +241,9 @@ class TestDividendPerformance(unittest.TestCase):
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)
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buy_txn = create_txn(1, 10.0, 100, events[1].dt)
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events.insert(2, buy_txn)
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events.insert(1, buy_txn)
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sell_txn = create_txn(1, 10.0, -100, events[3].dt)
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events.insert(4, sell_txn)
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events.insert(3, sell_txn)
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events.insert(1, dividend)
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results = calculate_results(self, events)
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@@ -267,12 +269,16 @@ class TestDividendPerformance(unittest.TestCase):
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self.sim_params
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)
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pay_date = self.sim_params.first_open
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# find pay date that is much later.
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for i in xrange(30):
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pay_date = factory.get_next_trading_dt(pay_date, oneday)
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dividend = factory.create_dividend(
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1,
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10.00,
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events[0].dt,
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events[1].dt,
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events[-1].dt + 10 * oneday
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pay_date
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)
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buy_txn = create_txn(1, 10.0, 100, events[1].dt)
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@@ -308,9 +314,11 @@ class TestDividendPerformance(unittest.TestCase):
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dividend = factory.create_dividend(
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1,
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10.00,
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# declare at open of test
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events[0].dt,
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events[1].dt,
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events[2].dt
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# ex_date same as trade 2
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events[2].dt,
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events[3].dt
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)
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txn = create_txn(1, 10.0, -100, events[1].dt)
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@@ -321,14 +329,14 @@ class TestDividendPerformance(unittest.TestCase):
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self.assertEqual(len(results), 5)
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cumulative_returns = \
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[event['cumulative_perf']['returns'] for event in results]
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self.assertEqual(cumulative_returns, [0.0, 0.0, -0.1, -0.1, -0.1])
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self.assertEqual(cumulative_returns, [0.0, 0.0, 0.0, -0.1, -0.1])
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daily_returns = [event['daily_perf']['returns'] for event in results]
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self.assertEqual(daily_returns, [0.0, 0.0, -0.1, 0.0, 0.0])
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self.assertEqual(daily_returns, [0.0, 0.0, 0.0, -0.1, 0.0])
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cash_flows = [event['daily_perf']['capital_used'] for event in results]
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self.assertEqual(cash_flows, [1000, 0, -1000, 0, 0])
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self.assertEqual(cash_flows, [0, 1000, 0, -1000, 0])
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cumulative_cash_flows = \
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[event['cumulative_perf']['capital_used'] for event in results]
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self.assertEqual(cumulative_cash_flows, [1000, 1000, 0, 0, 0])
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self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 0, 0])
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def test_no_position_receives_no_dividend(self):
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#post some trades in the market
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@@ -349,24 +357,7 @@ class TestDividendPerformance(unittest.TestCase):
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)
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events.insert(1, dividend)
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perf_tracker = perf.PerformanceTracker(self.sim_params)
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all_events = (msg[1] for msg in heapq.merge(
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((event.dt, event) for event in events),
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((event.dt, event) for event in self.benchmark_events)))
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transformed_events = list(perf_tracker.transform(
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itertools.groupby(all_events, attrgetter('dt'))))
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#flatten the list of events
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results = []
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for te in transformed_events:
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for event in te[1]:
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for message in event.perf_messages:
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results.append(message)
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perf_messages, risk = perf_tracker.handle_simulation_end()
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results.append(perf_messages[0])
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results = calculate_results(self, events)
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self.assertEqual(len(results), 5)
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cumulative_returns = \
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@@ -972,19 +963,18 @@ class TestPerformanceTracker(unittest.TestCase):
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((event.dt, event) for event in events),
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((event.dt, event) for event in benchmark_events)))
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# Extract events with transactions to use for verification.
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perf_messages = \
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[m for date, snapshot in
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perf_tracker.transform(
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itertools.groupby(all_events, attrgetter('dt')))
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for e in snapshot
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for m in e.perf_messages]
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filtered_events = [filt_event for filt_event
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in all_events if event.dt <= end_dt]
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filtered_events.sort(key=lambda x: x.dt)
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grouped_events = itertools.groupby(filtered_events, lambda x: x.dt)
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perf_messages = []
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end_perf_messages, risk_message = perf_tracker.handle_simulation_end()
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for date, group in grouped_events:
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for event in group:
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perf_tracker.process_event(event)
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msg = perf_tracker.handle_market_close()
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perf_messages.append(msg)
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perf_messages.extend(end_perf_messages)
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#we skip two trades, to test case of None transaction
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self.assertEqual(perf_tracker.txn_count, len(txns))
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self.assertEqual(perf_tracker.txn_count, len(orders))
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@@ -1074,11 +1064,17 @@ class TestPerformanceTracker(unittest.TestCase):
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bar_event_2,
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]
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messages = {date: snapshot[-1].perf_messages[0] for date, snapshot in
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tracker.transform(
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itertools.groupby(
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events,
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operator.attrgetter('dt')))}
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grouped_events = itertools.groupby(
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events, operator.attrgetter('dt'))
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messages = {}
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for date, group in grouped_events:
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tracker.set_date(date)
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for event in group:
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tracker.process_event(event)
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tracker.handle_minute_close(date)
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msg = tracker.to_dict()
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messages[date] = msg
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self.assertEquals(2, len(messages))
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