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BUG: Use context in lieu of "use_environment" decorator
The "use_environment" decorator is too side-effectful (e.g., connecting to Yahoo! Finance or another data source) to be used as a decorator to a function that gets evaluated during module load. This causes problems, e.g., if Zipline is being used in a gevent environment, when the trading environment created by the decorator argument tries to use greenlets when gevent hasn't been fully initialized. Since the decorator is nothing more than a context-manager wrapper, this commit removes the decorator and replaces its use with contexts, i.e., "with" statements.
This commit is contained in:
Jonathan Kamens
+152
-147
@@ -163,172 +163,177 @@ class TestEventsThroughRisk(unittest.TestCase):
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crm.sharpe[-1],
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decimal=6)
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@trading.use_environment(trading.TradingEnvironment())
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def test_minute_buy_and_hold(self):
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with trading.TradingEnvironment():
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start_date = datetime.datetime(
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year=2006,
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month=1,
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day=3,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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end_date = datetime.datetime(
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year=2006,
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month=1,
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day=5,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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start_date = datetime.datetime(
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year=2006,
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month=1,
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day=3,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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end_date = datetime.datetime(
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year=2006,
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month=1,
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day=5,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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sim_params = SimulationParameters(
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period_start=start_date,
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period_end=end_date,
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emission_rate='daily',
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data_frequency='minute')
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sim_params = SimulationParameters(
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period_start=start_date,
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period_end=end_date,
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emission_rate='daily',
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data_frequency='minute')
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algo = BuyAndHoldAlgorithm(
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sim_params=sim_params,
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data_frequency='minute')
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algo = BuyAndHoldAlgorithm(
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sim_params=sim_params,
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data_frequency='minute')
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first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
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first_open, first_close = \
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trading.environment.get_open_and_close(first_date)
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first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
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first_open, first_close = \
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trading.environment.get_open_and_close(first_date)
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second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
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second_open, second_close = \
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trading.environment.get_open_and_close(second_date)
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second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
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second_open, second_close = \
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trading.environment.get_open_and_close(second_date)
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third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
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third_open, third_close = \
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trading.environment.get_open_and_close(third_date)
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third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
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third_open, third_close = \
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trading.environment.get_open_and_close(third_date)
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benchmark_data = [
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Event({
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'returns': 0.1,
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'dt': first_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.2,
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'dt': second_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.4,
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'dt': third_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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]
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benchmark_data = [
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Event({
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'returns': 0.1,
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'dt': first_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.2,
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'dt': second_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.4,
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'dt': third_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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]
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trade_bar_data = [
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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]
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trade_bar_data = [
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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]
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algo.benchmark_return_source = benchmark_data
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algo.sources = list([trade_bar_data])
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gen = algo._create_generator(sim_params)
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algo.benchmark_return_source = benchmark_data
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algo.sources = list([trade_bar_data])
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gen = algo._create_generator(sim_params)
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crm = algo.perf_tracker.cumulative_risk_metrics
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crm = algo.perf_tracker.cumulative_risk_metrics
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first_msg = gen.next()
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first_msg = gen.next()
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self.assertIsNotNone(first_msg,
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"There should be a message emitted.")
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self.assertIsNotNone(first_msg, "There should be a message emitted.")
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# Protects against bug where the positions appeared to be
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# a day late, because benchmarks were triggering
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# calculations before the events for the day were
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# processed.
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self.assertEqual(1, len(algo.portfolio.positions), "There should "
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"be one position after the first day.")
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# Protects against bug where the positions appeared to be a day late,
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# because benchmarks were triggering calculations before the events
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# for the day were processed.
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self.assertEqual(1, len(algo.portfolio.positions),
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"There should be one position after the first day.")
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self.assertTrue(
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np.isnan(crm.algorithm_volatility[-1]),
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"On the first day algorithm volatility does not exist.")
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self.assertTrue(
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np.isnan(crm.algorithm_volatility[-1]),
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"On the first day algorithm volatility does not exist.")
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second_msg = gen.next()
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second_msg = gen.next()
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self.assertIsNotNone(second_msg, "There should be a message "
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"emitted.")
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self.assertIsNotNone(second_msg, "There should be a message emitted.")
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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# TODO: Hand derive. Current value is just a canary to
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# detect changes.
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np.testing.assert_almost_equal(
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0.050022510129558301,
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crm.algorithm_returns[-1],
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decimal=6)
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# TODO: Hand derive. Current value is just a canary to detect changes.
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np.testing.assert_almost_equal(
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0.050022510129558301,
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crm.algorithm_returns[-1],
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decimal=6)
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third_msg = gen.next()
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third_msg = gen.next()
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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self.assertIsNotNone(third_msg, "There should be a message "
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"emitted.")
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self.assertIsNotNone(third_msg, "There should be a message emitted.")
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# TODO: Hand derive. Current value is just a canary to detect changes.
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np.testing.assert_almost_equal(
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-0.047639464532418657,
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crm.algorithm_returns[-1],
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decimal=6)
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# TODO: Hand derive. Current value is just a canary to
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# detect changes.
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np.testing.assert_almost_equal(
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-0.047639464532418657,
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crm.algorithm_returns[-1],
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decimal=6)
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+119
-115
@@ -107,49 +107,51 @@ class TestDividendPerformance(unittest.TestCase):
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)
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self.assertEqual(after.hour, 13)
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@trading.use_environment(trading.TradingEnvironment())
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def test_long_position_receives_dividend(self):
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#post some trades in the market
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events = factory.create_trade_history(
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1,
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[10, 10, 10, 10, 10],
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[100, 100, 100, 100, 100],
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oneday,
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self.sim_params
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)
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with trading.TradingEnvironment():
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#post some trades in the market
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events = factory.create_trade_history(
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1,
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[10, 10, 10, 10, 10],
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[100, 100, 100, 100, 100],
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oneday,
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self.sim_params
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)
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dividend = factory.create_dividend(
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1,
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10.00,
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# declared date, when the algorithm finds out about
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# the dividend
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events[1].dt,
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# ex_date, when the algorithm is credited with the
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# dividend
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events[1].dt,
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# pay date, when the algorithm receives the dividend.
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events[2].dt
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)
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dividend = factory.create_dividend(
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1,
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10.00,
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# declared date, when the algorithm finds out about
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# the dividend
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events[1].dt,
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# ex_date, when the algorithm is credited with the
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# dividend
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events[1].dt,
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# pay date, when the algorithm receives the dividend.
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events[2].dt
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)
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txn = create_txn(events[0], 10.0, 100)
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events.insert(0, txn)
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events.insert(1, dividend)
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results = calculate_results(self, events)
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txn = create_txn(events[0], 10.0, 100)
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events.insert(0, txn)
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events.insert(1, dividend)
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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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[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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daily_returns = [event['daily_perf']['returns'] for event in results]
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self.assertEqual(daily_returns, [0.0, 0.0, 0.10, 0.0, 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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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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cash_pos = \
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[event['cumulative_perf']['ending_cash'] for event in results]
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self.assertEqual(cash_pos, [9000, 9000, 10000, 10000, 10000])
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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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daily_returns = [event['daily_perf']['returns']
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for event in results]
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self.assertEqual(daily_returns, [0.0, 0.0, 0.10, 0.0, 0.0])
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cash_flows = [event['daily_perf']['capital_used']
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for event in results]
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self.assertEqual(cash_flows, [-1000, 0, 1000, 0, 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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cash_pos = \
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[event['cumulative_perf']['ending_cash'] for event in results]
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self.assertEqual(cash_pos, [9000, 9000, 10000, 10000, 10000])
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def test_post_ex_long_position_receives_no_dividend(self):
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#post some trades in the market
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@@ -1026,92 +1028,94 @@ class TestPerformanceTracker(unittest.TestCase):
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else:
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yield event
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@trading.use_environment(trading.TradingEnvironment())
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def test_minute_tracker(self):
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""" Tests minute performance tracking."""
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start_dt = trading.environment.exchange_dt_in_utc(
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datetime.datetime(2013, 3, 1, 9, 31))
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end_dt = trading.environment.exchange_dt_in_utc(
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datetime.datetime(2013, 3, 1, 16, 0))
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with trading.TradingEnvironment():
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start_dt = trading.environment.exchange_dt_in_utc(
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datetime.datetime(2013, 3, 1, 9, 31))
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end_dt = trading.environment.exchange_dt_in_utc(
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datetime.datetime(2013, 3, 1, 16, 0))
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sim_params = SimulationParameters(
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period_start=start_dt,
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period_end=end_dt,
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emission_rate='minute'
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)
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tracker = perf.PerformanceTracker(sim_params)
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sim_params = SimulationParameters(
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period_start=start_dt,
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period_end=end_dt,
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emission_rate='minute'
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)
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tracker = perf.PerformanceTracker(sim_params)
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foo_event_1 = factory.create_trade('foo', 10.0, 20, start_dt)
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order_event_1 = Order(**{
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'sid': foo_event_1.sid,
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'amount': -25,
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'dt': foo_event_1.dt
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})
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bar_event_1 = factory.create_trade('bar', 100.0, 200, start_dt)
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txn_event_1 = Transaction(sid=foo_event_1.sid,
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amount=-25,
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dt=foo_event_1.dt,
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price=10.0,
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commission=0.50)
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benchmark_event_1 = Event({
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'dt': start_dt,
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'returns': 1.0,
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'type': DATASOURCE_TYPE.BENCHMARK
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||||
})
|
||||
foo_event_1 = factory.create_trade('foo', 10.0, 20, start_dt)
|
||||
order_event_1 = Order(**{
|
||||
'sid': foo_event_1.sid,
|
||||
'amount': -25,
|
||||
'dt': foo_event_1.dt
|
||||
})
|
||||
bar_event_1 = factory.create_trade('bar', 100.0, 200, start_dt)
|
||||
txn_event_1 = Transaction(sid=foo_event_1.sid,
|
||||
amount=-25,
|
||||
dt=foo_event_1.dt,
|
||||
price=10.0,
|
||||
commission=0.50)
|
||||
benchmark_event_1 = Event({
|
||||
'dt': start_dt,
|
||||
'returns': 1.0,
|
||||
'type': DATASOURCE_TYPE.BENCHMARK
|
||||
})
|
||||
|
||||
foo_event_2 = factory.create_trade(
|
||||
'foo', 11.0, 20, start_dt + datetime.timedelta(minutes=1))
|
||||
bar_event_2 = factory.create_trade(
|
||||
'bar', 11.0, 20, start_dt + datetime.timedelta(minutes=1))
|
||||
benchmark_event_2 = Event({
|
||||
'dt': start_dt + datetime.timedelta(minutes=1),
|
||||
'returns': 2.0,
|
||||
'type': DATASOURCE_TYPE.BENCHMARK
|
||||
})
|
||||
foo_event_2 = factory.create_trade(
|
||||
'foo', 11.0, 20, start_dt + datetime.timedelta(minutes=1))
|
||||
bar_event_2 = factory.create_trade(
|
||||
'bar', 11.0, 20, start_dt + datetime.timedelta(minutes=1))
|
||||
benchmark_event_2 = Event({
|
||||
'dt': start_dt + datetime.timedelta(minutes=1),
|
||||
'returns': 2.0,
|
||||
'type': DATASOURCE_TYPE.BENCHMARK
|
||||
})
|
||||
|
||||
events = [
|
||||
foo_event_1,
|
||||
order_event_1,
|
||||
benchmark_event_1,
|
||||
txn_event_1,
|
||||
bar_event_1,
|
||||
foo_event_2,
|
||||
benchmark_event_2,
|
||||
bar_event_2,
|
||||
]
|
||||
events = [
|
||||
foo_event_1,
|
||||
order_event_1,
|
||||
benchmark_event_1,
|
||||
txn_event_1,
|
||||
bar_event_1,
|
||||
foo_event_2,
|
||||
benchmark_event_2,
|
||||
bar_event_2,
|
||||
]
|
||||
|
||||
grouped_events = itertools.groupby(
|
||||
events, operator.attrgetter('dt'))
|
||||
grouped_events = itertools.groupby(
|
||||
events, operator.attrgetter('dt'))
|
||||
|
||||
messages = {}
|
||||
for date, group in grouped_events:
|
||||
tracker.set_date(date)
|
||||
for event in group:
|
||||
tracker.process_event(event)
|
||||
tracker.handle_minute_close(date)
|
||||
msg = tracker.to_dict()
|
||||
messages[date] = msg
|
||||
messages = {}
|
||||
for date, group in grouped_events:
|
||||
tracker.set_date(date)
|
||||
for event in group:
|
||||
tracker.process_event(event)
|
||||
tracker.handle_minute_close(date)
|
||||
msg = tracker.to_dict()
|
||||
messages[date] = msg
|
||||
|
||||
self.assertEquals(2, len(messages))
|
||||
self.assertEquals(2, len(messages))
|
||||
|
||||
msg_1 = messages[foo_event_1.dt]
|
||||
msg_2 = messages[foo_event_2.dt]
|
||||
msg_1 = messages[foo_event_1.dt]
|
||||
msg_2 = messages[foo_event_2.dt]
|
||||
|
||||
self.assertEquals(1, len(msg_1['minute_perf']['transactions']),
|
||||
"The first message should contain one transaction.")
|
||||
# Check that transactions aren't emitted for previous events.
|
||||
self.assertEquals(0, len(msg_2['minute_perf']['transactions']),
|
||||
"The second message should have no transactions.")
|
||||
self.assertEquals(1, len(msg_1['minute_perf']['transactions']),
|
||||
"The first message should contain one "
|
||||
"transaction.")
|
||||
# Check that transactions aren't emitted for previous events.
|
||||
self.assertEquals(0, len(msg_2['minute_perf']['transactions']),
|
||||
"The second message should have no "
|
||||
"transactions.")
|
||||
|
||||
self.assertEquals(1, len(msg_1['minute_perf']['orders']),
|
||||
"The first message should contain one orders.")
|
||||
# Check that orders aren't emitted for previous events.
|
||||
self.assertEquals(0, len(msg_2['minute_perf']['orders']),
|
||||
"The second message should have no orders.")
|
||||
self.assertEquals(1, len(msg_1['minute_perf']['orders']),
|
||||
"The first message should contain one orders.")
|
||||
# Check that orders aren't emitted for previous events.
|
||||
self.assertEquals(0, len(msg_2['minute_perf']['orders']),
|
||||
"The second message should have no orders.")
|
||||
|
||||
# Ensure that period_close moves through time.
|
||||
# Also, ensure that the period_closes are the expected dts.
|
||||
self.assertEquals(foo_event_1.dt,
|
||||
msg_1['minute_perf']['period_close'])
|
||||
self.assertEquals(foo_event_2.dt,
|
||||
msg_2['minute_perf']['period_close'])
|
||||
# Ensure that period_close moves through time.
|
||||
# Also, ensure that the period_closes are the expected dts.
|
||||
self.assertEquals(foo_event_1.dt,
|
||||
msg_1['minute_perf']['period_close'])
|
||||
self.assertEquals(foo_event_2.dt,
|
||||
msg_2['minute_perf']['period_close'])
|
||||
|
||||
@@ -18,7 +18,6 @@ import pytz
|
||||
import logbook
|
||||
import datetime
|
||||
|
||||
from functools import wraps
|
||||
from delorean import Delorean
|
||||
import pandas as pd
|
||||
from pandas import DatetimeIndex
|
||||
@@ -319,18 +318,3 @@ class SimulationParameters(object):
|
||||
emission_rate=self.emission_rate,
|
||||
first_open=self.first_open,
|
||||
last_close=self.last_close)
|
||||
|
||||
|
||||
class use_environment(object):
|
||||
"""A decorator to wrap a method in a particular
|
||||
trading environment."""
|
||||
|
||||
def __init__(self, environment):
|
||||
self.env = environment
|
||||
|
||||
def __call__(self, func):
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
with self.env:
|
||||
return func(*args, **kwargs)
|
||||
return wrapper
|
||||
|
||||
Reference in New Issue
Block a user