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https://github.com/wassname/catalyst.git
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Merge fixes for intraday perfomance.
Contains bugs and protocol compatibility changes.
This commit is contained in:
Eddie Hebert
@@ -1098,21 +1098,21 @@ class TestPerformanceTracker(unittest.TestCase):
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msg_1 = messages[foo_event_1.dt]
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msg_2 = messages[foo_event_2.dt]
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self.assertEquals(1, len(msg_1['intraday_perf']['transactions']),
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self.assertEquals(1, len(msg_1['minute_perf']['transactions']),
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"The first message should contain one transaction.")
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# Check that transactions aren't emitted for previous events.
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self.assertEquals(0, len(msg_2['intraday_perf']['transactions']),
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self.assertEquals(0, len(msg_2['minute_perf']['transactions']),
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"The second message should have no transactions.")
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self.assertEquals(1, len(msg_1['intraday_perf']['orders']),
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self.assertEquals(1, len(msg_1['minute_perf']['orders']),
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"The first message should contain one orders.")
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# Check that orders aren't emitted for previous events.
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self.assertEquals(0, len(msg_2['intraday_perf']['orders']),
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self.assertEquals(0, len(msg_2['minute_perf']['orders']),
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"The second message should have no orders.")
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# Ensure that period_close moves through time.
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# Also, ensure that the period_closes are the expected dts.
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self.assertEquals(foo_event_1.dt,
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msg_1['intraday_perf']['period_close'])
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msg_1['minute_perf']['period_close'])
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self.assertEquals(foo_event_2.dt,
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msg_2['intraday_perf']['period_close'])
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msg_2['minute_perf']['period_close'])
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@@ -270,25 +270,16 @@ class PerformanceTracker(object):
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'period_end': self.period_end,
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'capital_base': self.capital_base,
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'cumulative_perf': self.cumulative_performance.to_dict(),
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'progress': self.progress
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'progress': self.progress,
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'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
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}
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if emission_type == 'daily':
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_dict.update({'cumulative_risk_metrics':
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self.cumulative_risk_metrics.to_dict(),
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'daily_perf':
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self.todays_performance.to_dict()})
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if emission_type == 'minute':
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# Currently reusing 'todays_performance' for intraday trading
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# result, should be analogous, but has the potential for needing
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# its own configuration down the line.
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# Naming as intraday to make clear that these results are
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# being updated per minute
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_dict['intraday_risk_metrics'] = \
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self.intraday_risk_metrics.to_dict()
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_dict['intraday_perf'] = self.todays_performance.to_dict(
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self.saved_dt)
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_dict['cumulative_risk_metrics'] = \
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self.cumulative_risk_metrics.to_dict()
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_dict.update({'daily_perf': self.todays_performance.to_dict()})
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elif emission_type == 'minute':
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_dict.update({
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'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(),
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'minute_perf': self.todays_performance.to_dict(self.saved_dt)
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})
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return _dict
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@@ -327,9 +318,7 @@ class PerformanceTracker(object):
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perf_period.calculate_performance()
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def handle_minute_close(self, dt):
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todays_date = self.market_close.replace(hour=0, minute=0, second=0,
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microsecond=0)
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todays_date = dt.replace(hour=0, minute=0, second=0, microsecond=0)
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minute_returns = self.minute_performance.returns
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self.minute_performance.rollover()
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@@ -340,22 +329,20 @@ class PerformanceTracker(object):
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self.intraday_risk_metrics.update(dt,
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algo_minute_returns,
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bench_minute_returns)
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# the intraday risk metrics compound the minutely returns of the
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# benchmark.
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bench_since_open = self.intraday_risk_metrics.benchmark_returns[-1]
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benchmark_returns = pd.Series({dt: bench_since_open})
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bench_since_open = \
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self.intraday_risk_metrics.benchmark_period_returns[-1]
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benchmark_returns = pd.Series({todays_date: bench_since_open})
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# if we've reached market close, check on dividends
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if dt == self.market_close:
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for perf_period in self.perf_periods:
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perf_period.update_dividends(todays_date)
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algorithm_returns = pd.Series({dt: self.todays_performance.returns})
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self.intraday_risk_metrics.update(dt,
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algorithm_returns,
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benchmark_returns)
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algorithm_returns = pd.Series({
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todays_date: self.todays_performance.returns
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})
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self.cumulative_risk_metrics.update(todays_date,
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algorithm_returns,
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benchmark_returns)
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@@ -369,6 +356,18 @@ class PerformanceTracker(object):
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)
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self.returns.append(todays_return_obj)
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def handle_intraday_close(self):
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self.intraday_risk_metrics = \
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risk.RiskMetricsIterative(self.sim_params)
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# increment the day counter before we move markers forward.
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self.day_count += 1.0
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# move the market day markers forward
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if self.market_close < trading.environment.last_trading_day:
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_, self.market_close = \
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trading.environment.next_open_and_close(self.market_open)
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else:
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self.market_close = self.sim_params.last_close
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def handle_market_close(self):
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# add the return results from today to the list of DailyReturn objects.
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todays_date = self.market_close.replace(hour=0, minute=0, second=0,
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+4
-11
@@ -649,7 +649,7 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
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self.algorithm_volatility.append(
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self.calculate_volatility(self.algorithm_returns))
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# caching the treasury rates for the live case is a
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# caching the treasury rates for the minutely case is a
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# big speedup, because it avoids searching the treasury
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# curves on every minute.
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treasury_end = self.algorithm_returns.index[-1].replace(
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@@ -693,16 +693,9 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
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'period_label': period_label
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}
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if self.sim_params.emission_rate == 'daily':
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# Some risk metrics only make sense in a context of daily
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# risk calculations.
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rval['sharpe'] = self.sharpe[-1]
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rval['sortino'] = self.sortino[-1]
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rval['information'] = self.information[-1]
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elif self.sim_params.emission_rate == 'minute':
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rval['sharpe'] = 0.0
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rval['sortino'] = 0.0
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rval['information'] = 0.0
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rval['sharpe'] = self.sharpe[-1]
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rval['sortino'] = self.sortino[-1]
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rval['information'] = self.information[-1]
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return {k: None
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if check_entry(k, v)
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@@ -26,7 +26,7 @@ log = Logger('Trade Simulation')
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class AlgorithmSimulator(object):
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EMISSION_TO_PERF_KEY_MAP = {
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'minute': 'intraday_perf',
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'minute': 'minute_perf',
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'daily': 'daily_perf'
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}
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@@ -170,6 +170,7 @@ class AlgorithmSimulator(object):
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tp.rollover()
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if mkt_close < self.algo.perf_tracker.last_close:
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mkt_close = self.get_next_close(mkt_close)
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self.algo.perf_tracker.handle_intraday_close()
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risk_message = self.algo.perf_tracker.handle_simulation_end()
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yield risk_message
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@@ -185,7 +186,7 @@ class AlgorithmSimulator(object):
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elif self.algo.perf_tracker.emission_rate == 'minute':
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self.algo.perf_tracker.handle_minute_close(date)
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perf_message = self.algo.perf_tracker.to_dict()
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perf_message['intraday_perf']['recorded_vars'] = rvars
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perf_message['minute_perf']['recorded_vars'] = rvars
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return perf_message
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def get_next_close(self, mkt_close):
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