mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-03 02:28:28 +08:00
Eddie Hebert
7cc24cec1f
The calculations that are expected to change are:
- cumulative.beta
- cumulative.alpha
- cumulative.information
- cumulative.sharpe
- period.sortino
* Explanation of how risk calculations are changing
** Risk Fixes for Both Period and Cumulative
*** Downside Risk
Use sample instead of population for standard deviation.
Add a rounding factor, so that if the two values are close for a given
dt, that they do not count as a downside value, which would throw off
the denominator of the standard deviation of the downside diffs.
*** Standard Deviation Type
Across the board the standard deviation has been standardized to using
a 'sample' calculation, whereas before cumulative risk was monstly using
'population'. Using `ddof=1` with `np.std` calculates as if the values
are a sample.
** Cumulative Risk Fixes
*** Beta
Use the daily algorithm returns and benchmarks instead of annualized
mean returns.
*** Volatility
Use sample instead of population with standard deviation.
The volatility is an input to other calculations so this change affects
Sharpe and Information ratio calculations.
*** Information Ratio
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
*** Alpha
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
** Period Risk Fixes
*** Sortino
Use the downside risk of the daily return vs. the mean algorithm returns
for the minimum acceptable return instead of the treasury return.
The above required adding the calculation of the mean algorithm returns
for period risk.
Also, use algorithm_period_returns and tresaury_period_return as the
cumulative Sortino does, instead of using algorithm returns for both
inputs into the Sortino calculation.
* Other Supporting Changes
** answer_key
Add new mappings for downside risk and Sortino as well as
re-address the index mappings because of changes to the answer key
spread sheet.
** test_risk_cumulative
Change the decimal precision to expect higher precision.
The calculations are now more aligned with the answer key, so we can
expect higher precision. In particular now that the standard deviation
type matches everywhere in both the Python implementation and the answer
sheet, the precision of the first value no longer has to be glossed over.
** test_events_through_risk
Change the results which are used as a canary for risk changes,
since we do expect Sharpe to change with this change..
342 lines
11 KiB
Python
342 lines
11 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import datetime
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import pytz
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import numpy as np
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from zipline.finance.trading import SimulationParameters
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from zipline.finance import trading
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from zipline.algorithm import TradingAlgorithm
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from zipline.protocol import (
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Event,
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DATASOURCE_TYPE
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)
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class BuyAndHoldAlgorithm(TradingAlgorithm):
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SID_TO_BUY_AND_HOLD = 1
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def initialize(self):
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self.holding = False
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def handle_data(self, data):
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if not self.holding:
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self.order(self.SID_TO_BUY_AND_HOLD, 100)
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self.holding = True
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class TestEventsThroughRisk(unittest.TestCase):
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def test_daily_buy_and_hold(self):
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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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)
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algo = BuyAndHoldAlgorithm(
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sim_params=sim_params,
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data_frequency='daily')
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first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
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second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
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third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
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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_date,
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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_date,
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'source_id': 'test_list',
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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_date,
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'source_id': 'test_list',
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'type': DATASOURCE_TYPE.TRADE
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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_date,
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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_date,
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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_date,
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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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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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# TODO: Hand derive these results.
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# Currently, the output from the time of this writing to
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# at least be an early warning against changes.
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expected_algorithm_returns = {
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first_date: 0.0,
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second_date: -0.000350,
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third_date: -0.050018
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}
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# TODO: Hand derive these results.
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# Currently, the output from the time of this writing to
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# at least be an early warning against changes.
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expected_sharpe = {
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first_date: np.nan,
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second_date: -22.322677,
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third_date: -9.353741
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}
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for bar in gen:
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current_dt = algo.datetime
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crm = algo.perf_tracker.cumulative_risk_metrics
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np.testing.assert_almost_equal(
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crm.algorithm_returns[current_dt],
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expected_algorithm_returns[current_dt],
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decimal=6)
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np.testing.assert_almost_equal(
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crm.metrics.sharpe[current_dt],
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expected_sharpe[current_dt],
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decimal=6,
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err_msg="Mismatch at %s" % (current_dt,))
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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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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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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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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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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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crm = algo.perf_tracker.cumulative_risk_metrics
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first_msg = next(gen)
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self.assertIsNotNone(first_msg,
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"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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self.assertEquals(
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0,
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crm.metrics.algorithm_volatility[algo.datetime.date()],
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"On the first day algorithm volatility does not exist.")
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second_msg = next(gen)
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self.assertIsNotNone(second_msg, "There should be a message "
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"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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# 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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third_msg = next(gen)
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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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# 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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