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DEV: Adjust performance calculations for capital changes
Refactor PerformancePeriod so that it creates a sub-period every time a capital change happens within the period
This commit is contained in:
Andrew Liang
@@ -17,6 +17,7 @@ import datetime
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from datetime import timedelta
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from textwrap import dedent
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from unittest import TestCase, skip
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from copy import deepcopy
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import logbook
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import toolz
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@@ -1854,6 +1855,548 @@ def handle_data(context, data):
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algo.run(self.data_portal)
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class TestCapitalChanges(WithLogger,
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WithDataPortal,
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WithSimParams,
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ZiplineTestCase):
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sids = 0, 1
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@classmethod
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def make_equity_info(cls):
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data = make_simple_equity_info(
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cls.sids,
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pd.Timestamp('2006-01-03', tz='UTC'),
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pd.Timestamp('2006-01-09', tz='UTC'),
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)
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return data
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@classmethod
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def make_minute_bar_data(cls):
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minutes = cls.env.minutes_for_days_in_range(
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pd.Timestamp('2006-01-03', tz='UTC'),
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pd.Timestamp('2006-01-09', tz='UTC')
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)
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return trades_by_sid_to_dfs(
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{
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1: factory.create_trade_history(
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1,
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np.arange(100.0, 100.0 + len(minutes), 1),
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[10000] * len(minutes),
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timedelta(minutes=1),
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cls.sim_params,
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cls.env),
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},
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index=pd.DatetimeIndex(minutes),
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)
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@classmethod
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def make_daily_bar_data(cls):
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days = cls.env.days_in_range(
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pd.Timestamp('2006-01-03', tz='UTC'),
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pd.Timestamp('2006-01-09', tz='UTC')
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)
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return trades_by_sid_to_dfs(
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{
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0: factory.create_trade_history(
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0,
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np.arange(10.0, 10.0 + len(days), 1.0),
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[10000] * len(days),
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timedelta(days=1),
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cls.sim_params,
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cls.env),
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},
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index=pd.DatetimeIndex(days),
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)
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def test_capital_changes_daily_mode(self):
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sim_params = factory.create_simulation_parameters(
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start=pd.Timestamp('2006-01-03', tz='UTC'),
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end=pd.Timestamp('2006-01-09', tz='UTC')
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)
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capital_changes = {
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pd.Timestamp('2006-01-06', tz='UTC'): 50000
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}
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algocode = """
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from zipline.api import set_slippage, set_commission, slippage, commission, \
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schedule_function, time_rules, order, sid
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def initialize(context):
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set_slippage(slippage.FixedSlippage(spread=0))
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set_commission(commission.PerShare(0, 0))
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schedule_function(order_stuff, time_rule=time_rules.market_open())
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def order_stuff(context, data):
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order(sid(0), 1000)
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"""
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algo = TradingAlgorithm(
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script=algocode,
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sim_params=sim_params,
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env=self.env,
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data_portal=self.data_portal,
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capital_changes=capital_changes
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)
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gen = algo.get_generator()
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results = list(gen)
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cumulative_perf = \
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[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
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daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
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# 1/03: price = 10, place orders
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# 1/04: orders execute at price = 11, place orders
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# 1/05: orders execute at price = 12, place orders
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# 1/06: +50000 capital change,
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# orders execute at price = 13, place orders
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# 1/09: orders execute at price = 14, place orders
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expected_daily = {}
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expected_capital_changes = np.array([
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0.0, 0.0, 0.0, 50000.0, 0.0
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])
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# Day 1, no transaction. Day 2, we transact, but the price of our stock
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# does not change. Day 3, we start getting returns
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expected_daily['returns'] = np.array([
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0.0,
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0.0,
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# 1000 shares * gain of 1
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(100000.0 + 1000.0)/100000.0 - 1.0,
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# 2000 shares * gain of 1, capital change of +5000
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(151000.0 + 2000.0)/151000.0 - 1.0,
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# 3000 shares * gain of 1
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(153000.0 + 3000.0)/153000.0 - 1.0,
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])
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expected_daily['pnl'] = np.array([
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0.0,
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0.0,
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1000.00, # 1000 shares * gain of 1
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2000.00, # 2000 shares * gain of 1
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3000.00, # 3000 shares * gain of 1
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])
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expected_daily['capital_used'] = np.array([
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0.0,
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-11000.0, # 1000 shares at price = 11
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-12000.0, # 1000 shares at price = 12
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-13000.0, # 1000 shares at price = 13
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-14000.0, # 1000 shares at price = 14
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])
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expected_daily['ending_cash'] = \
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np.array([100000.0] * 5) + \
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np.cumsum(expected_capital_changes) + \
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np.cumsum(expected_daily['capital_used'])
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expected_daily['starting_cash'] = \
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expected_daily['ending_cash'] - \
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expected_daily['capital_used']
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expected_daily['starting_value'] = [
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0.0,
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0.0,
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11000.0, # 1000 shares at price = 11
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24000.0, # 2000 shares at price = 12
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39000.0, # 3000 shares at price = 13
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]
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expected_daily['ending_value'] = \
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expected_daily['starting_value'] + \
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expected_daily['pnl'] - \
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expected_daily['capital_used']
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expected_daily['portfolio_value'] = \
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expected_daily['ending_value'] + \
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expected_daily['ending_cash']
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stats = [
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'returns', 'pnl', 'capital_used', 'starting_cash', 'ending_cash',
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'starting_value', 'ending_value', 'portfolio_value'
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]
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expected_cumulative = {
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'returns': np.cumprod(expected_daily['returns'] + 1) - 1,
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'pnl': np.cumsum(expected_daily['pnl']),
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'capital_used': np.cumsum(expected_daily['capital_used']),
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'starting_cash':
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np.repeat(expected_daily['starting_cash'][0:1], 5),
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'ending_cash': expected_daily['ending_cash'],
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'starting_value':
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np.repeat(expected_daily['starting_value'][0:1], 5),
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'ending_value': expected_daily['ending_value'],
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'portfolio_value': expected_daily['portfolio_value'],
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}
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for stat in stats:
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np.testing.assert_array_almost_equal(
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np.array([perf[stat] for perf in daily_perf]),
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expected_daily[stat]
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)
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np.testing.assert_array_almost_equal(
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np.array([perf[stat] for perf in cumulative_perf]),
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expected_cumulative[stat]
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)
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@parameterized.expand([('interday',), ('intraday',)])
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def test_capital_changes_minute_mode_daily_emission(self, change):
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sim_params = factory.create_simulation_parameters(
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start=pd.Timestamp('2006-01-03', tz='UTC'),
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end=pd.Timestamp('2006-01-05', tz='UTC'),
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data_frequency='minute',
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capital_base=1000.0
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)
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if change == 'intraday':
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capital_changes = {
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pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
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pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0,
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}
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else:
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capital_changes = {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
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algocode = """
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from zipline.api import set_slippage, set_commission, slippage, commission, \
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schedule_function, time_rules, order, sid
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def initialize(context):
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set_slippage(slippage.FixedSlippage(spread=0))
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set_commission(commission.PerShare(0, 0))
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schedule_function(order_stuff, time_rule=time_rules.market_open())
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def order_stuff(context, data):
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order(sid(1), 1)
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"""
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algo = TradingAlgorithm(
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script=algocode,
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sim_params=sim_params,
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env=self.env,
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data_portal=self.data_portal,
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capital_changes=capital_changes
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)
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gen = algo.get_generator()
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results = list(gen)
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cumulative_perf = \
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[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
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daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
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# 1/03: place orders at price = 100, execute at 101
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# 1/04: place orders at price = 490, execute at 491,
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# +500 capital change at 17:00 and 18:00 (intraday)
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# or +1000 at 00:00 (interday),
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# 1/05: place orders at price = 880, execute at 881
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expected_daily = {}
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expected_capital_changes = np.array([
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0.0, 1000.0, 0.0
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])
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if change == 'intraday':
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# Fills at 491, +500 capital change comes at 638 (17:00) and
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# 698 (18:00), ends day at 879
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day2_return = (1388.0 + 149.0 + 147.0)/1388.0 * \
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(2184.0 + 60.0 + 60.0)/2184.0 * \
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(2804.0 + 181.0 + 181.0)/2804.0 - 1.0
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else:
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# Fills at 491, ends day at 879, capital change +1000
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day2_return = (2388.0 + 390.0 + 388.0)/2388.0 - 1
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expected_daily['returns'] = np.array([
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# Fills at 101, ends day at 489
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(1000.0 + 388.0)/1000.0 - 1.0,
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day2_return,
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# Fills at 881, ends day at 1269
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(3166.0 + 390.0 + 390.0 + 388.0)/3166.0 - 1.0,
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])
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expected_daily['pnl'] = np.array([
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388.0,
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390.0 + 388.0,
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390.0 + 390.0 + 388.0,
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])
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expected_daily['capital_used'] = np.array([
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-101.0, -491.0, -881.0
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])
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expected_daily['ending_cash'] = \
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np.array([1000.0] * 3) + \
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np.cumsum(expected_capital_changes) + \
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np.cumsum(expected_daily['capital_used'])
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expected_daily['starting_cash'] = \
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expected_daily['ending_cash'] - \
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expected_daily['capital_used']
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if change == 'intraday':
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# Capital changes come after day start
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expected_daily['starting_cash'] -= expected_capital_changes
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expected_daily['starting_value'] = np.array([
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0.0, 489.0, 879.0 * 2
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])
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expected_daily['ending_value'] = \
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expected_daily['starting_value'] + \
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expected_daily['pnl'] - \
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expected_daily['capital_used']
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expected_daily['portfolio_value'] = \
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expected_daily['ending_value'] + \
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expected_daily['ending_cash']
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stats = [
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'returns', 'pnl', 'capital_used', 'starting_cash', 'ending_cash',
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'starting_value', 'ending_value', 'portfolio_value'
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]
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expected_cumulative = {
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'returns': np.cumprod(expected_daily['returns'] + 1) - 1,
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'pnl': np.cumsum(expected_daily['pnl']),
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'capital_used': np.cumsum(expected_daily['capital_used']),
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'starting_cash':
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np.repeat(expected_daily['starting_cash'][0:1], 3),
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'ending_cash': expected_daily['ending_cash'],
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'starting_value':
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np.repeat(expected_daily['starting_value'][0:1], 3),
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'ending_value': expected_daily['ending_value'],
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'portfolio_value': expected_daily['portfolio_value'],
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}
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for stat in stats:
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np.testing.assert_array_almost_equal(
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np.array([perf[stat] for perf in daily_perf]),
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expected_daily[stat]
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)
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np.testing.assert_array_almost_equal(
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np.array([perf[stat] for perf in cumulative_perf]),
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expected_cumulative[stat]
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)
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@parameterized.expand([('interday',), ('intraday',)])
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def test_capital_changes_minute_mode_minute_emission(self, change):
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sim_params = factory.create_simulation_parameters(
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start=pd.Timestamp('2006-01-03', tz='UTC'),
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end=pd.Timestamp('2006-01-05', tz='UTC'),
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data_frequency='minute',
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emission_rate='minute',
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capital_base=1000.0
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)
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if change == 'intraday':
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capital_changes = {
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pd.Timestamp('2006-01-04 17:00', tz='UTC'): 500.0,
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pd.Timestamp('2006-01-04 18:00', tz='UTC'): 500.0,
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}
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else:
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capital_changes = {pd.Timestamp('2006-01-04', tz='UTC'): 1000.0}
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algocode = """
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from zipline.api import set_slippage, set_commission, slippage, commission, \
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schedule_function, time_rules, order, sid
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def initialize(context):
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set_slippage(slippage.FixedSlippage(spread=0))
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set_commission(commission.PerShare(0, 0))
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schedule_function(order_stuff, time_rule=time_rules.market_open())
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def order_stuff(context, data):
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order(sid(1), 1)
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"""
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algo = TradingAlgorithm(
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script=algocode,
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sim_params=sim_params,
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env=self.env,
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data_portal=self.data_portal,
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capital_changes=capital_changes
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)
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gen = algo.get_generator()
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results = list(gen)
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cumulative_perf = \
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[r['cumulative_perf'] for r in results if 'cumulative_perf' in r]
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minute_perf = [r['minute_perf'] for r in results if 'minute_perf' in r]
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daily_perf = [r['daily_perf'] for r in results if 'daily_perf' in r]
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# 1/03: place orders at price = 100, execute at 101
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# 1/04: place orders at price = 490, execute at 491,
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# +500 capital change at 17:00 and 18:00 (intraday)
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# or +1000 at 00:00 (interday),
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# 1/05: place orders at price = 880, execute at 881
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# Minute perfs are cumulative for the day
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expected_minute = {}
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capital_changes_after_start = np.array([0.0] * 1170)
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if change == 'intraday':
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capital_changes_after_start[539:599] = 500.0
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capital_changes_after_start[599:780] = 1000.0
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expected_minute['pnl'] = np.array([0.0] * 1170)
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expected_minute['pnl'][:2] = 0.0
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expected_minute['pnl'][2:392] = 1.0
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expected_minute['pnl'][392:782] = 2.0
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expected_minute['pnl'][782:] = 3.0
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for start, end in ((0, 390), (390, 780), (780, 1170)):
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expected_minute['pnl'][start:end] = \
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np.cumsum(expected_minute['pnl'][start:end])
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expected_minute['capital_used'] = np.concatenate((
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[0.0] * 1, [-101.0] * 389,
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[0.0] * 1, [-491.0] * 389,
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[0.0] * 1, [-881.0] * 389,
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))
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# +1000 capital changes comes before the day start if interday
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day2adj = 0.0 if change == 'intraday' else 1000.0
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expected_minute['starting_cash'] = np.concatenate((
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[1000.0] * 390,
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# 101 spent on 1/03
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[1000.0 - 101.0 + day2adj] * 390,
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# 101 spent on 1/03, 491 on 1/04, +1000 capital change on 1/04
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[1000.0 - 101.0 - 491.0 + 1000] * 390
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))
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expected_minute['ending_cash'] = \
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expected_minute['starting_cash'] + \
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expected_minute['capital_used'] + \
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capital_changes_after_start
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expected_minute['starting_value'] = np.concatenate((
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[0.0] * 390,
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[489.0] * 390,
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[879.0 * 2] * 390
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))
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expected_minute['ending_value'] = \
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expected_minute['starting_value'] + \
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expected_minute['pnl'] - \
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expected_minute['capital_used']
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expected_minute['portfolio_value'] = \
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expected_minute['ending_value'] + \
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expected_minute['ending_cash']
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expected_minute['returns'] = \
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expected_minute['pnl'] / \
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(expected_minute['starting_value'] +
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expected_minute['starting_cash'])
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# If the change is interday, we can just calculate the returns from
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# the pnl, starting_value and starting_cash. If the change is intraday,
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# the returns after the change have to be calculated from two
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# subperiods
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if change == 'intraday':
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# The last packet (at 1/04 16:59) before the first capital change
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prev_subperiod_return = expected_minute['returns'][538]
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# From 1/04 17:00 to 17:59
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cur_subperiod_pnl = \
|
||||
expected_minute['pnl'][539:599] - expected_minute['pnl'][538]
|
||||
cur_subperiod_starting_value = \
|
||||
np.array([expected_minute['ending_value'][538]] * 60)
|
||||
cur_subperiod_starting_cash = \
|
||||
np.array([expected_minute['ending_cash'][538] + 500] * 60)
|
||||
|
||||
cur_subperiod_returns = cur_subperiod_pnl / \
|
||||
(cur_subperiod_starting_value + cur_subperiod_starting_cash)
|
||||
expected_minute['returns'][539:599] = \
|
||||
(cur_subperiod_returns + 1.0) * \
|
||||
(prev_subperiod_return + 1.0) - \
|
||||
1.0
|
||||
|
||||
# The last packet (at 1/04 17:59) before the second capital change
|
||||
prev_subperiod_return = expected_minute['returns'][598]
|
||||
|
||||
# From 1/04 18:00 to 21:00
|
||||
cur_subperiod_pnl = \
|
||||
expected_minute['pnl'][599:780] - expected_minute['pnl'][598]
|
||||
cur_subperiod_starting_value = \
|
||||
np.array([expected_minute['ending_value'][598]] * 181)
|
||||
cur_subperiod_starting_cash = \
|
||||
np.array([expected_minute['ending_cash'][598] + 500] * 181)
|
||||
|
||||
cur_subperiod_returns = cur_subperiod_pnl / \
|
||||
(cur_subperiod_starting_value + cur_subperiod_starting_cash)
|
||||
expected_minute['returns'][599:780] = \
|
||||
(cur_subperiod_returns + 1.0) * \
|
||||
(prev_subperiod_return + 1.0) - \
|
||||
1.0
|
||||
|
||||
# The last minute packet of each day
|
||||
expected_daily = {
|
||||
k: np.array([v[389], v[779], v[1169]])
|
||||
for k, v in iteritems(expected_minute)
|
||||
}
|
||||
|
||||
stats = [
|
||||
'pnl', 'capital_used', 'starting_cash', 'ending_cash',
|
||||
'starting_value', 'ending_value', 'portfolio_value', 'returns'
|
||||
]
|
||||
|
||||
expected_cumulative = deepcopy(expected_minute)
|
||||
|
||||
# "Add" daily return from 1/03 to minute returns on 1/04 and 1/05
|
||||
# "Add" daily return from 1/04 to minute returns on 1/05
|
||||
expected_cumulative['returns'][390:] = \
|
||||
(expected_cumulative['returns'][390:] + 1) * \
|
||||
(expected_daily['returns'][0] + 1) - 1
|
||||
expected_cumulative['returns'][780:] = \
|
||||
(expected_cumulative['returns'][780:] + 1) * \
|
||||
(expected_daily['returns'][1] + 1) - 1
|
||||
|
||||
# Add daily pnl/capital_used from 1/03 to 1/04 and 1/05
|
||||
# Add daily pnl/capital_used from 1/04 to 1/05
|
||||
expected_cumulative['pnl'][390:] += expected_daily['pnl'][0]
|
||||
expected_cumulative['pnl'][780:] += expected_daily['pnl'][1]
|
||||
expected_cumulative['capital_used'][390:] += \
|
||||
expected_daily['capital_used'][0]
|
||||
expected_cumulative['capital_used'][780:] += \
|
||||
expected_daily['capital_used'][1]
|
||||
|
||||
# starting_cash, starting_value are same as those of the first daily
|
||||
# packet
|
||||
expected_cumulative['starting_cash'] = \
|
||||
np.repeat(expected_daily['starting_cash'][0:1], 1170)
|
||||
expected_cumulative['starting_value'] = \
|
||||
np.repeat(expected_daily['starting_value'][0:1], 1170)
|
||||
|
||||
# extra cumulative packet per day from the daily packet
|
||||
for stat in stats:
|
||||
for i in (390, 781, 1172):
|
||||
expected_cumulative[stat] = np.insert(
|
||||
expected_cumulative[stat],
|
||||
i,
|
||||
expected_cumulative[stat][i-1]
|
||||
)
|
||||
|
||||
for stat in stats:
|
||||
np.testing.assert_array_almost_equal(
|
||||
np.array([perf[stat] for perf in minute_perf]),
|
||||
expected_minute[stat]
|
||||
)
|
||||
np.testing.assert_array_almost_equal(
|
||||
np.array([perf[stat] for perf in daily_perf]),
|
||||
expected_daily[stat]
|
||||
)
|
||||
np.testing.assert_array_almost_equal(
|
||||
np.array([perf[stat] for perf in cumulative_perf]),
|
||||
expected_cumulative[stat]
|
||||
)
|
||||
|
||||
|
||||
class TestGetDatetime(WithLogger,
|
||||
WithSimParams,
|
||||
WithDataPortal,
|
||||
|
||||
Reference in New Issue
Block a user