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Adds dividends to performance tracking.

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Algorithm returns and the risk calculations that depend on them now include
cash dividends. This commit does _not_ provide an API for user algorithms to
access dividends.

PerformanceTracker expects the dividend data to arrive as events, similar to
the way that Trades arrive. Dividends are expected to have adjusted payment
amounts that are inline with adjusted trades.

PerformanceTracker maintains state of all the unpaid dividends in the position
objects held in PerformancePeriod. Dividend objects contain all the relevant
dates (declared, ex, payment) as well as net and gross amounts. Dividends are
removed from the list as they are paid. Cash flow is not incremented until the
payment day. This creates the possibility of a dividend being owed but not
paid or realized before the end of a test. For example, a dividend with an
ex_date of today may have a pay date 2 weeks in the future. Right now the
algorithm does not receive any credit for unpaid dividends.

Tests cover buying/selling around the ex_date and payment_date, and checking
that the performance calculated is as expected.
This commit is contained in:
fawce
2013-02-06 16:39:39 -05:00
committed by Eddie Hebert
parent 372a714eb8
commit 817ed88e38
5 changed files with 530 additions and 84 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_perf_tracking.py
+377 -56
View File
@@ -17,7 +17,6 @@ import collections
import unittest
from nose_parameterized import parameterized
import random
import datetime
import pytz
import itertools
@@ -28,55 +27,377 @@ import zipline.finance.performance as perf
from zipline.utils.protocol_utils import ndict
from zipline.gens.composites import date_sorted_sources
from zipline.finance.trading import TradingEnvironment
from zipline.utils.factory import create_random_trading_environment
class TestPerformance(unittest.TestCase):
onesec = datetime.timedelta(seconds=1)
oneday = datetime.timedelta(days=1)
tradingday = datetime.timedelta(hours=6, minutes=30)
class TestDividendPerformance(unittest.TestCase):
def setUp(self):
self.onesec = datetime.timedelta(seconds=1)
self.oneday = datetime.timedelta(days=1)
self.tradingday = datetime.timedelta(hours=6, minutes=30)
self.trading_environment, self.dt, self.end_dt = self.create_env()
self.trading_environment, self.dt, self.end_dt = \
create_random_trading_environment()
def create_env(self, start_dt=None):
benchmark_returns, treasury_curves = \
factory.load_market_data()
self.trading_environment.capital_base = 10e3
if not start_dt:
for n in range(100):
random_index = random.randint(
0,
len(treasury_curves)
)
start_dt = treasury_curves.keys()[random_index]
end_dt = start_dt + datetime.timedelta(days=365)
now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
if end_dt <= now:
break
else:
end_dt = start_dt + datetime.timedelta(days=365)
now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
assert end_dt <= now, """
failed to find a date suitable daterange after 100 attempts. please double
check treasury and benchmark data in findb, and re-run the test."""
assert start_dt < end_dt, "start_dt must be less than end_dt"
trading_environment = TradingEnvironment(
benchmark_returns,
treasury_curves,
period_start=start_dt,
period_end=end_dt
def test_long_position_receives_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
return trading_environment, start_dt, end_dt
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, 100, self.dt+oneday)
events[2].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(results[0]['daily_perf']['period_open'], events[0].dt)
self.assertEqual(
results[-1]['daily_perf']['period_open'],
events[-1].dt
)
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.1, 0.1, 0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.10, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 0, 0])
def test_post_ex_long_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, 100, events[3].dt)
events[3].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0, 0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 0, -1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 0, -1000, -1000, -1000])
def test_selling_before_dividend_payment_still_gets_paid(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[3].dt
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
sell_txn = factory.create_txn(1, 10.0, -100, events[2].dt)
events[2].TRANSACTION = sell_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0.1, 0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0.1, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 1000, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 1000, 1000])
def test_buy_and_sell_before_ex(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[3].dt,
events[4].dt,
events[5].dt
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
sell_txn = factory.create_txn(1, 10.0, -100, events[2].dt)
events[2].TRANSACTION = sell_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0, 0, 0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 0, 0, 0])
def test_ending_before_pay_date(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[-1].dt + 10*oneday
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(
cumulative_cash_flows,
[0, -1000, -1000, -1000, -1000]
)
def test_short_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, -100, self.dt+oneday)
events[2].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 1000, 1000])
def test_no_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 0, 0, 0, 0])
class TestPositionPerformance(unittest.TestCase):
def setUp(self):
self.trading_environment, self.dt, self.end_dt = \
create_random_trading_environment()
def test_long_position(self):
"""
@@ -88,11 +409,11 @@ check treasury and benchmark data in findb, and re-run the test."""
1,
[10, 10, 10, 11],
[100, 100, 100, 100],
self.onesec,
onesec,
self.trading_environment
)
txn = factory.create_txn(1, 10.0, 100, self.dt + self.onesec)
txn = factory.create_txn(1, 10.0, 100, self.dt + onesec)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
@@ -102,7 +423,7 @@ check treasury and benchmark data in findb, and re-run the test."""
pp.calculate_performance()
self.assertEqual(
pp.period_capital_used,
pp.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction \
cost of sole txn in test"
@@ -157,13 +478,13 @@ single short-sale transaction"""
1,
[10, 10, 10, 11, 10, 9],
[100, 100, 100, 100, 100, 100],
self.onesec,
onesec,
self.trading_environment
)
trades_1 = trades[:-2]
txn = factory.create_txn(1, 10.0, -100, self.dt + self.onesec)
txn = factory.create_txn(1, 10.0, -100, self.dt + onesec)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
@@ -173,7 +494,7 @@ single short-sale transaction"""
pp.calculate_performance()
self.assertEqual(
pp.period_capital_used,
pp.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction\
cost of sole txn in test"
@@ -230,7 +551,7 @@ single short-sale transaction"""
pp.calculate_performance()
self.assertEqual(
pp.period_capital_used,
pp.period_cash_flow,
0,
"capital used should be zero, there were no transactions in \
performance period"
@@ -292,7 +613,7 @@ single short-sale transaction"""
ppTotal.calculate_performance()
self.assertEqual(
ppTotal.period_capital_used,
ppTotal.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction \
cost of sole txn in test"
@@ -347,7 +668,7 @@ trade after cover"""
1,
[10, 10, 10, 11, 9, 8, 7, 8, 9, 10],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
self.onesec,
onesec,
self.trading_environment
)
@@ -355,10 +676,10 @@ trade after cover"""
1,
10.0,
-100,
self.dt + self.onesec
self.dt + onesec
)
cover_txn = factory.create_txn(1, 7.0, 100, self.dt + self.onesec * 6)
cover_txn = factory.create_txn(1, 7.0, 100, self.dt + onesec * 6)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(short_txn)
@@ -373,7 +694,7 @@ trade after cover"""
cover_txn_cost = cover_txn.price * cover_txn.amount
self.assertEqual(
pp.period_capital_used,
pp.period_cash_flow,
-1 * short_txn_cost - cover_txn_cost,
"capital used should be equal to the net transaction costs"
)
@@ -426,7 +747,7 @@ shares in position"
1,
[10, 11, 11, 12],
[100, 100, 100, 100],
self.onesec,
onesec,
self.trading_environment
)
@@ -434,7 +755,7 @@ shares in position"
1,
[10, 11, 11, 12],
[100, 100, 100, 100],
self.onesec,
onesec,
self.trading_environment
)
@@ -470,13 +791,13 @@ shares in position"
1,
10.0,
-100,
self.dt + self.onesec * 4)
self.dt + onesec * 4)
down_tick = factory.create_trade(
1,
10.0,
100,
trades[-1].dt + self.onesec)
trades[-1].dt + onesec)
pp.rollover()
tests/test_transforms.py
+1
View File
@@ -302,6 +302,7 @@ class TestBatchTransform(TestCase):
"First three iterations should return None." + "\n" +
"i.e. no returned values until window is full'" +
"%s" % (algo.history_return_price_decorator,))
# After three Nones, the next value should be a data frame
self.assertTrue(isinstance(
algo.history_return_price_class[wl],