def test_algo_with_rl_violation(self):
sim_params = factory.create_simulation_parameters(start=list(
LEVERAGED_ETFS.keys())[0],
num_days=4)
trade_history = factory.create_trade_history('BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='BZQ', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
# repeat with a symbol from a different lookup date
trade_history = factory.create_trade_history('JFT',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='JFT', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_algo_with_rl_violation(self):
sim_params = factory.create_simulation_parameters(
start=list(LEVERAGED_ETFS.keys())[0], num_days=4)
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='BZQ', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
# repeat with a symbol from a different lookup date
trade_history = factory.create_trade_history(
'JFT',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='JFT', sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_tracker(self):
trade_count = 100
sid = 133
price = 10.1
price_list = [price] * trade_count
volume = [100] * trade_count
trade_time_increment = datetime.timedelta(days=1)
trade_history = factory.create_trade_history(sid, price_list, volume,
trade_time_increment,
self.trading_environment)
sid2 = 134
price2 = 12.12
price2_list = [price2] * trade_count
trade_history2 = factory.create_trade_history(sid2, price2_list,
volume,
trade_time_increment,
self.trading_environment)
trade_history.extend(trade_history2)
self.trading_environment.period_start = trade_history[0].dt
self.trading_environment.period_end = trade_history[-1].dt
self.trading_environment.capital_base = 1000.0
self.trading_environment.frame_index = [
'sid', 'volume', 'dt', 'price', 'changed'
]
perf_tracker = perf.PerformanceTracker(self.trading_environment)
for event in trade_history:
#create a transaction for all but
#first trade in each sid, to simulate None transaction
if (event.dt != self.trading_environment.period_start):
txn = ndict({
'sid': event.sid,
'amount': -25,
'dt': event.dt,
'price': 10.0,
'commission': 0.50
})
else:
txn = None
event['TRANSACTION'] = txn
perf_tracker.process_event(event)
#we skip two trades, to test case of None transaction
txn_count = len(trade_history) - 2
self.assertEqual(perf_tracker.txn_count, txn_count)
cumulative_pos = perf_tracker.cumulative_performance.positions[sid]
expected_size = txn_count / 2 * -25
self.assertEqual(cumulative_pos.amount, expected_size)
self.assertEqual(
perf_tracker.period_end.replace(hour=0, minute=0, second=0),
perf_tracker.cumulative_risk_metrics.end_date)
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.sim_params
)
pay_date = self.sim_params.first_open
# find pay date that is much later.
for i in xrange(30):
pay_date = factory.get_next_trading_dt(pay_date, oneday)
dividend = factory.create_dividend(1, 10.00, events[0].dt, events[1].dt, pay_date)
buy_txn = create_txn(1, 10.0, 100, events[1].dt)
events.insert(2, buy_txn)
events.insert(1, dividend)
results = calculate_results(self, events)
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_returns(self):
# Daily returns.
returns = Returns(1)
transformed = list(returns.transform(self.source))
tnfm_vals = [message.tnfm_value for message in transformed]
# No returns for the first event because we don't have a
# previous close.
expected = [0.0, 0.0, 0.1, 0.0]
assert tnfm_vals == expected
# Two-day returns. An extra kink here is that the
# factory will automatically skip a weekend for the
# last event. Results shouldn't notice this blip.
trade_history = factory.create_trade_history(
133, [10.0, 15.0, 13.0, 12.0, 13.0], [100, 100, 100, 300, 100],
timedelta(days=1), self.trading_environment)
self.source = SpecificEquityTrades(event_list=trade_history)
returns = StatefulTransform(Returns, 2)
transformed = list(returns.transform(self.source))
tnfm_vals = [message.tnfm_value for message in transformed]
expected = [
0.0, 0.0, (13.0 - 10.0) / 10.0, (12.0 - 15.0) / 15.0,
(13.0 - 13.0) / 13.0
]
assert tnfm_vals == expected
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.sim_params)
dividend = factory.create_dividend(1, 10.00, events[3].dt,
events[4].dt, events[5].dt)
buy_txn = create_txn(events[1], 10.0, 100)
events.insert(1, buy_txn)
sell_txn = create_txn(events[3], 10.0, -100)
events.insert(3, sell_txn)
events.insert(1, dividend)
results = calculate_results(self, events)
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_short_position_pays_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.sim_params)
dividend = factory.create_dividend(
1,
10.00,
# declare at open of test
events[0].dt,
# ex_date same as trade 2
events[2].dt,
events[3].dt)
txn = create_txn(events[1], 10.0, -100)
events.insert(1, txn)
events.insert(0, dividend)
results = calculate_results(self, events)
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, -0.1, -0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, -0.1, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 1000, 0, -1000, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 0, 0])
def test_algo_with_rl_violation_cumulative(self):
"""
Add a new restriction, run a test long after both
knowledge dates, make sure stock from original restriction
set is still disallowed.
"""
sim_params = factory.create_simulation_parameters(
start=list(
LEVERAGED_ETFS.keys())[0] + timedelta(days=7), num_days=4)
with security_list_copy():
add_security_data(['AAPL'], [])
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params,
env=self.env,
)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
algo = RestrictedAlgoWithoutCheck(
symbol='BZQ', sim_params=sim_params, env=self.env)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
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.sim_params)
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.sim_params)
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])
def test_cost_basis_calc_close_pos(self):
history_args = (
1,
[10, 9, 11, 8, 9, 12, 13, 14],
[200, -100, -100, 100, -300, 100, 500, 400],
onesec,
self.sim_params
)
cost_bases = [10, 10, 0, 8, 9, 9, 13, 13.5]
trades = factory.create_trade_history(*history_args)
transactions = factory.create_txn_history(*history_args)
pp = perf.PerformancePeriod(1000.0)
for txn, cb in zip(transactions, cost_bases):
pp.execute_transaction(txn)
self.assertEqual(pp.positions[1].cost_basis, cb)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(pp.positions[1].cost_basis, cost_bases[-1])
def test_commission_zero_position(self):
"""
Ensure no div-by-zero errors.
"""
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
# Buy and sell the same sid so that we have a zero position by the
# time of events[3].
txns = [
create_txn(events[0], 20, 1),
create_txn(events[1], 20, -1),
]
# Add a cash adjustment at the time of event[3].
cash_adj_dt = events[3].dt
cash_adjustment = factory.create_commission(1, 300.0, cash_adj_dt)
events.append(cash_adjustment)
results = calculate_results(self, events, txns=txns)
# Validate that we lost 300 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9700)
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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
results = calculate_results(
self,
events,
dividend_events=[dividend],
)
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])
def test_short_position_pays_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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
# declare at open of test
events[0].dt,
# ex_date same as trade 2
events[2].dt,
events[3].dt,
)
txn = create_txn(1, 10.0, -100, events[1].dt)
events.insert(1, txn)
events.insert(0, dividend)
results = calculate_results(self, events)
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, -0.1, -0.1])
daily_returns = [event["daily_perf"]["returns"] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, -0.1, 0.0])
cash_flows = [event["daily_perf"]["capital_used"] for event in results]
self.assertEqual(cash_flows, [0, 1000, 0, -1000, 0])
cumulative_cash_flows = [event["cumulative_perf"]["capital_used"] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 0, 0])
def test_algo_with_rl_violation_cumulative(self):
"""
Add a new restriction, run a test long after both
knowledge dates, make sure stock from original restriction
set is still disallowed.
"""
sim_params = factory.create_simulation_parameters(
start=list(LEVERAGED_ETFS.keys())[0] + timedelta(days=7),
num_days=4)
with security_list_copy():
add_security_data(['AAPL'], [])
trade_history = factory.create_trade_history(
'BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params,
env=self.env,
)
self.source = SpecificEquityTrades(event_list=trade_history,
env=self.env)
algo = RestrictedAlgoWithoutCheck(symbol='BZQ',
sim_params=sim_params,
env=self.env)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 0)
def test_set_max_order_count(self):
# Override the default setUp to use six-hour intervals instead of full
# days so we can exercise trading-session rollover logic.
trade_history = factory.create_trade_history(
self.sid,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(hours=6),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
def handle_data(algo, data):
for i in range(5):
algo.order(self.sid, 1)
algo.order_count += 1
algo = SetMaxOrderCountAlgorithm(3)
self.check_algo_fails(algo, handle_data, 3)
# Second call to handle_data is the same day as the first, so the last
# order of the second call should fail.
algo = SetMaxOrderCountAlgorithm(9)
self.check_algo_fails(algo, handle_data, 9)
# Only ten orders are placed per day, so this should pass even though
# in total more than 20 orders are placed.
algo = SetMaxOrderCountAlgorithm(10)
self.check_algo_succeeds(algo, handle_data, order_count=20)
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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
results = calculate_results(self, events)
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])
def test_commission_zero_position(self):
"""
Ensure no div-by-zero errors.
"""
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
cash_adj_dt = self.sim_params.first_open \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
# Sell that order.
events.insert(1, create_txn(events[1], 20, -1))
events.insert(2, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 300 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9700)
def test_commission_zero_position(self):
"""
Ensure no div-by-zero errors.
"""
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
cash_adj_dt = self.sim_params.first_open \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
# Sell that order.
events.insert(1, create_txn(events[1], 20, -1))
events.insert(2, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 300 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9700)
def test_moving_stddev(self):
trade_history = factory.create_trade_history(
133,
[10.0, 15.0, 13.0, 12.0],
[100, 100, 100, 100],
timedelta(days=1),
self.sim_params
)
stddev = MovingStandardDev(
market_aware=True,
window_length=3,
)
self.source = SpecificEquityTrades(event_list=trade_history)
transformed = list(stddev.transform(self.source))
vals = [message[stddev.get_hash()] for message in transformed]
expected = [
None,
np.std([10.0, 15.0], ddof=1),
np.std([10.0, 15.0, 13.0], ddof=1),
np.std([15.0, 13.0, 12.0], ddof=1),
]
# np has odd rounding behavior, cf.
# http://docs.scipy.org/doc/np/reference/generated/np.std.html
for v1, v2 in zip(vals, expected):
if v1 is None:
self.assertIsNone(v2)
continue
self.assertEquals(round(v1, 5), round(v2, 5))
def test_cost_basis_calc_close_pos(self):
history_args = (
1,
[10, 9, 11, 8, 9, 12, 13, 14],
[200, -100, -100, 100, -300, 100, 500, 400],
onesec,
self.sim_params
)
cost_bases = [10, 10, 0, 8, 9, 9, 13, 13.5]
trades = factory.create_trade_history(*history_args)
transactions = factory.create_txn_history(*history_args)
pp = perf.PerformancePeriod(1000.0)
for txn, cb in zip(transactions, cost_bases):
pp.execute_transaction(txn)
self.assertEqual(pp.positions[1].cost_basis, cb)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(pp.positions[1].cost_basis, cost_bases[-1])
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
cash_adj_dt = self.sim_params.first_open \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
events.insert(1, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
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.sim_params)
pay_date = self.sim_params.first_open
# find pay date that is much later.
for i in xrange(30):
pay_date = factory.get_next_trading_dt(pay_date, oneday)
dividend = factory.create_dividend(1, 10.00, events[0].dt,
events[1].dt, pay_date)
buy_txn = create_txn(events[1], 10.0, 100)
events.insert(2, buy_txn)
events.insert(1, dividend)
results = calculate_results(self, events)
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_moving_stddev(self):
trade_history = factory.create_trade_history(
133,
[10.0, 15.0, 13.0, 12.0],
[100, 100, 100, 100],
timedelta(hours=1),
self.trading_environment
)
stddev = MovingStandardDev(
market_aware=False,
delta=timedelta(minutes=150),
)
self.source = SpecificEquityTrades(event_list=trade_history)
transformed = list(stddev.transform(self.source))
vals = [message.tnfm_value for message in transformed]
expected = [
None,
np.std([10.0, 15.0], ddof=1),
np.std([10.0, 15.0, 13.0], ddof=1),
np.std([15.0, 13.0, 12.0], ddof=1),
]
# np has odd rounding behavior, cf.
# http://docs.scipy.org/doc/np/reference/generated/np.std.html
for v1, v2 in zip(vals, expected):
if v1 is None:
assert v2 is None
continue
assert round(v1, 5) == round(v2, 5)
def test_moving_stddev(self):
trade_history = factory.create_trade_history(133,
[10.0, 15.0, 13.0, 12.0],
[100, 100, 100, 100],
timedelta(hours=1),
self.trading_environment)
stddev = MovingStandardDev(
market_aware=False,
delta=timedelta(minutes=150),
)
self.source = SpecificEquityTrades(event_list=trade_history)
transformed = list(stddev.transform(self.source))
vals = [message.tnfm_value for message in transformed]
expected = [
None,
np.std([10.0, 15.0], ddof=1),
np.std([10.0, 15.0, 13.0], ddof=1),
np.std([15.0, 13.0, 12.0], ddof=1),
]
# np has odd rounding behavior, cf.
# http://docs.scipy.org/doc/np/reference/generated/np.std.html
for v1, v2 in zip(vals, expected):
if v1 is None:
assert v2 is None
continue
assert round(v1, 5) == round(v2, 5)
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
cash_adj_dt = self.sim_params.period_start \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
events.insert(1, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
def test_returns(self):
# Daily returns.
returns = Returns(1)
transformed = list(returns.transform(self.source))
tnfm_vals = [message[returns.get_hash()] for message in transformed]
# No returns for the first event because we don't have a
# previous close.
expected = [0.0, 0.0, 0.1, 0.0]
self.assertEquals(tnfm_vals, expected)
# Two-day returns. An extra kink here is that the
# factory will automatically skip a weekend for the
# last event. Results shouldn't notice this blip.
trade_history = factory.create_trade_history(
133, [10.0, 15.0, 13.0, 12.0, 13.0], [100, 100, 100, 300, 100], timedelta(days=1), self.trading_environment
)
self.source = SpecificEquityTrades(event_list=trade_history)
returns = StatefulTransform(Returns, 2)
transformed = list(returns.transform(self.source))
tnfm_vals = [message[returns.get_hash()] for message in transformed]
expected = [0.0, 0.0, (13.0 - 10.0) / 10.0, (12.0 - 15.0) / 15.0, (13.0 - 13.0) / 13.0]
self.assertEquals(tnfm_vals, expected)
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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
# declared date, when the algorithm finds out about
# the dividend
events[1].dt,
# ex_date, when the algorithm is credited with the
# dividend
events[1].dt,
# pay date, when the algorithm receives the dividend.
events[2].dt,
)
txn = create_txn(1, 10.0, 100, events[0].dt)
events.insert(0, txn)
events.insert(1, dividend)
results = calculate_results(self, events)
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, [-1000, 0, 1000, 0, 0])
cumulative_cash_flows = [event["cumulative_perf"]["capital_used"] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000, -1000, 0, 0, 0])
cash_pos = [event["cumulative_perf"]["ending_cash"] for event in results]
self.assertEqual(cash_pos, [9000, 9000, 10000, 10000, 10000])
def setUp(self):
self.sim_params = factory.create_simulation_parameters()
setup_logger(self)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = factory.create_test_df_source(self.sim_params)
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
self.sid = 133
self.trade_history = factory.create_trade_history(
self.sid, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300],
timedelta(days=1), self.sim_params)
self.source = SpecificEquityTrades(event_list=self.trade_history)
def setUp(self):
self.trading_environment = factory.create_trading_environment()
setup_logger(self)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.trading_environment
)
self.source = SpecificEquityTrades(event_list=trade_history)
def test_split_long_position(self):
events = factory.create_trade_history(1, [20, 20], [100, 100], oneday,
self.sim_params)
# set up a long position in sid 1
# 100 shares at $20 apiece = $2000 position
txns = [create_txn(events[0], 20, 100)]
# set up a split with ratio 3 occurring at the start of the second
# day.
splits = [
factory.create_split(
1,
3,
events[1].dt,
),
]
results = calculate_results(self, events, txns=txns, splits=splits)
# should have 33 shares (at $60 apiece) and $20 in cash
self.assertEqual(2, len(results))
latest_positions = results[1]['daily_perf']['positions']
self.assertEqual(1, len(latest_positions))
# check the last position to make sure it's been updated
position = latest_positions[0]
self.assertEqual(1, position['sid'])
self.assertEqual(33, position['amount'])
self.assertEqual(60, position['cost_basis'])
self.assertEqual(60, position['last_sale_price'])
# since we started with $10000, and we spent $2000 on the
# position, but then got $20 back, we should have $8020
# (or close to it) in cash.
# we won't get exactly 8020 because sometimes a split is
# denoted as a ratio like 0.3333, and we lose some digits
# of precision. thus, make sure we're pretty close.
daily_perf = results[1]['daily_perf']
self.assertTrue(
zp_math.tolerant_equals(8020, daily_perf['ending_cash'], 1))
for i, result in enumerate(results):
for perf_kind in ('daily_perf', 'cumulative_perf'):
perf_result = result[perf_kind]
# prices aren't changing, so pnl and returns should be 0.0
self.assertEqual(
0.0, perf_result['pnl'],
"day %s %s pnl %s instead of 0.0" %
(i, perf_kind, perf_result['pnl']))
self.assertEqual(
0.0, perf_result['returns'],
"day %s %s returns %s instead of 0.0" %
(i, perf_kind, perf_result['returns']))
def test_split_long_position(self):
with trading.TradingEnvironment() as env:
events = factory.create_trade_history(
1,
[20, 20],
[100, 100],
oneday,
self.sim_params
)
# set up a long position in sid 1
# 100 shares at $20 apiece = $2000 position
events.insert(0, create_txn(events[0], 20, 100))
# set up a split with ratio 3
events.append(factory.create_split(1, 3,
env.next_trading_day(events[1].dt)))
results = calculate_results(self, events)
# should have 33 shares (at $60 apiece) and $20 in cash
self.assertEqual(2, len(results))
latest_positions = results[1]['daily_perf']['positions']
self.assertEqual(1, len(latest_positions))
# check the last position to make sure it's been updated
position = latest_positions[0]
self.assertEqual(1, position['sid'])
self.assertEqual(33, position['amount'])
self.assertEqual(60, position['cost_basis'])
self.assertEqual(60, position['last_sale_price'])
# since we started with $10000, and we spent $2000 on the
# position, but then got $20 back, we should have $8020
# (or close to it) in cash.
# we won't get exactly 8020 because sometimes a split is
# denoted as a ratio like 0.3333, and we lose some digits
# of precision. thus, make sure we're pretty close.
daily_perf = results[1]['daily_perf']
self.assertTrue(
zp_math.tolerant_equals(8020,
daily_perf['ending_cash'], 1))
for i, result in enumerate(results):
for perf_kind in ('daily_perf', 'cumulative_perf'):
perf_result = result[perf_kind]
# prices aren't changing, so pnl and returns should be 0.0
self.assertEqual(0.0, perf_result['pnl'],
"day %s %s pnl %s instead of 0.0" %
(i, perf_kind, perf_result['pnl']))
self.assertEqual(0.0, perf_result['returns'],
"day %s %s returns %s instead of 0.0" %
(i, perf_kind, perf_result['returns']))
def test_long_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
# post some trades in the market
trades = factory.create_trade_history(1, [10, 10, 10, 11],
[100, 100, 100, 100], onesec,
self.sim_params)
txn = create_txn(trades[1], 10.0, 100)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
# This verifies that the last sale price is being correctly
# set in the positions. If this is not the case then returns can
# incorrectly show as sharply dipping if a transaction arrives
# before a trade. This is caused by returns being based on holding
# stocks with a last sale price of 0.
self.assertEqual(pp.positions[1].last_sale_price, 10.0)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
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")
self.assertEqual(len(pp.positions), 1, "should be just one position")
self.assertEqual(pp.positions[1].sid, txn.sid,
"position should be in security with id 1")
self.assertEqual(
pp.positions[1].amount, txn.amount,
"should have a position of {sharecount} shares".format(
sharecount=txn.amount))
self.assertEqual(pp.positions[1].cost_basis, txn.price,
"should have a cost basis of 10")
self.assertEqual(
pp.positions[1].last_sale_price, trades[-1]['price'],
"last sale should be same as last trade. \
expected {exp} actual {act}".format(
exp=trades[-1]['price'], act=pp.positions[1].last_sale_price))
self.assertEqual(
pp.ending_value, 1100,
"ending value should be price of last trade times number of \
shares in position")
self.assertEqual(pp.pnl, 100, "gain of 1 on 100 shares should be 100")
def setUp(self):
self.trading_environment = factory.create_trading_environment()
setup_logger(self)
trade_history = factory.create_trade_history(133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.trading_environment)
self.source = SpecificEquityTrades(event_list=trade_history)
def setUp(self):
self.sim_params = factory.create_simulation_parameters()
trade_history = factory.create_trade_history(133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = \
factory.create_test_df_source(self.sim_params)
def setUp(self):
self.sim_params = factory.create_simulation_parameters()
setup_logger(self)
trade_history = factory.create_trade_history(133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params)
self.source = trade_history
def test_covering_short(self):
"""verify performance where short is bought and covered, and shares \
trade after cover"""
trades = factory.create_trade_history(
1, [10, 10, 10, 11, 9, 8, 7, 8, 9, 10],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100], onesec,
self.sim_params)
short_txn = create_txn(
trades[1],
10.0,
-100,
)
cover_txn = create_txn(trades[6], 7.0, 100)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(short_txn)
pp.execute_transaction(cover_txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
short_txn_cost = short_txn.price * short_txn.amount
cover_txn_cost = cover_txn.price * cover_txn.amount
self.assertEqual(
pp.period_cash_flow, -1 * short_txn_cost - cover_txn_cost,
"capital used should be equal to the net transaction costs")
self.assertEqual(len(pp.positions), 1, "should be just one position")
self.assertEqual(
pp.positions[1].sid, short_txn.sid,
"position should be in security from the transaction")
self.assertEqual(pp.positions[1].amount, 0,
"should have a position of -100 shares")
self.assertEqual(pp.positions[1].cost_basis, 0,
"a covered position should have a cost basis of 0")
self.assertEqual(pp.positions[1].last_sale_price, trades[-1].price,
"last sale should be price of last trade")
self.assertEqual(
pp.ending_value, 0,
"ending value should be price of last trade times number of \
shares in position")
self.assertEqual(pp.pnl, 300, "gain of 1 on 100 shares should be 300")
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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[-1].dt + 10 * oneday
)
buy_txn = create_txn(1, 10.0, 100, events[1].dt)
events.insert(2, buy_txn)
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.sim_params)
all_events = (msg[1] for msg in heapq.merge(
((event.dt, event) for event in events),
((event.dt, event) for event in self.benchmark_events)))
transformed_events = list(perf_tracker.transform(
itertools.groupby(all_events, attrgetter('dt'))))
#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_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
)
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_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.sim_params)
dividend = factory.create_dividend(
1,
10.00,
# declared date, when the algorithm finds out about
# the dividend
events[1].dt,
# ex_date, when the algorithm is credited with the
# dividend
events[1].dt,
# pay date, when the algorithm receives the dividend.
events[2].dt)
txn = factory.create_txn(1, 10.0, 100, events[0].dt)
events[0].TRANSACTION = txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.sim_params)
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, [-1000, 0, 1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000, -1000, 0, 0, 0])
cash_pos = \
[event['cumulative_perf']['ending_cash'] for event in results]
self.assertEqual(cash_pos, [9000, 9000, 10000, 10000, 10000])
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.sim_params
)
dividend = factory.create_dividend(
1,
10.00,
events[3].dt,
events[4].dt,
events[5].dt
)
buy_txn = create_txn(1, 10.0, 100, events[1].dt)
events.insert(2, buy_txn)
sell_txn = create_txn(1, 10.0, -100, events[3].dt)
events.insert(4, sell_txn)
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.sim_params)
all_events = heapq.merge(
((event.dt, event) for event in events),
((event.dt, event) for event in self.benchmark_events))
transformed_events = list(perf_tracker.transform(
(event[0], [event[1]]) for event in all_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_long_position_receives_stock_dividend(self):
with trading.TradingEnvironment():
# post some trades in the market
events = []
for sid in (1, 2):
events.extend(
factory.create_trade_history(
sid,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params)
)
dividend = factory.create_stock_dividend(
1,
payment_sid=2,
ratio=2,
# declared date, when the algorithm finds out about
# the dividend
declared_date=events[0].dt,
# ex_date, the date before which the algorithm must hold stock
# to receive the dividend
ex_date=events[1].dt,
# pay date, when the algorithm receives the dividend.
pay_date=events[2].dt
)
txns = [create_txn(events[0], 10.0, 100)]
results = calculate_results(
self,
events,
dividend_events=[dividend],
txns=txns,
)
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.2, 0.2, 0.2])
daily_returns = [event['daily_perf']['returns']
for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.2, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used']
for event in results]
self.assertEqual(cash_flows, [-1000, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000] * 5)
cash_pos = \
[event['cumulative_perf']['ending_cash'] for event in results]
self.assertEqual(cash_pos, [9000] * 5)
def setUp(self):
self.sim_params = factory.create_simulation_parameters(num_days=4)
self.sidint = 133
self.trade_history = factory.create_trade_history(
self.sidint,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.sim_params
)
self.source = SpecificEquityTrades(event_list=self.trade_history)
def test_long_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
# post some trades in the market
trades = factory.create_trade_history(1, [10, 10, 10, 11], [100, 100, 100, 100], onesec, self.sim_params)
txn = create_txn(1, 10.0, 100, self.dt + onesec)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
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",
)
self.assertEqual(len(pp.positions), 1, "should be just one position")
self.assertEqual(pp.positions[1].sid, txn.sid, "position should be in security with id 1")
self.assertEqual(
pp.positions[1].amount,
txn.amount,
"should have a position of {sharecount} shares".format(sharecount=txn.amount),
)
self.assertEqual(pp.positions[1].cost_basis, txn.price, "should have a cost basis of 10")
self.assertEqual(
pp.positions[1].last_sale_price,
trades[-1]["price"],
"last sale should be same as last trade. \
expected {exp} actual {act}".format(
exp=trades[-1]["price"], act=pp.positions[1].last_sale_price
),
)
self.assertEqual(
pp.ending_value,
1100,
"ending value should be price of last trade times number of \
shares in position",
)
self.assertEqual(pp.pnl, 100, "gain of 1 on 100 shares should be 100")
def test_covering_short(self):
"""verify performance where short is bought and covered, and shares \
trade after cover"""
trades = factory.create_trade_history(
1,
[10, 10, 10, 11, 9, 8, 7, 8, 9, 10],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
onesec,
self.sim_params,
)
short_txn = create_txn(1, 10.0, -100, self.dt + onesec)
cover_txn = create_txn(1, 7.0, 100, self.dt + onesec * 6)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(short_txn)
pp.execute_transaction(cover_txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
short_txn_cost = short_txn.price * short_txn.amount
cover_txn_cost = cover_txn.price * cover_txn.amount
self.assertEqual(
pp.period_cash_flow,
-1 * short_txn_cost - cover_txn_cost,
"capital used should be equal to the net transaction costs",
)
self.assertEqual(len(pp.positions), 1, "should be just one position")
self.assertEqual(pp.positions[1].sid, short_txn.sid, "position should be in security from the transaction")
self.assertEqual(pp.positions[1].amount, 0, "should have a position of -100 shares")
self.assertEqual(pp.positions[1].cost_basis, 0, "a covered position should have a cost basis of 0")
self.assertEqual(pp.positions[1].last_sale_price, trades[-1].price, "last sale should be price of last trade")
self.assertEqual(
pp.ending_value,
0,
"ending value should be price of last trade times number of \
shares in position",
)
self.assertEqual(pp.pnl, 300, "gain of 1 on 100 shares should be 300")
def test_algo_without_rl_violation(self):
sim_params = factory.create_simulation_parameters(start=list(
LEVERAGED_ETFS.keys())[0],
num_days=4)
trade_history = factory.create_trade_history('AAPL',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='AAPL', sim_params=sim_params)
algo.run(self.source)
def setUp(self):
days = 251
self.sim_params = factory.create_simulation_parameters(num_days=days)
setup_logger(self)
trade_history = factory.create_trade_history(
133, [10.0] * days, [100] * days, timedelta(days=1), self.sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
self.df_source, self.df = factory.create_test_df_source(self.sim_params)
self.zipline_test_config = {"sid": 0}
def test_long_position_receives_stock_dividend(self):
with trading.TradingEnvironment():
# post some trades in the market
events = []
for sid in (1, 2):
events.extend(
factory.create_trade_history(sid, [10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday, self.sim_params))
dividend = factory.create_stock_dividend(
1,
payment_sid=2,
ratio=2,
# declared date, when the algorithm finds out about
# the dividend
declared_date=events[0].dt,
# ex_date, the date before which the algorithm must hold stock
# to receive the dividend
ex_date=events[1].dt,
# pay date, when the algorithm receives the dividend.
pay_date=events[2].dt)
txns = [create_txn(events[0], 10.0, 100)]
results = calculate_results(
self,
events,
dividend_events=[dividend],
txns=txns,
)
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.2, 0.2, 0.2])
daily_returns = [
event['daily_perf']['returns'] for event in results
]
self.assertEqual(daily_returns, [0.0, 0.0, 0.2, 0.0, 0.0])
cash_flows = [
event['daily_perf']['capital_used'] for event in results
]
self.assertEqual(cash_flows, [-1000, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000] * 5)
cash_pos = \
[event['cumulative_perf']['ending_cash'] for event in results]
self.assertEqual(cash_pos, [9000] * 5)
def test_long_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
#post some trades in the market
trades = factory.create_trade_history(1, [10, 10, 10, 11],
[100, 100, 100, 100],
self.onesec,
self.trading_environment)
txn = factory.create_txn(1, 10.0, 100, self.dt + self.onesec)
pp = perf.PerformancePeriod({}, 0.0, 1000.0)
pp.execute_transaction(txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
pp.period_capital_used, -1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction \
cost of sole txn in test")
self.assertEqual(len(pp.positions), 1, "should be just one position")
self.assertEqual(pp.positions[1].sid, txn.sid,
"position should be in security with id 1")
self.assertEqual(
pp.positions[1].amount, txn.amount,
"should have a position of {sharecount} shares".format(
sharecount=txn.amount))
self.assertEqual(pp.positions[1].cost_basis, txn.price,
"should have a cost basis of 10")
self.assertEqual(
pp.positions[1].last_sale_price, trades[-1]['price'],
"last sale should be same as last trade. \
expected {exp} actual {act}".format(
exp=trades[-1]['price'], act=pp.positions[1].last_sale_price))
self.assertEqual(
pp.ending_value, 1100,
"ending value should be price of last trade times number of \
shares in position")
self.assertEqual(pp.pnl, 100, "gain of 1 on 100 shares should be 100")
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
# Test commission models and validate result
# Expected commission amounts:
# PerShare commission: 1.00, 1.00, 1.50 = $3.50
# PerTrade commission: 5.00, 5.00, 5.00 = $15.00
# PerDollar commission: 1.50, 3.00, 4.50 = $9.00
# Total commission = $3.50 + $15.00 + $9.00 = $27.50
# Create 3 transactions: 50, 100, 150 shares traded @ $20
transactions = [create_txn(events[0], 20, i)
for i in [50, 100, 150]]
# Create commission models and validate that produce expected
# commissions.
models = [PerShare(cost=0.01, min_trade_cost=1.00),
PerTrade(cost=5.00),
PerDollar(cost=0.0015)]
expected_results = [3.50, 15.0, 9.0]
for model, expected in zip(models, expected_results):
total_commission = 0
for trade in transactions:
total_commission += model.calculate(trade)[1]
self.assertEqual(total_commission, expected)
# Verify that commission events are handled correctly by
# PerformanceTracker.
cash_adj_dt = events[0].dt
cash_adjustment = factory.create_commission(1, 300.0, cash_adj_dt)
events.append(cash_adjustment)
# Insert a purchase order.
txns = [create_txn(events[0], 20, 1)]
results = calculate_results(self, events, txns=txns)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
def test_algo_without_rl_violation(self):
sim_params = factory.create_simulation_parameters(
start=list(LEVERAGED_ETFS.keys())[0], num_days=4)
trade_history = factory.create_trade_history(
'AAPL',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params
)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(sid='AAPL', sim_params=sim_params)
algo.run(self.source)
def test_iterate_over_rl(self):
sim_params = factory.create_simulation_parameters(start=list(
LEVERAGED_ETFS.keys())[0],
num_days=4)
trade_history = factory.create_trade_history('BZQ',
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = IterateRLAlgo(symbol='BZQ', sim_params=sim_params)
algo.run(self.source)
self.assertTrue(algo.found)
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
# Test commission models and validate result
# Expected commission amounts:
# PerShare commission: 1.00, 1.00, 1.50 = $3.50
# PerTrade commission: 5.00, 5.00, 5.00 = $15.00
# PerDollar commission: 1.50, 3.00, 4.50 = $9.00
# Total commission = $3.50 + $15.00 + $9.00 = $27.50
# Create 3 transactions: 50, 100, 150 shares traded @ $20
transactions = [create_txn(events[0], 20, i)
for i in [50, 100, 150]]
# Create commission models
models = [PerShare(cost=0.01, min_trade_cost=1.00),
PerTrade(cost=5.00),
PerDollar(cost=0.0015)]
# Aggregate commission amounts
total_commission = 0
for model in models:
for trade in transactions:
total_commission += model.calculate(trade)[1]
self.assertEqual(total_commission, 27.5)
cash_adj_dt = self.sim_params.first_open \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
events.insert(1, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(1, [10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday, self.sim_params)
# Test commission models and validate result
# Expected commission amounts:
# PerShare commission: 1.00, 1.00, 1.50 = $3.50
# PerTrade commission: 5.00, 5.00, 5.00 = $15.00
# PerDollar commission: 1.50, 3.00, 4.50 = $9.00
# Total commission = $3.50 + $15.00 + $9.00 = $27.50
# Create 3 transactions: 50, 100, 150 shares traded @ $20
transactions = [
create_txn(events[0], 20, i) for i in [50, 100, 150]
]
# Create commission models and validate that produce expected
# commissions.
models = [
PerShare(cost=0.01, min_trade_cost=1.00),
PerTrade(cost=5.00),
PerDollar(cost=0.0015)
]
expected_results = [3.50, 15.0, 9.0]
for model, expected in zip(models, expected_results):
total_commission = 0
for trade in transactions:
total_commission += model.calculate(trade)[1]
self.assertEqual(total_commission, expected)
# Verify that commission events are handled correctly by
# PerformanceTracker.
cash_adj_dt = events[0].dt
cash_adjustment = factory.create_commission(1, 300.0, cash_adj_dt)
events.append(cash_adjustment)
# Insert a purchase order.
txns = [create_txn(events[0], 20, 1)]
results = calculate_results(self, events, txns=txns)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(
results[-1]['daily_perf']['positions'][0]['cost_basis'], 320.0)
def test_commission_event(self):
with trading.TradingEnvironment():
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params
)
# Test commission models and validate result
# Expected commission amounts:
# PerShare commission: 1.00, 1.00, 1.50 = $3.50
# PerTrade commission: 5.00, 5.00, 5.00 = $15.00
# PerDollar commission: 1.50, 3.00, 4.50 = $9.00
# Total commission = $3.50 + $15.00 + $9.00 = $27.50
# Create 3 transactions: 50, 100, 150 shares traded @ $20
transactions = [create_txn(events[0], 20, i)
for i in [50, 100, 150]]
# Create commission models
models = [PerShare(cost=0.01, min_trade_cost=1.00),
PerTrade(cost=5.00),
PerDollar(cost=0.0015)]
# Aggregate commission amounts
total_commission = 0
for model in models:
for trade in transactions:
total_commission += model.calculate(trade)[1]
self.assertEqual(total_commission, 27.5)
cash_adj_dt = self.sim_params.first_open \
+ datetime.timedelta(hours=3)
cash_adjustment = factory.create_commission(1, 300.0,
cash_adj_dt)
# Insert a purchase order.
events.insert(0, create_txn(events[0], 20, 1))
events.insert(1, cash_adjustment)
results = calculate_results(self, events)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680)
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
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.sim_params
)
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[0].dt)
events[0].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.sim_params)
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, [-1000, 0, 1000, 1000, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000, -1000, 0, 1000, 1000])
def test_algo_with_rl_violation_after_add(self):
with security_list_copy():
add_security_data(['AAPL'], [])
sim_params = factory.create_simulation_parameters(
start=self.trading_day_before_first_kd, num_days=4)
trade_history = factory.create_trade_history(
'AAPL', [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300],
timedelta(days=1), sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
algo = RestrictedAlgoWithoutCheck(symbol='AAPL',
sim_params=sim_params)
with self.assertRaises(TradingControlViolation) as ctx:
algo.run(self.source)
self.check_algo_exception(algo, ctx, 2)
