def test_history_daily_data_1m_window(self):
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count=1, frequency='1m', field='price')
def handle_data(context, data):
prices = history(bar_count=3, frequency='1d', field='price')
""".strip()
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-30', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
with self.assertRaises(IncompatibleHistoryFrequency):
algo = TradingAlgorithm(
script=algo_text,
data_frequency='daily',
sim_params=sim_params
)
source = RandomWalkSource(start=start, end=end)
algo.run(source)
def test_history_container_constructed_at_runtime(self):
algo_text = dedent(
"""\
from zipline.api import history
def handle_data(context, data):
context.prices = history(2, '1d', 'price')
"""
)
start = pd.Timestamp('2007-04-05', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='daily'
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params
)
source = RandomWalkSource(start=start, end=end)
self.assertIsNone(test_algo.history_container)
test_algo.run(source)
self.assertIsNotNone(
test_algo.history_container,
msg='HistoryContainer was not constructed at runtime',
)
container = test_algo.history_container
self.assertEqual(
container.buffer_panel.window_length,
Frequency.MAX_MINUTES['d'],
msg='HistoryContainer.buffer_panel was not large enough to service'
' the given HistorySpec',
)
self.assertEqual(
len(container.digest_panels),
1,
msg='The HistoryContainer created too many digest panels',
)
freq, digest = list(container.digest_panels.items())[0]
self.assertEqual(
freq.unit_str,
'd',
)
self.assertEqual(
digest.window_length,
1,
msg='The digest panel is not large enough to service the given'
' HistorySpec',
)
def test_history_container_constructed_at_runtime(self):
algo_text = dedent("""\
from zipline.api import history
def handle_data(context, data):
context.prices = history(2, '1d', 'price')
""")
start = pd.Timestamp('2007-04-05', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='daily')
test_algo = TradingAlgorithm(script=algo_text,
data_frequency='minute',
sim_params=sim_params)
source = RandomWalkSource(start=start, end=end)
self.assertIsNone(test_algo.history_container)
test_algo.run(source)
self.assertIsNotNone(
test_algo.history_container,
msg='HistoryContainer was not constructed at runtime',
)
container = test_algo.history_container
self.assertEqual(
container.buffer_panel.window_length,
Frequency.MAX_MINUTES['d'],
msg='HistoryContainer.buffer_panel was not large enough to service'
' the given HistorySpec',
)
self.assertEqual(
len(container.digest_panels),
1,
msg='The HistoryContainer created too many digest panels',
)
freq, digest = list(container.digest_panels.items())[0]
self.assertEqual(
freq.unit_str,
'd',
)
self.assertEqual(
digest.window_length,
1,
msg='The digest panel is not large enough to service the given'
' HistorySpec',
)
def test_history_passed_to_talib(self):
"""
Had an issue where MagicMock was causing errors during validation
with talib.
We don't officially support a talib integration, yet.
But using talib directly should work.
"""
algo_text = """
import talib
import numpy as np
from zipline.api import history, add_history, record
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
ma_result = talib.MA(np.asarray(prices[0]), timeperiod=2)
record(ma=ma_result[-1])
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
# Eddie: this was set to 04-10 but I don't see how that makes
# sense as it does not generate enough data to get at -2 index
# below.
start = pd.Timestamp("2007-04-05", tz="UTC")
end = pd.Timestamp("2007-04-10", tz="UTC")
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency="minute",
emission_rate="daily",
)
test_algo = TradingAlgorithm(
script=algo_text, data_frequency="minute", sim_params=sim_params, env=TestHistoryAlgo.env
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
# At this point, just ensure that there is no crash.
self.assertIsNotNone(output)
recorded_ma = output.ix[-2, "ma"]
self.assertFalse(pd.isnull(recorded_ma))
# Depends on seed
np.testing.assert_almost_equal(recorded_ma, 159.76304468946876)
def get_results(self, algo_code):
algo = TradingAlgorithm(
script=algo_code,
env=self.env,
sim_params=self.sim_params
)
return algo.run(self.data_portal)
def test_history_grow_length_intra_bar(self, incr):
"""
Tests growing the length of a digest panel with different date_buf
deltas in a single bar.
"""
algo_text = dedent(
"""\
from zipline.api import history
def initialize(context):
context.bar_count = 1
def handle_data(context, data):
prices = history(context.bar_count, '1d', 'price')
context.test_case.assertEqual(len(prices), context.bar_count)
context.bar_count += {incr}
prices = history(context.bar_count, '1d', 'price')
context.test_case.assertEqual(len(prices), context.bar_count)
"""
).format(incr=incr)
start = pd.Timestamp('2007-04-05', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='daily',
env=self.env,
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=self.env,
)
test_algo.test_case = self
source = RandomWalkSource(start=start, end=end)
self.assertIsNone(test_algo.history_container)
test_algo.run(source)
def get_results(self, algo_code):
algo = TradingAlgorithm(
script=algo_code,
env=self.env,
sim_params=self.sim_params
)
return algo.run(self.data_portal)
def test_history_grow_length_intra_bar(self, incr):
"""
Tests growing the length of a digest panel with different date_buf
deltas in a single bar.
"""
algo_text = dedent(
"""\
from zipline.api import history
def initialize(context):
context.bar_count = 1
def handle_data(context, data):
prices = history(context.bar_count, '1d', 'price')
context.test_case.assertEqual(len(prices), context.bar_count)
context.bar_count += {incr}
prices = history(context.bar_count, '1d', 'price')
context.test_case.assertEqual(len(prices), context.bar_count)
"""
).format(incr=incr)
start = pd.Timestamp('2007-04-05', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='daily',
env=self.env,
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=self.env,
)
test_algo.test_case = self
source = RandomWalkSource(start=start, end=end)
self.assertIsNone(test_algo.history_container)
test_algo.run(source)
def test_history_passed_to_talib(self):
"""
Had an issue where MagicMock was causing errors during validation
with talib.
We don't officially support a talib integration, yet.
But using talib directly should work.
"""
algo_text = """
import talib
import numpy as np
from zipline.api import history, add_history, record
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
ma_result = talib.MA(np.asarray(prices[0]), timeperiod=2)
record(ma=ma_result[-1])
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
# Eddie: this was set to 04-10 but I don't see how that makes
# sense as it does not generate enough data to get at -2 index
# below.
start = pd.Timestamp('2007-04-05', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='daily')
test_algo = TradingAlgorithm(script=algo_text,
data_frequency='minute',
sim_params=sim_params)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
# At this point, just ensure that there is no crash.
self.assertIsNotNone(output)
recorded_ma = output.ix[-2, 'ma']
self.assertFalse(pd.isnull(recorded_ma))
# Depends on seed
np.testing.assert_almost_equal(recorded_ma, 159.76304468946876)
def run_algo(self, code, sim_params=None, data_frequency="daily"):
if sim_params is None:
sim_params = self.sim_params
test_algo = TradingAlgorithm(script=code, sim_params=sim_params, env=self.env, data_frequency=data_frequency)
results = test_algo.run(FetcherDataPortal(self.env))
return results
def test_basic_history_positional_args(self):
"""
Ensure that positional args work.
"""
algo_text = """
import copy
from zipline.api import history, add_history
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
context.last_prices = copy.deepcopy(prices)
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-21', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
oldest_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
self.assertEquals(139.36946942498648, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def test_basic_history_positional_args(self):
"""
Ensure that positional args work.
"""
algo_text = """
import copy
from zipline.api import history, add_history
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
context.last_prices = copy.deepcopy(prices)
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-21', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
oldest_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
self.assertEquals(139.36946942498648, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def run_algo(self, code, sim_params=None, data_frequency="daily"):
if sim_params is None:
sim_params = self.sim_params
test_algo = TradingAlgorithm(script=code,
sim_params=sim_params,
env=self.env,
data_frequency=data_frequency)
results = test_algo.run(FetcherDataPortal(self.env))
return results
def test_basic_history_one_day(self):
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count=1, frequency='1d', field='price')
def handle_data(context, data):
prices = history(bar_count=1, frequency='1d', field='price')
context.last_prices = prices
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-21', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
# oldest and newest should be the same if there is only 1 bar
oldest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
# Random, depends on seed
self.assertEquals(180.15661995395106, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def test_basic_history_one_day(self):
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count=1, frequency='1d', field='price')
def handle_data(context, data):
prices = history(bar_count=1, frequency='1d', field='price')
context.last_prices = prices
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-21', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
# oldest and newest should be the same if there is only 1 bar
oldest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
# Random, depends on seed
self.assertEquals(180.15661995395106, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def test_current_contract_in_algo(self):
code = dedent("""
from zipline.api import (
record,
continuous_future,
schedule_function,
get_datetime,
)
def initialize(algo):
algo.primary_cl = continuous_future('FO', 0, 'calendar')
algo.secondary_cl = continuous_future('FO', 1, 'calendar')
schedule_function(record_current_contract)
def record_current_contract(algo, data):
record(datetime=get_datetime())
record(primary=data.current(algo.primary_cl, 'contract'))
record(secondary=data.current(algo.secondary_cl, 'contract'))
""")
algo = TradingAlgorithm(script=code,
sim_params=self.sim_params,
trading_calendar=self.trading_calendar,
env=self.env)
results = algo.run(self.data_portal)
result = results.iloc[0]
self.assertEqual(result.primary.symbol,
'FOF16',
'Primary should be FOF16 on first session.')
self.assertEqual(result.secondary.symbol,
'FOG16',
'Secondary should be FOG16 on first session.')
result = results.iloc[1]
# Second day, primary should switch to FOG
self.assertEqual(result.primary.symbol,
'FOG16',
'Primary should be FOG16 on second session, auto '
'close is at beginning of the session.')
self.assertEqual(result.secondary.symbol,
'FOH16',
'Secondary should be FOH16 on second session, auto '
'close is at beginning of the session.')
result = results.iloc[2]
# Second day, primary should switch to FOG
self.assertEqual(result.primary.symbol,
'FOG16',
'Primary should remain as FOG16 on third session.')
self.assertEqual(result.secondary.symbol,
'FOH16',
'Secondary should remain as FOH16 on third session.')
def test_current_contract_in_algo(self):
code = dedent("""
from zipline.api import (
record,
continuous_future,
schedule_function,
get_datetime,
)
def initialize(algo):
algo.primary_cl = continuous_future('FO', 0, 'calendar')
algo.secondary_cl = continuous_future('FO', 1, 'calendar')
schedule_function(record_current_contract)
def record_current_contract(algo, data):
record(datetime=get_datetime())
record(primary=data.current(algo.primary_cl, 'contract'))
record(secondary=data.current(algo.secondary_cl, 'contract'))
""")
algo = TradingAlgorithm(script=code,
sim_params=self.sim_params,
trading_calendar=self.trading_calendar,
env=self.env)
results = algo.run(self.data_portal)
result = results.iloc[0]
self.assertEqual(result.primary.symbol,
'FOF16',
'Primary should be FOF16 on first session.')
self.assertEqual(result.secondary.symbol,
'FOG16',
'Secondary should be FOG16 on first session.')
result = results.iloc[1]
# Second day, primary should switch to FOG
self.assertEqual(result.primary.symbol,
'FOG16',
'Primary should be FOG16 on second session, auto '
'close is at beginning of the session.')
self.assertEqual(result.secondary.symbol,
'FOH16',
'Secondary should be FOH16 on second session, auto '
'close is at beginning of the session.')
result = results.iloc[2]
# Second day, primary should switch to FOG
self.assertEqual(result.primary.symbol,
'FOG16',
'Primary should remain as FOG16 on third session.')
self.assertEqual(result.secondary.symbol,
'FOH16',
'Secondary should remain as FOH16 on third session.')
def test_history_daily(self):
bar_count = 3
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace = []
def handle_data(context, data):
prices = history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace.append(prices)
""".format(bar_count=bar_count).strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-30', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start,
end=end,
data_frequency='daily',
env=self.env,
)
_, df = factory.create_test_df_source(sim_params, self.env)
df = df.astype(np.float64)
source = DataFrameSource(df)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='daily',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
output = test_algo.run(source)
self.assertIsNotNone(output)
df.columns = self.env.asset_finder.retrieve_all(df.columns)
for i, received in enumerate(test_algo.history_trace[bar_count - 1:]):
expected = df.iloc[i:i + bar_count]
assert_frame_equal(expected, received)
def test_history_container_constructed_at_runtime(self, data_freq):
algo_text = dedent(
"""\
from zipline.api import history
def handle_data(context, data):
context.prices = history(2, '1d', 'price')
"""
)
start = pd.Timestamp("2007-04-05", tz="UTC")
end = pd.Timestamp("2007-04-10", tz="UTC")
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency=data_freq,
emission_rate=data_freq,
)
test_algo = TradingAlgorithm(
script=algo_text, data_frequency=data_freq, sim_params=sim_params, env=TestHistoryAlgo.env
)
source = RandomWalkSource(start=start, end=end, freq=data_freq)
self.assertIsNone(test_algo.history_container)
test_algo.run(source)
self.assertIsNotNone(test_algo.history_container, msg="HistoryContainer was not constructed at runtime")
container = test_algo.history_container
self.assertEqual(len(container.digest_panels), 1, msg="The HistoryContainer created too many digest panels")
freq, digest = list(container.digest_panels.items())[0]
self.assertEqual(freq.unit_str, "d")
self.assertEqual(
digest.window_length, 1, msg="The digest panel is not large enough to service the given" " HistorySpec"
)
def test_history_passed_to_func(self):
"""
Had an issue where MagicMock was causing errors during validation
with rolling mean.
"""
algo_text = """
from zipline.api import history, add_history
import pandas as pd
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
pd.rolling_mean(prices, 2)
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
start = pd.Timestamp('2007-04-10', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='minute'
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
# At this point, just ensure that there is no crash.
self.assertIsNotNone(output)
def test_history_passed_to_func(self):
"""
Had an issue where MagicMock was causing errors during validation
with rolling mean.
"""
algo_text = """
from zipline.api import history, add_history
import pandas as pd
def initialize(context):
add_history(2, '1d', 'price')
def handle_data(context, data):
prices = history(2, '1d', 'price')
pd.rolling_mean(prices, 2)
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
start = pd.Timestamp('2007-04-10', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='minute'
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
# At this point, just ensure that there is no crash.
self.assertIsNotNone(output)
def test_history_daily(self):
bar_count = 3
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace = []
def handle_data(context, data):
prices = history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace.append(prices)
""".format(bar_count=bar_count).strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-30', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end, data_frequency='daily', env=self.env,
)
_, df = factory.create_test_df_source(sim_params, self.env)
df = df.astype(np.float64)
source = DataFrameSource(df)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='daily',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
output = test_algo.run(source)
self.assertIsNotNone(output)
df.columns = self.env.asset_finder.retrieve_all(df.columns)
for i, received in enumerate(test_algo.history_trace[bar_count - 1:]):
expected = df.iloc[i:i + bar_count]
assert_frame_equal(expected, received)
def run_algo_single(**algo_descr):
if 'constraint_func' in algo_descr:
if algo_descr['constraint_func'](algo_descr['param_set']):
return np.nan
try:
algo = TradingAlgorithm(initialize=algo_descr['initialize'],
handle_data=algo_descr['handle_data'],
**algo_descr['param_set'])
perf = algo.run(algo_descr['data'])
daily_rets = perf.portfolio_value.pct_change().dropna()
if daily_rets.std() > 0:
sharpe_ratio_calc = daily_rets.mean() / daily_rets.std() * np.sqrt(
252)
else:
sharpe_ratio_calc = -999
risk_report = algo.risk_report
risk_cum = pd.Series(
algo.perf_tracker.cumulative_risk_metrics.to_dict())
except ImportError as e:
print(e)
return np.nan
# Apply objective functions
objective = algo_descr.get('objective', 'none')
if objective == 'none':
obj = (perf, risk_cum, risk_report)
elif objective == 'sharpe':
obj = sharpe_ratio_calc
elif objective == 'total_return':
obj = perf['portfolio_value'][-1] / perf['portfolio_value'][0] - 1
elif callable(objective):
obj = objective(perf, risk_cum, risk_report)
else:
raise NotImplemented('Objective %s not implemented.' %
algo_descr['objective'])
print "Sharpe: " + str(sharpe_ratio_calc) + " %_Return: " + str(
perf.portfolio_value[-1] / perf.portfolio_value[0] -
1) + " MaxDD: " + str(perf.max_drawdown[-1]) + " MaxExp: " + str(
perf.max_leverage[-1])
return obj
def test_history_with_volume(self):
algo_text = """
from zipline.api import history, add_history, record
def initialize(context):
add_history(3, '1d', 'volume')
def handle_data(context, data):
volume = history(3, '1d', 'volume')
record(current_volume=volume[0].ix[-1])
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
start = pd.Timestamp('2007-04-10', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='minute'
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
np.testing.assert_equal(output.ix[0, 'current_volume'],
212218404.0)
def test_history_with_open(self):
algo_text = """
from zipline.api import history, add_history, record
def initialize(context):
add_history(3, '1d', 'open_price')
def handle_data(context, data):
opens = history(3, '1d', 'open_price')
record(current_open=opens[0].ix[-1])
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
start = pd.Timestamp('2007-04-10', tz='UTC')
end = pd.Timestamp('2007-04-10', tz='UTC')
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency='minute',
emission_rate='minute'
)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start,
end=end)
output = test_algo.run(source)
np.testing.assert_equal(output.ix[0, 'current_open'],
99.991436939669939)
def markowitz(stocks, cash):
warnings.filterwarnings("once")
solvers.options['show_progress'] = False
end = pd.Timestamp.utcnow()
start = end - 50 * pd.tseries.offsets.BDay()
data = load_bars_from_yahoo(stocks=stocks, start=start, end=end)
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
cash=cash)
# Run algorithm
results = algo.run(data)
# portfolio value plot
raw_plot = results.portfolio_value.plot()
raw_fig = raw_plot.get_figure()
returns_plot = mpld3.fig_to_html(raw_fig)
raw_fig.clf()
#stock price plot
raw_price_data = data.loc[:, :, 'price'].pct_change(1).fillna(0).applymap(
lambda x: x + 1).cumprod().applymap(lambda x: x * 100)
raw_price_plot = raw_price_data.plot(figsize=(8, 5))
raw_price_fig = raw_price_plot.get_figure()
price_plot = mpld3.fig_to_html(raw_price_fig)
raw_price_fig.clf()
#final returns
equalweight_returns = sum(map(list,
raw_price_data.tail(1).values)[0]) / 4 - 100
equalweight_returns = '{0:.2f}%'.format(float(equalweight_returns))
optimal_returns = (results.portfolio_value.tail(1).iloc[0] - 100000) / 1000
optimal_returns = '{0:.2f}%'.format(float(optimal_returns))
#efficient frontier plot
frontier_plot_data = open("plot.png", "rb").read()
# serialize to HTTP response
frontier_plot = HttpResponse(frontier_plot_data, content_type="image/png")
return (results, returns_plot, price_plot, frontier_plot,
equalweight_returns, optimal_returns)
def test_basic_history(self):
algo_text = """
from zipline.api import history, add_history
def initialize(context):
add_history(bar_count=2, frequency='1d', field='price')
def handle_data(context, data):
prices = history(bar_count=2, frequency='1d', field='price')
prices['prices_times_two'] = prices[1] * 2
context.last_prices = prices
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp("2006-03-20", tz="UTC")
end = pd.Timestamp("2006-03-21", tz="UTC")
sim_params = factory.create_simulation_parameters(start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text, data_frequency="minute", sim_params=sim_params, env=TestHistoryAlgo.env
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
last_prices = test_algo.last_prices[0]
oldest_dt = pd.Timestamp("2006-03-20 4:00 PM", tz="US/Eastern").tz_convert("UTC")
newest_dt = pd.Timestamp("2006-03-21 4:00 PM", tz="US/Eastern").tz_convert("UTC")
self.assertEquals(oldest_dt, last_prices.index[0])
self.assertEquals(newest_dt, last_prices.index[-1])
# Random, depends on seed
self.assertEquals(139.36946942498648, last_prices[oldest_dt])
self.assertEquals(180.15661995395106, last_prices[newest_dt])
def markowitz(stocks, cash):
warnings.filterwarnings("once")
solvers.options['show_progress'] = False
end = pd.Timestamp.utcnow()
start = end - 50 * pd.tseries.offsets.BDay()
data = load_bars_from_yahoo(stocks=stocks,
start=start, end=end)
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data, cash=cash)
# Run algorithm
results = algo.run(data)
# portfolio value plot
raw_plot = results.portfolio_value.plot()
raw_fig = raw_plot.get_figure()
returns_plot = mpld3.fig_to_html(raw_fig)
raw_fig.clf()
#stock price plot
raw_price_data = data.loc[:, :, 'price'].pct_change(1).fillna(0).applymap(lambda x: x + 1).cumprod().applymap(lambda x: x * 100)
raw_price_plot = raw_price_data.plot(figsize=(8,5))
raw_price_fig = raw_price_plot.get_figure()
price_plot = mpld3.fig_to_html(raw_price_fig)
raw_price_fig.clf()
#final returns
equalweight_returns = sum(map(list, raw_price_data.tail(1).values)[0]) / 4 - 100
equalweight_returns = '{0:.2f}%'.format(float(equalweight_returns))
optimal_returns = (results.portfolio_value.tail(1).iloc[0] - 100000) / 1000
optimal_returns = '{0:.2f}%'.format(float(optimal_returns))
#efficient frontier plot
frontier_plot_data = open("plot.png", "rb").read()
# serialize to HTTP response
frontier_plot = HttpResponse(frontier_plot_data, content_type="image/png")
return(results, returns_plot, price_plot, frontier_plot, equalweight_returns, optimal_returns)
def run_algo_single(**algo_descr):
if 'constraint_func' in algo_descr:
if algo_descr['constraint_func'](algo_descr['param_set']):
return np.nan
try:
algo = TradingAlgorithm(initialize=algo_descr['initialize'],
handle_data=algo_descr['handle_data'],
**algo_descr['param_set']
)
perf = algo.run(algo_descr['data'])
daily_rets = perf.portfolio_value.pct_change().dropna()
if daily_rets.std() > 0:
sharpe_ratio_calc = daily_rets.mean() / daily_rets.std() * np.sqrt(252)
else:
sharpe_ratio_calc = -999
risk_report = algo.risk_report
risk_cum = pd.Series(algo.perf_tracker.cumulative_risk_metrics.to_dict())
except ImportError as e:
print(e)
return np.nan
# Apply objective functions
objective = algo_descr.get('objective', 'none')
if objective == 'none':
obj = (perf, risk_cum, risk_report)
elif objective == 'sharpe':
obj = sharpe_ratio_calc
elif objective == 'total_return':
obj = perf['portfolio_value'][-1] / perf['portfolio_value'][0] - 1
elif callable(objective):
obj = objective(perf, risk_cum, risk_report)
else:
raise NotImplemented('Objective %s not implemented.' % algo_descr['objective'])
print "Sharpe: " + str(sharpe_ratio_calc) + " %_Return: " + str(perf.portfolio_value[-1]/perf.portfolio_value[0]-1) + " MaxDD: " + str(perf.max_drawdown[-1]) + " MaxExp: " + str(perf.max_leverage[-1])
return obj
def test_history_with_high(self):
algo_text = """
from zipline.api import history, add_history, record
def initialize(context):
add_history(3, '1d', 'high')
def handle_data(context, data):
highs = history(3, '1d', 'high')
record(current_high=highs[0].ix[-1])
""".strip()
# April 2007
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
start = pd.Timestamp("2007-04-10", tz="UTC")
end = pd.Timestamp("2007-04-10", tz="UTC")
sim_params = SimulationParameters(
period_start=start,
period_end=end,
capital_base=float("1.0e5"),
data_frequency="minute",
emission_rate="minute",
)
test_algo = TradingAlgorithm(
script=algo_text, data_frequency="minute", sim_params=sim_params, env=TestHistoryAlgo.env
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
np.testing.assert_equal(output.ix[0, "current_high"], 139.5370641791925)
def startbacktest():
starttime = request.args.get('starttime')
endtime = request.args.get('endtime')
code = request.args.get('code')
#处理代码
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = mystdout = StringIO()
sys.stderr = mystderr = StringIO()
try:
algo = TradingAlgorithm(script=code, startdate=starttime,enddate=endtime,capital_base=10000,benchmark='sz399004')
results = algo.run(input_data)
print results
except Exception as error:
print('caught this error: ' + repr(error))
sys.stdout = old_stdout
sys.stderr = old_stderr
print mystdout.getvalue()
print mystderr.getvalue()
json_results = []
return toJson(json_results)
def test_current_chain_in_algo(self):
code = dedent("""
from zipline.api import (
record,
continuous_future,
schedule_function,
get_datetime,
)
def initialize(algo):
algo.primary_cl = continuous_future('FO', 0, 'calendar')
algo.secondary_cl = continuous_future('FO', 1, 'calendar')
schedule_function(record_current_contract)
def record_current_contract(algo, data):
record(datetime=get_datetime())
primary_chain = data.current_chain(algo.primary_cl)
secondary_chain = data.current_chain(algo.secondary_cl)
record(primary_len=len(primary_chain))
record(primary_first=primary_chain[0].symbol)
record(primary_last=primary_chain[-1].symbol)
record(secondary_len=len(secondary_chain))
record(secondary_first=secondary_chain[0].symbol)
record(secondary_last=secondary_chain[-1].symbol)
""")
algo = TradingAlgorithm(script=code,
sim_params=self.sim_params,
trading_calendar=self.trading_calendar,
env=self.env)
results = algo.run(self.data_portal)
result = results.iloc[0]
self.assertEqual(result.primary_len,
4,
'There should be only 4 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date.')
self.assertEqual(result.secondary_len,
3,
'There should be only 3 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. And the first is not included because it is '
'the primary on that date.')
self.assertEqual(result.primary_first,
'FOF16',
'Front of primary chain should be FOF16 on first '
'session.')
self.assertEqual(result.secondary_first,
'FOG16',
'Front of secondary chain should be FOG16 on first '
'session.')
self.assertEqual(result.primary_last,
'FOJ16',
'End of primary chain should be FOJ16 on first '
'session.')
self.assertEqual(result.secondary_last,
'FOJ16',
'End of secondary chain should be FOJ16 on first '
'session.')
# Second day, primary should switch to FOG
result = results.iloc[1]
self.assertEqual(result.primary_len,
3,
'There should be only 3 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. The first is not included because of roll.')
self.assertEqual(result.secondary_len,
2,
'There should be only 2 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. The first is not included because of roll, '
'the second is the primary on that date.')
self.assertEqual(result.primary_first,
'FOG16',
'Front of primary chain should be FOG16 on second '
'session.')
self.assertEqual(result.secondary_first,
'FOH16',
'Front of secondary chain should be FOH16 on second '
'session.')
# These values remain FOJ16 because fixture data is not exhaustive
# enough to move the end of the chain.
self.assertEqual(result.primary_last,
'FOJ16',
'End of primary chain should be FOJ16 on second '
'session.')
self.assertEqual(result.secondary_last,
'FOJ16',
'End of secondary chain should be FOJ16 on second '
'session.')
security.symbol:
data[:data.index[0] + datetime.timedelta(days=10)]
})
count += 1
print str(count) + " : " + security.symbol
except:
continue
def initialize(context):
context.stock = symbol(security.symbol)
context.traded = False
def handle_data(context, data):
if context.traded:
return
print data
order(context.stock, 10000)
order(context.stock,
-10000,
style=LimitOrder(data[context.stock]['close'] * 1.1))
context.traded = True
# NOTE: This cell will take a few minutes to run.
# Create algorithm object passing in initialize and
# handle_data functions
algo_obj = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
# Run algorithm
perf_manual[security.symbol] = algo_obj.run(data)
from zipline.api import order, sid
from zipline.data.loader import load_bars_from_yahoo
# creating time interval
start = pd.Timestamp('2008-01-01', tz='UTC')
end = pd.Timestamp('2013-01-01', tz='UTC')
# loading the data
input_data = load_bars_from_yahoo(
stocks=['AAPL', 'MSFT'],
start=start,
end=end,
)
#checking if I can merge this
def initialize(context):
context.has_ordered = False
def handle_data(context, data):
if not context.has_ordered:
for stock in data:
order(sid(stock), 100)
context.has_ordered = True
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
results = algo.run(input_data)
from datetime import datetime
import pytz
from zipline import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
from zipline.api import order
def initialize(context):
context.test = 10
def handle_date(context, data):
order('AAPL', 10)
print(context.test)
if __name__ == '__main__':
import pylab as pl
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
end=end)
data = data.dropna()
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_date)
results = algo.run(data)
results.portfolio_value.plot()
pl.show()
def test_history_in_bts_volume_days(self, data_freq):
"""
Test calling history() in before_trading_start()
with daily volume bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
volume_bts = history(bar_count=2, frequency='1d', field='volume')
context.volume_bts = volume_bts
context.first_bts_call = False
def handle_data(context, data):
volume_hd = history(bar_count=2, frequency='1d', field='volume')
context.volume_hd = volume_hd
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end, data_frequency=data_freq)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency=data_freq,
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end, freq=data_freq)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the volume recorded by history() within handle_data()
volume_hd_0 = test_algo.volume_hd[0]
volume_hd_1 = test_algo.volume_hd[1]
# Get the volume recorded by history() within BTS
volume_bts_0 = test_algo.volume_bts[0]
volume_bts_1 = test_algo.volume_bts[1]
penultimate_hd_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
# Midnight of the day on which BTS is invoked.
newest_bts_dt = normalize_date(pd.Timestamp(
'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
if data_freq == 'daily':
# If we're dealing with daily data, then we record
# canonicalized timestamps, so make conversion here:
penultimate_hd_dt = normalize_date(penultimate_hd_dt)
# When history() is called in BTS, its 'current' volume value
# should equal the sum of the previous day.
self.assertEquals(volume_hd_0[penultimate_hd_dt],
volume_bts_0[newest_bts_dt])
self.assertEquals(volume_hd_1[penultimate_hd_dt],
volume_bts_1[newest_bts_dt])
def test_history_in_bts_volume_minutes(self):
"""
Test calling history() in before_trading_start()
with minutely volume bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
volume_bts = history(bar_count=2, frequency='1m', field='volume')
context.volume_bts = volume_bts
context.first_bts_call = False
def handle_data(context, data):
pass
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the volumes recorded for sid 0 by history() within BTS
volume_bts_0 = test_algo.volume_bts[0]
# Get the volumes recorded for sid 1 by history() within BTS
volume_bts_1 = test_algo.volume_bts[1]
# The values recorded on 2006-03-22 by history() in BTS
# should equal the final volume values for the trading
# day 2006-03-21:
# 0 1
# 2006-03-21 20:59:00 215548 439908
# 2006-03-21 21:00:00 985645 664313
#
# Note: These are not 'real' volume values. They are the product of
# RandonWalkSource, which produces random walk OHLCV timeseries. For a
# given seed these values are deterministc.
self.assertEquals(215548, volume_bts_0.ix[0])
self.assertEquals(985645, volume_bts_0.ix[1])
self.assertEquals(439908, volume_bts_1.ix[0])
self.assertEquals(664313, volume_bts_1.ix[1])
def run_backtest(params, instruments = ['close'], filename = None, verbose = False, asset_type = 'Top'):
# instruments says to wichi value(s) we look at (open, close, etc)
# Set global variables
global PARAMS
try:
thismodule = sys.modules[__name__]
general_parameters = params['general_parameters']
for key, value in general_parameters.iteritems():
setattr(thismodule, key, value)
except:
print "Warning: General parameters loading was not completed successfully! However, going to proceed with the default parameters..."
PARAMS = params
stocks = np.unique(np.loadtxt(ASSET_READER, dtype=str, delimiter='/n'))
stocks = [str(stocks[i]) for i in xrange(len(stocks))] # convert elements in stocks from numpy._string to python.string
stocks.sort()
returns_method = RETS_READER if RETS_READER != 'None' else 'NONE'
volatility_method = VOL_READER if VOL_READER != 'None' else 'NONE'
correlation_method = CORR_READER if CORR_READER != 'None' else 'NONE'
start_t = time.time()
if verbose: print "Going to read data... "
last_date_db = START_DATE_BT_STR
db = utils.make_db_connection()
cursor = db.cursor()
query_1 = """select DT_DATE
from PASS_SYS.T_PASS_BACKTEST
where ST_OPTIMIZATIONMETHOD = '%s' and
ST_RETURNPREDICTIONMETHOD = '%s' and
ST_VOLATILITYPREDICTIONMETHOD = '%s' and
ST_CORRELATIONPREDICTIONMETHOD = '%s' and
ST_UNIVERSE = '%s'
order by DT_DATE
""" % (ID,
returns_method,
volatility_method,
correlation_method,
'Top')#TODO should be ASSET_READER instead of hardcode 'Top'
select_1= pd.read_sql_query(query_1, db, index_col='DT_DATE')
if len(select_1) > 0:
last_date_db = select_1.index[-1]
db.close()
#TODO END_DATE_BT_STR should be recalculated to the last rebalance date so we don't have the last month with 0 allocations
data = utils.load_data_from_passdb(stocks, instruments = instruments, set_price = REBALANCE_ACTION_TYPE, start = last_date_db,
end = END_DATE_BT_STR, align_dates = False, transform_to_weights = False, add_dummy_volume = True,
output_asset_info = False, convert_curr = REF_CURNCY)
df_info = pd.read_csv('./Info/assets_info.csv')
data_price_df = utils.truncate_data(data.minor_xs('price'), df_info)
symbols = data_price_df.columns.tolist()
data = data[symbols]
for symbol_ in symbols:
data[symbol_]['price'] = data_price_df[symbol_]
if verbose: print "Time to read data: ", time.time() - start_t
date_limits = utils.get_date_limits(data.minor_xs('price'))
print 'get_date_limits'
print date_limits
print ''
print ''
print agdjagj
algo = TradingAlgorithm(initialize=initialize, handle_data = handle_data, date_limits = date_limits, verbose = verbose, symbols = symbols)
if verbose: print "Going to run..."
results = algo.run(data)
results.index = results.index.date
#TODO should be ASSET_READER instead of hardcode 'Top'
store_backtest_results_db(ID, returns_method, volatility_method, correlation_method, 'Top', results)
#TODO don't send to csv unless there's some option to do it
if filename is not None:
results.to_csv(filename)
return results
def test_history_in_bts_volume_minutes(self):
"""
Test calling history() in before_trading_start()
with minutely volume bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
volume_bts = history(bar_count=2, frequency='1m', field='volume')
context.volume_bts = volume_bts
context.first_bts_call = False
def handle_data(context, data):
pass
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the volumes recorded for sid 0 by history() within BTS
volume_bts_0 = test_algo.volume_bts[0]
# Get the volumes recorded for sid 1 by history() within BTS
volume_bts_1 = test_algo.volume_bts[1]
# The values recorded on 2006-03-22 by history() in BTS
# should equal the final volume values for the trading
# day 2006-03-21:
# 0 1
# 2006-03-21 20:59:00 215548 439908
# 2006-03-21 21:00:00 985645 664313
#
# Note: These are not 'real' volume values. They are the product of
# RandonWalkSource, which produces random walk OHLCV timeseries. For a
# given seed these values are deterministc.
self.assertEquals(215548, volume_bts_0.ix[0])
self.assertEquals(985645, volume_bts_0.ix[1])
self.assertEquals(439908, volume_bts_1.ix[0])
self.assertEquals(664313, volume_bts_1.ix[1])
def test_history_in_bts_volume_days(self, data_freq):
"""
Test calling history() in before_trading_start()
with daily volume bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
volume_bts = history(bar_count=2, frequency='1d', field='volume')
context.volume_bts = volume_bts
context.first_bts_call = False
def handle_data(context, data):
volume_hd = history(bar_count=2, frequency='1d', field='volume')
context.volume_hd = volume_hd
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end, data_frequency=data_freq)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency=data_freq,
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end, freq=data_freq)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the volume recorded by history() within handle_data()
volume_hd_0 = test_algo.volume_hd[0]
volume_hd_1 = test_algo.volume_hd[1]
# Get the volume recorded by history() within BTS
volume_bts_0 = test_algo.volume_bts[0]
volume_bts_1 = test_algo.volume_bts[1]
penultimate_hd_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
# Midnight of the day on which BTS is invoked.
newest_bts_dt = normalize_date(pd.Timestamp(
'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
if data_freq == 'daily':
# If we're dealing with daily data, then we record
# canonicalized timestamps, so make conversion here:
penultimate_hd_dt = normalize_date(penultimate_hd_dt)
# When history() is called in BTS, its 'current' volume value
# should equal the sum of the previous day.
self.assertEquals(volume_hd_0[penultimate_hd_dt],
volume_bts_0[newest_bts_dt])
self.assertEquals(volume_hd_1[penultimate_hd_dt],
volume_bts_1[newest_bts_dt])
def test_history_in_bts_price_minutes(self):
"""
Test calling history() in before_trading_start()
with minutely price bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
price_bts = history(bar_count=1, frequency='1m', field='price')
context.price_bts = price_bts
context.first_bts_call = False
def handle_data(context, data):
pass
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the prices recorded by history() within BTS
price_bts_0 = test_algo.price_bts[0]
price_bts_1 = test_algo.price_bts[1]
# The prices recorded by history() in BTS should
# be the closing price of the previous day, which are:
#
# sid | close on 2006-03-21
# ----------------------------
# 0 | 180.15661995395106
# 1 | 578.41665003444723
# These are not 'real' price values. They are the product of
# RandonWalkSource, which produces random walk OHLCV timeseries. For a
# given seed these values are deterministc.
self.assertEquals(180.15661995395106, price_bts_0.ix[0])
self.assertEquals(578.41665003444723, price_bts_1.ix[0])
def test_history_in_bts_price_days(self, data_freq):
"""
Test calling history() in before_trading_start()
with daily price bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
prices_bts = history(bar_count=3, frequency='1d', field='price')
context.prices_bts = prices_bts
context.first_bts_call = False
def handle_data(context, data):
prices_hd = history(bar_count=3, frequency='1d', field='price')
context.prices_hd = prices_hd
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end, data_frequency=data_freq)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency=data_freq,
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end, freq=data_freq)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the prices recorded by history() within handle_data()
prices_hd = test_algo.prices_hd[0]
# Get the prices recorded by history() within BTS
prices_bts = test_algo.prices_bts[0]
# before_trading_start() is timestamp'd to midnight prior to
# the day's trading. Since no equity trades occur at midnight,
# the price recorded for this time is forward filled from the
# last trade - typically ~4pm the previous day. This results
# in the OHLCV data recorded by history() in BTS lagging
# that recorded by history in handle_data().
# The trace of the pricing data from history() called within
# handle_data() vs. BTS in the above algo is as follows:
# When called within handle_data()
# ---------------------------------
# 2006-03-20 21:00:00 139.369469
# 2006-03-21 21:00:00 180.156620
# 2006-03-22 21:00:00 221.344654
# When called within BTS
# ---------------------------------
# 2006-03-17 21:00:00 NaN
# 2006-03-20 21:00:00 139.369469
# 2006-03-22 00:00:00 180.156620
# Get relevant Timestamps for the history() call within handle_data()
oldest_hd_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
penultimate_hd_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
# Get relevant Timestamps for the history() call within BTS
penultimate_bts_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_bts_dt = normalize_date(pd.Timestamp(
'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
if data_freq == 'daily':
# If we're dealing with daily data, then we record
# canonicalized timestamps, so make conversion here:
oldest_hd_dt = normalize_date(oldest_hd_dt)
penultimate_hd_dt = normalize_date(penultimate_hd_dt)
penultimate_bts_dt = normalize_date(penultimate_bts_dt)
self.assertEquals(prices_hd[oldest_hd_dt],
prices_bts[penultimate_bts_dt])
self.assertEquals(prices_hd[penultimate_hd_dt],
prices_bts[newest_bts_dt])
if False:
log.info("do some testing ONLY")
createStrategy(createStatements())
quit()
log.info("it's __main__, do something")
log.info('load data')
data = OrderedDict()
# df = pd.read_csv('data/603997.csv', index_col='date', parse_dates=['date']).tail(2770)
df = pd.read_csv('data/603997.csv', index_col='date',
parse_dates=['date']).tail(100)
df['test'] = [i for i in range(len(df.index))]
log.info(df.columns)
log.info(df.head(5))
data['603997'] = df
panel = pd.Panel(data)
algo_obj = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
log.info('run algorithm')
perf_manual = algo_obj.run(panel)
print(perf_manual)
if True:
import pickle
file = "e:\\perf.p"
log.info("write performance to pickle : %s" % file)
pickle.dump(perf_manual, open(file, 'wb'))
log.info('done')
def test_history_in_bts_price_days(self, data_freq):
"""
Test calling history() in before_trading_start()
with daily price bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
prices_bts = history(bar_count=3, frequency='1d', field='price')
context.prices_bts = prices_bts
context.first_bts_call = False
def handle_data(context, data):
prices_hd = history(bar_count=3, frequency='1d', field='price')
context.prices_hd = prices_hd
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end, data_frequency=data_freq)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency=data_freq,
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end, freq=data_freq)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the prices recorded by history() within handle_data()
prices_hd = test_algo.prices_hd[0]
# Get the prices recorded by history() within BTS
prices_bts = test_algo.prices_bts[0]
# before_trading_start() is timestamp'd to midnight prior to
# the day's trading. Since no equity trades occur at midnight,
# the price recorded for this time is forward filled from the
# last trade - typically ~4pm the previous day. This results
# in the OHLCV data recorded by history() in BTS lagging
# that recorded by history in handle_data().
# The trace of the pricing data from history() called within
# handle_data() vs. BTS in the above algo is as follows:
# When called within handle_data()
# ---------------------------------
# 2006-03-20 21:00:00 139.369469
# 2006-03-21 21:00:00 180.156620
# 2006-03-22 21:00:00 221.344654
# When called within BTS
# ---------------------------------
# 2006-03-17 21:00:00 NaN
# 2006-03-20 21:00:00 139.369469
# 2006-03-22 00:00:00 180.156620
# Get relevant Timestamps for the history() call within handle_data()
oldest_hd_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
penultimate_hd_dt = pd.Timestamp(
'2006-03-21 4:00 PM', tz='US/Eastern').tz_convert('UTC')
# Get relevant Timestamps for the history() call within BTS
penultimate_bts_dt = pd.Timestamp(
'2006-03-20 4:00 PM', tz='US/Eastern').tz_convert('UTC')
newest_bts_dt = normalize_date(pd.Timestamp(
'2006-03-22 04:00 PM', tz='US/Eastern').tz_convert('UTC'))
if data_freq == 'daily':
# If we're dealing with daily data, then we record
# canonicalized timestamps, so make conversion here:
oldest_hd_dt = normalize_date(oldest_hd_dt)
penultimate_hd_dt = normalize_date(penultimate_hd_dt)
penultimate_bts_dt = normalize_date(penultimate_bts_dt)
self.assertEquals(prices_hd[oldest_hd_dt],
prices_bts[penultimate_bts_dt])
self.assertEquals(prices_hd[penultimate_hd_dt],
prices_bts[newest_bts_dt])
data = pd.Panel({security.symbol: data[:data.index[0] + datetime.timedelta(days=10)]} )
count +=1
print str(count) + " : " + security.symbol
except:
continue
def initialize(context):
context.stock = symbol(security.symbol)
context.traded = False
def handle_data(context, data):
if context.traded:
return
print data
order(context.stock, 10000)
order(context.stock, - 10000, style=LimitOrder(data[context.stock]['close'] * 1.1))
context.traded = True
# NOTE: This cell will take a few minutes to run.
# Create algorithm object passing in initialize and
# handle_data functions
algo_obj = TradingAlgorithm(
initialize=initialize,
handle_data=handle_data
)
# Run algorithm
perf_manual[security.symbol] = algo_obj.run(data)
def run_trading(self) :
algo = TradingAlgorithm(initialize=self.initialize, handle_data=self.handle_data)
results = algo.run(self.data)
return results
context: The same context object from the initialize function.
Stores the up to date portfolio as well as any state
variables defined.
Returns None
'''
# Allow history to accumulate 100 days of prices before trading
# and rebalance every day thereafter.
context.tick += 1
if context.tick < 100:
return
# Get rolling window of past prices and compute returns
prices = history(100, '1d', 'price').dropna()
returns = prices.pct_change().dropna()
try:
# Perform Markowitz-style portfolio optimization
weights, _, _ = optimal_portfolio(returns.T)
# Rebalance portfolio accordingly
for stock, weight in zip(prices.columns, weights):
order_target_percent(stock, weight)
except ValueError as e:
# Sometimes this error is thrown
# ValueError: Rank(A) < p or Rank([P; A; G]) < n
pass
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
# Run algorithm
results = algo.run(total_data.T)
results.portfolio_value.plot()
if __name__ =='__main__':
import sys
import pytz
import matplotlib.pyplot as plt
from zipline import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
import argparse
parser = argparse.ArgumentParser(description='predict/test using similarity-prediction')
parser.add_argument('-t', '--ticker', action='store', default='AAPL', help='tickers to predict/test')
parser.add_argument('-m', '--mamethod', action='store', choices=['ema','ma'], default='ema', help='ma method to pre-process the Close/Volume')
parser.add_argument('-p', '--maperiod', action='store', type=int, default=20, help='period to ma Close/Volume')
parser.add_argument('-w', '--window', action='store', type=int, default=20, help='window size to match')
parser.add_argument('-a', '--lookahead', action='store', type=int, default=1, help='days to lookahead when predict')
parser.add_argument('-c', '--mincorr', action='store', type=float, default=0.9, help='days to lookahead when predict')
parser.add_argument('-b', '--begin', action='store', type=str, default='20100101', help='start of the market data')
parser.add_argument('-e', '--end', action='store', type=str, default='20161221', help='end of the market data')
args = parser.parse_args()
#start = datetime.datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
#end = datetime.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
#data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end, adjusted=False)
tickers = [t.strip() for t in args.ticker.split(',') if t.strip()]
data = prepare_data(tickers, start=args.begin, end=args.end)
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data, capital_base=50000, xdata=data, xticker=tickers, xstart=args.begin, xend=args.end, window=args.window)
res = algo.run(data).dropna()
analyze(res, tickers[0])
# Plot the portfolio and asset data.
ax1 = plt.subplot(211) #211表示2行1列两个子图，现在正在画第一个portfolio_value图,ax是一个Axes对象
results['portfolio_value'].plot(
ax=ax1, color='b', grid=True, linewidth=1.6
) # results的index是日期，根据输出的csv结果，有列名SYMBOL600111，algo_volatility,algorithm_period_return,alpha,benchmark_period_return,benchmark_volatility,beta,capital_used,ending_cash,ending_exposure,ending_value,excess_return,gross_leverage,information,long_exposure,long_value,longs_count,max_drawdown,max_leverage,net_leverage,orders,pnl,portfolio_value,positions,returns,sharpe,short_exposure,short_value,shorts_count,sortino,starting_cash,starting_exposure,starting_value,trading_days,transactions,treasury_period_return
ax1.set_ylabel('Portfolio Performance') #把y轴命名为Portfolio Performance
ax2 = plt.subplot(
212, sharex=ax1) #取得另外一个Axes对象，212表示2行1列两个子图，现在正在画第二个get(SYMBOL)图
results.get(SYMBOL).plot(ax=ax2, grid=True,
linewidth=1.6) #dataframe.get方法,SYMBOL可以改为
ax2.set_ylabel('%s price ' % SYMBOL) #把第二个子图的名字命名为price，后面那个取SYMBOL的名字
results.to_csv('aaaaaaaaaaaaaa.csv')
# Show the plot.
plt.gcf().set_size_inches(
18, 8) # plt.gcf():Get a reference to the current figure.设置尺寸
plt.show()
bars = get_data_from_tushare( # Using tushare to get data which could be handled by zipline later.
stocks=[SYMBOL],
start="2015-10-09",
end="2016-02-07",
) #bars是一个DataFrame类型，索引就是日期，还有一列是'600111'的收盘价，从tushare调用数据
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
identifiers=[SYMBOL])
perf = algo.run(bars) #performance是一个DataFrame类型
plot_function(results=perf)
def test_history_in_bts_price_minutes(self):
"""
Test calling history() in before_trading_start()
with minutely price bars.
"""
algo_text = """
from zipline.api import history
def initialize(context):
context.first_bts_call = True
def before_trading_start(context, data):
if not context.first_bts_call:
price_bts = history(bar_count=1, frequency='1m', field='price')
context.price_bts = price_bts
context.first_bts_call = False
def handle_data(context, data):
pass
""".strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-22', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='minute',
sim_params=sim_params,
env=TestHistoryAlgo.env,
)
source = RandomWalkSource(start=start, end=end)
output = test_algo.run(source)
self.assertIsNotNone(output)
# Get the prices recorded by history() within BTS
price_bts_0 = test_algo.price_bts[0]
price_bts_1 = test_algo.price_bts[1]
# The prices recorded by history() in BTS should
# be the closing price of the previous day, which are:
#
# sid | close on 2006-03-21
# ----------------------------
# 0 | 180.15661995395106
# 1 | 578.41665003444723
# These are not 'real' price values. They are the product of
# RandonWalkSource, which produces random walk OHLCV timeseries. For a
# given seed these values are deterministc.
self.assertEquals(180.15661995395106, price_bts_0.ix[0])
self.assertEquals(578.41665003444723, price_bts_1.ix[0])
stocks = ['AAPL', 'MSFT']
def initialize(context):
context.has_ordered = False
context.stocks = stocks
def handle_data(context, data):
if not context.has_ordered:
for stock in context.stocks:
order(symbol(stock), 100)
context.has_ordered = True
if __name__ == '__main__':
# creating time interval
start = pd.Timestamp('2008-01-01', tz='UTC')
end = pd.Timestamp('2013-01-01', tz='UTC')
# loading the data
input_data = load_bars_from_yahoo(
stocks=stocks,
start=start,
end=end,
)
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
results = algo.run(input_data)
def run_trading(self):
algo = TradingAlgorithm(initialize=self.initialize,
handle_data=self.handle_data)
results = algo.run(self.data)
return results
try:
# Get the strongest 5 in momentum
mom = returns_6m.T.sum(axis=1)
selected_indices = mom[mom>0].order().tail(len(mom) /2).index
# selected_indices = mom.index
# selected_indices = mom[mom > 0 ].index
selected_returns = returns_60d[selected_indices]
weights = minimize_vol(selected_returns.T)
# weights = minimize_vol(returns_60d.T)
# Rebalance portfolio accordingly
for stock, weight in zip(selected_returns.columns, weights):
order_target_percent(stock, weight)
except :
# Sometimes this error is thrown
# ValueError: Rank(A) < p or Rank([P; A; G]) < n
pass
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
# Run algorithm
results = algo.run(dp.dropna())
ret_ports = pd.DataFrame()
ret_ports[5] = results.portfolio_value
ret_ports.plot(figsize=[20,10])
print results
def test_current_chain_in_algo(self):
code = dedent("""
from zipline.api import (
record,
continuous_future,
schedule_function,
get_datetime,
)
def initialize(algo):
algo.primary_cl = continuous_future('FO', 0, 'calendar')
algo.secondary_cl = continuous_future('FO', 1, 'calendar')
schedule_function(record_current_contract)
def record_current_contract(algo, data):
record(datetime=get_datetime())
primary_chain = data.current_chain(algo.primary_cl)
secondary_chain = data.current_chain(algo.secondary_cl)
record(primary_len=len(primary_chain))
record(primary_first=primary_chain[0].symbol)
record(primary_last=primary_chain[-1].symbol)
record(secondary_len=len(secondary_chain))
record(secondary_first=secondary_chain[0].symbol)
record(secondary_last=secondary_chain[-1].symbol)
""")
algo = TradingAlgorithm(script=code,
sim_params=self.sim_params,
trading_calendar=self.trading_calendar,
env=self.env)
results = algo.run(self.data_portal)
result = results.iloc[0]
self.assertEqual(result.primary_len,
4,
'There should be only 4 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date.')
self.assertEqual(result.secondary_len,
3,
'There should be only 3 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. And the first is not included because it is '
'the primary on that date.')
self.assertEqual(result.primary_first,
'FOF16',
'Front of primary chain should be FOF16 on first '
'session.')
self.assertEqual(result.secondary_first,
'FOG16',
'Front of secondary chain should be FOG16 on first '
'session.')
self.assertEqual(result.primary_last,
'FOJ16',
'End of primary chain should be FOJ16 on first '
'session.')
self.assertEqual(result.secondary_last,
'FOJ16',
'End of secondary chain should be FOJ16 on first '
'session.')
# Second day, primary should switch to FOG
result = results.iloc[1]
self.assertEqual(result.primary_len,
3,
'There should be only 3 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. The first is not included because of roll.')
self.assertEqual(result.secondary_len,
2,
'There should be only 2 contracts in the chain for '
'the primary, there are 5 contracts defined in the '
'fixture, but one has a start after the simulation '
'date. The first is not included because of roll, '
'the second is the primary on that date.')
self.assertEqual(result.primary_first,
'FOG16',
'Front of primary chain should be FOG16 on second '
'session.')
self.assertEqual(result.secondary_first,
'FOH16',
'Front of secondary chain should be FOH16 on second '
'session.')
# These values remain FOJ16 because fixture data is not exhaustive
# enough to move the end of the chain.
self.assertEqual(result.primary_last,
'FOJ16',
'End of primary chain should be FOJ16 on second '
'session.')
self.assertEqual(result.secondary_last,
'FOJ16',
'End of secondary chain should be FOJ16 on second '
'session.')
ax2 = fig.add_subplot(312)
portfolio_ratio = perf.portfolio_value/100000.0
portfolio_ratio.plot(ax=ax2, lw=2.)
ax2.plot(buys.index, portfolio_ratio.ix[buys.index], '^', markersize=10, color='m')
ax2.plot(sells.index, portfolio_ratio.ix[sells.index], 'v', markersize=10, color='k')
# ax3 = fig.add_subplot(313)
# perf.portfolio_value.plot(ax=ax3, lw=2.)
# ax3.plot(buys.index, perf.portfolio_value.ix[buys.index], '^', markersize=10, color='m')
# ax3.plot(sells.index, perf.portfolio_value.ix[sells.index], 'v', markersize=10, color='k')
pass
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
algo._analyze = analyze
perf = algo.run(df)
perf_trans = perf.ix[[t!=[] for t in perf.transactions]]
buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
sells = perf_trans.ix[[t[0]['amount'] < 0 for t in perf_trans.transactions]]
investDays = validInvestDays(buys, sells, perf)
print investDays
cashes = perf.portfolio_value.ix[sells.index]
returnRatArr = returnRatioArr(cashes.values)
final_return_ratio = returnRatio(perf.portfolio_value[-1])
print '总收益率：', final_return_ratio
print '年化收益率：', annualizedReturnRatio([final_return_ratio], T=investDays, D=250.0)
from zipline.api import order_target, record, symbol, history, add_history
def main():
print("Aqui porra")
alg_obj = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
perf_manual = alg_obj.run(DATA)
perf_manual[["MA1","MA2","Price"]].plot()
def start_algo3(data):
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
results = algo.run(data)
return results, algo.perf_tracker.cumulative_risk_metrics
