本文整理汇总了Python中zipline.TradingAlgorithm.run方法的典型用法代码示例。如果您正苦于以下问题:Python TradingAlgorithm.run方法的具体用法?Python TradingAlgorithm.run怎么用?Python TradingAlgorithm.run使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类zipline.TradingAlgorithm
的用法示例。
在下文中一共展示了TradingAlgorithm.run方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_history_container_constructed_at_runtime
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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',
)
示例2: test_history_daily_data_1m_window
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例3: test_history_passed_to_talib
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例4: test_history_grow_length_intra_bar
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例5: get_results
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例6: run_algo
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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
示例7: test_basic_history_positional_args
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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])
示例8: test_basic_history_one_day
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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])
示例9: test_current_contract_in_algo
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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.')
示例10: test_history_container_constructed_at_runtime
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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"
)
示例11: test_history_passed_to_func
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例12: test_history_daily
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例13: test_history_with_open
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)
示例14: test_basic_history
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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])
示例15: markowitz
# 需要导入模块: from zipline import TradingAlgorithm [as 别名]
# 或者: from zipline.TradingAlgorithm import run [as 别名]
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)