本文整理汇总了Python中zipline.finance.trading.TradingEnvironment.market_minute_window方法的典型用法代码示例。如果您正苦于以下问题：Python TradingEnvironment.market_minute_window方法的具体用法？Python TradingEnvironment.market_minute_window怎么用？Python TradingEnvironment.market_minute_window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.finance.trading.TradingEnvironment的用法示例。
在下文中一共展示了TradingEnvironment.market_minute_window方法的2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: HistorySpec
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import market_minute_window [as 别名]
"""
if frequency == '1d':
# First day of this test is July 3rd, which is a half day.
if count < 210:
return [np.nan, count]
else:
return [209, count]
elif frequency == '1m':
if count == 0:
return [np.nan, count]
else:
return [count - 1, count]
MIXED_FREQUENCY_MINUTES = _cases_env.market_minute_window(
to_utc('2013-07-03 9:31AM'), 600,
)
ONE_MINUTE_PRICE_ONLY_SPECS = [
HistorySpec(1, '1m', 'price', True, _cases_env, data_frequency='minute'),
]
DAILY_OPEN_CLOSE_SPECS = [
HistorySpec(3, '1d', 'open_price', False, _cases_env,
data_frequency='minute'),
HistorySpec(3, '1d', 'close_price', False, _cases_env,
data_frequency='minute'),
]
ILLIQUID_PRICES_SPECS = [
HistorySpec(3, '1m', 'price', False, _cases_env, data_frequency='minute'),
HistorySpec(5, '1m', 'price', True, _cases_env, data_frequency='minute'),
]
MIXED_FREQUENCY_SPECS = [示例2: transaction_sim
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import market_minute_window [as 别名]
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
tempdir = TempDirectory()
try:
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
env = TradingEnvironment()
sid = 1
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
data_frequency="minute"
)
minutes = env.market_minute_window(
sim_params.first_open,
int((trade_interval.total_seconds() / 60) * trade_count)
+ 100)
price_data = np.array([10.1] * len(minutes))
assets = {
sid: pd.DataFrame({
"open": price_data,
"high": price_data,
"low": price_data,
"close": price_data,
"volume": np.array([100] * len(minutes)),
"dt": minutes
}).set_index("dt")
}
write_bcolz_minute_data(
env,
env.days_in_range(minutes[0], minutes[-1]),
tempdir.path,
assets
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env,
equity_minute_reader=equity_minute_reader,
)
else:
sim_params = factory.create_simulation_parameters(
data_frequency="daily"
)
days = sim_params.trading_days
assets = {
1: pd.DataFrame({
"open": [10.1] * len(days),
"high": [10.1] * len(days),
"low": [10.1] * len(days),
"close": [10.1] * len(days),
"volume": [100] * len(days),
"day": [day.value for day in days]
}, index=days)
}
path = os.path.join(tempdir.path, "testdata.bcolz")
DailyBarWriterFromDataFrames(assets).write(
path, days, assets)
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env,
equity_daily_reader=equity_daily_reader,
)
if "default_slippage" not in params or \
not params["default_slippage"]:
slippage_func = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder,
slippage_func)
env.write_data(equities_data={
#.........这里部分代码省略.........