<NME> factory.py
<BEF> #
# Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


"""
Factory functions to prepare useful data.
"""
import pytz
import random
from collections import OrderedDict

import pandas as pd
import numpy as np
from zipline.sources import SpecificEquityTrades
from zipline.finance.trading import SimulationParameters
from zipline.sources.test_source import create_trade


                             DataPanelSource)
from zipline.finance.trading import SimulationParameters
from zipline.finance import trading
from zipline.sources.test_source import (
    date_gen,
    create_trade
)

# For backwards compatibility
from zipline.data.loader import (load_from_yahoo,

    if not trading_calendar:
        trading_calendar = get_calendar("NYSE")

    if start is None:
        start = pd.Timestamp("{0}-01-01".format(year), tz='UTC')
    elif type(start) == datetime:
        start = pd.Timestamp(start)

    if end is None:
        if num_days:
            start_index = trading_calendar.all_sessions.searchsorted(start)
            end = trading_calendar.all_sessions[start_index + num_days - 1]
        else:
            end = pd.Timestamp("{0}-12-31".format(year), tz='UTC')
    elif type(end) == datetime:
        end = pd.Timestamp(end)

    sim_params = SimulationParameters(
        start_session=start,
        end_session=end,
        capital_base=capital_base,
        data_frequency=data_frequency,
        emission_rate=emission_rate,
        trading_calendar=trading_calendar,
    )

    return sim_params


def get_next_trading_dt(current, interval, trading_calendar):
    next_dt = pd.Timestamp(current).tz_convert(trading_calendar.tz)

    while True:
        # Convert timestamp to naive before adding day, otherwise the when
        # stepping over EDT an hour is added.
        next_dt = pd.Timestamp(next_dt.replace(tzinfo=None))
        next_dt = next_dt + interval
        next_dt = pd.Timestamp(next_dt, tz=trading_calendar.tz)
        next_dt_utc = next_dt.tz_convert('UTC')
        if trading_calendar.is_open_on_minute(next_dt_utc):
            break
        next_dt = next_dt_utc.tz_convert(trading_calendar.tz)

    return next_dt_utc


def create_trade_history(sid, prices, amounts, interval, sim_params,
                         trading_calendar, source_id="test_factory"):
    trades = []
    current = sim_params.first_open

    oneday = timedelta(days=1)
    use_midnight = interval >= oneday
    for price, amount in zip(prices, amounts):
        if use_midnight:
            trade_dt = current.replace(hour=0, minute=0)
        else:
            trade_dt = current
        trade = create_trade(sid, price, amount, trade_dt, source_id)
        trades.append(trade)
        current = get_next_trading_dt(current, interval, trading_calendar)

    assert len(trades) == len(prices)
    return trades


def create_returns_from_range(sim_params):
    return pd.Series(index=sim_params.sessions,
                     data=np.random.rand(len(sim_params.sessions)))


def create_returns_from_list(returns, sim_params):
    return pd.Series(index=sim_params.sessions[:len(returns)],
                     data=returns)


def create_daily_trade_source(sids,
                              sim_params,
                              asset_finder,
                              trading_calendar):
    """
    creates trade_count trades for each sid in sids list.
    first trade will be on sim_params.start_session, and daily
    thereafter for each sid. Thus, two sids should result in two trades per
    day.
    """
    return create_trade_source(
        sids,
        timedelta(days=1),
        sim_params,
        asset_finder,
        trading_calendar=trading_calendar,
    )


def create_trade_source(sids,
                        trade_time_increment,
                        sim_params,
                        asset_finder,
                        trading_calendar):
    # If the sim_params define an end that is during market hours, that will be
    # used as the end of the data source
    if trading_calendar.is_open_on_minute(sim_params.end_session):
        end = sim_params.end_session
    # Otherwise, the last_close after the end_session is used as the end of the
    # data source
    else:
        end = sim_params.last_close

    args = tuple()
    kwargs = {
        'sids': sids,
        'start': sim_params.first_open,
        'end': end,
        'delta': trade_time_increment,
        'trading_calendar': trading_calendar,
        'asset_finder': asset_finder,
    }
    source = SpecificEquityTrades(*args, **kwargs)

    return source

<MSG> STY: Remove unused imports.

<DFF> @@ -19,7 +19,6 @@ Factory functions to prepare useful data.
 """
 import pytz
 import random
-from collections import OrderedDict
 
 import pandas as pd
 import numpy as np
@@ -31,10 +30,8 @@ from zipline.sources import (SpecificEquityTrades,
                              DataPanelSource)
 from zipline.finance.trading import SimulationParameters
 from zipline.finance import trading
-from zipline.sources.test_source import (
-    date_gen,
-    create_trade
-)
+from zipline.sources.test_source import create_trade
+
 
 # For backwards compatibility
 from zipline.data.loader import (load_from_yahoo,

{'addition_count': 2, 'commit_subject': 'STY: Remove unused imports.', 'deletion_count': 5, 'file_extension': '.py', 'lang': 'py', 'license': 'apache-2.0', 'repo_name': 'quantopian/zipline'}