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fawce
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
"""
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Factory functions to prepare useful data for optimize tests.
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Author: Thomas V. Wiecki (thomas.wiecki@gmail.com), 2012
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"""
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from datetime import datetime, timedelta
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import zipline.protocol as zp
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from zipline.utils.factory import get_next_trading_dt
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from zipline.finance.sources import SpecificEquityTrades
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from zipline.optimize.algorithms import BuySellAlgorithm
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from zipline.lines import SimulatedTrading
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def create_updown_trade_source(sid, trade_count, trading_environment, start_price, amplitude):
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from itertools import cycle
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volume = 1000
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events = []
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price = start_price-amplitude/2.
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cur = trading_environment.first_open
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one_day = timedelta(days = 1)
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#create iterator to cycle through up and down phases
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change = cycle([1,-1])
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for i in xrange(trade_count + 2):
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cur = get_next_trading_dt(cur, one_day, trading_environment)
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event = zp.ndict({
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"type" : zp.DATASOURCE_TYPE.TRADE,
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"sid" : sid,
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"price" : price,
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"volume" : volume,
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"dt" : cur,
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})
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events.append(event)
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price += change.next()*amplitude
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trading_environment.period_end = cur
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source = SpecificEquityTrades(sid, events)
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return source
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def create_predictable_zipline(config, sid=133, amplitude=10, base_price=50, offset=0):
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config = deepcopy(config)
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trading_environment = create_trading_environment()
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source = create_updown_trade_source(sid,
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config['trade_count'],
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trading_environment,
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base_price,
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amplitude)
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algo = RegularIntervalBuySellAlgorithm(sid, 100, offset)
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config['algorithm'] = algo
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config['trade_source'] = source
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config['environment'] = trading_environment
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zipline = SimulatedTrading.create_test_zipline(**config)
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zipline.simulate(blocking=True)
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return zipline
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