mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-23 12:50:22 +08:00
Eddie Hebert
800210fbb3
Instead of using all calendar days between start and end in test sources, use the trading calendar for test sources. Needed for an incoming refactoring of market open and close, where the opens and closes are indexed by market days.
403 lines
12 KiB
Python
403 lines
12 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Factory functions to prepare useful data.
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"""
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import pytz
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import random
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from collections import OrderedDict
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from delorean import Delorean
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from zipline.protocol import DailyReturn, Event, DATASOURCE_TYPE
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from zipline.sources import (SpecificEquityTrades,
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DataFrameSource,
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DataPanelSource)
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from zipline.finance.trading import SimulationParameters
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import zipline.finance.trading as trading
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from zipline.sources.test_source import (
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date_gen,
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create_trade
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)
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# For backwards compatibility
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from zipline.data.loader import (load_from_yahoo,
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load_bars_from_yahoo)
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__all__ = ['load_from_yahoo', 'load_bars_from_yahoo']
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def create_simulation_parameters(year=2006, start=None, end=None,
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capital_base=float("1.0e5"),
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num_days=None
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):
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"""Construct a complete environment with reasonable defaults"""
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if start is None:
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start = datetime(year, 1, 1, tzinfo=pytz.utc)
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if end is None:
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if num_days:
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trading.environment = trading.TradingEnvironment()
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start_index = trading.environment.trading_days.searchsorted(
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start)
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end = trading.environment.trading_days[start_index + num_days - 1]
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else:
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end = datetime(year, 12, 31, tzinfo=pytz.utc)
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sim_params = SimulationParameters(
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period_start=start,
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period_end=end,
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capital_base=capital_base,
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)
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return sim_params
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def create_noop_environment():
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oneday = timedelta(days=1)
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start = datetime(2006, 1, 1, tzinfo=pytz.utc)
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bm_returns = []
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tr_curves = OrderedDict()
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for day in date_gen(start=start, delta=oneday, count=252):
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dr = DailyReturn(day, 0.01)
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bm_returns.append(dr)
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curve = {
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'10year': 0.0799,
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'1month': 0.0799,
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'1year': 0.0785,
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'20year': 0.0765,
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'2year': 0.0794,
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'30year': 0.0804,
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'3month': 0.0789,
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'3year': 0.0796,
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'5year': 0.0792,
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'6month': 0.0794,
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'7year': 0.0804,
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'tid': 1752
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}
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tr_curves[day] = curve
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load_nodata = lambda x: (bm_returns, tr_curves)
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return trading.TradingEnvironment(load=load_nodata)
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def create_random_simulation_parameters():
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trading.environment = trading.TradingEnvironment()
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treasury_curves = trading.environment.treasury_curves
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for n in range(100):
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random_index = random.randint(
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0,
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len(treasury_curves) - 1
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)
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start_dt = treasury_curves.index[random_index]
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end_dt = start_dt + timedelta(days=365)
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now = datetime.utcnow().replace(tzinfo=pytz.utc)
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if end_dt <= now:
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break
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assert end_dt <= now, """
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failed to find a suitable daterange after 100 attempts. please double
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check treasury and benchmark data in findb, and re-run the test."""
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sim_params = SimulationParameters(
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period_start=start_dt,
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period_end=end_dt
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)
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return sim_params, start_dt, end_dt
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def get_next_trading_dt(current, interval):
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naive = current.replace(tzinfo=None)
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delo = Delorean(naive, pytz.utc.zone)
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ex_tz = trading.environment.exchange_tz
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next_dt = delo.shift(ex_tz).datetime
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while True:
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next_dt = next_dt + interval
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next_delo = Delorean(next_dt.replace(tzinfo=None), ex_tz)
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next_utc = next_delo.shift(pytz.utc.zone).datetime
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if trading.environment.is_market_hours(next_utc):
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break
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return next_utc
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def create_trade_history(sid, prices, amounts, interval, sim_params,
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source_id="test_factory"):
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trades = []
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current = sim_params.first_open
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oneday = timedelta(days=1)
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use_midnight = interval >= oneday
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for price, amount in zip(prices, amounts):
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if use_midnight:
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trade_dt = current.replace(hour=0, minute=0)
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else:
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trade_dt = current
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trade = create_trade(sid, price, amount, trade_dt, source_id)
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trades.append(trade)
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current = get_next_trading_dt(current, interval)
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assert len(trades) == len(prices)
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return trades
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def create_dividend(sid, payment, declared_date, ex_date, pay_date):
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div = Event({
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'sid': sid,
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'gross_amount': payment,
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'net_amount': payment,
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'dt': declared_date.replace(hour=0, minute=0, second=0, microsecond=0),
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'ex_date': ex_date.replace(hour=0, minute=0, second=0, microsecond=0),
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'pay_date': pay_date.replace(hour=0, minute=0, second=0,
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microsecond=0),
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'type': DATASOURCE_TYPE.DIVIDEND
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})
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return div
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def create_split(sid, ratio, date):
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return Event({
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'sid': sid,
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'ratio': ratio,
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'dt': date.replace(hour=0, minute=0, second=0, microsecond=0),
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'type': DATASOURCE_TYPE.SPLIT
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})
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def create_txn(sid, price, amount, datetime):
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txn = Event({
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'sid': sid,
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'amount': amount,
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'dt': datetime,
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'price': price,
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'type': DATASOURCE_TYPE.TRANSACTION
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})
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return txn
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def create_commission(sid, value, datetime):
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txn = Event({
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'dt': datetime,
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'type': DATASOURCE_TYPE.COMMISSION,
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'cost': value,
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'sid': sid
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})
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return txn
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def create_txn_history(sid, priceList, amtList, interval, sim_params):
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txns = []
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current = sim_params.first_open
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for price, amount in zip(priceList, amtList):
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current = get_next_trading_dt(current, interval)
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txns.append(create_txn(sid, price, amount, current))
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current = current + interval
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return txns
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def create_returns_from_range(sim_params):
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current = sim_params.first_open
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end = sim_params.last_close
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test_range = []
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while current <= end:
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r = DailyReturn(current, random.random())
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test_range.append(r)
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current = trading.environment.next_trading_day(current)
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return test_range
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def create_returns_from_list(returns, sim_params):
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current = sim_params.first_open
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test_range = []
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# sometimes the range starts with a non-trading day.
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if not trading.environment.is_trading_day(current):
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current = trading.environment.next_trading_day(current)
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for return_val in returns:
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r = DailyReturn(current, return_val)
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test_range.append(r)
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current = trading.environment.next_trading_day(current)
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return test_range
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def create_daily_trade_source(sids, trade_count, sim_params,
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concurrent=False):
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"""
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creates trade_count trades for each sid in sids list.
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first trade will be on sim_params.period_start, and daily
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thereafter for each sid. Thus, two sids should result in two trades per
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day.
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Important side-effect: sim_params.period_end will be modified
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to match the day of the final trade.
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"""
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return create_trade_source(
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sids,
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trade_count,
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timedelta(days=1),
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sim_params,
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concurrent=concurrent
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)
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def create_minutely_trade_source(sids, trade_count, sim_params,
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concurrent=False):
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"""
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creates trade_count trades for each sid in sids list.
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first trade will be on sim_params.period_start, and every minute
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thereafter for each sid. Thus, two sids should result in two trades per
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minute.
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Important side-effect: sim_params.period_end will be modified
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to match the day of the final trade.
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"""
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return create_trade_source(
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sids,
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trade_count,
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timedelta(minutes=1),
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sim_params,
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concurrent=concurrent
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)
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def create_trade_source(sids, trade_count,
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trade_time_increment, sim_params,
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concurrent=False):
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args = tuple()
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kwargs = {
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'count': trade_count,
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'sids': sids,
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'start': sim_params.first_open,
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'delta': trade_time_increment,
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'filter': sids,
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'concurrent': concurrent
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}
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source = SpecificEquityTrades(*args, **kwargs)
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# TODO: do we need to set the trading environment's end to same dt as
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# the last trade in the history?
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# sim_params.period_end = trade_history[-1].dt
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return source
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def create_test_df_source(sim_params=None, bars='daily'):
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if bars == 'daily':
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freq = pd.datetools.BDay()
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elif bars == 'minute':
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freq = pd.datetools.Minute()
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else:
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raise ValueError('%s bars not understood.' % freq)
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if sim_params:
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index = sim_params.trading_days
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else:
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if trading.environment is None:
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trading.environment = trading.TradingEnvironment()
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start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
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end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
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days = trading.environment.days_in_range(start, end)
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if bars == 'daily':
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index = days
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if bars == 'minute':
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index = pd.DatetimeIndex([], freq=freq)
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for day in days:
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day_index = trading.environment.market_minutes_for_day(day)
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index = index.append(day_index)
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x = np.arange(1, len(index) + 1)
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df = pd.DataFrame(x, index=index, columns=[0])
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return DataFrameSource(df), df
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def create_test_panel_source(sim_params=None):
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start = sim_params.first_open \
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if sim_params else pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
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end = sim_params.last_close \
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if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
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if trading.environment is None:
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trading.environment = trading.TradingEnvironment()
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index = trading.environment.days_in_range(start, end)
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price = np.arange(0, len(index))
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volume = np.ones(len(index)) * 1000
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arbitrary = np.ones(len(index))
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df = pd.DataFrame({'price': price,
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'volume': volume,
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'arbitrary': arbitrary},
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index=index)
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panel = pd.Panel.from_dict({0: df})
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return DataPanelSource(panel), panel
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def create_test_panel_ohlc_source(sim_params=None):
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start = sim_params.first_open \
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if sim_params else pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
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end = sim_params.last_close \
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if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
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if trading.environment is None:
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trading.environment = trading.TradingEnvironment()
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index = trading.environment.days_in_range(start, end)
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price = np.arange(0, len(index)) + 100
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high = price * 1.05
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low = price * 0.95
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open_ = price + .1 * (price % 2 - .5)
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volume = np.ones(len(index)) * 1000
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arbitrary = np.ones(len(index))
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df = pd.DataFrame({'price': price,
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'high': high,
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'low': low,
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'open': open_,
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'volume': volume,
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'arbitrary': arbitrary},
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index=index)
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panel = pd.Panel.from_dict({0: df})
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return DataPanelSource(panel), panel
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