mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-02 15:21:01 +08:00
Eddie Hebert
dd172dd42a
In the test factory creation of returns, the date creation was using a timedelta of one day instead of incrementing by trading days. Working towards changing risk module behavior which would leverage the trading day map, but tests fail because non-trading days are created. Remove `factory.create_returns`, moving uses of that function to us `factory.create_returns_from_period`, since the number of days input for `create_returns` was more difficult to use when specifying ranges over arbirtray dates.
445 lines
14 KiB
Python
445 lines
14 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 for tests.
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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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from pandas.io.data import DataReader
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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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def create_simulation_parameters(year=2006, start=None, end=None,
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capital_base=float("1.0e5")
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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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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)
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)
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start_dt = treasury_curves.keys()[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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for price, amount in zip(prices, amounts):
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trade = create_trade(sid, price, amount, current, 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),
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'ex_date': ex_date.replace(hour=0, minute=0, second=0),
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'pay_date': pay_date.replace(hour=0, minute=0, second=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_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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})
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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):
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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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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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index = pd.DatetimeIndex(
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start=start,
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end=end,
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freq=pd.datetools.BDay()
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)
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x = np.arange(0, len(index))
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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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index = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.day)
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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 _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
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"""Load closing prices from yahoo finance.
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:Optional:
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indexes : dict (Default: {'SPX': '^GSPC'})
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Financial indexes to load.
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stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
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'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
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Stock closing prices to load.
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start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices from start date on.
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end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices until end date.
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:Note:
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This is based on code presented in a talk by Wes McKinney:
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http://wesmckinney.com/files/20111017/notebook_output.pdf
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"""
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if indexes is None:
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indexes = {'SPX': '^GSPC'}
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if stocks is None:
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stocks = ['AAPL', 'GE', 'IBM', 'MSFT', 'XOM', 'AA', 'JNJ', 'PEP', 'KO']
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if start is None:
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start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
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if end is None:
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end = pd.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
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assert start < end, "start date is later than end date."
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data = OrderedDict()
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for stock in stocks:
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print stock
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stkd = DataReader(stock, 'yahoo', start, end).sort_index()
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data[stock] = stkd
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for name, ticker in indexes.iteritems():
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print name
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stkd = DataReader(ticker, 'yahoo', start, end).sort_index()
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data[name] = stkd
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return data
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def load_from_yahoo(indexes=None,
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stocks=None,
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start=None,
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end=None,
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adjusted=True):
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"""
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Loads price data from Yahoo into a dataframe for each of the indicated
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securities. By default, 'price' is taken from Yahoo's 'Adjusted Close',
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which removes the impact of splits and dividends. If the argument
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'adjusted' is False, then the non-adjusted 'close' field is used instead.
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:Arguments:
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indexes : dict (Default: {'SPX': '^GSPC'})
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Financial indexes to load.
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stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
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'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
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Stock closing prices to load.
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start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices from start date on.
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end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices until end date.
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adjusted : bool (Default: True)
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Adjust the price for splits and dividends.
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"""
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data = _load_raw_yahoo_data(indexes, stocks, start, end)
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if adjusted:
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close_key = 'Adj Close'
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else:
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close_key = 'Close'
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df = pd.DataFrame({key: d[close_key] for key, d in data.iteritems()})
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df.index = df.index.tz_localize(pytz.utc)
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return df
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def load_bars_from_yahoo(indexes=None,
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stocks=None,
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start=None,
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end=None,
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adjusted=True):
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"""
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Loads data from Yahoo into a panel with the following
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column names for each indicated security:
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- open
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- high
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- low
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- close
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- volume
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- price
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Note that 'price' is Yahoo's 'Adjusted Close', which removes the
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impact of splits and dividends. If the argument 'adjusted' is True, then
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the open, high, low, and close values are adjusted as well.
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:Arguments:
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indexes : dict (Default: {'SPX': '^GSPC'})
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Financial indexes to load.
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stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
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'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
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Stock closing prices to load.
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start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices from start date on.
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end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
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Retrieve prices until end date.
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adjusted : bool (Default: True)
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Adjust open/high/low/close for splits and dividends. The 'price'
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field is always adjusted.
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"""
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data = _load_raw_yahoo_data(indexes, stocks, start, end)
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panel = pd.Panel(data)
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# Rename columns
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panel.minor_axis = ['open', 'high', 'low', 'close', 'volume', 'price']
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panel.major_axis = panel.major_axis.tz_localize(pytz.utc)
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# Adjust data
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if adjusted:
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adj_cols = ['open', 'high', 'low', 'close']
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for ticker in panel.items:
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ratio = (panel[ticker]['price'] / panel[ticker]['close'])
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ratio_filtered = ratio.fillna(0).values
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for col in adj_cols:
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panel[ticker][col] *= ratio_filtered
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return panel
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