mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-02 17:38:21 +08:00
Richard Frank
f21bbe58fc
recent 2 for 1 stock split, where 1 class C share was distributed for each share of class A held. Now a dividend can specify a sid and ratio of stock that will be paid to owners of the original security. If the ratio is 2.0, then for every existing share, two shares will be paid.
374 lines
11 KiB
Python
374 lines
11 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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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 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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from zipline.finance import trading
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from zipline.sources.test_source import create_trade
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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, load=None,
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sids=None):
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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(load=load)
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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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sids=sids,
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)
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return sim_params
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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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next_dt = pd.Timestamp(current).tz_convert(trading.environment.exchange_tz)
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while True:
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# Convert timestamp to naive before adding day, otherwise the when
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# stepping over EDT an hour is added.
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next_dt = pd.Timestamp(next_dt.replace(tzinfo=None))
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next_dt = next_dt + interval
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next_dt = pd.Timestamp(next_dt, tz=trading.environment.exchange_tz)
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next_dt_utc = next_dt.tz_convert('UTC')
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if trading.environment.is_market_hours(next_dt_utc):
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break
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next_dt = next_dt_utc.tz_convert(trading.environment.exchange_tz)
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return next_dt_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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'payment_sid': None,
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'ratio': None,
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'dt': pd.tslib.normalize_date(declared_date),
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'ex_date': pd.tslib.normalize_date(ex_date),
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'pay_date': pd.tslib.normalize_date(pay_date),
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'type': DATASOURCE_TYPE.DIVIDEND,
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'source_id': 'MockDividendSource'
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})
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return div
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def create_stock_dividend(sid, payment_sid, ratio, declared_date,
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ex_date, pay_date):
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return Event({
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'sid': sid,
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'payment_sid': payment_sid,
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'ratio': ratio,
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'net_amount': None,
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'gross_amount': None,
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'dt': pd.tslib.normalize_date(declared_date),
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'ex_date': pd.tslib.normalize_date(ex_date),
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'pay_date': pd.tslib.normalize_date(pay_date),
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'type': DATASOURCE_TYPE.DIVIDEND,
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'source_id': 'MockDividendSource'
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})
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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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'source_id': 'MockSplitSource'
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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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'source_id': 'MockTransactionSource'
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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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'source_id': 'MockCommissionSource'
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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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return pd.Series(index=sim_params.trading_days,
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data=np.random.rand(len(sim_params.trading_days)))
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def create_returns_from_list(returns, sim_params):
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return pd.Series(index=sim_params.trading_days[:len(returns)],
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data=returns)
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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.' % bars)
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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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