mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-06 05:14:38 +08:00
Thomas Wiecki
10885e1b77
There were sevaral places you could supply sim_params in TradingAlgorithm (__init__, run). This got confusing as its not clear who updated what and which one was the correct one to use at each time. Then there were to ways to define data_frequency, one in __init__() and one in the sim_params which also added code complexity. This refactor makes it explicit that sim_params are to be passed to __init__() only. Moreover, data_frequency is only stored in sim_params. For backwards compatibility, it can still be supplied separately but will link to the one in sim_params. For example, you could create new sim params via: sim_params = create_simulation_parameters(data_frequency='minute') algo = MyAlgo(sim_params) algo.run(data) In addition, perf_tracker only gets initialized in one place: _create_generator() which should also make the various ways of running an algorithm more deterministic. This also fixes a bug with SimulationParameters where you could not change the period_start. Unfortunately, the current implementation still requieres an implicit call to update the internal variables.
377 lines
11 KiB
Python
377 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, data_frequency='daily',
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emission_rate='daily'):
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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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data_frequency=data_frequency,
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emission_rate=emission_rate,
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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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