mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-12 11:38:29 +08:00
Eddie Hebert
57db5bc17c
The start and end of the simulation parameters should be 'normalized' i.e. midnight timestamped. However, the algorithm tests were using the timestamp of the first and last trade, which were in market times, i.e. 9:30 AM and 4:00 PM EST. Fix passing the sim_params that is used to create the trade_history, instead of having the sim_params inferred from the source. (Also may want to consider fixing the logic that infers the date range from the sources provided.) Also, add a `num_days` option to `factory.create_simulation_parameters` so that the a date range that covers the desired number of days is covered. Since the default sim_params were covering a year, while the test only supplies 4 values, causing an alignment issue with the record test, since a years worth of results were returned, but there were only 4 events.
452 lines
14 KiB
Python
452 lines
14 KiB
Python
#
|
|
# Copyright 2013 Quantopian, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
"""
|
|
Factory functions to prepare useful data.
|
|
"""
|
|
import pytz
|
|
import random
|
|
from collections import OrderedDict
|
|
from delorean import Delorean
|
|
|
|
import pandas as pd
|
|
from pandas.io.data import DataReader
|
|
import numpy as np
|
|
from datetime import datetime, timedelta
|
|
|
|
from zipline.protocol import DailyReturn, Event, DATASOURCE_TYPE
|
|
from zipline.sources import (SpecificEquityTrades,
|
|
DataFrameSource,
|
|
DataPanelSource)
|
|
from zipline.finance.trading import SimulationParameters
|
|
import zipline.finance.trading as trading
|
|
from zipline.sources.test_source import (
|
|
date_gen,
|
|
create_trade
|
|
)
|
|
|
|
|
|
def create_simulation_parameters(year=2006, start=None, end=None,
|
|
capital_base=float("1.0e5"),
|
|
num_days=None
|
|
):
|
|
"""Construct a complete environment with reasonable defaults"""
|
|
if start is None:
|
|
start = datetime(year, 1, 1, tzinfo=pytz.utc)
|
|
if end is None:
|
|
if num_days:
|
|
trading.environment = trading.TradingEnvironment()
|
|
start_index = trading.environment.trading_days.searchsorted(
|
|
start)
|
|
end = trading.environment.trading_days[start_index + num_days - 1]
|
|
else:
|
|
end = datetime(year, 12, 31, tzinfo=pytz.utc)
|
|
sim_params = SimulationParameters(
|
|
period_start=start,
|
|
period_end=end,
|
|
capital_base=capital_base,
|
|
)
|
|
|
|
return sim_params
|
|
|
|
|
|
def create_noop_environment():
|
|
oneday = timedelta(days=1)
|
|
start = datetime(2006, 1, 1, tzinfo=pytz.utc)
|
|
|
|
bm_returns = []
|
|
tr_curves = OrderedDict()
|
|
for day in date_gen(start=start, delta=oneday, count=252):
|
|
dr = DailyReturn(day, 0.01)
|
|
bm_returns.append(dr)
|
|
curve = {
|
|
'10year': 0.0799,
|
|
'1month': 0.0799,
|
|
'1year': 0.0785,
|
|
'20year': 0.0765,
|
|
'2year': 0.0794,
|
|
'30year': 0.0804,
|
|
'3month': 0.0789,
|
|
'3year': 0.0796,
|
|
'5year': 0.0792,
|
|
'6month': 0.0794,
|
|
'7year': 0.0804,
|
|
'tid': 1752
|
|
}
|
|
tr_curves[day] = curve
|
|
|
|
load_nodata = lambda x: (bm_returns, tr_curves)
|
|
|
|
return trading.TradingEnvironment(load=load_nodata)
|
|
|
|
|
|
def create_random_simulation_parameters():
|
|
trading.environment = trading.TradingEnvironment()
|
|
treasury_curves = trading.environment.treasury_curves
|
|
|
|
for n in range(100):
|
|
|
|
random_index = random.randint(
|
|
0,
|
|
len(treasury_curves)
|
|
)
|
|
|
|
start_dt = treasury_curves.keys()[random_index]
|
|
end_dt = start_dt + timedelta(days=365)
|
|
|
|
now = datetime.utcnow().replace(tzinfo=pytz.utc)
|
|
|
|
if end_dt <= now:
|
|
break
|
|
|
|
assert end_dt <= now, """
|
|
failed to find a suitable daterange after 100 attempts. please double
|
|
check treasury and benchmark data in findb, and re-run the test."""
|
|
|
|
sim_params = SimulationParameters(
|
|
period_start=start_dt,
|
|
period_end=end_dt
|
|
)
|
|
|
|
return sim_params, start_dt, end_dt
|
|
|
|
|
|
def get_next_trading_dt(current, interval):
|
|
naive = current.replace(tzinfo=None)
|
|
delo = Delorean(naive, pytz.utc.zone)
|
|
ex_tz = trading.environment.exchange_tz
|
|
next_dt = delo.shift(ex_tz).datetime
|
|
|
|
while True:
|
|
next_dt = next_dt + interval
|
|
next_delo = Delorean(next_dt.replace(tzinfo=None), ex_tz)
|
|
next_utc = next_delo.shift(pytz.utc.zone).datetime
|
|
if trading.environment.is_market_hours(next_utc):
|
|
break
|
|
|
|
return next_utc
|
|
|
|
|
|
def create_trade_history(sid, prices, amounts, interval, sim_params,
|
|
source_id="test_factory"):
|
|
trades = []
|
|
current = sim_params.first_open
|
|
|
|
for price, amount in zip(prices, amounts):
|
|
trade = create_trade(sid, price, amount, current, source_id)
|
|
trades.append(trade)
|
|
current = get_next_trading_dt(current, interval)
|
|
|
|
assert len(trades) == len(prices)
|
|
return trades
|
|
|
|
|
|
def create_dividend(sid, payment, declared_date, ex_date, pay_date):
|
|
div = Event({
|
|
'sid': sid,
|
|
'gross_amount': payment,
|
|
'net_amount': payment,
|
|
'dt': declared_date.replace(hour=0, minute=0, second=0),
|
|
'ex_date': ex_date.replace(hour=0, minute=0, second=0),
|
|
'pay_date': pay_date.replace(hour=0, minute=0, second=0),
|
|
'type': DATASOURCE_TYPE.DIVIDEND
|
|
})
|
|
|
|
return div
|
|
|
|
|
|
def create_txn(sid, price, amount, datetime):
|
|
txn = Event({
|
|
'sid': sid,
|
|
'amount': amount,
|
|
'dt': datetime,
|
|
'price': price,
|
|
})
|
|
return txn
|
|
|
|
|
|
def create_txn_history(sid, priceList, amtList, interval, sim_params):
|
|
txns = []
|
|
current = sim_params.first_open
|
|
|
|
for price, amount in zip(priceList, amtList):
|
|
current = get_next_trading_dt(current, interval)
|
|
|
|
txns.append(create_txn(sid, price, amount, current))
|
|
current = current + interval
|
|
return txns
|
|
|
|
|
|
def create_returns_from_range(sim_params):
|
|
current = sim_params.first_open
|
|
end = sim_params.last_close
|
|
test_range = []
|
|
while current <= end:
|
|
r = DailyReturn(current, random.random())
|
|
test_range.append(r)
|
|
current = trading.environment.next_trading_day(current)
|
|
|
|
return test_range
|
|
|
|
|
|
def create_returns_from_list(returns, sim_params):
|
|
current = sim_params.first_open
|
|
test_range = []
|
|
|
|
#sometimes the range starts with a non-trading day.
|
|
if not trading.environment.is_trading_day(current):
|
|
current = trading.environment.next_trading_day(current)
|
|
|
|
for return_val in returns:
|
|
r = DailyReturn(current, return_val)
|
|
test_range.append(r)
|
|
current = trading.environment.next_trading_day(current)
|
|
|
|
return test_range
|
|
|
|
|
|
def create_daily_trade_source(sids, trade_count, sim_params,
|
|
concurrent=False):
|
|
"""
|
|
creates trade_count trades for each sid in sids list.
|
|
first trade will be on sim_params.period_start, and daily
|
|
thereafter for each sid. Thus, two sids should result in two trades per
|
|
day.
|
|
|
|
Important side-effect: sim_params.period_end will be modified
|
|
to match the day of the final trade.
|
|
"""
|
|
return create_trade_source(
|
|
sids,
|
|
trade_count,
|
|
timedelta(days=1),
|
|
sim_params,
|
|
concurrent=concurrent
|
|
)
|
|
|
|
|
|
def create_minutely_trade_source(sids, trade_count, sim_params,
|
|
concurrent=False):
|
|
"""
|
|
creates trade_count trades for each sid in sids list.
|
|
first trade will be on sim_params.period_start, and every minute
|
|
thereafter for each sid. Thus, two sids should result in two trades per
|
|
minute.
|
|
|
|
Important side-effect: sim_params.period_end will be modified
|
|
to match the day of the final trade.
|
|
"""
|
|
return create_trade_source(
|
|
sids,
|
|
trade_count,
|
|
timedelta(minutes=1),
|
|
sim_params,
|
|
concurrent=concurrent
|
|
)
|
|
|
|
|
|
def create_trade_source(sids, trade_count,
|
|
trade_time_increment, sim_params,
|
|
concurrent=False):
|
|
|
|
args = tuple()
|
|
kwargs = {
|
|
'count': trade_count,
|
|
'sids': sids,
|
|
'start': sim_params.first_open,
|
|
'delta': trade_time_increment,
|
|
'filter': sids,
|
|
'concurrent': concurrent
|
|
}
|
|
source = SpecificEquityTrades(*args, **kwargs)
|
|
|
|
# TODO: do we need to set the trading environment's end to same dt as
|
|
# the last trade in the history?
|
|
#sim_params.period_end = trade_history[-1].dt
|
|
|
|
return source
|
|
|
|
|
|
def create_test_df_source(sim_params=None):
|
|
|
|
if sim_params:
|
|
index = sim_params.trading_days
|
|
else:
|
|
start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
|
|
end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
|
|
index = pd.DatetimeIndex(
|
|
start=start,
|
|
end=end,
|
|
freq=pd.datetools.BDay()
|
|
)
|
|
|
|
x = np.arange(0, len(index))
|
|
|
|
df = pd.DataFrame(x, index=index, columns=[0])
|
|
|
|
return DataFrameSource(df), df
|
|
|
|
|
|
def create_test_panel_source(sim_params=None):
|
|
start = sim_params.first_open \
|
|
if sim_params else pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
|
|
|
|
end = sim_params.last_close \
|
|
if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
|
|
|
|
index = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.day)
|
|
price = np.arange(0, len(index))
|
|
volume = np.ones(len(index)) * 1000
|
|
arbitrary = np.ones(len(index))
|
|
|
|
df = pd.DataFrame({'price': price,
|
|
'volume': volume,
|
|
'arbitrary': arbitrary},
|
|
index=index)
|
|
panel = pd.Panel.from_dict({0: df})
|
|
|
|
return DataPanelSource(panel), panel
|
|
|
|
|
|
def _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
|
|
"""Load closing prices from yahoo finance.
|
|
|
|
:Optional:
|
|
indexes : dict (Default: {'SPX': '^GSPC'})
|
|
Financial indexes to load.
|
|
stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
|
|
'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
|
|
Stock closing prices to load.
|
|
start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices from start date on.
|
|
end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices until end date.
|
|
|
|
:Note:
|
|
This is based on code presented in a talk by Wes McKinney:
|
|
http://wesmckinney.com/files/20111017/notebook_output.pdf
|
|
"""
|
|
|
|
assert indexes is not None or stocks is not None, """
|
|
must specify stocks or indexes"""
|
|
|
|
if start is None:
|
|
start = pd.datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
|
|
|
|
if not start is None and not end is None:
|
|
assert start < end, "start date is later than end date."
|
|
|
|
data = OrderedDict()
|
|
|
|
if stocks is not None:
|
|
for stock in stocks:
|
|
print stock
|
|
stkd = DataReader(stock, 'yahoo', start, end).sort_index()
|
|
data[stock] = stkd
|
|
|
|
if indexes is not None:
|
|
for name, ticker in indexes.iteritems():
|
|
print name
|
|
stkd = DataReader(ticker, 'yahoo', start, end).sort_index()
|
|
data[name] = stkd
|
|
|
|
return data
|
|
|
|
|
|
def load_from_yahoo(indexes=None,
|
|
stocks=None,
|
|
start=None,
|
|
end=None,
|
|
adjusted=True):
|
|
"""
|
|
Loads price data from Yahoo into a dataframe for each of the indicated
|
|
securities. By default, 'price' is taken from Yahoo's 'Adjusted Close',
|
|
which removes the impact of splits and dividends. If the argument
|
|
'adjusted' is False, then the non-adjusted 'close' field is used instead.
|
|
|
|
:Arguments:
|
|
indexes : dict (Default: {'SPX': '^GSPC'})
|
|
Financial indexes to load.
|
|
stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
|
|
'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
|
|
Stock closing prices to load.
|
|
start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices from start date on.
|
|
end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices until end date.
|
|
adjusted : bool (Default: True)
|
|
Adjust the price for splits and dividends.
|
|
|
|
"""
|
|
data = _load_raw_yahoo_data(indexes, stocks, start, end)
|
|
if adjusted:
|
|
close_key = 'Adj Close'
|
|
else:
|
|
close_key = 'Close'
|
|
df = pd.DataFrame({key: d[close_key] for key, d in data.iteritems()})
|
|
df.index = df.index.tz_localize(pytz.utc)
|
|
return df
|
|
|
|
|
|
def load_bars_from_yahoo(indexes=None,
|
|
stocks=None,
|
|
start=None,
|
|
end=None,
|
|
adjusted=True):
|
|
"""
|
|
Loads data from Yahoo into a panel with the following
|
|
column names for each indicated security:
|
|
- open
|
|
- high
|
|
- low
|
|
- close
|
|
- volume
|
|
- price
|
|
|
|
Note that 'price' is Yahoo's 'Adjusted Close', which removes the
|
|
impact of splits and dividends. If the argument 'adjusted' is True, then
|
|
the open, high, low, and close values are adjusted as well.
|
|
|
|
:Arguments:
|
|
indexes : dict (Default: {'SPX': '^GSPC'})
|
|
Financial indexes to load.
|
|
stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
|
|
'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
|
|
Stock closing prices to load.
|
|
start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices from start date on.
|
|
end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
|
|
Retrieve prices until end date.
|
|
adjusted : bool (Default: True)
|
|
Adjust open/high/low/close for splits and dividends. The 'price'
|
|
field is always adjusted.
|
|
|
|
"""
|
|
data = _load_raw_yahoo_data(indexes, stocks, start, end)
|
|
panel = pd.Panel(data)
|
|
# Rename columns
|
|
panel.minor_axis = ['open', 'high', 'low', 'close', 'volume', 'price']
|
|
panel.major_axis = panel.major_axis.tz_localize(pytz.utc)
|
|
# Adjust data
|
|
if adjusted:
|
|
adj_cols = ['open', 'high', 'low', 'close']
|
|
for ticker in panel.items:
|
|
ratio = (panel[ticker]['price'] / panel[ticker]['close'])
|
|
ratio_filtered = ratio.fillna(0).values
|
|
for col in adj_cols:
|
|
panel[ticker][col] *= ratio_filtered
|
|
return panel
|