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Merge branch 'history_support_daily_input'

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This commit is contained in:
Thomas Wiecki
2014-07-16 17:22:32 +02:00
parent 4980928394 78d2a7ef9a
commit 443bc718f4
5 changed files with 96 additions and 20 deletions
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tests/history_cases.py
-1
View File
@@ -246,7 +246,6 @@ HISTORY_CONTAINER_TEST_CASES = {
],
},
},
'test illiquid prices': {
# A list of HistorySpec objects.
tests/test_history.py
+54 -3
View File
@@ -18,6 +18,7 @@ from unittest import TestCase
from nose_parameterized import parameterized
import numpy as np
import pandas as pd
from pandas.util.testing import assert_frame_equal
from zipline.history import history
from zipline.history.history_container import HistoryContainer
@@ -26,7 +27,7 @@ import zipline.utils.factory as factory
from zipline import TradingAlgorithm
from zipline.finance.trading import SimulationParameters, TradingEnvironment
from zipline.sources import RandomWalkSource
from zipline.sources import RandomWalkSource, DataFrameSource
from .history_cases import (
HISTORY_CONTAINER_TEST_CASES,
@@ -128,7 +129,8 @@ def get_index_at_dt(case_input):
case_input['bar_count'],
case_input['frequency'],
None,
False
False,
daily_at_midnight=False
)
return history.index_at_dt(history_spec, case_input['algo_dt'])
@@ -217,7 +219,8 @@ class TestHistoryContainer(TestCase):
bar_count=3,
frequency='1d',
field='price',
ffill=True
ffill=True,
daily_at_midnight=False
)
specs = {spec.key_str: spec}
initial_sids = [1, ]
@@ -342,6 +345,54 @@ class TestHistoryAlgo(TestCase):
def setUp(self):
np.random.seed(123)
def test_history_daily(self):
bar_count = 3
algo_text = """
from zipline.api import history, add_history
from copy import deepcopy
def initialize(context):
add_history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace = []
def handle_data(context, data):
prices = history(bar_count={bar_count}, frequency='1d', field='price')
context.history_trace.append(deepcopy(prices))
""".format(bar_count=bar_count).strip()
# March 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start = pd.Timestamp('2006-03-20', tz='UTC')
end = pd.Timestamp('2006-03-30', tz='UTC')
sim_params = factory.create_simulation_parameters(
start=start, end=end)
_, df = factory.create_test_df_source(sim_params)
df = df.astype(np.float64)
source = DataFrameSource(df, sids=[0])
test_algo = TradingAlgorithm(
script=algo_text,
data_frequency='daily',
sim_params=sim_params
)
output = test_algo.run(source)
self.assertIsNotNone(output)
history_trace = test_algo.history_trace
for i, received in enumerate(history_trace[bar_count - 1:]):
expected = df.iloc[i:i + bar_count]
assert_frame_equal(expected, received)
def test_basic_history(self):
algo_text = """
from zipline.api import history, add_history
zipline/algorithm.py
+4 -1
View File
@@ -819,7 +819,10 @@ class TradingAlgorithm(object):
@api_method
def add_history(self, bar_count, frequency, field,
ffill=True):
history_spec = HistorySpec(bar_count, frequency, field, ffill)
daily_at_midnight = (self.sim_params.data_frequency == 'daily')
history_spec = HistorySpec(bar_count, frequency, field, ffill,
daily_at_midnight=daily_at_midnight)
self.history_specs[history_spec.key_str] = history_spec
@api_method
zipline/history/history.py
+29 -14
View File
@@ -16,6 +16,7 @@
from __future__ import division
import numpy as np
import pandas as pd
import re
from zipline.finance import trading
@@ -41,7 +42,7 @@ class Frequency(object):
SUPPORTED_FREQUENCIES = frozenset({'1d', '1m'})
MAX_MINUTES = {'m': 1, 'd': 390}
def __init__(self, freq_str):
def __init__(self, freq_str, daily_at_midnight=False):
if freq_str not in self.SUPPORTED_FREQUENCIES:
raise ValueError(
@@ -56,25 +57,31 @@ class Frequency(object):
# unit_str - The unit type, e.g. 'd'
self.num, self.unit_str = parse_freq_str(freq_str)
self.daily_at_midnight = daily_at_midnight
def next_window_start(self, previous_window_close):
"""
Get the first minute of the window starting after a window that
finished on @previous_window_close.
"""
if self.unit_str == 'd':
return self.next_day_window_start(previous_window_close)
return self.next_day_window_start(previous_window_close,
self.daily_at_midnight)
elif self.unit_str == 'm':
return self.next_minute_window_start(previous_window_close)
@staticmethod
def next_day_window_start(previous_window_close):
def next_day_window_start(previous_window_close, daily_at_midnight=False):
"""
Get the next day window start after @previous_window_close. This is
defined as the first market open strictly greater than
@previous_window_close.
"""
env = trading.environment
next_open, _ = env.next_open_and_close(previous_window_close)
if daily_at_midnight:
next_open = env.next_trading_day(previous_window_close)
else:
next_open, _ = env.next_open_and_close(previous_window_close)
return next_open
@staticmethod
@@ -107,8 +114,7 @@ class Frequency(object):
elif self.unit_str == 'm':
return self.minute_window_close(window_start, self.num)
@staticmethod
def day_window_open(window_close, num_days):
def day_window_open(self, window_close, num_days):
"""
Get the first minute for a daily window of length @num_days with last
minute @window_close. This is calculated by searching backward until
@@ -120,10 +126,13 @@ class Frequency(object):
1,
offset=-(num_days - 1)
).market_open.iloc[0]
if self.daily_at_midnight:
open_ = pd.tslib.normalize_date(open_)
return open_
@staticmethod
def minute_window_open(window_close, num_minutes):
def minute_window_open(self, window_close, num_minutes):
"""
Get the first minute for a minutely window of length @num_minutes with
last minute @window_close.
@@ -138,8 +147,7 @@ class Frequency(object):
env = trading.environment
return env.market_minute_window(window_close, count=-num_minutes)[-1]
@staticmethod
def day_window_close(window_start, num_days):
def day_window_close(self, window_start, num_days):
"""
Get the last minute for a daily window of length @num_days with first
minute @window_start. This is calculated by searching forward until
@@ -174,10 +182,13 @@ class Frequency(object):
1,
offset=num_days - 1
).market_close.iloc[0]
if self.daily_at_midnight:
close = pd.tslib.normalize_date(close)
return close
@staticmethod
def minute_window_close(window_start, num_minutes):
def minute_window_close(self, window_start, num_minutes):
"""
Get the last minute for a minutely window of length @num_minutes with
first minute @window_start.
@@ -229,11 +240,12 @@ class HistorySpec(object):
return "{0}:{1}:{2}:{3}".format(
bar_count, freq_str, field, ffill)
def __init__(self, bar_count, frequency, field, ffill):
def __init__(self, bar_count, frequency, field, ffill,
daily_at_midnight=False):
# Number of bars to look back.
self.bar_count = bar_count
if isinstance(frequency, str):
frequency = Frequency(frequency)
frequency = Frequency(frequency, daily_at_midnight)
# The frequency at which the data is sampled.
self.frequency = frequency
# The field, e.g. 'price', 'volume', etc.
@@ -272,6 +284,9 @@ def days_index_at_dt(history_spec, algo_dt):
step=history_spec.frequency.num,
).market_close
if history_spec.frequency.daily_at_midnight:
market_closes = market_closes.apply(pd.tslib.normalize_date)
# Append the current algo_dt as the last index value.
# Using the 'rawer' numpy array values here because of a bottleneck
# that appeared when using DatetimeIndex
zipline/history/history_container.py
+9 -1
View File
@@ -246,6 +246,13 @@ class HistoryContainer(object):
)
return rp
def convert_columns(self, values):
"""
If columns have a specific type you want to enforce, overwrite this
method and return the transformed values.
"""
return values
def create_return_frames(self, algo_dt):
"""
Populates the return frame cache.
@@ -257,7 +264,8 @@ class HistoryContainer(object):
index = pd.to_datetime(index_at_dt(history_spec, algo_dt))
frame = pd.DataFrame(
index=index,
columns=map(int, self.buffer_panel.minor_axis.values),
columns=self.convert_columns(
self.buffer_panel.minor_axis.values),
dtype=np.float64)
self.return_frames[spec_key] = frame