def test_combine_date_time():
date = 20170101
time = 145930
assert jutil.combine_date_time(date, time) == 20170101145930
a = np.arange(20170101, 20170101 + 500, dtype=np.int64)
b = np.arange(93000, 93000 + 500, dtype=np.int64)
assert np.all(jutil.combine_date_time(a, b) == a * 1000000 + b)
def test_dtutil():
import datetime
date = 20170105
dt = jutil.convert_int_to_datetime(date)
assert jutil.shift(date, 2) == 20170119
assert jutil.convert_datetime_to_int(jutil.shift(dt, 2)) == 20170119
t = 145539
dt = jutil.combine_date_time(date, t)
assert jutil.split_date_time(dt) == (date, t)
assert jutil.get_next_period_day(date, 'day', 1, 1) == 20170109
assert jutil.get_next_period_day(date, 'week', 2, 0) == 20170116
assert jutil.get_next_period_day(date, 'month', 1, 0) == 20170201
date = 20170808
assert jutil.get_next_period_day(20170831, 'day',
extra_offset=1) == 20170904
assert jutil.get_next_period_day(20170831, 'day', n=2,
extra_offset=0) == 20170904
assert jutil.get_next_period_day(20170831, 'day', n=7,
extra_offset=0) == 20170911
assert jutil.get_next_period_day(20170831, 'week',
extra_offset=1) == 20170905
assert jutil.get_next_period_day(20170831, 'month',
extra_offset=0) == 20170901
monthly = 20170101
while monthly < 20180301:
monthly = jutil.get_next_period_day(monthly, 'month', extra_offset=0)
assert datetime.datetime.strptime(str(monthly), "%Y%m%d").weekday() < 5
def _init_trades(self, df):
"""Add datetime column. """
df.loc[:, 'fill_dt'] = jutil.combine_date_time(df.loc[:, 'fill_date'], df.loc[:, 'fill_time'])
df = df.set_index(['symbol', 'fill_dt']).sort_index(axis=0)
# self._trades = jutil.group_df_to_dict(df, by='symbol')
self._trades = df
def _init_trades(self, df):
"""
Modify trading records dataframe. (Add datetime column)
Parameters
----------
df : pd.DataFrame
Each row represents a single trading record.
"""
df.loc[:, 'fill_dt'] = jutil.combine_date_time(df.loc[:, 'fill_date'],
df.loc[:, 'fill_time'])
df = df.set_index(['symbol', 'fill_dt']).sort_index(axis=0)
# self._trades = jutil.group_df_to_dict(df, by='symbol')
self._trades = df
def _init_trades(self, df):
"""
Modify trading records dataframe. (Add datetime column)
Parameters
----------
df : pd.DataFrame
Each row represents a single trading record.
"""
self._raw_trades = df.copy()
df.loc[:, 'fill_dt'] = jutil.combine_date_time(df.loc[:, 'fill_date'], df.loc[:, 'fill_time'])
df = df.set_index(['symbol', 'fill_dt']).sort_index(axis=0)
# self._trades = jutil.group_df_to_dict(df, by='symbol')
self._trades = df
def _create_time_symbol_bars(self, date):
from collections import defaultdict
# query quotes data
symbols_str = ','.join(self.ctx.universe)
df_quotes, msg = self.ctx.data_api.bar(symbol=symbols_str, start_time=200000, end_time=160000,
trade_date=date, freq=self.bar_type)
if msg != '0,':
print msg
if df_quotes is None or df_quotes.empty:
return dict()
# create nested dict
quotes_list = Bar.create_from_df(df_quotes)
dic = defaultdict(dict)
for quote in quotes_list:
dic[jutil.combine_date_time(quote.date, quote.time)][quote.symbol] = quote
return dic
