def test_ema_20_initial_calculation(self):
# INTC 2019-12-03 to 2019-12-31
price_data = [
56.07, 56.02, 56.08, 56.81, 56.53, 56.59, 57.07, 57.55, 57.79, 57.70,
57.23, 57.17, 57.96, 58.95, 59.23, 59.41, 59.82, 60.08, 59.62, 59.85,
] # yapf: disable
date = time.CivilTime(2019, 12, 31)
close_prices = [
_make_close_price(value, time.FromCivil(d))
for value, d in zip(price_data, trading_days.get_last_n(date, 20))
]
# Note: this is different than the actual EMA20 for Intel on 2019/12/31,
# which is 58.46, because EMA accounts for *all* past data
assert_that(
ema.make_ema_20d_producer().calculate(close_prices),
equals_proto(f"""
symbol: "TEST"
data_space: STOCK_DATA
data_type: EMA_20D
value: 58.255796513052346
timestamp {{
seconds: {time.as_seconds(2019, 12, 31)}
}}"""))
def test_raise_when_inputs_do_not_meet_accpeted_input_shapes(self):
date = time.CivilTime(2017, 10, 10)
close_prices = [
_make_close_price(200.0, time.FromCivil(d))
for d in trading_days.get_last_n(date, 19)
]
with self.assertRaisesRegex(
errors.InvalidArgumentError,
'Expecting data with input shape: data_type: CLOSE_PRICE'):
ema.make_ema_20d_producer().calculate(close_prices)
def get_close_prices_for_ema20(t: time.Time, symbol: str) -> List[DataEntry]:
# INTC 2019-12-03 to 2019-12-31
price_data = [
56.07, 56.02, 56.08, 56.81, 56.53, 56.59, 57.07, 57.55, 57.79, 57.70,
57.23, 57.17, 57.96, 58.95, 59.23, 59.41, 59.82, 60.08, 59.62, 59.85,
] # yapf: disable
close_prices = [
_make_close_price(symbol, value, time.FromCivil(d))
for value, d in zip(price_data,
trading_days.get_last_n(time.ToCivil(t), 20))
]
random.shuffle(close_prices)
return close_prices
def recursive_inputs_shape(
base_calc_type: DataType.Enum, incr_calc_type: DataType.Enum,
t: time.Time, time_spec: calc.CalcTimeSpecs) -> calc.RecursiveInputs:
# TODO: same here, think about how to get it work with period other than
# days. e.g week-based moving averages.
the_day_before = trading_days.get_last_n(time.ToCivil(t),
1,
include_input_date=False)[0]
return (DataEntry(
data_type=base_calc_type,
timestamp=time_util.from_time(time.FromCivil(the_day_before)),
), DataEntry(
data_type=incr_calc_type,
timestamp=time_util.from_time(t),
))
def test_ema_20_for_constant_list(self):
date = time.CivilTime(2017, 10, 10)
close_prices = [
_make_close_price(200.0, time.FromCivil(d))
for d in trading_days.get_last_n(date, 20)
]
assert_that(
ema.make_ema_20d_producer().calculate(close_prices),
equals_proto(f"""
symbol: "TEST"
data_space: STOCK_DATA
data_type: EMA_20D
value: 200.0
timestamp {{
seconds: {time.as_seconds(2017, 10, 10)}
}}"""))
def series_source_inputs_shape(
source_calc_type: DataType.Enum, t: time.Time,
time_spec: calc.CalcTimeSpecs) -> calc.SourceInputs:
# TODO: right now this is still no properly generalized. For time span
# longer than a day, they should all work. However for day and shorter span,
# they need to be checked against trading days and trading hours.
# NOTE: both FromCivil and ToCivil assumes UTC, which later might
# be updated to take into account the exchange timezone of a
# security.
timestamps = [
time.FromCivil(day) for day in trading_days.get_last_n(
time.ToCivil(t), time_spec.num_periods)
]
return [
DataEntry(
data_type=source_calc_type,
timestamp=time_util.from_time(timestmap),
) for timestmap in timestamps
]