mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-30 04:01:25 +08:00
Scott Sanderson
406 lines
12 KiB
Python
406 lines
12 KiB
Python
from __future__ import division
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from nose_parameterized import parameterized
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from six.moves import range
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import numpy as np
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import pandas as pd
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import talib
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from zipline.lib.adjusted_array import AdjustedArray
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from zipline.pipeline.data import USEquityPricing
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from zipline.pipeline.factors import (
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BollingerBands,
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Aroon,
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FastStochasticOscillator,
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IchimokuKinkoHyo,
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LinearWeightedMovingAverage,
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RateOfChangePercentage,
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TrueRange,
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)
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from zipline.testing import parameter_space
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from zipline.testing.fixtures import ZiplineTestCase
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from zipline.testing.predicates import assert_equal
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from .base import BasePipelineTestCase
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class BollingerBandsTestCase(BasePipelineTestCase):
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def closes(self, mask_last_sid):
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data = self.arange_data(dtype=np.float64)
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if mask_last_sid:
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data[:, -1] = np.nan
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return data
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def expected_bbands(self, window_length, k, closes):
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"""Compute the expected data (without adjustments) for the given
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window, k, and closes array.
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This uses talib.BBANDS to generate the expected data.
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"""
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lower_cols = []
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middle_cols = []
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upper_cols = []
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ndates, nassets = closes.shape
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for n in range(nassets):
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close_col = closes[:, n]
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if np.isnan(close_col).all():
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# ta-lib doesn't deal well with all nans.
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upper, middle, lower = [np.full(ndates, np.nan)] * 3
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else:
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upper, middle, lower = talib.BBANDS(
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close_col,
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window_length,
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k,
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k,
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)
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upper_cols.append(upper)
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middle_cols.append(middle)
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lower_cols.append(lower)
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# Stack all of our uppers, middles, lowers into three 2d arrays
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# whose columns are the sids. After that, slice off only the
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# rows we care about.
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where = np.s_[window_length - 1:]
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uppers = np.column_stack(upper_cols)[where]
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middles = np.column_stack(middle_cols)[where]
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lowers = np.column_stack(lower_cols)[where]
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return uppers, middles, lowers
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@parameter_space(
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window_length={5, 10, 20},
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k={1.5, 2, 2.5},
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mask_last_sid={True, False},
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__fail_fast=True,
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)
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def test_bollinger_bands(self, window_length, k, mask_last_sid):
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closes = self.closes(mask_last_sid=mask_last_sid)
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mask = ~np.isnan(closes)
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bbands = BollingerBands(window_length=window_length, k=k)
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expected = self.expected_bbands(window_length, k, closes)
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self.check_terms(
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terms={
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'upper': bbands.upper,
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'middle': bbands.middle,
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'lower': bbands.lower,
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},
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expected={
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'upper': expected[0],
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'middle': expected[1],
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'lower': expected[2],
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},
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initial_workspace={
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USEquityPricing.close: AdjustedArray(
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data=closes,
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mask=mask,
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adjustments={},
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missing_value=np.nan,
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),
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},
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mask=self.build_mask(mask),
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)
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def test_bollinger_bands_output_ordering(self):
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bbands = BollingerBands(window_length=5, k=2)
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lower, middle, upper = bbands
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self.assertIs(lower, bbands.lower)
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self.assertIs(middle, bbands.middle)
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self.assertIs(upper, bbands.upper)
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class AroonTestCase(ZiplineTestCase):
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window_length = 10
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nassets = 5
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dtype = [('down', 'f8'), ('up', 'f8')]
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@parameterized.expand([
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(np.arange(window_length),
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np.arange(window_length) + 1,
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np.recarray(shape=(nassets,), dtype=dtype,
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buf=np.array([0, 100] * nassets, dtype='f8'))),
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(np.arange(window_length, 0, -1),
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np.arange(window_length, 0, -1) - 1,
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np.recarray(shape=(nassets,), dtype=dtype,
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buf=np.array([100, 0] * nassets, dtype='f8'))),
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(np.array([10, 10, 10, 1, 10, 10, 10, 10, 10, 10]),
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np.array([1, 1, 1, 1, 1, 10, 1, 1, 1, 1]),
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np.recarray(shape=(nassets,), dtype=dtype,
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buf=np.array([100 * 3 / 9, 100 * 5 / 9] * nassets,
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dtype='f8'))),
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])
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def test_aroon_basic(self, lows, highs, expected_out):
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aroon = Aroon(window_length=self.window_length)
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today = pd.Timestamp('2014', tz='utc')
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assets = pd.Index(np.arange(self.nassets, dtype=np.int64))
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shape = (self.nassets,)
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out = np.recarray(shape=shape, dtype=self.dtype,
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buf=np.empty(shape=shape, dtype=self.dtype))
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aroon.compute(today, assets, out, lows, highs)
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assert_equal(out, expected_out)
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class TestFastStochasticOscillator(ZiplineTestCase):
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"""
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Test the Fast Stochastic Oscillator
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"""
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def test_fso_expected_basic(self):
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"""
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Simple test of expected output from fast stochastic oscillator
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"""
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fso = FastStochasticOscillator()
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today = pd.Timestamp('2015')
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assets = np.arange(3, dtype=np.float64)
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out = np.empty(shape=(3,), dtype=np.float64)
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highs = np.full((50, 3), 3, dtype=np.float64)
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lows = np.full((50, 3), 2, dtype=np.float64)
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closes = np.full((50, 3), 4, dtype=np.float64)
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fso.compute(today, assets, out, closes, lows, highs)
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# Expected %K
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assert_equal(out, np.full((3,), 200, dtype=np.float64))
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@parameter_space(seed=range(5))
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def test_fso_expected_with_talib(self, seed):
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"""
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Test the output that is returned from the fast stochastic oscillator
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is the same as that from the ta-lib STOCHF function.
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"""
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window_length = 14
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nassets = 6
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rng = np.random.RandomState(seed=seed)
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input_size = (window_length, nassets)
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# values from 9 to 12
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closes = 9.0 + (rng.random_sample(input_size) * 3.0)
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# Values from 13 to 15
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highs = 13.0 + (rng.random_sample(input_size) * 2.0)
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# Values from 6 to 8.
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lows = 6.0 + (rng.random_sample(input_size) * 2.0)
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expected_out_k = []
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for i in range(nassets):
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fastk, fastd = talib.STOCHF(
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high=highs[:, i],
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low=lows[:, i],
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close=closes[:, i],
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fastk_period=window_length,
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fastd_period=1,
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)
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expected_out_k.append(fastk[-1])
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expected_out_k = np.array(expected_out_k)
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today = pd.Timestamp('2015')
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out = np.empty(shape=(nassets,), dtype=np.float)
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assets = np.arange(nassets, dtype=np.float)
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fso = FastStochasticOscillator()
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fso.compute(
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today, assets, out, closes, lows, highs
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)
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assert_equal(out, expected_out_k, array_decimal=6)
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class IchimokuKinkoHyoTestCase(ZiplineTestCase):
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def test_ichimoku_kinko_hyo(self):
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window_length = 52
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today = pd.Timestamp('2014', tz='utc')
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nassets = 5
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assets = pd.Index(np.arange(nassets))
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days_col = np.arange(window_length)[:, np.newaxis]
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highs = np.arange(nassets) + 2 + days_col
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closes = np.arange(nassets) + 1 + days_col
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lows = np.arange(nassets) + days_col
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tenkan_sen_length = 9
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kijun_sen_length = 26
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chikou_span_length = 26
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ichimoku_kinko_hyo = IchimokuKinkoHyo(
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window_length=window_length,
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tenkan_sen_length=tenkan_sen_length,
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kijun_sen_length=kijun_sen_length,
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chikou_span_length=chikou_span_length,
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)
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dtype = [
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('tenkan_sen', 'f8'),
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('kijun_sen', 'f8'),
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('senkou_span_a', 'f8'),
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('senkou_span_b', 'f8'),
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('chikou_span', 'f8'),
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]
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out = np.recarray(
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shape=(nassets,),
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dtype=dtype,
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buf=np.empty(shape=(nassets,), dtype=dtype),
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)
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ichimoku_kinko_hyo.compute(
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today,
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assets,
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out,
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highs,
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lows,
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closes,
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tenkan_sen_length,
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kijun_sen_length,
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chikou_span_length,
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)
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expected_tenkan_sen = np.array([
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(53 + 43) / 2,
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(54 + 44) / 2,
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(55 + 45) / 2,
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(56 + 46) / 2,
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(57 + 47) / 2,
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])
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expected_kijun_sen = np.array([
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(53 + 26) / 2,
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(54 + 27) / 2,
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(55 + 28) / 2,
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(56 + 29) / 2,
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(57 + 30) / 2,
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])
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expected_senkou_span_a = (expected_tenkan_sen + expected_kijun_sen) / 2
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expected_senkou_span_b = np.array([
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(53 + 0) / 2,
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(54 + 1) / 2,
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(55 + 2) / 2,
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(56 + 3) / 2,
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(57 + 4) / 2,
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])
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expected_chikou_span = np.array([
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27.0,
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28.0,
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29.0,
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30.0,
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31.0,
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])
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assert_equal(
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out.tenkan_sen,
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expected_tenkan_sen,
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msg='tenkan_sen',
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)
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assert_equal(
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out.kijun_sen,
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expected_kijun_sen,
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msg='kijun_sen',
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)
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assert_equal(
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out.senkou_span_a,
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expected_senkou_span_a,
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msg='senkou_span_a',
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)
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assert_equal(
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out.senkou_span_b,
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expected_senkou_span_b,
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msg='senkou_span_b',
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)
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assert_equal(
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out.chikou_span,
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expected_chikou_span,
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msg='chikou_span',
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)
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@parameter_space(
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arg={'tenkan_sen_length', 'kijun_sen_length', 'chikou_span_length'},
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)
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def test_input_validation(self, arg):
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window_length = 52
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with self.assertRaises(ValueError) as e:
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IchimokuKinkoHyo(**{arg: window_length + 1})
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assert_equal(
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str(e.exception),
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'%s must be <= the window_length: 53 > 52' % arg,
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)
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class TestRateOfChangePercentage(ZiplineTestCase):
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@parameterized.expand([
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('constant', [2.] * 10, 0.0),
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('step', [2.] + [1.] * 9, -50.0),
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('linear', [2. + x for x in range(10)], 450.0),
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('quadratic', [2. + x**2 for x in range(10)], 4050.0),
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])
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def test_rate_of_change_percentage(self, test_name, data, expected):
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window_length = len(data)
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rocp = RateOfChangePercentage(
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inputs=(USEquityPricing.close,),
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window_length=window_length,
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)
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today = pd.Timestamp('2014')
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assets = np.arange(5, dtype=np.int64)
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# broadcast data across assets
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data = np.array(data)[:, np.newaxis] * np.ones(len(assets))
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out = np.zeros(len(assets))
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rocp.compute(today, assets, out, data)
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assert_equal(out, np.full((len(assets),), expected))
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class TestLinearWeightedMovingAverage(ZiplineTestCase):
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def test_wma1(self):
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wma1 = LinearWeightedMovingAverage(
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inputs=(USEquityPricing.close,),
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window_length=10
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)
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today = pd.Timestamp('2015')
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assets = np.arange(5, dtype=np.int64)
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data = np.ones((10, 5))
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out = np.zeros(data.shape[1])
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wma1.compute(today, assets, out, data)
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assert_equal(out, np.ones(5))
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def test_wma2(self):
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wma2 = LinearWeightedMovingAverage(
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inputs=(USEquityPricing.close,),
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window_length=10
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)
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today = pd.Timestamp('2015')
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assets = np.arange(5, dtype=np.int64)
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data = np.arange(50, dtype=np.float64).reshape((10, 5))
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out = np.zeros(data.shape[1])
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wma2.compute(today, assets, out, data)
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assert_equal(out, np.array([30., 31., 32., 33., 34.]))
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class TestTrueRange(ZiplineTestCase):
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def test_tr_basic(self):
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tr = TrueRange()
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today = pd.Timestamp('2014')
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assets = np.arange(3, dtype=np.int64)
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out = np.empty(3, dtype=np.float64)
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highs = np.full((2, 3), 3.)
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lows = np.full((2, 3), 2.)
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closes = np.full((2, 3), 1.)
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tr.compute(today, assets, out, highs, lows, closes)
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assert_equal(out, np.full((3,), 2.))
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