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https://github.com/wassname/rl-portfolio-management.git
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wassname
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
from rl_portfolio_management.data.utils import random_shift, normalize, scale_to_start
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from rl_portfolio_management.util import sharpe, MDD, softmax
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import pandas as pd
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import numpy as np
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def test_softmax():
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x = np.random.random((20, 20))
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y = softmax(x)
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np.testing.assert_almost_equal(y.sum(), 1)
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x = np.random.random([0, 1])
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y = softmax(x)
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assert y[0] < y[1]
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assert (y > 0).all()
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assert (y < 1).all()
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def test_maxdrawdown():
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assert MDD(np.array([0, 0, 0, 0, 1, 2, 3])) == 0
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assert MDD(np.array([0, 0, 0, 0, 1, 2, 1])) == -1
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assert MDD(np.array([0, 0, 0, 3, 1, 2, 1])) == -2
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assert MDD(np.array([0, 0, 0, 3, 1, 2, 1, 1, 2])) == -2
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def test_sharpe():
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x = np.random.normal(loc=1, scale=1, size=1000000)
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np.testing.assert_almost_equal(sharpe(
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x, freq=1), 1, decimal=2, err_msg='sharpe of normal dist with mean=1, std=1 should be ~1')
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x = np.random.normal(loc=1 / np.sqrt(30), scale=1, size=1000000)
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np.testing.assert_almost_equal(sharpe(
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x, freq=30), 1, decimal=2, err_msg='sharpe of normal dist with mean=1/sqrt(30), std=1, at freq of 30 should be ~1')
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def test_random_shift():
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s = pd.Series(np.random.random(100)) * 10 + 3
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s1 = random_shift(s.copy(), 0)
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assert (s == s1).all(), 'should not do anything if given 0'
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s2 = random_shift(s.copy(), 0.05)
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shift = (s2 / s)
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np.testing.assert_almost_equal(shift.mean(), 1.00, 2)
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np.testing.assert_almost_equal(shift.max(), 1.05, 2)
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def test_normalize():
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s = pd.Series(np.random.random(10)) * 10 + 3
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s1 = normalize(s)
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np.testing.assert_almost_equal(s1.std(), 1, err_msg='should make std 0')
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np.testing.assert_almost_equal(s1.mean(), 0, err_msg='')
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def test_scale_to_start():
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s = pd.Series(np.random.random(10))
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s1 = scale_to_start(s)
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assert s1[0] == 1, 'start should be 1'
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assert s1[1] != 1, 'not start should not be 1'
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