mirror of
https://github.com/wassname/rl-portfolio-management.git
synced 2026-06-27 18:06:31 +08:00
wassname
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from rl_portfolio_management.data.utils import random_shift, normalize, scale_to_start
|
|
from rl_portfolio_management.util import sharpe, MDD
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
|
|
def test_maxdrawdown():
|
|
assert MDD(np.array([0, 0, 0, 0, 1, 2, 3])) == 0
|
|
assert MDD(np.array([0, 0, 0, 0, 1, 2, 1])) == -1
|
|
assert MDD(np.array([0, 0, 0, 3, 1, 2, 1])) == -2
|
|
assert MDD(np.array([0, 0, 0, 3, 1, 2, 1, 1, 2])) == -2
|
|
|
|
|
|
def test_sharpe():
|
|
x = np.random.normal(loc=1, scale=1, size=1000000)
|
|
np.testing.assert_almost_equal(sharpe(
|
|
x, freq=1), 1, decimal=2, err_msg='sharpe of normal dist with mean=1, std=1 should be ~1')
|
|
|
|
x = np.random.normal(loc=1 / np.sqrt(30), scale=1, size=1000000)
|
|
np.testing.assert_almost_equal(sharpe(
|
|
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')
|
|
|
|
|
|
def test_random_shift():
|
|
s = pd.Series(np.random.random(100)) * 10 + 3
|
|
s1 = random_shift(s.copy(), 0)
|
|
assert (s == s1).all(), 'should not do anything if given 0'
|
|
|
|
s2 = random_shift(s.copy(), 0.05)
|
|
shift = (s2 / s)
|
|
np.testing.assert_almost_equal(shift.mean(), 1.00, 2)
|
|
np.testing.assert_almost_equal(shift.max(), 1.05, 2)
|
|
|
|
|
|
def test_normalize():
|
|
s = pd.Series(np.random.random(10)) * 10 + 3
|
|
s1 = normalize(s)
|
|
np.testing.assert_almost_equal(s1.std(), 1, err_msg='should make std 0')
|
|
np.testing.assert_almost_equal(s1.mean(), 0, err_msg='')
|
|
|
|
|
|
def test_scale_to_start():
|
|
s = pd.Series(np.random.random(10))
|
|
s1 = scale_to_start(s)
|
|
assert s1[0] == 1, 'start should be 1'
|
|
assert s1[1] != 1, 'not start should not be 1'
|