mirror of
https://github.com/robertmartin8/PyPortfolioOpt.git
synced 2022-11-27 18:02:41 +03:00
599 lines
20 KiB
Python
Executable File
599 lines
20 KiB
Python
Executable File
import warnings
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import numpy as np
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import pandas as pd
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import pytest
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from pypfopt.efficient_frontier import EfficientFrontier
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from tests.utilities_for_tests import get_data, setup_efficient_frontier
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from pypfopt import risk_models
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def test_data_source():
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df = get_data()
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assert isinstance(df, pd.DataFrame)
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assert df.shape[1] == 20
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assert len(df) == 7126
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assert df.index.is_all_dates
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def test_returns_dataframe():
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df = get_data()
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returns_df = df.pct_change().dropna(how="all")
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assert isinstance(returns_df, pd.DataFrame)
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assert returns_df.shape[1] == 20
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assert len(returns_df) == 7125
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assert returns_df.index.is_all_dates
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assert not ((returns_df > 1) & returns_df.notnull()).any().any()
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def test_portfolio_performance():
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ef = setup_efficient_frontier()
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with pytest.raises(ValueError):
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ef.portfolio_performance()
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ef.max_sharpe()
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assert ef.portfolio_performance()
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def test_efficient_frontier_inheritance():
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ef = setup_efficient_frontier()
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assert ef.clean_weights
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assert isinstance(ef.initial_guess, np.ndarray)
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assert isinstance(ef.constraints, list)
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def test_max_sharpe_long_only():
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ef = setup_efficient_frontier()
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w = ef.max_sharpe()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.3303554227420522, 0.21671629569400466, 1.4320816150358278),
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)
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def test_max_sharpe_short():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
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)
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w = ef.max_sharpe()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.4072375737868628, 0.24823079606119094, 1.5599900573634125)
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)
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sharpe = ef.portfolio_performance()[2]
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ef_long_only = setup_efficient_frontier()
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ef_long_only.max_sharpe()
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long_only_sharpe = ef_long_only.portfolio_performance()[2]
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assert sharpe > long_only_sharpe
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def test_weight_bounds_minus_one_to_one():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
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)
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assert ef.max_sharpe()
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assert ef.min_volatility()
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assert ef.efficient_return(0.05)
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assert ef.efficient_risk(0.05)
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def test_max_sharpe_L2_reg():
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ef = setup_efficient_frontier()
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ef.gamma = 1
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w = ef.max_sharpe()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.3062919877378972, 0.20291366982652356, 1.4109053765705188),
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)
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def test_max_sharpe_L2_reg_many_values():
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ef = setup_efficient_frontier()
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ef.max_sharpe()
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# Count the number of weights more 1%
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initial_number = sum(ef.weights > 0.01)
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for a in np.arange(0.5, 5, 0.5):
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ef.gamma = a
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ef.max_sharpe()
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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new_number = sum(ef.weights > 0.01)
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# Higher gamma should reduce the number of small weights
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assert new_number >= initial_number
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initial_number = new_number
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def test_max_sharpe_L2_reg_limit_case():
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ef = setup_efficient_frontier()
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ef.gamma = 1e10
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ef.max_sharpe()
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equal_weights = np.array([1 / ef.n_assets] * ef.n_assets)
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np.testing.assert_array_almost_equal(ef.weights, equal_weights)
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def test_max_sharpe_L2_reg_reduces_sharpe():
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# L2 reg should reduce the number of small weights at the cost of Sharpe
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ef_no_reg = setup_efficient_frontier()
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ef_no_reg.max_sharpe()
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sharpe_no_reg = ef_no_reg.portfolio_performance()[2]
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ef = setup_efficient_frontier()
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ef.gamma = 1
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ef.max_sharpe()
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sharpe = ef.portfolio_performance()[2]
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assert sharpe < sharpe_no_reg
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def test_max_sharpe_L2_reg_with_shorts():
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ef_no_reg = setup_efficient_frontier()
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ef_no_reg.max_sharpe()
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initial_number = sum(ef_no_reg.weights > 0.01)
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
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)
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ef.gamma = 1
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w = ef.max_sharpe()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.32360478341793864, 0.20241509658051923, 1.499911758296975),
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)
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new_number = sum(ef.weights > 0.01)
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assert new_number >= initial_number
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def test_max_sharpe_risk_free_rate():
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ef = setup_efficient_frontier()
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ef.max_sharpe()
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_, _, initial_sharpe = ef.portfolio_performance()
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ef.max_sharpe(risk_free_rate=0.10)
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_, _, new_sharpe = ef.portfolio_performance(risk_free_rate=0.10)
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assert new_sharpe <= initial_sharpe
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ef.max_sharpe(risk_free_rate=0)
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_, _, new_sharpe = ef.portfolio_performance(risk_free_rate=0)
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assert new_sharpe >= initial_sharpe
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def test_max_sharpe_input_errors():
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with pytest.raises(ValueError):
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), gamma="2"
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)
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with warnings.catch_warnings(record=True) as w:
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), gamma=-1)
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assert len(w) == 1
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assert issubclass(w[0].category, UserWarning)
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assert (
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str(w[0].message)
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== "in most cases, gamma should be positive"
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)
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with pytest.raises(ValueError):
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ef.max_sharpe(risk_free_rate="0.2")
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def test_min_volatility():
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ef = setup_efficient_frontier()
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w = ef.min_volatility()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.1791557243114251, 0.15915426422116669, 1.0000091740567905),
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)
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def test_min_volatility_short():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
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)
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w = ef.min_volatility()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.1719799152621441, 0.1555954785460613, 0.9767630568850568),
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)
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# Shorting should reduce volatility
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volatility = ef.portfolio_performance()[1]
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ef_long_only = setup_efficient_frontier()
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ef_long_only.min_volatility()
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long_only_volatility = ef_long_only.portfolio_performance()[1]
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assert volatility < long_only_volatility
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def test_min_volatility_L2_reg():
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ef = setup_efficient_frontier()
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ef.gamma = 1
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w = ef.min_volatility()
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.23136193240984504, 0.1955259140191799, 1.0809919159314694),
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)
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def test_min_volatility_L2_reg_many_values():
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ef = setup_efficient_frontier()
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ef.min_volatility()
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# Count the number of weights more 1%
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initial_number = sum(ef.weights > 0.01)
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for a in np.arange(0.5, 5, 0.5):
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ef.gamma = a
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ef.min_volatility()
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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new_number = sum(ef.weights > 0.01)
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# Higher gamma should reduce the number of small weights
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assert new_number >= initial_number
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initial_number = new_number
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def test_efficient_risk():
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ef = setup_efficient_frontier()
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w = ef.efficient_risk(0.19)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(), (0.2857747021087114, 0.19, 1.3988133092245933), atol=1e-6
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)
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def test_efficient_risk_many_values():
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ef = setup_efficient_frontier()
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for target_risk in np.arange(0.16, 0.21, 0.01):
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ef.efficient_risk(target_risk)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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volatility = ef.portfolio_performance()[1]
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assert abs(target_risk - volatility) < 0.05
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def test_efficient_risk_short():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
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)
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w = ef.efficient_risk(0.19)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.30468522897430295, 0.19, 1.4983424153337392),
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atol=1e-6,
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)
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sharpe = ef.portfolio_performance()[2]
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ef_long_only = setup_efficient_frontier()
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ef_long_only.efficient_return(0.25)
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long_only_sharpe = ef_long_only.portfolio_performance()[2]
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assert sharpe > long_only_sharpe
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def test_efficient_risk_L2_reg():
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ef = setup_efficient_frontier()
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ef.gamma = 1
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w = ef.efficient_risk(0.19)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.28438883284316746, 0.19, 1.3915199577262938),
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atol=1e-6,
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)
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def test_efficient_risk_L2_reg_many_values():
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ef = setup_efficient_frontier()
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ef.efficient_risk(0.19)
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# Count the number of weights more 1%
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initial_number = sum(ef.weights > 0.01)
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for a in np.arange(0.5, 5, 0.5):
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ef.gamma = a
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ef.efficient_risk(0.19)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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new_number = sum(ef.weights > 0.01)
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# Higher gamma should reduce the number of small weights
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assert new_number >= initial_number
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initial_number = new_number
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def test_efficient_risk_market_neutral():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
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)
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w = ef.efficient_risk(0.19, market_neutral=True)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 0)
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assert (ef.weights < 1).all() and (ef.weights > -1).all()
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np.testing.assert_allclose(
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ef.portfolio_performance(),
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(0.2309497469633197, 0.19, 1.1102605909328953),
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atol=1e-6
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)
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sharpe = ef.portfolio_performance()[2]
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ef_long_only = setup_efficient_frontier()
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ef_long_only.efficient_return(0.25)
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long_only_sharpe = ef_long_only.portfolio_performance()[2]
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assert long_only_sharpe > sharpe
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def test_efficient_risk_market_neutral_warning():
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ef = setup_efficient_frontier()
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with warnings.catch_warnings(record=True) as w:
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ef.efficient_risk(0.19, market_neutral=True)
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assert len(w) == 1
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assert issubclass(w[0].category, RuntimeWarning)
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assert (
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str(w[0].message)
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== "Market neutrality requires shorting - bounds have been amended"
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)
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def test_efficient_return():
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ef = setup_efficient_frontier()
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w = ef.efficient_return(0.25)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(), (0.25, 0.1738877891235972, 1.3226920714748545), atol=1e-6
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)
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def test_efficient_return_many_values():
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ef = setup_efficient_frontier()
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for target_return in np.arange(0.19, 0.30, 0.01):
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ef.efficient_return(target_return)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in ef.weights])
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mean_return = ef.portfolio_performance()[0]
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assert abs(target_return - mean_return) < 0.05
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def test_efficient_return_short():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
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)
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w = ef.efficient_return(0.25)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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np.testing.assert_allclose(
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ef.portfolio_performance(), (0.25, 0.1682647442258144, 1.3668935881968987)
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)
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sharpe = ef.portfolio_performance()[2]
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ef_long_only = setup_efficient_frontier()
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ef_long_only.efficient_return(0.25)
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long_only_sharpe = ef_long_only.portfolio_performance()[2]
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assert sharpe > long_only_sharpe
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def test_efficient_return_L2_reg():
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ef = setup_efficient_frontier()
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ef.gamma = 1
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w = ef.efficient_return(0.25)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in w.values()])
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np.testing.assert_allclose(
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ef.portfolio_performance(), (0.25, 0.20032972845476912, 1.1481071819692497)
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)
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def test_efficient_return_L2_reg_many_values():
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ef = setup_efficient_frontier()
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ef.efficient_return(0.25)
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# Count the number of weights more 1%
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initial_number = sum(ef.weights > 0.01)
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for a in np.arange(0.5, 5, 0.5):
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ef.gamma = a
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ef.efficient_return(0.25)
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np.testing.assert_almost_equal(ef.weights.sum(), 1)
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assert all([i >= 0 for i in ef.weights])
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new_number = sum(ef.weights > 0.01)
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# Higher gamma should reduce the number of small weights
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assert new_number >= initial_number
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initial_number = new_number
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def test_efficient_return_market_neutral():
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ef = EfficientFrontier(
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*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
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)
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w = ef.efficient_return(0.25, market_neutral=True)
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assert isinstance(w, dict)
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assert set(w.keys()) == set(ef.tickers)
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assert set(w.keys()) == set(ef.expected_returns.index)
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np.testing.assert_almost_equal(ef.weights.sum(), 0)
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assert (ef.weights < 1).all() and (ef.weights > -1).all()
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np.testing.assert_almost_equal(
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|
ef.portfolio_performance(),
|
|
(0.25, 0.20567621957479246, 1.1182624830289896)
|
|
)
|
|
sharpe = ef.portfolio_performance()[2]
|
|
ef_long_only = setup_efficient_frontier()
|
|
ef_long_only.efficient_return(0.25)
|
|
long_only_sharpe = ef_long_only.portfolio_performance()[2]
|
|
assert long_only_sharpe > sharpe
|
|
|
|
|
|
def test_efficient_return_market_neutral_warning():
|
|
ef = setup_efficient_frontier()
|
|
with warnings.catch_warnings(record=True) as w:
|
|
ef.efficient_return(0.25, market_neutral=True)
|
|
assert len(w) == 1
|
|
assert issubclass(w[0].category, RuntimeWarning)
|
|
assert (
|
|
str(w[0].message)
|
|
== "Market neutrality requires shorting - bounds have been amended"
|
|
)
|
|
|
|
|
|
def test_max_sharpe_semicovariance():
|
|
df = get_data()
|
|
ef = setup_efficient_frontier()
|
|
ef.cov_matrix = risk_models.semicovariance(df, benchmark=0)
|
|
w = ef.max_sharpe()
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
assert all([i >= 0 for i in w.values()])
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.2972237371625498, 0.06443267303123411, 4.302533545801584)
|
|
)
|
|
|
|
|
|
def test_max_sharpe_short_semicovariance():
|
|
df = get_data()
|
|
ef = EfficientFrontier(
|
|
*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
|
|
)
|
|
ef.cov_matrix = risk_models.semicovariance(df, benchmark=0)
|
|
w = ef.max_sharpe()
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.3564654865246848, 0.07202031837368413, 4.671813373260894)
|
|
)
|
|
|
|
|
|
def test_min_volatilty_semicovariance_L2_reg():
|
|
df = get_data()
|
|
ef = setup_efficient_frontier()
|
|
ef.gamma = 1
|
|
ef.cov_matrix = risk_models.semicovariance(df, benchmark=0)
|
|
w = ef.min_volatility()
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
assert all([i >= 0 for i in w.values()])
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.23803779483710888, 0.0962263031034166, 2.265885603053655)
|
|
)
|
|
|
|
|
|
def test_efficient_return_semicovariance():
|
|
df = get_data()
|
|
ef = setup_efficient_frontier()
|
|
ef.cov_matrix = risk_models.semicovariance(df, benchmark=0)
|
|
w = ef.efficient_return(0.12)
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
assert all([i >= 0 for i in w.values()])
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.11999999997948813, 0.06948386215256849, 1.4391830977949114)
|
|
)
|
|
|
|
|
|
def test_max_sharpe_exp_cov():
|
|
df = get_data()
|
|
ef = setup_efficient_frontier()
|
|
ef.cov_matrix = risk_models.exp_cov(df)
|
|
w = ef.max_sharpe()
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
assert all([i >= 0 for i in w.values()])
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.3678835305574766, 0.17534146043561463, 1.9840346355802103)
|
|
)
|
|
|
|
|
|
def test_min_volatility_exp_cov_L2_reg():
|
|
df = get_data()
|
|
ef = setup_efficient_frontier()
|
|
ef.gamma = 1
|
|
ef.cov_matrix = risk_models.exp_cov(df)
|
|
w = ef.min_volatility()
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
|
assert all([i >= 0 for i in w.values()])
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.24340406492258035, 0.17835396894670616, 1.2525881326999546)
|
|
)
|
|
|
|
|
|
def test_efficient_risk_exp_cov_market_neutral():
|
|
df = get_data()
|
|
ef = EfficientFrontier(
|
|
*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
|
|
)
|
|
ef.cov_matrix = risk_models.exp_cov(df)
|
|
w = ef.efficient_risk(0.19, market_neutral=True)
|
|
assert isinstance(w, dict)
|
|
assert set(w.keys()) == set(ef.tickers)
|
|
assert set(w.keys()) == set(ef.expected_returns.index)
|
|
np.testing.assert_almost_equal(ef.weights.sum(), 0)
|
|
assert (ef.weights < 1).all() and (ef.weights > -1).all()
|
|
np.testing.assert_allclose(
|
|
ef.portfolio_performance(),
|
|
(0.39089308906686077, 0.19, 1.9520670176494717),
|
|
atol=1e-6
|
|
)
|