def test_short_returns():
b_data = prep_data.get_data('^GSPC', '2018-05-01', '2019-05-01', 'monthly')
b_transformed_data, dates = prep_data.transform_yahoo_finance_dict(b_data)
b_asset_data = prep_data.generate_asset_data_array(b_transformed_data)
# MSFT is a winner, X is a loser during this period
data = prep_data.get_data(['MSFT', 'X'], '2018-05-01', '2019-05-01',
'monthly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
msft_start = asset_data[0].price_data[-1]
msft_end = asset_data[0].price_data[0]
msft_ret = (msft_end - msft_start) / msft_start
x_start = asset_data[1].price_data[-1]
x_end = asset_data[1].price_data[0]
x_ret = (x_end - x_start) / x_start
# Calculate long only returns
long_p_ret = PortfolioReturns(asset_data, np.array([0.5, 0.5]),
b_asset_data[0])
expected_return = (msft_ret + x_ret) / 2
assert round(long_p_ret.total_return, 3) == round(expected_return, 3)
# Calculate long / short returns
long_short_p_ret = PortfolioReturns(asset_data, np.array([0.5, -0.5]),
b_asset_data[0])
expected_return = (msft_ret - x_ret) / 2
assert round(long_short_p_ret.total_return, 3) == round(expected_return, 3)
def do_task_optimize(name,
tickers,
benchmark_index: str,
start_date,
end_date,
user_id,
task_id,
interval='weekly'):
job_start = datetime.now()
# Get & prepare portfolio data
data = prep_data.get_data(tickers, start_date, end_date, interval)
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
# Get benchmark data
benchmark_data = prep_data.get_data(benchmark_index, start_date, end_date,
interval)
benchmark_transformed_data, _ = prep_data.transform_yahoo_finance_dict(
benchmark_data)
benchmark_asset_data = prep_data.generate_asset_data_array(
benchmark_transformed_data)
benchmark_returns = PortfolioReturns(benchmark_asset_data)
# Optimize portfolio
matrices = prep_data.AssetMatrices(asset_data)
optimizer = optimize.Optimize(matrices, benchmark_asset_data[0])
results = optimizer.optimize_all()
job_end = datetime.now()
job = {
'job_start': job_start,
'job_end': job_end,
'asset_data': [a.as_dict() for a in asset_data],
'matrices': matrices.as_dict(),
'price_dates': dates,
'parameters': {
'tickers': tickers,
'start_date': start_date,
'end_date': end_date,
'interval': interval
},
'benchmark_index': {
'asset_data': benchmark_asset_data[0].as_dict(),
'returns': benchmark_returns.as_dict()
},
'results': [res.as_dict() for res in results],
'user_id': user_id,
'task_id': task_id,
'published': False,
'name': name
}
return insert_job(job)
def test_short_returns_complex():
b_data = prep_data.get_data('^GSPC', '2018-05-13', '2019-05-13', 'weekly')
b_transformed_data, dates = prep_data.transform_yahoo_finance_dict(b_data)
b_asset_data = prep_data.generate_asset_data_array(b_transformed_data)
data = prep_data.get_data(['MSFT', 'X', 'AA', 'SPLK'], '2018-05-13',
'2019-05-13', 'weekly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
long_p_ret = PortfolioReturns(asset_data, np.array([1., 0., 0., 0.]),
b_asset_data[0])
long_short_p_ret = PortfolioReturns(
asset_data, np.array([1., -0.3847, -0.3808, 0.7655]), b_asset_data[0])
assert 1 == 1
def test_returns_s_and_p_500():
data = prep_data.get_data('^GSPC', '2018-05-01', '2019-02-01', 'weekly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
portfolio_returns = PortfolioReturns(asset_data)
assert len(portfolio_returns.portfolio_returns) == len(
asset_data[0].returns)
assert len(portfolio_returns.portfolio_values) == len(
asset_data[0].price_data)
def test_alpha_beta():
b_data = prep_data.get_data('^GSPC', '2016-05-01', '2019-05-01', 'monthly')
b_transformed_data, dates = prep_data.transform_yahoo_finance_dict(b_data)
b_asset_data = prep_data.generate_asset_data_array(b_transformed_data)
data = prep_data.get_data(['FB', 'MSFT'], '2016-05-01', '2019-05-01',
'monthly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
matrices = prep_data.AssetMatrices(asset_data)
optimizer = optimize.Optimize(matrices, b_asset_data[0])
results = optimizer.optimize_all()
portfolio_returns = results[0].portfolio_returns
assert len(portfolio_returns.portfolio_returns) == len(
asset_data[0].returns)
assert len(portfolio_returns.portfolio_values) == len(
asset_data[0].price_data)
portfolio_returns_dict = portfolio_returns.as_dict()
assert portfolio_returns_dict['total_return'] > 0
def test_optimize():
data = prep_data.get_data(sample_tickers, '2006-07-01', '2012-08-01',
'monthly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
matrices = prep_data.AssetMatrices(asset_data)
optimizer = optimize.Optimize(matrices, None)
results = optimizer.optimize_all()
# Test max sharpe
test_cases = [
optimizer.equal_weights_results['sharpe_ratio'],
*(opt.sharpe_ratio for opt in results if not opt.shorting_ok
and opt.goal != optimize.OptimizeGoal.MAX_SHARPE)
]
max_sharpe = [
opt.sharpe_ratio for opt in results
if not opt.shorting_ok and opt.goal == optimize.OptimizeGoal.MAX_SHARPE
][0]
for test_case in test_cases:
assert max_sharpe > test_case
# Test max returns
max_returns = [
opt.returns for opt in results if not opt.shorting_ok
and opt.goal == optimize.OptimizeGoal.MAX_RETURNS
][0]
idx_of_min_std_dev = np.where(
optimizer.asset_matrices.std_dev_vec == optimizer.min_std_dev)[0]
assert ~idx_of_min_std_dev
returns_of_min_asset = optimizer.asset_matrices.avg_returns_vec[
idx_of_min_std_dev][0]
assert max_returns >= returns_of_min_asset
# Test min standard deviation
min_std_dev = [
opt.std_dev for opt in results if not opt.shorting_ok
and opt.goal == optimize.OptimizeGoal.MIN_STD_DEV
][0]
idx_of_max_returns = np.where(
optimizer.asset_matrices.avg_returns_vec == optimizer.max_returns)[0]
assert ~idx_of_max_returns
std_dev_of_max_returns_asset = optimizer.asset_matrices.std_dev_vec[
idx_of_max_returns][0]
# Add padding, this is due to "greater than" as opposed to "greater than or equal to" logic in the constraint
std_dev_of_max_returns_asset = std_dev_of_max_returns_asset + 0.02
assert min_std_dev <= std_dev_of_max_returns_asset
def test_returns():
data = prep_data.get_data(sample_tickers, '2018-05-01', '2019-02-01',
'weekly')
transformed_data, dates = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
matrices = prep_data.AssetMatrices(asset_data)
optimizer = optimize.Optimize(matrices)
results = optimizer.optimize_all()
portfolio_returns = results[0].portfolio_returns
assert len(portfolio_returns.portfolio_returns) == len(
asset_data[0].returns)
assert len(portfolio_returns.portfolio_values) == len(
asset_data[0].price_data)
portfolio_returns_dict = portfolio_returns.as_dict()
assert portfolio_returns_dict['total_return'] > 0
def test_matrix_data():
data = prep_data.get_data(sample_tickers, '2012-01-01', '2019-01-01')
transformed_data = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
matrices = prep_data.AssetMatrices(asset_data)
assert hasattr(matrices, 'correlation_matrix')
def test_generate_asset_data_array():
data = prep_data.get_data(sample_tickers, '2012-01-01', '2019-01-01')
transformed_data = prep_data.transform_yahoo_finance_dict(data)
asset_data = prep_data.generate_asset_data_array(transformed_data)
assert len(asset_data) == len(sample_tickers)