def test_portfolio_to_dict_empty_portfolio():
"""
Test 'portfolio_to_dict' method for an empty Portfolio.
"""
start_dt = pd.Timestamp('2017-10-05 08:00:00', tz=pytz.UTC)
port = Portfolio(start_dt)
port.subscribe_funds(start_dt, 100000.0)
port_dict = port.portfolio_to_dict()
assert port_dict == {}
def test_portfolio_to_dict_for_two_holdings():
"""
Test portfolio_to_dict for two holdings.
"""
start_dt = pd.Timestamp('2017-10-05 08:00:00', tz=pytz.UTC)
asset1_dt = pd.Timestamp('2017-10-06 08:00:00', tz=pytz.UTC)
asset2_dt = pd.Timestamp('2017-10-07 08:00:00', tz=pytz.UTC)
update_dt = pd.Timestamp('2017-10-08 08:00:00', tz=pytz.UTC)
asset1 = Equity("AAA Inc.", "EQ:AAA", tax_exempt=False)
asset2 = Equity("BBB Inc.", "EQ:BBB", tax_exempt=False)
port = Portfolio(start_dt, portfolio_id='1234')
port.subscribe_funds(start_dt, 100000.0)
tn_asset1 = Transaction(asset=asset1.symbol,
quantity=100,
dt=asset1_dt,
price=567.0,
order_id=1,
commission=15.78)
port.transact_asset(tn_asset1)
tn_asset2 = Transaction(asset=asset2.symbol,
quantity=100,
dt=asset2_dt,
price=123.0,
order_id=2,
commission=7.64)
port.transact_asset(tn_asset2)
port.update_market_value_of_asset(asset2.symbol, 134.0, update_dt)
test_holdings = {
asset1.symbol: {
"quantity": 100,
"book_cost": 56715.78,
"market_value": 56700.0,
"gain": -15.78,
"perc_gain": -0.027822944513854874
},
asset2.symbol: {
"quantity": 100,
"book_cost": 12307.64,
"market_value": 13400.0,
"gain": 1092.3600000000006,
"perc_gain": 8.8754627207165679
}
}
port_holdings = port.portfolio_to_dict()
# This is needed because we're not using Decimal
# datatypes and have to compare slightly differing
# floating point representations
for asset in (asset1.symbol, asset2.symbol):
for key, val in test_holdings[asset].items():
assert port_holdings[asset][key] == pytest.approx(
test_holdings[asset][key])
def test_portfolio_to_dict_for_two_holdings():
"""
Test portfolio_to_dict for two holdings.
"""
start_dt = pd.Timestamp('2017-10-05 08:00:00', tz=pytz.UTC)
asset1_dt = pd.Timestamp('2017-10-06 08:00:00', tz=pytz.UTC)
asset2_dt = pd.Timestamp('2017-10-07 08:00:00', tz=pytz.UTC)
update_dt = pd.Timestamp('2017-10-08 08:00:00', tz=pytz.UTC)
asset1 = 'EQ:AAA'
asset2 = 'EQ:BBB'
port = Portfolio(start_dt, portfolio_id='1234')
port.subscribe_funds(start_dt, 100000.0)
tn_asset1 = Transaction(asset=asset1,
quantity=100,
dt=asset1_dt,
price=567.0,
order_id=1,
commission=15.78)
port.transact_asset(tn_asset1)
tn_asset2 = Transaction(asset=asset2,
quantity=100,
dt=asset2_dt,
price=123.0,
order_id=2,
commission=7.64)
port.transact_asset(tn_asset2)
port.update_market_value_of_asset(asset2, 134.0, update_dt)
test_holdings = {
asset1: {
"quantity": 100,
"market_value": 56700.0,
"unrealised_pnl": -15.78,
"realised_pnl": 0.0,
"total_pnl": -15.78
},
asset2: {
"quantity": 100,
"market_value": 13400.0,
"unrealised_pnl": 1092.3600000000006,
"realised_pnl": 0.0,
"total_pnl": 1092.3600000000006
}
}
port_holdings = port.portfolio_to_dict()
# This is needed because we're not using Decimal
# datatypes and have to compare slightly differing
# floating point representations
for asset in (asset1, asset2):
for key, val in test_holdings[asset].items():
assert port_holdings[asset][key] == pytest.approx(
test_holdings[asset][key])
