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])