def test_find_zero_turnover_and_weight():
date = pd.Timestamp("2018-12-18")
positions = dict(KAZT=1, KAZTP=0, CHMF=20000, TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
tickers = finder.find_zero_turnover_and_weight(port)
assert "KAZT" not in tickers
assert "KAZTP" in tickers
def test_add_tickers(capsys):
date = pd.Timestamp("2018-12-19")
positions = dict(KAZT=1, KAZTP=0, CHMF=20000, TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
finder.add_tickers(port)
captured = capsys.readouterr()
assert "МОМЕНТУМ ТИКЕРЫ" in captured.out
assert "ДИВИДЕНДНЫЕ ТИКЕРЫ" in captured.out
def make_optimizer(monkeypatch):
monkeypatch.setattr(config, "ML_PARAMS", ML_PARAMS)
monkeypatch.setattr(config, "TURNOVER_CUT_OFF", 0.0016)
date = pd.Timestamp("2018-12-17")
positions = dict(
KZOS=800, MGNT=0, PIKK=800, MSTT=0, MTLRP=0, GMKN=21, CBOM=0, SNGSP=13000
)
port = portfolio.Portfolio(date, 1000, positions)
return optimizer.Optimizer(port, months=11)
def test_remove_tickers(capsys):
date = pd.Timestamp("2018-12-19")
positions = dict(KAZT=1, KAZTP=0, CHMF=20000, TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
opt = optimizer.Optimizer(port, months=11)
finder.remove_tickers(opt)
captured = capsys.readouterr()
assert "БУМАГИ С НУЛЕВЫМ ОБОРОТОМ И ВЕСОМ" in captured.out
assert "БУМАГИ С НИЗКИМ ГРАДИЕНТОМ" in captured.out
def test_get_turnover(monkeypatch):
monkeypatch.setattr(config, "TURNOVER_CUT_OFF", 0.0012)
monkeypatch.setattr(config, "TURNOVER_PERIOD", 21)
date = pd.Timestamp("2018-12-18")
positions = dict(TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
df = finder.get_turnover(port, ("KZOS", "AKRN"))
assert isinstance(df, pd.Series)
assert df.size == 4
assert df["KZOS"] == pytest.approx(0.986873)
def get_turnover(port, tickers):
"""Получает фактор оборота для тикеров."""
date = port.date
cash = port.shares[CASH]
positions = {ticker: port.shares[ticker] for ticker in port.index[:-2]}
for ticker in tickers:
if ticker not in positions:
positions[ticker] = 0
all_tickers_port = portfolio.Portfolio(date, cash, positions)
return all_tickers_port.turnover_factor[:-2]
def test_update_data_bad_date():
port = portfolio.Portfolio(date=DATE_OLD, cash=CASH, positions=POSITIONS)
with pytest.raises(POptimizerError) as error:
pdf.update_data(
"test",
port.date,
port.value[PORTFOLIO],
dict(WLMike=1000, Igor=-2000),
1234,
)
assert str(error.value) == "В этом месяце данные уже вносились в отчет"
def test_report_new_month():
port = portfolio.Portfolio(date=DATE_NEW, cash=CASH, positions=POSITIONS)
pdf.report("test", port, dict(WLMike=10000, Igor=-5000), 4321)
df = pdf.read_data("test")
date = port.date
assert df.index[-1] == date
assert df.loc[date, "WLMike"] == pytest.approx(10000)
assert df.loc[date, "Igor"] == pytest.approx(-5000)
assert df.loc[date, "Value_WLMike"] == pytest.approx(387_550.829_708_377)
assert df.loc[date, "Value_Igor"] == pytest.approx(4972.170_292)
assert df.loc[date, "Value"] == pytest.approx(392_523)
assert df.loc[date, "Dividends"] == pytest.approx(4321)
def test_find_dividends(monkeypatch):
monkeypatch.setattr(config, "TURNOVER_CUT_OFF", 0.0022)
monkeypatch.setattr(config, "ML_PARAMS", ML_PARAMS)
date = pd.Timestamp("2018-12-18")
positions = dict(CHMF=20000, TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
df = finder.find_dividends(port, 0.02)
assert isinstance(df, pd.DataFrame)
assert df.shape == (5, 4)
assert list(df.columns) == ["DivYield_0", "TURNOVER", "SCORE", "ADD"]
assert list(df.index) == ["CHMF", "MTLRP", "MRKV", "MRKP", "LSNGP"]
assert df.loc["CHMF", "ADD"] == ""
assert df.loc["MTLRP", "ADD"] == "ADD"
def test_find_momentum(monkeypatch):
monkeypatch.setattr(config, "TURNOVER_CUT_OFF", 0.0022)
monkeypatch.setattr(finder, "feature_params", lambda x: {"days": 252})
date = pd.Timestamp("2018-12-18")
positions = dict(TATN=20000, KZOS=20000, LKOH=20000)
port = portfolio.Portfolio(date, 0, positions)
df = finder.find_momentum(port, 0.02)
assert isinstance(df, pd.DataFrame)
assert df.shape == (5, 5)
assert list(df.columns) == ["Mom12m", "STD", "TURNOVER", "_DRAW_DOWN", "ADD"]
assert list(df.index) == ["AKRN", "RTKMP", "BANEP", "KZOS", "CBOM"]
assert df.loc["AKRN", "ADD"] == "ADD"
assert df.loc["KZOS", "ADD"] == ""
assert df.loc["BANEP", "ADD"] == "ADD"
def test_update_data():
port = portfolio.Portfolio(date=DATE_NEW, cash=CASH, positions=POSITIONS)
pdf.update_data("test", port.date, port.value[PORTFOLIO],
dict(WLMike=1000, Igor=-2000), 1234)
df = pdf.read_data("test")
assert df.shape == (241, 6)
assert df.index[-1] == pd.Timestamp("2018-05-07")
assert df.loc["2018-05-07", "WLMike"] == pytest.approx(1000)
assert df.loc["2018-05-07", "Igor"] == pytest.approx(-2000)
assert df.loc["2018-05-07",
"Value_WLMike"] == pytest.approx(384_396.431_074_62)
assert df.loc["2018-05-07",
"Value_Igor"] == pytest.approx(8126.568_925_380_33)
assert df.loc["2018-05-07", "Value"] == pytest.approx(392_523)
assert df.loc["2018-05-07", "Dividends"] == pytest.approx(1234)
def test_find_low_gradient(monkeypatch):
monkeypatch.setattr(config, "ML_PARAMS", ML_PARAMS)
date = pd.Timestamp("2018-12-19")
positions = dict(
AKRN=563,
BANE=236,
CHMF=2000,
BANEP=1644,
KAZT=0,
KAZTP=0,
KZOS=3400,
LKOH=270,
TATN=420,
MGNT=0,
MTLRP=0,
)
port = portfolio.Portfolio(date, 0, positions)
opt = optimizer.Optimizer(port, months=11)
bad_tickers = finder.find_low_gradient(opt)
assert len(bad_tickers) == 1
MVID=3260,
NMTP=0,
PHOR=0,
PMSBP=17290,
PRTK=7000,
RSTIP=87000,
RTKM=0,
RTKMP=182_600,
SNGSP=23500,
TTLK=0,
UPRO=1_267_000,
VSMO=73,
)
CASH = 1_548_264
DATE = "2018-04-19"
TEST_PORTFOLIO = portfolio.Portfolio(date=DATE, cash=CASH, positions=POSITIONS)
def test_drop_small_positions():
df = pdf_lower.drop_small_positions(TEST_PORTFOLIO)
index = df.index
assert len(df) == pdf_lower.MAX_TABLE_ROWS + 2
assert index[-1] == PORTFOLIO
assert df[PORTFOLIO] == pytest.approx(39_226_178)
assert index[-2] == OTHER
assert df[OTHER] == pytest.approx(6_523_354)
assert index[0] == "RTKMP"
assert df.iloc[0] == pytest.approx(11_167_816)
assert index[-3] == "MVID"
assert df.iloc[-3] == pytest.approx(1_310_520)
def test_update_data_bad_investor_name():
port = portfolio.Portfolio(date=DATE_NEW, cash=CASH, positions=POSITIONS)
with pytest.raises(POptimizerError) as error:
pdf.update_data("test", port.date, port.value[PORTFOLIO],
dict(WLMike1=1000), 1234)
assert str(error.value) == "Неверное имя инвестора - WLMike1"