Function metrics

quantstats/reports.py:721–1202

(
    returns,
    benchmark=None,
    rf=0.0,
    display=True,
    mode="basic",
    sep=False,
    compounded=True,
    periods_per_year=252,
    prepare_returns=True,
    match_dates=True,
    **kwargs,
)

def metrics(
    returns,
    benchmark=None,
    rf=0.0,
    display=True,
    mode="basic",
    sep=False,
    compounded=True,
    periods_per_year=252,
    prepare_returns=True,
    match_dates=True,
    **kwargs,
):

    if match_dates:
        returns = returns.dropna()
    returns.index = returns.index.tz_localize(None)
    win_year, _ = _get_trading_periods(periods_per_year)

    benchmark_colname = kwargs.get("benchmark_title", "Benchmark")
    strategy_colname = kwargs.get("strategy_title", "Strategy")

    if benchmark is not None:
        if isinstance(benchmark, str):
            benchmark_colname = f"Benchmark ({benchmark.upper()})"
        elif isinstance(benchmark, _pd.DataFrame) and len(benchmark.columns) > 1:
            raise ValueError(
                "`benchmark` must be a pandas Series, "
                "but a multi-column DataFrame was passed"
            )

    if isinstance(returns, _pd.DataFrame):
        if len(returns.columns) > 1:
            blank = [""] * len(returns.columns)
            if isinstance(strategy_colname, str):
                strategy_colname = list(returns.columns)
    else:
        blank = [""]

    # if isinstance(returns, _pd.DataFrame):
    #     if len(returns.columns) > 1:
    #         raise ValueError("`returns` needs to be a Pandas Series or one column DataFrame. multi colums DataFrame was passed")
    #     returns = returns[returns.columns[0]]

    if prepare_returns:
        df = _utils._prepare_returns(returns)

    if isinstance(returns, _pd.Series):
        df = _pd.DataFrame({"returns": returns})
    elif isinstance(returns, _pd.DataFrame):
        df = _pd.DataFrame(
            {
                "returns_" + str(i + 1): returns[strategy_col]
                for i, strategy_col in enumerate(returns.columns)
            }
        )

    if benchmark is not None: