26import inspect
 27
 28
 29def _mtd(df): 30    return df[df.index >= _dt.datetime.now().strftime("%Y-%m-01")]
 31
 32

 30    return df[df.index >= _dt.datetime.now().strftime("%Y-%m-01")]
 31
 32
 33def _qtd(df): 34    date = _dt.datetime.now()
 35    for q in [1, 4, 7, 10]:
 36        if date.month <= q:

 38    return df[df.index >= date.strftime("%Y-%m-01")]
 39
 40
 41def _ytd(df): 42    return df[df.index >= _dt.datetime.now().strftime("%Y-01-01")]
 43
 44

 42    return df[df.index >= _dt.datetime.now().strftime("%Y-01-01")]
 43
 44
 45def _pandas_date(df, dates): 46    if not isinstance(dates, list):
 47        dates = [dates]
 48    return df[df.index.isin(dates)]

 48    return df[df.index.isin(dates)]
 49
 50
 51def _pandas_current_month(df): 52    n = _dt.datetime.now()
 53    daterange = _pd.date_range(_dt.date(n.year, n.month, 1), n)
 54    return df[df.index.isin(daterange)]

230    return data
231
232
233def download_returns(ticker, period="max", proxy=None):234    params = {
235        "tickers": ticker,
236        "proxy": proxy,

312def _count_consecutive(data):
313    """Counts consecutive data (like cumsum() with reset on zeroes)"""
314
315    def _count(data):316        return data * (data.groupby((data != data.shift(1)).cumsum()).cumcount() + 1)
317
318    if isinstance(data, _pd.DataFrame):

  46    return returns.add(1).prod() - 1
  47
  48
  49def distribution(returns, compounded=True, prepare_returns=True):  50    def get_outliers(data):
  51        # https://datascience.stackexchange.com/a/57199
  52        Q1 = data.quantile(0.25)

  47
  48
  49def distribution(returns, compounded=True, prepare_returns=True):
  50    def get_outliers(data):  51        # https://datascience.stackexchange.com/a/57199
  52        Q1 = data.quantile(0.25)
  53        Q3 = data.quantile(0.75)

 154    if prepare_returns:
 155        returns = _utils._prepare_returns(returns)
 156
 157    def _exposure(ret): 158        ex = len(ret[(~_np.isnan(ret)) & (ret != 0)]) / len(ret)
 159        return _ceil(ex * 100) / 100
 160

 169def win_rate(returns, aggregate=None, compounded=True, prepare_returns=True):
 170    """Calculates the win ratio for a period"""
 171
 172    def _win_rate(series): 173        try:
 174            return len(series[series > 0]) / len(series[series != 0])
 175        except Exception:

 234    return std
 235
 236
 237def rolling_volatility( 238    returns, rolling_period=126, periods_per_year=252, prepare_returns=True
 239):
 240    if prepare_returns:

 299    return res
 300
 301
 302def smart_sharpe(returns, rf=0.0, periods=252, annualize=True): 303    return sharpe(returns, rf, periods, annualize, True)
 304
 305

 303    return sharpe(returns, rf, periods, annualize, True)
 304
 305
 306def rolling_sharpe( 307    returns,
 308    rf=0.0,
 309    rolling_period=126,

 354    return res
 355
 356
 357def smart_sortino(returns, rf=0, periods=252, annualize=True): 358    return sortino(returns, rf, periods, annualize, True)
 359
 360

 358    return sortino(returns, rf, periods, annualize, True)
 359
 360
 361def rolling_sortino( 362    returns, rf=0, rolling_period=126, annualize=True, periods_per_year=252, **kwargs
 363):
 364    if rf != 0 and rolling_period is None:

 390    return data / _sqrt(2)
 391
 392
 393def probabilistic_ratio( 394    series, rf=0.0, base="sharpe", periods=252, annualize=False, smart=False
 395):
 396

 427    return psr
 428
 429
 430def probabilistic_sharpe_ratio( 431    series, rf=0.0, periods=252, annualize=False, smart=False
 432):
 433    return probabilistic_ratio(

 435    )
 436
 437
 438def probabilistic_sortino_ratio( 439    series, rf=0.0, periods=252, annualize=False, smart=False
 440):
 441    return probabilistic_ratio(

 443    )
 444
 445
 446def probabilistic_adjusted_sortino_ratio( 447    series, rf=0.0, periods=252, annualize=False, smart=False
 448):
 449    return probabilistic_ratio(

 786    for every drawdown period
 787    """
 788
 789    def _drawdown_details(drawdown): 790        # mark no drawdown
 791        no_dd = drawdown == 0
 792

1603        iDisplay(iHTML(jscode))
1604
1605
1606def _embed_figure(figfiles, figfmt):1607    if isinstance(figfiles, list):
1608        embed_string = "\n"
1609        for figfile in figfiles:

1588        iDisplay(iHTML(jscode.replace("{{filename}}", filename)))
1589
1590
1591def _open_html(html):1592    jscode = _regex.sub(
1593        " +",
1594        " ",

1568    return obj
1569
1570
1571def _download_html(html, filename="quantstats-tearsheet.html"):1572    jscode = _regex.sub(
1573        " +",
1574        " ",

1554    return dd_stats.T
1555
1556
1557def _html_table(obj, showindex="default"):1558    obj = _tabulate(
1559        obj, headers="keys", tablefmt="html", floatfmt=".2f", showindex=showindex
1560    )