Misc pd/np compatibility stuff

quantstats/_plotting/core.py

@@ -100,7 +100,7 @@ def plot_returns_bars(
     hlw=None,
     hlcolor="red",
     hllabel="",
-    resample="A",
+    resample="YE",
     title="Returns",
     match_volatility=False,
     log_scale=False,
@@ -1018,10 +1018,10 @@ def plot_distribution(
     port["Monthly"] = port["Daily"].resample("ME").apply(apply_fnc)
     port["Monthly"].ffill(inplace=True)
 
-    port["Quarterly"] = port["Daily"].resample("Q").apply(apply_fnc)
+    port["Quarterly"] = port["Daily"].resample("QE").apply(apply_fnc)
     port["Quarterly"].ffill(inplace=True)
 
-    port["Yearly"] = port["Daily"].resample("A").apply(apply_fnc)
+    port["Yearly"] = port["Daily"].resample("YE").apply(apply_fnc)
     port["Yearly"].ffill(inplace=True)
 
     fig, ax = _plt.subplots(figsize=figsize)

quantstats/_plotting/wrappers.py

@@ -552,20 +552,20 @@ def yearly_returns(
         title += "  vs Benchmark"
         benchmark = (
             _utils._prepare_benchmark(benchmark, returns.index)
-            .resample("A")
+            .resample("YE")
             .apply(_stats.comp)
-            .resample("A")
+            .resample("YE")
             .last()
         )
 
     if prepare_returns:
         returns = _utils._prepare_returns(returns)
 
     if compounded:
-        returns = returns.resample("A").apply(_stats.comp)
+        returns = returns.resample("YE").apply(_stats.comp)
     else:
-        returns = returns.resample("A").apply(_df.sum)
-    returns = returns.resample("A").last()
+        returns = returns.resample("YE").apply(_df.sum)
+    returns = returns.resample("YE").last()
 
     fig = _core.plot_returns_bars(
         returns,
@@ -646,9 +646,9 @@ def histogram(
         title = "Weekly "
     elif resample == "ME":
         title = "Monthly "
-    elif resample == "Q":
+    elif resample == "QE":
         title = "Quarterly "
-    elif resample == "A":
+    elif resample == "YE":
         title = "Annual "
     else:
         title = ""

quantstats/reports.py

@@ -155,7 +155,7 @@ def html(
 
     if benchmark is not None:
         yoy = _stats.compare(
-            returns, benchmark, "A", compounded=compounded, prepare_returns=False
+            returns, benchmark, "YE", compounded=compounded, prepare_returns=False
         )
         if isinstance(returns, _pd.Series):
             yoy.columns = [benchmark_title, strategy_title, "Multiplier", "Won"]
@@ -859,7 +859,7 @@ def metrics(
         # metrics['Prob. Sortino/√2 Ratio %'] = _stats.probabilistic_adjusted_sortino_ratio(df, rf, win_year, False) * pct
         metrics["Smart Sortino/√2"] = metrics["Smart Sortino"] / _sqrt(2)
         # metrics['Prob. Smart Sortino/√2 Ratio %'] = _stats.probabilistic_adjusted_sortino_ratio(df, rf, win_year, False, True) * pct
-    metrics["Omega"] = _stats.omega(df, rf, 0.0, win_year)
+    metrics["Omega"] = _stats.omega(df["returns"], rf, 0.0, win_year)
 
     metrics["~~~~~~~~"] = blank
     metrics["Max Drawdown %"] = blank
@@ -936,7 +936,7 @@ def metrics(
             _stats.expected_return(df, compounded=compounded, aggregate="ME", prepare_returns=False) * pct
         )
         metrics["Expected Yearly %%"] = (
-            _stats.expected_return(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.expected_return(df, compounded=compounded, aggregate="YE", prepare_returns=False) * pct
         )
         metrics["Kelly Criterion %"] = (
             _stats.kelly_criterion(df, prepare_returns=False) * pct
@@ -959,9 +959,9 @@ def metrics(
     metrics["Gain/Pain Ratio"] = _stats.gain_to_pain_ratio(df, rf)
     metrics["Gain/Pain (1M)"] = _stats.gain_to_pain_ratio(df, rf, "ME")
     # if mode.lower() == 'full':
-    #     metrics['GPR (3M)'] = _stats.gain_to_pain_ratio(df, rf, "Q")
+    #     metrics['GPR (3M)'] = _stats.gain_to_pain_ratio(df, rf, "QE")
     #     metrics['GPR (6M)'] = _stats.gain_to_pain_ratio(df, rf, "2Q")
-    #     metrics['GPR (1Y)'] = _stats.gain_to_pain_ratio(df, rf, "A")
+    #     metrics['GPR (1Y)'] = _stats.gain_to_pain_ratio(df, rf, "YE")
     metrics["~~~~~~~"] = blank
 
     metrics["Payoff Ratio"] = _stats.payoff_ratio(df, prepare_returns=False)
@@ -1013,10 +1013,10 @@ def metrics(
             _stats.worst(df, aggregate="ME", prepare_returns=False) * pct
         )
         metrics["Best Year %"] = (
-            _stats.best(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.best(df, compounded=compounded, aggregate="YE", prepare_returns=False) * pct
         )
         metrics["Worst Year %"] = (
-            _stats.worst(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.worst(df, compounded=compounded, aggregate="YE", prepare_returns=False) * pct
         )
 
     # dd
@@ -1041,10 +1041,10 @@ def metrics(
             _stats.win_rate(df, compounded=compounded, aggregate="ME", prepare_returns=False) * pct
         )
         metrics["Win Quarter %%"] = (
-            _stats.win_rate(df, compounded=compounded, aggregate="Q", prepare_returns=False) * pct
+            _stats.win_rate(df, compounded=compounded, aggregate="QE", prepare_returns=False) * pct
         )
         metrics["Win Year %%"] = (
-            _stats.win_rate(df, compounded=compounded, aggregate="A", prepare_returns=False) * pct
+            _stats.win_rate(df, compounded=compounded, aggregate="YE", prepare_returns=False) * pct
         )
 
         if "benchmark" in df:
@@ -1574,7 +1574,7 @@ def _download_html(html, filename="quantstats-tearsheet.html"):
         " ",
         """<script>
     var bl=new Blob(['{{html}}'],{type:"text/html"});
-    var a=document.createElement("a");
+    var a=document.createElement("YE");
     a.href=URL.createObjectURL(bl);
     a.download="{{filename}}";
     a.hidden=true;document.body.appendChild(a);

quantstats/stats.py

@@ -80,8 +80,8 @@ def get_outliers(data):
         "Daily": get_outliers(daily),
         "Weekly": get_outliers(daily.resample("W-MON").apply(apply_fnc)),
         "Monthly": get_outliers(daily.resample("ME").apply(apply_fnc)),
-        "Quarterly": get_outliers(daily.resample("Q").apply(apply_fnc)),
-        "Yearly": get_outliers(daily.resample("A").apply(apply_fnc)),
+        "Quarterly": get_outliers(daily.resample("QE").apply(apply_fnc)),
+        "Yearly": get_outliers(daily.resample("YE").apply(apply_fnc)),
     }
 
 
@@ -93,17 +93,17 @@ def expected_return(returns, aggregate=None, compounded=True, prepare_returns=Tr
     if prepare_returns:
         returns = _utils._prepare_returns(returns)
     returns = _utils.aggregate_returns(returns, aggregate, compounded)
-    return _np.product(1 + returns) ** (1 / len(returns)) - 1
+    return _np.prod(1 + returns) ** (1 / len(returns)) - 1
 
 
-def geometric_mean(retruns, aggregate=None, compounded=True):
+def geometric_mean(returns, aggregate=None, compounded=True):
     """Shorthand for expected_return()"""
-    return expected_return(retruns, aggregate, compounded)
+    return expected_return(returns, aggregate, compounded)
 
 
-def ghpr(retruns, aggregate=None, compounded=True):
+def ghpr(returns, aggregate=None, compounded=True):
     """Shorthand for expected_return()"""
-    return expected_return(retruns, aggregate, compounded)
+    return expected_return(returns, aggregate, compounded)
 
 
 def outliers(returns, quantile=0.95):
@@ -410,12 +410,7 @@ def probabilistic_ratio(
     n = len(series)
 
     sigma_sr = _np.sqrt(
-        (
-            1
-            + (0.5 * base**2)
-            - (skew_no * base)
-            + (((kurtosis_no - 3) / 4) * base**2)
-        )
+        (1 + (0.5 * base**2) - (skew_no * base) + (((kurtosis_no - 3) / 4) * base**2))
         / (n - 1)
     )
 
@@ -493,8 +488,8 @@ def omega(returns, rf=0.0, required_return=0.0, periods=252):
         return_threshold = (1 + required_return) ** (1.0 / periods) - 1
 
     returns_less_thresh = returns - return_threshold
-    numer = returns_less_thresh[returns_less_thresh > 0.0].sum().values[0]
-    denom = -1.0 * returns_less_thresh[returns_less_thresh < 0.0].sum().values[0]
+    numer = returns_less_thresh[returns_less_thresh > 0.0].sum()
+    denom = -1.0 * returns_less_thresh[returns_less_thresh < 0.0].sum()
 
     if denom > 0.0:
         return numer / denom
@@ -747,7 +742,7 @@ def outlier_loss_ratio(returns, quantile=0.01, prepare_returns=True):
     return returns.quantile(quantile).mean() / returns[returns < 0].mean()
 
 
-def recovery_factor(returns, rf=0., prepare_returns=True):
+def recovery_factor(returns, rf=0.0, prepare_returns=True):
     """Measures how fast the strategy recovers from drawdowns"""
     if prepare_returns:
         returns = _utils._prepare_returns(returns)
@@ -910,7 +905,10 @@ def information_ratio(returns, benchmark, prepare_returns=True):
         returns = _utils._prepare_returns(returns)
     diff_rets = returns - _utils._prepare_benchmark(benchmark, returns.index)
 
-    return diff_rets.mean() / diff_rets.std()
+    std = diff_rets.std()
+    if std != 0:
+        return diff_rets.mean() / diff_rets.std()
+    return 0
 
 
 def greeks(returns, benchmark, periods=252.0, prepare_returns=True):

quantstats/utils.py

@@ -62,7 +62,7 @@ def multi_shift(df, shift=3):
     dfs = [df.shift(i) for i in _np.arange(shift)]
     for ix, dfi in enumerate(dfs[1:]):
         dfs[ix + 1].columns = [str(col) for col in dfi.columns + str(ix + 1)]
-    return _pd.concat(dfs, 1, sort=True)
+    return _pd.concat(dfs, axis=1, sort=True)
 
 
 def to_returns(prices, rf=0.0):
@@ -133,7 +133,7 @@ def aggregate_returns(returns, period=None, compounded=True):
     if "quarter" in period:
         return group_returns(returns, index.quarter, compounded=compounded)
 
-    if period == "A" or any(x in period for x in ["year", "eoy", "yoy"]):
+    if period == "YE" or any(x in period for x in ["year", "eoy", "yoy"]):
         return group_returns(returns, index.year, compounded=compounded)
 
     if "week" in period:
@@ -145,7 +145,7 @@ def aggregate_returns(returns, period=None, compounded=True):
     if "eom" in period or period == "ME":
         return group_returns(returns, [index.year, index.month], compounded=compounded)
 
-    if "eoq" in period or period == "Q":
+    if "eoq" in period or period == "QE":
         return group_returns(
             returns, [index.year, index.quarter], compounded=compounded
         )
@@ -179,7 +179,9 @@ def to_excess_returns(returns, rf, nperiods=None):
         # deannualize
         rf = _np.power(1 + rf, 1.0 / nperiods) - 1.0
 
-    return returns - rf
+    df = returns - rf
+    df = df.tz_localize(None)
+    return df
 
 
 def _prepare_prices(data, base=1.0):
@@ -198,6 +200,7 @@ def _prepare_prices(data, base=1.0):
     if isinstance(data, (_pd.DataFrame, _pd.Series)):
         data = data.fillna(0).replace([_np.inf, -_np.inf], float("NaN"))
 
+    data = data.tz_localize(None)
     return data
 
 
@@ -227,19 +230,26 @@ def _prepare_returns(data, rf=0.0, nperiods=None):
     if function not in unnecessary_function_calls:
         if rf > 0:
             return to_excess_returns(data, rf, nperiods)
+
+    data = data.tz_localize(None)
     return data
 
 
 def download_returns(ticker, period="max", proxy=None):
     params = {
         "tickers": ticker,
         "proxy": proxy,
+        "auto_adjust": True,
+        "multi_level_index": False,
+        "progress": False,
     }
     if isinstance(period, _pd.DatetimeIndex):
         params["start"] = period[0]
     else:
         params["period"] = period
-    return _yf.download(**params)["Close"].pct_change()
+    df = _yf.download(**params)["Close"].pct_change()
+    df = df.tz_localize(None)
+    return df
 
 
 def _prepare_benchmark(benchmark=None, period="max", rf=0.0, prepare_returns=True):
@@ -271,7 +281,7 @@ def _prepare_benchmark(benchmark=None, period="max", rf=0.0, prepare_returns=Tru
         )
         benchmark = benchmark[benchmark.index.isin(period)]
 
-    benchmark.index = benchmark.index.tz_localize(None)
+    benchmark = benchmark.tz_localize(None)
 
     if prepare_returns:
         return _prepare_returns(benchmark.dropna(), rf=rf)