Misc pd/np compatibility stuff

quantstats/reports.py

@@ -972,23 +972,25 @@ def metrics(
     metrics["Outlier Win Ratio"] = _stats.outlier_win_ratio(df, prepare_returns=False)
     metrics["Outlier Loss Ratio"] = _stats.outlier_loss_ratio(df, prepare_returns=False)
 
-    # returns
+    # # returns
     metrics["~~"] = blank
-    comp_func = _stats.comp if compounded else _np.sum
 
     today = df.index[-1]  # _dt.today()
-    metrics["MTD %"] = comp_func(df[df.index >= _dt(today.year, today.month, 1)]) * pct
-
-    d = today - relativedelta(months=3)
-    metrics["3M %"] = comp_func(df[df.index >= d]) * pct
-
-    d = today - relativedelta(months=6)
-    metrics["6M %"] = comp_func(df[df.index >= d]) * pct
-
-    metrics["YTD %"] = comp_func(df[df.index >= _dt(today.year, 1, 1)]) * pct
-
-    d = today - relativedelta(years=1)
-    metrics["1Y %"] = comp_func(df[df.index >= d]) * pct
+    m3 = today - relativedelta(months=3)
+    m6 = today - relativedelta(months=6)
+    y1 = today - relativedelta(years=1)
+    if compounded:
+        metrics["MTD %"] = _stats.comp(df[df.index >= _dt(today.year, today.month, 1)]) * pct
+        metrics["3M %"] = _stats.comp(df[df.index >= m3]) * pct
+        metrics["6M %"] = _stats.comp(df[df.index >= m6]) * pct
+        metrics["YTD %"] = _stats.comp(df[df.index >= _dt(today.year, 1, 1)]) * pct
+        metrics["1Y %"] = _stats.comp(df[df.index >= y1]) * pct
+    else:
+        metrics["MTD %"] = _np.sum(df[df.index >= _dt(today.year, today.month, 1)], axis=0) * pct
+        metrics["3M %"] = _np.sum(df[df.index >= m3], axis=0) * pct
+        metrics["6M %"] = _np.sum(df[df.index >= m6], axis=0) * pct
+        metrics["YTD %"] = _np.sum(df[df.index >= _dt(today.year, 1, 1)], axis=0) * pct
+        metrics["1Y %"] = _np.sum(df[df.index >= y1], axis=0) * pct
 
     d = today - relativedelta(months=35)
     metrics["3Y (ann.) %"] = _stats.cagr(df[df.index >= d], 0.0, compounded) * pct

quantstats/stats.py

@@ -38,12 +38,12 @@ def pct_rank(prices, window=60):
 
 def compsum(returns):
     """Calculates rolling compounded returns"""
-    return returns.add(1).cumprod() - 1
+    return returns.add(1).cumprod(axis=0) - 1
 
 
 def comp(returns):
     """Calculates total compounded returns"""
-    return returns.add(1).prod() - 1
+    return returns.add(1).prod(axis=0) - 1
 
 
 def distribution(returns, compounded=True, prepare_returns=True):
@@ -93,7 +93,7 @@ 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.prod(1 + returns) ** (1 / len(returns)) - 1
+    return _np.prod(1 + returns, axis=0) ** (1 / len(returns)) - 1
 
 
 def geometric_mean(returns, aggregate=None, compounded=True):
@@ -519,7 +519,7 @@ def cagr(returns, rf=0.0, compounded=True, periods=252):
     if compounded:
         total = comp(total)
     else:
-        total = _np.sum(total)
+        total = _np.sum(total, axis=0)
 
     years = (returns.index[-1] - returns.index[0]).days / periods