diff --git a/setup.py b/setup.py
index 65e4321..62e5a75 100644
--- a/setup.py
+++ b/setup.py
@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='ttools',
-    version='0.3.1',
+    version='0.3.2',
     packages=find_packages(),
     install_requires=[
         'vectorbtpro',
diff --git a/tests/test_ntb.ipynb b/tests/test_ntb.ipynb
index 1c9168f..93e81ec 100644
--- a/tests/test_ntb.ipynb
+++ b/tests/test_ntb.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -16,10 +16,10 @@
     {
      "data": {
       "text/plain": [
-       "['CUVWAP']"
+       "['CUVWAP', 'DIVRELN']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -38,7 +38,10 @@
     "#vwap_cum_d = vbt.indicator(\"ttools:CUVWAP\").run(s12_data.high, s12_data.low, s12_data.close, s12_data.volume, anchor=\"D\", drag=50)\n",
     "#vwap_lin_angle = vbt.indicator(\"talib:LINEARREG_ANGLE\").run(vwap_cum_d.vwap, timeperiod=2)\n",
     "\n",
-    "vbt.IF.list_indicators(\"ttools\")\n"
+    "vbt.IF.list_indicators(\"ttools\")\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   }
  ],
diff --git a/ttools/indicators/CUVWAP.py b/ttools/indicators/CUVWAP.py
index 916a424..b4016b0 100644
--- a/ttools/indicators/CUVWAP.py
+++ b/ttools/indicators/CUVWAP.py
@@ -9,6 +9,8 @@ from vectorbtpro.utils.template import RepFunc
 Cumulative Anchored VWAP indicator on HLCC4 price, anchor = "D", "h", or "min" ...
 drag = 0 - overlap with previous group. takes into account last N elements from previous group
 when calculating (simulating v2realbot logic)
+
+HLCC4 price is rounded to hlcc4_round decimas (default is 3)
 """
 
 def substitute_anchor(wrapper: ArrayWrapper, anchor: tp.Optional[tp.FrequencyLike]) -> tp.Array1d:
@@ -18,16 +20,22 @@ def substitute_anchor(wrapper: ArrayWrapper, anchor: tp.Optional[tp.FrequencyLik
     return wrapper.get_index_grouper(anchor).get_group_lens()
 
 @jit(nopython=True)
-def vwap_cum(high, low, close, volume, group_lens, drag):
+def vwap_cum(high, low, close, volume, group_lens, drag, hlcc4_round):
     #anchor based grouping - prepare group indexes
     group_end_idxs = np.cumsum(group_lens)
     group_start_idxs = group_end_idxs - group_lens
 
     #prepare output
     out = np.full(volume.shape, np.nan, dtype=np.float_)
+    #hlcc4 = np.empty_like(close)
 
     hlcc4 = (high + low + close + close) / 4
 
+    for i in range(hlcc4.shape[0]):
+        hlcc4[i] = np.round(hlcc4[i], hlcc4_round)
+
+    #hlcc4 = np.floor(hlcc4 * 1000 + 0.5) / 1000 #nworkaround for np.round(hlcc4, 3) which doesnt work without iterating
+
     #iterate over groups
     for group in range(len(group_lens)):
         from_i = group_start_idxs[group]
@@ -43,7 +51,7 @@ def vwap_cum(high, low, close, volume, group_lens, drag):
                 out[i] = np.nan
             else:
                 out[i] = nom_cumsum / denum_cumsum
-    return out
+    return out, hlcc4
 
 """
 cumulative anchored vwap indicator on HLCC4 price, anchor = "D", "h", or "min" ...
@@ -55,12 +63,13 @@ IND_CUVWAP = vbt.IF(
     module_name='ttools',
     input_names=['high', 'low', 'close', 'volume'],
     param_names=['anchor', "drag"],
-    output_names=['vwap']
+    output_names=['vwap','hlcc4']
 ).with_apply_func(vwap_cum,
                 takes_1d=True,
                 param_settings=dict(
                     anchor=dict(template=RepFunc(substitute_anchor)),
                 ),
                 anchor="D",
-                drag=0
+                drag=0,
+                hlcc4_round=3
                 )
\ No newline at end of file
diff --git a/ttools/vbtutils.py b/ttools/vbtutils.py
index 76f1607..0d49ed3 100644
--- a/ttools/vbtutils.py
+++ b/ttools/vbtutils.py
@@ -7,6 +7,7 @@ import datetime
 def isrising(series: pd.Series, n: int) -> pd.Series:
     """
     Checks if a series is rising over a given window size.
+    Returns True for windows where values are either strictly increasing or staying the same
     
     Parameters
     ----------
@@ -20,12 +21,12 @@ def isrising(series: pd.Series, n: int) -> pd.Series:
     pd.Series
         Boolean mask indicating when the series is falling
     """
-    return series.rolling(n).apply(lambda x: (x == sorted(x, reverse=True)).all(), raw=False).fillna(False).astype(bool)
+    return series.rolling(n).apply(lambda x: (x == sorted(x, reverse=False)).all(), raw=False).fillna(False).astype(bool)
 
 def isfalling(series: pd.Series, n: int) -> pd.Series:
     """
     Checks if a series is falling over a given window size.
-    
+        Returns True for windows where values are either strictly decreasing or staying the same
     Parameters
     ----------
     series : pd.Series