diff --git a/pandas_ta/candles/cdl_inside.py b/pandas_ta/candles/cdl_inside.py
index 9b2bbe6d..43ff7852 100644
--- a/pandas_ta/candles/cdl_inside.py
+++ b/pandas_ta/candles/cdl_inside.py
@@ -62,7 +62,7 @@ def cdl_inside(
     offset = v_offset(offset)
 
     # Calculate
-    np_high, np_low = high.values, low.values
+    np_high, np_low = high.to_numpy(), low.to_numpy()
     np_inside = np_cdl_inside(np_high, np_low)
     inside = Series(np_inside, index=close.index, dtype=bool)
 
diff --git a/pandas_ta/candles/ha.py b/pandas_ta/candles/ha.py
index 993c7e3b..2d10b1fb 100644
--- a/pandas_ta/candles/ha.py
+++ b/pandas_ta/candles/ha.py
@@ -65,8 +65,8 @@ def ha(
         return
 
     # Calculate
-    np_open, np_high = open_.values, high.values
-    np_low, np_close = low.values, close.values
+    np_open, np_high = open_.to_numpy(), high.to_numpy()
+    np_low, np_close = low.to_numpy(), close.to_numpy()
     ha_open, ha_high, ha_low, ha_close = np_ha(np_open, np_high, np_low, np_close)
     df = DataFrame({
         "HA_open": ha_open,
diff --git a/pandas_ta/core.py b/pandas_ta/core.py
index b2be5634..adbe0abf 100644
--- a/pandas_ta/core.py
+++ b/pandas_ta/core.py
@@ -1806,10 +1806,10 @@ def obv(self, offset=None, **kwargs: DictLike):
         result = obv(close=close, volume=volume, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def pvi(self, length=None, initial=None, mamode=None, offset=None, **kwargs: DictLike):
+    def pvi(self, length=None, initial=None, mamode=None, overlay=None, offset=None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         volume = self._get_column(kwargs.pop("volume", "volume"))
-        result = pvi(close=close, volume=volume, length=length, initial=initial, mamode=mamode, offset=offset, **kwargs)
+        result = pvi(close=close, volume=volume, length=length, initial=initial, mamode=mamode, overlay=overlay, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
     def pvo(self, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs: DictLike):
diff --git a/pandas_ta/cycles/reflex.py b/pandas_ta/cycles/reflex.py
index ccdaa2d7..9313d8a3 100644
--- a/pandas_ta/cycles/reflex.py
+++ b/pandas_ta/cycles/reflex.py
@@ -93,7 +93,7 @@ def reflex(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     result = np_reflex(np_close, length, smooth, alpha, pi, sqrt2)
     result[:length] = nan
     result = Series(result, index=close.index)
diff --git a/pandas_ta/momentum/crsi.py b/pandas_ta/momentum/crsi.py
index 46bfcaf9..7dd64c11 100644
--- a/pandas_ta/momentum/crsi.py
+++ b/pandas_ta/momentum/crsi.py
@@ -145,7 +145,7 @@ def crsi(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     streak = Series(consecutive_streak(np_close), index=close.index)
 
     if Imports["talib"] and mode_tal:
diff --git a/pandas_ta/momentum/rvgi.py b/pandas_ta/momentum/rvgi.py
index ded6ad52..d027427f 100644
--- a/pandas_ta/momentum/rvgi.py
+++ b/pandas_ta/momentum/rvgi.py
@@ -63,7 +63,7 @@ def rvgi(
     rvgi = numerator / denominator
     signal = swma(rvgi, length=swma_length)
 
-    if all(isnan(signal.values)):
+    if all(isnan(signal.to_numpy())):
         return  # Emergency Break
 
     # Offset
diff --git a/pandas_ta/overlap/alma.py b/pandas_ta/overlap/alma.py
index b0667f34..15a89c24 100644
--- a/pandas_ta/overlap/alma.py
+++ b/pandas_ta/overlap/alma.py
@@ -55,7 +55,7 @@ def alma(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     x = arange(length)
     k = floor(offset_ * (length - 1))
     weights = exp(-0.5 * ((sigma / length) * (x - k)) ** 2)
diff --git a/pandas_ta/overlap/dema.py b/pandas_ta/overlap/dema.py
index 4315d1c6..b0892a9e 100644
--- a/pandas_ta/overlap/dema.py
+++ b/pandas_ta/overlap/dema.py
@@ -52,7 +52,7 @@ def dema(
         ema2 = ema(close=ema1, length=length, talib=mode_tal)
         dema = 2 * ema1 - ema2
 
-    if all(isnan(dema.values)):
+    if all(isnan(dema.to_numpy())):
         return  # Emergency Break
 
     # Offset
diff --git a/pandas_ta/overlap/hl2.py b/pandas_ta/overlap/hl2.py
index c0ef8336..57f04c06 100644
--- a/pandas_ta/overlap/hl2.py
+++ b/pandas_ta/overlap/hl2.py
@@ -35,7 +35,7 @@ def hl2(
         return
 
     # Calculate
-    avg = 0.5 * (high.values + low.values)
+    avg = 0.5 * (high.to_numpy() + low.to_numpy())
     hl2 = Series(avg, index=high.index)
 
     # Offset
diff --git a/pandas_ta/overlap/hlc3.py b/pandas_ta/overlap/hlc3.py
index 185cc735..219d1c54 100644
--- a/pandas_ta/overlap/hlc3.py
+++ b/pandas_ta/overlap/hlc3.py
@@ -43,7 +43,7 @@ def hlc3(
         from talib import TYPPRICE
         hlc3 = TYPPRICE(high, low, close)
     else:
-        avg = (high.values + low.values + close.values) / 3.0
+        avg = (high.to_numpy() + low.to_numpy() + close.to_numpy()) / 3.0
         hlc3 = Series(avg, index=close.index)
 
     # Offset
diff --git a/pandas_ta/overlap/linreg.py b/pandas_ta/overlap/linreg.py
index bb5db7e7..ad7e324f 100644
--- a/pandas_ta/overlap/linreg.py
+++ b/pandas_ta/overlap/linreg.py
@@ -70,7 +70,7 @@ def linreg(
     tsf = kwargs.pop("tsf", False)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
 
     if Imports["talib"] and mode_tal and not r:
         from talib import LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE, TSF
diff --git a/pandas_ta/overlap/mama.py b/pandas_ta/overlap/mama.py
index 97b5160e..035c006b 100644
--- a/pandas_ta/overlap/mama.py
+++ b/pandas_ta/overlap/mama.py
@@ -143,7 +143,7 @@ def mama(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     if Imports["talib"] and mode_tal:
         from talib import MAMA
         mama, fama = MAMA(np_close, fastlimit, slowlimit)
diff --git a/pandas_ta/overlap/ohlc4.py b/pandas_ta/overlap/ohlc4.py
index f89c4abb..0b4dc6a1 100644
--- a/pandas_ta/overlap/ohlc4.py
+++ b/pandas_ta/overlap/ohlc4.py
@@ -36,7 +36,7 @@ def ohlc4(
     offset = v_offset(offset)
 
     # Calculate
-    avg = 0.25 * (open_.values + high.values + low.values + close.values)
+    avg = 0.25 * (open_.to_numpy() + high.to_numpy() + low.to_numpy() + close.to_numpy())
     ohlc4 = Series(avg, index=close.index)
 
     # Offset
diff --git a/pandas_ta/overlap/pivots.py b/pandas_ta/overlap/pivots.py
index 30ab778c..41db0443 100644
--- a/pandas_ta/overlap/pivots.py
+++ b/pandas_ta/overlap/pivots.py
@@ -190,10 +190,10 @@ def pivots(
             index=dt_index
         )
 
-    np_open = df.open.values
-    np_high = df.high.values
-    np_low = df.low.values
-    np_close = df.close.values
+    np_open = df.open.to_numpy()
+    np_high = df.high.to_numpy()
+    np_low = df.low.to_numpy()
+    np_close = df.close.to_numpy()
 
     # Create nan arrays for "demark" and "fibonacci" pivots
     _nan_array = zeros_like(np_close)
diff --git a/pandas_ta/overlap/sma.py b/pandas_ta/overlap/sma.py
index afe15209..eefa3294 100644
--- a/pandas_ta/overlap/sma.py
+++ b/pandas_ta/overlap/sma.py
@@ -80,7 +80,7 @@ def sma(
         from talib import SMA
         sma = SMA(close, length)
     else:
-        np_close = close.values
+        np_close = close.to_numpy()
         sma = np_sma(np_close, length)
         sma = Series(sma, index=close.index)
 
diff --git a/pandas_ta/overlap/ssf.py b/pandas_ta/overlap/ssf.py
index ea01c5cc..4cc79378 100644
--- a/pandas_ta/overlap/ssf.py
+++ b/pandas_ta/overlap/ssf.py
@@ -95,7 +95,7 @@ def ssf(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     if everget:
         ssf = np_ssf_everget(np_close, length, pi, sqrt2)
     else:
diff --git a/pandas_ta/overlap/ssf3.py b/pandas_ta/overlap/ssf3.py
index 1a596dc7..36121dd0 100644
--- a/pandas_ta/overlap/ssf3.py
+++ b/pandas_ta/overlap/ssf3.py
@@ -77,7 +77,7 @@ def ssf3(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     ssf = np_ssf3(np_close, length, pi, sqrt3)
     ssf = Series(ssf, index=close.index)
 
diff --git a/pandas_ta/overlap/wcp.py b/pandas_ta/overlap/wcp.py
index 30d8dd1c..07d09955 100644
--- a/pandas_ta/overlap/wcp.py
+++ b/pandas_ta/overlap/wcp.py
@@ -49,7 +49,7 @@ def wcp(
         from talib import WCLPRICE
         wcp = WCLPRICE(high, low, close)
     else:
-        weight = high.values + low.values + 2 * close.values
+        weight = high.to_numpy() + low.to_numpy() + 2 * close.to_numpy()
         wcp = Series(weight, index=close.index)
 
     # Offset
diff --git a/pandas_ta/overlap/wma.py b/pandas_ta/overlap/wma.py
index c98d448c..fb1be19b 100644
--- a/pandas_ta/overlap/wma.py
+++ b/pandas_ta/overlap/wma.py
@@ -76,7 +76,7 @@ def wma(
         from talib import WMA
         wma = WMA(close, length)
     else:
-        np_close = close.values
+        np_close = close.to_numpy()
         wma_ = np_wma(np_close, length, asc, True)
         wma = Series(wma_, index=close.index)
 
diff --git a/pandas_ta/performance/log_return.py b/pandas_ta/performance/log_return.py
index 872fe393..e4076026 100644
--- a/pandas_ta/performance/log_return.py
+++ b/pandas_ta/performance/log_return.py
@@ -42,7 +42,7 @@ def log_return(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     if cumulative:
         r = np_close / np_close[0]
     else:
diff --git a/pandas_ta/performance/percent_return.py b/pandas_ta/performance/percent_return.py
index d5750c70..c7d222f2 100644
--- a/pandas_ta/performance/percent_return.py
+++ b/pandas_ta/performance/percent_return.py
@@ -42,7 +42,7 @@ def percent_return(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     if cumulative:
         pr = (np_close / np_close[0]) - 1
     else:
diff --git a/pandas_ta/transform/ifisher.py b/pandas_ta/transform/ifisher.py
index 3e752ff4..cad5e569 100644
--- a/pandas_ta/transform/ifisher.py
+++ b/pandas_ta/transform/ifisher.py
@@ -55,14 +55,14 @@ def ifisher(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     is_remapped = logical_and(np_close >= -1, np_close <= 1)
     if not all(is_remapped):
         np_max, np_min = max(np_close), min(np_close)
         close_map = remap(close, from_min=np_min, from_max=np_max, to_min=-1, to_max=1)
-        if close_map is None or all(isnan(close_map.values)):
+        if close_map is None or all(isnan(close_map.to_numpy())):
             return  # Emergency Break
-        np_close = close_map.values
+        np_close = close_map.to_numpy()
     amped = exp(amp * np_close)
     result = (amped - 1) / (amped + 1)
 
diff --git a/pandas_ta/transform/remap.py b/pandas_ta/transform/remap.py
index ae4e7f50..d3b0dd69 100644
--- a/pandas_ta/transform/remap.py
+++ b/pandas_ta/transform/remap.py
@@ -50,7 +50,7 @@ def remap(
     frange, trange = from_max - from_min, to_max - to_min
     if frange <= 0 or trange <= 0:
         return
-    result = to_min + (trange / frange) * (close.values - from_min)
+    result = to_min + (trange / frange) * (close.to_numpy() - from_min)
     result = Series(result, index=close.index)
 
     # Offset
diff --git a/pandas_ta/trend/alphatrend.py b/pandas_ta/trend/alphatrend.py
index f4a39252..d36496a0 100644
--- a/pandas_ta/trend/alphatrend.py
+++ b/pandas_ta/trend/alphatrend.py
@@ -126,9 +126,9 @@ def alphatrend(
     if momo is None:
         return
 
-    np_upper_atr, np_lower_atr = upper_atr.values, lower_atr.values
+    np_upper_atr, np_lower_atr = upper_atr.to_numpy(), lower_atr.to_numpy()
 
-    at = np_alpha(np_lower_atr, np_upper_atr, momo.values >= threshold)
+    at = np_alpha(np_lower_atr, np_upper_atr, momo.to_numpy() >= threshold)
     at = Series(at, index=close.index)
 
     atl = at.shift(lag)
diff --git a/pandas_ta/trend/decay.py b/pandas_ta/trend/decay.py
index 03590a8b..5316aca0 100644
--- a/pandas_ta/trend/decay.py
+++ b/pandas_ta/trend/decay.py
@@ -71,7 +71,7 @@ def decay(
     offset = v_offset(offset)
 
     # Calculate
-    _mode, np_close = "L", close.values
+    _mode, np_close = "L", close.to_numpy()
 
     if mode in ["exp", "exponential"]:
         _mode = "EXP"
diff --git a/pandas_ta/trend/ht_trendline.py b/pandas_ta/trend/ht_trendline.py
index 4e626713..efdbaa1a 100644
--- a/pandas_ta/trend/ht_trendline.py
+++ b/pandas_ta/trend/ht_trendline.py
@@ -123,7 +123,7 @@ def ht_trendline(
         from talib import HT_TRENDLINE
         tl = HT_TRENDLINE(close)
     else:
-        np_close = close.values
+        np_close = close.to_numpy()
         np_tl = np_ht_trendline(np_close)
 
         if prenan > 0:
diff --git a/pandas_ta/trend/trendflex.py b/pandas_ta/trend/trendflex.py
index b5bab922..ffee5ba4 100644
--- a/pandas_ta/trend/trendflex.py
+++ b/pandas_ta/trend/trendflex.py
@@ -92,7 +92,7 @@ def trendflex(
     offset = v_offset(offset)
 
     # Calculate
-    np_close = close.values
+    np_close = close.to_numpy()
     result = np_trendflex(np_close, length, smooth, alpha, pi, sqrt2)
     result[:length] = nan
     result = Series(result, index=close.index)
diff --git a/pandas_ta/trend/xsignals.py b/pandas_ta/trend/xsignals.py
index 19933c8f..96897dbd 100644
--- a/pandas_ta/trend/xsignals.py
+++ b/pandas_ta/trend/xsignals.py
@@ -93,7 +93,7 @@ def xsignals(
 
     # Modify trades to fill gaps for trends
     trades.replace({0: nan}, inplace=True)
-    trades.interpolate(method="pad", inplace=True)
+    trades.ffill(limit_area="inside", inplace=True) # or trades.bfill(limit_area="inside", inplace=True)
     trades.fillna(0, inplace=True)
 
     trends = (trades > 0).astype(int)
diff --git a/pandas_ta/trend/zigzag.py b/pandas_ta/trend/zigzag.py
index eb6a4176..6f010f0f 100644
--- a/pandas_ta/trend/zigzag.py
+++ b/pandas_ta/trend/zigzag.py
@@ -60,7 +60,7 @@ def zigzag(
 
     if close is not None:
         close = v_series(close, _length + 1)
-        np_close = close.values
+        np_close = close.to_numpy()
         if close is None:
             return
 
@@ -70,7 +70,7 @@ def zigzag(
     offset = v_offset(offset)
 
     # Calculation
-    np_high, np_low = high.values, low.values
+    np_high, np_low = high.to_numpy(), low.to_numpy()
     highest_high = high.rolling(window=pivot_leg, center=True, min_periods=0).max()
     lowest_low = low.rolling(window=pivot_leg, center=True, min_periods=0).min()
 
diff --git a/pandas_ta/utils/_math.py b/pandas_ta/utils/_math.py
index c2f174cb..9ca844ba 100644
--- a/pandas_ta/utils/_math.py
+++ b/pandas_ta/utils/_math.py
@@ -104,7 +104,7 @@ def geometric_mean(series: Series) -> Float:
     if n < 1:
         return series.iloc[0]
 
-    has_zeros = 0 in series.values
+    has_zeros = 0 in series.to_numpy()
     if has_zeros:
         series = series.fillna(0) + 1
     if all(series > 0):
diff --git a/pandas_ta/volatility/atrts.py b/pandas_ta/volatility/atrts.py
index 18965805..d6d102ca 100644
--- a/pandas_ta/volatility/atrts.py
+++ b/pandas_ta/volatility/atrts.py
@@ -122,7 +122,7 @@ def atrts(
     atr_ *= multiplier
     ma_ = _ma(mamode, close, length=ma_length, talib=mode_tal)
 
-    np_close, np_ma, np_atr = close.values, ma_.values, atr_.values
+    np_close, np_ma, np_atr = close.to_numpy(), ma_.to_numpy(), atr_.to_numpy()
     np_atrts_, _, _ = np_atrts(np_close, np_ma, np_atr, length, ma_length)
 
     percent = kwargs.pop("percent", False)
diff --git a/pandas_ta/volume/kvo.py b/pandas_ta/volume/kvo.py
index f6089595..685d046e 100644
--- a/pandas_ta/volume/kvo.py
+++ b/pandas_ta/volume/kvo.py
@@ -69,11 +69,11 @@ def kvo(
     sv = signed_volume.loc[signed_volume.first_valid_index():, ]
 
     kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow)
-    if kvo is None or all(isnan(kvo.values)):
+    if kvo is None or all(isnan(kvo.to_numpy())):
         return  # Emergency Break
 
     kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():, ], length=signal)
-    if kvo_signal is None or all(isnan(kvo_signal.values)):
+    if kvo_signal is None or all(isnan(kvo_signal.to_numpy())):
         return  # Emergency Break
 
     # Offset
diff --git a/pandas_ta/volume/mfi.py b/pandas_ta/volume/mfi.py
index f5a4c546..89f1fc84 100644
--- a/pandas_ta/volume/mfi.py
+++ b/pandas_ta/volume/mfi.py
@@ -68,8 +68,8 @@ def mfi(
     else:
         m, _ones = close.size, ones(length)
 
-        tp = (high.values + low.values + close.values) / 3.0
-        smf = tp * volume.values * where(tp > roll(tp, shift=drift), 1, -1)
+        tp = (high.to_numpy() + low.to_numpy() + close.to_numpy()) / 3.0
+        smf = tp * volume.to_numpy() * where(tp > roll(tp, shift=drift), 1, -1)
 
         pos, neg = maximum(smf, 0), maximum(-smf, 0)
         avg_gain, avg_loss = convolve(pos, _ones)[:m], convolve(neg, _ones)[:m]
diff --git a/pandas_ta/volume/pvi.py b/pandas_ta/volume/pvi.py
index f3bf046a..509d86ce 100644
--- a/pandas_ta/volume/pvi.py
+++ b/pandas_ta/volume/pvi.py
@@ -1,15 +1,38 @@
 # -*- coding: utf-8 -*-
-import pandas as pd
-import numpy as np
-from pandas import Series
+from numba import njit
+from numpy import empty, float64, zeros_like
+from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_offset, v_mamode, v_pos_default, v_series
+from pandas_ta.utils import (
+    v_bool,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
+
+
+@njit(cache=True)
+def np_pvi(np_close, np_volume, initial):
+    result = zeros_like(np_close, dtype=float64)
+    result[0] = initial
+
+    m = np_close.size
+    for i in range(1, m):
+        if np_volume[i] > np_volume[i - 1]:
+            result[i] = result[i - i] * (np_close[i] / np_close[i - 1])
+        else:
+            result[i] = result[i - i]
+
+    return result
+
 
 def pvi(
     close: Series, volume: Series, length: Int = None, initial: Int = None,
-    mamode: str = None, offset: Int = None, **kwargs: DictLike
-) -> pd.DataFrame:
+    mamode: str = None, overlay: bool = None, offset: Int = None,
+    **kwargs: DictLike
+) -> DataFrame:
     """Positive Volume Index (PVI)
 
     The Positive Volume Index is a cumulative indicator that uses volume
@@ -17,6 +40,7 @@ def pvi(
     conjunction with NVI.
 
     Sources:
+        https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=101
         https://www.investopedia.com/terms/p/pvi.asp
 
     Args:
@@ -25,6 +49,8 @@ def pvi(
         length (int): The short period. Default: 255
         initial (int): The short period. Default: 100
         mamode (str): See ``help(ta.ma)``. Default: 'ema'
+        overlay (bool): Sets the initial value to initial close so PVI
+            can be overlaid the price chart. Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -32,52 +58,55 @@ def pvi(
         fill_method (value, optional): Type of fill method
 
     Returns:
-        pd.DataFrame: New DataFrame with ['PVI_1', 'PVIs_<length>']
+        pd.DataFrame: DataFrame with PVI and PVI Signal columns
     """
-
     # Validate
-    mamode = v_mamode(mamode, "ema")
     length = v_pos_default(length, 255)
     close = v_series(close, length + 1)
     volume = v_series(volume, length + 1)
-    initial = v_pos_default(initial, 100)
-    offset = v_offset(offset)
 
     if close is None or volume is None:
         return
 
-    # Get numpy arrays of the data
-    close_prices = close.to_numpy()
-    volumes = volume.to_numpy()
-    pvis = np.empty(len(close_prices))
-
-    # Set the first value from from initial
-    pvis[0] = initial
+    mamode = v_mamode(mamode, "ema")
+    overlay = v_bool(overlay, False)
+    if overlay:
+        initial = close.iloc[0]
+    initial = v_pos_default(initial, 100)
+    offset = v_offset(offset)
 
     # Calculate
-    for i in range(1, len(close_prices)):
-        if volumes[i] > volumes[i-1]:
-            # PVI = Yesterday’s PVI + [[(Close – Yesterday’s Close) / Yesterday’s Close] * Yesterday’s PVI
-            pvis[i] = pvis[i-1] + (((close_prices[i] - close_prices[i-1]) / close_prices[i-1]) * pvis[i-1])
-        else:
-            # PVI = Yesterday’s PVI
-            pvis[i] = pvis[i-1]
+    np_close, np_volume = close.to_numpy(), volume.to_numpy()
+    _pvi = np_pvi(np_close, np_volume, initial)
 
-    data = {
-        'PVI_1': pvis,
-    }
-    df = pd.DataFrame(data, index=close.index)
+    pvi = Series(_pvi, index=close.index)
+    pvi_ma = ma(mamode, pvi, length=length)
 
+    # Offset
     if offset != 0:
-        df['PVI_1'] = df['PVI_1'].shift(offset)
-
-    sig_series = ma(mamode, df['PVI_1'], length=length)
-    df[f'PVIs_{length}'] = sig_series
+        pvi = pvi.shift(offset)
+        pvi_ma = pvi_ma.shift(offset)
 
     # Fill
     if "fillna" in kwargs:
-        df.fillna(kwargs["fillna"], inplace=True)
+        pvi.fillna(kwargs["fillna"], inplace=True)
+        pvi_ma.fillna(kwargs["fillna"], inplace=True)
     if "fill_method" in kwargs:
-        df.fillna(method=kwargs["fill_method"], inplace=True)
+        pvi.fillna(method=kwargs["fill_method"], inplace=True)
+        pvi_ma.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Category
+    _mode = mamode.lower()[0] if len(mamode) else ""
+    _props = f"{_mode}_{length}"
+    pvi.name = f"PVI"
+    pvi_ma.name = f"PVI{_props}"
+    pvi.category = pvi_ma.category = "volume"
+
+    data = { pvi.name: pvi,  pvi_ma.name: pvi_ma }
+    df = DataFrame(data, index=close.index)
+
+    # Name and Category
+    df.name = pvi.name
+    df.category = pvi.category
 
     return df
diff --git a/tests/conftest.py b/tests/conftest.py
index 67829934..301fe42e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -9,7 +9,7 @@
 
 from pandas import read_csv
 
-TEST_ROWS = 200
+TEST_ROWS = 300
 TEST_CSV = f"data/SPY_D.csv"
 
 BEEP = False
diff --git a/tests/test_indicator_volume.py b/tests/test_indicator_volume.py
index 88307bb5..15ccb39a 100644
--- a/tests/test_indicator_volume.py
+++ b/tests/test_indicator_volume.py
@@ -137,9 +137,13 @@ def test_obv(df):
 
 
 def test_pvi(df):
-    result = ta.pvi(df.close, df.volume)
+    result = ta.pvi(df.close, df.volume, length=10)
     assert isinstance(result, DataFrame)
-    assert result.name == "PVI_1"
+    assert result.name == "PVI"
+
+    result = ta.pvi(df.close, df.volume, length=10, overlay=True)
+    assert isinstance(result, DataFrame)
+    assert df.close.iloc[0] == result.iloc[0,0]
 
 
 def test_pvol(df):
@@ -251,9 +255,9 @@ def test_ext_nvi(df):
     assert df.columns[-1] == "NVI_1"
 
 
-def test_ext_pvi(df):
-    df.ta.pvi(append=True)
-    assert list(df.columns[-2:]) == ["PVI_1", "PVIs_255"]
+# def test_ext_pvi(df):
+    df.ta.pvi(length=10, append=True)
+    assert list(df.columns[-2:]) == ["PVI", "PVIe_10"]
 
 
 def test_ext_pvol(df):
diff --git a/tests/test_studies.py b/tests/test_studies.py
index e57b1781..0256acde 100644
--- a/tests/test_studies.py
+++ b/tests/test_studies.py
@@ -9,7 +9,7 @@
 [pytest.param(ta.CommonStudy, id="common"), pytest.param(ta.AllStudy, id="all")]
 
 # +/- when adding/removing indicators
-ALL_COLUMNS = 324
+ALL_COLUMNS = 325
 
 
 def test_all_study_props(all_study):
@@ -33,7 +33,7 @@ def test_common_study_props(common_study):
 @pytest.mark.parametrize("category,columns", [
     ("candles", 70), ("cycles", 2), ("momentum", 78), ("overlap", 56),
     ("performance", 2), ("statistics", 16), ("transform", 5), ("trend", 31),
-    ("volatility", 36), ("volume", 28),
+    ("volatility", 36), ("volume", 29),
     pytest.param(ta.AllStudy, ALL_COLUMNS, id=f"all-{ALL_COLUMNS}"),
     pytest.param(ta.CommonStudy, 5, id="common-5"),
 ])
@@ -89,7 +89,7 @@ def test_study_custom_e_talib(df, custom_study_e, talib):
 
 @pytest.mark.parametrize("talib", [False, True])
 def test_study_all_multirun_talib(df, all_study, talib):
-    new_columns = 613  # +/- when adding/removing indicators
+    new_columns = 619  # +/- when adding/removing indicators
     initial_columns = df.shape[1]
     df.ta.study(all_study, length=10, cores=0, talib=talib)
     df.ta.study(all_study, length=50, cores=0, talib=talib)