Added TV_TSV.py (Time Segmented Volume) indicator under volume

.gitignore

@@ -146,4 +146,5 @@ examples/*.csv
 jnb/*
 
 *.txt
-df_file
+df_file
+/.vs/slnx.sqlite

pandas_ta/volume/__init__.py

@@ -13,4 +13,5 @@
 from .pvol import pvol
 from .pvr import pvr
 from .pvt import pvt
+from .tv_tsv import tv_tsv
 from .vp import vp

pandas_ta/volume/tv_tsv.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+import pandas as pd
+from pandas import DataFrame, Series
+from pandas_ta.utils import verify_series, signed_series
+import statistics
+import time
+from datetime import date, datetime, timedelta, timezone
+
+def tv_tsv(close=None, volume=None, length=None, signal=None, mamode=None, **kwargs):
+    """Indicator: Time Segmented Value (TSV)"""
+    # Validate Arguments
+    drift = kwargs.pop("drift", 1)
+    length = int(length) if length and length > 0 else 18
+    signal = int(signal) if signal and signal > 0 else 10
+    mamode = mamode if isinstance(mamode, str) else "sma"
+
+    # Trading View
+    # https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/
+    # t = sum( close > close[1] ? volume*(close-close[1]) : close < close[1] ? volume*(close-close[1]) : 0,l)
+    # m = sma(t ,l_ma )
+
+    # Calculate Result
+    signed_volume = volume * ta.signed_series(close, 1)  # > 0
+    signed_volume[signed_volume < 0 ] = -signed_volume   # < 0
+    signed_volume.apply(ta.zero)                         # ~ 0
+
+    cvd = (close.diff(drift) * signed_volume).fillna(0)
+
+    tsv = (cvd.rolling(length).sum()).fillna(0)
+    signal = (ta.ma(mamode, tsv, length=signal)).fillna(0)
+    tsv_ratio = (tsv / signal).fillna(0)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        tsv.fillna(kwargs["fillna"], inplace=True)
+        signal.fillna(kwargs["fillna"], inplace=True)
+        tsv_ratio.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        tsv.fillna(method=kwargs["fill_method"], inplace=True)
+        signal.fillna(method=kwargs["fill_method"], inplace=True)
+        tsv_ratio.fillna(method=kwargs["fill_method"], inplace=True)
+
+
+
+    # Name & Category
+    _props = f"_{length}_{signal}"
+    tv_tsv.name = f"TV_TSV{_props}"
+    tv_tsv.category = "volume"
+
+    #tsv.name = "tsv"
+
+
+    df = pd.DataFrame({
+        "close": close, "volume": volume,
+        "signed_volume": signed_volume, "cvd": cvd,
+        "tsv": tsv, "signal": signal, "tsv_ratio": tsv_ratio
+    })
+
+    return df
+
+
+
+tv_tsv.__doc__ = \
+"""Time Segmented Value (TSV)
+
+TSV is a proprietary technical indicator developed by Worden Brothers Inc., classified as an oscillator. 
+It is calculated by comparing various time segments of both price and volume. 
+TSV essentially measures the amount of money flowing in or out of a particular stock. 
+The baseline represents the zero line.
+
+TSV is a leading indicator because its movement is based on both the stock's price fluctuation and volume. 
+Ideal entry and exit points are commonly found as the stock moves across the baseline level. 
+This indicator is similar to on-balance volume (OBV) because it measures the amount of money flowing in or out of a particular stock.
+
+Sources:
+    https://www.investopedia.com/terms/t/tsv.asp
+
+Calculation:
+    Default Inputs:
+        length=18, signal=10
+
+
+Args:
+    close (pd.Series): Series of 'close's
+    volume (pd.Series): Series of 'volume's
+    length (int): It's period. Default: 18
+    signal (int): It's avg period. Default: 10
+    mamode (str): See ```help(ta.ma)```. Default: 'sma'
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.DataFrame: tsv, signal, tsv_ratio
+"""