Added signal line to TSI and updated SMI to account for the change

README.md

@@ -986,6 +986,7 @@ of the last bars defined by the length parameter. See ```help(ta.tos_stdevall)``
 * _Increasing_ (**increasing**): New argument ```strict``` checks if the series is continuously increasing over period ```length``` with a faster calculation. Default: ```False```. The ```percent``` argument has also been added with default None. See ```help(ta.increasing)```.
 * _Parabolic Stop and Reverse_ (**psar**): Bug fix and adjustment to match TradingView's ```sar```. New argument ```af0``` to initialize the Acceleration Factor. See ```help(ta.psar)```.
 * _Percentage Price Oscillator_ (**ppo**): Included new argument ```mamode``` as an option. Default is **sma** to match TA Lib. See ```help(ta.ppo)```.
+* _True Strength Index_ (**tsi**): Added ```signal``` argument. See ```help(ta.tsi)```.
 * _Volume Profile_ (**vp**): Calculation improvements. See [Pull Request #320](https://github.com/twopirllc/pandas-ta/pull/320) See ```help(ta.vp)```.
 * _Volume Weighted Moving Average_ (**vwma**): Fixed bug in DataFrame Extension call. See ```help(ta.vwma)```.
 * _Volume Weighted Average Price_ (**vwap**): Added a new parameter called ```anchor```. Default: "D" for "Daily". See [Timeseries Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) for additional options. **Requires** the DataFrame index to be a DatetimeIndex. See ```help(ta.vwap)```.

pandas_ta/momentum/smi.py

@@ -20,8 +20,9 @@ def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kw
     if close is None: return
 
     # Calculate Result
-    smi = tsi(close, fast=fast, slow=slow, scalar=scalar)
-    signalma = ema(smi, signal)
+    tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar)
+    smi = tsi_df.iloc[:, 0]
+    signalma = tsi_df.iloc[:, 1]
     osc = smi - signalma
 
     # Offset

pandas_ta/momentum/tsi.py

@@ -1,13 +1,15 @@
 # -*- coding: utf-8 -*-
+from pandas import DataFrame
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwargs):
+def tsi(close, fast=None, slow=None, signal=None, scalar=None, drift=None, offset=None, **kwargs):
     """Indicator: True Strength Index (TSI)"""
     # Validate Arguments
     fast = int(fast) if fast and fast > 0 else 13
     slow = int(slow) if slow and slow > 0 else 25
+    signal = int(signal) if signal and signal > 0 else 13
     # if slow < fast:
     #     fast, slow = slow, fast
     scalar = float(scalar) if scalar else 100
@@ -29,21 +31,32 @@ def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwa
 
     tsi = scalar * fast_slow_ema / abs_fast_slow_ema
 
+    tsi_signal = ema(close=tsi, length=signal)
+
     # Offset
     if offset != 0:
         tsi = tsi.shift(offset)
+        tsi_signal = tsi_signal.shift(offset)
 
     # Handle fills
     if "fillna" in kwargs:
         tsi.fillna(kwargs["fillna"], inplace=True)
+        tsi_signal.fillna(kwargs["fillna"], inplace=True)
     if "fill_method" in kwargs:
         tsi.fillna(method=kwargs["fill_method"], inplace=True)
+        tsi_signal.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
-    tsi.name = f"TSI_{fast}_{slow}"
-    tsi.category = "momentum"
+    tsi.name = f"TSI_{fast}_{slow}_{signal}"
+    tsi_signal.name = f"TSIs_{fast}_{slow}_{signal}"
+    tsi.category = tsi_signal.category =  "momentum"
+
+    # Prepare DataFrame to return
+    df = DataFrame({tsi.name: tsi, tsi_signal.name: tsi_signal})
+    df.name = f"TSI_{fast}_{slow}_{signal}"
+    df.category = "momentum"
 
-    return tsi
+    return df
 
 
 tsi.__doc__ = \
@@ -58,7 +71,7 @@ def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwa
 
 Calculation:
     Default Inputs:
-        fast=13, slow=25, scalar=100, drift=1
+        fast=13, slow=25, signal=13, scalar=100, drift=1
     EMA = Exponential Moving Average
     diff = close.diff(drift)
 
@@ -69,11 +82,13 @@ def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwa
     abema = abs_diff_fast_slow_ema = EMA(abs_diff_slow_ema, fast)
 
     TSI = scalar * fast_slow_ema / abema
+    Signal = EMA(TSI, signal)
 
 Args:
     close (pd.Series): Series of 'close's
     fast (int): The short period. Default: 13
     slow (int): The long period. Default: 25
+    signal (int): The signal period. Default: 13
     scalar (float): How much to magnify. Default: 100
     drift (int): The difference period. Default: 1
     offset (int): How many periods to offset the result. Default: 0
@@ -83,5 +98,5 @@ def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwa
     fill_method (value, optional): Type of fill method
 
 Returns:
-    pd.Series: New feature generated.
+    pd.DataFrame: tsi, signal.
 """

tests/test_indicator_momentum.py

@@ -453,8 +453,8 @@ def test_trix(self):
 
     def test_tsi(self):
         result = pandas_ta.tsi(self.close)
-        self.assertIsInstance(result, Series)
-        self.assertEqual(result.name, "TSI_13_25")
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "TSI_13_25_13")
 
     def test_uo(self):
         result = pandas_ta.uo(self.high, self.low, self.close)