ENH #92 + smi indicator MAINT refactoring

.gitignore

@@ -120,9 +120,9 @@ data/datas.csv
 data/GLD_D_tv.csv
 data/SPY_5min.csv
 data/SPY_1min.csv
-data/SPY_D_adxfish.csv
-data/SPY_D_lbsz.csv
-data/similang-ch.csv
+data/SPY_D_TV1.csv
+data/SPY_D_TV2.csv
+data/SPY_D_TV3.csv
 data/TV_5min.csv
 data/tulip.csv
 

README.md

@@ -58,6 +58,7 @@ and _Weighted Moving Average_.
 ## __New Indicators__
 * _Squeeze_ (**squeeze**). A Momentum indicator. Both John Carter's TTM **and** Lazybear's TradingView versions are implemented. The default is John Carter's, or ```lazybear=False```. Set ```lazybear=True``` to enable Lazybear's.
 * _TTM Trend_ (**ttm_trend**). A trend indicator inspired from John Carter's book "Mastering the Trade".
+* _SMI Ergodic_ (**smi**) Developed by William Blau, the SMI Ergodic Indicator is the same as the True Strength Index (TSI) except the SMI includes a signal line and oscillator.
 
 ## __Updated Indicators__
 * _Fisher Transform_ (**fisher**): Added Fisher's default **ema** signal line. To change the length of the signal line, use the argument: ```signal=5```. Default: 5
@@ -316,7 +317,7 @@ print(bothhl2.name)  # "pre_HL2_post"
 * _Doji_: **cdl_doji**
 * _Heikin-Ashi_: **ha**
 
-## _Momentum_ (31)
+## _Momentum_ (32)
 
 * _Awesome Oscillator_: **ao**
 * _Absolute Price Oscillator_: **apo**
@@ -343,6 +344,7 @@ print(bothhl2.name)  # "pre_HL2_post"
 * _Relative Strength Index_: **rsi**
 * _Relative Vigor Index_: **rvgi**
 * _Slope_: **slope**
+* _SMI Ergodic_ **smi**
 * _Squeeze_: **squeeze**
     * Default is John Carter's. Enable Lazybear's by ```lazybear=True```
 * _Stochastic Oscillator_: **stoch**

pandas_ta/__init__.py

@@ -25,7 +25,7 @@ Category = {
     "candles": ["cdl_doji", "ha"],
 
     # Momentum
-    "momentum": ["ao", "apo", "bias", "bop", "brar", "cci", "cg", "cmo", "coppock", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "pvo", "roc", "rsi", "rvgi", "slope", "squeeze", "stoch", "trix", "tsi", "uo", "willr"],
+    "momentum": ["ao", "apo", "bias", "bop", "brar", "cci", "cg", "cmo", "coppock", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "pvo", "roc", "rsi", "rvgi", "slope", "smi", "squeeze", "stoch", "trix", "tsi", "uo", "willr"],
 
     # Overlap
     "overlap": ["dema", "ema", "fwma", "hl2", "hlc3", "hma", "ichimoku", "kama", "linreg", "midpoint", "midprice", "ohlc4", "pwma", "rma", "sinwma", "sma", "supertrend", "swma", "t3", "tema", "trima", "vwap", "vwma", "wcp", "wma", "zlma"],

pandas_ta/candles/cdl_doji.py

@@ -14,7 +14,7 @@ def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asi
     factor = float(factor) if is_percent(factor) else 10
     scalar = float(scalar) if scalar else 100
     offset = get_offset(offset)
-    naive = kwargs.pop('naive', False)
+    naive = kwargs.pop("naive", False)
 
     # Calculate Result
     body = real_body(open_, close).abs()
@@ -32,10 +32,10 @@ def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asi
         doji = doji.shift(offset)
 
     # Handle fills
-    if 'fillna' in kwargs:
-        doji.fillna(kwargs['fillna'], inplace=True)
-    if 'fill_method' in kwargs:
-        doji.fillna(method=kwargs['fill_method'], inplace=True)
+    if "fillna" in kwargs:
+        doji.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        doji.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
     doji.name = f"CDL_DOJI_{length}_{0.01 * factor}"
@@ -69,9 +69,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): The period.  Default: 10
-    factor (float): Doji value.  Default: 100
-    scalar (float): How much to magnify.  Default: 100
+    length (int): The period. Default: 10
+    factor (float): Doji value. Default: 100
+    scalar (float): How much to magnify. Default: 100
     asint (bool): Keep results numerical instead of boolean. Default: True
 
 Kwargs:

pandas_ta/core.py

@@ -23,7 +23,7 @@ from pandas_ta.volatility import *
 from pandas_ta.volume import *
 from pandas_ta.utils import *
 
-version = ".".join(("0", "1", "97b"))
+version = ".".join(("0", "1", "98b"))
 
 
 def mp_worker(args):
@@ -820,6 +820,13 @@ class AnalysisIndicators(BasePandasObject):
         result = slope(close=close, length=length, offset=offset, **kwargs)
         return result
 
+    @finalize
+    def smi(self, close=None, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs):
+        close = self._get_column(close, "close")
+
+        result = smi(close=close, fast=fast, slow=slow, signal=signal, scalar=scalar, offset=offset, **kwargs)
+        return result
+
     @finalize
     def squeeze(self, high=None, low=None, close=None, bb_length=None, bb_std=None, kc_length=None, kc_scalar=None, mom_length=None, mom_smooth=None, use_tr=None, offset=None, **kwargs):
         high = self._get_column(high, "high")
@@ -846,10 +853,10 @@ class AnalysisIndicators(BasePandasObject):
         return result
 
     @finalize
-    def tsi(self, close=None, fast=None, slow=None, drift=None, offset=None, **kwargs):
+    def tsi(self, close=None, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwargs):
         close = self._get_column(close, "close")
 
-        result = tsi(close=close, fast=fast, slow=slow, drift=drift, offset=offset, **kwargs)
+        result = tsi(close=close, fast=fast, slow=slow, scalar=scalar, drift=drift, offset=offset, **kwargs)
         return result
 
     @finalize

pandas_ta/momentum/__init__.py

@@ -24,6 +24,7 @@ from .roc import roc
 from .rsi import rsi
 from .rvgi import rvgi
 from .slope import slope
+from .smi import smi
 from .squeeze import squeeze
 from .stoch import stoch
 from .trix import trix

pandas_ta/momentum/macd.py

@@ -29,14 +29,14 @@ def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
         signalma = signalma.shift(offset)
 
     # Handle fills
-    if 'fillna' in kwargs:
-        macd.fillna(kwargs['fillna'], inplace=True)
-        histogram.fillna(kwargs['fillna'], inplace=True)
-        signalma.fillna(kwargs['fillna'], inplace=True)
-    if 'fill_method' in kwargs:
-        macd.fillna(method=kwargs['fill_method'], inplace=True)
-        histogram.fillna(method=kwargs['fill_method'], inplace=True)
-        signalma.fillna(method=kwargs['fill_method'], inplace=True)
+    if "fillna" in kwargs:
+        macd.fillna(kwargs["fillna"], inplace=True)
+        histogram.fillna(kwargs["fillna"], inplace=True)
+        signalma.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        macd.fillna(method=kwargs["fill_method"], inplace=True)
+        histogram.fillna(method=kwargs["fill_method"], inplace=True)
+        signalma.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
     _props = f"_{fast}_{slow}_{signal}"
@@ -51,31 +51,31 @@ def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
     df.name = f"MACD{_props}"
     df.category = macd.category
 
-    signal_indicators = kwargs.pop('signal_indicators', False)
+    signal_indicators = kwargs.pop("signal_indicators", False)
     if signal_indicators:
         signalsdf = concat(
             [
                 df,
                 signals(
                     indicator=histogram,
-                    xa=kwargs.pop('xa', 0),
-                    xb=kwargs.pop('xb', None),
-                    xserie=kwargs.pop('xserie', None),
-                    xserie_a=kwargs.pop('xserie_a', None),
-                    xserie_b=kwargs.pop('xserie_b', None),
-                    cross_values=kwargs.pop('cross_values', True),
-                    cross_series=kwargs.pop('cross_series', True),
+                    xa=kwargs.pop("xa", 0),
+                    xb=kwargs.pop("xb", None),
+                    xserie=kwargs.pop("xserie", None),
+                    xserie_a=kwargs.pop("xserie_a", None),
+                    xserie_b=kwargs.pop("xserie_b", None),
+                    cross_values=kwargs.pop("cross_values", True),
+                    cross_series=kwargs.pop("cross_series", True),
                     offset=offset,
                 ),
                 signals(
                     indicator=macd,
-                    xa=kwargs.pop('xa', 0),
-                    xb=kwargs.pop('xb', None),
-                    xserie=kwargs.pop('xserie', None),
-                    xserie_a=kwargs.pop('xserie_a', None),
-                    xserie_b=kwargs.pop('xserie_b', None),
-                    cross_values=kwargs.pop('cross_values', False),
-                    cross_series=kwargs.pop('cross_series', True),
+                    xa=kwargs.pop("xa", 0),
+                    xb=kwargs.pop("xb", None),
+                    xserie=kwargs.pop("xserie", None),
+                    xserie_a=kwargs.pop("xserie_a", None),
+                    xserie_b=kwargs.pop("xserie_b", None),
+                    cross_values=kwargs.pop("cross_values", False),
+                    cross_series=kwargs.pop("cross_series", True),
                     offset=offset,
                 ),
             ],

pandas_ta/momentum/smi.py

@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+from pandas import DataFrame, concat
+from .tsi import tsi
+from pandas_ta.overlap import ema
+from pandas_ta.utils import get_offset, verify_series, signals
+
+def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs):
+    """Indicator: SMI Ergodic Indicator (SMIIO)"""
+    # Validate arguments
+    close = verify_series(close)
+    fast = int(fast) if fast and fast > 0 else 5
+    slow = int(slow) if slow and slow > 0 else 20
+    signal = int(signal) if signal and signal > 0 else 5
+    if slow < fast:
+        fast, slow = slow, fast
+    scalar = float(scalar) if scalar else 1
+    offset = get_offset(offset)
+
+    # Calculate Result
+    smi = tsi(close, fast=fast, slow=slow, scalar=scalar)
+    signalma = ema(smi, signal)
+    osc = smi - signalma
+
+    # Offset
+    if offset != 0:
+        smi = smi.shift(offset)
+        signalma = signalma.shift(offset)
+        osc = osc.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        smi.fillna(kwargs["fillna"], inplace=True)
+        signalma.fillna(kwargs["fillna"], inplace=True)
+        osc.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        smi.fillna(method=kwargs["fill_method"], inplace=True)
+        signalma.fillna(method=kwargs["fill_method"], inplace=True)
+        osc.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Categorize it
+    _scalar = f"_{scalar}" if scalar != 1 else ""
+    _props = f"_{fast}_{slow}_{signal}{_scalar}"
+    smi.name = f"SMI{_props}"
+    signalma.name = f"SMIs{_props}"
+    osc.name = f"SMIo{_props}"
+    smi.category = signalma.category = osc.category = "momentum"
+
+    # Prepare DataFrame to return
+    data = {smi.name: smi, signalma.name: signalma, osc.name: osc}
+    df = DataFrame(data)
+    df.name = f"SMI{_props}"
+    df.category = smi.category
+
+    return df
+
+
+
+smi.__doc__ = \
+"""SMI Ergodic Indicator (SMI)
+
+The SMI Ergodic Indicator is the same as the True Strength Index (TSI) developed
+by William Blau, except the SMI includes a signal line. The SMI uses double
+moving averages of price minus previous price over 2 time frames. The signal
+line, which is an EMA of the SMI, is plotted to help trigger trading signals.
+The trend is bullish when crossing above zero and bearish when crossing below
+zero. This implementation includes both the SMI Ergodic Indicator and SMI
+Ergodic Oscillator.
+
+Sources:
+    https://www.motivewave.com/studies/smi_ergodic_indicator.htm
+    https://www.tradingview.com/script/Xh5Q0une-SMI-Ergodic-Oscillator/
+    https://www.tradingview.com/script/cwrgy4fw-SMIIO/
+
+Calculation:
+    Default Inputs:
+        fast=5, slow=20, signal=5
+    TSI = True Strength Index
+    EMA = Exponential Moving Average
+
+    ERG = TSI(close, fast, slow)
+    Signal = EMA(ERG, signal)
+    OSC = ERG - Signal
+
+Args:
+    close (pd.Series): Series of 'close's
+    fast (int): The short period. Default: 5
+    slow (int): The long period. Default: 20
+    signal (int): The signal period. Default: 5
+    scalar (float): How much to magnify. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.DataFrame: smi, signal, oscillator columns.
+"""

pandas_ta/momentum/tsi.py

@@ -1,15 +1,16 @@
 # -*- coding: utf-8 -*-
-from ..overlap.ema import ema
-from ..utils import get_drift, get_offset, verify_series
+from pandas_ta.overlap import ema
+from pandas_ta.utils import get_drift, get_offset, verify_series
 
-def tsi(close, fast=None, slow=None, drift=None, offset=None, **kwargs):
+def tsi(close, fast=None, slow=None, scalar=None, drift=None, offset=None, **kwargs):
     """Indicator: True Strength Index (TSI)"""
     # Validate Arguments
     close = verify_series(close)
     fast = int(fast) if fast and fast > 0 else 13
     slow = int(slow) if slow and slow > 0 else 25
-    if slow < fast:
-        fast, slow = slow, fast
+    # if slow < fast:
+    #     fast, slow = slow, fast
+    scalar = float(scalar) if scalar else 100
     drift = get_drift(drift)
     offset = get_offset(offset)
 
@@ -22,21 +23,21 @@ def tsi(close, fast=None, slow=None, drift=None, offset=None, **kwargs):
     abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs)
     abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs)
 
-    tsi = 100 * fast_slow_ema / abs_fast_slow_ema
+    tsi = scalar * fast_slow_ema / abs_fast_slow_ema
 
     # Offset
     if offset != 0:
         tsi = tsi.shift(offset)
 
     # Handle fills
-    if 'fillna' in kwargs:
-        tsi.fillna(kwargs['fillna'], inplace=True)
-    if 'fill_method' in kwargs:
-        tsi.fillna(method=kwargs['fill_method'], inplace=True)
+    if "fillna" in kwargs:
+        tsi.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        tsi.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
     tsi.name = f"TSI_{fast}_{slow}"
-    tsi.category = 'momentum'
+    tsi.category = "momentum"
 
     return tsi
 
@@ -54,7 +55,7 @@ Sources:
 
 Calculation:
     Default Inputs:
-        fast=13, slow=25, drift=1
+        fast=13, slow=25, scalar=100, drift=1
     EMA = Exponential Moving Average
     diff = close.diff(drift)
 
@@ -64,13 +65,14 @@ Calculation:
     abs_diff_slow_ema = absolute_diff_ema = EMA(ABS(diff), slow)
     abema = abs_diff_fast_slow_ema = EMA(abs_diff_slow_ema, fast)
 
-    TSI = 100 * fast_slow_ema / abema
+    TSI = scalar * fast_slow_ema / abema
 
 Args:
     close (pd.Series): Series of 'close's
-    fast (int): The short period.  Default: 13
-    slow (int): The long period.   Default: 25
-    drift (int): The difference period.   Default: 1
+    fast (int): The short period. Default: 13
+    slow (int): The long period. Default: 25
+    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
 
 Kwargs:

pandas_ta/utils.py

@@ -10,7 +10,7 @@ from functools import reduce
 from operator import mul
 from sys import float_info as sflt
 
-TRADING_DAYS_PER_YEAR = 251
+TRADING_DAYS_PER_YEAR = 252 # Keep even
 TRADING_HOURS_PER_DAY = 6.5
 MINUTES_PER_HOUR = 60
 

setup.py

@@ -2,7 +2,7 @@
 from distutils.core import setup
 from pandas_ta.core import version
 
-long_description = "An easy to use Python 3 Pandas Extension with 100+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib."
+long_description = "An easy to use Python 3 Pandas Extension with 115+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib."
 
 setup(
     name ="pandas_ta",

tests/test_indicator_momentum.py

@@ -306,6 +306,18 @@ class TestMomentum(TestCase):
         self.assertIsInstance(result, Series)
         self.assertEqual(result.name, "ANGLEd_1")
 
+    def test_smi(self):
+        result = pandas_ta.smi(self.close)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "SMI_5_20_5")
+        self.assertEqual(len(result.columns), 3)
+
+    def test_smi_scalar(self):
+        result = pandas_ta.smi(self.close, scalar=10)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "SMI_5_20_5_10.0")
+        self.assertEqual(len(result.columns), 3)
+
     def test_squeeze(self):
         result = pandas_ta.squeeze(self.high, self.low, self.close)
         self.assertIsInstance(result, DataFrame)

tests/test_indicator_momentum_ext.py

@@ -166,6 +166,15 @@ class TestMomentumExtension(TestCase):
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(self.data.columns[-1], "ANGLEd_1")
 
+    def test_smi_ext(self):
+        self.data.ta.smi(append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(list(self.data.columns[-3:]), ["SMI_5_20_5", "SMIs_5_20_5", "SMIo_5_20_5"])
+
+        self.data.ta.smi(scalar=10, append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(list(self.data.columns[-3:]), ["SMI_5_20_5_10.0", "SMIs_5_20_5_10.0", "SMIo_5_20_5_10.0"])
+
     def test_squeeze_ext(self):
         self.data.ta.squeeze(append=True)
         self.assertIsInstance(self.data, DataFrame)