ENH #86 fisher indicator fix and TV correlation

.gitignore

@@ -119,6 +119,7 @@ pandas_ta/_wrapper.py
 data/datas.csv
 data/SPY_5min.csv
 data/SPY_1min.csv
+data/SPY_D_adxfish.csv
 data/SPY_D_lbsz.csv
 data/TV_5min.csv
 data/similang-ch.csv

examples/watchlist.py

@@ -9,7 +9,7 @@ import pandas as pd # pip install pandas
 from alphaVantageAPI.alphavantage import AlphaVantage  # pip install alphaVantage-api
 import pandas_ta as ta # pip install pandas_ta
 
-
+AV = AlphaVantage(api_key="YOUR API KEY", premium=False, clean=True, output_size="full")
 
 class Watchlist(object):
     """Watchlist Class (** This is subject to change! **)
@@ -44,7 +44,7 @@ class Watchlist(object):
         self.data = None
         self.kwargs = kwargs
 
-        self.ds = ds if ds is not None else None
+        self.ds = ds if ds is not None else AV
         self.strategy = strategy
 
 
@@ -105,7 +105,6 @@ class Watchlist(object):
 
         if kwargs.pop("analyze", True):
             if self.debug: print(f"[+] TA[{len(self.strategy.ta)}]: {self.strategy.name}")
-            # df.ta.strategy(name=self.strategy.name, ta=self.strategy.ta, **kwargs)
             df.ta.strategy(self.strategy, **kwargs)
 
         df.ticker = ticker # Attach ticker to the DataFrame

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", "92b"))
+version = ".".join(("0", "1", "93b"))
 
 
 def mp_worker(args):

pandas_ta/momentum/fisher.py

@@ -3,7 +3,7 @@ from numpy import log as nplog
 from numpy import NaN as npNaN
 from pandas import DataFrame, Series
 from pandas_ta.overlap import ema, hl2
-from pandas_ta.utils import get_offset, verify_series, zero
+from pandas_ta.utils import get_offset, high_low_range, verify_series, zero
 
 def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
     """Indicator: Fisher Transform (FISHT)"""
@@ -11,30 +11,29 @@ def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
     high = verify_series(high)
     low = verify_series(low)
     length = int(length) if length and length > 0 else 9
-    signal = int(signal) if signal and signal > 0 else 5
+    signal = int(signal) if signal and signal > 0 else 1
     offset = get_offset(offset)
 
     # Calculate Result
-    m = high.size
     hl2_ = hl2(high, low)
-    max_high = hl2_.rolling(length).max()
-    min_low = hl2_.rolling(length).min()
-    hl2_range = max_high - min_low
-    hl2_range[hl2_range < 1e-5] = 0.001
-    position = (hl2_ - min_low) / hl2_range
-    
+    highest_hl2 = hl2_.rolling(length).max()
+    lowest_hl2 = hl2_.rolling(length).min()
+
+    hlr = high_low_range(highest_hl2, lowest_hl2)
+    hlr[hlr < 0.001] = 0.001
+
+    position = ((hl2_ - lowest_hl2) / hlr) - 0.5
+
     v = 0
-    fish = 0
-    result = [npNaN for _ in range(0, length - 1)]
-    for i in range(length - 1, m):
-        v = 0.66 * (position[i] - 0.5) + 0.67 * v
-        if v >  0.99: v =  0.999
+    m = high.size
+    result = [npNaN for _ in range(0, length - 1)] + [0]
+    for i in range(length, m):
+        v = 0.66 * position[i] + 0.67 * v
         if v < -0.99: v = -0.999
-        fish = 0.5 * (fish + nplog((1 + v) / (1 - v)))
-        result.append(fish)
-        
+        if v >  0.99: v =  0.999
+        result.append(0.5 * (nplog((1 + v) / (1 - v)) + result[i - 1]))
     fisher = Series(result, index=high.index)
-    signalma = ema(fisher, length=signal)
+    signalma = fisher.shift(signal)
 
     # Offset
     if offset != 0:
@@ -73,37 +72,36 @@ user-specified number of periods. A reversal signal is suggested when the the
 two lines cross.
 
 Sources:
-    https://tulipindicators.org/fisher
-    https://library.tradingtechnologies.com/trade/chrt-ti-ehler-fisher-transformation.html
-    TradingView
+    TradingView (Correlation >99%)
 
 Calculation:
     Default Inputs:
-        length=10, signal=5
-    EMA = Exponential Moving Average
-    HL2 = 0.5 * (high + low)
-    Max_HL2 = HL2.rolling(length).max()
-    Min_HL2 = HL2.rolling(length).min()
-    HL2R = Max_HL2 - Min_HL2
-    HL2R[HL2R < 1e-5] = 0.001 # Set small values to 0.001
-    position = (HL2 - Min_HL2) / HL2R
-    
-    FISH = 0.5 * log((1 + position) / (1 - position)) 
-    Signal = EMA(FISH, signal)
-    
-    # Fix 
-    position = position > .99 ? .999 : position < -.99 ? -.999 : position
+        length=9, signal=1
+    HL2 = hl2(high, low)
+    HHL2 = HL2.rolling(length).max()
+    LHL2 = HL2.rolling(length).min()
 
-    position := round_(.66 * ((hl2 - low_) / max(high_ - low_, .001) - .5) + .67 * nz(value[1]))
+    HLR = HHL2 - LHL2
+    HLR[HLR < 0.001] = 0.001
 
-    fish1 = 0.0
-    fish1 := .5 * log((1 + value) / max(1 - value, .001)) + .5 * nz(fish1[1])
+    position = ((HL2 - LHL2) / HLR) - 0.5
+
+    v = 0
+    m = high.size
+    FISHER = [npNaN for _ in range(0, length - 1)] + [0]
+    for i in range(length, m):
+        v = 0.66 * position[i] + 0.67 * v
+        if v < -0.99: v = -0.999
+        if v >  0.99: v =  0.999
+        FISHER.append(0.5 * (nplog((1 + v) / (1 - v)) + FISHER[i - 1]))
+
+    SIGNAL = FISHER.shift(signal)
 
 Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     length (int): Fisher period.  Default: 9
-    signal (int): Fisher Signal period.  Default: 5
+    signal (int): Fisher Signal period.  Default: 1
     offset (int): How many periods to offset the result.  Default: 0
 
 Kwargs:

tests/test_indicator_momentum.py

@@ -157,15 +157,7 @@ class TestMomentum(TestCase):
     def test_fisher(self):
         result = pandas_ta.fisher(self.high, self.low)
         self.assertIsInstance(result, DataFrame)
-        self.assertEqual(result.name, "FISHERT_9_5")
-
-        result = pandas_ta.fisher(self.high, self.low, tt=True)
-        self.assertIsInstance(result, DataFrame)
-        self.assertEqual(result.name, "FISHERT_9_5")
-
-        result = pandas_ta.fisher(self.high, self.low, tulip=True)
-        self.assertIsInstance(result, DataFrame)
-        self.assertEqual(result.name, "FISHERT_9_5")
+        self.assertEqual(result.name, "FISHERT_9_1")
 
     def test_inertia(self):
         result = pandas_ta.inertia(self.close)

tests/test_indicator_momentum_ext.py

@@ -81,7 +81,7 @@ class TestMomentumExtension(TestCase):
     def test_fisher_ext(self):
         self.data.ta.fisher(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(list(self.data.columns[-2:]), ["FISHERT_9_5", "FISHERTs_9_5"])
+        self.assertEqual(list(self.data.columns[-2:]), ["FISHERT_9_1", "FISHERTs_9_1"])
 
     def test_inertia_ext(self):
         self.data.ta.inertia(append=True)