Added some styles, debuged to get the same result from pandas-ta and …

…TALIB versions.

pandas_ta/trend/ht_trendline.py

@@ -1,62 +1,63 @@
 # -*- coding: utf-8 -*-
-from numpy import nan, zeros_like, arctan
+from numpy import nan, zeros_like, arctan, zeros
 from numba import njit
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import v_offset, v_pos_default, v_series, zero
+from pandas_ta.maps import Imports
+from pandas_ta.utils import v_offset, v_series, v_talib
 
 
 @njit
 def np_ht_trendline(x):
     # Variables used for the Hilbert Transformation
     a, b = 0.0962, 0.5769
+    rad_to_deg = 45 / arctan(1)
+    period, smooth_period = 0.0, 0.0
 
     m = x.size
-    smooth_price = zeros_like(m)
-    de_trender = zeros_like(m)
-    q1 = zeros_like(m)
-    i1 = zeros_like(m)
-    i2 = zeros_like(m)
-    q2 = zeros_like(m)
-    _re = zeros_like(m)
-    _im = zeros_like(m)
-    i_trend = zeros_like(m)
-
-    period, _prev_i2, _prev_q2, _re, _im, smooth_period = 0, 0, 0, 0, 0, 0
+    smooth_price = zeros(m)
+    de_trender = zeros(m)
+    q1 = zeros(m)
+    i1 = zeros(m)
+    i2 = zeros(m)
+    q2 = zeros(m)
+    _re = zeros(m)
+    _im = zeros(m)
+    i_trend = zeros(m)
+    trend_line = zeros(m)
 
     for i in range(x.size):
-        if i > 50:
-            smooth_price[i] = (4 * x[i] + 3 * x[i - 1] + 2 * x[i - 2] + x[i - 3]) / 10
-        else:
+        if i < 50:
             smooth_price[i] = 0
+        else:
+            smooth_price[i] = (4 * x[i] + 3 * x[i - 1] + 2 * x[i - 2] + x[i - 3]) / 10
 
-    for i in range(x.size):
         adjusted_prev_period = 0.075 * period + 0.54
 
-        de_trender = (a * smooth_price[i] + b * smooth_price[i - 2] -
-                      b * smooth_price[i - 4] - a * smooth_price[i - 6]) * adjusted_prev_period
+        de_trender[i] = (a * smooth_price[i] + b * smooth_price[i - 2] -
+                         b * smooth_price[i - 4] - a * smooth_price[i - 6]) * adjusted_prev_period
 
-        q1[i] = (a * de_trender[i] + b * de_trender[i-2] -
-                 b * de_trender[i-4] - a * de_trender[i-6]) * adjusted_prev_period
-        i1[i] = de_trender[i-3]
-        ji = (a * i1[i] + b * i1[i-2] - b * i1[i-4] - a * i1[i-6]) * adjusted_prev_period
-        jq = (a * q1[i] + b * q1[i-2] - b * q1[i-4] - a * q1[i-6]) * adjusted_prev_period
+        q1[i] = (a * de_trender[i] + b * de_trender[i - 2] -
+                 b * de_trender[i - 4] - a * de_trender[i - 6]) * adjusted_prev_period
+        i1[i] = de_trender[i - 3]
+        ji = (a * i1[i] + b * i1[i - 2] - b * i1[i - 4] - a * i1[i - 6]) * adjusted_prev_period
+        jq = (a * q1[i] + b * q1[i - 2] - b * q1[i - 4] - a * q1[i - 6]) * adjusted_prev_period
 
         i2[i] = i1[i] - jq
         q2[i] = q1[i] + ji
 
-        i2 = 0.2 * i2[i] + 0.8 * i2[i-1]
-        q2 = 0.2 * q2[i] + 0.8 * q2[i-1]
+        i2[i] = 0.2 * i2[i] + 0.8 * i2[i - 1]
+        q2[i] = 0.2 * q2[i] + 0.8 * q2[i - 1]
 
-        _re[i] = i2[i] * i2[i-1] + q2[i] * q2[i-1]
-        _im[i] = i2[i] * q2[i-1] - q2[i] * i2[i-1]
+        _re[i] = i2[i] * i2[i - 1] + q2[i] * q2[i - 1]
+        _im[i] = i2[i] * q2[i - 1] - q2[i] * i2[i - 1]
 
-        _re[i] = 0.2 * _re[i] + 0.8 * _re[i-1]
-        _im[i] = 0.2 * _im[i] + 0.8 * _im[i-1]
+        _re[i] = 0.2 * _re[i] + 0.8 * _re[i - 1]
+        _im[i] = 0.2 * _im[i] + 0.8 * _im[i - 1]
 
         new_period = 0
-        if _re[i] and _im[i]:
-            new_period = 360 / arctan(_re[i]/_im[i])
+        if _re[i] != 0 and _im[i] != 0:
+            new_period = 360 / (arctan(_im[i]/_re[i]) * rad_to_deg)
         if new_period > 1.5 * period:
             new_period = 1.5 * period
         if new_period < 0.67 * period:
@@ -71,13 +72,17 @@ def np_ht_trendline(x):
         dc_period = int(smooth_period + 0.5)
         temp_real = 0
         for k in range(dc_period):
-            temp_real += x[i-k]
+            temp_real += x[i - k]
 
         if dc_period > 0:
             temp_real /= dc_period
 
         i_trend[i] = temp_real
-        trend_line = (4 * i_trend[i] + 3 * i_trend[i-1] + 2 * i_trend[i-2] + i_trend[i-3]) / 10.0
+
+        if i < 12:
+            trend_line[i] = x[i]
+        else:
+            trend_line[i] = (4 * i_trend[i] + 3 * i_trend[i - 1] + 2 * i_trend[i - 2] + i_trend[i - 3]) / 10.0
 
     return trend_line
 
@@ -90,6 +95,7 @@ def ht_trendline(
 
     Sources:
         https://c.mql5.com/forextsd/forum/59/023inst.pdf
+        https://github.com/TA-Lib/ta-lib/blob/main/src/ta_func/ta_HT_TRENDLINE.c
 
     Args:
         close (pd.Series): Series of 'close's.