Merge remote-tracking branch 'origin/development' into pr_issue_700

pandas_ta/candles/cdl_z.py

@@ -60,12 +60,13 @@ def cdl_z(
 
     _full = "a" if full else ""
     _props = _full if full else f"_{length}_{ddof}"
-    df = DataFrame({
+    data = {
         f"open_Z{_props}": z_open,
         f"high_Z{_props}": z_high,
         f"low_Z{_props}": z_low,
         f"close_Z{_props}": z_close,
-    })
+    }
+    df = DataFrame(data, index=close.index)
 
     if full:
         df.fillna(method="backfill", axis=0, inplace=True)

pandas_ta/core.py

@@ -1447,6 +1447,21 @@ def adx(self, length=None, lensig=None, mamode=None, scalar=None, drift=None, of
                      drift=drift, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def alphatrend(self, volume=None, src=None, length=None, multiplier=None, threshold=None, lag=None, mamode=None, talib=None, offset=None, **kwargs: DictLike):
+        open_ = self._get_column(kwargs.pop("open", "open"))
+        high = self._get_column(kwargs.pop("high", "high"))
+        low = self._get_column(kwargs.pop("low", "low"))
+        close = self._get_column(kwargs.pop("close", "close"))
+        if volume is not None:
+            volume = self._get_column(kwargs.pop("volume", "volume"))
+        result = alphatrend(
+            open_=open_, high=high, low=low, close=close, volume=volume,
+            src=src, length=length, multiplier=multiplier,
+            threshold=threshold, lag=lag, mamode=mamode,
+            talib=talib, offset=offset, **kwargs
+        )
+        return self._post_process(result, **kwargs)
+
     def amat(self, fast=None, slow=None, mamode=None, lookback=None, offset=None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         result = amat(close=close, fast=fast, slow=slow, mamode=mamode, lookback=lookback, offset=offset, **kwargs)
@@ -1575,6 +1590,18 @@ def xsignals(self, signal=None, xa=None, xb=None, above=None, long=None, asbool=
                               trend_offset=trend_offset, trend_reset=trend_reset, offset=offset, **kwargs)
             return self._post_process(result, **kwargs)
 
+    # def zigzag(self, close=None, pivot_leg=None, price_deviation=None, retrace=None, last_extreme=None, offset=None, **kwargs: DictLike):
+    #     high = self._get_column(kwargs.pop("high", "high"))
+    #     low = self._get_column(kwargs.pop("low", "low"))
+    #     if close is not None:
+    #         close = self._get_column(kwargs.pop("close", "close"))
+    #     result = zigzag(
+    #         high=high, low=low, close=close,
+    #         pivot_leg=pivot_leg, price_deviation=price_deviation,
+    #         retrace=retrace, last_extreme=last_extreme,
+    #         offset=offset, **kwargs)
+    #     return self._post_process(result, **kwargs)
+
     # Volatility
     def aberration(self, length=None, atr_length=None, offset=None, **kwargs: DictLike):
         high = self._get_column(kwargs.pop("high", "high"))

pandas_ta/maps.py

@@ -73,9 +73,10 @@
     "transform": ["cube", "ifisher", "remap"],
     # Trend
     "trend": [
-        "adx", "amat", "aroon", "chop", "cksp", "decay", "decreasing", "dpo",
-        "increasing", "long_run", "psar", "qstick", "rwi", "short_run",
-        "trendflex", "tsignals", "ttm_trend", "vhf", "vortex", "xsignals"
+        "adx", "alphatrend", "amat", "aroon", "chop", "cksp", "decay",
+        "decreasing", "dpo", "increasing", "long_run", "psar", "qstick",
+        "rwi", "short_run", "trendflex", "tsignals", "ttm_trend", "vhf",
+        "vortex", "xsignals"
     ],
     # Volatility
     "volatility": [

pandas_ta/momentum/brar.py

@@ -89,8 +89,9 @@ def brar(
     br.name = f"BR{_props}"
     ar.category = br.category = "momentum"
 
-    brardf = DataFrame({ar.name: ar, br.name: br})
-    brardf.name = f"BRAR{_props}"
-    brardf.category = "momentum"
+    data = {ar.name: ar, br.name: br}
+    df = DataFrame(data, index=close.index)
+    df.name = f"BRAR{_props}"
+    df.category = "momentum"
 
-    return brardf
+    return df

pandas_ta/momentum/dm.py

@@ -90,11 +90,10 @@ def dm(
         neg.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Category
-    _params = f"_{length}"
-    data = {f"DMP{_params}": pos, f"DMN{_params}": neg, }
+    _props = f"_{length}"
+    data = {f"DMP{_props}": pos, f"DMN{_props}": neg}
+    df = DataFrame(data, index=high.index)
+    df.name = f"DM{_props}"
+    df.category = "momentum"
 
-    dmdf = DataFrame(data)
-    dmdf.name = f"DM{_params}"
-    dmdf.category = "momentum"
-
-    return dmdf
+    return df

pandas_ta/momentum/eri.py

@@ -72,7 +72,7 @@ def eri(
     bull.category = bear.category = "momentum"
 
     data = {bull.name: bull, bear.name: bear}
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = f"ERI_{length}"
     df.category = bull.category
 

pandas_ta/momentum/fisher.py

@@ -92,7 +92,7 @@ def fisher(
     fisher.category = signalma.category = "momentum"
 
     data = {fisher.name: fisher, signalma.name: signalma}
-    df = DataFrame(data)
+    df = DataFrame(data, index=high.index)
     df.name = f"FISHERT{_props}"
     df.category = fisher.category
 

pandas_ta/momentum/kdj.py

@@ -89,8 +89,9 @@ def kdj(
     j.name = f"J{_params}"
     k.category = d.category = j.category = "momentum"
 
-    kdjdf = DataFrame({k.name: k, d.name: d, j.name: j})
-    kdjdf.name = f"KDJ{_params}"
-    kdjdf.category = "momentum"
+    data = {k.name: k, d.name: d, j.name: j}
+    df = DataFrame(data, index=close.index)
+    df.name = f"KDJ{_params}"
+    df.category = "momentum"
 
-    return kdjdf
+    return df

pandas_ta/momentum/kst.py

@@ -92,8 +92,8 @@ def kst(
     kst.category = kst_signal.category = "momentum"
 
     data = {kst.name: kst, kst_signal.name: kst_signal}
-    kstdf = DataFrame(data)
-    kstdf.name = f"KST_{roc1}_{roc2}_{roc3}_{roc4}_{sma1}_{sma2}_{sma3}_{sma4}_{signal}"
-    kstdf.category = "momentum"
+    df = DataFrame(data, index=close.index)
+    df.name = f"KST_{roc1}_{roc2}_{roc3}_{roc4}_{sma1}_{sma2}_{sma3}_{sma4}_{signal}"
+    df.category = "momentum"
 
-    return kstdf
+    return df

pandas_ta/momentum/macd.py

@@ -108,8 +108,9 @@ def macd(
     data = {
         macd.name: macd,
         histogram.name: histogram,
-        signalma.name: signalma}
-    df = DataFrame(data)
+        signalma.name: signalma
+    }
+    df = DataFrame(data, index=close.index)
     df.name = f"MACD{_asmode}{_props}"
     df.category = macd.category
 

pandas_ta/momentum/ppo.py

@@ -100,8 +100,12 @@ def ppo(
     signalma.name = f"PPOs{_props}"
     ppo.category = histogram.category = signalma.category = "momentum"
 
-    data = {ppo.name: ppo, histogram.name: histogram, signalma.name: signalma}
-    df = DataFrame(data)
+    data = {
+        ppo.name: ppo,
+        histogram.name: histogram,
+        signalma.name: signalma
+    }
+    df = DataFrame(data, index=close.index)
     df.name = f"PPO{_props}"
     df.category = ppo.category
 

pandas_ta/momentum/qqe.py

@@ -165,11 +165,14 @@ def qqe(
     qqe_long.category = qqe_short.category = qqe.category
 
     data = {
-        qqe.name: qqe, rsi_ma.name: rsi_ma,
-        # long.name: long, short.name: short
-        qqe_long.name: qqe_long, qqe_short.name: qqe_short
+        qqe.name: qqe,
+        rsi_ma.name: rsi_ma,
+        # long.name: long,
+        # short.name: short
+        qqe_long.name: qqe_long,
+        qqe_short.name: qqe_short
     }
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = f"QQE{_props}"
     df.category = qqe.category
 

pandas_ta/momentum/rvgi.py

@@ -84,7 +84,8 @@ def rvgi(
     signal.name = f"RVGIs_{length}_{swma_length}"
     rvgi.category = signal.category = "momentum"
 
-    df = DataFrame({rvgi.name: rvgi, signal.name: signal})
+    data = {rvgi.name: rvgi, signal.name: signal}
+    df = DataFrame(data, index=close.index)
     df.name = f"RVGI_{length}_{swma_length}"
     df.category = rvgi.category
 

pandas_ta/momentum/smi.py

@@ -93,7 +93,7 @@ def smi(
     smi.category = signalma.category = osc.category = "momentum"
 
     data = {smi.name: smi, signalma.name: signalma, osc.name: osc}
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = f"SMI{_props}"
     df.category = smi.category
 

pandas_ta/momentum/squeeze.py

@@ -166,9 +166,9 @@ def squeeze(
         squeeze.name: squeeze,
         f"SQZ_ON": squeeze_on,
         f"SQZ_OFF": squeeze_off,
-        f"SQZ_NO": no_squeeze,
+        f"SQZ_NO": no_squeeze
     }
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = squeeze.name
     df.category = squeeze.category = "momentum"
 

pandas_ta/momentum/squeeze_pro.py

@@ -190,9 +190,9 @@ def squeeze_pro(
         f"SQZPRO_ON_NORMAL": squeeze_on_normal,
         f"SQZPRO_ON_NARROW": squeeze_on_narrow,
         f"SQZPRO_OFF": squeeze_off_wide,
-        f"SQZPRO_NO": no_squeeze,
+        f"SQZPRO_NO": no_squeeze
     }
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = squeeze.name
     df.category = squeeze.category = "momentum"
 

pandas_ta/momentum/stc.py

@@ -147,7 +147,7 @@ def stc(
         macd.name: macd,
         stoch.name: stoch
     }
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = f"STC{_props}"
     df.category = stc.category
 

pandas_ta/momentum/stochf.py

@@ -97,10 +97,7 @@ def stochf(
     stochf_d.name = f"{_name}d{_props}"
     stochf_k.category = stochf_d.category = "momentum"
 
-    data = {
-        stochf_k.name: stochf_k,
-        stochf_d.name: stochf_d
-    }
+    data = {stochf_k.name: stochf_k, stochf_d.name: stochf_d}
     df = DataFrame(data, index=close.index)
     df.name = f"{_name}{_props}"
     df.category = stochf_k.category

pandas_ta/momentum/stochrsi.py

@@ -93,11 +93,8 @@ def stochrsi(
     stochrsi_d.name = f"{_name}d{_props}"
     stochrsi_k.category = stochrsi_d.category = "momentum"
 
-    data = {
-        stochrsi_k.name: stochrsi_k,
-        stochrsi_d.name: stochrsi_d
-    }
-    df = DataFrame(data)
+    data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d}
+    df = DataFrame(data, index=close.index)
     df.name = f"{_name}{_props}"
     df.category = stochrsi_k.category
 

pandas_ta/momentum/td_seq.py

@@ -72,7 +72,7 @@ def td_seq(
     up_seq.category = down_seq.category = "momentum"
 
     data = {up_seq.name: up_seq, down_seq.name: down_seq}
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.index = close.index  # Only works here for some reason?
     df.name = "TD_SEQ"
     df.category = up_seq.category

pandas_ta/momentum/tsi.py

@@ -103,7 +103,8 @@ def tsi(
     tsi_signal.name = f"TSIs_{fast}_{slow}_{signal}"
     tsi.category = tsi_signal.category = "momentum"
 
-    df = DataFrame({tsi.name: tsi, tsi_signal.name: tsi_signal})
+    data = {tsi.name: tsi, tsi_signal.name: tsi_signal}
+    df = DataFrame(data, index=close.index)
     df.name = f"TSI_{fast}_{slow}_{signal}"
     df.category = "momentum"
 

pandas_ta/overlap/hilo.py

@@ -99,7 +99,8 @@ def hilo(
     data = {
         f"HILO{_props}": hilo,
         f"HILOl{_props}": long,
-        f"HILOs{_props}": short}
+        f"HILOs{_props}": short
+    }
     df = DataFrame(data, index=close.index)
 
     df.name = f"HILO{_props}"

pandas_ta/overlap/mama.py

@@ -156,10 +156,8 @@ def mama(
 
     # Name and Category
     _props = f"_{fastlimit}_{slowlimit}"
-    df = DataFrame({
-        f"MAMA{_props}": mama,
-        f"FAMA{_props}": fama,
-    }, index=close.index)
+    data = {f"MAMA{_props}": mama, f"FAMA{_props}": fama}
+    df = DataFrame(data, index=close.index)
 
     df.name = f"MAMA{_props}"
     df.category = "overlap"

pandas_ta/overlap/supertrend.py

@@ -3,14 +3,15 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import hl2
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
 from pandas_ta.volatility import atr
 
 
 def supertrend(
     high: Series, low: Series, close: Series,
     length: Int = None, atr_length: Int = None,
     multiplier: IntFloat = None,
+    atr_mamode : str = None,
     offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Supertrend (supertrend)
@@ -31,6 +32,8 @@ def supertrend(
             variable of control. Default: length
         multiplier (float): Coefficient for upper and lower band distance to
             midrange. Default: 3.0
+        atr_mamode (str) : MA type to be used for ATR calculation.
+            See ``help(ta.ma)``. Default: 'rma'
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -52,6 +55,7 @@ def supertrend(
         return
 
     multiplier = v_pos_default(multiplier, 3.0)
+    atr_mamode = v_mamode(atr_mamode, "rma")
     offset = v_offset(offset)
 
     # Calculate
@@ -60,9 +64,10 @@ def supertrend(
     long, short = [nan] * m, [nan] * m
 
     hl2_ = hl2(high, low)
-    matr = multiplier * atr(high, low, close, atr_length)
-    lb = hl2_ - matr  # Lowerband
-    ub = hl2_ + matr  # Upperband
+    matr = multiplier * atr(high, low, close, atr_length, mamode=atr_mamode)
+    lb = hl2_ - matr
+    ub = hl2_ + matr
+
     for i in range(1, m):
         if close.iat[i] > ub.iat[i - 1]:
             dir_[i] = 1
@@ -84,12 +89,13 @@ def supertrend(
     dir_[:length] = [nan] * length
 
     _props = f"_{length}_{multiplier}"
-    df = DataFrame({
+    data = {
         f"SUPERT{_props}": trend,
         f"SUPERTd{_props}": dir_,
         f"SUPERTl{_props}": long,
-        f"SUPERTs{_props}": short,
-    }, index=close.index)
+        f"SUPERTs{_props}": short
+    }
+    df = DataFrame(data, index=close.index)
 
     df.name = f"SUPERT{_props}"
     df.category = "overlap"

pandas_ta/performance/drawdown.py

@@ -65,7 +65,7 @@ def drawdown(
     dd.category = dd_pct.category = dd_log.category = "performance"
 
     data = {dd.name: dd, dd_pct.name: dd_pct, dd_log.name: dd_log}
-    df = DataFrame(data)
+    df = DataFrame(data, index=close.index)
     df.name = dd.name
     df.category = dd.category
 

pandas_ta/transform/cube.py

@@ -75,7 +75,8 @@ def cube(
     ct_signal.name = f"CUBEs{_props}"
     ct.category = ct_signal.category = "transform"
 
-    df = DataFrame({ct.name: ct, ct_signal.name: ct_signal})
+    data = {ct.name: ct, ct_signal.name: ct_signal}
+    df = DataFrame(data, index=close.index)
     df.name = f"CUBE{_props}"
     df.category = ct.category