From 2f7aec18eb87dec93b9e9d6472d4e50b9203f887 Mon Sep 17 00:00:00 2001
From: fff-git <fsdfgs@gmx.com>
Date: Tue, 26 May 2020 12:56:50 +0200
Subject: [PATCH] Add Heikin Ashi ti

---
 pandas_ta/overlap/ha.py | 103 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 103 insertions(+)
 create mode 100644 pandas_ta/overlap/ha.py

diff --git a/pandas_ta/overlap/ha.py b/pandas_ta/overlap/ha.py
new file mode 100644
index 0000000..ea6219a
--- /dev/null
+++ b/pandas_ta/overlap/ha.py
@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+from pandas import DataFrame
+from ..utils import get_offset, verify_series
+
+
+def ha(open, high, low, close, offset=None, **kwargs):
+    # indicator : Heiken Ashi
+    # Validate Arguments
+    open = verify_series(open)
+    high = verify_series(high)
+    low = verify_series(low)
+    close = verify_series(close)
+    offset = get_offset(offset)
+
+    # Initialization of the ha_open serie
+    ha_open = np.zeros(shape=(len(close)))
+
+    # ha_open of the first element
+    ha_open[0] = 0.5 * (open[0] + close[0])
+
+    # shift open and close series by one to calculate ha_open other elements
+    open_shifted = np.empty_like(open)
+    open_shifted[:1] = np.nan
+    open_shifted[1:] = open[:-1]
+    close_shifted = np.empty_like(close)
+    close_shifted[:1] = np.nan
+    close_shifted[1:] = close[:-1]
+    # Calculation of ha_open except first element
+    ha_open[1:] = 0.5 * (open_shifted[1:] + close_shifted[1:])
+
+    # calculation of ha_close, ha_high, ha_low
+    ha_close = 0.25 * (open + high + low + close)
+    ha_high = np.maximum.reduce([high, ha_open, ha_close])
+    ha_low = np.minimum.reduce([low, ha_open, ha_close])
+
+    # Prepare DataFrame to return
+    data = {'ha_open': ha_open, 'ha_high': ha_high, 'ha_low': ha_low, 'ha_close': ha_close}
+    hadf = DataFrame(data)
+    hadf.name = "Heiken-Ashi"
+    hadf.category = 'overlap'
+
+    # Apply offset if needed
+    if offset != 0:
+        hadf = hadf.shift(offset)
+
+    # Handle fills
+    if 'fillna' in kwargs:
+        hadf.fillna(kwargs['fillna'], inplace=True)
+
+    if 'fill_method' in kwargs:
+        hadf.fillna(method=kwargs['fill_method'], inplace=True)
+
+    return hadf
+
+
+ha.__doc__ = \
+    """Heiken Ashi (HA)
+
+The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise. 
+Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, 
+share some characteristics with standard candlestick charts but differ based on the values used to create each candle. 
+Instead of using the open, high, low, and close like standard candlestick charts, 
+the Heikin-Ashi technique uses a modified formula based on two-period averages. 
+This gives the chart a smoother appearance, making it easier to spots trends and reversals, 
+but also obscures gaps and some price data.
+
+Sources:
+    https://www.investopedia.com/terms/h/heikinashi.asp
+
+Calculation:
+     The Formula for the Heikin-Ashi Technique Is:
+
+Heikin-Ashi Close=(Open0+High0+Low0+Close0)/4
+Heikin-Ashi Open=(HA Open−1+HA Close−1)/2
+Heikin-Ashi High=Max (High0,HA Open0,HA Close0)
+Heikin-Ashi Low=Min (Low0,HA Open0,HA Close0)
+where:Open0 etc.=Values from the current period
+Open−1 etc.=Values from the prior period
+HA=Heikin-Ashi
+
+ How to Calculate Heikin-Ashi
+
+    Use one period to create the first Heikin-Ashi (HA) candle, using the formulas. 
+    For example use the high, low, open, and close to create the first HA close price. 
+    Use the open and close to create the first HA open. 
+    The high of the period will be the first HA high, and the low will be the first HA low.
+    With the first HA calculated, it is now possible to continue computing the HA candles per the formulas.
+​​
+Args:
+    open (pd.Series): Series of 'open's
+    high (pd.Series): Series of 'high's
+    low (pd.Series): Series of 'low's
+    close (pd.Series): Series of 'close's
+    
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
+"""