Smoothed Heikin Ashi candlesticks¶
In this article we will discuss alternative usage of Heikin Ashi candlestics (HA) for stocks/crypto analytics and trading in general. Regular Heikin Ashi candlestics make it easier to determine longer term trend by filtering out noise from OHLC price data. If we want to suppress noise further, we can take moving averages (Simple/Exponencial) of OHLC prices to get Smoothed Heikin Ashi candlesticks. It might prove useful to plot both regular OHLC candlesticks alongside (Smoothed/Regular) Heikin Ashi candlesticks.
Heikin Ashi constructed from Moving averages of OHLC prices behave similarly to moving averages. With one benefit - we can plot green/red candle of such HA candlestics to give us info about bullish/bearish price trend. With that said, we can make simplification during visualization - instead of plotting actual candlestics, we will plot HA candlestics as line graph and will color space between the Smoothed Heikin Ashi Open and Close lines accordingly. It will give us pretty much the same info as the actual HA candlestics. For candlestics graphs please check sources at the bottom of the article.
Regular Heikin Ashi: $$ C_{ha} = \frac{1}{4}(Open + High + Low + Close) $$ $$ O_{ha} = \frac{1}{2}(Open_{i-1} + Close_{i-1}) $$ $$ H_{ha} = Max[High, Open, Close] $$ $$ L_{ha} = Min[Low, Open, Close] $$
Smoothed Heikin Ashi: $$ \overline{C_{ha}} = \frac{1}{4}( \overline{Open} + \overline{High} + \overline{Low} + \overline{Close}) $$ $$ \overline{O_{ha}} = \frac{1}{2}(\overline{Open_{i-1}} + \overline{Close_{i-1}}) $$ $$ \overline{H_{ha}} = Max[\overline{High}, \overline{Open}, \overline{Close}] $$ $$ \overline{L_{ha}} = Min[\overline{Low}, \overline{Open}, \overline{Close}] $$
Optional installs
# uncomment on first run in google colab
#!pip3 install yfinance
#!pip3 install pandas_ta
Imports
import sys
import os
# ___library_import_statements___
import pandas as pd
import pandas_ta as ta # for exponencial moving averages (better than EWM in regular pandas)
# make pandas to print dataframes nicely
pd.set_option('expand_frame_repr', False)
import numpy as np
import matplotlib.pyplot as plt
import datetime
import time
#newest yahoo API
import yfinance as yf
Variables definitions
ticker = 'FB'
start_time = datetime.datetime(2021, 6, 6)
#end_time = datetime.datetime(2022, 1, 1)
end_time = datetime.datetime.now().date().isoformat() # today
# Heikin Ashi related vars
M=40 # lookback period for averaging OHLC values
#ma_type = 'SMA' # Simple Moving Average
ma_type = 'EMA' # Exponencial Moving Average
Function definitions
def get_data(ticker):
ticker_df =yf.download(ticker, start=start_time, end=end_time)
# use numerical integer index instead of date
ticker_df = ticker_df.reset_index()
#print(ticker_df.head(5))
return ticker_df
def construct_df(ticker):
#get data from yahoo API
df = get_data(ticker)
return df
def plot_data_HA(df, markers=False):
# plots price data with Smoothed Heikin Ashi overlay
plt.figure(figsize=(30,14))
# stock price Adj. Close
plt.title('Price chart (Adj Close) ' + ticker)
plt.plot(df['Adj Close'], alpha=0.99)
# stock OHLC line visualization
plt.plot(df['Open'] , alpha=0.2)
plt.plot(df['High'] , alpha=0.2)
plt.plot(df['Low'] , alpha=0.2)
plt.plot(df['Close'] , alpha=0.2)
# heikin ashi graph for reference
plt.plot(df['O_ha_avg'], alpha=0.5)
plt.plot(df['H_ha_avg'], alpha=0.25)
plt.plot(df['L_ha_avg'], alpha=0.25)
plt.plot(df['C_ha_avg'], alpha=0.5)
# heikin ashi red/green trend visualization
plt.fill_between(df.index, df['O_ha_avg'], df['C_ha_avg'],
where=(df['O_ha_avg'] < df['C_ha_avg']),
color='green', interpolate=True)
plt.fill_between(df.index, df['O_ha_avg'], df['C_ha_avg'],
where=(df['O_ha_avg'] > df['C_ha_avg']),
color='red', interpolate=True)
plt.legend(loc='upper left')
plt.show()
df = construct_df(ticker)
# SMA/EMA of OHLC values with sliding window of M per paper
if ma_type == 'EMA':
df['O_avg'] = ta.ema( df['Open'], length=M )
df['H_avg'] = ta.ema( df['High'], length=M )
df['L_avg'] = ta.ema( df['Low'], length=M )
df['C_avg'] = ta.ema( df['Close'], length=M )
elif ma_type == 'SMA':
df['O_avg'] = ta.sma( df['Open'], length=M )
df['H_avg'] = ta.sma( df['High'], length=M )
df['L_avg'] = ta.sma( df['Low'], length=M )
df['C_avg'] = ta.sma( df['Close'], length=M )
else:
print('invalid paramater specified')
# Smoothed Heikin Ashi computation
df['O_ha_avg'] = (df['O_avg'].shift(1) + df['C_avg'].shift(1) ) / 2 # shift(1) means value of df in index i-1
df['L_ha_avg'] = df[['L_avg','O_avg','C_avg']].min(axis=1)
df['H_ha_avg'] = df[['H_avg','O_avg','C_avg']].max(axis=1)
df['C_ha_avg'] = (df['O_avg'] + df['H_avg'] + df['L_avg'] + df['C_avg'] ) / 4
df.head()
OHLC price data with Smoothed Heikin Ashi "candlesticks".
# whole data history
plot_data_HA(df)
Summary: Smoothed Heikin Ashi can be used as an alternative for Simple/Exponencial moving averages. As a bonus it shows bullish/bearish price trend by its color by default.