Weekly & Daily MACD

References

traders.com: TradersTips 2017-12

█ OVERVIEW

In the article "Weekly & Daily MACD" in this issue, author Vitali Apirine introduces a novel approach to using the classic MACD indicator in a way that simulates calculations based on different timeframes while using just a daily-interval chart. He describes a number of ways to use this new indicator that allows traders to adapt it to differing markets and conditions.

Load basic packages

import pandas as pd
import numpy as np
import os
import gc
import copy
from pathlib import Path
from datetime import datetime, timedelta, time, date
  

#this package is to download equity price data from yahoo finance
#the source code of this package can be found here: https://github.com/ranaroussi/yfinance/blob/main
import yfinance as yf
  

pd.options.display.max_rows = 100
pd.options.display.max_columns = 100

import warnings
warnings.filterwarnings("ignore")

import pytorch_lightning as pl
random_seed=1234
pl.seed_everything(random_seed)
  

Global seed set to 1234

1234

Download data

##### Download data#S&P 500 (^GSPC),  Dow Jones Industrial Average (^DJI), NASDAQ Composite (^IXIC)
#Russell 2000 (^RUT), Crude Oil Nov 21 (CL=F), Gold Dec 21 (GC=F)
#Treasury Yield 10 Years (^TNX)
#CBOE Volatility Index (^VIX) Chicago Options - Chicago Options Delayed Price. Currency in USD

#benchmark_tickers = ['^GSPC', '^DJI', '^IXIC', '^RUT',  'CL=F', 'GC=F', '^TNX']

benchmark_tickers = ['^GSPC', '^VIX']
tickers = benchmark_tickers + ['GSK', 'BST', 'PFE']
  

#https://github.com/ranaroussi/yfinance/blob/main/yfinance/base.py
#     def history(self, period="1mo", interval="1d",
#                 start=None, end=None, prepost=False, actions=True,
#                 auto_adjust=True, back_adjust=False,
#                 proxy=None, rounding=False, tz=None, timeout=None, **kwargs):

dfs = {}

for ticker in tickers:
    cur_data = yf.Ticker(ticker)
    hist = cur_data.history(period="max", start='2000-01-01')
    print(datetime.now(), ticker, hist.shape, hist.index.min(), hist.index.max())
    dfs[ticker] = hist
  

2022-09-04 23:00:36.564993 ^GSPC (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-04 23:00:36.879870 ^VIX (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-04 23:00:37.172501 GSK (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-04 23:00:37.447286 BST (1976, 7) 2014-10-29 00:00:00 2022-09-02 00:00:00
2022-09-04 23:00:37.832612 PFE (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
  

ticker = 'PFE'
dfs[ticker].tail(5)
  

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2022-08-29	46.380001	46.689999	46.119999	46.230000	13400500	0.0	0.0
2022-08-30	46.340000	46.349998	45.799999	45.849998	16303000	0.0	0.0
2022-08-31	46.009998	46.290001	45.130001	45.230000	26416800	0.0	0.0
2022-09-01	45.139999	46.650002	45.139999	46.630001	19947600	0.0	0.0
2022-09-02	46.740002	46.799999	45.529999	45.700001	14662700	0.0	0.0

Calculate the technical indicators and Find signals

from core.finta import TA
  

df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]
df = df.round(2)
  

df_ta = TA.BBANDS(df, period = 20, std_multiplier=2.2, column="close")
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.XSII(df, slow_period = 102, fast_period = 7)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.MACD(df, fast_period = 12, slow_period = 26, signal = 9, column = 'close')
df_ta.columns = [f'DAILY_{c}' for c in df_ta.columns]
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.MACD(df, fast_period = 60, slow_period = 130, signal = 9, column = 'close')
df_ta.columns = [f'WEEKLY_{c}' for c in df_ta.columns]
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()
  

df['B'] = 0
  

display(df.head(5))
display(df.tail(5))
  

	Open	High	Low	Close	Volume	BB_UPPER	BB_MIDDLE	BB_LOWER	XSII1	XSII2	XSII3	XSII4	DAILY_MACD	DAILY_SIGNAL	WEEKLY_MACD	WEEKLY_SIGNAL	B
Date
1999-12-31	14.25	14.31	14.11	14.22	5939817	NaN	NaN	NaN	NaN	NaN	15.2154	13.2246	0.000000	0.000000	0.000000	0.000000	0
2000-01-03	14.06	14.20	13.87	13.98	12873345	NaN	NaN	NaN	NaN	NaN	14.9586	13.0014	-0.005385	-0.002991	-0.001077	-0.000598	0
2000-01-04	13.70	13.81	13.16	13.46	14208974	NaN	NaN	NaN	NaN	NaN	14.4022	12.5178	-0.022986	-0.011186	-0.004560	-0.002222	0
2000-01-05	13.54	13.98	13.51	13.68	12981591	NaN	NaN	NaN	NaN	NaN	14.6376	12.7224	-0.022485	-0.015014	-0.004749	-0.003078	0
2000-01-06	13.70	14.36	13.68	14.17	11115273	NaN	NaN	NaN	14.17851	13.62249	15.1619	13.1781	-0.002242	-0.011214	-0.001242	-0.002532	0

	Open	High	Low	Close	Volume	BB_UPPER	BB_MIDDLE	BB_LOWER	XSII1	XSII2	XSII3	XSII4	DAILY_MACD	DAILY_SIGNAL	WEEKLY_MACD	WEEKLY_SIGNAL	B
Date
2022-08-29	46.38	46.69	46.12	46.23	13400500	51.392579	48.9140	46.435421	48.30414	46.40986	53.102620	46.154613	-0.906411	-0.633650	-0.073433	0.103551	0
2022-08-30	46.34	46.35	45.80	45.85	16303000	51.584454	48.7220	45.859546	47.87319	45.99581	52.873627	45.955582	-1.021035	-0.711127	-0.141759	0.054489	0
2022-08-31	46.01	46.29	45.13	45.23	26416800	51.761125	48.4905	45.219875	47.44632	45.58568	52.594719	45.713167	-1.148664	-0.798635	-0.217627	0.000066	0
2022-09-01	45.14	46.65	45.14	46.63	19947600	51.640812	48.3290	45.017188	47.14185	45.29315	52.479243	45.612800	-1.123887	-0.863685	-0.265250	-0.052997	0
2022-09-02	46.74	46.80	45.53	45.70	14662700	51.663455	48.1505	44.637545	46.90878	45.06922	52.314323	45.469459	-1.165855	-0.924119	-0.326770	-0.107752	0

Visual

from core.visuals import *
  

start = -350
end = df.shape[0]
df_sub = df.iloc[start:end]
# df_sub = df[(df.index<='2019-04-01') & (df.index>='2019-01-24')]
names = {'main_title': f'{ticker}'}
  

lines0 = basic_lines(df_sub[['XSII1', 'XSII2', 'XSII3', 'XSII4']], 
                     colors = [], 
                     **dict(panel=0, width=1.5, secondary_y=False))

lines1 = basic_lines(df_sub[['DAILY_SIGNAL', 'WEEKLY_SIGNAL']], 
                     colors = ['cadetblue', 'lightcoral'], 
                     **dict(panel=1, width=1, secondary_y=False))
lines3 = basic_lines(df_sub[['DAILY_MACD', 'WEEKLY_MACD']], 
                     colors = ['cadetblue', 'lightcoral'], 
                     **dict(panel=2, width=1, secondary_y=False))

lines2 = basic_lines(df_sub[[ 'B']],
                     colors = ['navy'], 
                     **dict(panel=0, type='scatter', marker=r'${B}$' , markersize=100, secondary_y=False))


lines_ = dict(**lines0, **lines1)
lines_.update(lines2)
lines_.update(lines3)

#shadows_ = basic_shadows(bands=[-0.01, 0.01], nsamples=df.iloc[start:end].shape[0], **dict(panel=1, color="lightskyblue",alpha=0.1,interpolate=True))
shadows_ = []
fig_config_ = dict(figratio=(18,10), volume=False, volume_panel=2,panel_ratios=(4,2, 2), tight_layout=True, returnfig=True,)

ax_cfg_ = {0:dict(basic=[6, 2, ['XSII1', 'XSII2', 'XSII3', 'XSII4']], 
                 title=dict(label = 'XSII', fontsize=9, style='italic',  loc='left'), 
                ),
           2:dict(basic=[2, 0, ['DAILY_SIGNAL', 'WEEKLY_SIGNAL']]
                ),
           4:dict(basic=[2, 0, ['DAILY_MACD', 'WEEKLY_MACD']]
                ),
          }


names = {'main_title': f'{ticker}'}

aa_, bb_ = make_panels(main_data = df_sub[['Open', 'High', 'Low', 'Close', 'Volume']], 
                       added_plots = lines_,
                       fill_betweens = shadows_, 
                       fig_config = fig_config_, 
                       axes_config = ax_cfg_,  
                       names = names)
  

png