Update – Python Tutorial: MACD (Moving Average Convergence/Divergence)

Download the accompanying IPython Notebook for this Tutorial from Github. 

I received a question from Sam Khorsand about applying the Python Tutorial: MACD (Moving Average Convergence/Divergence) Tutorial   to multiple stocks. The code below will produce yesterday’s MACD Crossover for a list of stocks. Also, by adding ‘to_string(index = False’), you can clean up the date formatting. Enjoy!

import pandas as pd
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime as dt
%matplotlib inline

def MACD(stock, start, end):
    df = pd.DataFrame(web.DataReader(stock,'google',start,end)['Close'])
    df = df.reset_index()
    df['30 mavg'] = pd.rolling_mean(df['Close'], 30)
    df['26 ema'] = pd.ewma(df['Close'], span=26)
    df['12 ema'] = pd.ewma(df['Close'], span=12)
    df['MACD'] = (df['12 ema'] - df['26 ema'])
    df['Signal'] = pd.ewma(df['MACD'], span=9)
    df['Crossover'] = df['MACD'] - df['Signal']
    return stock, df['Date'][-1:].to_string(),df['Crossover'][-1:].mean()
    

stocks = ['FB', 'AAPL', 'GOOG', 'AMZN', 'TSLA']

d = []

for stock in stocks:
    stock, date, macd = MACD(stock, '1/1/2016', dt.datetime.today())
    d.append({'Stock':stock, 'Date':date, 'MACD':macd})
    
df2 = pd.DataFrame(d)
df2[['Date', 'Stock', 'MACD']]
Date Stock MACD
0 249 2017-09-20 FB -0.211440
1 249 2017-09-20 AAPL -0.828956
2 249 2017-09-20 GOOG -0.069812
3 249 2017-09-20 AMZN 1.028655
4 249 2017-09-20 TSLA 2.287354

 

Python Tutorial: ROC

Download the accompanying IPython Notebook for this Tutorial from Github. 

Python streamlines tasks requiring multiple steps in a single block of code. For this reason, it is a great tool for querying and performing analysis on data.

Last Tutorial, we outlined steps for calculating Relative Strength Index (RSI).
In this Tutorial, we introduce a new technical indicator, the Rate of Change (ROC).

‘The only thing constant is change’

The Rate of Change (ROC) is a technical indicator of momentum that measures the percentage change in price between the current price and the price n periods in the past.

The Rate of Change (ROC) is calculated as follows:

ROC = ((Most recent closing price - Closing price n periods ago) / Closing price n periods ago) x 100

The Rate of Change (ROC) is classed as a momentum indicator because it measures strength of price momentum. For example, if a stock’s price at the close of trading today is 10, and the closing price five trading days prior was 7, then the Rate of Change (ROC) over that time frame is approximately 43, calculated as (10 – 7 / 7) x 100 = 42.85.

 Positive values indicate upward buying pressure or momentum, while negative values below zero indicate selling pressure or downward momentum. Increasing values in either direction, positive or negative, indicate increasing momentum, and decreasing values indicate waning momentum.

The Rate of Change (ROC) is also sometimes used to indicate overbought or oversold conditions for a security. Positive values that are greater than 30 are generally interpreted as indicating overbought conditions, while negative values lower than negative 30 indicate oversold conditions.

 Let’s use Python to compute the Rate of Change (ROC).

1.) Import modules.

import pandas as pd
import numpy as np
from pandas_datareader import data as web
import matplotlib.pyplot as plt
%matplotlib inline

2.) Define function for querying daily close.

def get_stock(stock,start,end):
     return web.DataReader(stock,'google',start,end)['Close']

3.) Define function for Rate of Change (ROC).

def ROC(df, n):  
    M = df.diff(n - 1)  
    N = df.shift(n - 1)  
    ROC = pd.Series(((M / N) * 100), name = 'ROC_' + str(n))   
    return ROC

How does the ROC function work?

3.a.) Function calculates difference in most recent closing price from closing price n periods ago. Sets the value to variable M.
#M = df.diff(n - 1)

3.b.) Function calculates closing price n periods ago. Sets the value to variable N.

#N = df.shift(n - 1)

3.c.) Function creates series called ROC that is ((M/N) * 100)

#ROC = pd.Series(((M / N) * 100), name = 'ROC_' + str(n))

3.d.) Function returns ROC

#return ROC

4.) Query daily close for ‘FB’ during 2016.

df = pd.DataFrame(get_stock('FB', '1/1/2016', '12/31/2016'))

5.) Run daily close through ROC function. Save series to new column in dataframe.

df['ROC'] = ROC(df['Close'], 12)
df.tail()

6.) Plot daily close and ROC.

df.plot(y=['Close'])
df.plot(y=['ROC'])

There you have it! We created our ROC indicator. Here’s the full code:

import pandas as pd
import numpy as np
from pandas_datareader import data as web
import matplotlib.pyplot as plt
%matplotlib inline

def get_stock(stock,start,end):
     return web.DataReader(stock,'google',start,end)['Close']
    
def ROC(df, n):  
    M = df.diff(n - 1)  
    N = df.shift(n - 1)  
    ROC = pd.Series(((M / N) * 100), name = 'ROC_' + str(n))   
    return ROC
    
df = pd.DataFrame(get_stock('FB', '1/1/2016', '12/31/2016'))
df['ROC'] = ROC(df['Close'], 12)
df.tail()

Python Tutorial: MACD Signal Line & Centerline Crossovers

Python streamlines tasks requiring multiple steps in a single block of code. For this reason, it is a great tool for querying and performing analysis on data.

Last post, we outlined steps for calculating a stock’s MACD indicator.

In this post, we take MACD a step further by introducing Signal Line and Centerline Crossovers.

Signal Line Crossovers

Signal Line is defined as:

Signal Line: 9-day EMA of MACD Line

Signal line crossovers are the most common MACD signals. The signal line is a 9-day EMA of the MACD Line. As a moving average of the indicator, it trails the MACD and makes it easier to spot MACD turns. A bullish crossover occurs when the MACD turns up and crosses above the signal line. A bearish crossover occurs when the MACD turns down and crosses below the signal line. Crossovers can last a few days or a few weeks, it all depends on the strength of the move.

Let’s use Python to compute the Signal Line.

1.  Start with the MACD Tutorial code.

import pandas.io.data as web 
import pandas as pd 
%matplotlib inline 
import matplotlib.pyplot as plt 

names = ['FB'] 

def get_px(stock, start, end): 
     return web.get_data_yahoo(stock, start, end)['Adj Close'] 
px = pd.DataFrame({n: get_px(n, '1/1/2016', '1/17/2017') for n in names}) 
px['26 ema'] = pd.ewma(px["FB"], span=26) 
px['12 ema'] = pd.ewma(px["FB"], span=12) 
px['MACD'] = (px['12 ema'] - px['26 ema'])

2. Compute the 9 Day Exponential Moving Average of MACD.

px['Signal Line'] = pd.ewma(px['MACD'], span=9)

3. Create Signal Line Crossover Indicator. When MACD > Signal Line, 1. When MACD < Signal Line, 0.

px['Signal Line Crossover'] = np.where(px['MACD'] > px['Signal Line'], 1, 0)
px['Signal Line Crossover'] = np.where(px['MACD'] < px['Signal Line'], -1, px['Signal Line Crossover'])

Centerline Crossovers

Centerline crossovers are the next most common MACD signals. A bullish centerline crossover occurs when the MACD Line moves above the zero line to turn positive. This happens when the 12-day EMA of the underlying security moves above the 26-day EMA. A bearish centerline crossover occurs when the MACD moves below the zero line to turn negative. This happens when the 12-day EMA moves below the 26-day EMA.

Centerline crossovers can last a few days or a few months. It all depends on the strength of the trend. The MACD will remain positive as long as there is a sustained uptrend. The MACD will remain negative when there is a sustained downtrend.

4. Create Centerline Crossover Indicator. When MACD > 0, 1. When MACD < 0, 0.

px['Centerline Crossover'] = np.where(px['MACD'] > 0, 1, 0)
px['Centerline Crossover'] = np.where(px['MACD'] < 0, -1, px['Centerline Crossover'])

Plotting Crossovers

Last post, we posed the question: ‘When would you enter the position 😕 ?’

Now that we understand Signal Line Crossovers, let’s propose that we enter the position, ‘buy’, on 1, and we exit the position, ‘sell’, on -1.

5. Create Buy/Sell Indicator, based on Signal Line Crossovers. Multiply by 2 to increase size of indicator when plotted, so ‘buy’ on 2 and ‘sell’ on -2.

px['Buy Sell'] = (2*(np.sign(px['Signal Line Crossover'] - px['Signal Line Crossover'].shift(1))))

6. Plot close price, MACD & Signal Line, and Signal Line & Centerline Crossovers.

px.plot(y=['FB'], title='Close')
px.plot(y= ['MACD', 'Signal Line'], title='MACD & Signal Line')
px.plot(y= ['Centerline Crossover', 'Buy Sell'], title='Signal Line & Centerline Crossovers', ylim=(-3,3))

There you have it! We created MACD Signal Line and Centerline Crossovers, and based on the Crossovers, plotted ‘buy’ and ‘sell’ indicators.

Based on the entry and exit points, can you calculate the P&L? Stay tuned to find out.

Here’s the full code:

import pandas.io.data as web 
import pandas as pd 
%matplotlib inline 
import matplotlib.pyplot as plt 

names = ['FB'] 

def get_px(stock, start, end): 
     return web.get_data_yahoo(stock, start, end)['Adj Close'] 
px = pd.DataFrame({n: get_px(n, '1/1/2016', '1/17/2017') for n in names}) 
px['26 ema'] = pd.ewma(px["FB"], span=26) 
px['12 ema'] = pd.ewma(px["FB"], span=12) 
px['MACD'] = (px['12 ema'] - px['26 ema'])
px['Signal Line'] = pd.ewma(px['MACD'], span=9)
px['Signal Line Crossover'] = np.where(px['MACD'] > px['Signal Line'], 1, 0)
px['Signal Line Crossover'] = np.where(px['MACD'] < px['Signal Line'], -1, px['Signal Line Crossover'])
px['Centerline Crossover'] = np.where(px['MACD'] > 0, 1, 0)
px['Centerline Crossover'] = np.where(px['MACD'] < 0, -1, px['Centerline Crossover'])
px['Buy Sell'] = (2*(np.sign(px['Signal Line Crossover'] - px['Signal Line Crossover'].shift(1))))

px.plot(y=['FB'], title='Close')
px.plot(y= ['MACD', 'Signal Line'], title='MACD & Signal Line')
px.plot(y= ['Centerline Crossover', 'Buy Sell'], title='Signal Line & Centerline Crossovers', ylim=(-3,3))

Python Tutorial: MACD (Moving Average Convergence/Divergence)

Python streamlines tasks requiring multiple steps in a single block of code. For this reason, it is a great tool for querying and performing analysis on data.

In this post, we outline steps for calculating a stock’s MACD indicator. But first, what is MACD (Moving Average Convergence/Divergence)?

Developed by Gerald Appel in the late seventies, MACD is one of the simplest and most effective momentum indicators available. MACD turns two trend-following indicators, moving averages, into a momentum oscillator by subtracting the longer moving average from the shorter moving average. As a result, MACD offers the best of both worlds: trend following and momentum.

To calculate MACD, the formula is:

MACD: (12-day EMA - 26-day EMA)

EMA stands for Exponential Moving Average.

With that background, let’s use Python to compute MACD.

1. Start with the 30 Day Moving Average Tutorial code.

import pandas as pd
import pandas.io.data as web

stocks = ['FB']
def get_stock(stock, start, end):
     return web.get_data_yahoo(stock, start, end)['Adj Close']
px = pd.DataFrame({n: get_px(n, '1/1/2016', '12/31/2016') for n in names})
px

2. Compute the 26 Day Exponential Moving Average. We must call the column by the stock ticker.

px['26 ema'] = pd.ewma(px["FB"], span=26)

3. Then the 12 Day Exponential Moving Average.

px['12 ema'] = pd.ewma(px["FB"], span=12)

4. Subtract the 26 Day EMA from the 12 Day EMA, arriving at the MACD.

px['MACD'] = (px['12 ema'] - px['26 ema'])

5. Plot close price against MACD.

px.plot(y= ['FB'], title='FB')
px.plot(y= ['MACD'], title='MACD')

There you have it! We created our MACD indicator. Here’s the full code:

import pandas.io.data as web
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt

names = ['FB']
def get_px(stock, start, end): 
     return web.get_data_yahoo(stock, start, end)['Adj Close']
px = pd.DataFrame({n: get_px(n, '1/1/2016', '1/17/2017') for n in names})
px['26 ema'] = pd.ewma(px["FB"], span=26)
px['12 ema'] = pd.ewma(px["FB"], span=12)
px['MACD'] = (px['12 ema'] - px['26 ema'])
px.plot(y= ['FB'], title='FB')
px.plot(y= ['MACD'], title='MACD')

So when would you enter the position 😕 ?