moving-average

avgutil.py

import numpy as np


def do_cma(s):
    return s.expanding().mean()

def do_sma(s, N=15):
    return s.rolling(N, min_periods=1).mean()

def do_ema(s, N=15):
    return s.ewm(alpha=2/(N+1)).mean()


w = lambda x: np.arange(1, len(x) + 1)
def do_wma(s, N=15):
    # w = np.arange(1, N + 1)
    return s.rolling(N, min_periods=2).apply(lambda x: np.dot(x, w(x))/w(x).sum(), raw=True)

ma.py

import numpy as np

import pandas as pd

import matplotlib.dates as mdates

from avgutils import do_cma, do_sma, do_ema, do_wma

from matplotlib import rcParams
rcParams['font.family'] = 'monospace'
rcParams['font.sans-serif'] = ['Tahoma']
import matplotlib.pyplot as plt

N = 15

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

all_frames = pd.read_csv('data.csv')

df = all_frames.tail(365)

# df = all_frames

date = pd.to_datetime(df.Date)
high = df.High 
low  = df.Low
price = df.Close

cma = do_cma(price)
sma = do_sma(price, N=N)
ema = do_ema(price, N=N)
wma = do_wma(price, N=N)



ax = pd.DataFrame({
    'Date': date, 
    'Price': price, 
    'CMA': cma, 
    'SMA': sma,
    'WMA': wma, 
    'EMA': ema
    }).plot(x='Date', kind='line', figsize=(16, 9))

fmt_month = mdates.MonthLocator()
ax.xaxis.set_minor_locator(fmt_month)
# Text in the x axis will be displayed in 'YYYY-mm' format.
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))

plt.legend(labels=[
    'Closing Price', 
    'Cumulative Moving Average',
    'Simple Moving Average',
    'Weighted Moving Average',
    'Exponential Moving Average',
    ], fontsize=12)


plt.xlim(date.iat[0], date.iat[-1])

plt.title(f'Moving Average(N={N})', fontsize=14)

plt.xticks(rotation=45)

plt.tight_layout()

plt.savefig('all.svg', format='svg')

weight.py

import numpy as np

import pandas as pd

from matplotlib import rcParams
rcParams['font.family'] = 'monospace'
rcParams['font.sans-serif'] = ['Tahoma']
import matplotlib.pyplot as plt

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)


# smoothing factor and number of data points
N = 15
ALPHA = 2/(N+1)

NUM_X = N * 4

# simple moving average
sma = [0 if i > N else 1/N for i in range(NUM_X)]

# weighted moving average
wma =  [0 if i > N else (N - i)/(N * (N + 1) / 2) for i in range(NUM_X)]

# exponential moving average alpha=2/(N+1)
ema = [ALPHA*(1-ALPHA)**i for i in range(NUM_X)]

# store the values in a data frame 
pd.DataFrame({'sma': sma, 'wma': wma, 'ema': ema}).plot(kind='bar', figsize=(16,9))

plt.xticks(np.arange(0, NUM_X, 5), rotation=0)
plt.yticks(fontsize=10)

plt.legend(labels=[
    'SMA', 
    'WMA',
    'EMA, α=2/(N+1)'
    ], fontsize=12)

# title and labels
plt.title(f'Weights of Moving Average(N={N})', fontsize=14)
plt.ylabel('Weights', fontsize=12)
plt.xlabel('n-th Most Recent Smple', fontsize=12)
plt.tight_layout()

# plt.savefig('weight.svg', format='svg')
plt.show()