import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import movingAverage as ma
TSMC = pd.read_csv('2330.csv', sep='\t')
TSMC.index = pd.to_datetime(TSMC.Date)
Close = TSMC.Close
Close.describe()
Close15 = Close['2015']
sma10 = ma.smaCal(Close15, 10)
sma10.tail(n=3)
weight = np.array(range(1, 11)) / sum(range(1, 11))
wma10 = ma.wmaCal(Close15, weight)
wma10.tail(n=3)
expo = 2 / (len(Close15) + 1)
ewma10 = ma.ewmaCal(Close15, 10, expo)
ewma10.tail(n=3)
plt.plot(Close15[10:], label="Close", color='k')
plt.plot(sma10[10:], label="sma10", color='r', linestyle='dashed')
plt.plot(wma10[10:], label="wma10", color='b', linestyle=':')
plt.plot(ewma10[10:], label="ewma10", color='G', linestyle='-.')
plt.title('台積電股價和三種均線圖')
plt.legend()
sma5 = ma.smaCal(Close15, 5)
sma30 = ma.smaCal(Close15, 30)
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import movingAverage as ma
#1.
dow = pd.read_csv('Data/Part5/004/problem30-1.csv', index_col='date')
dow.index.name = 'Date'
dow.index = pd.to_datetime(dow.index, format='%Y-%m-%d')
sma = ma.smaCal(dow.Close, 30)
wma = ma.wmaCal(dow.Close, [(i + 1) / 465 for i in range(30)])
ema = ma.ewmaCal(dow.Close, 30, 0.8)
maValues = pd.concat([sma, wma, ema], 1).replace(0, np.nan).dropna()
maValues.columns = ['sma', 'wma', 'ema']
maValues.plot()
#2.
zgyh = pd.read_csv('Data/Part5/004/problem30-2.csv', index_col='date')
zgyh.index.name = 'Date'
zgyh.index = pd.to_datetime(zgyh.index, format='%Y-%m-%d')
import matplotlib.pyplot as plt
mom14 = (zgyh.Close - zgyh.Close.shift(14)) / zgyh.Close.shift(14)
ma14 = ma.smaCal(zgyh.Close, 14)