58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
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import os
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import time
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import statistics
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from matplotlib.pyplot import ylabel
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import pandas as pd
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import numpy as np
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from pandas_datareader import data
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dir_path = os.path.dirname(os.path.realpath(__file__))
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start_date = '2014-01-01'
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end_date = '2018-01-01'
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SRC_DATA_FILENAME = dir_path + '/goog_data.pkl'
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try:
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goog_data = pd.read_pickle(SRC_DATA_FILENAME)
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print('File data found...reading GOOG data')
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except FileNotFoundError:
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print('File not found...downloading the GOOG data')
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goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)
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goog_data.to_pickle(SRC_DATA_FILENAME)
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goog_data_signal = pd.DataFrame(index=goog_data.index)
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goog_data_signal['price'] = goog_data['Adj Close']
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close = goog_data_signal['price']
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exe_start_time = time.time()
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""" Calculate EMA """
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num_periods = 20 # number of days over which to average
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K = 2/(num_periods + 1) # smoothing constant
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ema_p = 0
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ema_values = []
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for close_price in close:
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if (ema_p == 0):
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# first observation, EMA = current price
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ema_p = close_price
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else:
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ema_p = (close_price - ema_p) * K + ema_p
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ema_values.append(ema_p)
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goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
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goog_data = goog_data.assign(Exponential120DayMovingAverage=pd.Series(ema_values, index=goog_data.index))
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close_price = goog_data['ClosePrice']
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ema = goog_data['Exponential120DayMovingAverage']
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""" Draw plot """
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax1 = fig.add_subplot(111, ylabel='Google price in $')
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close_price.plot(ax=ax1, color='g', lw=2., legend=True)
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ema.plot(ax=ax1, color='b', lw=2., legend=True)
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plt.savefig(dir_path + '/ema.png')
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plt.show()
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