97 lines
3.1 KiB
Python
97 lines
3.1 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 legend, 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 MACD """
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num_periods_fast = 10 # fast EMA time period
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K_fast = 2/(num_periods_fast+1)
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ema_fast = 0
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num_periods_slow = 40 # slow EMA time period
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K_slow = 2/(num_periods_slow+1)
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ema_slow = 0
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num_periods_macd = 20 # MACD EMA time period
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K_macd = 2/(num_periods_macd+1)
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ema_macd=0
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ema_fast_values = []
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ema_slow_values = []
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macd_values = []
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macd_signal_values = []
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macd_histogram_values = []
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for close_price in close:
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if (ema_fast == 0):
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ema_fast = close_price
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ema_slow = close_price
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else:
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ema_fast = (close_price - ema_fast) * K_fast + ema_fast
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ema_slow = (close_price - ema_slow) * K_slow + ema_slow
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ema_fast_values.append(ema_fast)
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ema_slow_values.append(ema_slow)
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macd = ema_fast - ema_slow # calculate MACD
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# Based on APO(MACD), calculate EMA_MACD
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if ema_macd == 0:
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ema_macd = macd
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else:
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ema_macd = (macd - ema_macd) * K_slow + ema_macd
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macd_values.append(macd)
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macd_signal_values.append(ema_macd)
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macd_histogram_values.append(macd - ema_macd)
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""" Visualization """
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# assign data back to goog_data to get index and aligned
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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(Fast_EMA_110Days=pd.Series(ema_fast_values, index=goog_data.index))
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goog_data = goog_data.assign(Slow_EMA_140Days=pd.Series(ema_slow_values, index=goog_data.index))
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goog_data = goog_data.assign(MACD=pd.Series(macd_values, index=goog_data.index))
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goog_data = goog_data.assign(EMA_of_MACD_120Days=pd.Series(macd_signal_values, index=goog_data.index))
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goog_data = goog_data.assign(MACDHistogram=pd.Series(macd_histogram_values, index=goog_data.index))
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print(goog_data['MACDHistogram'])
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax1 = fig.add_subplot(311, ylabel='Google price in $')
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goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
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goog_data['Fast_EMA_110Days'].plot(ax=ax1, color='b', lw=2., legend=True)
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goog_data['Slow_EMA_140Days'].plot(ax=ax1, color='r', lw=2., legend=True)
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ax2 = fig.add_subplot(312, ylabel='MACD')
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goog_data['MACD'].plot(ax=ax2, color='black', lw=2., legend=True)
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goog_data['EMA_of_MACD_120Days'].plot(ax=ax2, color='g', lw=2., legend=True)
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ax3 = fig.add_subplot(313, ylabel='MACD')
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goog_data['MACDHistogram'].plot(ax=ax3, color='r', kind='bar', legend=True, use_index=False)
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plt.savefig(dir_path + "/macd.png")
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plt.show()
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