54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
import os
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import time
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import statistics
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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 momentum """
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time_period = 20 # lookback period
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history = [] # history of observed prices to use in momentum calculation
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mom_values = [] # track momentum values for visualization purposes
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for close_price in close:
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history.append(close_price)
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if len(history) > time_period:
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del(history[0])
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mom = close_price - history[0]
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mom_values.append(mom)
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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(MomentumFromPrice20DaysAgo=pd.Series(mom_values, index=goog_data.index))
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""" Visualization """
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax1 = fig.add_subplot(211, ylabel='Google price in $')
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goog_data['ClosePrice'].plot(ax=ax1, color='g', lw=2., legend=True)
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ax2 = fig.add_subplot(212, ylabel='Momentum in $')
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goog_data['MomentumFromPrice20DaysAgo'].plot(ax=ax2, color='b', lw=2., legend=True)
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plt.savefig(dir_path + '/momentum.png')
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plt.show() |