81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
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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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import statistics as stats
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time_period = 20 # look back period to compute gains & losses
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gain_history = [] # history of gains over look back period (0 if no gain, magnitude of gain if gain)
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loss_history = [] # history of losses over look back period (0 if no loss, magnitude of loss if loss)
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avg_gain_values = [] # track avg gains for visualization purposes
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avg_loss_values = []
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rsi_values = [] # track computed RSI values
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last_price = 0 # current_price - last_price > 0 => gain ; current_price - last_price < 0 => loss.
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for close_price in close:
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if last_price == 0:
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last_price = close_price
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gain_history.append(max(0, close_price - last_price))
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loss_history.append(max(0, last_price - close_price))
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last_price = close_price
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if len(gain_history) > time_period:
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del(gain_history[0])
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del(loss_history[0])
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avg_gain = stats.mean(gain_history) # average gain over lookback period
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avg_loss = stats.mean(loss_history) # average loss over lookback period
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avg_gain_values.append(avg_gain)
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avg_loss_values.append(avg_loss)
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rs = 0
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if avg_loss > 0: # to avoid division by 0, which is undefined
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rs = avg_gain /avg_loss
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rsi = 100 - (100/(1+rs))
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rsi_values.append(rsi)
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""" Data Visualization """
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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(RelativeStrengthAvgGainOver20Days=pd.Series(avg_gain_values, index=goog_data.index))
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goog_data = goog_data.assign(RelativeStrengthAvgLossOver20Days=pd.Series(avg_loss_values, index=goog_data.index))
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goog_data = goog_data.assign(RelativeStrengthIndicatorOver20Days=pd.Series(rsi_values, index=goog_data.index))
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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='black', lw=2., legend=True)
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ax2 = fig.add_subplot(312, ylabel='RS')
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goog_data['RelativeStrengthAvgGainOver20Days'].plot(ax=ax2, color='g', lw=2., legend=True)
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goog_data['RelativeStrengthAvgLossOver20Days'].plot(ax=ax2, color='r', lw=2., legend=True)
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ax3 = fig.add_subplot(313, ylabel='RSI')
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goog_data['RelativeStrengthIndicatorOver20Days'].plot(ax=ax3, color='b', lw=2., legend=True)
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plt.savefig(dir_path + '/rsi.png')
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plt.show()
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