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i04_5M Gain.py
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i04_5M Gain.py
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"""
5/30/2019
Robert Sloan
Replicating Al Zmyslowski's AAII CIMI Market Review - Technical & Economic Indicators
Using Yahoo Adjusted Close for SPY and BIL in calculations.
5 month gain - Compare 5 month gain of SPY vs. BIL
TODO
Find out why some areas of the graph are not boxed in green (SPY better) or red (BIL better)
"""
# load libraries
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import os
# this library load is because pandas matplotlib FutureWarning
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
indicatorType = "5M Gain"
# from data in dataframe return Indicator
def Indicator(dataframe, record_date, last_EOM_date, previous_EOM_date):
print("Calculating", indicatorType)
# get SPY and BIL data from passed dataframe
spy_data = dataframe["SPY"]
bil_data = dataframe["BIL"]
# Calculate 5 month gains
spy_5M_gain = spy_data.pct_change(105)
bil_5M_gain = bil_data.pct_change(105)
# Determine which has greater gain SPY or BIL
gain_flag_5M = spy_5M_gain - bil_5M_gain
gain_flag_5M = gain_flag_5M.dropna()
# gain is a series with 0 if SPY performed better or -1 if BIL performed better
gain_flag_5M = gain_flag_5M.apply(lambda x: 0 if x >= 0 else -1)
# truncate SPY and BIL data to match the length of the gain series
lengthOfGainFlag = len(gain_flag_5M)
trunc_spy_data = spy_data[0:lengthOfGainFlag]
trunc_bil_data = bil_data[0:lengthOfGainFlag]
# Plot SPY and BIL
fig, ax = plt.subplots(figsize=(16,9))
title = indicatorType + "\n" + record_date + "\n areas in green SPY 5M gain is greater areas in red BIL 5M gain is greater"
ax.set_title(title)
ax.plot(trunc_spy_data.index, trunc_spy_data, label='SPY')
ax.plot(trunc_bil_data.index, trunc_bil_data, label='BIL')
# Add labels and legend
ax.set_xlabel('Date')
ax.set_ylabel('Adjusted closing price SPY and BIL ($)')
ax.legend()
# fillin green or red bars depending if SPY or BIL had a better 5M year gain
ax.fill_between(trunc_spy_data.index, 80, 300, where = gain_flag_5M==0, facecolor = 'green', alpha=0.5)
ax.fill_between(trunc_spy_data.index, 80, 300, where = gain_flag_5M<0, facecolor = 'red', alpha=0.5)
indicatorName = "i04_5M Gain"
filepath = "Figures/"+record_date+"_"+indicatorName+".png"
if os.path.exists(filepath) == False:
fig.savefig(filepath)
#fig.savefig("Figures/" + indicatorType +".png")
#plt.show()
# Calculate for Last day of last month
# If the 5 month SPY gain greater than BIL go LONG
# otherwise go Short
endOfLastMonth_gain_flag_5M = gain_flag_5M.loc[last_EOM_date]
endOfLastMonth_spy_5M_gain = str(round((spy_5M_gain.loc[last_EOM_date][0])*100,2))
endOfLastMonth_bil_5M_gain = str(round((bil_5M_gain.loc[last_EOM_date][0])*100,2))
if (endOfLastMonth_gain_flag_5M[0] == 0):
lastMonth_status_5M = 'LONG'
else:
lastMonth_status_5M = 'SHORT'
# Calculate for Last day of month before last
# If the 5 month SPY gain greater than BIL go LONG
# otherwise go Short
endOfMonthBeforeLast_gain_flag_5M = gain_flag_5M.loc[previous_EOM_date]
endOfMonthBeforeLast_spy_5M_gain = str(round((spy_5M_gain[previous_EOM_date][0])*100,2))
endOfMonthBeforeLast_bil_5M_gain = str(round((bil_5M_gain[previous_EOM_date][0])*100,2))
if (endOfMonthBeforeLast_gain_flag_5M[0] == 0):
monthBeforeLast_status_5M = 'LONG'
else:
monthBeforeLast_status_5M = 'SHORT'
strLast_EOM_date = str(last_EOM_date.strftime('%Y-%m-%d')[0])
strPrevious_EOM_date = str(previous_EOM_date.strftime('%Y-%m-%d')[0])
comment = "SPY 5M Gain > BIL 5M Gain"
lastMonth_status_5M_str = lastMonth_status_5M + "(" + endOfLastMonth_spy_5M_gain + "%/" + endOfLastMonth_bil_5M_gain + "%)"
monthBeforeLast_status_5M_str = monthBeforeLast_status_5M + "(" + endOfMonthBeforeLast_spy_5M_gain + "%/" + endOfMonthBeforeLast_bil_5M_gain + "%)"
indicators = pd.DataFrame([{'Technical Indicator': indicatorType,
strLast_EOM_date:lastMonth_status_5M_str,
strPrevious_EOM_date:monthBeforeLast_status_5M_str,
'Comment': comment}],
columns=['Technical Indicator', strLast_EOM_date, strPrevious_EOM_date,
'Comment'])
#print (indicators)
return {
"Technical Indicator": indicatorType,
"Frequency":"Monthly",
"MonthBeforeLast":monthBeforeLast_status_5M_str,
"LastMonth":lastMonth_status_5M_str,
"Comment":comment}