April 30, 2020 Python Q&A

Topics covered in this session include:

  • Continued development on the Risk on / Risk off tool
  • Colorized visualization of the risk on and risk off indicator
  • Added a function to loop over two variables to optimize indicator

RiskOnOff.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 11:02:36 2020

@author: Bruce1
"""

# =======================================================================
# Import Libraries
# =======================================================================
import pandas as pd 
import pandas_datareader.data as web
from   pathlib import Path
import matplotlib.pyplot as plt
#import numpy as np

# =======================================================================
# Setup Porgram Variables
# =======================================================================
symList = ['SPY', 'TLT', 'GLD']

startDate = '01/01/2000'

refreshData = False

lookback = 10

threashold = 1.75

# =======================================================================
# Gather data function
# =======================================================================
def gatherData(sym, startDate):
    #import pdb; pdb.set_trace()

    savedFile = Path('./{}.xlsx'.format(sym))
    
    if savedFile.exists() == False or refreshData == True:
        print("")
        print("-> Fetching data from the web")
        df = web.DataReader(sym, data_source='yahoo', start=startDate)
    
        print("")
        print("-> Save data to file")  
        df.to_excel("{}.xlsx".format(sym))

    else:
        print("")
        print("-> Fetching data from file") 
        df = pd.read_excel(savedFile, index_col='Date', parse_dates=True)

    # =======================================================================
    # Inspect/Report on data
    # =======================================================================
    firstIndex = df.index.min()
    lastIndex  = df.index.max()
    records = len(df)
    print("")
    print("-> Importing ", sym)
    print("First Date = ", firstIndex)
    print("Last Date  = ", lastIndex)
    print("Total Days = ", records)

    if df.isnull().values.any() == True:
        print("WARNING: there are {} NaN in the data".format(df.isnull().values.sum()))
        print(df.isnull().values)
        
    return df

dfDict = {}

for sym in symList:
    
    dfDict[sym] = gatherData(sym, startDate)


# =======================================================================
# Combining Data
# =======================================================================
cdf = pd.DataFrame() 
first = True

for sym in symList:
    
    tdf = dfDict[sym].copy()
    tdf.rename(columns={'Close':sym}, inplace=True)
    
    if first:
        cdf = tdf.loc[:,sym]
        first = False
        
    else:
        cdf = pd.merge(cdf, tdf.loc[:,sym], left_index=True, \
                       right_index=True,  how='inner')
    
#import pdb; pdb.set_trace()

firstIndex = cdf.index.min().date()
lastIndex  = cdf.index.max().date()

print ("")
print ("Combined Date Range = {} to {}".format(firstIndex, lastIndex))
print ("Total Trading Days = {}".format(len(cdf)))

# =======================================================================
# Calculate Correlation
# =======================================================================
#import pdb; pdb.set_trace()
 
first = True
   
for ii in range(len(symList) - 1):
    
    for jj in range(ii + 1, len(symList)):
        
        print ("Comparing {} with {}".format(ii, jj))
        
        xdf = cdf.loc[:,symList[ii]].rolling(lookback). \
                       corr(cdf.loc[:,symList[jj]])
                       
        xdf = xdf.rename('{}_{}'.format(symList[ii],symList[jj]))
        
        plt.figure()
        t = 'Correlation of {} to {} over {} bar lookback'. \
                   format(symList[ii],symList[jj],lookback )
        xdf.plot(kind='hist', bins=20, title = t)
        plt.show()
        
        if first:
            finalDf = xdf.copy()
            first = False
        else:
            finalDf = pd.merge(finalDf, xdf, left_index=True, \
                               right_index=True, how='inner')


# =======================================================================
# Visualize Data
# =======================================================================
finalDf.loc[:,'total'] = finalDf.sum(axis=1)

t = 'Sum of Correlations between {} over {} bar lookback'.format(symList, lookback)
plt.figure()
finalDf.total.plot(kind='hist', bins=20, title=t)
plt.show()

plt.figure()
finalDf.total.plot(kind='line', title=t)
plt.show()


rdf = pd.merge(cdf.SPY, finalDf, left_index=True, right_index=True, how='inner')
cdf.to_excel('temp.xlsx')

# Calculation of price direction
import pdb; pdb.set_trace()
rdf.loc[:,'SPYdiff'] = rdf.SPY.diff()
#rdf.loc[:,'SPYup'] = (rdf.SPYdiff - rdf.SPYdiff.shift(3)) > 0
rdf.loc[:,'SPYup'] = rdf.SPYdiff > 0

rdf.loc[:,'thresh'] = rdf.total > threashold

minVal = rdf.SPY.min()
#rdf.loc[:,'filterSig'] = rdf.apply(lambda row: row.SPY if row.thresh else minVal, axis=1)
rdf.loc[:,'RiskOn']  = rdf.apply(lambda row: row.SPY if row.thresh &  row.SPYup else minVal, axis=1)
rdf.loc[:,'RiskOff'] = rdf.apply(lambda row: row.SPY if row.thresh & ~row.SPYup else minVal, axis=1)

plt.figure()
cols = ['SPY','RiskOn', 'RiskOff']
rdf[cols].plot(kind='line', title=t)
plt.show()

plt.figure()
rdf['2020'][cols].plot(kind='line', title=t)
plt.show()

import pdb; pdb.set_trace()

RiskOnOff_loop.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 11:02:36 2020

@author: Bruce1
"""

# =======================================================================
# Import Libraries
# =======================================================================
import pandas as pd 
import pandas_datareader.data as web
from   pathlib import Path
import matplotlib.pyplot as plt
#import numpy as np

# =======================================================================
# Gather data function
# =======================================================================
def gatherData(sym, startDate):
    #import pdb; pdb.set_trace()

    savedFile = Path('./{}.xlsx'.format(sym))
    
    if savedFile.exists() == False or refreshData == True:
        print("")
        print("-> Fetching data from the web")
        df = web.DataReader(sym, data_source='yahoo', start=startDate)
    
        print("")
        print("-> Save data to file")  
        df.to_excel("{}.xlsx".format(sym))

    else:
        print("")
        print("-> Fetching data from file") 
        df = pd.read_excel(savedFile, index_col='Date', parse_dates=True)

    # =======================================================================
    # Inspect/Report on data
    # =======================================================================
    firstIndex = df.index.min()
    lastIndex  = df.index.max()
    records = len(df)
    print("")
    print("-> Importing ", sym)
    print("First Date = ", firstIndex)
    print("Last Date  = ", lastIndex)
    print("Total Days = ", records)

    if df.isnull().values.any() == True:
        print("WARNING: there are {} NaN in the data".format(df.isnull().values.sum()))
        print(df.isnull().values)
        
    return df


# =======================================================================
# Gather data function
# =======================================================================
def calcRiskOnRiskOff (symList, cdf, lookback, threshold):
    #import pdb; pdb.set_trace()

    # =======================================================================
    # Calculate Correlation
    # =======================================================================
    #import pdb; pdb.set_trace()
     
    first = True
       
    for ii in range(len(symList) - 1):
        
        for jj in range(ii + 1, len(symList)):
            
            print ("Comparing {} with {}".format(ii, jj))
            
            xdf = cdf.loc[:,symList[ii]].rolling(lookback). \
                           corr(cdf.loc[:,symList[jj]])
                           
            xdf = xdf.rename('{}_{}'.format(symList[ii],symList[jj]))
            
            #plt.figure()
            t = 'Correlation of {} to {} over {} bar lookback'. \
                       format(symList[ii],symList[jj],lookback )
            #xdf.plot(kind='hist', bins=20, title = t)
            #plt.show()
            
            if first:
                finalDf = xdf.copy()
                first = False
            else:
                finalDf = pd.merge(finalDf, xdf, left_index=True, \
                                   right_index=True, how='inner')
    
    
    # =======================================================================
    # Visualize Data
    # =======================================================================
    finalDf.loc[:,'total'] = finalDf.sum(axis=1)
    
    t = 'Sum of Correlations between {} over {} bar lookback'.format(symList, lookback)
    #plt.figure()
    #finalDf.total.plot(kind='hist', bins=20, title=t)
    #plt.show()
    
    #plt.figure()
    #finalDf.total.plot(kind='line', title=t)
    #plt.show()
    
    
    rdf = pd.merge(cdf.SPY, finalDf, left_index=True, right_index=True, how='inner')
    #cdf.to_excel('temp.xlsx')
    
    # Calculation of price direction

    rdf.loc[:,'SPYdiff'] = rdf.SPY.diff()
    rdf.loc[:,'SPYup'] = rdf.SPYdiff > 0
    
    rdf.loc[:,'thresh'] = rdf.total > threashold
    
    minVal = rdf.SPY.min()

    rdf.loc[:,'RiskOn']  = rdf.apply(lambda row: row.SPY if row.thresh &  row.SPYup else minVal, axis=1)
    rdf.loc[:,'RiskOff'] = rdf.apply(lambda row: row.SPY if row.thresh & ~row.SPYup else minVal, axis=1)
    
    t = 'Risk On / Risk Off : {} lookback {} threshold'.format(lookback, threshold)
    
    plt.figure()
    cols = ['SPY','RiskOn', 'RiskOff']
    #rdf[cols].plot(kind='line', title=t)
    plt.show()
    
    plt.figure()
    rdf['2019':'2020'][cols].plot(kind='line', title=t)
    plt.show()
    
    return

# =======================================================================
# Setup Porgram Variables
# =======================================================================
symList = ['SPY', 'TLT', 'GLD']

startDate = '01/01/2000'

refreshData = False

lookback = 10

threashold = 1.75


dfDict = {}

for sym in symList:
    
    dfDict[sym] = gatherData(sym, startDate)


# =======================================================================
# Combining Data
# =======================================================================
cdf = pd.DataFrame() 
first = True

for sym in symList:
    
    tdf = dfDict[sym].copy()
    tdf.rename(columns={'Close':sym}, inplace=True)
    
    if first:
        cdf = tdf.loc[:,sym]
        first = False
        
    else:
        cdf = pd.merge(cdf, tdf.loc[:,sym], left_index=True, \
                       right_index=True,  how='inner')

firstIndex = cdf.index.min().date()
lastIndex  = cdf.index.max().date()

print ("")
print ("Combined Date Range = {} to {}".format(firstIndex, lastIndex))
print ("Total Trading Days = {}".format(len(cdf)))



#import pdb; pdb.set_trace()

# =======================================================================
# Set Threashold, looback to optimize indicator
# =======================================================================

thresholdList = [1.25, 1.5, 1.75]
lookbackList  = [5, 10, 15]

for threshold in thresholdList:
    for lookback in lookbackList:
        
        calcRiskOnRiskOff(symList, cdf, lookback, threshold)
Scroll to Top