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Find ETF Bond funds that move counter to SPY





In [359]:



    


import pandas as pd
import matplotlib.pyplot as pyplot
import seaborn as sns



    











In [360]:



    


df_ETFs = local_csv('ETFs.csv')
df_ETFs = df_ETFs.set_index('Symbol')
df_ETFs[:2]



    


















    
Out[360]:










  
    

      
      ETF Name
      Tracks This Index
      Index Description
      Category
      Provider
      Expense Ratio
      Dividend
      Div Yield
      Market Cap (millions)
      Shares (millions)
      ...
      Beta
      Asset Class
      Asset Class Size
      Asset Class Style
      Region
      Sector
      Leveraged
      Inverse
      Top Holdings
      Chart
    

    

      Symbol
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      AAXJ
       iShares MSCI All Country Asia ex Japan Index Fund
       MSCI All Country Asia ex Japan Index
       The index measures the performance of 11 devel...
       Asia Pacific Equities
       iShares
       0.7400%
       $0.36
       1.8400%
       $1,687.89
       31
      ...
       1
       Equity
             NaN
         NaN
          Pacific ex-Japan
       NaN
       NaN
       NaN
       click
       click
    

    

      ACWI
                       MSCI All Country World Index Fund
               MSCI All Country World Index
       The Index is designed to measure the performan...
             Global Equities
       iShares
       0.3500%
       $0.36
       1.4100%
       $1,176.64
       30
      ...
       1
       Equity
       Large-Cap
       Blend
       Global/Multi-Region
       NaN
       NaN
       NaN
       click
       click
    

  



2 rows × 21 columns



















In [361]:



    


df_ETFs.Category.unique()



    


















    
Out[361]:








array(['Asia Pacific Equities', 'Global Equities',
       'Foreign Large Cap Equities', 'Emerging Markets Equities',
       'Europe Equities', 'Inverse Commodities', 'Leveraged Commodities',
       'Agricultural Commodities', 'Total Bond Market', 'Government Bonds',
       'Hedge Fund', 'Energy Equities', 'Diversified Portfolio',
       'Commodities', 'Financials Equities', 'Currency', 'National Munis',
       'Health & Biotech Equities', 'Metals', 'Technology Equities',
       'Leveraged Equities', 'Money Market',
       'Miscellaneous Sector Equities', 'Latin America Equities',
       'Small Cap Value Equities', 'Large Cap Value Equities',
       'Long-Short', 'International Government Bonds', 'Inverse Equities',
       'Corporate Bonds', 'China Equities', 'California Munis',
       'Foreign Small & Mid Cap Equities', 'Commodity Producers Equities',
       'Quantitative Methodology Indexes',
       'Preferred Stock/Convertible Bonds', 'Mid Cap Blend Equities',
       'Oil & Gas', 'Materials', 'Precious Metals', 'Utilities Equities',
       'Industrials Equities', 'Japan Equities', 'Communications Equities',
       'Leveraged Real Estate', 'Mid Cap Value Equities',
       'Consumer Discretionary Equities', 'Consumer Staples Equities',
       'Leveraged Currency', 'Global Real Estate',
       'Small Cap Blend Equities', 'Small Cap Growth Equities',
       'Large Cap Blend Equities', 'Large Cap Growth Equities',
       'Emerging Markets Bonds', 'Mid Cap Growth Equities',
       'All Cap Equities', 'Transportation Equities', 'Real Estate',
       'High Yield Bonds', 'New York Munis', 'Inflation-Protected Bonds',
       'Mortgage Backed Securities', 'Leveraged Bonds',
       'Target Retirement Date', 'Volatility', nan], dtype=object)

















In [362]:



    


df_ETFs.Sector.unique()



    


















    
Out[362]:








array([nan, 'Energy', 'Financial', 'Health & Biotech', 'Technology',
       'Retail & Consumer', 'Utilities', 'Industrial', 'Materials',
       'Consumer Discretionary', 'Agriculture', 'Communications',
       'Consumer Staples', 'Environmental', 'Transportation',
       'Building & Construction', 'Aerospace & Defense'], dtype=object)

















In [363]:



    


df_ETFs['Asset Class'].unique()



    


















    
Out[363]:








array(['Equity', 'Commodity', 'Bond', 'Multi-Asset', 'Currency',
       'Real Estate', nan], dtype=object)

















In [364]:



    


bonds = df_ETFs[df_ETFs['Asset Class'] == 'Bond']
bonds[:2]



    


















    
Out[364]:










  
    

      
      ETF Name
      Tracks This Index
      Index Description
      Category
      Provider
      Expense Ratio
      Dividend
      Div Yield
      Market Cap (millions)
      Shares (millions)
      ...
      Beta
      Asset Class
      Asset Class Size
      Asset Class Style
      Region
      Sector
      Leveraged
      Inverse
      Top Holdings
      Chart
    

    

      Symbol
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      AGG
       iShares Barclays Aggregate Bond Fund
       Barclays Capital U.S. Aggregate Bond Index
       The index measures the performance of the U.S....
       Total Bond Market
       iShares
       0.2400%
       $0.31
       3.8500%
       $11,688.03
       110
      ...
       0
       Bond
       NaN
       NaN
       NaN
       NaN
       NaN
       NaN
       click
       click
    

    

      AGZ
               iShares Barclays Agency Bond
          Barclays Capital U.S. Agency Bond Index
       The index measures the performance of the agen...
        Government Bonds
       iShares
        0.200%
       $0.17
       0.2400%
          $318.54
         3
      ...
       0
       Bond
       NaN
       NaN
       NaN
       NaN
       NaN
       NaN
       click
       click
    

  



2 rows × 21 columns



















In [365]:



    


# find ETFs' with Bond in their Category
bond_cat = list(df_ETFs[df_ETFs.Category.str.contains('Bond').fillna(False)].index)
bond_cat[:2]



    


















    
Out[365]:








['AGG', 'AGZ']

















In [366]:



    


# ETFs with Bond in title that are not a Bond in Asset Class
set(bond_cat) - set(bonds.index)



    


















    
Out[366]:








{'PFF', 'PGF', 'PGX'}

















In [367]:



    


# apparently these are all equity
df_ETFs.ix['PGX']



    


















    
Out[367]:








ETF Name                                   PowerShares Preferred Portfolio
Tracks This Index        BofA Merrill Lynch Core Fixed Rate Preferred S...
Index Description        The Index is designed to replicate the total r...
Category                                 Preferred Stock/Convertible Bonds
Provider                                               Invesco PowerShares
Expense Ratio                                                       0.500%
Dividend                                                             $0.08
Div Yield                                                           7.100%
Market Cap (millions)                                            $1,020.21
Shares (millions)                                                       74
Avg Vol                                                            512,816
Beta                                                                     1
Asset Class                                                         Equity
Asset Class Size                                                       NaN
Asset Class Style                                                      NaN
Region                                                                U.S.
Sector                                                           Financial
Leveraged                                                              NaN
Inverse                                                                NaN
Top Holdings                                                         click
Chart                                                                click
Name: PGX, dtype: object

















In [368]:



    


bonds = bonds.drop(bonds[bonds.Leveraged.notnull()].index) # 5 are leveraged
bonds = bonds.drop(bonds[bonds.Inverse.notnull()].index)
len(bonds)



    


















    
Out[368]:








95

















In [369]:



    


bond_prices = pd.DataFrame(index=get_pricing('AGG', fields='close_price', start_date=start, end_date=end, frequency='daily').index)
non_bonds = []
for b in bonds.index:
    try:
        bond_prices[b] = get_pricing(b, fields='close_price', start_date=start, end_date=end, frequency='daily')
    except:
        non_bonds += [b]
non_bonds



    


















    
Out[369]:








['CFT', 'GLJ', 'MUAD', 'MUAE', 'MUAF', 'UBD', 'ULQ']

















In [370]:



    


bonds = bonds.drop(non_bonds)
len(bonds)



    


















    
Out[370]:








88

















In [371]:



    


start = pd.Timestamp('2007-03-02 00:00:00+0000', tz='UTC')
end = pd.Timestamp('2015-02-06 23:59:00+0000', tz='UTC')



    











In [398]:



    


# add spy to the price df
spy = get_pricing('SPY', fields='close_price', start_date=start, end_date=end, frequency='daily')
bond_prices['SPY'] = spy



    











In [442]:



    


# plot the Bond ETF's that have data for the 2008 - 2009 stock market drop
bond_prices.ix['2008-09-01':'2009-06-01'].dropna(axis=1,how='any').plot(colormap='rainbow')
pyplot.title("Bond Prices")
#pyplot.legend(loc='best')
pyplot.ylabel("Bond Price")
pyplot.xlabel("Time")



    


















    
Out[442]:








<matplotlib.text.Text at 0x7fdccdfcfcd0>










    
























In [406]:



    


# plot the Bond ETF's that have a price > 62 , those below don't seem to move
bond_prices.ix['2008-09-01':'2009-06-01'][(bond_prices.ix['2008-09-01':'2009-06-01'].dropna(axis=1,how='any') > 62)].plot(colormap='rainbow')
pyplot.title("Bond Prices")
pyplot.ylabel("Bond Price")
pyplot.xlabel("Time")



    


















    
Out[406]:








<matplotlib.text.Text at 0x7fdcceef9e10>










    
























In [441]:



    


# plot the Bond ETF's that have a price > 62 and max > 100, they broke to the upside
bonds2 = bond_prices.ix['2008-09-01':'2009-06-01'].dropna(axis=1,how='any')
bonds2[bonds2.columns[bonds2.max() > 100]].plot(colormap='rainbow')



    


















    
Out[441]:








<matplotlib.axes._subplots.AxesSubplot at 0x7fdccde42450>










    
























In [ ]:

Content source: paulperry/quant

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