COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/13/2016)

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FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE

LAYOUT (Organized by Time Period): 

  1. Composite ETF Cumulative Returns Momentum Bar plot

  2. Composite ETF Cumulative Returns Line plot

  3. Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean)

  4. Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot)

  5. Implied Cost of Capital Estimates

  6. Composite ETF Cumulative Return Tables

  7. Notable Trends and Observations

COMPOSITE ETF COMPONENTS:

LAST 252 TRADING DAYS

LAST 126 TRADING DAYS

LAST 63 TRADING DAYS

year-to-date LAST 31 TRADING DAYS

LAST 21 TRADING DAYS

LAST 10 TRADING DAYS

Implied Cost of Capital Estimates:

To learn more about the Implied Cost of Capital see here.

CATEGORY AVERAGE ICC ESTIMATES

ALL ETF ICC ESTIMATES BY CATEGORY

Cumulative Return Tables:

Notable Observations and Trends:

  • Investors appear to be increasing their defensive positioning in the market as evidenced by the continued relative strength in the Precious Metals/Precious Metals Miners and Treasury Bond composites.
  • Investors appear to be liquidating previous high performing asset classes as evidenced by Healthcare being among the bottom 3 performers off all composites across 5/6 timeframes beginning over the last 126 days. 
    • This is also supported by further deterioration in the relative performance of the Technology composite, which appears as a bottom 3 performer year-to-date.
  • Correlations still appear relatively binary. However, a notable change is occurring in the correlation of Oil and Gas with the rest of the Sector based composites. It appears to be weakening year-to-date as compared to the last 126/252 trading days. 
  • The Treasury bond composite is seeing a notable increase in its negative correlation with the rest of the market as well. I look for this trend to continue as long as the global Negative-Interest Rate Policy trend continues. The US offers relatively high yield when compared to negative rates!

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (2/06/16)

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To see the origin of this series click here

In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name equities. I will then track the results of the Long/Short portfolio, in equity returns, cumulatively for 4 weeks before rotating out of that portfolio. The ETF's are selected from the following groups:

PORTFOLIO TWO

Longs: VPU, IJR, FEZ, IWB, INDA, HEDJ, IYT

Shorts: LQD, EWW, IAU, VDE, EWT, EEM, EWH

PORTFOLIO THREE

Longs: VWO, KRE, XLU, EEM, HEDJ

Shorts: EWW, HACK, JNK, XLP, IYR

PORTFOLIO FOUR

Longs: XRT,  XLY,  XLP,  XHB,  GDXJ,  IYT,  XME,  MDY

Shorts: EPI, XLU, HEDJ, JNK, EWQ, VEU, XLI

PORTFOLIO FIVE

Longs: VO, GDX, XHB, XLB, HACK, XLY, XLP, XLU

Shorts: ACWI, VWO, IYJ, VB, VPU, ECH, VGK, IWB

CUMULATIVE GROSS PRICE RETURN (ALL PORTFOLIOS)

PORTFOLIO 6:

LONGS: IJR, ACWI, IJH, KBE, VWO, XLY, XLU, IYG

SHORTS: EWU, XHB, VXUS, VPU, IXC, EWW, VGK, EPI

ETF SKEW LONGS

IJR

ACWI

IJH

KBE

VWO

XLY

XLU

IYG

ETF SKEW SHORTS

EWU

XHB

VXUS

VPU

IXC

EWW

VGK

EPI

COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/06/2016)

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FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE

LAYOUT (Organized by Time Period): 

  1. Composite ETF Cumulative Returns Momentum Bar plot

  2. Composite ETF Cumulative Returns Line plot

  3. Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean)

  4. Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot)

  5. Implied Cost of Capital Estimates

  6. Composite ETF Cumulative Return Tables

  7. Notable Trends and Observations

COMPOSITE ETF COMPONENTS:

LAST 252 TRADING DAYS

LAST 126 TRADING DAYS

LAST 63 TRADING DAYS

LAST 21 TRADING DAYS

LAST 10 TRADING DAYS

Implied Cost of Capital Estimates:

To learn more about the Implied Cost of Capital see here.

CATEGORY AVERAGE ICC ESTIMATES

ALL ETF ICC ESTIMATES BY CATEGORY

Cumulative Return Tables:

Notable Observations and Trends:

  • Market crash risk is rising. What is currently a correction is showing the potential to become much worse.
  • T-Bond Yields are the worst performers over the last 21 and 10 day periods. Recall that Bond yields and prices have an inverse relationship, meaning that Treasury Bonds are seeing a strong bid from the market.
  • Precious Metal Miners and Precious Metals have been among the top 3 performers for 3 out of 5 time periods starting over the last 126 days. These composites are usually bid up as a form of crash protection when investors fear 1) systematic devaluation of the USD and other major currencies 2) global market crash.
  • Utilities being among the top 3 performers across all time periods also support the rising crash risk that markets appear to be pricing in.

For a deeper dive into ETF performance and relative value subscribe to the ETF Internal Analytics package here

Implied Volatility Skew Strategy Mid-Week Update Using Python (2/03/2016)

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To see this weekend's prediction click here.

Overall this strategy has been impressive in its trial run over the last 4.5 weeks. I figured, given the volatility and uncertainty in the broad markets this week I'd like to see a mid-week update of the strategy using Python and the BarChart OnDemand API. To see my original article on the basics of using the BarChart OnDemand API click here.

First I import the basic modules needed to execute the script:

from copy import copy
import pandas as pd
import pandas_datareader.data as web
from pandas.tseries.offsets import *

From there I define a couple convenience functions. The first is a one off function for querying the BarChart API for singular symbol names. The other is a bulk function to aggregate the portfolio symbol price data into a HDF5 format for easy querying later on.  Remember the api key is your api key.

# ================================================================== #
def _get_barChart_px(sym):
    start = '20160201'
    freq = 'minutes' 
    api_url = construct_barChart_url(sym, start, freq, api_key=apikey)  
    csvfile = pd.read_csv(api_url, parse_dates=['timestamp'])
    csvfile.set_index('timestamp', inplace=True)
    csvfile.index = csvfile.index.tz_localize('utc').tz_convert('US/Eastern')
    return csvfile
# ================================================================== #
def construct_barChart_url(sym, start_date, freq, api_key=apikey):
    '''Function to construct barchart api url'''    
    url = 'http://marketdata.websol.barchart.com/getHistory.csv?' +\
            'key={}&symbol={}&type={}&startDate={}'.format(api_key, sym, freq, start_date)
    return url
   
# ================================================================== #
def get_minute_data(syms):
    '''Function to retrieve minute data for multiple stocks'''
    print('Running Get Minute Data')
    # This is the required format for datetimes to access the API
    # You could make a function to translate datetime to this format
    start = '20160201'
    #end = d
    freq = 'minutes'    
    symbol_count = len(syms)
    N = copy(symbol_count)
    try:
        for i, sym in enumerate(syms, start=1):
            api_url = construct_barChart_url(sym, start, freq, api_key=apikey)
            try:
                csvfile = pd.read_csv(api_url, parse_dates=['timestamp'])
                csvfile.set_index('timestamp', inplace=True)
                # convert timestamps to EST
                csvfile.index = csvfile.index.tz_localize('utc').tz_convert('US/Eastern')
                symbol_store.put('{}'.format(sym), csvfile, format='table')
            except:
                continue
            N -= 1
            pct_total_left = (N/symbol_count)
            print('{}..[done] | {} of {} symbols collected | percent remaining: {:>.2%}'.format(sym, i, symbol_count, pct_total_left)) 
    except Exception as e: 
        print(e)
    finally:
        pass

Next I run the function for aggregating the portfolio stocks prices. 

longs = ['VO', 'GDX', 'XHB', 'XLB', 'HACK', 'XLY', 'XLP', 'XLU']
shorts = ['ACWI', 'VWO', 'IYJ', 'VB', 'VPU', 'ECH', 'VGK', 'IWB']

today = pd.datetime.today().date()
symbol_store = pd.HDFStore(price_path + 'Implied_Volatility_ETF_Tracking_{}.h5'.format(today.strftime('%m-%d-%y')))

symbols = longs+shorts
get_minute_data(symbols)
symbol_store.close()

After aggregating the data I perform some simple clean up operations along with calculation of the mid-prices for each minute of available data. 

'''grab data from our previously created hdf5 file using keys'''
data = {}
loc = price_path + 'Implied_Volatility_ETF_Tracking_{}.h5'.format(today.strftime('%m-%d-%y'))
with pd.HDFStore(loc, 'r') as DATA:
    for key in DATA.keys():
        data[key] = DATA[key]
dat = pd.Panel.from_dict(data) # convert Python Dict to Pandas Panel        

'''construct minute mid-prices'''
mids = pd.DataFrame()
for symbol in dat:
    mids[symbol] = (dat[symbol]['open'] + dat[symbol]['close']) / 2

'''Remove unnecessary forward slash from default HDF5 key label'''
cols = []
for sym in mids.columns:
    symz = sym.replace('/','')
    cols.append(symz)

mids.columns = cols
mids.head()

Because each ETF did not record a trade for every minute I perform a forward fill of the previous price before calculating the log returns. Then I calculate the cumulative sum of the the returns for both the long and short legs of the portfolio for comparison. 

mids = mids.ffill()
lrets = np.log(mids / mids.shift(1))

crets = lrets.cumsum()
last_cret = crets.ix[-1]

long_rets = pd.Series()
for long in longs:
    long_rets.loc[long] = last_cret.loc[long]
    
short_rets = pd.Series()
for short in shorts:
    short_rets.loc[short] = last_cret.loc[short]

Finally, we arrive at the moment of truth. How has the strategy performed for the first 3 trading sessions of the week?

net_gain = long_rets.mean() - short_rets.mean()
print('long positions mean return: {:.3%}\nshort positions mean return: {:.3%}\ngross strategy pnl: {:.3%}'.format(long_rets.mean(), short_rets.mean(), net_gain))

Not bad at all! How does that compare to the US Major Market Averages? I perform almost the same process for the SPY, QQQ, and DIA ETF's except I use the singular BarChart API function defined above. I also calculate the mid-price for each minute of data. 

'''get price data from BarChart'''
spy = _get_barChart_px('SPY')
qqq = _get_barChart_px('QQQ')
dia = _get_barChart_px('DIA')

'''calculate the midprice'''
spy_mid = (spy['open'] + spy['close']) / 2
qqq_mid = (qqq['open'] + qqq['close']) / 2
dia_mid = (dia['open'] + dia['close']) / 2

'''calculate the returns'''
spyr = np.log(spy_mid / spy_mid.shift(1))
qqqr = np.log(qqq_mid / qqq_mid.shift(1))
diar = np.log(dia_mid / dia_mid.shift(1))

'''calculate the cumulative sum of the returns'''
spyc = spyr.cumsum().ix[-1]
diac = diar.cumsum().ix[-1]
qqqc = qqqr.cumsum().ix[-1]

print('spy returns: {:.2%}\ndia returns: {:.2%}\nqqq returns: {:.2%}\nstrategy gross return: {:.2%}'.format(spyc, diac, qqqc, net_gain))

Wow! Not even close.

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (1/30/16)

/

To see the origin of this series click here

In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name equities. I will then track the results of the Long/Short portfolio, in equity returns, cumulatively for 4 weeks before rotating out of that portfolio. The ETF's are selected from the following groups:

PORTFOLIO ONE

Longs: XLF, EPI, VOX, XLI, XLP, XLV, HEDJ, IYT

Shorts: EZU, XLB, GDXJ, XRT, XHB, VGK, KRE, EWT

Week 1, Week 2, Week 3, Week 4 and Cumulative Results: 

PORTFOLIO TWO

Longs: VPU, IJR, FEZ, IWB, INDA, HEDJ, IYT

Shorts: LQD, EWW, IAU, VDE, EWT, EEM, EWH

Week 1, Week 2, Week 3 and Cumulative Results:

PORTFOLIO THREE

Longs: VWO, KRE, XLU, EEM, HEDJ

Shorts: EWW, HACK, JNK, XLP, IYR

Week 1, Week 2 and Cumulative Results:

PORTFOLIO FOUR

Longs: XRT,  XLY,  XLP,  XHB,  GDXJ,  IYT,  XME,  MDY

Shorts: EPI, XLU, HEDJ, JNK, EWQ, VEU, XLI

Week 1 Results:

PORTFOLIO FIVE

Longs: VO, GDX, XHB, XLB, HACK, XLY, XLP, XLU

Shorts: ACWI, VWO, IYJ, VB, VPU, ECH, VGK, IWB

ETF SKEW LONGS

VO

GDX

XHB

XLB

HACK

XLY

XLP

XLU

ETF SKEW SHORTS

ACWI

VWO

IYJ

VB

VPU

ECH

VGK

IWB