def holtpc( data, yearly=256, alpha=hw_alpha, beta=hw_beta ):
'''Annualized percentage growth dataframe from H-W growth model.'''
# yearly is the multiplier to annualize Growth.
#
# MOST VALUABLE H-W function <= !!
# It contains the HISTORY of FORECASTED RATES!
#
holtdf = holt( data, alpha, beta )
level = todf( holtdf['Level'] )
grow = todf( holtdf['Growth'] )
growan = todf( grow * yearly )
return todf( 100 * ( growan / level ) )
def holtpc(data, yearly=256, alpha=hw_alpha, beta=hw_beta):
'''Annualized percentage growth dataframe from H-W growth model.'''
# yearly is the multiplier to annualize Growth.
#
# MOST VALUABLE H-W function <= !!
# It contains the HISTORY of FORECASTED RATES!
#
holtdf = holt(data, alpha, beta)
level = todf(holtdf['Level'])
grow = todf(holtdf['Growth'])
growan = todf(grow * yearly)
return todf(100 * (growan / level))
def stock_one( slang, maxi=3650, col='Close' ):
'''slang string retrieves SINGLE column for said stock.
Available col include: Open, High, Low, Close, Volume
'''
df = stock_all( slang, maxi )
# return just a single column dataframe:
return tools.todf( df[[ col ]] )
def cotr_position_metals():
'''Market position for precious metals from COTR of GC and SI.'''
pos1 = cotr_position( 'GC' )
# Gold Comex
pos2 = cotr_position( 'SI' )
# Silver Comex
#
# _Average reading between two contracts.
return tools.todf( (pos1 + pos2) / 2.0 )
def simu_prices( N, yarray ):
'''Convert bootstrap returns to price time-series into pandas DATAFRAME.'''
# Initial price implicitly starts at 1.
# Realize that its history is just the products of the returns.
ret = bootstrap( N, yarray )
# Cumulative product of array elements:
# cumprod is very fast, and keeps interim results!
# http://docs.scipy.org/doc/numpy/reference/generated/numpy.cumprod.html
return todf( np.cumprod( ret ) )
def cotr_position_bonds():
'''Market position for bonds from COTR of TY and ED.'''
pos1 = cotr_position( 'TY' )
# TY is 10-years.
pos2 = cotr_position( 'ED' )
# Eurodollar strips.
#
# _Average reading between two contracts.
return tools.todf( (pos1 + pos2) / 2.0 )
def cotr_position_metals():
'''Market position for precious metals from COTR of GC and SI.'''
pos1 = cotr_position('GC')
# Gold Comex
pos2 = cotr_position('SI')
# Silver Comex
#
# _Average reading between two contracts.
return tools.todf((pos1 + pos2) / 2.0)
def cotr_position_bonds():
'''Market position for bonds from COTR of TY and ED.'''
pos1 = cotr_position('TY')
# TY is 10-years.
pos2 = cotr_position('ED')
# Eurodollar strips.
#
# _Average reading between two contracts.
return tools.todf((pos1 + pos2) / 2.0)
def cotr_position_equities():
'''Market position for equities from COTR of both SP and ES.'''
pos1 = cotr_position('SP')
# SP better for options reading.
pos2 = cotr_position('ES')
# Minis better for reading futures.
#
# _Average reading between two contracts.
return tools.todf((pos1 + pos2) / 2.0)
def cotr_position_equities():
'''Market position for equities from COTR of both SP and ES.'''
pos1 = cotr_position( 'SP' )
# SP better for options reading.
pos2 = cotr_position( 'ES' )
# Minis better for reading futures.
#
# _Average reading between two contracts.
return tools.todf( (pos1 + pos2) / 2.0 )
def cotr_position_usd():
'''Market position for USD from COTR of JY and EC.'''
# We ignore USD index DX from ICE.
pos1 = cotr_position( 'JY' )
# JPY futures.
pos2 = cotr_position( 'EC' )
# EUR futures.
#
# _Inverts position relative to quotation styles.
# _Average reading between two contracts.
return tools.todf( 1 - ((pos1 + pos2) / 2.0) )
def holtforecast( holtdf, h=12 ):
'''Given a dataframe from holt, forecast ahead h periods.'''
# N.B. - holt forecasts by multiplying latest growth
# by the number of periods ahead. Somewhat naive...
# notice that the growth is based on smoothed levels.
last = holtdf[-1:]
y, l, b = last.values.tolist()[0]
# df to array to list, but extract first element :-(
forecasts = [y] + [ l + (b*(i+1)) for i in range(h) ]
# ^last actual point
return todf( forecasts, 'Forecast' )
def holtforecast(holtdf, h=12):
'''Given a dataframe from holt, forecast ahead h periods.'''
# N.B. - holt forecasts by multiplying latest growth
# by the number of periods ahead. Somewhat naive...
# notice that the growth is based on smoothed levels.
last = holtdf[-1:]
y, l, b = last.values.tolist()[0]
# df to array to list, but extract first element :-(
forecasts = [y] + [l + (b * (i + 1)) for i in range(h)]
# ^last actual point
return todf(forecasts, 'Forecast')
def cotr_position_usd():
'''Market position for USD from COTR of JY and EC.'''
# We ignore USD index DX from ICE.
pos1 = cotr_position('JY')
# JPY futures.
pos2 = cotr_position('EC')
# EUR futures.
#
# _Inverts position relative to quotation styles.
# _Average reading between two contracts.
return tools.todf(1 - ((pos1 + pos2) / 2.0))
def holt(data, alpha=hw_alpha, beta=hw_beta):
'''Holt-Winters growth (linear) model outputs workout dataframe.'''
# holt is an EXPENSIVE function, so retain its output for later.
holtdf = todf(data).dropna()
# 'Y' ^else:
# "ValueError: Length of values does not match length of index"
y = holtdf.values # Convert to array.
l, b = holt_winters_growth(y, alpha, beta)
holtdf['Level'] = l
holtdf['Growth'] = b
# In effect, additional columns 'Level' and 'Growth'
# for smoothed data and local slope,
# along side the original index and given data:
return holtdf
def holt( data, alpha=hw_alpha, beta=hw_beta ):
'''Holt-Winters growth (linear) model outputs workout dataframe.'''
# holt is an EXPENSIVE function, so retain its output for later.
holtdf = todf( data ).dropna()
# 'Y' ^else:
# "ValueError: Length of values does not match length of index"
y = holtdf.values # Convert to array.
l, b = holt_winters_growth( y, alpha, beta )
holtdf['Level'] = l
holtdf['Growth'] = b
# In effect, additional columns 'Level' and 'Growth'
# for smoothed data and local slope,
# along side the original index and given data:
return holtdf
def getfut(slang, maxi=512, col='Settle'):
'''slang string retrieves single column for one futures contract.
The string consists of a key from fut_dict concatenated with
'yym' where yy is shorthand for year and m is the month symbol
all in lower case, e.g. 'f4xau15z' for December 2015 Comex Gold.
Available col are: Open, High, Low, Last, Change, Settle,
Volume, 'Open Interest'
'''
# Other than Eurodollars, we should not need more than 512 days
# of data due to finite life of a futures contract.
# 2015-09-11 quandl default seems to be maxi around 380.
#
fut = quandl(fut_decode(slang), rows=maxi)
# return just a single column dataframe:
return tools.todf(fut[[col]])
def getfut( slang, maxi=512, col='Settle' ):
'''slang string retrieves single column for one futures contract.
The string consists of a key from fut_dict concatenated with
'yym' where yy is shorthand for year and m is the month symbol
all in lower case, e.g. 'f4xau15z' for December 2015 Comex Gold.
Available col are: Open, High, Low, Last, Change, Settle,
Volume, 'Open Interest'
'''
# Other than Eurodollars, we should not need more than 512 days
# of data due to finite life of a futures contract.
# 2015-09-11 quandl default seems to be maxi around 380.
#
fut = quandl( fut_decode( slang ), rows=maxi )
# return just a single column dataframe:
return tools.todf( fut[[ col ]] )
def cotr_position(futures='GC'):
'''Extract market position from CFTC Commitment of Traders Report.'''
cotr = cotr_get(futures)
# Report for both futures and options requested by implicit "FO".
#
# For directionality we use these categories:
try:
longs = cotr['Asset Manager Longs']
shorts = cotr['Asset Manager Shorts']
# "Leveraged Funds" for FINANCIALS appear short-term, whereas
# "Asset Manager" takes longer term perspective.
except:
longs = cotr['Money Manager Longs']
shorts = cotr['Money Manager Shorts']
# "Money Manager" for COMMODITIES.
# The report is structured differently than financials.
#
# _Scale-free between 0 and 1 indicating bullishness.
return tools.todf(longs / (longs + shorts))
def cotr_position( futures='GC' ):
'''Extract market position from CFTC Commitment of Traders Report.'''
cotr = cotr_get( futures )
# Report for both futures and options requested by implicit "FO".
#
# For directionality we use these categories:
try:
longs = cotr['Asset Manager Longs']
shorts = cotr['Asset Manager Shorts']
# "Leveraged Funds" for FINANCIALS appear short-term, whereas
# "Asset Manager" takes longer term perspective.
except:
longs = cotr['Money Manager Longs']
shorts = cotr['Money Manager Shorts']
# "Money Manager" for COMMODITIES.
# The report is structured differently than financials.
#
# _Scale-free between 0 and 1 indicating bullishness.
return tools.todf( longs / (longs + shorts ))
def holtgrow( data, alpha=hw_alpha, beta=hw_beta ):
'''Just the Growth dataframe from Holt-Winters growth model.'''
# In terms of units expressed in data.
return todf( holt( data, alpha, beta )['Growth'] )
def holtlevel(data, alpha=hw_alpha, beta=hw_beta):
'''Just smoothed Level dataframe from Holt-Winters growth model.'''
# Useful to filter out seasonals, e.g. see X-11 method:
# http://www.sa-elearning.eu/basic-algorithm-x-11
return todf(holt(data, alpha, beta)['Level'])
def holtgrow(data, alpha=hw_alpha, beta=hw_beta):
'''Just the Growth dataframe from Holt-Winters growth model.'''
# In terms of units expressed in data.
return todf(holt(data, alpha, beta)['Growth'])
def holtlevel( data, alpha=hw_alpha, beta=hw_beta ):
'''Just smoothed Level dataframe from Holt-Winters growth model.'''
# Useful to filter out seasonals, e.g. see X-11 method:
# http://www.sa-elearning.eu/basic-algorithm-x-11
return todf( holt( data, alpha, beta )['Level'] )
