from matplotlib.pyplot import plot, show, xticks, xlabel, ylabel, legend, yscale, title, savefig, rcParams, figure, hist, text, bar, subplots
import matplotlib.pyplot as plt
import Image
from scipy import stats
import pandas as pd
import numpy as np
from datetime import datetime as dt
def read_ts_csv(fname, dindex="Date"):
data=pd.read_csv(fname)
dateindex=[dt.strptime(dx, "%d/%m/%y") for dx in list(data[dindex])]
data.index=dateindex
del(data[dindex])
return data
def get_div_yield(tickname, rawdata):
price_data=rawdata[tickname+"_PRICE"]
total_returns=rawdata[tickname+"_TR"]
tr_return = get_monthly_tr(tickname, rawdata)
price_return = (price_data / price_data.shift(1)) - 1.0
div = tr_return - price_return
div_tr = div * total_returns
last_year_divs = pd.rolling_mean(div_tr, 12, min_periods=1)*12.0
div_yield = last_year_divs / total_returns
return div_yield
def get_monthly_tr(tickname, rawdata):
total_returns=rawdata[tickname+"_TR"]
return (total_returns / total_returns.shift(1)) - 1.0
def get_annual_tr(tickname, rawdata):
total_returns=rawdata[tickname+"_TR"]
return (total_returns / total_returns.shift(12)) - 1.0
def get_forward_tr(tickname, rawdata, months):
total_returns=rawdata[tickname+"_TR"]
return (total_returns.shift(-months) / total_returns) - 1.0
def risk_weight_with_model(SRdiff, old_weight):
SRdifftable=[ -0.25, -0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.1, 0.15, 0.2, 0.25]
multipliers=[.6, .66, .77, .85, .94, 1.0, 1.11, 1.19, 1.3, 1.37, 1.48]
"""
if SRdiff>0.25:
SRdiff=0.25
if SRdiff<-0.25:
SRdiff=-0.25
"""
multiplier=np.interp(SRdiff, SRdifftable, multipliers)
new_weight=old_weight*multiplier
return new_weight
def new_risk_weights(SRdiff_list, old_risk_weights):
new_weights=[risk_weight_with_model(SRdiff, old_weight)
for (SRdiff, old_weight) in zip(SRdiff_list, old_risk_weights)]
total_weight=sum(new_weights)
new_weights=[new_wt/total_weight for new_wt in new_weights]
return new_weights
## 1/N
def calc_ts_weights(tickers, rawdata, SRdiff_list_ts):
old_risk_weights=[1.0/len(tickers)]*len(tickers)
new_weights=[new_risk_weights(SRdiff_list, old_risk_weights) for SRdiff_list
in SRdiff_list_ts]
return pd.DataFrame(new_weights, rawdata.index, tickers)
def calc_ts_fixed_weights(rawdata, tickers, risk_weights):
return pd.DataFrame([risk_weights]*len(rawdata.index), rawdata.index, columns=tickers)
def calc_cash_weights(risk_weights):
cash_weights=[rw/vl for (rw, vl) in zip(risk_weights, vols)]
cash_weights=[wt/sum(cash_weights) for wt in cash_weights]
return cash_weights
def ts_calc_cash_weights(risk_weights_ts):
return risk_weights_ts.apply(calc_cash_weights, 1)
def calc_asset_returns(rawdata, tickers):
asset_returns=pd.concat([get_monthly_tr(tickname, rawdata) for tickname in tickers], axis=1)
asset_returns.columns=tickers
return asset_returns
def expand_boring(boring, tickers):
return pd.DataFrame(np.array([boring.values]*len(tickers), ndmin=2).transpose(), boring.index, columns=tickers)
def two_year_fix(weights):
fix_weights=weights.resample("1A")
fix_weights=fix_weights.reindex(weights.index).ffill()
return fix_weights
def relative_div_predictor(tickers, rawdata):
all_divs=pd.concat([get_div_yield(tickname, rawdata) for tickname in tickers], axis=1, names=tickers)
all_divs.columns=tickers
vol_ts=pd.DataFrame([vols]*len(rawdata.index), rawdata.index, columns=tickers)
divs_SR=all_divs / vol_ts
SR_avg=divs_SR.median(axis=1)
SR_avg=expand_boring(SR_avg, tickers)
rel_SR=divs_SR - SR_avg
return rel_SR
def relative_div(tickers, rawdata):
rel_SR=relative_div_predictor(tickers, rawdata)
SRdiff_list_ts=[list(rel_SR.irow(idx).values*2.5) for idx in range(len(rawdata.index))]
risk_weights_ts=calc_ts_weights(tickers, rawdata, SRdiff_list_ts)
return risk_weights_ts
def relative_ts_div_predictor(tickers, rawdata):
all_divs=pd.concat([get_div_yield(tickname, rawdata) for tickname in tickers], axis=1, names=tickers)
all_divs.columns=tickers
vol_ts=pd.DataFrame([vols]*len(rawdata.index), rawdata.index, columns=tickers)
divs_SR=all_divs / vol_ts
divs_SR_ts_avg=pd.rolling_mean(divs_SR, 600, min_periods=1)
norm_SR=divs_SR - divs_SR_ts_avg
SR_avg=norm_SR.median(axis=1)
SR_avg=expand_boring(SR_avg, tickers)
rel_SR=norm_SR - SR_avg
return rel_SR
def relative_ts_div(tickers, rawdata):
rel_SR = relative_ts_div_predictor(tickers, rawdata)
SRdiff_list_ts=[list(rel_SR.irow(idx).values*2.5) for idx in range(len(rawdata.index))]
risk_weights_ts=calc_ts_weights(tickers, rawdata, SRdiff_list_ts)
return two_year_fix(risk_weights_ts)
def mom_predictor(tickers, rawdata):
all_mom=[get_annual_tr(tickname, rawdata) for tickname in tickers]
all_mom=pd.concat(all_mom, axis=1)
all_mom.columns=tickers
vol_ts=pd.DataFrame([vols]*len(rawdata.index), rawdata.index, columns=tickers)
mom_SR=all_mom / vol_ts
SR_avg=mom_SR.median(axis=1)
SR_avg=expand_boring(SR_avg, tickers)
rel_SR=mom_SR - SR_avg
return rel_SR
def momentum(tickers, rawdata):
rel_SR=mom_predictor(tickers, rawdata)
SRdiff_list_ts=[list(rel_SR.irow(idx).values*.5) for idx in range(len(rawdata.index))]
risk_weights_ts=calc_ts_weights(tickers, rawdata, SRdiff_list_ts)
return risk_weights_ts
def mom_rel(tickers, rawdata):
a1=momentum(tickers, rawdata)
a2=relative_ts_div(tickers, rawdata)
return (a1+a2)/2.0
def fixed(tickers, rawdata):
SRdiff_list_ts=[[0.0]*len(tickers)]*len(rawdata.index)
risk_weights_ts=calc_ts_weights(tickers, rawdata, SRdiff_list_ts)
return risk_weights_ts
def portfolio_return(asset_returns, cash_weights):
portfolio_returns=asset_returns*cash_weights
portfolio_returns=portfolio_returns.sum(axis=1)
return portfolio_returns
def port_return(tickers, rawdata, risk_wt_function):
risk_weights_ts=risk_wt_function(tickers, rawdata)
asset_returns=calc_asset_returns(rawdata, tickers)
cash_weights=ts_calc_cash_weights(risk_weights_ts)
cash_weights=~np.isnan(asset_returns)*cash_weights
def normalise_weights(new_weights):
total_weight=sum(new_weights)
return [new_wt/total_weight for new_wt in new_weights]
cash_weights=cash_weights.apply(normalise_weights, axis=1)
cash_weights=cash_weights.shift(1)
ans=portfolio_return(asset_returns, cash_weights)
return ans
def sign(x):
if x is None:
return None
if np.isnan(x):
return np.NAN
if x==0:
return 0.0
if x<0:
return -1.0
if x>0:
return 1.0
from matplotlib.pyplot import show, plot, legend, title
rawdata=read_ts_csv("/home/rob/workspace/systematictradingexamples/plots_for_perhaps/MSCI_data.csv")
refdata=pd.read_csv("/home/rob/workspace/systematictradingexamples/plots_for_perhaps/MSCI_ref.csv")
## Comparisions:
## Type=Country, etc
caselist=["emdev", "devregions", "emregions", "devemea", "devams", "devasia", "emasia", "emams",
"ememea", "devall", "emall", "all", "emea", "america", "asia"]
caselist=["all"]
"""
if case=="emdev":
tickers=["EM", "DEV"] #mom 9bp
elif case=="devregions":
tickers=list(refdata[(refdata.EmorDEV=="DEV") & (refdata.Type=="Region")].Country.values) #mom 2sr
elif case=="emregions":
tickers=list(refdata[(refdata.EmorDEV=="EM") & (refdata.Type=="Region")].Country.values) #divs 3sr
elif case=="devemea":
tickers=list(refdata[(refdata.EmorDEV=="DEV") & (refdata.Region=="EMEA")].Country.values) #mom
elif case=="devams":
tickers=list(refdata[(refdata.EmorDEV=="DEV") & (refdata.Region=="America")].Country.values) #mom
elif case=="devasia":
tickers=list(refdata[(refdata.EmorDEV=="DEV") & (refdata.Region=="Asia")].Country.values) #divs
elif case=="emasia":
tickers=list(refdata[(refdata.EmorDEV=="EM") & (refdata.Region=="Asia")].Country.values) #flat
elif case=="emams":
tickers=list(refdata[(refdata.EmorDEV=="EM") & (refdata.Region=="America")].Country.values) #divs
elif case=="ememea":
tickers=list(refdata[(refdata.EmorDEV=="EM") & (refdata.Region=="EMEA")].Country.values) #divs
elif case=="devall":
tickers=list(refdata[(refdata.EmorDEV=="DEV") & (refdata.Type=="Country")].Country.values) #mom 12bp
elif case=="emall":
tickers=list(refdata[(refdata.EmorDEV=="EM") & (refdata.Type=="Country")].Country.values) #mom 7bp
elif case=="all":
tickers=list(refdata[refdata.Type=="Country"].Country.values) #mom 17bp
elif case=="emea":
tickers=list(refdata[refdata.Region=="EMEA"].Country.values) #mom
elif case=="america":
tickers=list(refdata[refdata.Region=="America"].Country.values) #mom
elif case=="asia":
tickers=list(refdata[refdata.Region=="Asia"].Country.values) #flat
else:
raise Exception()
"""
tickers=list(refdata[refdata.Type=="Country"].Country.values) #mom 17bp
def vl(emordev):
if emordev=="EM":
return .23
else:
return .15
vols=[vl(refdata[refdata.Country==ticker].EmorDEV.values[0]) for ticker in tickers]
## Use a filter function for each comparision
## Returns a list of "tickers"
## replace this function
weights=pd.read_csv("/home/rob/workspace/systematictradingexamples/plots_for_perhaps/allhcweights.csv")
p1=port_return(tickers, rawdata, fixed)
p2=port_return(tickers, rawdata, relative_div)
#p3=port_return(tickers, rawdata, relative_ts_div)
p4=port_return(tickers, rawdata, momentum)
p5=port_return(tickers, rawdata, mom_rel)
"""
mom_predictor(tickers, rawdata).plot()
title("mom")
show()
"""
all=pd.concat([p1,p2,p4], axis=1)
#all.columns=[ "Fixed","Relative div", "Norm div", "mom", "mom+ndiv"]
def sharpe(x):
return (12*.5)*x.mean()/x.std()
print(all.apply(sharpe, axis=0))
def file_process(filename):
fig = plt.gcf()
fig.set_size_inches(18.5,10.5)
fig.savefig("/home/rob/%s.png" % filename,dpi=300)
fig.savefig("/home/rob/%sLOWRES.png" % filename,dpi=50)
Image.open("/home/rob/%s.png" % filename).convert('L').save("/home/rob/%s.jpg" % filename)
Image.open("/home/rob/%sLOWRES.png" % filename).convert('L').save("/home/rob/%sLOWRES.jpg" % filename)
p1.cumsum().plot(color="blue", linestyle="-.")
p2.cumsum().plot(color="gray")
p4.cumsum().plot(color="black")
legend( ["Fixed weight", "Dividend yield", "Relative momentum"], loc="upper left")
frame=plt.gca()
frame.get_yaxis().set_visible(False)
rcParams.update({'font.size': 18})
file_process("equitycountrymodel")
plt.show()
