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Portfolio.py
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import numpy
import sys
import math
import pickle
import os
import urllib
import datetime
from scipy.stats import norm
from cvxopt import matrix
from cvxopt import solvers
import shutil
base = "BA"
PC = {}
globals()["riskmodel"] = "s" #d = downside s = sharpes m=compared to market(QQQ)
class Portfolio:
def __init__(self):
self.lib = {}
def add(self,sym,shares,purchase):
self.lib[sym]=[shares,purchase]
def allocation(self):
total = self.eval()
retr = datetime.datetime.now() + datetime.timedelta(-1)
alloc = []
for i in self.lib:
p = GetPrice(i,str(retr.month),str(retr.day),str(retr.year))
a = 100*self.lib[i][0]*p/total
alloc.append(a)
return alloc
def realloc(self,dist):
retr = datetime.datetime.now() + datetime.timedelta(-1)
orders = []
total = self.eval()
alloc = self.allocation()
i = 0
while i<len(self.lib):
p = GetPrice(self.lib.keys()[i],str(retr.month),str(retr.day),str(retr.year))
bs = "S"
if float(dist[i])-alloc[i] > 0:
bs = "B"
orders.append([self.lib.keys()[i],bs,abs((float(dist[i])-alloc[i])*total/100/p)])
i+=1
return orders
def list(self):
#print self.lib.keys()
self.sigma = AllCov(self.lib.keys())
#print "sigma:",self.sigma
self.cv = 2*self.sigma
return self.lib.keys()
def load(self,name):
pass
def loadlist(self):
self.lib = {}
port = open("./Portfolios/test.txt","r")
for i in port:
i = i.replace("\n","")
i = i.replace("\r","")
if i.find(",")>0:
i = i[:i.find(",")]
self.add(i,0,0)
return self.lib.keys()
def save(self,name):
pass
def eval(self):
retr = datetime.datetime.now() + datetime.timedelta(-1)
total = 0
for i in self.lib:
p = GetPrice(i,str(retr.month),str(retr.day),str(retr.year))
#print i,self.lib[i],p,self.lib[i][0]*p
total += self.lib[i][0]*p
return total
def evalasof(self,y,m,d):
total = 0
for i in self.lib:
p = GetPrice(i,str(m),str(d),str(y))
#print i,self.lib[i],p,self.lib[i][0]*p
total += self.lib[i][0]*p
return total
def efficient(self):
pass
def tangency(self):
pass
def targetreturn(self,p,r,strat="N"):
r = float(r)/12.0
#print "S1:",self.sigma
a,b,d,e = "","","",""
if strat == "N":
a,b,d,e = self.test(p)
else:
#CURRENTLY FAILS TO GIVE VALID OUTPUT
a,b,d,e = self.noshort(p)
#print "S2:",d
i = 0
while i < len(e):
e[i]/=1200
i+=1
g = numpy.hstack((d,numpy.transpose(numpy.matrix(e))))
g = numpy.hstack((g,numpy.transpose(numpy.matrix([1 for i in range(len(d))]))))
g = numpy.vstack((g,numpy.matrix(e+[0,0])))
g = numpy.vstack((g,numpy.matrix([1 for i in range(len(d))]+[0,0])))
bvec = numpy.transpose(numpy.matrix(([0 for i in range(len(d))]+[r,1])))
solu=numpy.linalg.solve(g,bvec)
pct = "["
ct = 1
totalpospct,totalnegpct=0,0
for i in solu:
pct += "[" + str(float(i)) + "],"
if float(i)<0:
totalnegpct+=float(i)
else:
totalpospct+=float(i)
if ct == len(solu)-2:
break
ct +=1
pct = pct[:-1]+"]"
yvec=numpy.matrix(pct)
print "yvec:",yvec
self.sd = yvec*d*numpy.transpose(yvec)
self.ret = r*12
print totalpospct,totalnegpct
return solu,g,bvec,yvec
def noshort(self,t):
r=[]
for i in t:
r.append(LookupReturn(i))
Q = matrix(self.cv)
p = matrix([0.0]*len(t))
A = matrix([1.0]*len(t),(1,len(t)))
b = matrix(1.0)
G = matrix(numpy.identity(len(t)))
h = matrix([0.0]*len(t))
sol = solvers.qp(Q,p,-G,-h,A,b)
ret,i = 0,0
pcts = "["
while i < len(sol['x']):
pcov = PairCov(t[i],t[i],PC)[1]
rt = round(r[i],2)
print str(round(float(sol['x'][i]),4)).rjust(5),t[i].rjust(4) + " Ret",rt, "SD",pcov,"5%,1% VaR on $100K:",round(100000*norm.ppf(0.05,rt/1200.0,pcov),2),round(norm.ppf(0.01,rt/1200.0,pcov)*100000,2)
pcts+="["+str(round(float(sol['x'][i]),4))+"],"
ret += float(r[i])/100.0*round(float(sol['x'][i]),4)
i += 1
pcts = pcts[:-1] + "]"
yvec = numpy.transpose(numpy.matrix(pcts))
sd = numpy.transpose(yvec)*self.sigma*yvec
self.ret=ret
self.sd=sd
return ret,yvec,self.sigma,r
def test(self,t):
r=[]
for i in t:
r.append(LookupReturn(i))
tpospct, tnegpct = 0.0,0.0
#CALC MIN VARIANCE PORTFOLIO
if len(self.cv)>0 and str(self.cv[0][0])!="nan":
newcol = ",[1]" * (len(t)-1)
newcol = "[[1]"+str(newcol)+"]"
newcol = numpy.transpose(numpy.matrix(newcol))
self.cv = numpy.hstack((self.cv,newcol))
newrow = numpy.matrix("["+"1,"*len(t)+ "0]")
self.cv = numpy.vstack((self.cv,newrow))
bcol="[0],"*(len(t))+"[1]"
b=numpy.transpose(numpy.matrix(bcol))
solu=numpy.linalg.solve(self.cv,b)
#CALC PORTFOLIO RETURN
i,ret = 0,0
pcts = "["
pctss = []
while i < len(t):
pcov = PairCov(t[i],t[i],PC)[1]
rt = round(r[i],2)
print str(round(100*float(solu[i][0]),2)).rjust(5),t[i].rjust(4) + " Ret",rt, "SD",pcov,"5%,1% VaR on $100K:",round(100000*norm.ppf(0.05,rt/1200.0,pcov),2),round(norm.ppf(0.01,rt/1200.0,pcov)*100000,2)
ret += float(r[i])/100.0*float(solu[i][0])
pcts+="["+str(float(solu[i][0]))+"],"
pctss.append(float(solu[i][0]))
i += 1
if round(float(solu[i][0]),3) > 0:
tpospct += round(float(solu[i][0]),3)
else:
tnegpct += round(float(solu[i][0]),3)
pcts = pcts[:-1] + "]"
yvec = numpy.transpose(numpy.matrix(pcts))
sd = numpy.transpose(yvec)*self.sigma*yvec
print tpospct,tnegpct
#CALC PORTFOLIO SHARPE RATIO
#APPEND TO LIST OF SHARPE RATIOS
self.ret=ret
self.sd=sd
return ret,yvec,self.sigma,r
def plot(self):
pass
def summarize(self):
print "Ret:",self.ret*100,"SD:",math.sqrt(abs(self.sd)),"Sharpe:",float(self.ret/12)/math.sqrt(abs(self.sd))
print "5%,1% VaR on $100000:",round(100000*norm.ppf(0.05,self.ret/12.0,math.sqrt(abs(self.sd))),2),round(100000*norm.ppf(0.01,self.ret/12.0,math.sqrt(abs(self.sd))),2)
def IsFloat(f):
try:
float(f)
except:
return False
return True
def LookupReturn(l):
bret = False
g = open("nasd_output_monthly.txt","r")
for ln in g:
if ln[:ln.find(",")]==l:
bret = ln[ln.rfind(",")+1:]
break
g.close()
if bret == False:
g = open("nyse_output_monthly.txt","r")
for ln in g:
if ln[:ln.find(",")]==l:
bret = ln[ln.rfind(",")+1:]
break
g.close()
return float(bret)
def ReturnPoint(p):
r = LookupReturn(p)
x,y,z = PairCov(p,p,PC)
return r,y
def PairCov(t1,t2,PC,riskmodel="s"):
#print "RISK:",riskmodel
if t1>t2:
t3=t1
t1=t2
t2=t3
if (t1,t2) in PC:
return PC[t1,t2]
try:
f = open("./monthly/"+t1+".txt","r")
except:
return "",0,0
st1,st2,stq=[],[],[]
downside1,downside2,downsideq=[],[],[]
q = open("./monthly/QQQ.txt","r")
chgq=0
oldstq=""
ct = 0
for ln in f:
ct +=1
#print "T2:",t2
try:
g = open("./monthly/"+t2+".txt","r")
except:
return "",0,0
try:
q = open("./monthly/QQQ.txt","r")
except:
return "",0,0
for ln3 in q:
#print ln3[:ln3.find(",")],ln[:ln.find(",")]
if ln3[:ln3.find(",")] == ln[:ln.find(",")] and ct != 1:
#print ln3[:ln3.find(",")],ln[:ln.find(",")],ct
if ct!=2:
chgq = ((oldstq / float(ln3[ln3.rfind(",")+1:]))-1)
#print "chq:",chq
stq.append(chgq)
if chgq<0:
downsideq.append(chgq)
else:
downsideq.append(0)
if chgq<0:
downsideq.append(chgq)
else:
downsideq.append(0)
oldstq = float(ln3[ln3.rfind(",")+1:])
break
for ln2 in g:
if ln2[:ln2.find(",")] == ln[:ln.find(",")] and ct != 1:
if ct!=2:
chg1 = ((oldst1 / float(ln[ln.rfind(",")+1:]))-1)
chg2 = ((oldst2 / float(ln2[ln2.rfind(",")+1:]))-1)
if riskmodel != "m":
st1.append(chg1)
st2.append(chg2)
#print "a",riskmodel,st1[0]
else:
st1.append(chg1-chgq)
st2.append(chg2-chgq)
chg1 = chg1-chgq
chg2 = chg2-chgq
#print "b",riskmodel,st1[0],chgq
if chg1<0:
downside1.append(chg1)
else:
downside1.append(0)
if chg2<0:
downside2.append(chg2)
else:
downside2.append(0)
try:
oldst1 = float(ln[ln.rfind(",")+1:])
oldst2 = float(ln2[ln2.rfind(",")+1:])
except:
return "",0,0
break
g.close()
if riskmodel=="s":
PC[t1,t2]=numpy.cov(st1,st2),numpy.std(st1),numpy.std(st2)
return numpy.cov(st1,st2),numpy.std(st1),numpy.std(st2)
elif riskmodel =="d":
PC[t1,t2]=numpy.cov(downside1,downside2),numpy.std(downside1),numpy.std(downside2)
return numpy.cov(downside1,downside2),numpy.std(downside1),numpy.std(downside2)
elif riskmodel == "m":
PC[t1,t2]=numpy.cov(st1,st2),numpy.std(st1),numpy.std(st2)
return numpy.cov(st1,st2),numpy.std(st1),numpy.std(st2)
def AllCov(n):
PC={}
#print "RISK1:",riskmodel
if os.path.exists("pickle_"+riskmodel+".dmp") and len(n)>25:
dstr = open("pickle_"+riskmodel+".dmp","r")
PC = pickle.load(dstr)
dstr.close()
row = []
totalpairs = 0
i=1
while i<=len(n):
row.append(0.0)
totalpairs += i
i +=1
i=0
col = []
while i<len(n):
col.append(row)
i+=1
sigma = numpy.matrix(col)
orig_n=len(n)
ct = 0
for x in range(len(n)):
#print x
for y in range(len(n)):
ct += 1
#print x,n[x],y,n[y],ct
o,p,q = PairCov(n[x],n[y],PC,riskmodel)
if x==y:
sigma[[x],[x]]=float(p)*float(p)
else:
sigma[[x],[y]]=float(o[0][1])
if len(n)>25 and ct % 72500 == 0 and ct > 797000:
try:
shutil.copy2("pickle_"+riskmodel+".dmp", "pickle_"+riskmodel+".bak")
except:
pass
dser = open("pickle_"+riskmodel+".dmp","w")
pickle.dump(PC,dser)
dser.close()
if len(n)>25:
try:
shutil.copy2("pickle_"+riskmodel+".dmp", "pickle_"+riskmodel+".bak")
except:
pass
dser = open("pickle_"+riskmodel+".dmp","w")
pickle.dump(PC,dser)
dser.close()
return sigma
def EvalPortfolio(t):
rets,eqs = [],{}
ret,pct,sigma,r=TestPortfolio(t)
if ret:
rets.append(ret)
eqs[ret]=[t[0],pct]
return sigma
def Calculate(sigma,vec,t):
pts=[]
ret,ct = 0,0
for i in vec:
ret+=float(LookupReturn(t[ct]))/100.0*float(vec[ct])
ct+=1
xvec = numpy.matrix(vec)
sd = numpy.transpose(xvec)*sigma*xvec
if sd < 0:
sd = -math.sqrt(abs(sd))
else:
sd = math.sqrt(sd)
pts.append((ret,float(sd)))
return pts
def EnumeratePortfolio(t):
pts=[]
sigma = EvalPortfolio(t)
b,s = EfficientPortfolio(t)
for i in range(200):
vec = b + (i * s)
pts.append(Calculate(sigma,vec,t))
if i != 0:
vec = b + (-i * s)
pts.append(Calculate(sigma,vec,t))
return pts
def LTestPortfolio(riskmodel="s"):
#FULLY DEPRICATED
p = []
port = open("./Portfolios/test.txt","r")
for i in port:
i = i.replace("\n","")
i = i.replace("\r","")
if i.find(",")>0:
i = i[:i.find(",")]
p.append(i)
return TestPortfolio(p,riskmodel)
def EfficientPortfolio(t):
a,b,d,e = TestPortfolio(t)
g,h,i = TargetReturn(t,a+0.01)
scalar = g[0:len(t)]-b
return b,scalar
def TestPortfolio(t,riskmodel="s"):
globals()["riskmodel"] = riskmodel
#FULLY DEPRICATED
r=[]
for i in t:
r.append(LookupReturn(i))
mr = r[0]
ret,pct="",""
#CALC COVARIANCE
sigma = AllCov(t)
cv = 2*sigma
tpospct, tnegpct = 0.0,0.0
#CALC MIN VARIANCE PORTFOLIO
if len(cv)>0 and str(cv[0][0])!="nan":
newcol = ",[1]" * (len(t)-1)
newcol = "[[1]"+str(newcol)+"]"
newcol = numpy.transpose(numpy.matrix(newcol))
cv = numpy.hstack((cv,newcol))
newrow = numpy.matrix("["+"1,"*len(t)+ "0]")
cv = numpy.vstack((cv,newrow))
bcol="[0],"*(len(t))+"[1]"
b=numpy.transpose(numpy.matrix(bcol))
solu=numpy.linalg.solve(cv,b)
#CALC PORTFOLIO RETURN
i = 0
ret = 0
pcts = "["
pctss = []
while i < len(t):
pcov = PairCov(t[i],t[i],PC,riskmodel)[1]
rt = round(r[i],2)
print str(round(100*float(solu[i][0]),2)).rjust(5),t[i].rjust(4) + " Ret",rt, "SD",pcov,"5%,1% VaR on $100K:",round(100000*norm.ppf(0.05,rt/1200.0,pcov),2),round(norm.ppf(0.01,rt/1200.0,pcov)*100000,2)
ret += float(r[i])/100.0*float(solu[i][0])
pcts+="["+str(float(solu[i][0]))+"],"
pctss.append(float(solu[i][0]))
i += 1
if round(float(solu[i][0]),3) > 0:
tpospct += round(float(solu[i][0]),3)
else:
tnegpct += round(float(solu[i][0]),3)
pcts = pcts[:-1] + "]"
yvec = numpy.transpose(numpy.matrix(pcts))
sd = numpy.transpose(yvec)*sigma*yvec
if sd == 0:
sd = 0.000001
print "Ret:",ret*100,"SD:",math.sqrt(abs(sd)),"Sharpe:",float(ret/12)/math.sqrt(abs(sd))
print "5%,1% VaR on $100000:",round(100000*norm.ppf(0.05,ret/12.0,math.sqrt(abs(sd))),2),round(100000*norm.ppf(0.01,ret/12.0,math.sqrt(abs(sd))),2)
print tpospct,tnegpct
#CALC PORTFOLIO SHARPE RATIO
#APPEND TO LIST OF SHARPE RATIOS
#print "RISK:",riskmodel
return ret,yvec,sigma,r
def TargetReturn(p,r,riskmodel="s"):
globals()["riskmodel"] = riskmodel
r = float(r)/12.0
a,b,d,e = TestPortfolio(p,riskmodel)
i = 0
while i < len(e):
e[i]/=1200
i+=1
g = numpy.hstack((d,numpy.transpose(numpy.matrix(e))))
g = numpy.hstack((g,numpy.transpose(numpy.matrix([1] *len(d)))))
g = numpy.vstack((g,numpy.matrix(e+[0,0])))
g = numpy.vstack((g,numpy.matrix([1] * len(d)+[0,0])))
#CHANGED r,1 to r,0 to attempt 0 bias portfolio
bvec = numpy.transpose(numpy.matrix(([0]*len(d)+[r,1])))
solu=numpy.linalg.solve(g,bvec)
pct = "["
ct = 1
totalpospct,totalnegpct=0,0
for i in solu:
print round(float(i)*100.0,2)
pct += "[" + str(float(i)) + "],"
if float(i)<0:
totalnegpct+=float(i)
else:
totalpospct+=float(i)
if ct == len(solu)-2:
break
ct +=1
pct = pct[:-1]+"]"
yvec=numpy.matrix(pct)
sd = math.sqrt(abs(yvec*d*numpy.transpose(yvec)))
print "RET:",r*1200,"s.d",sd,"Sharpe:",float(r)/sd
print "5%,1% VaR on $100000:",round(100000*norm.ppf(0.05,r,sd),2),round(100000*norm.ppf(0.01,r,sd),2)
print totalpospct,totalnegpct
return solu,g,bvec
def LoadTargetReturn(r,riskmodel="s"):
globals()["riskmodel"] = riskmodel
p=[]
z = open(".\\portfolios\\test.txt","r")
for ln in z:
if ln[-1]=="\r\n" or ln[-1]=="\n":
ln = ln[:-1]
if ln.find(",")>0:
ln = ln[:ln.find(",")]
p.append(ln)
return TargetReturn(p,r,riskmodel)
def LTargetReturn(l,r,riskmodel="s"):
globals()["riskmodel"] = riskmodel
p=[]
z = open(".\\portfolios\\"+l+".txt","r")
for ln in z:
if ln[-1]=="\r\n" or ln[-1]=="\n":
ln = ln[:-1]
if ln.find(",")>0:
ln = ln[:ln.find(",")]
p.append(ln)
return TargetReturn(p,r,riskmodel)
def EvaluatePortfolio(p,r):
startdir = "./daily/2012-10-31/"
stopdir = "./daily/2012-11-01/"
Investment = 100000
solu,g,bvec=TargetReturn(p,r)
i,total = 0,0
while i < len(p):
start = open(startdir+p[i]+".txt","r")
stop = open(stopdir+p[i]+".txt","r")
#BEGIN DATE
st,stp = "",""
ct = 0
for ln in start:
if ct==1:
print "start:",ln[ln.rfind(",")+1:]
st = float(ln[ln.rfind(",")+1:])
ct+=1
ct = 0
for ln in stop:
if ct==1:
print "stop:",ln[ln.rfind(",")+1:]
stp = float(ln[ln.rfind(",")+1:])
ct+=1
chg = stp-st
shares = Investment*float(solu[i])/st
print str(round(float(solu[i]),4))+"%",p[i],chg,shares
#ALLOCATE SHARES
#PRINT TOTAL
#END DATE
#CALC DIFFERENCES
#PRINT TOTAL, ANNUALIZED PROFIT/LOSS
total+=chg*shares
i += 1
print "Total:",total
def GetPrice(symbol,m,d,y):
#IF PRICE NOT AVAILABLE IN CACHE - GRAB FROM YAHOO
url = 'http://ichart.finance.yahoo.com/table.csv?s='+symbol+'&a='+str(int(m)-1)+'&b='+d+'&c='+y+'&d='+m+'&e='+d+'&f='+y+'&g=d&ignore=.csv'
f = urllib.urlopen(url)
s = f.read()
f.close
ln = ""
lines = []
for ch in s:
if ch != '\n':
ln += ch
else:
lines.append(ln)
ln = ""
return float(lines[1][lines[1].rfind(",")+1:])
def LAllocatePortfolio(r):
p = []
port = open("./Portfolios/test.txt","r")
for i in port:
i = i.replace("\n","")
i = i.replace("\r","")
p.append(i)
h = open("holding.txt","w")
Investment = 100000
solu,g,bvec=TargetReturn(p,r)
i,total,totalpct,totalpospct,totalnegpct = 0,0,0,0,0
while i < len(p):
#BEGIN DATE
st= GetPrice(p[i],"10","31","2012")
ret=float(solu[i])
shares = round(Investment*ret/st,2)
print str(round(ret*100,1))+"%",p[i],shares
bs = "BUY"
if shares<0:
bs="SELL"
csvstr = ",".join([p[i],bs,str(abs(shares)),"11-02-2012",str(st)])
h.write(csvstr+"\r\n")
#ALLOCATE SHARES
#PRINT TOTAL
#END DATE
#CALC DIFFERENCES
#PRINT TOTAL, ANNUALIZED PROFIT/LOSS
totalpct += ret
if ret > 0:
totalpospct += ret
else:
totalnegpct += ret
i += 1
h.close()
print "Total:",total
print "TotalPct:",totalpct,totalpospct,totalnegpct