def fetchPriceHistory(self, dialog):
"""Parse the text in the input box"""
stockstr = self.m_symbolinput.Value.encode('ascii')
stocks = stockstr.split(",")
stocks = [s.strip() for s in stocks]
"""Get the historical prices and
create a new Asset object for each stock symbol"""
curSymbols = [a.symbol for a in self.portfolio.assets]
inc = 100 / len(stocks)
i = 0
for s in stocks:
if s not in curSymbols:
wx.CallAfter(dialog.Update, i * inc,
"Fetching %s historical data" % (s))
i = i + 1
try:
prices = u.getHistoricalPrices(s)
asset = fin.Asset(s, prices)
self.portfolio.addAsset(asset)
except:
message = "Could not find data for %s \n Perhaps it's not a real stock symbol?" % (
s)
wx.MessageBox(message, 'Error',
wx.OK | wx.ICON_INFORMATION)
continue
self.m_symbolinput.SetValue("")
self.updateGridSymbols()
wx.CallAfter(dialog.Destroy)
def fetchPriceHistory(self, dialog):
"""Parse the text in the input box"""
stockstr = self.m_symbolinput.Value.encode('ascii')
stocks = stockstr.split(",")
stocks = [s.strip() for s in stocks]
"""Get the historical prices and
create a new Asset object for each stock symbol"""
curSymbols = [a.symbol for a in self.portfolio.assets]
inc = 100/len(stocks)
i = 0
for s in stocks:
if s not in curSymbols:
wx.CallAfter(dialog.Update, i*inc, "Fetching %s historical data"%(s))
i = i+1
try:
prices = u.getHistoricalPrices(s)
asset = fin.Asset(s, prices)
self.portfolio.addAsset(asset)
except:
message = "Could not find data for %s \n Perhaps it's not a real stock symbol?"%(s)
wx.MessageBox(message, 'Error', wx.OK | wx.ICON_INFORMATION)
continue
self.m_symbolinput.SetValue("")
self.updateGridSymbols()
wx.CallAfter(dialog.Destroy)
def calculateGrid(self):
colcount = self.m_stocklist.ColumnCount
if colcount < 4:
self.m_stocklist.InsertColumn(0, "Symbol")
self.m_stocklist.InsertColumn(1, "Mean Rate")
self.m_stocklist.InsertColumn(2, "Std. Deviation", width=120)
self.m_stocklist.InsertColumn(3, "Allocation")
self.m_stocklist.InsertColumn(4, "Correlation")
self.m_stocklist.InsertColumn(5, "Beta")
self.m_stocklist.InsertColumn(6, "Sharpe Ratio")
if len(self.portfolio.assets) != 0:
MRAprices = u.getHistoricalPrices("SPY")
marketRetAsset = fin.Asset("SPY", MRAprices)
rfr = self.m_rfRadBox.GetStringSelection()
rfRates = u.getHistoricalRates(rfr)
mrRates = marketRetAsset.getRatesOfReturn(
self.portfolio.startdate, self.portfolio.ratemethod)
dates = u.date_range(self.portfolio.startdate)
meanrates = []
ratesmatrix = []
sharpes = {}
for asset in self.portfolio.assets:
rates = asset.getRatesOfReturn(self.portfolio.startdate,
self.portfolio.ratemethod)
rateBundle = []
for d in dates:
if rfRates.has_key(d) and mrRates.has_key(
d) and rates.has_key(d):
rateBundle.append((rates[d], rfRates[d], mrRates[d]))
asset.correlation = asset.getCorrelation(rateBundle)
asset.beta = asset.getBeta(rateBundle, asset.correlation)
asset.sharpe = asset.getSharpe(rateBundle)
mostrecentrfr = max(rfRates.keys())
if self.portfolio.returnmethod == "CAPM":
marketrr = float(self.m_marketReturnRate.Value)
"""We are multiplying percentages by 100 (for displaying) but then we continue to use the standard deviations."""
mean = asset.getMeanROR(rates,
annualized=False,
returnmethod="CAPM",
marketrate=marketrr,
rfrate=rfRates[mostrecentrfr],
B=asset.beta)
else:
mean = asset.getMeanROR(rates,
annualized=False,
returnmethod="Historical")
asset.rates = rates
asset.mean = mean
""" This will make annmeans a percentage of a percentage of annualized means?
SHOULDN'T BE USING ANNUALIZED FOR OPTIMIZATION?"""
"""annmeans.append(annmean/10000)"""
meanrates.append(mean)
sharpes[asset] = asset.sharpe
ratesmatrix.append(rateBundle)
self.portfolio.ratesmatrix = ratesmatrix
cormatrix = self.portfolio.getMatrix(ratesmatrix)
self.portfolio.cormatrix = cormatrix
cvmatrix, stdmarket = self.portfolio.getMatrix(
ratesmatrix, "covariance")
self.portfolio.cvmatrix = cvmatrix
self.portfolio.stdmarket = stdmarket
self.portfolio.meanrates = meanrates
if self.m_allocRads.GetSelection() == 1:
#results = u.optimizePortfolio(self.portfolio, step=0.001)
results = u.efficientFrontier(self.portfolio, step=0.0001)
self.portfolio.optimizationResults = results
e = min([res['ExpectedReturn'] for res in results])
minrisk = 10000000
optimalSolution = None
for s in results:
tempweights = []
for asset in self.portfolio.assets:
tempweights.extend([
w['Allocation'] for w in s['Results']
if w['Symbol'] == asset.symbol
])
tempwsharpe = 0
i = 0
for asset in self.portfolio.assets:
tempwsharpe += sharpes[asset] * tempweights[i]
i = i + 1
print "Weighted Sharpe:"
print tempwsharpe
tempwcovwithmarket = self.portfolio.getWeightedCovariance(
cvmatrix, tempweights, True)
tempwvar = self.portfolio.getWeightedCovariance(
cvmatrix, tempweights, False)
""" getWeightedReturn annualizes the data. I think this means we end up annualizing it twice."""
tempwrr = self.portfolio.getWeightedReturn(
meanrates, tempweights) * 100
tempwcor = self.portfolio.getWeightedCorrelation(
tempwcovwithmarket, tempwvar, stdmarket)
tempwbeta = self.portfolio.getWeightedBeta(
tempwcor, tempwvar, stdmarket)
tempwstd = num.sqrt(tempwvar) * num.sqrt(252) * 100
if tempwstd < minrisk:
i = 0
for asset in self.portfolio.assets:
asset.weight = tempweights[i]
i = i + 1
minrisk = tempwstd
wrr = tempwrr
wcor = tempwcor
wbeta = tempwbeta
wstd = tempwstd
wsharpe = tempwsharpe
self.m_EFbutton.Show()
self.m_allocations.Show()
self.SetSize((self.GetSize().width, self.GetSize().height + 1))
self.SetSize((self.GetSize().width, self.GetSize().height - 1))
else:
weights = [a.weight for a in reversed(self.portfolio.assets)]
wsharpe = 0
i = 0
for asset in self.portfolio.assets:
wsharpe += sharpes[asset] * weights[i]
i = i + 1
print "Weighted Sharpe:"
print wsharpe
covwithmarket = self.portfolio.getWeightedCovariance(
cvmatrix, weights, True)
wvar = self.portfolio.getWeightedCovariance(
cvmatrix, weights, False)
wrr = self.portfolio.getWeightedReturn(meanrates,
weights) * 100
wcor = self.portfolio.getWeightedCorrelation(
covwithmarket, wvar, stdmarket)
wbeta = self.portfolio.getWeightedBeta(wcor, wvar, stdmarket)
wstd = num.sqrt(wvar) * num.sqrt(252) * 100
self.m_EFbutton.Hide()
#wrr = wsharpe*wstd
"""
print "Weighted annualized mean return rate?"
print wrr
print "Weight standard deviation:"
print wstd
"""
for asset in self.portfolio.assets:
pos = self.m_stocklist.FindItem(-1, asset.symbol)
"""We're for displaying! ANd only displaying!"""
annmean = 100 * 252 * asset.mean
annstd = 100 * asset.getStd(asset.rates, annualized=True)
if pos == -1:
pos = self.m_stocklist.ItemCount
self.m_stocklist.InsertStringItem(pos, asset.symbol)
self.m_stocklist.SetStringItem(0, 1,
str("%.2f" % asset.weight))
self.m_stocklist.SetStringItem(pos, 2,
str("%.2f" % annmean) + "%")
self.m_stocklist.SetStringItem(pos, 3,
str("%.2f" % annstd) + "%")
self.m_stocklist.SetStringItem(
pos, 4, str("%.2f" % asset.correlation))
self.m_stocklist.SetStringItem(pos, 5,
str("%.2f" % asset.beta))
self.m_stocklist.SetStringItem(pos, 6,
str("%.2f" % asset.sharpe))
else:
self.m_stocklist.SetStringItem(pos, 1,
str("%.2f" % asset.weight))
self.m_stocklist.SetStringItem(pos, 2,
str("%.2f" % annmean) + "%")
self.m_stocklist.SetStringItem(pos, 3,
str("%.2f" % annstd) + "%")
self.m_stocklist.SetStringItem(
pos, 4, str("%.2f" % asset.correlation))
self.m_stocklist.SetStringItem(pos, 5,
str("%.2f" % asset.beta))
self.m_stocklist.SetStringItem(pos, 6,
str("%.2f" % asset.sharpe))
self.m_portfoliolist.SetStringItem(0, 1, str("%.2f" % 1.00))
self.m_portfoliolist.SetStringItem(0, 2, str("%.2f" % wrr) + "%")
self.m_portfoliolist.SetStringItem(0, 3, str("%.2f" % wstd) + "%")
self.m_portfoliolist.SetStringItem(0, 4, str("%.2f" % wcor))
self.m_portfoliolist.SetStringItem(0, 5, str("%.2f" % wbeta))
self.m_portfoliolist.SetStringItem(0, 6, str("%.2f" % wsharpe))
assets = []
assets.extend(self.portfolio.assets)
assets.append(marketRetAsset)
grid = self.m_corgrid
grid.ClearGrid()
while self.m_corgrid.NumberRows != 0:
self.m_corgrid.DeleteCols()
self.m_corgrid.DeleteRows()
grid.AppendCols(len(assets))
grid.AppendRows(len(assets))
i = 0
for asset in assets:
self.m_corgrid.SetRowLabelValue(i, asset.symbol)
self.m_corgrid.SetColLabelValue(i, asset.symbol)
asset.covariances = {}
i = i + 1
for i in range(len(assets)):
for j in range(len(assets)):
self.m_corgrid.SetCellValue(i, j,
str("%.2f" % cormatrix[i][j]))
def calculateGrid(self):
colcount = self.m_stocklist.ColumnCount
if colcount < 4:
self.m_stocklist.InsertColumn(0, "Symbol")
self.m_stocklist.InsertColumn(1, "Mean Rate")
self.m_stocklist.InsertColumn(2, "Std. Deviation", width=120)
self.m_stocklist.InsertColumn(3, "Allocation")
self.m_stocklist.InsertColumn(4, "Correlation")
self.m_stocklist.InsertColumn(5, "Beta")
self.m_stocklist.InsertColumn(6, "Sharpe Ratio")
if len(self.portfolio.assets) != 0:
MRAprices = u.getHistoricalPrices("SPY")
marketRetAsset = fin.Asset("SPY", MRAprices)
rfr = self.m_rfRadBox.GetStringSelection()
rfRates = u.getHistoricalRates(rfr)
mrRates = marketRetAsset.getRatesOfReturn(self.portfolio.startdate, self.portfolio.ratemethod)
dates = u.date_range(self.portfolio.startdate)
meanrates = []
ratesmatrix = []
sharpes = {}
for asset in self.portfolio.assets:
rates = asset.getRatesOfReturn(self.portfolio.startdate, self.portfolio.ratemethod)
rateBundle = []
for d in dates:
if rfRates.has_key(d) and mrRates.has_key(d) and rates.has_key(d):
rateBundle.append((rates[d],rfRates[d],mrRates[d]))
asset.correlation = asset.getCorrelation(rateBundle)
asset.beta = asset.getBeta(rateBundle, asset.correlation)
asset.sharpe = asset.getSharpe(rateBundle)
mostrecentrfr = max(rfRates.keys())
if self.portfolio.returnmethod=="CAPM":
marketrr = float(self.m_marketReturnRate.Value)
"""We are multiplying percentages by 100 (for displaying) but then we continue to use the standard deviations."""
mean = asset.getMeanROR(rates, annualized=False, returnmethod="CAPM", marketrate=marketrr, rfrate=rfRates[mostrecentrfr], B=asset.beta)
else:
mean = asset.getMeanROR(rates, annualized=False, returnmethod="Historical")
asset.rates =rates
asset.mean = mean
""" This will make annmeans a percentage of a percentage of annualized means?
SHOULDN'T BE USING ANNUALIZED FOR OPTIMIZATION?"""
"""annmeans.append(annmean/10000)"""
meanrates.append(mean)
sharpes[asset] = asset.sharpe
ratesmatrix.append(rateBundle)
self.portfolio.ratesmatrix = ratesmatrix
cormatrix = self.portfolio.getMatrix(ratesmatrix)
self.portfolio.cormatrix = cormatrix
cvmatrix,stdmarket = self.portfolio.getMatrix(ratesmatrix, "covariance")
self.portfolio.cvmatrix = cvmatrix
self.portfolio.stdmarket = stdmarket
self.portfolio.meanrates = meanrates
if self.m_allocRads.GetSelection() == 1:
#results = u.optimizePortfolio(self.portfolio, step=0.001)
results = u.efficientFrontier(self.portfolio, step=0.0001)
self.portfolio.optimizationResults = results
e = min([res['ExpectedReturn'] for res in results])
minrisk = 10000000
optimalSolution = None
for s in results:
tempweights = []
for asset in self.portfolio.assets:
tempweights.extend([w['Allocation'] for w in s['Results'] if w['Symbol'] == asset.symbol])
tempwsharpe = 0
i=0
for asset in self.portfolio.assets:
tempwsharpe += sharpes[asset]*tempweights[i]
i=i+1
print "Weighted Sharpe:"
print tempwsharpe
tempwcovwithmarket = self.portfolio.getWeightedCovariance(cvmatrix, tempweights, True)
tempwvar = self.portfolio.getWeightedCovariance(cvmatrix, tempweights, False)
""" getWeightedReturn annualizes the data. I think this means we end up annualizing it twice."""
tempwrr = self.portfolio.getWeightedReturn(meanrates, tempweights)*100
tempwcor = self.portfolio.getWeightedCorrelation(tempwcovwithmarket,tempwvar,stdmarket)
tempwbeta = self.portfolio.getWeightedBeta(tempwcor, tempwvar,stdmarket)
tempwstd = num.sqrt(tempwvar)*num.sqrt(252)*100
if tempwstd<minrisk:
i=0
for asset in self.portfolio.assets:
asset.weight =tempweights[i]
i = i+1
minrisk=tempwstd
wrr = tempwrr
wcor = tempwcor
wbeta = tempwbeta
wstd=tempwstd
wsharpe=tempwsharpe
self.m_EFbutton.Show()
self.m_allocations.Show()
self.SetSize((self.GetSize().width, self.GetSize().height+1))
self.SetSize((self.GetSize().width, self.GetSize().height-1))
else:
weights = [a.weight for a in reversed(self.portfolio.assets)]
wsharpe = 0
i=0
for asset in self.portfolio.assets:
wsharpe += sharpes[asset]*weights[i]
i=i+1
print "Weighted Sharpe:"
print wsharpe
covwithmarket = self.portfolio.getWeightedCovariance(cvmatrix, weights, True)
wvar = self.portfolio.getWeightedCovariance(cvmatrix, weights, False)
wrr = self.portfolio.getWeightedReturn(meanrates, weights)*100
wcor = self.portfolio.getWeightedCorrelation(covwithmarket,wvar,stdmarket)
wbeta = self.portfolio.getWeightedBeta(wcor, wvar,stdmarket)
wstd = num.sqrt(wvar)*num.sqrt(252)*100
self.m_EFbutton.Hide()
#wrr = wsharpe*wstd
"""
print "Weighted annualized mean return rate?"
print wrr
print "Weight standard deviation:"
print wstd
"""
for asset in self.portfolio.assets:
pos = self.m_stocklist.FindItem(-1, asset.symbol)
"""We're for displaying! ANd only displaying!"""
annmean = 100*252*asset.mean
annstd = 100 * asset.getStd(asset.rates, annualized=True)
if pos ==-1:
pos = self.m_stocklist.ItemCount
self.m_stocklist.InsertStringItem(pos, asset.symbol)
self.m_stocklist.SetStringItem(0,1, str("%.2f" % asset.weight))
self.m_stocklist.SetStringItem(pos,2, str("%.2f" % annmean)+"%")
self.m_stocklist.SetStringItem(pos,3, str("%.2f" % annstd)+"%")
self.m_stocklist.SetStringItem(pos,4, str("%.2f" % asset.correlation))
self.m_stocklist.SetStringItem(pos,5, str("%.2f" % asset.beta))
self.m_stocklist.SetStringItem(pos,6, str("%.2f" % asset.sharpe))
else:
self.m_stocklist.SetStringItem(pos,1, str("%.2f" % asset.weight))
self.m_stocklist.SetStringItem(pos,2, str("%.2f" % annmean)+"%")
self.m_stocklist.SetStringItem(pos,3, str("%.2f" % annstd)+"%")
self.m_stocklist.SetStringItem(pos,4, str("%.2f" % asset.correlation))
self.m_stocklist.SetStringItem(pos,5, str("%.2f" % asset.beta))
self.m_stocklist.SetStringItem(pos,6, str("%.2f" % asset.sharpe))
self.m_portfoliolist.SetStringItem(0,1, str("%.2f" % 1.00))
self.m_portfoliolist.SetStringItem(0,2, str("%.2f" % wrr)+"%")
self.m_portfoliolist.SetStringItem(0,3, str("%.2f" % wstd)+"%")
self.m_portfoliolist.SetStringItem(0,4, str("%.2f" % wcor))
self.m_portfoliolist.SetStringItem(0,5, str("%.2f" % wbeta))
self.m_portfoliolist.SetStringItem(0,6, str("%.2f" % wsharpe))
assets = []
assets.extend(self.portfolio.assets)
assets.append(marketRetAsset)
grid = self.m_corgrid
grid.ClearGrid()
while self.m_corgrid.NumberRows != 0:
self.m_corgrid.DeleteCols()
self.m_corgrid.DeleteRows()
grid.AppendCols(len(assets))
grid.AppendRows(len(assets))
i = 0
for asset in assets:
self.m_corgrid.SetRowLabelValue(i, asset.symbol)
self.m_corgrid.SetColLabelValue(i, asset.symbol)
asset.covariances = {}
i = i+1
for i in range(len(assets)):
for j in range(len(assets)):
self.m_corgrid.SetCellValue(i, j, str("%.2f" % cormatrix[i][j]))