def run_portfolio_analysis(self, filename):
print "Run portfolio analysis %s" % (filename)
myPortfolio = read_portfolio_xml.read_portfolio(filename)
for ix1 in myPortfolio:
symbol = ix1['symbol']
symbolData = symbol_analysis(symbol, self)
retString = symbolData._analyze()
print "%2s %s" % (ix1['pctg'], retString)
def run_portfolio_analysis(self,filename): print "Run portfolio analysis %s" % (filename) myPortfolio = read_portfolio_xml.read_portfolio(filename) for ix1 in myPortfolio: symbol = ix1['symbol']; symbolData = symbol_analysis(symbol,self) retString = symbolData._analyze() print "%2s %s" % (ix1['pctg'],retString)
def _rebalance(self):
# Rebalance Formula is
# set all falling trend to 5%
# For rising trend symbols,
# First set all to equal
# For reversal += 2
# For accelerating += 2
# For sharp += 2
# Now calculate Total and reduce all by same amount as
# necessary to get the required total
#
# Currently user defined weights are not implemented.
# Else, first apply user defined weights to get the starting
# values and then apply above algorithm
#
print "Run portfolio rebalance %s" % (self._filename)
myPortfolio = read_portfolio_xml.read_portfolio(self._filename)
myhash = {}
pctg = {}
for ix1 in myPortfolio:
symbol = ix1['symbol']
symbolData = symbol_analysis(symbol, self._sma_ptr)
myhash[symbol] = symbolData
totalNum = len(myPortfolio)
totalNumRem = totalNum
totalPctSoFar = 0
for symbol in myhash:
print symbol
symbolData = myhash[symbol]
retString = symbolData._analyze()
curPct = 0
if (symbolData._newTrend and symbolData._reverseTrend):
curPct += 2
if (symbolData._newTrend and symbolData._trendSpeed):
curPct += 2
if (symbolData._newTrend and symbolData._trendSlope):
curPct += 2
pctg[symbol] = curPct
totalPctSoFar += curPct
if (not symbolData._newTrend):
pctg[symbol] = 5
totalPctSoFar += 5
totalNumRem -= 1
DivideEq = int((100 - totalPctSoFar) / totalNumRem)
for symbol in myhash:
if (pctg[symbol] != 5):
pctg[symbol] += DivideEq
for ix1 in myPortfolio:
symbol = ix1['symbol']
symbolData = myhash[symbol]
print_colors.my_print(
"%2s %2s %s" %
(ix1['pctg'], pctg[symbol], symbolData._analyze()), 'RED')
def _rebalance(self):
# Rebalance Formula is
# set all falling trend to 5%
# For rising trend symbols,
# First set all to equal
# For reversal += 2
# For accelerating += 2
# For sharp += 2
# Now calculate Total and reduce all by same amount as
# necessary to get the required total
#
# Currently user defined weights are not implemented.
# Else, first apply user defined weights to get the starting
# values and then apply above algorithm
#
print "Run portfolio rebalance %s" % (self._filename)
myPortfolio = read_portfolio_xml.read_portfolio(self._filename)
myhash = {};
pctg = {};
for ix1 in myPortfolio:
symbol = ix1['symbol'];
symbolData = symbol_analysis(symbol,self._sma_ptr)
myhash[symbol] = symbolData
totalNum = len(myPortfolio)
totalNumRem = totalNum
totalPctSoFar = 0;
for symbol in myhash:
print symbol
symbolData = myhash[symbol]
retString = symbolData._analyze()
curPct = 0;
if (symbolData._newTrend and symbolData._reverseTrend):
curPct += 2;
if (symbolData._newTrend and symbolData._trendSpeed):
curPct += 2;
if (symbolData._newTrend and symbolData._trendSlope):
curPct += 2;
pctg[symbol] = curPct;
totalPctSoFar += curPct
if (not symbolData._newTrend):
pctg[symbol] = 5 ;
totalPctSoFar += 5;
totalNumRem -= 1;
DivideEq = int((100 - totalPctSoFar) / totalNumRem)
for symbol in myhash:
if (pctg[symbol] != 5):
pctg[symbol] += DivideEq
for ix1 in myPortfolio:
symbol = ix1['symbol'];
symbolData = myhash[symbol]
print_colors.my_print ( "%2s %2s %s" % (ix1['pctg'],pctg[symbol], symbolData._analyze()),'RED')
