def fit(self): pso.dmn = self.dimensions pso.searchRange = float(1)/float(pso.dmn) pso.maxIterations = 100 s,i,g = pso.particleSwarmOptimize(self.divinecost, True, True) self.weights = s return s,i,g
def learn(search):
pso.dmn = len(qds[0])
pso.searchRange = float(search)
s,i,g = pso.particleSwarmOptimize(MCC, True, True)
f = open('weight.txt', 'w')
f.write(str(s))
f.close()
return s, i, g
def fit(self): pso.dmn = self.dimensions s, i, g = pso.particleSwarmOptimize(self.costfunction, True, True) self.ar = s[:self.p] self.ma = s[self.p:self.p+self.q] #self.varC = s[self.p+self.q] #self.wnC = s[self.p+self.q+1] #self.error = s[self.p+self.q] print s, i, g return s, i, g
def famaLearn():
print "the year is now " + year
logname = 'results/newfamaWeights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(famaFrench, True, True)
yearString = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
famaPlot(ans)
f.write("\n")
f.write(str(year) + " to " + str(int(year) + 4))
f.write("\n")
f.write(report)
f.close()
return ans
def famaLearn():
print "the year is now " + year
logname = 'results/newfamaWeights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(famaFrench, True, True)
yearString = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
famaPlot(ans)
f.write("\n")
f.write(str(year) + " to " + str(int(year) + 4))
f.write("\n")
f.write(report)
f.close()
return ans
def learn(y):
print "the year is now " + year
logname = 'results/weights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(calculateFitness, True, True)
yearstring = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
ps = plotFitness(ans)
f.write("\n")
f.write(yearstring + ", Portfolio Size: " + str(ps) + ", 30 Portfolios")
f.write("\n")
f.write(report)
f.close()
def spearLearn():
global year
print "the year is now " + year
logname = 'results/avgSpearWeights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(bestAvgSpear, True, True)
yearString = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
f.write("\n")
f.write(str(year) + " to " + str(int(year) + 3))
f.write("\n")
f.write(report)
f.close()
return ans
def learn(y):
print "the year is now " + year
logname = 'results/weights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(calculateFitness, True, True)
yearstring = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
ps = plotFitness(ans)
f.write("\n")
f.write(yearstring + ", Portfolio Size: " + str(ps) + ", 30 Portfolios")
f.write("\n")
f.write(report)
f.close()
def spearLearn():
global year
print "the year is now " + year
logname = 'results/avgSpearWeights.txt'
f = open(logname, 'a')
pso.dmn = len(ratios)
ans, i, opt = pso.particleSwarmOptimize(bestAvgSpear, True, True)
yearString = str(year)
report = "Weights: " + str(ans) + ", Spearman: " + str(opt)
f.write("\n")
f.write(str(year) + " to " + str(int(year) + 3))
f.write("\n")
f.write(report)
f.close()
return ans
