def saveTrades(testingdata, classifiersTest, cutoffTest, printToScreen=False):
goodTrades = testingdata[classifiersTest > cutoffTest][:,[1,2,3,4,5]] # FIXME FIXME FIXME
# goodTrades = testingdata[testingdata[:,0] > 0.5][classifiersTest[testingdata[:,0] > 0.5] > cutoffTest][:,[1,2,3,4,5]]
goodClassifiers = classifiersTest[classifiersTest > cutoffTest] # FIXME FIXME FIXME
# goodClassifiers = classifiersTest[testingdata[:,0] > 0.5][classifiersTest[testingdata[:,0] > 0.5] > cutoffTest]
for itrade, trade in enumerate(goodTrades):
day, gain1, gain2, iticker, close0 = trade
day, iticker, close0 = int(day), int(iticker), float(close0)
if printToScreen:
print dTickers[iticker], u.inum2tuple(day), close0, gain1, gain2, gain1+gain2
else:
dt = "%04i-%02i-%02i" % u.inum2tuple(day)
fhTrades.write("%s %s %.3f %.2f\n" % (dTickers[iticker], dt, goodClassifiers[itrade], close0))
def saveTrades(testingdata, classifiersTest, cutoffTest, printToScreen=False):
goodTrades = testingdata[classifiersTest > cutoffTest][:, [
1, 2, 3, 4, 5
]] # FIXME FIXME FIXME
# goodTrades = testingdata[testingdata[:,0] > 0.5][classifiersTest[testingdata[:,0] > 0.5] > cutoffTest][:,[1,2,3,4,5]]
goodClassifiers = classifiersTest[classifiersTest >
cutoffTest] # FIXME FIXME FIXME
# goodClassifiers = classifiersTest[testingdata[:,0] > 0.5][classifiersTest[testingdata[:,0] > 0.5] > cutoffTest]
for itrade, trade in enumerate(goodTrades):
day, gain1, gain2, iticker, close0 = trade
day, iticker, close0 = int(day), int(iticker), float(close0)
if printToScreen:
print dTickers[iticker], u.inum2tuple(
day), close0, gain1, gain2, gain1 + gain2
else:
dt = "%04i-%02i-%02i" % u.inum2tuple(day)
fhTrades.write(
"%s %s %.3f %.2f\n" %
(dTickers[iticker], dt, goodClassifiers[itrade], close0))
def crossover(quotes, d1, d2, params={}):
# define a function that takes a quotes object, so it is an array with elements [day,o,h,l,c]
# and returns a dictionary with keys of buy and sell times
# values are not used (1 here), so that gives us room to pack in more information with this function
print d1, d2
timesclose = quotes[:,[0,4]]
p1, p2 = params["p1"], params["p2"]
p1, p2 = min(p1,p2), max(p1,p2)
if(p2 - p1 <= 1): return { }, { }
emas = [ ind.ematimes(timesclose,i) for i in [p1, p2] ]
crossovertimes = ind.crossovertimes(emas)
for t in crossovertimes:
print u.inum2tuple(t[0])
dCrossovers = {} # turn into dict for fast lookup
for time,rising in crossovertimes: dCrossovers[time] = rising
dBuy = { }
dSell = { }
hasBought = False
boughtPrice = -1.0
for i,day in enumerate(quotes[:,0]):
# only trade within specified window
if( day > u.tuple2inum(d2) or day < u.tuple2inum(d1) ): continue
if(day in dCrossovers):
rising = dCrossovers[day]
if(rising):
dBuy[day] = 1
hasBought = True
boughtPrice = quotes[i][4]
else:
if(hasBought): # don't sell if we already sold
dSell[day] = 1
hasBought = False
# if price is x% lower than what we bought for, get the hell out and sell a certain %
if( hasBought and (0.0 < quotes[i][4] / boughtPrice < 0.90) ):
dSell[day] = 1
hasBought = False
return dBuy, dSell
def printLedger(self,showTrades=True):
self.cleanup()
print "-"*25
print "Money remaining: %.2f" % self.money
print "-"*25
if(showTrades):
for t in self.trades:
date = ", ".join(map(str,list(u.inum2tuple(t[3]))))
print "[%s] %-4s %i stocks of %s @ $%.2f" % (date,"BUY" if t[2]>0 else "SELL", abs(t[2]), t[0], t[1])
print "-"*25
for ass in self.assets.keys():
print "Have %i stocks of %s" % (self.assets[ass], ass)
print
def printLedger(self, showTrades=True):
self.cleanup()
print "-" * 25
print "Money remaining: %.2f" % self.money
print "-" * 25
if (showTrades):
for t in self.trades:
date = ", ".join(map(str, list(u.inum2tuple(t[3]))))
print "[%s] %-4s %i stocks of %s @ $%.2f" % (
date, "BUY" if t[2] > 0 else "SELL", abs(t[2]), t[0], t[1])
print "-" * 25
for ass in self.assets.keys():
print "Have %i stocks of %s" % (self.assets[ass], ass)
print
elif algorithm == "RandomForest": alg = RandomForestClassifier()
elif algorithm == "Bagging": alg = BaggingClassifier()
elif algorithm == "XGB": alg = XGBoostClassifier(num_boost_round=3000,num_class=2,eta=0.01,max_depth=9,sub_sample=0.9)
# elif algorithm == "XGB": alg = XGBoostClassifier(num_boost_round=30,num_class=2,eta=0.01,max_depth=9,sub_sample=0.9)
alg.fit(Xtrain, Ytrain)
# try: printImportances(alg.feature_importances_, whichIndicators)
# except: pass
# try: printImportances(alg.coef_, whichIndicators)
# except: pass
minday = int(np.min(testingdata[:,[1]]))
maxday = int(np.max(testingdata[:,[1]]))
mind, maxd = u.inum2tuple(minday), u.inum2tuple(maxday)
fhTrades.write("# %s %i to %i (%i-%i-%i to %i-%i-%i) \n" % (algorithm, minday, maxday, mind[0], mind[1], mind[2], maxd[0], maxd[1], maxd[2]) )
plotTrainTest(Xtrain, Ytrain, Xtest, Ytest, trainingdata, testingdata, \
# title = "%s LR: %.3f N-estimators: %i" % (algorithm, rate, nest), \
title = "%s" % (algorithm), \
# filename = basedir+"TrainTest_%s_%s_%s.png" % (algorithm, ("%.4f" % rate).replace(".",""), ("%i" % nest).replace(".","")) \
filename = basedir+"TrainTest_%s.png" % (algorithm) \
)
Yscore = alg.predict(Xtest)
# needed for when we test inclusively -1, 0, 1
# want to just make the -1 into background (0)
Ytest[Ytest < -0.5] = 0
fpr, tpr, _ = roc_curve(Ytest,Yscore)
forROCs[algorithm] = [fpr,tpr]
num_class=2,
eta=0.01,
max_depth=9,
sub_sample=0.9)
# elif algorithm == "XGB": alg = XGBoostClassifier(num_boost_round=30,num_class=2,eta=0.01,max_depth=9,sub_sample=0.9)
alg.fit(Xtrain, Ytrain)
# try: printImportances(alg.feature_importances_, whichIndicators)
# except: pass
# try: printImportances(alg.coef_, whichIndicators)
# except: pass
minday = int(np.min(testingdata[:, [1]]))
maxday = int(np.max(testingdata[:, [1]]))
mind, maxd = u.inum2tuple(minday), u.inum2tuple(maxday)
fhTrades.write("# %s %i to %i (%i-%i-%i to %i-%i-%i) \n" %
(algorithm, minday, maxday, mind[0], mind[1], mind[2],
maxd[0], maxd[1], maxd[2]))
plotTrainTest(Xtrain, Ytrain, Xtest, Ytest, trainingdata, testingdata, \
# title = "%s LR: %.3f N-estimators: %i" % (algorithm, rate, nest), \
title = "%s" % (algorithm), \
# filename = basedir+"TrainTest_%s_%s_%s.png" % (algorithm, ("%.4f" % rate).replace(".",""), ("%i" % nest).replace(".","")) \
filename = basedir+"TrainTest_%s.png" % (algorithm) \
)
Yscore = alg.predict(Xtest)
# needed for when we test inclusively -1, 0, 1
# want to just make the -1 into background (0)
Ytest[Ytest < -0.5] = 0
