#ga.setMutationRate(0.2)
#ga.setCrossoverRate(0.8)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setElitism(True)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
dbPath = createPath('pyevolve.db')
sqlite_adapter = DBAdapters.DBSQLite(dbname=dbPath, identify="timewindow" + str(timewindow), resetIdentify=True, resetDB=False)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump frequency of 20 generations
ga.evolve(freq_stats=20)
# Best individual
best = ga.bestIndividual()
stats = {'constant': best[0],
'EMA': best[1],
'RSI': best[2],
'MACD': best[3]
}
return stats
filename = createPath('gaValidation.csv')
timeseries = loadStock('KO')
cv.crossValidationFeatures(filename, GAForecaster())
x = np.vstack([ema, rsi, macd]).T
mymodel = ols.ols(y, x, 'price', ['EMA', 'RSI', 'MACD'])
labels = ['constant', 'EMA', 'RSI', 'MACD']
equation = "price(t+1) = "
stats = {}
for i in range(len(mymodel.b)):
l = labels[i]
c = mymodel.b[i]
stats[l] = c
if l == 'constant':
equation += str(c) + " + "
else:
equation += str(c) + "*" + l + "(t) + "
equation = equation[:-2]
return stats
stocks = ['NFLX', 'LUV']
for s in stocks:
timeseries = loadStock(s + '/' + s)
path = createPath(s + '/' + s + 'mrValidation.csv')
featuresPath = createPath(s + '/')
cv.crossValidationFeatures(featuresPath, path, timeseries,
MultipleRegression())
prices, ema, rsi, macd = self.tf.getTimewindow(windowSize)
y = np.array(prices)
x = np.vstack([ema, rsi, macd]).T
mymodel = ols.ols(y,x,'price',['EMA','RSI','MACD'])
labels = ['constant', 'EMA','RSI','MACD']
equation = "price(t+1) = "
stats = {}
for i in range(len(mymodel.b)):
l = labels[i]
c = mymodel.b[i]
stats[l] = c
if l == 'constant':
equation += str(c) + " + "
else:
equation += str(c) + "*" + l + "(t) + "
equation = equation[:-2]
return stats
stocks = ['NFLX', 'LUV']
for s in stocks:
timeseries = loadStock(s + '/' + s)
path = createPath(s + '/' + s + 'mrValidation.csv')
featuresPath = createPath(s + '/')
cv.crossValidationFeatures(featuresPath, path, timeseries, MultipleRegression())
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
dbPath = createPath('pyevolve.db')
sqlite_adapter = DBAdapters.DBSQLite(dbname=dbPath,
identify="timewindow" +
str(timewindow),
resetIdentify=True,
resetDB=False)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump frequency of 20 generations
ga.evolve(freq_stats=20)
# Best individual
best = ga.bestIndividual()
stats = {
'constant': best[0],
'EMA': best[1],
'RSI': best[2],
'MACD': best[3]
}
return stats
filename = createPath('gaValidation.csv')
timeseries = loadStock('KO')
cv.crossValidationFeatures(filename, GAForecaster())
