def predict2tmp(self):
old_code = ""
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
kabuka_generator = kf.g_get_kabuka(self.meigaras, self.startd, self.endd)
for line in kabuka_generator:
(code, date, open, high, low, close, volume) = line
if code != old_code and old_code != "":
if self.chart_prg == "gen_bollinger_field":
tpl = gen_pf_bollinger(dl, ol, hl, ll, cl, vl)
if len(tpl) == 4:
(d, X, Y) = tpl
else:
continue
if len(X) > 0:
labels = np.array(self.classifier.predict(X))
else:
continue
data = []
for i in range(len(d)):
data.append([old_code, d[i], "", labels[i], Y[i][0], Y[i][1]])
if len(data) > 0:
tbl.arr2table(data, self.table_name)
data = []
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
dl.append(date)
ol.append(open)
hl.append(high)
ll.append(low)
cl.append(close)
vl.append(volume)
old_code = code
def get_data(self, code):
g = kf.g_get_kabuka([code])
kabuka = []
dates = {}
i = 0
for row in g:
(code, date, open, high, low, close, vol) = row
kabuka.append(close)
dates[date] = i
i += 1
regressions = vct.get_moving_regression(kabuka, self.trend_days)
return dates, np.array(kabuka), regressions
def learn(self):
old_code = ""
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
X = []
kabuka_generator = kf.g_get_kabuka(self.meigaras, self.startd, self.endd)
for line in kabuka_generator:
(code, date, open, high, low, close, volume) = line
if code != old_code and old_code != "":
if self.chart_prg == "gen_bollinger_field":
tpl = gen_pf_bollinger(dl, ol, hl, ll, cl, vl, False)
if self.chart_prg == "gen_pnf_field":
tpl = gen_pf_pnf(dl, ol, hl, ll, cl, vl, False)
if self.chart_prg == "gen_ichimoku_field":
tpl = gen_pf_ichimoku(dl, hl, ll, cl)
if len(tpl) > 0:
(d, sz, xx) = tpl
else:
old_code = code
continue
debug = False
if debug:
labels = self.classifier.predict(xx)
X.extend(xx)
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
dl.append(date)
ol.append(open)
hl.append(high)
ll.append(low)
cl.append(close)
vl.append(volume)
old_code = code
self.classifier.fit(X)
self.classifier.save()
def learn(self):
old_code = ""
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
X = []
yma = []
ymi = []
kabuka_generator = kf.g_get_kabuka(self.meigaras, self.startd, self.endd)
for line in kabuka_generator:
(code, date, open, high, low, close, volume) = line
if code != old_code and old_code != "":
if self.chart_prg == "gen_bollinger_field":
tpl = gen_pf_bollinger(dl, ol, hl, ll, cl, vl)
if len(tpl) == 4:
(d, xx, ma, mi) = tpl
debug = False
if debug:
ma_labels = self.ma_classifier.predict(xx)
mi_labels = self.mi_classifier.predict(xx)
X.extend(xx)
yma.extend(ma)
ymi.extend(mi)
dl, ol, hl, ll, cl, vl = ([], [], [], [], [], [])
dl.append(date)
ol.append(open)
hl.append(high)
ll.append(low)
cl.append(close)
vl.append(volume)
old_code = code
self.ma_classifier.fit(X, yma)
self.ma_classifier.save()
self.mi_classifier.fit(X, ymi)
self.mi_classifier.save()