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 self.chart_prg == "gen_pnf_field":
tpl = gen_pf_pnf(dl, ol, hl, ll, cl, vl)
if self.chart_prg == "gen_ichimoku_field":
tpl = gen_pf_ichimoku(dl, hl, ll, cl)
if len(tpl) > 0:
(d, sz, X, yma, ymi) = tpl
else:
old_code = code
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], sz[i], yma[i], ymi[i]])
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 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()
