def insert_hist_trade(self):
self.set_data()
db = Db()
engine = db._get_engine()
sql_stocklist = "select code,name from stock_code"
codes = pd.read_sql_query(sql_stocklist, engine)
codes = codes.to_dict('records')
i = 1
for row in codes:
gta = td.get_hist_data(code=row['code'],
start=self.nowdate,
end=self.nowdate,
ktype='D',
retry_count=3,
pause=0.001)
gta['datain_date'] = self.nowtime
gta['code'] = row['code']
gta['name'] = row['name']
gta['c_yearmonthday'] = gta.index
gta = gta.to_dict('records')
try:
db.insertmany(
"""INSERT INTO trade_hist(c_yearmonthday,code,name,open,high,close,low,volume,price_change,p_change,ma5,ma10,ma20,v_ma5,v_ma10,v_ma20,turnover,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(open)s,%(high)s,%(close)s,%(low)s,%(volume)s,%(price_change)s,%(p_change)s,%(ma5)s,%(ma10)s,%(ma20)s,%(v_ma5)s,%(v_ma10)s,%(v_ma20)s,%(turnover)s,%(datain_date)s)""",
gta)
except Exception, e:
log.error('insert error:%s ', e)
log.info('%s stock insert finished,%s,%s', i, row['code'],
row['name'].decode('utf-8'))
i += 1
def predict_today(datatype, timesteps, data_dim=15):
# log = logger.log
nowdate = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
x_predict, id_predict, name_predict = trade.get_today(seg_len=timesteps,
datatype=datatype,
split=0.1,
debug=False)
network = policy.LSTMPolicy.create_network(timesteps=timesteps,
data_dim=data_dim)
USER_HOME = os.environ['HOME']
out_directory_path = USER_HOME + '/dw/'
meta_file = os.path.join(out_directory_path, 'metadata.json')
weights_path = policy_trainer.get_best_weights(meta_file)
network.load_weights(weights_path)
predicts = network.predict(x_predict, batch_size=16)
v_predicts = pd.DataFrame()
v_predicts['code'] = id_predict
v_predicts['name'] = name_predict
v_predicts['predict'] = predicts
v_predicts['datain_date'] = nowdate
db = Db()
v_predicts = v_predicts.to_dict('records')
db.insertmany(
"""INSERT INTO predicts(code,name,predict,datain_date)
VALUES (%(code)s,%(name)s,%(predict)s,%(datain_date)s)""", v_predicts)
log.info('predicts finished')
def insert_hist_trade(self):
self.set_data()
db = Db()
engine = db._get_engine()
sql_stocklist = "select code,name from stock_code"
codes = pd.read_sql_query(sql_stocklist, engine)
codes = codes.to_dict('records')
i = 1
for row in codes:
gta = td.get_hist_data(code=row['code'],
start=self.nowdate,
end=self.nowdate,
ktype='D',
retry_count=3,
pause=0.001)
gta['datain_date'] = self.nowtime
gta['code'] = row['code']
gta['name'] = row['name']
gta['c_yearmonthday'] = gta.index
gta = gta.to_dict('records')
try:
db.insertmany(
"""INSERT INTO trade_hist(c_yearmonthday,code,name,open,high,close,low,volume,price_change,p_change,ma5,ma10,ma20,v_ma5,v_ma10,v_ma20,turnover,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(open)s,%(high)s,%(close)s,%(low)s,%(volume)s,%(price_change)s,%(p_change)s,%(ma5)s,%(ma10)s,%(ma20)s,%(v_ma5)s,%(v_ma10)s,%(v_ma20)s,%(turnover)s,%(datain_date)s)""",
gta)
except Exception, e:
log.error('insert error:%s ', e)
log.info('%s stock insert finished,%s,%s', i, row['code'],
row['name'].decode('utf-8'))
i += 1
def predict_today(datatype, timesteps, data_dim=15):
# log = logger.log
nowdate = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
x_predict, id_predict, name_predict = trade.get_today(seg_len=timesteps,
datatype=datatype,
split=0.1,
debug=False)
network = policy.LSTMPolicy.create_network(timesteps=timesteps,
data_dim=data_dim)
USER_HOME = os.environ['HOME']
out_directory_path = USER_HOME + '/dw/'
meta_file = os.path.join(out_directory_path, 'metadata.json')
weights_path = policy_trainer.get_best_weights(meta_file)
network.load_weights(weights_path)
predicts = network.predict(x_predict, batch_size=16)
v_predicts = pd.DataFrame()
v_predicts['code'] = id_predict
v_predicts['name'] = name_predict
v_predicts['predict'] = predicts
v_predicts['datain_date'] = nowdate
db = Db()
v_predicts = v_predicts.to_dict('records')
db.insertmany("""INSERT INTO predicts(code,name,predict,datain_date)
VALUES (%(code)s,%(name)s,%(predict)s,%(datain_date)s)""", v_predicts)
log.info('predicts finished')
def insert_predict_statics():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
psummery,headpredict = trade.get_predict()
psummery['c_yearmonthday'] = nowdate
headpredict['c_yearmonthday'] = nowdate
psummery=psummery.to_dict('records')
headpredict=headpredict.to_dict('records')
db.insertmany("""INSERT INTO predict_head(c_yearmonthday,code,name,predict)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s)""", headpredict)
db.insertmany("""INSERT INTO predict_statics(c_yearmonthday,p_cnt,p_mean,p_std,p_min,p25,p50,p75,p_max)
VALUES (%(c_yearmonthday)s,%(p_cnt)s,%(p_mean)s,%(p_std)s,%(p_min)s,%(p25)s,%(p50)s,%(p75)s,%(p_max)s)""", psummery)
def insert_predict_acc():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
acc1 = trade.get_predict_acc1()
acc1['c_yearmonthday'] = nowdate
acc1=acc1.to_dict('records')
db.insertmany("""INSERT INTO acc1(c_yearmonthday,code,name,predict,p_change,acc)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s,%(p_change)s,%(acc)s)""", acc1)
acc2 = trade.get_predict_acc2()
acc2['c_yearmonthday'] = nowdate
acc2=acc2.to_dict('records')
db.insertmany("""INSERT INTO acc2(c_yearmonthday,p_acc,p_change,h_p_acc,h_p_change)
VALUES (%(c_yearmonthday)s,%(p_acc)s,%(p_change)s,%(h_p_acc)s,%(h_p_change)s)""", acc2)
def insert_today_trade(self):
self.set_data()
db = Db()
gta = td.get_today_all()
gta['datain_date'] = self.nowtime
gta['c_yearmonthday'] = self.nowdate
gta = gta.to_dict('records')
db.insertmany(
"""INSERT INTO trade_record(c_yearmonthday,code,name,changepercent,trade,open,high,low,settlement
,volume,turnoverratio,amount,per,pb,mktcap,nmc,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(changepercent)s,%(trade)s,%(open)s,%(high)s,%(low)s,%(settlement)s,%(volume)s,%(turnoverratio)s,%(amount)s,%(per)s,%(pb)s,%(mktcap)s,%(nmc)s,%(datain_date)s)""",
gta)
def insert_today_trade(self):
self.set_data()
db = Db()
gta = td.get_today_all()
gta['datain_date'] = self.nowtime
gta['c_yearmonthday'] = self.nowdate
gta = gta.to_dict('records')
db.insertmany(
"""INSERT INTO trade_record(c_yearmonthday,code,name,changepercent,trade,open,high,low,settlement
,volume,turnoverratio,amount,per,pb,mktcap,nmc,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(changepercent)s,%(trade)s,%(open)s,%(high)s,%(low)s,%(settlement)s,%(volume)s,%(turnoverratio)s,%(amount)s,%(per)s,%(pb)s,%(mktcap)s,%(nmc)s,%(datain_date)s)""",
gta)
def insert_predict_statics():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
psummery, headpredict = trade.get_predict()
psummery['c_yearmonthday'] = nowdate
headpredict['c_yearmonthday'] = nowdate
psummery = psummery.to_dict('records')
headpredict = headpredict.to_dict('records')
db.insertmany(
"""INSERT INTO predict_head(c_yearmonthday,code,name,predict)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s)""",
headpredict)
db.insertmany(
"""INSERT INTO predict_statics(c_yearmonthday,p_cnt,p_mean,p_std,p_min,p25,p50,p75,p_max)
VALUES (%(c_yearmonthday)s,%(p_cnt)s,%(p_mean)s,%(p_std)s,%(p_min)s,%(p25)s,%(p50)s,%(p75)s,%(p_max)s)""",
psummery)
def insert_predict_acc():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
acc1 = trade.get_predict_acc1()
acc1['c_yearmonthday'] = nowdate
acc1 = acc1.to_dict('records')
db.insertmany(
"""INSERT INTO acc1(c_yearmonthday,code,name,predict,p_change,acc)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s,%(p_change)s,%(acc)s)""",
acc1)
acc2 = trade.get_predict_acc2()
acc2['c_yearmonthday'] = nowdate
acc2 = acc2.to_dict('records')
db.insertmany(
"""INSERT INTO acc2(c_yearmonthday,p_acc,p_change,h_p_acc,h_p_change)
VALUES (%(c_yearmonthday)s,%(p_acc)s,%(p_change)s,%(h_p_acc)s,%(h_p_change)s)""",
acc2)
