def trading(trading_date,
percent=percent,
strategy_id='机器学习1号',
account1='name:client-1',
working_dir=working_dir,
ui_log=None):
try:
QA_util_log_info(
'##JOB01 Now Load Model ==== {}'.format(str(trading_date)), ui_log)
model_temp, info_temp = load_model('stock', working_dir=working_dir)
except:
send_email('错误报告', '无法正确加载模型,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'无法正确加载模型,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
QA_util_log_info(
'##JOB02 Now Model Predict ==== {}'.format(str(trading_date)), ui_log)
tar, tar1 = model_predict(model_temp,
str(trading_date[0:7]) + "-01", trading_date,
info_temp['cols'])
r_tar = tar[tar['RANK'] <= 5].loc[trading_date][[
'Z_PROB', 'O_PROB', 'RANK'
]]
try:
QA_util_log_info(
'##JOB03 Now Chect Account Server ==== {}'.format(
str(trading_date)), ui_log)
client = get_Client()
account1 = account1
client.cancel_all(account1)
account_info = client.get_account(account1)
print(account_info)
except:
send_email('错误报告', '云服务器错误,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'云服务器错误,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
h1 = int(datetime.datetime.now().strftime("%H"))
m1 = int(datetime.datetime.now().strftime("%M"))
while h1 == 14 and m1 <= 50:
h1 = int(datetime.datetime.now().strftime("%H"))
m1 = int(datetime.datetime.now().strftime("%M"))
time.sleep(30)
QA_util_log_info('##JOB04 Now Trading ==== {}'.format(str(trading_date)),
ui_log)
res = trade_roboot(r_tar, account1, trading_date, percent, strategy_id)
return (res)
def trading(trading_date, percent=percent, strategy_id= '机器学习1号', account1= 'name:client-1', working_dir= working_dir, ui_log= None, exceptions= exceptions):
try:
QA_util_log_info(
'##JOB01 Now Load Model ==== {}'.format(str(trading_date)), ui_log)
stock_model_temp,stock_info_temp = Stock.load_model('stock',working_dir = working_dir)
index_model_temp,index_info_temp = Index.load_model('index',working_dir = working_dir)
safe_model_temp,safe_info_temp = Index.load_model('safe',working_dir = working_dir)
except:
send_email('错误报告', '无法正确加载模型,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'无法正确加载模型,请检查',
direction = 'HOLD',
offset='HOLD',
volume=None
)
QA_util_log_info(
'##JOB02 Now Model Predict ==== {}'.format(str(trading_date)), ui_log)
#tar,tar1 = model_predict(model_temp, str(trading_date[0:7])+"-01",trading_date,info_temp['cols'])
#index_list,index_report,index_top_report = Index.check_model(index_model_temp, QA_util_get_last_day(trading_date),QA_util_get_last_day(trading_date),index_info_temp['cols'], 'INDEXT_TARGET5', 0.3)
index_tar,index_b = Index.model_predict(index_model_temp, str(trading_date[0:7])+"-01",trading_date,index_info_temp['cols'])
#safe_list,safe_report,safe_top_report = Index.check_model(safe_model_temp, QA_util_get_last_day(trading_date),QA_util_get_last_day(trading_date),safe_info_temp['cols'], 'INDEXT_TARGET', 0.3)
safe_tar,safe_b = Index.model_predict(safe_model_temp, str(trading_date[0:7])+"-01",trading_date,index_info_temp['cols'])
#stock_list,report,top_report = Stock.check_model(stock_model_temp, QA_util_get_last_day(trading_date),QA_util_get_last_day(trading_date),stock_info_temp['cols'], 0.42)
stock_tar,stock_b = Stock.model_predict(stock_model_temp, str(trading_date[0:7])+"-01",trading_date,stock_info_temp['cols'])
tar = combine_model(index_b, stock_b, safe_b, str(trading_date[0:7])+"-01",trading_date)
try:
r_tar = tar.loc[trading_date][['Z_PROB','O_PROB','RANK']]
except:
r_tar = None
send_email('交易报告:'+ trading_date, "空仓状态", 'date')
try:
QA_util_log_info(
'##JOB03 Now Chect Account Server ==== {}'.format(str(trading_date)), ui_log)
client = get_Client()
client.cancel_all(account1)
account_info = client.get_account(account1)
print(account_info)
except:
send_email('错误报告', '云服务器错误,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'云服务器错误,请检查',
direction = 'HOLD',
offset='HOLD',
volume=None
)
h1 = int(datetime.datetime.now().strftime("%H"))
m1 = int(datetime.datetime.now().strftime("%M"))
while h1 == 9 and m1 <= 29 :
h1 = int(datetime.datetime.now().strftime("%H"))
m1 = int(datetime.datetime.now().strftime("%M"))
time.sleep(30)
QA_util_log_info(
'##JOB04 Now Trading ==== {}'.format(str(trading_date)), ui_log)
res = trade_roboot(r_tar, account1, trading_date, percent, strategy_id, type='morning', exceptions = exceptions)
return(res)
def predict(trading_date,
strategy_id='机器学习1号',
account1='name:client-1',
working_dir=working_dir,
ui_log=None,
exceptions=exceptions):
try:
QA_util_log_info(
'##JOB01 Now Got Account Info ==== {}'.format(str(trading_date)),
ui_log)
client = get_Client()
account1 = account1
account_info = client.get_account(account1)
print(account_info)
sub_accounts = client.get_positions(account1)['sub_accounts']
try:
frozen = float(
client.get_positions(account1)['positions'].set_index(
'证券代码').loc[exceptions]['市值'].sum())
except:
frozen = 0
sub_accounts = sub_accounts - frozen
except:
send_email('错误报告', '云服务器错误,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'云服务器错误,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
try:
QA_util_log_info(
'##JOB02 Now Load Model ==== {}'.format(str(trading_date)), ui_log)
stock_model_temp, stock_info_temp = Stock.load_model(
'stock', working_dir=working_dir)
index_model_temp, index_info_temp = Index.load_model(
'index', working_dir=working_dir)
safe_model_temp, safe_info_temp = Index.load_model(
'safe', working_dir=working_dir)
except:
send_email('错误报告', '无法正确加载模型,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'无法正确加载模型,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
QA_util_log_info(
'##JOB03 Now Model Predict ==== {}'.format(str(trading_date)), ui_log)
#index_list,index_report,index_top_report = Index.check_model(index_model_temp, QA_util_get_last_day(trading_date),QA_util_get_last_day(trading_date),index_info_temp['cols'], 'INDEXT_TARGET5', 0.3)
index_tar, index_b = Index.model_predict(index_model_temp,
str(trading_date[0:7]) + "-01",
trading_date,
index_info_temp['cols'])
#safe_list,safe_report,safe_top_report = Index.check_model(safe_model_temp, QA_util_get_last_day(trading_date),QA_util_get_last_day(trading_date),safe_info_temp['cols'], 'INDEXT_TARGET', 0.3)
safe_tar, safe_b = Index.model_predict(safe_model_temp,
str(trading_date[0:7]) + "-01",
trading_date,
index_info_temp['cols'])
stock_list, report, top_report = Stock.check_model(
stock_model_temp, QA_util_get_last_day(trading_date),
QA_util_get_last_day(trading_date), stock_info_temp['cols'], 0.42)
stock_tar, stock_b = Stock.model_predict(stock_model_temp,
str(trading_date[0:7]) + "-01",
trading_date,
stock_info_temp['cols'])
tar = combine_model(index_b, stock_b, safe_b,
str(trading_date[0:7]) + "-01", trading_date)
QA_util_log_info(
'##JOB03 Now Concat Result ==== {}'.format(str(trading_date)), ui_log)
try:
tar1 = tar.loc[trading_date]
except:
tar1 = None
QA_util_log_info(
'##JOB04 Now Funding Decision ==== {}'.format(str(trading_date)),
ui_log)
if tar1 is None:
res = None
else:
tar2 = tar1[['Z_PROB', 'O_PROB', 'RANK']]
close = QA_fetch_stock_day_adv(
list(tar1.index), QA_util_get_last_day(trading_date, 60),
trading_date).to_qfq().data.loc[trading_date].reset_index(
'date')['close']
info = QA_fetch_stock_fianacial_adv(
list(tar1.index), trading_date,
trading_date).data.reset_index('date')[['NAME', 'INDUSTRY']]
res = tar2.join(close).join(info)
#res = pd.concat([tar2,close,info],axis=1)
avg_account = sub_accounts['总 资 产'] / tar1.shape[0]
res = res.assign(tar=avg_account[0] * percent)
res['cnt'] = (res['tar'] / res['close'] /
100).apply(lambda x: round(x, 0) * 100)
res['real'] = res['cnt'] * res['close']
QA_util_log_info(
'##JOB05 Now Current Report ==== {}'.format(str(trading_date)), ui_log)
table1 = tar[tar['RANK'] <= 5].groupby('date').mean()
if exceptions is not None:
frozen_positions = client.get_positions(account1)['positions'][[
'证券代码', '证券名称', '股票余额', '可用余额', '冻结数量', '参考盈亏', '盈亏比例(%)'
]].set_index('证券代码').loc[exceptions]
else:
frozen_positions = pd.DataFrame()
QA_util_log_info(
'##JOB06 Now Current Holding ==== {}'.format(str(trading_date)),
ui_log)
positions = client.get_positions(account1)['positions'][[
'证券代码', '证券名称', '股票余额', '可用余额', '冻结数量', '参考盈亏', '盈亏比例(%)'
]]
QA_util_log_info(
'##JOB07 Now Message Building ==== {}'.format(str(trading_date)),
ui_log)
try:
msg1 = '模型训练日期:{model_date}'.format(model_date=stock_info_temp['date'])
body1 = build_table(
table1,
'safe模型结果_{}'.format(str(QA_util_get_last_day(trading_date))))
body3 = build_table(positions, '目前持仓')
body4 = build_table(
pd.DataFrame(report),
'指数模型结果_{}'.format(str(QA_util_get_last_day(trading_date))))
body5 = build_table(
pd.DataFrame(top_report),
'选股模型结果_{}'.format(str(QA_util_get_last_day(trading_date))))
#body6 = build_table(stock_list, '上一交易日模型交易清单{}'.format(str(QA_util_get_last_day(trading_date))))
body7 = build_table(frozen_positions, '目前锁定持仓')
if res is not None:
body2 = build_table(res, '目标持仓')
msg = build_email(build_head(), msg1, body1, body3, body5, body4,
body2, body7)
title = '交易报告'
else:
msg = build_email(build_head(), msg1, body1, body3, body5, body4,
body1, body7)
title = '空仓交易报告'
send_email(title + trading_date, msg, 'date')
except:
send_email('交易报告:' + trading_date, "消息构建失败", 'date')
return (tar)
def predict(trading_date,
strategy_id='机器学习1号',
account1='name:client-1',
working_dir=working_dir,
ui_log=None):
try:
QA_util_log_info(
'##JOB01 Now Got Account Info ==== {}'.format(str(trading_date)),
ui_log)
client = get_Client()
account1 = account1
account_info = client.get_account(account1)
print(account_info)
sub_accounts = client.get_positions(account1)['sub_accounts']
except:
send_email('错误报告', '云服务器错误,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'云服务器错误,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
try:
QA_util_log_info(
'##JOB02 Now Load Model ==== {}'.format(str(trading_date)), ui_log)
model_temp, info_temp = load_model('index', working_dir=working_dir)
except:
send_email('错误报告', '无法正确加载模型,请检查', trading_date)
send_actionnotice(strategy_id,
'错误报告:{}'.format(trading_date),
'无法正确加载模型,请检查',
direction='HOLD',
offset='HOLD',
volume=None)
QA_util_log_info(
'##JOB03 Now Model Predict ==== {}'.format(str(trading_date)), ui_log)
stock_list, report, top_report = check_model(
model_temp, QA_util_get_last_day(trading_date),
QA_util_get_last_day(trading_date), info_temp['cols'], 0.42)
tar, b = model_predict(model_temp,
str(trading_date[0:7]) + "-01", trading_date,
info_temp['cols'])
QA_util_log_info(
'##JOB03 Now Concat Result ==== {}'.format(str(trading_date)), ui_log)
tar1 = tar[tar['RANK'] <= 5].loc[trading_date]
tar2 = tar1[['Z_PROB', 'O_PROB', 'RANK']]
print(trading_date)
close = QA_fetch_stock_day_adv(
list(tar1.index), trading_date,
trading_date).data.loc[trading_date].reset_index('date')['close']
info = QA_fetch_stock_fianacial_adv(
list(tar1.index), trading_date,
trading_date).data.reset_index('date')[['NAME', 'INDUSTRY']]
res = pd.concat([tar2, close, info], axis=1)
QA_util_log_info(
'##JOB04 Now Funding Decision ==== {}'.format(str(trading_date)),
ui_log)
avg_account = sub_accounts['总 资 产'] / tar1.shape[0]
res = res.assign(tar=avg_account[0] * percent)
res['cnt'] = (res['tar'] / res['close'] /
100).apply(lambda x: round(x, 0) * 100)
res['real'] = res['cnt'] * res['close']
QA_util_log_info(
'##JOB05 Now Current Report ==== {}'.format(str(trading_date)), ui_log)
table1 = tar[tar['RANK'] <= 5].groupby('date').mean()
QA_util_log_info(
'##JOB06 Now Current Holding ==== {}'.format(str(trading_date)),
ui_log)
positions = client.get_positions(account1)['positions'][[
'证券代码', '证券名称', '股票余额', '可用余额', '冻结数量', '参考盈亏', '盈亏比例(%)'
]]
QA_util_log_info(
'##JOB07 Now Message Building ==== {}'.format(str(trading_date)),
ui_log)
try:
msg1 = '模型训练日期:{model_date}'.format(model_date=info_temp['date'])
body1 = build_table(table1, '近段时间内模型盈利报告')
body2 = build_table(res, '目标持仓')
body3 = build_table(positions, '目前持仓')
body4 = build_table(
pd.DataFrame(report),
'上一交易日模型报告{}'.format(str(QA_util_get_last_day(trading_date))))
body5 = build_table(
pd.DataFrame(top_report),
'上一交易日模型报告Top{}'.format(str(QA_util_get_last_day(trading_date))))
body6 = build_table(
stock_list,
'上一交易日模型交易清单{}'.format(str(QA_util_get_last_day(trading_date))))
msg = build_email(build_head(), msg1, body5, body4, body6, body1,
body2, body3)
except:
send_email('交易报告:' + trading_date, "消息构建失败", 'date')
send_email('交易报告:' + trading_date, msg, 'date')