def is_dirty(self):
#return True
key = 'dirty'
key = self._genKey(key)
o = myredis.get_obj(key)
if agl.IsNone(o):
self.setDirty(True)
return True
return o
def HistoryWeiTuo(self):
key = 'HistoryChengJiao'
key = self._genKey(key)
if self.is_dirty():
df = TcAccount.HistoryWeiTuo(self)
myredis.set_obj(key, df)
else:
return myredis.get_obj(key)
return df
def _getUserStrategy(self, downloadStrategyInterval=60):
"""按固定的时间间隔下载用户代码
downloadStrategyInterval: int default=60 秒
return: df"""
k = "SignForWebUser_preLoadTime"
preLoadTime = myredis.get_obj(k)
if preLoadTime is None:
preLoadTime = datetime.datetime(2015, 10, 19, 15, 33, 47, 53000) #上一次下载的时间
#判断时间
if (agl.curTime() - preLoadTime).total_seconds() > downloadStrategyInterval:
url = "http://stocksign.sinaapp.com/query?cmd=query_strategy"
result = Http().get(url)
df_source = pd.read_json(result)
df_source.columns = ['id', 'user_id', 'title', 'code']
preLoadTime = agl.curTime()
myredis.set_obj(k, preLoadTime)
myredis.set_obj('mysource', df_source)
else:
df_source = myredis.get_obj('mysource')
if df_source is None:
df_source = pd.DataFrame([])
return df_source
def PostTask(fn, t, reset=False):
"""每隔t秒执行一次fn
主要是防止交易接口被短期内多次调用， 造成系统异常
"""
key = 'posttask_'+fn.func_name
cur_t = agl.curTime()
if reset:
myredis.delkey(key)
pre_t = myredis.get_obj(key)
if pre_t == None:
pre_t = cur_t - datetime.timedelta(seconds=t+1)
if cur_t - pre_t > datetime.timedelta(seconds=t):
fn()
myredis.set_obj(key, cur_t)
def get_zhijing_from_redis(is_have_return=False):
"""获取资金列表"""
key_df_stocklist = 'df_zhijing'
if 0: df = pd.DataFrame()
df = myredis.get_obj(key_df_stocklist)
if is_have_return:
return df
if df is not None and len(df) > 0:
from prettytable import PrettyTable
cols = '余额|可用|参考市值|资产'
cols = cols.split('|')
table = PrettyTable(cols)
for i, row in df.iterrows():
table.add_row(row[cols].tolist())
print table
def Reduce(self):
"""合并结果, 返回各任务的df
return: list, 有几个Map list里就有几个元素"""
df = self.pd_task
#按task_id来排序
max_id = np.max(df[0])
result = []
for task_id in range(max_id + 1):
df_result = pd.DataFrame([])
for i in df[df[0] == task_id].index:
r = myredis.get_obj(getResultName(i))
assert (isinstance(r, pd.DataFrame))
df_result = pd.concat([df_result, r])
result.append(df_result)
return result
def get_stocklist_from_redis(is_have_return=False):
"""临时函数， 因为策略basesign保存了列表， 因此可以直接取"""
key_df_stocklist = 'df_stocklist'
if 0: df = pd.DataFrame()
df = myredis.get_obj(key_df_stocklist)
if is_have_return:
return df
if df is not None:
from prettytable import PrettyTable
cols = '证券代码|证券名称|证券数量|库存数量|可卖数量|参考成本价|买入均价|参考盈亏成本价|当前价|最新市值|参考浮动盈亏|盈亏比例(%)'
cols = cols.split('|')
table = PrettyTable(cols)
for i, row in df.iterrows():
table.add_row(row[cols].tolist())
#table.sort_key("ferocity")
#table.reversesort = True
print table
def main(args):
os.chdir(getMainDir())
i = int(args[0])
#print 'arg=',i
pd_task = myredis.get_obj('multi')
if 0: pd_task = pd.DataFrame
#print pd_task.iloc[i]
v = tuple(pd_task.iloc[i].tolist()[1:]) #去掉task_id
#print v
v += (i, ) #加上task_id
s = 'import %s\n' % (v[0])
#module.fn
s += 'r = %s.%s(%s, %d)' % v
#print s
exec s
#print 'result=',r
#保存结果
myredis.set_obj(MultiSubProcess.getResultName(i), r)
def WeiTuoList(self):
"""历史成交加当日委托
主要用状态说明这个字段来判断, 通过枚举值来判断委托状态
操作日期|委托时间|股东代码|深0沪1|证券代码|证券名称|买0卖1|买卖标志|委托价格|委托数量|委托编号|成交数量|成交金额|撤单数量|状态说明|撤单标志|委托日期|备注|
return : df"""
key = 'WeiTuoList'
key = self._genKey(key)
if self.is_dirty():
df = self.HistoryWeiTuo()
df2 = TcAccount.WeiTuoList(self)
df = pd.concat([df2, df])
#对报单时间排序
df.index = pd.DatetimeIndex(df['操作日期'] + ' ' + df['委托时间'])
df = df.sort()
myredis.set_obj(key, df)
self.setDirty(False)
return df
else:
return myredis.get_obj(key)
def get_chengjiao_from_redis(is_have_return=False):
"""输出为成交的委托列表"""
key = 'df_chengjiao'
if 0: df = pd.DataFrame()
df = myredis.get_obj(key)
if is_have_return:
return df
if df is not None:
from prettytable import PrettyTable
cols = '成交时间|证券代码|证券名称|买卖标志|委托价格|委托数量|委托编号|成交价格|成交数量|成交金额|成交编号'
cols = cols.split('|')
df['成交金额'] = df['成交金额'].astype(float)
df = df[df['成交金额'] > 0]
table = PrettyTable(cols)
for i, row in df.iterrows():
table.add_row(row[cols].tolist())
#table.sort_key("ferocity")
#table.reversesort = True
print table
def get_weituo_from_redis(is_have_return=False):
"""输出为成交的委托列表"""
key = 'df_weituo'
if 0: df = pd.DataFrame()
df = myredis.get_obj(key)
if is_have_return:
return df
if df is not None and len(df) > 0:
from prettytable import PrettyTable
cols = '证券代码|证券名称|买卖标志|委托价格|委托数量|委托编号|成交数量|成交金额|撤单数量|状态说明'
cols = cols.split('|')
df = df[df['状态说明'] != '已成']
df = df[df['状态说明'] != '已撤']
df = df[df['买卖标志'] != '配售申购']
table = PrettyTable(cols)
for i, row in df.iterrows():
table.add_row(row[cols].tolist())
#table.sort_key("ferocity")
#table.reversesort = True
print table
