def get_basic_factor_val(factorname, date_list, stock_list):
#t = TushareBasedata.TushareBasedata()
#print factorname
#print date_list
#print stock_list
t = MysqlBasedata.MysqlBasedata()
if len(date_list) > 1:
start_date_str = datetime.strftime(date_list[0], '%Y-%m-%d')
end_date_str = datetime.strftime(date_list[1], '%Y-%m-%d')
if factorname == 'trade_closeprice':
factorDf = t.get_factor_data_by_datecode(stock_list,
start_date_str,
end_date_str, factorname,
0)
else:
factorDf = t.get_factor_data_by_datecode(stock_list,
start_date_str,
end_date_str, factorname,
-1)
else:
start_date_str = datetime.strftime(date_list[0], '%Y-%m-%d')
factorDf = t.get_factor_data_by_datecode(stock_list, start_date_str,
start_date_str, factorname,
-1)
factorDf['FACTOR_VALUE'] = factorDf['FACTOR_VALUE'].map(lambda x: float(x))
return factorDf
def getStockList(tradedate):
tbd = MysqlBasedata.MysqlBasedata()
s1 = str(tbd.get_stocklist_by_type(tradedate, '300'))
s2 = str(tbd.get_stocklist_by_type(tradedate, '500'))
s3 = str(tbd.get_stocklist_by_type(tradedate, '50'))
s = "(s1Ns2)Us3"
return tbd.get_stocklist_by_type(tradedate, '300')
def getSummary(strategyId, benchCode, startDateStr, endDateStr):
conn = cbt.getConnection()
t = MysqlBasedata.MysqlBasedata()
df_bench = t.get_history_index_data_by_date(benchCode, startDateStr,
endDateStr, None)
df_bench = df_bench.sort_values(by='tradedate', ascending=True)
#sql = "SELECT * from r_position where strategy_id='"+stategyId+"' and tradedate ORDER BY tradedate"
#df_position = pd.read_sql(sql, conn)
df_position = st.getPositionListByTradedate(strategyId, startDateStr,
endDateStr, conn)
rsumm = returnSummary.ReturnSummary(df_position, df_bench)
summ = rsumm.get_summary()
print summ
# total asset data
print rsumm.df_assets
# rsumm.df_assets = rsumm.df_assets[rsumm.df_assets]
rsumm.df_assets.to_csv('asserts_.csv', index=False, sep=',')
print pd.read_csv('asserts_.csv')
return rsumm, summ
def tradeNew(tradedate):
# 数据库操作类初始化
t = MysqlBasedata.MysqlBasedata()
# 连接池初始化
conn = cbt.getConnection()
# 日期格式转换
trade_date_str = datetime.strftime(tradedate, '%Y-%m-%d')
# 查询股票行情接口
stockPriceDf = t.get_history_data_by_stocklist(trade_date_str, None, 'D',
'B')
# 验证调仓日期：验证是否到达调仓日期
# 如果当前不是到调仓日期，新增资金账户信息
# 查询前一天是否存在仓位，如果存在，拷贝前一天仓位信息
if cc.currentPeriod % cc.changePeriod != 1:
# 如果是第一天，记录账户资金信息
# todo 否则，判断是否
# 拷贝前一天的仓位信息，更新仓位中的股票价格
updatePosition(tradedate, stockPriceDf, conn)
#updatePositionAccount(tradedate)
cc.currentPeriod = cc.currentPeriod + 1
return
cc.currentPeriod = cc.currentPeriod + 1
# 如果是调仓日期，进入策略逻辑
# 获取票池
stocklist = getStockList(tradedate)
print stocklist
# 如果股票池为0
if len(stocklist) == 0:
# 新增资金账户信息，拷贝前一天仓位信息
updatePosition(tradedate, stockPriceDf, conn)
return
# 定义买入股票列表、卖出股票列表
buystocklist = stocklist
# 计算得出中间条件、筛选股票的条件
factor_txt = 'MA5 = MA(trade_closeprice,5)\nMA10 = MA(trade_closeprice, 10)'
filter_txt = 'gx = CROSS(MA5, MA10)\nasc5 = SORT(MA5, desc, 5)'
d1, d2 = intp.interp(factor_txt, filter_txt)
# 通过买入条件得出买入股票列表
for key in ['asc5']:
buystocklist = d2[key].filter(buystocklist, tradedate)
# print 'Stock list by applying filter', key, ':', buystocklist
# 当前订单列表
currentOrderlist = []
lastAccountDateStr = cst.getLastAccountDate(cc.strategyId, conn)
positionList = []
# 如果仓位交易日不为空，查询仓位信息，复制仓位信息到新的交易日
if lastAccountDateStr != None:
positionDf = cst.getPositionList(cc.strategyId, lastAccountDateStr,
conn)
positionList = positionDf.values
# 通过卖出条件得出卖出股票列表,并将卖出股票列表放入订单
for position in positionList:
code = position[1]
if code in buystocklist:
buystocklist.remove(code)
continue
print code
print stockPriceDf.loc[code, 'FACTOR_VALUE']
o = StockOrder.StockOrder(cc.strategyId, code, tradedate,
stockPriceDf.loc[code, 'FACTOR_VALUE'], 0)
print o
currentOrderlist.append(o)
# 买入,并将买入股票列表放入订单
for code in buystocklist:
o = StockOrder.StockOrder(cc.strategyId, code, tradedate,
stockPriceDf.loc[code, 'FACTOR_VALUE'], 100)
currentOrderlist.append(o)
print currentOrderlist
# 订单入库，并更新仓位
cst.order(currentOrderlist, stockPriceDf, conn)
self.retrn, self.retrn_annual, self.retrn_bench,
self.retrn_bench_annual, self.alpha, self.beta, self.IR, self.vol,
self.vol_bench, self.win_daily, self.max_loss_daily
]
df_summary['FieldName'] = names
df_summary["FieldValue"] = values
return df_summary
if __name__ == "__main__":
# 连接池初始化
conn = cbt.getConnection()
sql = "SELECT * from r_position where strategy_id='2' ORDER BY tradedate"
df = pd.read_sql(sql, conn)
# print df
t = MysqlBasedata.MysqlBasedata()
df_bench_new = t.get_history_index_data_by_date('000300.SH', '2015-05-10',
'2017-05-10', None)
df_bench = pd.read_pickle('test_bench_returnSummary.pkl')
df_position = pd.read_pickle('test_data_returnSummary.pkl')
rsumm = ReturnSummary(df_position, df_bench)
summ = rsumm.get_summary()
print summ.T
print type(summ)
# total asset data
print rsumm.df_assets
#df['tradedate'] = df['tradedate'].map(lambda x:time.mktime(x.timetuple()))
#print df
# print datetime.now().microsecond
# print time.mktime(t.timetuple())