def cal_alphas(self, expr_ds):
# 计算信号
for i in [
'OpenPrice', 'ClosePrice', 'HighestPrice', 'LowestPrice',
'Volume', 'VWAP', 'Position', 'TurnOver'
]:
expr_tmp = expr_ds.values.expr.replace(i, 'self.eod.%s' % i)
alphas = eval(expr_tmp)
resample_wgts = pd.DataFrame(alphas[:-1] * 1.,
index=self.eod.dates[1:],
columns=self.eod.ticker_names)
bt = BackTest(resample_wgts,
returns=self.resample_returns,
cycle='day',
IS_OOS_ratio=None,
stat_info=False,
plot=False,
quintiles=3,
turnover=1,
cost=0.,
ticker_names=None,
output_dir=None,
test_mode=False,
signal_name=None)
bt.stat_quintiles()
bt.stat_quintiles_pnl()
bt.stat_alpha_pnl()
bt.stat_alpha_sharpe()
# 保存信号
print expr_ds.values.id, expr_ds.values.expr, 'sharpe:', bt.alpha_sharpe
