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分钟频数据回测问题 #1741
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还是没人回复吗?我自己实现了一个HighFreqTopkDropoutStragegy解决了这个问题...... |
其实你可以得到预测结果文件后,拿到其他回测平台去回测,比如放到backtrader去回测,这样可以完全自己控制t0问题。qlib系列二教程里有怎样在backtrader中拿到qlib预测结果文件,执行定制回测策略的例子。 |
请问你可以分享你实现的HighFreqTopkDropoutStragegy吗?下面卖课的真的是。 |
How to get the transaction detail in your picture in non_highfreq mode? |
To get the transaction details, you can also refer to #1655, where we discussed more detailed. In sort, change the yaml to this:
I add
to (It it not to the initial question) |
您好,请问您方便一下您写的这个策略学习一下吗?感觉逻辑像是添加一个$buyDate然后判断$buyDate需要和¥today不等于才能进行交易? |
@l0ngc 其实逻辑都差不多,就是对每一份买卖标注下日期 |
❓ Questions and Help
在实例examples/highfreq的yaml文件中没有回测部分的配置
我进行了简单的配置,用自己的因子数据进行训练,采用TopNDropout策略
回测出来发现无费收益跑赢了benchmark(沪深300 ),但如果算上手续费差异非常大,不应该如此离谱,然后对持仓进行了分析,发现它每分钟都会进行交易,前一分钟买入的股票,下一分钟可能就卖出了,这显然是不合理的,因为不符合A股的交易规则。如下图
我最终定位到qlib的持仓设计上 qlib\backtest\position.py::Position。对于A股来说,持仓需要考虑到三个概念,昨日持仓,总持仓,可卖持仓,但是qlib中仅仅只有一个amount市值。那么在回测时,就必然会出现把今天买入的持仓今日卖出,这是严格意义上的T0交易。
在Strategy和Executor、Exchange的实现上,也并没有发现阻止这种情况发生的代码。
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