Vincent yu
Vincent yu
同志们写的过于复杂 给出一个示例 ``` import QUANTAXIS as QA block=QA.QA_fetch_stock_block_adv() codes=block.data.reset_index().query('blockname == "5G概念" or blockname == "OLED概念"').code codes[codes.duplicated()] ``` ``` ['002217', '002288', '002384', '002547', '300134', '000063', '000070', '000586', '000636', '000801', '000823', '000836',...
今天我们拿到群友(浙江一柱擎天xinan)的一个通达信指标 让我们快速改写 展示下吧 这是原指标 ``` N:=20; N1:=7; N2:=69; T1:=IF((DATEVAR4),VAR3,VAR4,4,0),COLOR00FFFF; STICKLINE((VAR3=(1.5)*(MA(VOL,5))) AND COUNT((VARE>=VARF),3) AND (REF(LOW,1)=LLV(LOW,120)),1,0),LOW,'●买进'),COLORFF00FF; DRAWTEXT(IF(((COUNT((CLOSE0.6) AND COUNT((VARE>VARF),6) AND (REF(LOW,5)=LLV(LOW,120)) AND REF((CLOSE>=OPEN),4) AND REF((CLOSE>OPEN),3) AND REF((CLOSE>OPEN),2) AND REF((OPEN>CLOSE),1) AND (OPEN>REF(CLOSE,1)),1,0),LOW,'●买进'),COLOR00FFFF; VAR10:=(MA(CLOSE,80)-(MA(CLOSE,10))/(3))*(T1);...
# QUANTAXIS的跨周期函数教程贴 跨周期总是一个让人很难受的话题, 在纵观了一些平台 Tb/rq/jq的解决方案后, 我决定给出一个比较简单的解决方案 qa基于快速的resample试图给出一些方案 (首先 quantaxis先升级到最新版本 : git pull) ## 跨周期的函数 LLV_MT (举个例子) ```python def LLV_MT(Series,new_freq='15min',N=20): res =QA.QA_data_futuremin_resample_series(Series.reset_index(1),Series.name,new_freq) return res.rolling(N).min() ``` 让我们感受一下 如何把一个5min回测周期的数据, 获取在15min上的LLV指标结果 ``` data =...
知乎观光团~
``` λ npm run build > @oriolmirosa/[email protected] build D:\x\jupyterlab_materialdarker > run-p build:** > @oriolmirosa/[email protected] build:typescript D:\x\jupyterlab_materialdarker > tsc > @oriolmirosa/[email protected] build:webpack D:\x\jupyterlab_materialdarker > webpack Hash: 1cea5f270c54dfb58b31 Version: webpack 2.7.0 Time:...
8 | for (dog, prediction) in test_dogs.iter().zip(predictions.row_iter()).take(unprinted_total) { | ^^^^^^^^ method not found in `rusty_machine::prelude::Matrix`
#### Describe your feature request Please describe the behavior you want and the motivation. Please also provide examples of how polars would be used if your feature request were added....
看到有issue提出不知道如果下载完整的数据,关于数据位置指针的使用,给一个示例代码: ```python from pytdx.hq import TdxHq_API api=TdxHq_API() with api.connect(): data=[] for i in range(10): data+=api.get_security_bars(9,0,'000001',(9-i)*800,800) print(api.to_df(data)) ``` ```bash open | close | high | low | vol | amount |...
#### Describe your feature request currently the groupby_rolling, groupby_dynamic can only agg pl.col context, but sometimes we need to apply func over the whole dataframe for analysis, for sure, we...