From 2c1c91f3a40a674c926b509b08d147896bb498c4 Mon Sep 17 00:00:00 2001 From: laizn Date: Sun, 3 Nov 2024 00:02:07 +0800 Subject: [PATCH] docs: Make the examples in Readme work again after the refactor. --- README-cn.md | 10 +++++----- README.md | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/README-cn.md b/README-cn.md index b3305b0a..f01d46c7 100644 --- a/README-cn.md +++ b/README-cn.md @@ -380,7 +380,7 @@ zvt_context.schemas为系统支持的schema,schema即表结构，即数据，其 * 源码 -[domain](https://github.com/zvtvz/zvt/tree/master/zvt/domain)里的文件为schema的定义，查看相应字段的注释即可。 +[domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain)里的文件为schema的定义，查看相应字段的注释即可。 通过以上的例子，你应该掌握了统一的记录数据的方法： @@ -529,8 +529,8 @@ if __name__ == '__main__': 下面以技术因子为例对**计算流程**进行说明: ``` -In [7]: from zvt.factors.technical_factor import * -In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer()) +In [7]: from zvt.factors import * +In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True)) ``` ### data_df data_df为factor的原始数据，即通过query_data从数据库读取到的数据,为一个**二维索引**DataFrame @@ -555,7 +555,7 @@ stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_00 ``` ### factor_df -factor_df为transformer对data_df进行计算后得到的数据，设计因子即对[transformer](https://github.com/zvtvz/zvt/blob/master/zvt/factors/factor.py#L18)进行扩展，例子中用的是MacdTransformer()。 +factor_df为transformer对data_df进行计算后得到的数据，设计因子即对[transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34)进行扩展，例子中用的是MacdTransformer()。 ``` In [12]: factor.factor_df @@ -579,7 +579,7 @@ stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_00 ### result_df result_df为可用于选股器的**二维索引**DataFrame，通过对data_df或factor_df计算来实现。 -该例子在计算macd之后，利用factor_df,黄白线在0轴上为True,否则为False，[具体代码](https://github.com/zvtvz/zvt/blob/master/zvt/factors/technical_factor.py#L56) +该例子在计算macd之后，利用factor_df,黄白线在0轴上为True,否则为False，[具体代码](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56) ``` In [14]: factor.result_df diff --git a/README.md b/README.md index 8694bd7a..2d602092 100644 --- a/README.md +++ b/README.md @@ -365,7 +365,7 @@ type the schema. and press tab to show its fields or .help() * source code -Schemas defined in [domain](https://github.com/zvtvz/zvt/tree/master/zvt/domain) +Schemas defined in [domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain) From above examples, you should know the unified way of recording data: @@ -519,8 +519,8 @@ Now it's time to introduce the two-dimensional index multi-entity calculation mo Takes technical factors as an example to illustrate the **calculation process**: ``` -In [7]: from zvt.factors.technical_factor import * -In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer()) +In [7]: from zvt.factors import * +In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True)) ``` ### data_df @@ -546,7 +546,7 @@ stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_00 ``` ### factor_df -**two-dimensional index** DataFrame which calculating using data_df by [transformer](https://github.com/zvtvz/zvt/blob/master/zvt/factors/factor.py#L18) +**two-dimensional index** DataFrame which calculating using data_df by [transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34) e.g., MacdTransformer. ``` In [12]: factor.factor_df @@ -572,7 +572,7 @@ stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_00 **two-dimensional index** DataFrame which calculating using factor_df or(and) data_df. It's used by TargetSelector. -e.g.,[macd](https://github.com/zvtvz/zvt/blob/master/zvt/factors/technical_factor.py#L56) +e.g.,[macd](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56) ``` In [14]: factor.result_df