add readthedocs config

.readthedocs.yaml

@@ -0,0 +1,10 @@
+version: 2
+python:
+  install:
+    - requirements: requirements/docs.txt
+    - method: pip
+      path: .
+sphinx:
+  builder: dirhtml
+  fail_on_warning: true
+  configuration: docs/source/conf.py

docs/source/api/index.rst

@@ -6,6 +6,7 @@ API
    :template: custom-module-template.rst
    :recursive:
 
+    zvt
     zvt.api
     zvt.autocode
     zvt.consts

docs/source/user_guide/intro.rst

@@ -262,7 +262,7 @@ pre adjusted(qfq), post adjusted(hfq), or not adjusted(bfq).
 .. note:
     In order to be compatible with historical data, the qfq is an exception, {adjust_type} is left empty
 
-The pre defined kdata schema could be in :module:`~.zvt.domain.quotes`, it's seperated by
+The pre defined kdata schema could be in :py:mod:`~.zvt.domain.quotes`, it's seperated by
 entity_schema, level, and adjust type.
 
 Normal data

requirements/docs.txt

@@ -0,0 +1,4 @@
+sphinx==4.4.0
+sphinx_autodoc_typehints==1.15.3
+rst2pdf==0.99
+ipython==7.31.1

src/zvt/ml/ml.py

@@ -191,8 +191,7 @@ def build_feature(
         self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp
     ) -> pd.DataFrame:
         """
-        result df format:
-
+        result df format
                                   col1    col2    col3    ...
         entity_id    timestamp
                                   1.2     0.5     0.3     ...
@@ -214,6 +213,13 @@ class MaStockMLMachine(StockMLMachine):
     def build_feature(
         self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp
     ) -> pd.DataFrame:
+        """
+
+        :param entity_ids:
+        :param start_timestamp:
+        :param end_timestamp:
+        :return:
+        """
         t = MaTransformer(windows=[5, 10, 120, 250])
         df = t.transform(self.kdata_df)
         return df

src/zvt/recorders/exchange/exchange_etf_meta_recorder.py

@@ -71,12 +71,6 @@ def persist_etf_list(self, df: pd.DataFrame, exchange: str):
         df_to_db(df=df, data_schema=Etf, provider=self.provider, force_update=False)
 
     def download_sh_etf_component(self, df: pd.DataFrame):
-        """
-        ETF_CLASS => 1. 单市场 ETF 2.跨市场 ETF 3. 跨境 ETF
-                        5. 债券 ETF 6. 黄金 ETF
-        :param df: ETF 列表数据
-        :return: None
-        """
         query_url = (
             "http://query.sse.com.cn/infodisplay/queryConstituentStockInfo.do?" "isPagination=false&type={}&etfClass={}"
         )