添加行业NHNLX信号

B-因子构建类/行业有效量价因子与行业轮动策略/requirements.txt

@@ -0,0 +1,11 @@
+alphalens==0.4.0
+empyrical==0.5.5
+gradient_free_optimizers==1.2.3
+matplotlib==3.6.2
+numpy==1.21.5
+pandas==1.4.4
+pyqlib==0.9.1
+qlib==0.0.2.dev20
+scikit_learn==1.2.1
+seaborn==0.11.2
+TA_Lib==0.4.19

C-择时类/行业指数顶部和底部信号/requirements.txt

@@ -0,0 +1,3 @@
+numpy==1.21.5
+pandas==1.4.4
+plotly==5.5.0

C-择时类/行业指数顶部和底部信号/scr/load_data.py

@@ -0,0 +1,44 @@
+"""
+Author: hugo2046 [email protected]
+Date: 2023-03-07 19:02:14
+LastEditors: hugo2046 [email protected]
+LastEditTime: 2023-03-07 19:28:33
+Description: 数据加载
+"""
+import json
+from typing import Dict
+
+import pandas as pd
+
+__all__ = [
+    "sw_cons_name",
+    "price",
+    "pivot_table",
+    "ind_price",
+    "pivot_ind_price",
+    "classify_num",
+]
+
+with open("data/sw_cons_name.json", "r") as f:
+
+    sw_cons_name: Dict = json.load(f)
+
+price: pd.DataFrame = pd.read_csv("data/data.csv", index_col=[0], parse_dates=[0])
+
+pivot_table: pd.DataFrame = pd.pivot_table(
+    price, index="trade_date", columns="code", values=["low", "high"]
+)
+pivot_table.index = pd.to_datetime(pivot_table.index)
+
+ind_price: pd.DataFrame = pd.read_csv(
+    "data/industry_price.csv", index_col=[0], parse_dates=[0]
+)
+
+pivot_ind_price: pd.DataFrame = pd.pivot_table(
+    ind_price, index="trade_date", columns="name", values="close"
+)
+pivot_ind_price.index = pd.to_datetime(pivot_ind_price.index)
+
+classify_num: pd.DataFrame = pd.read_csv(
+    "data/industry_classify_num.csv", index_col=[0], parse_dates=[0]
+)