feat: 完善 QMT 交易模块文档和配置展示功能
- 优化前端仪表盘界面 - 添加配置文件可视化展示 - 编写 QMT 模块配置文档 - 完善项目规则体系（KiloCode）
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@@ -15,18 +15,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/mnt/d/PyProject/NewStock/main/train\n"
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]
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},
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'main.factor'; 'main' is not a package",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 9\u001b[39m \u001b[38;5;28mprint\u001b[39m(os.getcwd())\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_rolling_factor, get_simple_factor\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m read_industry_data\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor_processor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m calculate_score\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'main.factor'; 'main' is not a package"
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"/mnt/d/PyProject/NewStock\n"
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]
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}
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],
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@@ -53,20 +42,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "4a481c60",
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"outputs": [],
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"source": [
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"# 设置使用核心\n",
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"import os\n",
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@@ -75,7 +54,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "a79cafb06a7e0e43",
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"metadata": {
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"ExecuteTime": {
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@@ -99,7 +78,7 @@
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"cyq perf\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 9315967 entries, 0 to 9315966\n",
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"RangeIndex: 9359748 entries, 0 to 9359747\n",
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"Data columns (total 33 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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@@ -140,15 +119,6 @@
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"memory usage: 2.2+ GB\n",
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"None\n"
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]
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},
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"source": [
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@@ -184,7 +154,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"id": "cac01788dac10678",
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"metadata": {
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"ExecuteTime": {
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@@ -199,15 +169,6 @@
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"text": [
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"industry\n"
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]
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},
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"source": [
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@@ -261,7 +222,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"id": "c4e9e1d31da6dba6",
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"metadata": {
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"ExecuteTime": {
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@@ -272,17 +233,7 @@
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"outputs": [],
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"source": [
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"from main.factor.factor import *\n",
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"\n",
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@@ -371,7 +322,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"id": "a735bc02ceb4d872",
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"metadata": {
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"ExecuteTime": {
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@@ -379,17 +330,7 @@
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"start_time": "2025-04-03T12:47:10.751831Z"
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}
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},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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||||
]
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||||
}
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],
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"outputs": [],
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"source": [
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"import talib\n",
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"import numpy as np"
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@@ -397,7 +338,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "53f86ddc0677a6d7",
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"metadata": {
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"ExecuteTime": {
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@@ -409,17 +350,7 @@
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},
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"scrolled": true
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},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"outputs": [],
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"source": [
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"from main.utils.factor import get_act_factor\n",
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"\n",
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@@ -474,7 +405,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "dbe2fd8021b9417f",
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"metadata": {
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"ExecuteTime": {
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@@ -489,15 +420,6 @@
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"text": [
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"['ts_code', 'open', 'close', 'high', 'low', 'amount', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
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]
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},
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||||
{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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||||
"traceback": [
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||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
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||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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||||
]
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||||
}
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||||
],
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"source": [
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@@ -511,7 +433,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 9,
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"id": "85c3e3d0235ffffa",
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"metadata": {
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"ExecuteTime": {
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@@ -519,17 +441,7 @@
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"start_time": "2025-04-03T12:47:15.990101Z"
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}
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},
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"outputs": [
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{
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||||
"ename": "",
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||||
"evalue": "",
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||||
"output_type": "error",
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||||
"traceback": [
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||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
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||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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||||
]
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||||
}
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||||
],
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"outputs": [],
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"source": [
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"fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n",
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" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
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@@ -553,7 +465,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"id": "92d84ce15a562ec6",
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"metadata": {
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"ExecuteTime": {
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@@ -688,22 +600,13 @@
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"Calculating cs_rank_size...\n",
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"Finished cs_rank_size.\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 4910010 entries, 0 to 4910009\n",
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"RangeIndex: 5042085 entries, 0 to 5042084\n",
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"Columns: 181 entries, ts_code to cs_rank_size\n",
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"dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
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"memory usage: 6.3+ GB\n",
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"memory usage: 6.5+ GB\n",
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"None\n",
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"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
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]
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},
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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||||
}
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],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f80b2f9",
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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||||
]
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||||
}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 11,
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"id": "b87b938028afa206",
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"metadata": {
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"ExecuteTime": {
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@@ -837,17 +722,7 @@
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"start_time": "2025-04-03T13:08:02.469611Z"
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}
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},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
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||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
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||||
}
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],
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"outputs": [],
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"source": [
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"from scipy.stats import ks_2samp, wasserstein_distance\n",
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"\n",
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@@ -877,7 +752,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 12,
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"id": "f4f16d63ad18d1bc",
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"metadata": {
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"ExecuteTime": {
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@@ -885,17 +760,7 @@
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"start_time": "2025-04-03T13:08:03.665739Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import statsmodels.api as sm # 用于中性化回归\n",
|
||||
@@ -1113,7 +978,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"id": "40e6b68a91b30c79",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
@@ -1121,17 +986,7 @@
|
||||
"start_time": "2025-04-03T13:08:03.694904Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
|
||||
" log=True):\n",
|
||||
@@ -1443,7 +1298,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 14,
|
||||
"id": "47c12bb34062ae7a",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
@@ -1451,17 +1306,7 @@
|
||||
"start_time": "2025-04-03T14:49:25.889057Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"days = 5\n",
|
||||
"validation_days = 120\n",
|
||||
@@ -1487,7 +1332,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 15,
|
||||
"id": "29221dde",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1497,15 +1342,6 @@
|
||||
"text": [
|
||||
"191\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -1539,20 +1375,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 16,
|
||||
"id": "03ee5daf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# df = fill_nan_with_daily_median(df, feature_columns)\n",
|
||||
"for feature_col in [col for col in feature_columns if col in df.columns]:\n",
|
||||
@@ -1562,7 +1388,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 17,
|
||||
"id": "b76ea08a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1583,14 +1409,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"MAD Filtering: 62%|██████▏ | 81/131 [00:08<00:05, 9.28it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 9.63it/s]\n"
|
||||
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 9.39it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1605,7 +1424,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"MAD Filtering: 100%|██████████| 131/131 [00:14<00:00, 8.97it/s]\n"
|
||||
"MAD Filtering: 100%|██████████| 131/131 [00:15<00:00, 8.45it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1645,27 +1464,18 @@
|
||||
"截面 MAD 去极值处理完成。\n",
|
||||
"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||||
"df最小日期: 2019-01-02\n",
|
||||
"df最大日期: 2025-11-21\n",
|
||||
"2056030\n",
|
||||
"df最大日期: 2026-01-23\n",
|
||||
"2055951\n",
|
||||
"train_data最小日期: 2020-01-02\n",
|
||||
"train_data最大日期: 2022-12-30\n",
|
||||
"2135782\n",
|
||||
"2267560\n",
|
||||
"test_data最小日期: 2023-01-03\n",
|
||||
"test_data最大日期: 2025-11-21\n",
|
||||
"test_data最大日期: 2026-01-23\n",
|
||||
" ts_code trade_date log_circ_mv\n",
|
||||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||||
"2 000001.SZ 2019-01-04 16.633371\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -1796,20 +1606,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 18,
|
||||
"id": "3ff2d1c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
@@ -1947,28 +1747,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 19,
|
||||
"id": "a5bbb8be",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<catboost.core.CatBoostClassifier at 0x7602293f6030>"
|
||||
"<catboost.core.CatBoostClassifier at 0x7cca5687f800>"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -1981,7 +1772,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 20,
|
||||
"id": "5d1522a7538db91b",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
@@ -1989,19 +1780,10 @@
|
||||
"start_time": "2025-04-03T15:04:39.298483Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(300, 'total_mv'))\n",
|
||||
"score_df = score_df[~score_df['ts_code'].isin(['002856.SZ'])]\n",
|
||||
"# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n",
|
||||
"# score_df = score_df[score_df['pe_ttm'] > 0]\n",
|
||||
"score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||||
@@ -2029,7 +1811,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 21,
|
||||
"id": "09b1799e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -2041,15 +1823,6 @@
|
||||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||||
"[]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -2060,7 +1833,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 22,
|
||||
"id": "e53b209a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -2068,7 +1841,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"5587 2056030\n",
|
||||
"5584 2055951\n",
|
||||
" ts_code trade_date turnover_rate\n",
|
||||
"0 000001.SZ 2023-01-03 1.1307\n",
|
||||
"1 000001.SZ 2023-01-04 1.1284\n",
|
||||
@@ -2076,22 +1849,13 @@
|
||||
"3 000001.SZ 2023-01-06 0.6162\n",
|
||||
"4 000001.SZ 2023-01-09 0.5450\n",
|
||||
"... ... ... ...\n",
|
||||
"2135777 605599.SH 2025-11-17 0.3820\n",
|
||||
"2135778 605599.SH 2025-11-18 0.3565\n",
|
||||
"2135779 605599.SH 2025-11-19 0.3748\n",
|
||||
"2135780 605599.SH 2025-11-20 0.3132\n",
|
||||
"2135781 605599.SH 2025-11-21 0.4580\n",
|
||||
"2267555 605599.SH 2026-01-19 0.6158\n",
|
||||
"2267556 605599.SH 2026-01-20 0.9493\n",
|
||||
"2267557 605599.SH 2026-01-21 1.1732\n",
|
||||
"2267558 605599.SH 2026-01-22 0.8848\n",
|
||||
"2267559 605599.SH 2026-01-23 0.9550\n",
|
||||
"\n",
|
||||
"[2135782 rows x 3 columns]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||||
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
|
||||
"[2267560 rows x 3 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in New Issue
Block a user