diff --git "a/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\241\214\344\270\232\346\234\211\346\225\210\351\207\217\344\273\267\345\233\240\345\255\220\344\270\216\350\241\214\344\270\232\350\275\256\345\212\250\347\255\226\347\225\245/requirements.txt" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\241\214\344\270\232\346\234\211\346\225\210\351\207\217\344\273\267\345\233\240\345\255\220\344\270\216\350\241\214\344\270\232\350\275\256\345\212\250\347\255\226\347\225\245/requirements.txt" new file mode 100644 index 0000000..ec9634a --- /dev/null +++ "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\241\214\344\270\232\346\234\211\346\225\210\351\207\217\344\273\267\345\233\240\345\255\220\344\270\216\350\241\214\344\270\232\350\275\256\345\212\250\347\255\226\347\225\245/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 diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/requirements.txt" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/requirements.txt" new file mode 100644 index 0000000..ca51ae0 --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/requirements.txt" @@ -0,0 +1,3 @@ +numpy==1.21.5 +pandas==1.4.4 +plotly==5.5.0 diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-39.pyc" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-39.pyc" new file mode 100644 index 0000000..25a33c7 Binary files /dev/null and "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/__init__.cpython-39.pyc" differ diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/core.cpython-39.pyc" 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files /dev/null and "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/__pycache__/load_data.cpython-39.pyc" differ diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/load_data.py" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/load_data.py" new file mode 100644 index 0000000..6e146dd --- /dev/null +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/scr/load_data.py" @@ -0,0 +1,44 @@ +""" +Author: hugo2046 shen.lan123@gmail.com +Date: 2023-03-07 19:02:14 +LastEditors: hugo2046 shen.lan123@gmail.com +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] +) diff --git "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" index b1a5e03..f095d76 100644 --- "a/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" +++ "b/C-\346\213\251\346\227\266\347\261\273/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267/\350\241\214\344\270\232\346\214\207\346\225\260\351\241\266\351\203\250\345\222\214\345\272\225\351\203\250\344\277\241\345\217\267.ipynb" @@ -26,15 +26,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", - "from typing import Dict, List\n", - "from tqdm.notebook import tqdm\n", - "from scr import calc_industry_nhnl,plot_nhnl_signal" + "from typing import Dict, List" ] }, { @@ -103,7 +101,9 @@ "source": [ "### 数据获取\n", "\n", - "**此部分可以跳过直接读取data文件中的数据构建信号**" + "**此部分可以跳过直接读取data文件中的数据构建信号**\n", + "\n", + "[数据获取](https://drive.google.com/drive/folders/1AKnaQVwp_DdZ6wl27miQTWwuJAcAPDPv?usp=share_link)" ] }, { @@ -117,7 +117,8 @@ "from pathlib import Path\n", "\n", "sys.path.append(str(Path().resolve().parents[0]))\n", - "\n", + "from tqdm.notebook import tqdm\n", + "# 这里我使用的自己的数据库\n", "from knight_scr.scr.sql_service import (\n", " get_ts_price,\n", " get_ind_classify,\n", @@ -638,67 +639,9 @@ ")" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 信号构建\n", - "\n", - "这里直接构建已有数据构建" - ] - }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "with open('data/sw_cons_name.json','r') as f:\n", - " \n", - " sw_cons_name:Dict = json.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "price: pd.DataFrame = pd.read_csv(\"data/data.csv\", index_col=[0], parse_dates=[0])\n", - "\n", - "pivot_table: pd.DataFrame = pd.pivot_table(\n", - " price, index=\"trade_date\", columns=\"code\", values=[\"low\", \"high\"]\n", - ")\n", - "\n", - "ind_price: pd.DataFrame = pd.read_csv(\n", - " \"data/industry_price.csv\", index_col=[0], parse_dates=[0]\n", - ")\n", - "\n", - "pivot_ind_price:pd.DataFrame = pd.pivot_table(\n", - " ind_price, index=\"trade_date\", columns=\"name\", values='close'\n", - ")\n", - "classify_num: pd.DataFrame = pd.read_csv(\n", - " \"data/industry_classify_num.csv\", index_col=[0], parse_dates=[0]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "nhnl_df: pd.DataFrame = calc_industry_nhnl(\n", - " pivot_table, sw_cons_name, 52 * 5, classify_num,tradition=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [ 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name交通运输传媒公用事业非银金融食品饮料
trade_date
2014-01-021615.66869.631830.181910.495051.733068.11967.031820.032949.753110.232013-01-048278826319178571593853...1654.621633.074451.862442.391641.971446.491561.162085.201061.484658.9653248123475036165560
2014-01-031600.54883.521814.651896.495028.633047.10952.461803.592881.533041.162013-01-078279826319178571593853...1633.761608.184436.662468.341638.411445.331562.542051.371034.814615.0653248123475036165560
2014-01-061547.14864.401771.891818.074919.262953.29914.791752.002807.652945.862013-01-088279826319178571593853...1581.041562.434320.382402.111605.001403.291517.682028.911028.314500.2153248123475036165560
2014-01-071549.88877.561777.651818.744970.162966.80920.691759.832813.322943.222013-01-098279826319178571593853...1587.111569.974364.412418.801611.901410.631508.782022.181022.424521.7753248123475036165560
2014-01-081538.35914.001783.921818.815016.002961.86934.771755.682830.832939.19...1576.371564.094294.822466.131624.171427.981493.702032.831030.184552.21
...............................................................2013-01-108279826319178571593853...
2023-02-272386.26583.412250.093404.769416.412744.151588.844473.797041.166537.20...1695.062997.437324.374516.412586.312132.332554.973127.161547.2924368.50
2023-02-282400.09590.742271.143422.689577.382761.721593.424491.057045.126576.73...1725.233020.477378.864617.622605.862180.572563.843139.451562.7924394.10
2023-03-012424.65611.742283.593427.029572.932762.031601.774499.707169.866705.74...1724.573027.177356.284800.492625.232264.472574.993178.121592.5624651.70
2023-03-022436.05623.222282.973404.219469.682746.051604.844492.887141.956760.43...1728.873014.667351.534825.392627.052287.312586.223187.211591.1524573.10
2023-03-032451.72620.292302.853404.769473.962763.931629.944476.907165.706755.17...1717.343040.547315.954774.172599.052272.002600.953212.641600.3624429.9053248123475036165560
\n", - "

2231 rows × 31 columns

\n", + "

5 rows × 31 columns

\n", "" ], "text/plain": [ - "name 交通运输 传媒 公用事业 农林牧渔 医药生物 商贸零售 国防军工 \\\n", - "trade_date \n", - "2014-01-02 1615.66 869.63 1830.18 1910.49 5051.73 3068.11 967.03 \n", - "2014-01-03 1600.54 883.52 1814.65 1896.49 5028.63 3047.10 952.46 \n", - "2014-01-06 1547.14 864.40 1771.89 1818.07 4919.26 2953.29 914.79 \n", - "2014-01-07 1549.88 877.56 1777.65 1818.74 4970.16 2966.80 920.69 \n", - "2014-01-08 1538.35 914.00 1783.92 1818.81 5016.00 2961.86 934.77 \n", - "... ... ... ... ... ... ... ... \n", - "2023-02-27 2386.26 583.41 2250.09 3404.76 9416.41 2744.15 1588.84 \n", - "2023-02-28 2400.09 590.74 2271.14 3422.68 9577.38 2761.72 1593.42 \n", - "2023-03-01 2424.65 611.74 2283.59 3427.02 9572.93 2762.03 1601.77 \n", - "2023-03-02 2436.05 623.22 2282.97 3404.21 9469.68 2746.05 1604.84 \n", - "2023-03-03 2451.72 620.29 2302.85 3404.76 9473.96 2763.93 1629.94 \n", - "\n", - "name 基础化工 家用电器 建筑材料 ... 纺织服饰 综合 美容护理 \\\n", - "trade_date ... \n", - "2014-01-02 1820.03 2949.75 3110.23 ... 1654.62 1633.07 4451.86 \n", - "2014-01-03 1803.59 2881.53 3041.16 ... 1633.76 1608.18 4436.66 \n", - "2014-01-06 1752.00 2807.65 2945.86 ... 1581.04 1562.43 4320.38 \n", - "2014-01-07 1759.83 2813.32 2943.22 ... 1587.11 1569.97 4364.41 \n", - "2014-01-08 1755.68 2830.83 2939.19 ... 1576.37 1564.09 4294.82 \n", - "... ... ... ... ... ... ... ... \n", - "2023-02-27 4473.79 7041.16 6537.20 ... 1695.06 2997.43 7324.37 \n", - "2023-02-28 4491.05 7045.12 6576.73 ... 1725.23 3020.47 7378.86 \n", - "2023-03-01 4499.70 7169.86 6705.74 ... 1724.57 3027.17 7356.28 \n", - "2023-03-02 4492.88 7141.95 6760.43 ... 1728.87 3014.66 7351.53 \n", - "2023-03-03 4476.90 7165.70 6755.17 ... 1717.34 3040.54 7315.95 \n", + " 交通运输 传媒 公用事业 农林牧渔 医药生物 商贸零售 国防军工 基础化工 家用电器 建筑材料 ... \\\n", + "2013-01-04 82 78 82 63 191 78 57 159 38 53 ... \n", + "2013-01-07 82 79 82 63 191 78 57 159 38 53 ... \n", + "2013-01-08 82 79 82 63 191 78 57 159 38 53 ... \n", + "2013-01-09 82 79 82 63 191 78 57 159 38 53 ... \n", + "2013-01-10 82 79 82 63 191 78 57 159 38 53 ... \n", "\n", - "name 计算机 轻工制造 通信 钢铁 银行 非银金融 食品饮料 \n", - "trade_date \n", - "2014-01-02 2442.39 1641.97 1446.49 1561.16 2085.20 1061.48 4658.96 \n", - "2014-01-03 2468.34 1638.41 1445.33 1562.54 2051.37 1034.81 4615.06 \n", - "2014-01-06 2402.11 1605.00 1403.29 1517.68 2028.91 1028.31 4500.21 \n", - "2014-01-07 2418.80 1611.90 1410.63 1508.78 2022.18 1022.42 4521.77 \n", - "2014-01-08 2466.13 1624.17 1427.98 1493.70 2032.83 1030.18 4552.21 \n", - "... ... ... ... ... ... ... ... \n", - "2023-02-27 4516.41 2586.31 2132.33 2554.97 3127.16 1547.29 24368.50 \n", - "2023-02-28 4617.62 2605.86 2180.57 2563.84 3139.45 1562.79 24394.10 \n", - "2023-03-01 4800.49 2625.23 2264.47 2574.99 3178.12 1592.56 24651.70 \n", - "2023-03-02 4825.39 2627.05 2287.31 2586.22 3187.21 1591.15 24573.10 \n", - "2023-03-03 4774.17 2599.05 2272.00 2600.95 3212.64 1600.36 24429.90 \n", + " 纺织服饰 综合 美容护理 计算机 轻工制造 通信 钢铁 银行 非银金融 食品饮料 \n", + "2013-01-04 53 24 8 123 47 50 36 16 55 60 \n", + "2013-01-07 53 24 8 123 47 50 36 16 55 60 \n", + "2013-01-08 53 24 8 123 47 50 36 16 55 60 \n", + "2013-01-09 53 24 8 123 47 50 36 16 55 60 \n", + "2013-01-10 53 24 8 123 47 50 36 16 55 60 \n", "\n", - "[2231 rows x 31 columns]" + "[5 rows x 31 columns]" ] }, - "execution_count": 24, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pivot_ind_price" + "classify_num.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 信号构建\n", + "\n", + "这里直接构建已有数据构建\n", + "\n", + "这里有个细节问题:*行业每半年调整一次成分股,这里我近用了最近截面上的行业成分股生成信号,故前序行情可能失真*" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'电子'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: '电子'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m plot_nhnl_signal(pivot_ind_price[\u001b[39m\"\u001b[39;49m\u001b[39m电子\u001b[39;49m\u001b[39m\"\u001b[39;49m], nhnl_df[\u001b[39m\"\u001b[39m\u001b[39m电子\u001b[39m\u001b[39m\"\u001b[39m], \u001b[39m40\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m电子行业净新高占比\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m)\n", - "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3805\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3803\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m 3804\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3805\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m 3806\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3807\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m 3807\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3808\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3809\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3810\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[1;31mKeyError\u001b[0m: '电子'" - ] - } - ], + "outputs": [], "source": [ - "plot_nhnl_signal(pivot_ind_price[\"电子\"], nhnl_df[\"电子\"], 40, \"电子行业净新高占比\", True)" + "from scr import load_data\n", + "from scr import calc_industry_nhnl,plot_nhnl_signal" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "nhnl_df: pd.DataFrame = calc_industry_nhnl(\n", + " load_data.pivot_table, load_data.sw_cons_name, 52 * 5, load_data.classify_num,tradition=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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