├── transf.png ├── industry.png ├── funetuning.png ├── stacking_ct_0.16 ├── stacking_fw_0.05 ├── stacking_fw_0.08 ├── stacking_wy_0.07 ├── stacking_model_0.12 ├── stacking_model_0.125 ├── stacking_model_0.136 ├── .idea ├── .gitignore ├── vcs.xml ├── misc.xml ├── inspectionProfiles │ ├── profiles_settings.xml │ └── Project_Default.xml ├── modules.xml ├── Financial-fake-Predict.iml └── deployment.xml ├── README.md ├── Data Prepare.ipynb ├── pre_data_wy.csv └── PRED_wy.csv /transf.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/transf.png -------------------------------------------------------------------------------- /industry.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/industry.png -------------------------------------------------------------------------------- /funetuning.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/funetuning.png -------------------------------------------------------------------------------- /stacking_ct_0.16: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_ct_0.16 -------------------------------------------------------------------------------- /stacking_fw_0.05: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_fw_0.05 -------------------------------------------------------------------------------- /stacking_fw_0.08: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_fw_0.08 -------------------------------------------------------------------------------- /stacking_wy_0.07: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_wy_0.07 -------------------------------------------------------------------------------- /stacking_model_0.12: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_model_0.12 -------------------------------------------------------------------------------- /stacking_model_0.125: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_model_0.125 -------------------------------------------------------------------------------- /stacking_model_0.136: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cpuritan/Financial-fake-Predict/HEAD/stacking_model_0.136 -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | # 数据源本地存储已忽略文件 5 | /dataSources/ 6 | /dataSources.local.xml 7 | # 基于编辑器的 HTTP 客户端请求 8 | /httpRequests/ 9 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/Financial-fake-Predict.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 17 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Financial-fake-Predict 2 | 近年来不时出现上市公司财务数据造假及暴雷的情况。面对上市公司多年的财务数据报告，筛选数据指标进行跟踪分析和研究，识别真伪，避免投资踩雷🤣。谁造假谁是是是🐱‍🐉😒 3 | 4 | 本次项目源于泰迪杯数据挖掘挑战赛国二，代码未能较好包装略显凌乱。 5 | 6 | ### 数据 7 | 基于https://www.tipdm.org:10010/#/competition/1354705811842195456/question 的三个问题 8 | 9 | 本项目基于初始的行业分类，在此基础上又将其划分到，制造业，服务业，文娱行业，传统行业四个行业进行分析，行业重组如下 10 | ![image](https://github.com/Cpuritan/Financial-fake-Predict/blob/master/industry.png) 11 | 12 | 缺失值填补通过剔除和KNN缺失值填补结合。 13 | 14 | ### 模型 15 | 同时部分应用迁移学习思想，发掘不同行业的共同影响特征 16 | ![image](https://github.com/Cpuritan/Financial-fake-Predict/blob/master/transf.png) 17 | 18 | 主要是基于xgboost,randomforest,svc等机器学习模型，并结合stacking构建集成模型,部分调参如下 19 | ![image](https://github.com/Cpuritan/Financial-fake-Predict/blob/master/funetuning.png) 20 | -------------------------------------------------------------------------------- /Data Prepare.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 1 导入数据" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import numpy as np\n", 17 | "import pandas as pd" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "df_ = pd.read_csv(r'./附件2_num.csv')\n", 27 | "df = df_.iloc[:18060,:]" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 3, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "name": "stderr", 37 | "output_type": "stream", 38 | "text": [ 39 | "D:\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:1123: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation\n", 40 | " warnings.warn(\n" 41 | ] 42 | } 43 | ], 44 | "source": [ 45 | "df_ = pd.read_csv(r'./附件2_num.csv')\n", 46 | "df = df_.iloc[:18060,:]\n", 47 | "df_indus = pd.read_excel(r'./附件1.xlsx')\n", 48 | "df = pd.merge(df,df_indus,on=\"TICKER_SYMBOL\",how='left')\n", 49 | "#提取制造业分析\n", 50 | "df_manu=df[df['所属行业']=='制造业']\n", 51 | "#df_manu = pd.read_csv(r'./制造业_full.csv')#利用Excel将制造业数据划分" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "### 2 数据预处理\n", 59 | "#### 2.1 移除缺失大的特征" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 4, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [ 68 | "def find_missing(data):\n", 69 | " missing_num =data.isna().sum(axis=0).sort_values(ascending=False)\n", 70 | " missing_prop=missing_num/float(len(data))\n", 71 | " drop_index=missing_prop[missing_prop>0.80].index.tolist()#筛选出缺失值大于50%的特征\n", 72 | " return drop_index\n", 73 | "\n", 74 | "df_manu = df_manu.drop(find_missing(df_manu.iloc[:,1:-2]),axis=1)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "#### 2.2 移除低方差特征" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 5, 87 | "metadata": {}, 88 | "outputs": [ 89 | { 90 | "name": "stdout", 91 | "output_type": "stream", 92 | "text": [ 93 | "(9943, 268) (9943, 261)\n" 94 | ] 95 | }, 96 | { 97 | "data": { 98 | "text/html": [ 99 | "
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TICKER_SYMBOLACT_PUBTIMEPUBLISH_DATEEND_DATE_REPEND_DATECASH_C_EQUIVNOTES_RECEIVARPREPAYMENTINT_RECEIV...DAYS_ARAP_TURNOVERCA_TURNOVEROPER_CYCLEINVEN_TURNOVERFA_TURNOVERTFA_TURNOVERDAYS_APDAYS_INVENAR_TURNOVER
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" 265 | ], 266 | "text/plain": [ 267 | " TICKER_SYMBOL ACT_PUBTIME PUBLISH_DATE END_DATE_REP END_DATE \\\n", 268 | "0 4019.0 3.0 3.0 2.0 1.0 \n", 269 | "1 8166.0 3.0 3.0 2.0 1.0 \n", 270 | "2 11737.0 3.0 3.0 2.0 1.0 \n", 271 | "3 16479.0 3.0 3.0 2.0 1.0 \n", 272 | "4 16842.0 4.0 4.0 3.0 1.0 \n", 273 | "\n", 274 | " CASH_C_EQUIV NOTES_RECEIV AR PREPAYMENT INT_RECEIV ... \\\n", 275 | "0 6.719939e+08 1.643520e+08 3.451945e+08 1.039522e+07 2311657.01 ... \n", 276 | "1 1.010981e+09 2.155752e+09 2.887470e+08 1.075202e+08 NaN ... \n", 277 | "2 1.502023e+09 2.688988e+07 9.517806e+08 4.157376e+07 6464811.93 ... \n", 278 | "3 9.966031e+08 3.686740e+05 2.824134e+08 2.741431e+07 2528572.87 ... \n", 279 | "4 5.763404e+07 8.990000e+06 5.941825e+07 3.603420e+07 NaN ... \n", 280 | "\n", 281 | " DAYS_AR AP_TURNOVER CA_TURNOVER OPER_CYCLE INVEN_TURNOVER FA_TURNOVER \\\n", 282 | "0 NaN NaN NaN NaN NaN NaN \n", 283 | "1 NaN NaN NaN NaN NaN NaN \n", 284 | "2 NaN NaN NaN NaN NaN NaN \n", 285 | "3 NaN NaN NaN NaN NaN NaN \n", 286 | "4 NaN NaN NaN NaN NaN NaN \n", 287 | "\n", 288 | " TFA_TURNOVER DAYS_AP DAYS_INVEN AR_TURNOVER \n", 289 | "0 NaN NaN NaN NaN \n", 290 | "1 NaN NaN NaN NaN \n", 291 | "2 NaN NaN NaN NaN \n", 292 | "3 NaN NaN NaN NaN \n", 293 | "4 NaN NaN NaN NaN \n", 294 | "\n", 295 | "[5 rows x 261 columns]" 296 | ] 297 | }, 298 | "execution_count": 5, 299 | "metadata": {}, 300 | "output_type": "execute_result" 301 | } 302 | ], 303 | "source": [ 304 | "from sklearn.feature_selection import VarianceThreshold\n", 305 | "sel = VarianceThreshold(threshold=1)\n", 306 | "df1 = pd.DataFrame(sel.fit_transform(df_manu.iloc[:,:-2]))\n", 307 | "print(df_manu.shape,df1.shape)\n", 308 | "df1.columns = df_manu.iloc[:,:-2].columns[sel.get_support(indices=False)]\n", 309 | "df1.head()" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "#### 2.3 基于KNN模型的缺失值填补" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": 6, 322 | "metadata": {}, 323 | "outputs": [ 324 | { 325 | "name": "stdout", 326 | "output_type": "stream", 327 | "text": [ 328 | "80.0\n" 329 | ] 330 | }, 331 | { 332 | "data": { 333 | "text/html": [ 334 | "
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TICKER_SYMBOLACT_PUBTIMEPUBLISH_DATEEND_DATE_REPEND_DATECASH_C_EQUIVNOTES_RECEIVARPREPAYMENTINT_RECEIV...DAYS_ARAP_TURNOVERCA_TURNOVEROPER_CYCLEINVEN_TURNOVERFA_TURNOVERTFA_TURNOVERDAYS_APDAYS_INVENAR_TURNOVER
04019.03.03.02.01.06.719939e+081.643520e+083.451945e+081.039522e+072311657.010...25.957747.924181.23638194.739142.883501.563001.4279651.28988193.7624017.56994
18166.03.03.02.01.01.010981e+092.155752e+092.887470e+081.075202e+083560974.464...66.8610831.291722.18356191.368187.7942415.6650416.3829667.77566124.5071412.01438
211737.03.03.02.01.01.502023e+092.688988e+079.517806e+084.157376e+076464811.930...64.877989.481860.73224143.150227.492989.116507.8852458.1244878.272246.59040
316479.03.03.02.01.09.966031e+083.686740e+052.824134e+082.741431e+072528572.870...42.591525.031481.40024229.231744.219707.899387.7718491.31484186.6402213.56754
416842.04.04.03.01.05.763404e+078.990000e+065.941825e+073.603420e+0765976.256...120.135587.725941.3807657.3504434.9154225.7177026.8981671.9367277.276984.06470
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5 rows × 261 columns

\n", 499 | "
" 500 | ], 501 | "text/plain": [ 502 | " TICKER_SYMBOL ACT_PUBTIME PUBLISH_DATE END_DATE_REP END_DATE \\\n", 503 | "0 4019.0 3.0 3.0 2.0 1.0 \n", 504 | "1 8166.0 3.0 3.0 2.0 1.0 \n", 505 | "2 11737.0 3.0 3.0 2.0 1.0 \n", 506 | "3 16479.0 3.0 3.0 2.0 1.0 \n", 507 | "4 16842.0 4.0 4.0 3.0 1.0 \n", 508 | "\n", 509 | " CASH_C_EQUIV NOTES_RECEIV AR PREPAYMENT INT_RECEIV ... \\\n", 510 | "0 6.719939e+08 1.643520e+08 3.451945e+08 1.039522e+07 2311657.010 ... \n", 511 | "1 1.010981e+09 2.155752e+09 2.887470e+08 1.075202e+08 3560974.464 ... \n", 512 | "2 1.502023e+09 2.688988e+07 9.517806e+08 4.157376e+07 6464811.930 ... \n", 513 | "3 9.966031e+08 3.686740e+05 2.824134e+08 2.741431e+07 2528572.870 ... \n", 514 | "4 5.763404e+07 8.990000e+06 5.941825e+07 3.603420e+07 65976.256 ... \n", 515 | "\n", 516 | " DAYS_AR AP_TURNOVER CA_TURNOVER OPER_CYCLE INVEN_TURNOVER \\\n", 517 | "0 25.95774 7.92418 1.23638 194.73914 2.88350 \n", 518 | "1 66.86108 31.29172 2.18356 191.36818 7.79424 \n", 519 | "2 64.87798 9.48186 0.73224 143.15022 7.49298 \n", 520 | "3 42.59152 5.03148 1.40024 229.23174 4.21970 \n", 521 | "4 120.13558 7.72594 1.38076 57.35044 34.91542 \n", 522 | "\n", 523 | " FA_TURNOVER TFA_TURNOVER DAYS_AP DAYS_INVEN AR_TURNOVER \n", 524 | "0 1.56300 1.42796 51.28988 193.76240 17.56994 \n", 525 | "1 15.66504 16.38296 67.77566 124.50714 12.01438 \n", 526 | "2 9.11650 7.88524 58.12448 78.27224 6.59040 \n", 527 | "3 7.89938 7.77184 91.31484 186.64022 13.56754 \n", 528 | "4 25.71770 26.89816 71.93672 77.27698 4.06470 \n", 529 | "\n", 530 | "[5 rows x 261 columns]" 531 | ] 532 | }, 533 | "execution_count": 6, 534 | "metadata": {}, 535 | "output_type": "execute_result" 536 | } 537 | ], 538 | "source": [ 539 | "from sklearn.impute import KNNImputer\n", 540 | "\n", 541 | "imputer=KNNImputer()\n", 542 | "df1_full = pd.DataFrame(imputer.fit_transform(df1,df_manu['FLAG']))\n", 543 | "print(sum(df_manu.iloc[:,-2]))\n", 544 | "df1_full.columns = df_manu.iloc[:,:-2].columns[sel.get_support(indices=False)]\n", 545 | "df1_full.head()" 546 | ] 547 | } 548 | ], 549 | "metadata": { 550 | "kernelspec": { 551 | "display_name": "Python 3", 552 | "language": "python", 553 | "name": "python3" 554 | }, 555 | "language_info": { 556 | "codemirror_mode": { 557 | "name": "ipython", 558 | "version": 3 559 | }, 560 | "file_extension": ".py", 561 | "mimetype": "text/x-python", 562 | "name": "python", 563 | "nbconvert_exporter": "python", 564 | "pygments_lexer": "ipython3", 565 | "version": "3.8.5" 566 | }, 567 | "toc": { 568 | "base_numbering": 1, 569 | "nav_menu": {}, 570 | "number_sections": true, 571 | "sideBar": true, 572 | "skip_h1_title": false, 573 | "title_cell": "Table of Contents", 574 | "title_sidebar": "Contents", 575 | "toc_cell": false, 576 | "toc_position": {}, 577 | "toc_section_display": true, 578 | "toc_window_display": false 579 | } 580 | }, 581 | "nbformat": 4, 582 | "nbformat_minor": 4 583 | } 584 | 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