diff --git a/财报筛选/金字塔选股.ipynb b/财报筛选/金字塔选股.ipynb new file mode 100644 index 0000000..39a6cf0 --- /dev/null +++ b/财报筛选/金字塔选股.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-01-11T13:37:21.407428Z", + "start_time": "2025-01-11T13:37:18.600140Z" + } + }, + "source": [ + "import pandas as pd\n", + "import tushare as ts\n", + "from numpy.ma.extras import column_stack\n", + "\n", + "ts_pro = ts.pro_api(token=\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")" + ], + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-11T15:15:46.117153Z", + "start_time": "2025-01-11T15:15:46.107715Z" + } + }, + "cell_type": "code", + "source": [ + "def get_blancesheet_df(start_year, end_year):\n", + " fields = [\"ts_code\", \"end_date\", \"total_assets\", \"total_hldr_eqy_inc_min_int\", \"money_cap\", \"accounts_receiv\",\n", + " \"accounts_receiv_bill\",\n", + " \"oth_rcv_total\", \"inventories\"]\n", + " result = pd.DataFrame(columns=fields)\n", + " for year in range(start_year, end_year + 1):\n", + " period = f\"{year}1231\"\n", + " temp = ts_pro.balancesheet_vip(period=period, fields=\",\".join(fields))\n", + " result = pd.concat([result, temp], ignore_index=True)\n", + " return result\n", + "\n", + "\n", + "def get_income_df(start_year, end_year):\n", + " fields = [\"ts_code\", \"end_date\", \"n_income\", \"revenue\", \"total_revenue\", \"oper_cost\", \"operate_profit\",\n", + " \"total_cogs\"]\n", + " result = pd.DataFrame(columns=fields)\n", + " for year in range(start_year, end_year + 1):\n", + " period = f\"{year}1231\"\n", + " temp = ts_pro.income_vip(period=period, fields=\",\".join(fields))\n", + " result = pd.concat([result, temp], ignore_index=True)\n", + " return result\n", + "\n", + "\n", + "def clean_df(df):\n", + " df = df.drop_duplicates(subset=[\"ts_code\", \"end_date\"])\n", + " df[\"end_date\"] = df[\"end_date\"].str[:4]\n", + " return df" + ], + "id": "3100e2f517963834", + "outputs": [], + "execution_count": 107 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-11T15:16:10.137410Z", + "start_time": "2025-01-11T15:15:51.266092Z" + } + }, + "cell_type": "code", + "source": [ + "blancesheet_df = clean_df(get_blancesheet_df(2018, 2024))\n", + "income_df = clean_df(get_income_df(2018, 2024))" + ], + "id": "66ab3d52c88888ea", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\lanyuanxiaoyao\\AppData\\Local\\Temp\\ipykernel_32660\\3334449392.py:9: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " result = pd.concat([result, temp], ignore_index=True)\n", + "C:\\Users\\lanyuanxiaoyao\\AppData\\Local\\Temp\\ipykernel_32660\\3334449392.py:9: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " result = pd.concat([result, temp], ignore_index=True)\n", + "C:\\Users\\lanyuanxiaoyao\\AppData\\Local\\Temp\\ipykernel_32660\\3334449392.py:20: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " result = pd.concat([result, temp], ignore_index=True)\n", + "C:\\Users\\lanyuanxiaoyao\\AppData\\Local\\Temp\\ipykernel_32660\\3334449392.py:20: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " result = pd.concat([result, temp], ignore_index=True)\n" + ] + } + ], + "execution_count": 108 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-11T15:16:38.476024Z", + "start_time": "2025-01-11T15:16:38.460593Z" + } + }, + "cell_type": "code", + "source": [ + "def cal_roe(df):\n", + " df_group = stock_df.groupby(\"ts_code\")\n", + " df[\"prev_total_hldr_eqy_inc_min_int\"] = df_group[\"total_hldr_eqy_inc_min_int\"].shift(1)\n", + " df[\"roe\"] = df[\"n_income\"] / (\n", + " (df[\"prev_total_hldr_eqy_inc_min_int\"] + df[\"total_hldr_eqy_inc_min_int\"]) / 2)\n", + " df[\"roe\"] = df[\"roe\"].round(4)\n", + " df.drop(columns=[\"prev_total_hldr_eqy_inc_min_int\"], inplace=True)\n", + "\n", + "\n", + "def cal_roa(df):\n", + " df_group = stock_df.groupby(\"ts_code\")\n", + " df[\"prev_toral_assets\"] = df_group[\"total_assets\"].shift(1)\n", + " df[\"roa\"] = df[\"n_income\"] / ((df[\"prev_toral_assets\"] + df[\"total_assets\"]) / 2)\n", + " df[\"roa\"] = df[\"roa\"].round(4)\n", + " df.drop(columns=[\"prev_toral_assets\"], inplace=True)\n", + "\n", + "\n", + "# 总资产周转率\n", + "def cal_total_assets_turnover(df):\n", + " df_group = stock_df.groupby(\"ts_code\")\n", + " df[\"prev_toral_assets\"] = df_group[\"total_assets\"].shift(1)\n", + " df[\"total_assets_turnover\"] = df[\"total_revenue\"] / ((df[\"prev_toral_assets\"] + df[\"total_assets\"]) / 2)\n", + " df[\"total_assets_turnover\"] = df[\"total_assets_turnover\"].round(2)\n", + " df.drop(columns=[\"prev_toral_assets\"], inplace=True)\n", + "\n", + "\n", + "# 现金比率\n", + "def cal_cash_ratio(df):\n", + " df[\"cash_ratio\"] = df[\"money_cap\"] / df[\"total_assets\"]\n", + " df[\"cash_ratio\"] = df[\"cash_ratio\"].round(4)\n", + "\n", + "\n", + "# 毛利率\n", + "def cal_gross_profit(df):\n", + " df[\"gross_profit\"] = (df[\"total_revenue\"] - df[\"oper_cost\"]) / df[\"total_revenue\"]\n", + " df[\"gross_profit\"] = df[\"gross_profit\"].round(4)\n", + "\n", + "\n", + "# 经营利润率\n", + "def cal_operating_profit(df):\n", + " df[\"operating_profit\"] = df[\"operate_profit\"] / df[\"total_revenue\"]\n", + " df[\"operating_profit\"] = df[\"operating_profit\"].round(4)\n", + "\n", + "\n", + "# 经营安全边际\n", + "def cal_operating_safety_margin(df):\n", + " df[\"operating_safety_margin\"] = df[\"operating_profit\"] / df[\"gross_profit\"]\n", + " df[\"operating_safety_margin\"] = df[\"operating_safety_margin\"].round(4)\n", + "\n", + "\n", + "# 收现日数\n", + "def cal_collection_cash_period(df):\n", + " df[\"collection_cash_period\"] = 360 / (df[\"total_revenue\"] / df[\"accounts_receiv\"])\n", + " df[\"collection_cash_period\"] = df[\"collection_cash_period\"].round(2)\n", + "\n", + "\n", + "# 销货日数\n", + "def cal_sales_period(df):\n", + " df[\"sales_period\"] = 360 / (df[\"oper_cost\"] / df[\"inventories\"])\n", + " df[\"sales_period\"] = df[\"sales_period\"].round(2)" + ], + "id": "85618db7e29a25eb", + "outputs": [], + "execution_count": 110 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-11T15:24:45.366231Z", + "start_time": "2025-01-11T15:24:45.251939Z" + } + }, + "cell_type": "code", + "source": [ + "stock_df = pd.merge(blancesheet_df, income_df, on=[\"ts_code\", \"end_date\"])\n", + "\n", + "cal_roe(stock_df)\n", + "cal_roa(stock_df)\n", + "cal_total_assets_turnover(stock_df)\n", + "cal_cash_ratio(stock_df)\n", + "cal_gross_profit(stock_df)\n", + "cal_operating_profit(stock_df)\n", + "cal_operating_safety_margin(stock_df)\n", + "cal_collection_cash_period(stock_df)\n", + "cal_sales_period(stock_df)\n", + "\n", + "stock_df[\n", + " # [\"ts_code\", \"end_date\", \"roe\", \"roa\", \"total_assets_turnover\", \"cash_ratio\", \"gross_profit\", \"operating_profit\", \"operating_safety_margin\"]\n", + " [\"ts_code\", \"end_date\", \"collection_cash_period\", \"sales_period\", \"oper_cost\", \"total_cogs\"]\n", + "]" + ], + "id": "cc0d2f7406bb447e", + "outputs": [ + { + "data": { + "text/plain": [ + " ts_code end_date collection_cash_period sales_period oper_cost \\\n", + "0 301093.SZ 2018 93.76 137.78 2.160737e+08 \n", + "1 301092.SZ 2018 67.58 141.15 2.604383e+08 \n", + "2 688280.SH 2018 212.32 137.55 7.666165e+08 \n", + "3 688739.SH 2018 124.23 314.40 2.063751e+08 \n", + "4 688257.SH 2018 101.39 148.16 4.092993e+08 \n", + "... ... ... ... ... ... \n", + "31118 873896.BJ 2023 40.83 1.20 2.687648e+08 \n", + "31119 873911.BJ 2023 41.27 377.77 1.362851e+08 \n", + "31120 873755.BJ 2023 63.15 155.20 1.354382e+08 \n", + "31121 603091.SH 2023 36.89 93.67 9.023004e+08 \n", + "31122 301622.SZ 2023 134.91 116.69 7.020213e+08 \n", + "\n", + " total_cogs \n", + "0 3.352792e+08 \n", + "1 3.321345e+08 \n", + "2 1.110642e+09 \n", + "3 6.802215e+08 \n", + "4 5.354176e+08 \n", + "... ... \n", + "31118 3.891025e+08 \n", + "31119 2.304407e+08 \n", + "31120 1.716143e+08 \n", + "31121 1.057946e+09 \n", + "31122 8.257937e+08 \n", + "\n", + "[31123 rows x 6 columns]" + ], + "text/html": [ + "
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