{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-01-20T09:12:32.313699Z", "start_time": "2025-01-20T09:12:32.310753Z" } }, "source": [ "\n", "import pandas as pd\n", "import tushare as ts\n", "\n", "ts_pro = ts.pro_api(token=\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")" ], "outputs": [], "execution_count": 99 }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:35.800981Z", "start_time": "2025-01-20T09:12:32.319361Z" } }, "cell_type": "code", "source": [ "import loader as loader\n", "\n", "# 加载财报信息\n", "source_finance_df = loader.load_finance()\n", "finance_df = pd.DataFrame()\n", "finance_df[[\n", " \"code\",\n", " # 年份\n", " \"year\",\n", " # 股东权益合计（含少数股东权益）\n", " \"total_stockholder_interest\",\n", " # 净利润\n", " \"net_income\",\n", " # 总资产\n", " \"total_assets\",\n", " # 营业总收入\n", " \"total_revenue\",\n", " # 存货\n", " \"inventories\",\n", " # 应收账款\n", " \"accounts_receivable\",\n", " # 营业成本\n", " \"operating_costs\",\n", " # 营业利润\n", " \"operating_profit\",\n", " # 现金与现金等价物\n", " \"cash\",\n", " # 营业活动现金流量净值\n", " \"operating_net_cash_flow\",\n", "]] = source_finance_df[[\n", " \"ts_code\",\n", " \"end_date\",\n", " \"total_hldr_eqy_inc_min_int\",\n", " \"n_income\",\n", " \"total_assets\",\n", " \"total_revenue\",\n", " \"inventories\",\n", " \"accounts_receiv\",\n", " \"oper_cost\",\n", " \"operate_profit\",\n", " \"money_cap\",\n", " \"n_cashflow_act\",\n", "]]\n", "finance_df[\"score\"] = 0\n", "finance_df = finance_df.sort_values(by=[\"code\", \"year\"], ascending=True).reset_index(drop=True)" ], "id": "68b2debc14502fd5", "outputs": [], "execution_count": 100 }, { "metadata": {}, "cell_type": "markdown", "source": "过滤上市时间大于5年的企业，由于计算很多数值需要与前一年比，所以实际上需要的是上市时间至少6年的企业", "id": "3052b8c4442f3643" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:35.994681Z", "start_time": "2025-01-20T09:12:35.828990Z" } }, "cell_type": "code", "source": "finance_df = finance_df.groupby(\"code\").filter(lambda x: len(x) > 6)", "id": "4293bd93ea8f9ed", "outputs": [], "execution_count": 101 }, { "metadata": {}, "cell_type": "markdown", "source": [ "ROE\n", "\n", "股东权益报酬率(%) RoE\t股东权益报酬率RoE 是判断股票是否具备”长期上涨潜力“最重要的指标之一， RoE 如果既稳定又优秀，那这家公司的其他指标也基本不会差。 \n", "RoE均值 >= 35\t550分 \n", "35 > RoE均值 >= 30\t500分 \n", "30 > RoE均值 >= 25\t450分 \n", "25 > RoE均值 >= 20\t400分 \n", "20 > RoE均值 >= 15\t300分 \n", "15 > RoE均值 >= 10\t250分 \n", "10 > RoE均值 或 0 >= 其中一年\t0分 " ], "id": "336aa970ee46709d" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:38.908153Z", "start_time": "2025-01-20T09:12:36.022143Z" } }, "cell_type": "code", "source": [ "finance_df[\"prev_total_stockholder_interest\"] = finance_df.groupby(\"code\")[\"total_stockholder_interest\"].shift(1)\n", "finance_df[\"roe\"] = finance_df[\"net_income\"] / (\n", " (finance_df[\"prev_total_stockholder_interest\"] + finance_df[\"total_stockholder_interest\"]) / 2)\n", "finance_df[\"average_roe\"] = finance_df.groupby(\"code\")[\"roe\"].rolling(window=5).mean().reset_index(0, drop=True)\n", "finance_df[\"score_roe\"] = 0\n", "finance_df.loc[finance_df[\"average_roe\"] >= 0.35, \"score_roe\"] += 550\n", "finance_df.loc[(finance_df[\"average_roe\"] >= 0.30) & (finance_df[\"average_roe\"] < 0.35), \"score_roe\"] = 500\n", "finance_df.loc[(finance_df[\"average_roe\"] >= 0.25) & (finance_df[\"average_roe\"] < 0.30), \"score_roe\"] = 450\n", "finance_df.loc[(finance_df[\"average_roe\"] >= 0.20) & (finance_df[\"average_roe\"] < 0.25), \"score_roe\"] = 400\n", "finance_df.loc[(finance_df[\"average_roe\"] >= 0.15) & (finance_df[\"average_roe\"] < 0.20), \"score_roe\"] = 300\n", "finance_df.loc[(finance_df[\"average_roe\"] >= 0.10) & (finance_df[\"average_roe\"] < 0.15), \"score_roe\"] = 250\n", "finance_df[\"score\"] += finance_df[\"score_roe\"]\n", "\n", "\n", "def reset_score_for_average_roe(group):\n", " group.loc[group['average_roe'].rolling(window=5, min_periods=1).min() < 0, 'score'] = 0\n", " return group\n", "\n", "\n", "finance_df = finance_df.groupby(\"code\").apply(reset_score_for_average_roe).reset_index(drop=True)" ], "id": "f050d33c4a0cd720", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_72996/1604170078.py:20: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " finance_df = finance_df.groupby(\"code\").apply(reset_score_for_average_roe).reset_index(drop=True)\n" ] } ], "execution_count": 102 }, { "metadata": {}, "cell_type": "markdown", "source": [ "总资产报酬率(%) RoA\t总资产报酬率RoA 能真实反映出一家公司的获利能力， 相当于一家公司的 「投资年化率」，当然越高越好。\n", "RoA均值 >= 15\t100分\n", "15 > RoA均值 >= 11\t80分\n", "11 > RoA均值 >= 7\t50分\n", "7 > RoA均值\t0分" ], "id": "6c2e1c602898e342" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:39.103417Z", "start_time": "2025-01-20T09:12:38.937447Z" } }, "cell_type": "code", "source": [ "finance_df[\"prev_total_assets\"] = finance_df.groupby(\"code\")[\"total_assets\"].shift(1)\n", "finance_df[\"roa\"] = finance_df[\"net_income\"] / ((finance_df[\"prev_total_assets\"] + finance_df[\"total_assets\"]) / 2)\n", "finance_df[\"average_roa\"] = finance_df.groupby(\"code\")[\"roa\"].rolling(window=5).mean().reset_index(0, drop=True)\n", "finance_df[\"score_roa\"] = 0\n", "finance_df.loc[finance_df[\"average_roa\"] >= 0.15, \"score_roa\"] += 100\n", "finance_df.loc[(finance_df[\"average_roa\"] >= 0.11) & (finance_df[\"average_roa\"] < 0.15), \"score_roa\"] += 80\n", "finance_df.loc[(finance_df[\"average_roa\"] >= 0.07) & (finance_df[\"average_roa\"] < 0.11), \"score_roa\"] += 50\n", "finance_df[\"score\"] += finance_df[\"score_roa\"]" ], "id": "3b585cf8e2eb5e3", "outputs": [], "execution_count": 103 }, { "metadata": {}, "cell_type": "markdown", "source": [ "税后净利 规模(百万)\t获利规模大，那股价的”长期上涨潜力“也就会越稳健、越确定。选择时，大公司的优先级要高于小公司。\n", "税后净利均值 >= 10000\t150分\n", "10000 > 税后净利均值 >= 1000\t100分\n", "1000 > 税后净利均值\t0分" ], "id": "dbcb1d6b23582f3e" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:39.922683Z", "start_time": "2025-01-20T09:12:39.130104Z" } }, "cell_type": "code", "source": [ "finance_df['average_net_income'] = finance_df.groupby('code')['net_income'].transform(lambda x: x.rolling(5).mean())\n", "finance_df[\"score_net_income\"] = 0\n", "finance_df.loc[finance_df[\"average_net_income\"] >= 10000 * 10000000, \"score_net_income\"] = 150\n", "finance_df.loc[(finance_df[\"average_net_income\"] >= 1000 * 10000000) & (\n", " finance_df[\"average_net_income\"] < 10000 * 10000000), \"score_net_income\"] = 100\n", "finance_df[\"score\"] += finance_df[\"score_net_income\"]" ], "id": "fd5582e080102e20", "outputs": [], "execution_count": 104 }, { "metadata": {}, "cell_type": "markdown", "source": [ "现金状况 分析\t总资产周转率 与 现金与约当现金占总资产的关系，可以看出公司的现金状况是否健康。\n", "规则①：总资产周转率 > 0.8 且 现金与约当现金占总资产(%) >= 10\t50分\n", "规则②：总资产周转率 < 0.8 且 现金与约当现金占总资产(%) >= 20\t50分\n", "不符合 规则① 或 规则②\t0分" ], "id": "2c6c934a376eef0d" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:39.965030Z", "start_time": "2025-01-20T09:12:39.946246Z" } }, "cell_type": "code", "source": [ "finance_df[\"prev_total_assets\"] = finance_df.groupby(\"code\")[\"total_assets\"].shift(1)\n", "finance_df[\"total_assets_turnover_ratio\"] = finance_df[\"total_revenue\"] / (\n", " (finance_df[\"prev_total_assets\"] + finance_df[\"total_assets\"]) / 2)\n", "\n", "finance_df[\"cash_ratio\"] = finance_df[\"cash\"] / finance_df[\"total_assets\"]\n", "\n", "finance_df[\"score_assets_turnover_and_cash\"] = 0\n", "finance_df.loc[(finance_df[\"total_assets_turnover_ratio\"] >= 0.8) & (\n", " finance_df[\"cash_ratio\"] >= 0.1), \"score_assets_turnover_and_cash\"] = 50\n", "finance_df.loc[(finance_df[\"total_assets_turnover_ratio\"] < 0.8) & (\n", " finance_df[\"cash_ratio\"] >= 0.2), \"score_assets_turnover_and_cash\"] = 50\n", "finance_df[\"score\"] += finance_df[\"score_assets_turnover_and_cash\"]" ], "id": "bc92e050c82c3768", "outputs": [], "execution_count": 105 }, { "metadata": {}, "cell_type": "markdown", "source": [ "收现日数(日)\t平均收现日数越短，说明公司的经营能力越强。\n", "30 >= 平均收现日数\t20分\n", "平均收现日数 > 30\t0分\n", "\n", "销货日数(日)\t平均销货日数越短，说明公司商品越畅销。\n", "30 >= 平均销货日数\t20分\n", "平均销货日数 > 30\t0分\n", "\n", "收现日数+销货日数(日)\t生意周期 是否足够优秀，越短说明每年能做的生意趟数越多，经营能力就越强。\n", "40 >= 收现日数+销货日数\t20分\n", "60 >= 收现日数+销货日数 > 40\t10分\n", "平均销货日数 > 60\t0分" ], "id": "41ac526b0296b44b" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:40.003327Z", "start_time": "2025-01-20T09:12:39.990482Z" } }, "cell_type": "code", "source": [ "# 收现日数\n", "finance_df[\"collection_cash_period\"] = 360 / (finance_df[\"total_revenue\"] / finance_df[\"accounts_receivable\"])\n", "# 销货日数\n", "finance_df[\"sales_period\"] = 360 / (finance_df[\"operating_costs\"] / finance_df[\"accounts_receivable\"])\n", "\n", "finance_df[\"score_collection_cash_period\"] = 0\n", "finance_df.loc[(not finance_df[\"score_collection_cash_period\"].isna) & (\n", " finance_df[\"collection_cash_period\"] < 30), \"score_collection_cash_period\"] = 20\n", "finance_df[\"score\"] += finance_df[\"score_collection_cash_period\"]\n", "\n", "finance_df[\"score_sales_period\"] = 0\n", "finance_df.loc[\n", " (not finance_df[\"score_sales_period\"].isna) & (finance_df[\"score_sales_period\"] < 30), \"score_sales_period\"] = 20\n", "finance_df[\"score\"] += finance_df[\"score_sales_period\"]\n", "\n", "finance_df[\"score_collection_cash_period_and_sales_period\"] = 0\n", "finance_df.loc[(finance_df[\"collection_cash_period\"] + finance_df[\n", " \"sales_period\"]) < 40, \"score_collection_cash_period_and_sales_period\"] = 20\n", "finance_df.loc[(finance_df[\"collection_cash_period\"] + finance_df[\n", " \"sales_period\"]) < 60, \"score_collection_cash_period_and_sales_period\"] = 10\n", "finance_df[\"score\"] += finance_df[\"score_collection_cash_period_and_sales_period\"]" ], "id": "baeb44a4fb28b60b", "outputs": [], "execution_count": 106 }, { "metadata": {}, "cell_type": "markdown", "source": [ "毛利率(%)\t毛利率 是否保持平稳，不大起大落。\n", "30% >= 毛利率平均波动幅度\t50分\n", "毛利率平均波动幅度 > 30%\t0分" ], "id": "a53bfdb799df8f48" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:40.179998Z", "start_time": "2025-01-20T09:12:40.027705Z" } }, "cell_type": "code", "source": [ "finance_df[\"gross_profit_ratio\"] = (finance_df[\"total_revenue\"] - finance_df[\"operating_costs\"]) / finance_df[\n", " \"total_revenue\"]\n", "finance_df[\"gross_profit_ratio_std\"] = finance_df.groupby(\"code\")[\"gross_profit_ratio\"].rolling(\n", " window=5\n", ").std().reset_index(0, drop=True)\n", "finance_df[\"score_gross_profit_ratio\"] = 0\n", "finance_df[(not finance_df[\"gross_profit_ratio_std\"].isna) & (finance_df[\"gross_profit_ratio_std\"] < 30)] = 50\n", "finance_df[\"score\"] += finance_df[\"score_gross_profit_ratio\"]" ], "id": "9d23bdf60a1839c9", "outputs": [], "execution_count": 107 }, { "metadata": {}, "cell_type": "markdown", "source": [ "经营安全边际率(%)\t经营安全边际率越高，说明公司能够很好的控制成本，把大部分利润转化成实际收益， 获利能力很强，有利于抵御经济波动和价格竞争等影响。\n", "经营安全边际率 >= 70\t50分\n", "70 > 经营安全边际率 >= 50\t30分\n", "50 > 经营安全边际率 >= 30\t10分\n", "30 > 经营安全边际率\t0分" ], "id": "6613ab8263d4afef" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:40.213136Z", "start_time": "2025-01-20T09:12:40.205548Z" } }, "cell_type": "code", "source": [ "finance_df[\"operating_profit_ratio\"] = finance_df[\"operating_profit\"] / finance_df[\"total_revenue\"]\n", "finance_df[\"operating_safety_margin\"] = finance_df[\"operating_profit_ratio\"] / finance_df[\"gross_profit_ratio\"]\n", "finance_df[\"score_operating_safety_margin\"] = 0\n", "finance_df.loc[finance_df[\"operating_safety_margin\"] >= 70, \"score_operating_safety_margin\"] = 50\n", "finance_df.loc[finance_df[\"operating_safety_margin\"] >= 50, \"score_operating_safety_margin\"] = 30\n", "finance_df.loc[finance_df[\"operating_safety_margin\"] >= 30, \"score_operating_safety_margin\"] = 10\n", "finance_df[\"score\"] += finance_df[\"score_operating_safety_margin\"]" ], "id": "f7d9486af89cb710", "outputs": [], "execution_count": 108 }, { "metadata": {}, "cell_type": "markdown", "source": [ "税后净利\n", "年份由近(A)~远(E)\t公司赚的钱是否在逐年增长，是股价能长期上涨的基础。\n", "A年 > B年 (+) / A年 < B年 (-)\t+30分 / -30分\n", "B年 > C年 (+) / B年 < C年 (-)\t+25分 / -25分\n", "C年 > D年 (+) / C年 < D年 (-)\t+20分 / -20分\n", "D年 > E年 (+) / D年 < E年 (-)\t+15分 / -15分" ], "id": "2ee3712525455954" }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:12:57.833029Z", "start_time": "2025-01-20T09:12:40.241875Z" } }, "cell_type": "code", "source": [ "def score_by_net_income_ascending(group):\n", " # 计算 score_net_income_1\n", " group['net_income_shift_1'] = group['net_income'].shift(1)\n", " group['score_net_income_1'] = (group['net_income'] > group['net_income_shift_1']).map(\n", " {True: 30, False: -30}\n", " )\n", " # 计算 score_net_income_2\n", " group['net_income_shift_2'] = group['net_income'].shift(2)\n", " group['score_net_income_2'] = (group['net_income_shift_1'] > group['net_income_shift_2']).map(\n", " {True: 25, False: -25}\n", " )\n", " # 计算 score_net_income_3\n", " group['net_income_shift_3'] = group['net_income'].shift(3)\n", " group['score_net_income_3'] = (group['net_income_shift_2'] > group['net_income_shift_3']).map(\n", " {True: 20, False: -20}\n", " )\n", " # 计算 score_net_income_4\n", " group['net_income_shift_4'] = group['net_income'].shift(4)\n", " group['score_net_income_4'] = (group['net_income_shift_3'] > group['net_income_shift_4']).map(\n", " {True: 15, False: -15}\n", " )\n", " return group\n", "\n", "\n", "finance_df = finance_df.groupby(\"code\").apply(score_by_net_income_ascending).reset_index(drop=True)\n", "finance_df[\"score_net_income_ascending\"] = finance_df[\"score_net_income_1\"] + finance_df[\"score_net_income_2\"] + \\\n", " finance_df[\"score_net_income_3\"] + finance_df[\"score_net_income_4\"]" ], "id": "2d1ca7fc7873ce71", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_72996/432379325.py:25: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " finance_df = finance_df.groupby(\"code\").apply(score_by_net_income_ascending).reset_index(drop=True)\n" ] } ], "execution_count": 109 }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:13:15.433529Z", "start_time": "2025-01-20T09:12:57.864097Z" } }, "cell_type": "code", "source": [ "def score_by_operating_net_cash_flow_ascending(group):\n", " # 计算 score_operating_net_cash_flow_1\n", " group['operating_net_cash_flow_shift_1'] = group['operating_net_cash_flow'].shift(1)\n", " group['score_operating_net_cash_flow_1'] = (\n", " group['operating_net_cash_flow'] > group['operating_net_cash_flow_shift_1']).map(\n", " {True: 30, False: -30}\n", " )\n", " # 计算 score_operating_net_cash_flow_2\n", " group['operating_net_cash_flow_shift_2'] = group['operating_net_cash_flow'].shift(2)\n", " group['score_operating_net_cash_flow_2'] = (\n", " group['operating_net_cash_flow_shift_1'] > group['operating_net_cash_flow_shift_2']).map(\n", " {True: 25, False: -25}\n", " )\n", " # 计算 score_operating_net_cash_flow_3\n", " group['operating_net_cash_flow_shift_3'] = group['operating_net_cash_flow'].shift(3)\n", " group['score_operating_net_cash_flow_3'] = (\n", " group['operating_net_cash_flow_shift_2'] > group['operating_net_cash_flow_shift_3']).map(\n", " {True: 20, False: -20}\n", " )\n", " # 计算 score_operating_net_cash_flow_4\n", " group['operating_net_cash_flow_shift_4'] = group['operating_net_cash_flow'].shift(4)\n", " group['score_operating_net_cash_flow_4'] = (\n", " group['operating_net_cash_flow_shift_3'] > group['operating_net_cash_flow_shift_4']).map(\n", " {True: 15, False: -15}\n", " )\n", " return group\n", "\n", "\n", "finance_df = finance_df.groupby(\"code\").apply(score_by_operating_net_cash_flow_ascending).reset_index(drop=True)\n", "finance_df[\"score_operating_net_cash_flow_ascending\"] = finance_df[\"score_operating_net_cash_flow_1\"] + finance_df[\n", " \"score_operating_net_cash_flow_2\"] + finance_df[\"score_operating_net_cash_flow_3\"] + finance_df[\n", " \"score_operating_net_cash_flow_4\"]" ], "id": "a90f3721487bd635", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_72996/2138932640.py:29: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " finance_df = finance_df.groupby(\"code\").apply(score_by_operating_net_cash_flow_ascending).reset_index(drop=True)\n" ] } ], "execution_count": 110 }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-20T09:15:08.342704Z", "start_time": "2025-01-20T09:15:08.302792Z" } }, "cell_type": "code", "source": [ "result_df = finance_df.sort_values(by=['score'], ascending=False, inplace=False)[[\"code\", \"score\"]]\n", "result_df = result_df.drop_duplicates(\"code\")\n", "result_df[:20]" ], "id": "ef9e6259efc1c1d0", "outputs": [ { "data": { "text/plain": [ " code score\n", "48060 600519.SH 810\n", "45062 600309.SH 810\n", "58715 601919.SH 790\n", "17006 002415.SZ 750\n", "3252 000587.SZ 710\n", "4129 000651.SZ 710\n", "64547 603868.SH 710\n", "56747 601225.SH 710\n", "4457 000672.SZ 710\n", "3031 000568.SZ 710\n", "8872 001201.SZ 710\n", "31827 300390.SZ 710\n", "65769 605098.SH 710\n", "46474 600398.SH 710\n", "65749 605089.SH 710\n", "64240 603816.SH 710\n", "47860 600507.SH 710\n", "33696 300533.SZ 710\n", "27948 300146.SZ 710\n", "35448 300677.SZ 710" ], "text/html": [ "
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