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明确策略开发方向

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兰缘小妖
2025-01-17 18:20:28 +08:00
parent bd1131ee06
commit c711a8c94a
5 changed files with 731 additions and 193 deletions
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5
.idea/codeStyles/codeStyleConfig.xml generated Normal file
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@@ -0,0 +1,5 @@
<component name="ProjectCodeStyleConfiguration">
<state>
<option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
</state>
</component>

20
selector.py
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@@ -1,20 +0,0 @@
import pandas as pd
class Selector:
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
return codes
class Score:
def score(self, codes: [str], df: pd.DataFrame) -> [(str, int)]:
return list(map(lambda code: (code, 0), codes))
class PeriodSelector(Selector):
def __init__(self, period: int = 5):
self.__period = period
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
size_df = df.groupby("code").size()
return list(filter(lambda code: size_df[code] > self.__period, codes))

44
strategy.py Normal file
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@@ -0,0 +1,44 @@
import pandas as pd
class Selector:
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
return codes
class Strategy:
def __init__(self, selectors: [Selector]):
self.selectors = selectors
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
return list(map(lambda code: self.selectors.select(code, df), codes))
class PeriodSelector(Selector):
def __init__(self, period: int = 5):
self.__period = period
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
size_df = df.groupby("code").size()
return list(filter(lambda code: size_df[code] > self.__period, codes))
class PyramidSelector(Selector):
def select(self, codes: [str], df: pd.DataFrame) -> [str]:
target_df = df[df["code"].isin(codes)]
target_df["score"] = 0
group_df = target_df.groupby("code")
target_df["prev_total_stockholder_interest"] = group_df["total_stockholder_interest"].shift(1)
target_df["roe"] = target_df["net_income"] / ((target_df["prev_total_stockholder_interest"] + target_df["total_stockholder_interest"]) / 2)
target_df["average_roe"] = target_df["roe"].mean()
target_df[target_df["average_roe"] >= 35] = target_df["score"] + 550
target_df[(target_df["average_roe"] < 35) & (target_df["average_roe"] >= 30)] = target_df["score"] + 500
target_df[(target_df["average_roe"] < 30) & (target_df["average_roe"] >= 25)] = target_df["score"] + 450
target_df[(target_df["average_roe"] < 25) & (target_df["average_roe"] >= 15)] = target_df["score"] + 300
target_df[(target_df["average_roe"] < 15) & (target_df["average_roe"] >= 10)] = target_df["score"] + 250
target_df["prev_total_assets"] = group_df["total_assets"].shift(1)
target_df["roa"] = target_df["net_income"] / ((target_df["prev_total_assets"] + target_df["total_assets"]) / 2)
return super().select(codes, df)

2
财报筛选/选股测试.ipynb
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@@ -67,7 +67,7 @@
},
"cell_type": "code",
"source": [
"from selector import PeriodSelector\n",
"from strategy import PeriodSelector\n",
"\n",
"filter_df = finance_df[(2010 < finance_df[\"year\"]) & (finance_df[\"year\"] < 2025)]\n",
"codes = filter_df[\"code\"].unique().tolist()\n",

853
财报筛选/金字塔选股.ipynb
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@@ -6,8 +6,8 @@
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-01-14T14:10:53.801418Z",
"start_time": "2025-01-14T14:10:53.532671Z"
"end_time": "2025-01-17T02:07:55.001963Z",
"start_time": "2025-01-17T02:07:54.772694Z"
}
},
"source": [
@@ -22,8 +22,103 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-14T14:10:56.411909Z",
"start_time": "2025-01-14T14:10:55.020264Z"
"end_time": "2025-01-17T08:18:12.685428Z",
"start_time": "2025-01-17T08:18:12.676949Z"
}
},
"cell_type": "code",
"source": [
"df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20]])\n",
"df.loc[df[0] > 2, 2] += 100\n",
"df"
],
"id": "e19fd115ed8cb631",
"outputs": [
{
"data": {
"text/plain": [
" 0 1 2 3 4\n",
"0 1 2 3 4 5\n",
"1 6 7 108 9 10\n",
"2 11 12 113 14 15\n",
"3 16 17 118 19 20"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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" </tr>\n",
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" <td>16</td>\n",
" <td>17</td>\n",
" <td>118</td>\n",
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" </tr>\n",
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"</table>\n",
"</div>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 86
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-17T10:11:56.563929Z",
"start_time": "2025-01-17T10:11:52.872684Z"
}
},
"cell_type": "code",
@@ -72,46 +167,192 @@
" \"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)"
],
"id": "68b2debc14502fd5",
"outputs": [],
"execution_count": 3
"execution_count": 148
},
{
"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": "2025-01-14T14:22:17.070395Z",
"start_time": "2025-01-14T14:22:17.053180Z"
"end_time": "2025-01-17T10:11:57.531470Z",
"start_time": "2025-01-17T10:11:57.287352Z"
}
},
"cell_type": "code",
"source": "finance_df[finance_df[\"code\"] == \"600763.SH\"][[\"year\", \"net_income\"]]",
"id": "f0e8942b3403348c",
"source": "finance_df = finance_df.groupby(\"code\").filter(lambda x: len(x) > 6)",
"id": "4293bd93ea8f9ed",
"outputs": [],
"execution_count": 149
},
{
"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": "2025-01-17T10:12:05.859920Z",
"start_time": "2025-01-17T10:12:00.853021Z"
}
},
"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.loc[finance_df[\"average_roe\"] >= 0.35, \"score\"] += 550\n",
"finance_df.loc[(finance_df[\"average_roe\"] >= 0.30) & (finance_df[\"average_roe\"] < 0.35), \"score\"] += 500\n",
"finance_df.loc[(finance_df[\"average_roe\"] >= 0.25) & (finance_df[\"average_roe\"] < 0.30), \"score\"] += 450\n",
"finance_df.loc[(finance_df[\"average_roe\"] >= 0.20) & (finance_df[\"average_roe\"] < 0.25), \"score\"] += 400\n",
"finance_df.loc[(finance_df[\"average_roe\"] >= 0.15) & (finance_df[\"average_roe\"] < 0.20), \"score\"] += 300\n",
"finance_df.loc[(finance_df[\"average_roe\"] >= 0.10) & (finance_df[\"average_roe\"] < 0.15), \"score\"] += 250\n",
"finance_df[\"score\"] = finance_df.groupby(\"code\").apply(\n",
" lambda x: x[\"score\"].mask(x[\"average_roe\"].rolling(window=5).apply(lambda y: (y < 0).any(), raw=False) > 0, 0)\n",
").reset_index(drop=True)\n",
"\n",
"finance_df[finance_df[\"code\"] == '000002.SZ']"
],
"id": "f050d33c4a0cd720",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_54636/2126828454.py:12: 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[\"score\"] = finance_df.groupby(\"code\").apply(\n"
]
},
{
"data": {
"text/plain": [
" year net_income\n",
"1808 2005 3.467468e+06\n",
"3228 2006 1.212677e+06\n",
"4784 2007 1.015147e+07\n",
"8425 2008 1.466373e+07\n",
"9811 2009 2.172666e+07\n",
"12426 2010 4.982651e+07\n",
"17669 2011 7.169039e+07\n",
"19876 2012 9.554584e+07\n",
"25082 2013 1.024329e+08\n",
"28480 2014 1.138340e+08\n",
"33116 2015 1.936414e+08\n",
"37622 2016 1.328421e+08\n",
"41906 2017 2.266875e+08\n",
"47465 2018 3.592014e+08\n",
"51158 2019 5.077704e+08\n",
"54614 2020 5.449616e+08\n",
"61814 2021 7.861501e+08\n",
"66356 2022 6.156781e+08\n",
"71478 2023 5.784234e+08"
" code year total_stockholder_interest net_income \\\n",
"1945 000002.SZ 2005 8.581223e+09 1.433426e+09 \n",
"3432 000002.SZ 2006 1.745350e+10 2.422997e+09 \n",
"5449 000002.SZ 2007 3.391952e+10 5.317501e+09 \n",
"7921 000002.SZ 2008 3.881855e+10 4.639869e+09 \n",
"10605 000002.SZ 2009 4.540851e+10 6.430008e+09 \n",
"13934 000002.SZ 2010 5.458620e+10 8.839611e+09 \n",
"17289 000002.SZ 2011 6.783254e+10 1.159961e+10 \n",
"20376 000002.SZ 2012 8.213819e+10 1.566259e+10 \n",
"25215 000002.SZ 2013 1.054394e+11 1.829755e+10 \n",
"29387 000002.SZ 2014 1.158936e+11 1.928752e+10 \n",
"33753 000002.SZ 2015 1.363096e+11 2.594944e+10 \n",
"37463 000002.SZ 2016 1.616766e+11 2.835026e+10 \n",
"42678 000002.SZ 2017 1.866739e+11 3.720839e+10 \n",
"47756 000002.SZ 2018 2.356207e+11 4.927229e+10 \n",
"52944 000002.SZ 2019 2.705791e+11 5.513161e+10 \n",
"57414 000002.SZ 2020 3.498445e+11 5.929812e+10 \n",
"59180 000002.SZ 2021 3.927728e+11 3.806953e+10 \n",
"62929 000002.SZ 2022 4.049915e+11 3.755091e+10 \n",
"68422 000002.SZ 2023 4.029335e+11 2.045556e+10 \n",
"\n",
" total_assets total_revenue inventories accounts_receivable \\\n",
"1945 2.199239e+10 1.055885e+10 1.484948e+10 3.773077e+08 \n",
"3432 4.991984e+10 1.791833e+10 3.416711e+10 3.646097e+08 \n",
"5449 1.000945e+11 3.552661e+10 6.647288e+10 8.648830e+08 \n",
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"\n",
" operating_costs operating_profit cash \\\n",
"1945 6.884921e+09 1.958371e+09 3.249035e+09 \n",
"3432 1.144126e+10 3.426907e+09 1.074370e+10 \n",
"5449 2.060734e+10 7.652897e+09 1.704650e+10 \n",
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"68422 3.947839e+11 2.925170e+10 9.981376e+10 \n",
"\n",
" operating_net_cash_flow score prev_total_stockholder_interest \\\n",
"1945 8.434391e+08 0.0 NaN \n",
"3432 -3.024121e+09 0.0 8.581223e+09 \n",
"5449 -1.043772e+10 0.0 1.745350e+10 \n",
"7921 -3.415183e+07 0.0 3.391952e+10 \n",
"10605 9.253351e+09 0.0 3.881855e+10 \n",
"13934 2.237255e+09 250.0 4.540851e+10 \n",
"17289 3.389425e+09 0.0 5.458620e+10 \n",
"20376 3.725958e+09 0.0 6.783254e+10 \n",
"25215 1.923869e+09 300.0 8.213819e+10 \n",
"29387 4.172482e+10 0.0 1.054394e+11 \n",
"33753 1.604602e+10 0.0 1.158936e+11 \n",
"37463 3.956613e+10 400.0 1.363096e+11 \n",
"42678 8.232283e+10 250.0 1.616766e+11 \n",
"47756 3.361818e+10 0.0 1.866739e+11 \n",
"52944 4.568681e+10 400.0 2.356207e+11 \n",
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"59180 4.113161e+09 250.0 3.498445e+11 \n",
"62929 2.750449e+09 0.0 3.927728e+11 \n",
"68422 3.912324e+09 300.0 4.049915e+11 \n",
"\n",
" roe average_roe \n",
"1945 NaN NaN \n",
"3432 0.186136 NaN \n",
"5449 0.207015 NaN \n",
"7921 0.127577 NaN \n",
"10605 0.152683 NaN \n",
"13934 0.176802 0.170043 \n",
"17289 0.189507 0.170717 \n",
"20376 0.208875 0.171089 \n",
"25215 0.195093 0.184592 \n",
"29387 0.174285 0.188912 \n",
"33753 0.205782 0.194708 \n",
"37463 0.190279 0.194863 \n",
"42678 0.213626 0.195813 \n",
"47756 0.233355 0.203465 \n",
"52944 0.217825 0.212174 \n",
"57414 0.191154 0.209248 \n",
"59180 0.102528 0.191698 \n",
"62929 0.094140 0.167801 \n",
"68422 0.050637 0.131257 "
],
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"<div>\n",
@@ -132,223 +373,487 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>year</th>\n",
" <th>total_stockholder_interest</th>\n",
" <th>net_income</th>\n",
" <th>total_assets</th>\n",
" <th>total_revenue</th>\n",
" <th>inventories</th>\n",
" <th>accounts_receivable</th>\n",
" <th>operating_costs</th>\n",
" <th>operating_profit</th>\n",
" <th>cash</th>\n",
" <th>operating_net_cash_flow</th>\n",
" <th>score</th>\n",
" <th>prev_total_stockholder_interest</th>\n",
" <th>roe</th>\n",
" <th>average_roe</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1808</th>\n",
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" <td>6.884921e+09</td>\n",
" <td>1.958371e+09</td>\n",
" <td>3.249035e+09</td>\n",
" <td>8.434391e+08</td>\n",
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" <td>1.745350e+10</td>\n",
" <td>2.422997e+09</td>\n",
" <td>4.991984e+10</td>\n",
" <td>1.791833e+10</td>\n",
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" <td>3.646097e+08</td>\n",
" <td>1.144126e+10</td>\n",
" <td>3.426907e+09</td>\n",
" <td>1.074370e+10</td>\n",
" <td>-3.024121e+09</td>\n",
" <td>0.0</td>\n",
" <td>8.581223e+09</td>\n",
" <td>0.186136</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4784</th>\n",
" <th>5449</th>\n",
" <td>000002.SZ</td>\n",
" <td>2007</td>\n",
" <td>1.015147e+07</td>\n",
" <td>3.391952e+10</td>\n",
" <td>5.317501e+09</td>\n",
" <td>1.000945e+11</td>\n",
" <td>3.552661e+10</td>\n",
" <td>6.647288e+10</td>\n",
" <td>8.648830e+08</td>\n",
" <td>2.060734e+10</td>\n",
" <td>7.652897e+09</td>\n",
" <td>1.704650e+10</td>\n",
" <td>-1.043772e+10</td>\n",
" <td>0.0</td>\n",
" <td>1.745350e+10</td>\n",
" <td>0.207015</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8425</th>\n",
" <th>7921</th>\n",
" <td>000002.SZ</td>\n",
" <td>2008</td>\n",
" <td>1.466373e+07</td>\n",
" <td>3.881855e+10</td>\n",
" <td>4.639869e+09</td>\n",
" <td>1.192366e+11</td>\n",
" <td>4.099178e+10</td>\n",
" <td>8.589870e+10</td>\n",
" <td>9.227748e+08</td>\n",
" <td>2.500527e+10</td>\n",
" <td>6.364790e+09</td>\n",
" <td>1.997829e+10</td>\n",
" <td>-3.415183e+07</td>\n",
" <td>0.0</td>\n",
" <td>3.391952e+10</td>\n",
" <td>0.127577</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9811</th>\n",
" <th>10605</th>\n",
" <td>000002.SZ</td>\n",
" <td>2009</td>\n",
" <td>2.172666e+07</td>\n",
" <td>4.540851e+10</td>\n",
" <td>6.430008e+09</td>\n",
" <td>1.376086e+11</td>\n",
" <td>4.888101e+10</td>\n",
" <td>9.008529e+10</td>\n",
" <td>7.131919e+08</td>\n",
" <td>3.451472e+10</td>\n",
" <td>8.685083e+09</td>\n",
" <td>2.300192e+10</td>\n",
" <td>9.253351e+09</td>\n",
" <td>0.0</td>\n",
" <td>3.881855e+10</td>\n",
" <td>0.152683</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12426</th>\n",
" <th>13934</th>\n",
" <td>000002.SZ</td>\n",
" <td>2010</td>\n",
" <td>4.982651e+07</td>\n",
" <td>5.458620e+10</td>\n",
" <td>8.839611e+09</td>\n",
" <td>2.156376e+11</td>\n",
" <td>5.071385e+10</td>\n",
" <td>1.333335e+11</td>\n",
" <td>1.594025e+09</td>\n",
" <td>3.007350e+10</td>\n",
" <td>1.189489e+10</td>\n",
" <td>3.781693e+10</td>\n",
" <td>2.237255e+09</td>\n",
" <td>250.0</td>\n",
" <td>4.540851e+10</td>\n",
" <td>0.176802</td>\n",
" <td>0.170043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17669</th>\n",
" <th>17289</th>\n",
" <td>000002.SZ</td>\n",
" <td>2011</td>\n",
" <td>7.169039e+07</td>\n",
" <td>6.783254e+10</td>\n",
" <td>1.159961e+10</td>\n",
" <td>2.962084e+11</td>\n",
" <td>7.178275e+10</td>\n",
" <td>2.083355e+11</td>\n",
" <td>1.514814e+09</td>\n",
" <td>4.322816e+10</td>\n",
" <td>1.576322e+10</td>\n",
" <td>3.423951e+10</td>\n",
" <td>3.389425e+09</td>\n",
" <td>0.0</td>\n",
" <td>5.458620e+10</td>\n",
" <td>0.189507</td>\n",
" <td>0.170717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19876</th>\n",
" <th>20376</th>\n",
" <td>000002.SZ</td>\n",
" <td>2012</td>\n",
" <td>9.554584e+07</td>\n",
" <td>8.213819e+10</td>\n",
" <td>1.566259e+10</td>\n",
" <td>3.788016e+11</td>\n",
" <td>1.031162e+11</td>\n",
" <td>2.551641e+11</td>\n",
" <td>1.886549e+09</td>\n",
" <td>6.542161e+10</td>\n",
" <td>2.101304e+10</td>\n",
" <td>5.229154e+10</td>\n",
" <td>3.725958e+09</td>\n",
" <td>0.0</td>\n",
" <td>6.783254e+10</td>\n",
" <td>0.208875</td>\n",
" <td>0.171089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25082</th>\n",
" <th>25215</th>\n",
" <td>000002.SZ</td>\n",
" <td>2013</td>\n",
" <td>1.024329e+08</td>\n",
" <td>1.054394e+11</td>\n",
" <td>1.829755e+10</td>\n",
" <td>4.792053e+11</td>\n",
" <td>1.354188e+11</td>\n",
" <td>3.311332e+11</td>\n",
" <td>3.078970e+09</td>\n",
" <td>9.279765e+10</td>\n",
" <td>2.426134e+10</td>\n",
" <td>4.436541e+10</td>\n",
" <td>1.923869e+09</td>\n",
" <td>300.0</td>\n",
" <td>8.213819e+10</td>\n",
" <td>0.195093</td>\n",
" <td>0.184592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28480</th>\n",
" <th>29387</th>\n",
" <td>000002.SZ</td>\n",
" <td>2014</td>\n",
" <td>1.138340e+08</td>\n",
" <td>1.158936e+11</td>\n",
" <td>1.928752e+10</td>\n",
" <td>5.084088e+11</td>\n",
" <td>1.463880e+11</td>\n",
" <td>3.177264e+11</td>\n",
" <td>1.894072e+09</td>\n",
" <td>1.025571e+11</td>\n",
" <td>2.497936e+10</td>\n",
" <td>6.271525e+10</td>\n",
" <td>4.172482e+10</td>\n",
" <td>0.0</td>\n",
" <td>1.054394e+11</td>\n",
" <td>0.174285</td>\n",
" <td>0.188912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33116</th>\n",
" <th>33753</th>\n",
" <td>000002.SZ</td>\n",
" <td>2015</td>\n",
" <td>1.936414e+08</td>\n",
" <td>1.363096e+11</td>\n",
" <td>2.594944e+10</td>\n",
" <td>6.112956e+11</td>\n",
" <td>1.955491e+11</td>\n",
" <td>3.681219e+11</td>\n",
" <td>2.510653e+09</td>\n",
" <td>1.381506e+11</td>\n",
" <td>3.312278e+10</td>\n",
" <td>5.318038e+10</td>\n",
" <td>1.604602e+10</td>\n",
" <td>0.0</td>\n",
" <td>1.158936e+11</td>\n",
" <td>0.205782</td>\n",
" <td>0.194708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37622</th>\n",
" <th>37463</th>\n",
" <td>000002.SZ</td>\n",
" <td>2016</td>\n",
" <td>1.328421e+08</td>\n",
" <td>1.616766e+11</td>\n",
" <td>2.835026e+10</td>\n",
" <td>8.306742e+11</td>\n",
" <td>2.404772e+11</td>\n",
" <td>4.673613e+11</td>\n",
" <td>2.075257e+09</td>\n",
" <td>1.697424e+11</td>\n",
" <td>3.902378e+10</td>\n",
" <td>8.703212e+10</td>\n",
" <td>3.956613e+10</td>\n",
" <td>400.0</td>\n",
" <td>1.363096e+11</td>\n",
" <td>0.190279</td>\n",
" <td>0.194863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41906</th>\n",
" <th>42678</th>\n",
" <td>000002.SZ</td>\n",
" <td>2017</td>\n",
" <td>2.266875e+08</td>\n",
" <td>1.866739e+11</td>\n",
" <td>3.720839e+10</td>\n",
" <td>1.165347e+12</td>\n",
" <td>2.428971e+11</td>\n",
" <td>5.980877e+11</td>\n",
" <td>1.432734e+09</td>\n",
" <td>1.600799e+11</td>\n",
" <td>5.081292e+10</td>\n",
" <td>1.741210e+11</td>\n",
" <td>8.232283e+10</td>\n",
" <td>250.0</td>\n",
" <td>1.616766e+11</td>\n",
" <td>0.213626</td>\n",
" <td>0.195813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47465</th>\n",
" <th>47756</th>\n",
" <td>000002.SZ</td>\n",
" <td>2018</td>\n",
" <td>3.592014e+08</td>\n",
" <td>2.356207e+11</td>\n",
" <td>4.927229e+10</td>\n",
" <td>1.528579e+12</td>\n",
" <td>2.976793e+11</td>\n",
" <td>7.503026e+11</td>\n",
" <td>1.586181e+09</td>\n",
" <td>1.861042e+11</td>\n",
" <td>6.749861e+10</td>\n",
" <td>1.884174e+11</td>\n",
" <td>3.361818e+10</td>\n",
" <td>0.0</td>\n",
" <td>1.866739e+11</td>\n",
" <td>0.233355</td>\n",
" <td>0.203465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51158</th>\n",
" <th>52944</th>\n",
" <td>000002.SZ</td>\n",
" <td>2019</td>\n",
" <td>5.077704e+08</td>\n",
" <td>2.705791e+11</td>\n",
" <td>5.513161e+10</td>\n",
" <td>1.729929e+12</td>\n",
" <td>3.678939e+11</td>\n",
" <td>8.970190e+11</td>\n",
" <td>1.988076e+09</td>\n",
" <td>2.345503e+11</td>\n",
" <td>7.661314e+10</td>\n",
" <td>1.661946e+11</td>\n",
" <td>4.568681e+10</td>\n",
" <td>400.0</td>\n",
" <td>2.356207e+11</td>\n",
" <td>0.217825</td>\n",
" <td>0.212174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54614</th>\n",
" <th>57414</th>\n",
" <td>000002.SZ</td>\n",
" <td>2020</td>\n",
" <td>5.449616e+08</td>\n",
" <td>3.498445e+11</td>\n",
" <td>5.929812e+10</td>\n",
" <td>1.869177e+12</td>\n",
" <td>4.191117e+11</td>\n",
" <td>1.002063e+12</td>\n",
" <td>2.992423e+09</td>\n",
" <td>2.965407e+11</td>\n",
" <td>7.995864e+10</td>\n",
" <td>1.952307e+11</td>\n",
" <td>5.318802e+10</td>\n",
" <td>0.0</td>\n",
" <td>2.705791e+11</td>\n",
" <td>0.191154</td>\n",
" <td>0.209248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61814</th>\n",
" <th>59180</th>\n",
" <td>000002.SZ</td>\n",
" <td>2021</td>\n",
" <td>7.861501e+08</td>\n",
" <td>3.927728e+11</td>\n",
" <td>3.806953e+10</td>\n",
" <td>1.938638e+12</td>\n",
" <td>4.527978e+11</td>\n",
" <td>1.075617e+12</td>\n",
" <td>4.743597e+09</td>\n",
" <td>3.539771e+11</td>\n",
" <td>5.253100e+10</td>\n",
" <td>1.493524e+11</td>\n",
" <td>4.113161e+09</td>\n",
" <td>250.0</td>\n",
" <td>3.498445e+11</td>\n",
" <td>0.102528</td>\n",
" <td>0.191698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66356</th>\n",
" <th>62929</th>\n",
" <td>000002.SZ</td>\n",
" <td>2022</td>\n",
" <td>6.156781e+08</td>\n",
" <td>4.049915e+11</td>\n",
" <td>3.755091e+10</td>\n",
" <td>1.757124e+12</td>\n",
" <td>5.038384e+11</td>\n",
" <td>9.070569e+11</td>\n",
" <td>7.504692e+09</td>\n",
" <td>4.053193e+11</td>\n",
" <td>5.200694e+10</td>\n",
" <td>1.372076e+11</td>\n",
" <td>2.750449e+09</td>\n",
" <td>0.0</td>\n",
" <td>3.927728e+11</td>\n",
" <td>0.094140</td>\n",
" <td>0.167801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71478</th>\n",
" <th>68422</th>\n",
" <td>000002.SZ</td>\n",
" <td>2023</td>\n",
" <td>5.784234e+08</td>\n",
" <td>4.029335e+11</td>\n",
" <td>2.045556e+10</td>\n",
" <td>1.504850e+12</td>\n",
" <td>4.657391e+11</td>\n",
" <td>7.016958e+11</td>\n",
" <td>7.293628e+09</td>\n",
" <td>3.947839e+11</td>\n",
" <td>2.925170e+10</td>\n",
" <td>9.981376e+10</td>\n",
" <td>3.912324e+09</td>\n",
" <td>300.0</td>\n",
" <td>4.049915e+11</td>\n",
" <td>0.050637</td>\n",
" <td>0.131257</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 4,
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 4
},
{
"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"
"execution_count": 150
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-14T14:22:57.782337Z",
"start_time": "2025-01-14T14:22:57.774714Z"
"end_time": "2025-01-17T10:19:25.031141Z",
"start_time": "2025-01-17T10:19:25.020325Z"
}
},
"cell_type": "code",
"source": [
"from datetime import datetime\n",
"\n",
"select_df = stock_df[[\"ts_code\", \"list_date\"]]\n",
"select_df.loc[:, \"list_date\"] = pd.to_datetime(select_df[\"list_date\"], format=\"%Y%m%d\").dt.year\n",
"select_df = select_df[select_df[\"list_date\"] < datetime.now().year - 6]\n",
"codes = select_df[\"ts_code\"].tolist()\n",
"codes = map(lambda x: [x, stock_df[stock_df[\"ts_code\"] == x][\"name\"].values[0], 0], codes)"
"df = pd.DataFrame([[1, 2, 0], [1, 3, 0], [1, -4, 0]], columns=[\"code\", \"average\", \"score\"])\n",
"df[\"condition\"] = df.groupby(\"code\")[\"average\"].transform(\n",
" lambda x: x.rolling(window=2).apply(lambda y: (y < 0).any(), raw=False)\n",
") > 0\n",
"df[\"score\"].mask(\n",
" df.groupby(\"code\")[\"average\"].transform(\n",
" lambda x: x.rolling(window=2).apply(lambda y: (y < 0).any(), raw=False)\n",
" ) > 0,\n",
" 0\n",
")\n",
"df"
],
"id": "4293bd93ea8f9ed",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-14T14:22:59.917536Z",
"start_time": "2025-01-14T14:22:59.914056Z"
"id": "1a6a739df5b6f3a9",
"outputs": [
{
"data": {
"text/plain": [
" code average score condition\n",
"0 1 2 0 False\n",
"1 1 3 0 False\n",
"2 1 -4 0 True"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>average</th>\n",
" <th>score</th>\n",
" <th>condition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>-4</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
},
"cell_type": "code",
"source": [
"def add_score(code_list, score):\n",
" code_list[2] = code_list[2] + score\n",
" code_list.append(score)\n",
" return code_list"
],
"id": "fa22f7c620511f0a",
"outputs": [],
"execution_count": 6
},
{
"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": "2025-01-14T14:23:11.032339Z",
"start_time": "2025-01-14T14:23:02.481495Z"
}
},
"cell_type": "code",
"source": [
"def cal_roe(df):\n",
" df[\"prev_total_stockholder_interest\"] = df[\"total_stockholder_interest\"].shift(1)\n",
" df[\"roe\"] = df[\"net_income\"] / ((df[\"prev_total_stockholder_interest\"] + df[\"total_stockholder_interest\"]) / 2)\n",
" df.drop(columns=[\"prev_total_stockholder_interest\"], inplace=True)\n",
"\n",
"\n",
"def score_by_roe(code):\n",
" temp_df = finance_df[finance_df[\"code\"] == code].copy()\n",
" cal_roe(temp_df)\n",
"\n",
" if any(roe < 0 for roe in temp_df[\"roe\"]):\n",
" return 0\n",
"\n",
" average_roe = temp_df.nlargest(5, \"year\")[\"roe\"].mean() * 100\n",
"\n",
" if average_roe >= 35:\n",
" return 550\n",
" elif average_roe >= 30:\n",
" return 500\n",
" elif average_roe >= 25:\n",
" return 450\n",
" elif average_roe >= 15:\n",
" return 300\n",
" elif average_roe >= 10:\n",
" return 250\n",
" else:\n",
" return 0\n",
"\n",
"\n",
"codes = list(map(lambda x: add_score(x, score_by_roe(x[0])), codes))"
],
"id": "f050d33c4a0cd720",
"outputs": [],
"execution_count": 7
"execution_count": 165
},
{
"metadata": {},
@@ -763,11 +1268,13 @@
},
"cell_type": "code",
"source": [
"df = pd.DataFrame(codes,\n",
" columns=[\"code\", \"name\", \"score\", \"roe_score\", \"roa_score\", \"net_income\", \"assets_turnover_and_cash\",\n",
" \"collection_cash_period_and_sales_period\", \"gross_profit_ratio_volatility\",\n",
" \"operating_safety_margin\", \"net_income_ascending\",\n",
" \"operating_net_cash_flow_ascending\"])\n",
"df = pd.DataFrame(\n",
" codes,\n",
" columns=[\"code\", \"name\", \"score\", \"roe_score\", \"roa_score\", \"net_income\", \"assets_turnover_and_cash\",\n",
" \"collection_cash_period_and_sales_period\", \"gross_profit_ratio_volatility\",\n",
" \"operating_safety_margin\", \"net_income_ascending\",\n",
" \"operating_net_cash_flow_ascending\"]\n",
")\n",
"df.sort_values(by=\"score\", ascending=False, inplace=True)\n",
"df"
],
@@ -1444,8 +1951,10 @@
],
"execution_count": 206,
"source": [
"temp_df = ts_pro.fina_indicator(ts_code=\"600763.SH\", start_date=\"20140101\", end_date=\"20241231\",\n",
" fields=\"ts_code,end_date,roe,roa\")\n",
"temp_df = ts_pro.fina_indicator(\n",
" ts_code=\"600763.SH\", start_date=\"20140101\", end_date=\"20241231\",\n",
" fields=\"ts_code,end_date,roe,roa\"\n",
")\n",
"temp_df = temp_df[temp_df[\"end_date\"].str.endswith(\"1231\")]\n",
"# temp_df[\"end_date\"] = temp_df[\"end_date\"].str[:4]\n",
"# temp_df = temp_df.drop_duplicates(subset=[\"end_date\"], keep=\"last\")\n",