finance-consumer/data/adata.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import adata\n",
"\n",
"res_df = adata.stock.info.all_code()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"\n",
"conn = sqlite3.connect('big-a.db')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5661"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_df.to_sql('all_code', con=conn, if_exists='replace', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"no_exit = res_df['short_name'].map(lambda x: not x.endswith('退'))\n",
"have_date = res_df['list_date'].map(lambda x: not (not x))\n",
"no_st = res_df['short_name'].map(lambda x: 'ST' not in x)\n",
"no_pt = res_df['short_name'].map(lambda x: 'PT' not in x)\n",
"\n",
"filtered = res_df[no_exit & no_st & no_pt & res_df['list_date'].notnull()]\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5255"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered.to_sql('all_code', con=conn, if_exists='replace', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"\n",
"local = pandas.read_sql_table('all_code', con='sqlite:///big-a.db', parse_dates=['list_date'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "big-a",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}