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兰缘小妖
2026-01-29 18:10:40 +08:00
parent 9f06ebf87d
commit b90d030899
9 changed files with 499 additions and 75 deletions
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16
.idea/csv-editor.xml generated Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="CsvFileAttributes">
<option name="attributeMap">
<map>
<entry key="/dumpDataPreview">
<value>
<Attribute>
<option name="separator" value="," />
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</value>
</entry>
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</option>
</component>
</project>

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.idea/dataSources.xml generated
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<jdbc-url>jdbc:postgresql://81.71.3.24:6785/leopard_dev</jdbc-url>
<working-dir>$ProjectFileDir$</working-dir>
</data-source>
<data-source source="LOCAL" name="leopard.sqlite" uuid="c9e16f8e-81be-45cf-847c-47a6750eeee2">
<driver-ref>sqlite.xerial</driver-ref>
<synchronize>true</synchronize>
<jdbc-driver>org.sqlite.JDBC</jdbc-driver>
<jdbc-url>jdbc:sqlite:$USER_HOME$/Documents/leopard_data/leopard.sqlite</jdbc-url>
<working-dir>$ProjectFileDir$</working-dir>
</data-source>
</component>
</project>

1
.idea/sqldialects.xml generated
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="SqlDialectMappings">
<file url="file://$PROJECT_DIR$/sql/initial.sql" dialect="SQLite" />
<file url="PROJECT" dialect="PostgreSQL" />
</component>
</project>

1
.idea/vcs.xml generated
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@@ -2,6 +2,5 @@
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
<mapping directory="$PROJECT_DIR$/backtestingpy" vcs="Git" />
</component>
</project>

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notebook/datasource/data_old.ipynb Normal file
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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-29T10:09:22.171634Z",
"start_time": "2026-01-29T08:27:03.148937Z"
}
},
"cell_type": "code",
"source": [
"import urllib.parse\n",
"\n",
"import pandas as pd\n",
"import sqlalchemy\n",
"from sqlalchemy.orm import DeclarativeBase, Session\n",
"\n",
"postgresql_engin = sqlalchemy.create_engine(\n",
" f\"postgresql://leopard:{urllib.parse.quote_plus(\"9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X\")}@81.71.3.24:6785/leopard\"\n",
")\n",
"sqlite_engine = sqlalchemy.create_engine(f\"sqlite:////Users/lanyuanxiaoyao/Documents/leopard_data/leopard.sqlite\")\n",
"\n",
"\n",
"class Base(DeclarativeBase):\n",
" pass\n",
"\n",
"\n",
"class Daily(Base):\n",
" __tablename__ = 'daily'\n",
"\n",
" code = sqlalchemy.Column(sqlalchemy.String, primary_key=True)\n",
" trade_date = sqlalchemy.Column(sqlalchemy.Date)\n",
" open = sqlalchemy.Column(sqlalchemy.Double)\n",
" close = sqlalchemy.Column(sqlalchemy.Double)\n",
" high = sqlalchemy.Column(sqlalchemy.Double)\n",
" low = sqlalchemy.Column(sqlalchemy.Double)\n",
" previous_close = sqlalchemy.Column(sqlalchemy.Double)\n",
" turnover = sqlalchemy.Column(sqlalchemy.Double)\n",
" volume = sqlalchemy.Column(sqlalchemy.Integer)\n",
" price_change_amount = sqlalchemy.Column(sqlalchemy.Double)\n",
" factor = sqlalchemy.Column(sqlalchemy.Double)\n",
"\n",
"\n",
"try:\n",
" stock_df = pd.read_sql_table(\"stock\", sqlite_engine)\n",
" for index, code in enumerate(stock_df[\"code\"]):\n",
" print(code)\n",
" daily_df = pd.read_sql(\n",
" f\"\"\"\n",
" select code,\n",
" trade_date,\n",
" open,\n",
" close,\n",
" high,\n",
" low,\n",
" previous_close,\n",
" turnover,\n",
" volume,\n",
" price_change_amount,\n",
" factor\n",
" from leopard_daily d\n",
" left join leopard_stock s on d.stock_id = s.id\n",
" where s.code = '{code}'\n",
" \"\"\",\n",
" postgresql_engin\n",
" )\n",
" with Session(sqlite_engine) as session:\n",
" for _, row in daily_df.iterrows():\n",
" session.add(\n",
" Daily(\n",
" code=row[\"code\"],\n",
" trade_date=row[\"trade_date\"],\n",
" open=row[\"open\"],\n",
" close=row[\"close\"],\n",
" high=row[\"high\"],\n",
" low=row[\"low\"],\n",
" previous_close=row[\"previous_close\"],\n",
" turnover=row[\"turnover\"],\n",
" volume=row[\"volume\"],\n",
" price_change_amount=row[\"price_change_amount\"],\n",
" factor=row[\"factor\"]\n",
" )\n",
" )\n",
" session.flush()\n",
" session.commit()\n",
"finally:\n",
" postgresql_engin.dispose()\n",
" sqlite_engine.dispose()"
],
"id": "48821306efc640a1",
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[26]\u001B[39m\u001B[32m, line 37\u001B[39m\n\u001B[32m 35\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index, code \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(stock_df[\u001B[33m\"\u001B[39m\u001B[33mcode\u001B[39m\u001B[33m\"\u001B[39m]):\n\u001B[32m 36\u001B[39m \u001B[38;5;28mprint\u001B[39m(code)\n\u001B[32m---> \u001B[39m\u001B[32m37\u001B[39m daily_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_sql\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 38\u001B[39m \u001B[43m \u001B[49m\u001B[33;43mf\u001B[39;49m\u001B[33;43m\"\"\"\u001B[39;49m\n\u001B[32m 39\u001B[39m \u001B[33;43m select code,\u001B[39;49m\n\u001B[32m 40\u001B[39m \u001B[33;43m trade_date,\u001B[39;49m\n\u001B[32m 41\u001B[39m \u001B[33;43m open,\u001B[39;49m\n\u001B[32m 42\u001B[39m \u001B[33;43m close,\u001B[39;49m\n\u001B[32m 43\u001B[39m \u001B[33;43m high,\u001B[39;49m\n\u001B[32m 44\u001B[39m \u001B[33;43m low,\u001B[39;49m\n\u001B[32m 45\u001B[39m \u001B[33;43m previous_close,\u001B[39;49m\n\u001B[32m 46\u001B[39m \u001B[33;43m turnover,\u001B[39;49m\n\u001B[32m 47\u001B[39m \u001B[33;43m volume,\u001B[39;49m\n\u001B[32m 48\u001B[39m \u001B[33;43m price_change_amount,\u001B[39;49m\n\u001B[32m 49\u001B[39m \u001B[33;43m factor\u001B[39;49m\n\u001B[32m 50\u001B[39m \u001B[33;43m from leopard_daily d\u001B[39;49m\n\u001B[32m 51\u001B[39m \u001B[33;43m left join leopard_stock s on d.stock_id = s.id\u001B[39;49m\n\u001B[32m 52\u001B[39m \u001B[33;43m where s.code = \u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[43mcode\u001B[49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m'\u001B[39;49m\n\u001B[32m 53\u001B[39m \u001B[33;43m \u001B[39;49m\u001B[33;43m\"\"\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 54\u001B[39m \u001B[43m \u001B[49m\u001B[43mpostgresql_engin\u001B[49m\n\u001B[32m 55\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 56\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m Session(sqlite_engine) \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[32m 57\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m _, row \u001B[38;5;129;01min\u001B[39;00m daily_df.iterrows():\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:736\u001B[39m, in \u001B[36mread_sql\u001B[39m\u001B[34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize, dtype_backend, dtype)\u001B[39m\n\u001B[32m 726\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m pandas_sql.read_table(\n\u001B[32m 727\u001B[39m sql,\n\u001B[32m 728\u001B[39m index_col=index_col,\n\u001B[32m (...)\u001B[39m\u001B[32m 733\u001B[39m dtype_backend=dtype_backend,\n\u001B[32m 734\u001B[39m )\n\u001B[32m 735\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m736\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpandas_sql\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_query\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 737\u001B[39m \u001B[43m \u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 738\u001B[39m \u001B[43m \u001B[49m\u001B[43mindex_col\u001B[49m\u001B[43m=\u001B[49m\u001B[43mindex_col\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 739\u001B[39m \u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 740\u001B[39m \u001B[43m \u001B[49m\u001B[43mcoerce_float\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcoerce_float\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 741\u001B[39m \u001B[43m \u001B[49m\u001B[43mparse_dates\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparse_dates\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 742\u001B[39m \u001B[43m \u001B[49m\u001B[43mchunksize\u001B[49m\u001B[43m=\u001B[49m\u001B[43mchunksize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 743\u001B[39m \u001B[43m \u001B[49m\u001B[43mdtype_backend\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype_backend\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 744\u001B[39m \u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 745\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:1848\u001B[39m, in \u001B[36mSQLDatabase.read_query\u001B[39m\u001B[34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype, dtype_backend)\u001B[39m\n\u001B[32m 1791\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mread_query\u001B[39m(\n\u001B[32m 1792\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1793\u001B[39m sql: \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m (...)\u001B[39m\u001B[32m 1800\u001B[39m dtype_backend: DtypeBackend | Literal[\u001B[33m\"\u001B[39m\u001B[33mnumpy\u001B[39m\u001B[33m\"\u001B[39m] = \u001B[33m\"\u001B[39m\u001B[33mnumpy\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 1801\u001B[39m ) -> DataFrame | Iterator[DataFrame]:\n\u001B[32m 1802\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 1803\u001B[39m \u001B[33;03m Read SQL query into a DataFrame.\u001B[39;00m\n\u001B[32m 1804\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 1846\u001B[39m \n\u001B[32m 1847\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1848\u001B[39m result = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1849\u001B[39m columns = result.keys()\n\u001B[32m 1851\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:1671\u001B[39m, in \u001B[36mSQLDatabase.execute\u001B[39m\u001B[34m(self, sql, params)\u001B[39m\n\u001B[32m 1669\u001B[39m args = [] \u001B[38;5;28;01mif\u001B[39;00m params \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m [params]\n\u001B[32m 1670\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(sql, \u001B[38;5;28mstr\u001B[39m):\n\u001B[32m-> \u001B[39m\u001B[32m1671\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mcon\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexec_driver_sql\u001B[49m\u001B[43m(\u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1672\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.con.execute(sql, *args)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1779\u001B[39m, in \u001B[36mConnection.exec_driver_sql\u001B[39m\u001B[34m(self, statement, parameters, execution_options)\u001B[39m\n\u001B[32m 1774\u001B[39m execution_options = \u001B[38;5;28mself\u001B[39m._execution_options.merge_with(\n\u001B[32m 1775\u001B[39m execution_options\n\u001B[32m 1776\u001B[39m )\n\u001B[32m 1778\u001B[39m dialect = \u001B[38;5;28mself\u001B[39m.dialect\n\u001B[32m-> \u001B[39m\u001B[32m1779\u001B[39m ret = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_execute_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1780\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1781\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecution_ctx_cls\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_init_statement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1782\u001B[39m \u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1783\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 1784\u001B[39m \u001B[43m \u001B[49m\u001B[43mexecution_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1785\u001B[39m \u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1786\u001B[39m \u001B[43m \u001B[49m\u001B[43mdistilled_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1787\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1789\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ret\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1846\u001B[39m, in \u001B[36mConnection._execute_context\u001B[39m\u001B[34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001B[39m\n\u001B[32m 1844\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._exec_insertmany_context(dialect, context)\n\u001B[32m 1845\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1846\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_exec_single_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1847\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\n\u001B[32m 1848\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1986\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1983\u001B[39m result = context._setup_result_proxy()\n\u001B[32m 1985\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m-> \u001B[39m\u001B[32m1986\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_handle_dbapi_exception\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1987\u001B[39m \u001B[43m \u001B[49m\u001B[43me\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1988\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1990\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:2366\u001B[39m, in \u001B[36mConnection._handle_dbapi_exception\u001B[39m\u001B[34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001B[39m\n\u001B[32m 2364\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 2365\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m exc_info[\u001B[32m1\u001B[39m] \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2366\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m exc_info[\u001B[32m1\u001B[39m].with_traceback(exc_info[\u001B[32m2\u001B[39m])\n\u001B[32m 2367\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m 2368\u001B[39m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m._reentrant_error\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1967\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1965\u001B[39m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[32m 1966\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1967\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1968\u001B[39m \u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1969\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1971\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m 1972\u001B[39m \u001B[38;5;28mself\u001B[39m.dispatch.after_cursor_execute(\n\u001B[32m 1973\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1974\u001B[39m cursor,\n\u001B[32m (...)\u001B[39m\u001B[32m 1978\u001B[39m context.executemany,\n\u001B[32m 1979\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/default.py:952\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m 951\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m952\u001B[39m \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/.local/share/uv/python/cpython-3.14.2-macos-x86_64-none/lib/python3.14/encodings/utf_8.py:15\u001B[39m, in \u001B[36mdecode\u001B[39m\u001B[34m(input, errors)\u001B[39m\n\u001B[32m 11\u001B[39m \u001B[38;5;66;03m### Codec APIs\u001B[39;00m\n\u001B[32m 13\u001B[39m encode = codecs.utf_8_encode\n\u001B[32m---> \u001B[39m\u001B[32m15\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdecode\u001B[39m(\u001B[38;5;28minput\u001B[39m, errors=\u001B[33m'\u001B[39m\u001B[33mstrict\u001B[39m\u001B[33m'\u001B[39m):\n\u001B[32m 16\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m codecs.utf_8_decode(\u001B[38;5;28minput\u001B[39m, errors, \u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 18\u001B[39m \u001B[38;5;28;01mclass\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mIncrementalEncoder\u001B[39;00m(codecs.IncrementalEncoder):\n",
"\u001B[31mKeyboardInterrupt\u001B[39m: "
]
}
],
"execution_count": 26
}
],
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"language": "python",
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101
notebook/indicator.ipynb
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File diff suppressed because one or more lines are too long

2
pyproject.toml
View File

@@ -4,6 +4,7 @@ version = "0.1.0"
description = "Stock analysis"
requires-python = ">=3.14"
dependencies = [
"akshare>=1.18.20",
"backtesting~=0.6.5",
"duckdb>=1.4.4",
"jupyter~=1.1.1",
@@ -18,4 +19,5 @@ dependencies = [
"ta-lib>=0.6.8",
"tabulate>=0.9.0",
"tqdm>=4.67.1",
"tushare>=1.4.24",
]

92
sql/initial.sql Normal file
View File

@@ -0,0 +1,92 @@
CREATE TABLE stock
(
code varchar primary key,
fullname varchar,
industry varchar,
listed_date date,
market varchar,
name varchar
);
CREATE TABLE daily
(
code varchar not null,
trade_date date not null,
open double not null,
close double not null,
high double not null,
low double not null,
previous_close double not null,
turnover double not null,
volume integer not null,
price_change_amount double not null,
factor double not null,
primary key (code, trade_date)
);
CREATE TABLE finance_indicator
(
code varchar,
year integer,
accounts_payable double,
accounts_payable_to_total_assets_ratio double,
accounts_receivable double,
accounts_receivable_to_total_assets_ratio double,
accounts_receivable_turnover double,
capital_surplus double,
cash_and_cash_equivalents double,
cash_and_cash_equivalents_to_total_assets_ratio double,
cash_flow_adequacy_ratio double,
cash_flow_from_financing_activities double,
cash_flow_from_investing_activities double,
cash_flow_from_operating_activities double,
cash_flow_ratio double,
cash_reinvestment_ratio double,
current_assets double,
current_assets_to_total_assets_ratio double,
current_liabilities double,
current_liabilities_to_total_assets_ratio double,
current_liabilities_to_total_liabilities_ratio double,
current_ratio double,
days_accounts_receivable_turnover double,
days_fixed_assets_turnover double,
days_inventory_turnover double,
days_total_assets_turnover double,
earnings_per_share double,
fixed_assets double,
fixed_assets_to_total_assets_ratio double,
fixed_assets_turnover double,
goodwill double,
goodwill_to_total_assets_ratio double,
inventory double,
inventory_to_total_assets_ratio double,
inventory_turnover double,
liabilities_to_total_assets_ratio double,
long_term_funds_to_fixed_assets_ratio double,
long_term_liabilities double,
long_term_liabilities_to_total_assets_ratio double,
long_term_liabilities_to_total_liabilities_ratio double,
net_cash_flow_from_operating_activities double,
net_profit double,
net_profit_margin double,
operating_cost double,
operating_expenses double,
operating_gross_profit_margin double,
operating_profit double,
operating_profit_margin double,
operating_revenue double,
operating_safety_margin_ratio double,
quick_ratio double,
return_on_assets double,
return_on_equity double,
shareholders_equity double,
shareholders_equity_to_total_assets_ratio double,
surplus_reserve double,
total_assets double,
total_assets_turnover double,
total_liabilities double,
total_share_capital double,
undistributed_profit double,
primary key (code, year)
)

216
uv.lock generated
View File

@@ -2,6 +2,42 @@ version = 1
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