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Author SHA1 Message Date
lanyuanxiaoyao
5554abc245 移除不使用的依赖 2026-01-29 09:56:30 +08:00
lanyuanxiaoyao
9759720fec 改用marimo编辑notebook方便跨平台使用 2026-01-29 09:55:45 +08:00
20 changed files with 3221 additions and 4587 deletions
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7
.idea/dataSources.xml generated
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@@ -8,12 +8,5 @@
<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>

7
.idea/data_source_mapping.xml generated
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DataSourcePerFileMappings">
<file url="file://$APPLICATION_CONFIG_DIR$/consoles/db/bd7b5f2a-eb99-4aad-81ec-1fec76b3d7fc/console.sql" value="bd7b5f2a-eb99-4aad-81ec-1fec76b3d7fc" />
<file url="file://$APPLICATION_CONFIG_DIR$/consoles/db/c9e16f8e-81be-45cf-847c-47a6750eeee2/console.sql" value="c9e16f8e-81be-45cf-847c-47a6750eeee2" />
</component>
</project>

6
.idea/db-forest-config.xml generated
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="db-tree-configuration">
<option name="data" value="" />
</component>
</project>

1
.idea/sqldialects.xml generated
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@@ -1,7 +1,6 @@
<?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>

136
data.py
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from datetime import date, datetime, timedelta
from time import sleep
from sqlalchemy import Column, Double, Integer, String, create_engine
from sqlalchemy.orm import DeclarativeBase, Session
from tushare import pro_api
TUSHARE_API_KEY = '64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e'
class Base(DeclarativeBase):
pass
class Stock(Base):
__tablename__ = 'stock'
code = Column(String, primary_key=True, comment="代码")
name = Column(String, comment="名称")
fullname = Column(String, comment="全名")
market = Column(String, comment="市场")
exchange = Column(String, comment="交易所")
industry = Column(String, comment="行业")
list_date = Column(String, comment="上市日期")
class Daily(Base):
__tablename__ = 'daily'
code = Column(String, primary_key=True)
trade_date = Column(String, primary_key=True)
open = Column(Double)
close = Column(Double)
high = Column(Double)
low = Column(Double)
previous_close = Column(Double)
turnover = Column(Double)
volume = Column(Integer)
price_change_amount = Column(Double)
factor = Column(Double)
def main():
print("开始更新数据")
engine = create_engine(f"sqlite:////Users/lanyuanxiaoyao/Documents/leopard_data/leopard.sqlite")
try:
Stock.metadata.create_all(engine, checkfirst=True)
Daily.metadata.create_all(engine, checkfirst=True)
pro = pro_api(TUSHARE_API_KEY)
# with engine.connect() as connection:
# stocks = pro.stock_basic(list_status="L", market="主板", fields="ts_code,name,fullname,market,exchange,industry,list_date")
# for row in stocks.itertuples():
# stmt = insert(Stock).values(
# code=row.ts_code,
# name=row.name,
# fullname=row.fullname,
# market=row.market,
# exchange=row.exchange,
# industry=row.industry,
# list_date=row.list_date,
# )
# stmt = stmt.on_conflict_do_update(
# index_elements=["code"],
# set_={
# "name": stmt.excluded.name,
# "fullname": stmt.excluded.fullname,
# "market": stmt.excluded.market,
# "exchange": stmt.excluded.exchange,
# "industry": stmt.excluded.industry,
# "list_date": stmt.excluded.list_date,
# },
# )
# print(stmt)
# connection.execute(stmt)
# connection.commit()
#
# print("清理行情数据")
# connection.execute(text("delete from daily where code not in (select distinct code from stock)"))
# connection.commit()
#
# print("清理财务数据")
# connection.execute(text("delete from finance_indicator where code not in (select distinct code from stock)"))
# connection.commit()
with Session(engine) as session:
stock_codes = [row[0] for row in session.query(Stock.code).all()]
latest_date = session.query(Daily.trade_date).order_by(Daily.trade_date.desc()).first()
if latest_date is None:
latest_date = '1990-12-19'
else:
latest_date = latest_date.trade_date
latest_date = datetime.strptime(latest_date, '%Y-%m-%d').date()
current_date = date.today() - timedelta(days=1)
delta = (current_date - latest_date).days
print(f"最新数据日期：{latest_date}，当前日期：{current_date}，待更新天数：{delta}")
if delta > 0:
update_dates = []
for i in range(delta):
latest_date = latest_date + timedelta(days=1)
update_dates.append(latest_date.strftime('%Y%m%d'))
for target_date in update_dates:
print(f"正在采集：{target_date}")
dailies = pro.daily(trade_date=target_date)
dailies.set_index("ts_code", inplace=True)
factors = pro.adj_factor(trade_date=target_date)
factors.set_index("ts_code", inplace=True)
results = dailies.join(factors, lsuffix="_daily", rsuffix="_factor", how="left")
rows = []
for row in results.itertuples():
if row.Index in stock_codes:
rows.append(
Daily(
code=row.Index,
trade_date=datetime.strptime(target_date, '%Y%m%d').strftime("%Y-%m-%d"),
open=row.open,
close=row.close,
high=row.high,
low=row.low,
previous_close=row.pre_close,
turnover=row.amount,
volume=row.vol,
price_change_amount=row.pct_chg,
factor=row.adj_factor,
)
)
session.add_all(rows)
session.commit()
sleep(1)
finally:
engine.dispose()
if __name__ == '__main__':
main()

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notebook/__marimo__/session/indicator.py.json Normal file
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notebook/backtest.ipynb
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184
notebook/backtest.py Normal file
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import marimo
__generated_with = "0.19.6"
app = marimo.App(width="full", auto_download=["ipynb"], sql_output="pandas")
@app.cell
def _():
import marimo as mo
return (mo,)
@app.cell
def _():
import urllib
import sqlalchemy
host = "81.71.3.24"
port = 6785
username = "leopard"
password = urllib.parse.quote_plus("9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X")
database = "leopard_dev"
engine = sqlalchemy.create_engine(f"postgresql://{username}:{password}@{host}:{port}/{database}")
return (engine,)
@app.cell
def _(engine, mo):
dailies_df = mo.sql(
f"""
select trade_date,
open * factor as Open,
close * factor as Close,
high * factor as High,
low * factor as Low,
volume as Volume,
coalesce(factor, 1.0) as factor
from leopard_daily daily
left join leopard_stock stock on stock.id = daily.stock_id
where stock.code = '000001.SZ'
and daily.trade_date between '2024-01-01 00:00:00' and '2025-12-31 23:59:59'
order by daily.trade_date
""",
engine=engine
)
return (dailies_df,)
@app.cell
def _(dailies_df):
import pandas as pd
dailies_df.rename(
columns={'open': 'Open', 'close': 'Close', 'high': 'High', 'low': 'Low', 'volume': 'Volume'}, inplace=True
)
dailies_df['trade_date'] = pd.to_datetime(dailies_df['trade_date'], format='%Y-%m-%d')
dailies_df.set_index('trade_date', inplace=True)
dailies_df
return
@app.cell
def _(dailies_df):
import talib
dailies_df['sma10'] = talib.SMA(dailies_df['Close'], timeperiod=10)
dailies_df['sma30'] = talib.SMA(dailies_df['Close'], timeperiod=30)
dailies_df['sma60'] = talib.SMA(dailies_df['Close'], timeperiod=60)
dailies_df['sma120'] = talib.SMA(dailies_df['Close'], timeperiod=120)
dailies_df
return
@app.cell
def _(dailies_df):
# 指标计算完成后截取后面指标的完整的部份使用
target_dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']
target_dailies_df
return (target_dailies_df,)
@app.cell
def _():
from backtesting import Strategy
from backtesting.lib import crossover
class SmaCross(Strategy):
def init(self):
self.sma10 = self.I(lambda x: x, self.data.sma10)
self.sma30 = self.I(lambda x: x, self.data.sma30)
self.sma60 = self.I(lambda x: x, self.data.sma60)
# self.sma120 = self.I(lambda x: x, self.data.sma120)
def next(self):
if self.sma60 > 0 and crossover(self.data.sma10, self.data.sma30):
self.buy()
elif self.position.size > 0 and crossover(self.data.sma30, self.data.sma10):
self.position.close()
return (SmaCross,)
@app.function
def stats_print(stats):
indicator_name_mapping = {
# 'Start': '回测开始时间',
# 'End': '回测结束时间',
# 'Duration': '回测持续时长',
# 'Exposure Time [%]': '持仓时间占比（%）',
'Equity Final [$]': '最终收益',
'Equity Peak [$]': '峰值收益',
'Return [%]': '总收益率（%）',
'Buy & Hold Return [%]': '买入并持有收益率（%）',
'Return (Ann.) [%]': '年化收益率（%）',
'Volatility (Ann.) [%]': '年化波动率（%）',
# 'CAGR [%]': '复合年均增长率（%）',
# 'Sharpe Ratio': '夏普比率',
'Sortino Ratio': '索提诺比率',
'Calmar Ratio': '卡尔玛比率',
# 'Alpha [%]': '阿尔法系数（%）',
# 'Beta': '贝塔系数',
'Max. Drawdown [%]': '最大回撤（%）',
'Avg. Drawdown [%]': '平均回撤（%）',
'Max. Drawdown Duration': '最大回撤持续时长',
'Avg. Drawdown Duration': '平均回撤持续时长',
'# Trades': '总交易次数',
'Win Rate [%]': '胜率（%）',
# 'Best Trade [%]': '最佳单笔交易收益率（%）',
# 'Worst Trade [%]': '最差单笔交易收益率（%）',
# 'Avg. Trade [%]': '平均单笔交易收益率（%）',
# 'Max. Trade Duration': '单笔交易最长持有时长',
# 'Avg. Trade Duration': '单笔交易平均持有时长',
# 'Profit Factor': '盈利因子',
# 'Expectancy [%]': '期望收益（%）',
'SQN': '系统质量数',
# 'Kelly Criterion': '凯利准则',
}
for k, v in stats.items():
if k in indicator_name_mapping:
cn_name = indicator_name_mapping.get(k, k)
if isinstance(v, (int, float)):
if "%" in cn_name or k in ['Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio', 'Profit Factor']:
formatted_value = f"{v:.2f}"
elif "$" in cn_name:
formatted_value = f"{v:.2f}"
elif "次数" in cn_name:
formatted_value = f"{v:.0f}"
else:
formatted_value = f"{v:.4f}"
else:
formatted_value = str(v)
print(f'{cn_name}: {formatted_value}')
@app.cell
def _(SmaCross, target_dailies_df):
from backtesting import Backtest
import backtesting._plotting as plotting
from bokeh.colors.named import tomato, lime
plotting.BULL_COLOR = tomato
plotting.BEAR_COLOR = lime
bt = Backtest(target_dailies_df, SmaCross, cash=100000, commission=.002, finalize_trades=True)
stats = bt.run()
stats_print(stats)
return bt, stats
@app.cell
def _(stats):
stats._trades
# stats._equity_curve
return
@app.cell
def _(bt):
bt.plot()
return
if __name__ == "__main__":
app.run()

1300
notebook/datasource/data.ipynb
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455
notebook/datasource/data_old.ipynb
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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-30T05:41:51.291397Z",
"start_time": "2026-01-30T04:34:22.917761Z"
}
},
"cell_type": "code",
"source": [
"import urllib.parse\n",
"\n",
"import pandas as pd\n",
"import sqlalchemy\n",
"from sqlalchemy import text\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, primary_key=True)\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",
" with Session(postgresql_engin) as pg_session:\n",
" results = pg_session.execute(text(\"select distinct trade_date from leopard_daily\")).fetchall()\n",
" results = list(map(lambda x: x[0].strftime(\"%Y-%m-%d\"), results))\n",
" dates = [results[i: i + 30] for i in range(0, len(results), 30)]\n",
"\n",
" for index, date in enumerate(dates):\n",
" print(date)\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 d.trade_date in ('{\"','\".join(date)}')\n",
" \"\"\",\n",
" postgresql_engin\n",
" )\n",
" with Session(sqlite_engine) as session:\n",
" rows = []\n",
" for _, row in daily_df.iterrows():\n",
" rows.append(\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.add_all(rows)\n",
" session.commit()\n",
"finally:\n",
" postgresql_engin.dispose()\n",
" sqlite_engine.dispose()"
],
"id": "48821306efc640a1",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2025-12-25', '2025-12-26', '2025-12-29', '2025-12-30', '2025-12-31', '2026-01-05', '2026-01-06', '2026-01-07', '2026-01-08', '2026-01-09']\n"
]
}
],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-30T09:24:09.859231Z",
"start_time": "2026-01-30T09:24:09.746912Z"
}
},
"cell_type": "code",
"source": [
"import tushare as ts\n",
"\n",
"pro = ts.pro_api(\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")\n",
"# stocks = pro.stock_basic(ts_code=\"600200.SH\", list_status=\"D\", fields=\"ts_code,name,fullname,market,exchange,industry,list_date,delist_date\")\n",
"# stocks"
],
"id": "ed58a1faaf2cdb8e",
"outputs": [],
"execution_count": 34
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-30T07:14:29.897120Z",
"start_time": "2026-01-30T07:14:29.664124Z"
}
},
"cell_type": "code",
"source": "# stocks.to_csv(\"dlist.csv\")",
"id": "3c8c0a38d6b2992e",
"outputs": [],
"execution_count": 24
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-30T09:46:34.808300Z",
"start_time": "2026-01-30T09:46:34.129412Z"
}
},
"cell_type": "code",
"source": [
"daily_df = pro.daily(trade_date=\"20251231\")\n",
"daily_df.set_index(\"ts_code\", inplace=True)\n",
"factor_df = pro.adj_factor(trade_date=\"20251231\")\n",
"factor_df.set_index(\"ts_code\", inplace=True)"
],
"id": "c052a945869aa329",
"outputs": [],
"execution_count": 50
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-30T09:46:36.697015Z",
"start_time": "2026-01-30T09:46:36.642975Z"
}
},
"cell_type": "code",
"source": [
"result_df = daily_df.join(factor_df, lsuffix=\"_daily\", rsuffix=\"_factor\", how=\"left\")\n",
"result_df\n",
"# factor_df"
],
"id": "d61ee80d2cd9f06b",
"outputs": [
{
"data": {
"text/plain": [
" trade_date_daily open high low close pre_close change \\\n",
"ts_code \n",
"000001.SZ 20251231 11.48 11.49 11.40 11.41 11.48 -0.07 \n",
"000002.SZ 20251231 4.66 4.68 4.62 4.65 4.62 0.03 \n",
"000004.SZ 20251231 11.30 11.35 11.07 11.08 11.27 -0.19 \n",
"000006.SZ 20251231 9.95 10.03 9.69 9.95 9.86 0.09 \n",
"000007.SZ 20251231 11.72 11.75 11.28 11.44 11.62 -0.18 \n",
"... ... ... ... ... ... ... ... \n",
"920978.BJ 20251231 37.64 38.39 36.88 36.90 37.78 -0.88 \n",
"920981.BJ 20251231 32.20 32.29 31.75 31.96 32.07 -0.11 \n",
"920982.BJ 20251231 233.00 238.49 232.10 233.70 234.80 -1.10 \n",
"920985.BJ 20251231 7.32 7.35 7.17 7.19 7.30 -0.11 \n",
"920992.BJ 20251231 17.33 17.60 17.29 17.39 17.38 0.01 \n",
"\n",
" pct_chg vol amount trade_date_factor adj_factor \n",
"ts_code \n",
"000001.SZ -0.6098 590620.37 675457.357 20251231 134.5794 \n",
"000002.SZ 0.6494 1075561.25 499883.113 20251231 181.7040 \n",
"000004.SZ -1.6859 18056.00 20248.567 20251231 4.0640 \n",
"000006.SZ 0.9128 270369.08 267758.676 20251231 39.7400 \n",
"000007.SZ -1.5491 80556.00 92109.366 20251231 8.2840 \n",
"... ... ... ... ... ... \n",
"920978.BJ -2.3293 33945.04 126954.937 20251231 1.2885 \n",
"920981.BJ -0.3430 8237.16 26301.206 20251231 1.4343 \n",
"920982.BJ -0.4685 5210.09 122452.646 20251231 4.2831 \n",
"920985.BJ -1.5068 35174.30 25350.257 20251231 1.6280 \n",
"920992.BJ 0.0575 6991.87 12193.445 20251231 1.4932 \n",
"\n",
"[5458 rows x 12 columns]"
],
"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>trade_date_daily</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>pre_close</th>\n",
" <th>change</th>\n",
" <th>pct_chg</th>\n",
" <th>vol</th>\n",
" <th>amount</th>\n",
" <th>trade_date_factor</th>\n",
" <th>adj_factor</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ts_code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>000001.SZ</th>\n",
" <td>20251231</td>\n",
" <td>11.48</td>\n",
" <td>11.49</td>\n",
" <td>11.40</td>\n",
" <td>11.41</td>\n",
" <td>11.48</td>\n",
" <td>-0.07</td>\n",
" <td>-0.6098</td>\n",
" <td>590620.37</td>\n",
" <td>675457.357</td>\n",
" <td>20251231</td>\n",
" <td>134.5794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000002.SZ</th>\n",
" <td>20251231</td>\n",
" <td>4.66</td>\n",
" <td>4.68</td>\n",
" <td>4.62</td>\n",
" <td>4.65</td>\n",
" <td>4.62</td>\n",
" <td>0.03</td>\n",
" <td>0.6494</td>\n",
" <td>1075561.25</td>\n",
" <td>499883.113</td>\n",
" <td>20251231</td>\n",
" <td>181.7040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000004.SZ</th>\n",
" <td>20251231</td>\n",
" <td>11.30</td>\n",
" <td>11.35</td>\n",
" <td>11.07</td>\n",
" <td>11.08</td>\n",
" <td>11.27</td>\n",
" <td>-0.19</td>\n",
" <td>-1.6859</td>\n",
" <td>18056.00</td>\n",
" <td>20248.567</td>\n",
" <td>20251231</td>\n",
" <td>4.0640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000006.SZ</th>\n",
" <td>20251231</td>\n",
" <td>9.95</td>\n",
" <td>10.03</td>\n",
" <td>9.69</td>\n",
" <td>9.95</td>\n",
" <td>9.86</td>\n",
" <td>0.09</td>\n",
" <td>0.9128</td>\n",
" <td>270369.08</td>\n",
" <td>267758.676</td>\n",
" <td>20251231</td>\n",
" <td>39.7400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000007.SZ</th>\n",
" <td>20251231</td>\n",
" <td>11.72</td>\n",
" <td>11.75</td>\n",
" <td>11.28</td>\n",
" <td>11.44</td>\n",
" <td>11.62</td>\n",
" <td>-0.18</td>\n",
" <td>-1.5491</td>\n",
" <td>80556.00</td>\n",
" <td>92109.366</td>\n",
" <td>20251231</td>\n",
" <td>8.2840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920978.BJ</th>\n",
" <td>20251231</td>\n",
" <td>37.64</td>\n",
" <td>38.39</td>\n",
" <td>36.88</td>\n",
" <td>36.90</td>\n",
" <td>37.78</td>\n",
" <td>-0.88</td>\n",
" <td>-2.3293</td>\n",
" <td>33945.04</td>\n",
" <td>126954.937</td>\n",
" <td>20251231</td>\n",
" <td>1.2885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920981.BJ</th>\n",
" <td>20251231</td>\n",
" <td>32.20</td>\n",
" <td>32.29</td>\n",
" <td>31.75</td>\n",
" <td>31.96</td>\n",
" <td>32.07</td>\n",
" <td>-0.11</td>\n",
" <td>-0.3430</td>\n",
" <td>8237.16</td>\n",
" <td>26301.206</td>\n",
" <td>20251231</td>\n",
" <td>1.4343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920982.BJ</th>\n",
" <td>20251231</td>\n",
" <td>233.00</td>\n",
" <td>238.49</td>\n",
" <td>232.10</td>\n",
" <td>233.70</td>\n",
" <td>234.80</td>\n",
" <td>-1.10</td>\n",
" <td>-0.4685</td>\n",
" <td>5210.09</td>\n",
" <td>122452.646</td>\n",
" <td>20251231</td>\n",
" <td>4.2831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920985.BJ</th>\n",
" <td>20251231</td>\n",
" <td>7.32</td>\n",
" <td>7.35</td>\n",
" <td>7.17</td>\n",
" <td>7.19</td>\n",
" <td>7.30</td>\n",
" <td>-0.11</td>\n",
" <td>-1.5068</td>\n",
" <td>35174.30</td>\n",
" <td>25350.257</td>\n",
" <td>20251231</td>\n",
" <td>1.6280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920992.BJ</th>\n",
" <td>20251231</td>\n",
" <td>17.33</td>\n",
" <td>17.60</td>\n",
" <td>17.29</td>\n",
" <td>17.39</td>\n",
" <td>17.38</td>\n",
" <td>0.01</td>\n",
" <td>0.0575</td>\n",
" <td>6991.87</td>\n",
" <td>12193.445</td>\n",
" <td>20251231</td>\n",
" <td>1.4932</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5458 rows × 12 columns</p>\n",
"</div>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 51
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

1157
notebook/indicator.ipynb
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131
notebook/indicator.py Normal file
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@@ -0,0 +1,131 @@
import marimo
__generated_with = "0.19.6"
app = marimo.App(width="full", auto_download=["ipynb"], sql_output="pandas")
@app.cell
def _():
import marimo as mo
return (mo,)
@app.cell
def _():
import urllib
import sqlalchemy
host = "81.71.3.24"
port = 6785
username = "leopard"
password = urllib.parse.quote_plus("9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X")
database = "leopard_dev"
engine = sqlalchemy.create_engine(f"postgresql://{username}:{password}@{host}:{port}/{database}")
return (engine,)
@app.cell
def _(engine, mo):
dailies_df = mo.sql(
f"""
select trade_date, open * factor as "Open", close * factor as "Close", high * factor as "High", low * factor as "Low", volume as "Volume", coalesce(factor, 1.0) as factor
from leopard_daily daily
left join leopard_stock stock on stock.id = daily.stock_id
where stock.code = '000001.SZ'
and daily.trade_date between '2024-01-01 00:00:00' and '2025-12-31 23:59:59'
order by daily.trade_date
""",
engine=engine
)
return (dailies_df,)
@app.cell
def _(dailies_df):
import pandas as pd
dailies_df["trade_date"] = pd.to_datetime(dailies_df["trade_date"], format='%Y-%m-%d')
dailies_df.set_index("trade_date", inplace=True)
dailies_df
return
@app.cell
def _(dailies_df):
import talib
dailies_df['sma30'] = talib.SMA(dailies_df['Close'], timeperiod=30)
dailies_df['sma60'] = talib.SMA(dailies_df['Close'], timeperiod=60)
dailies_df['sma120'] = talib.SMA(dailies_df['Close'], timeperiod=120)
macd, signal, hist = talib.MACD(dailies_df["Close"], fastperiod=10, slowperiod=20, signalperiod=9)
dailies_df['macd'] = macd
dailies_df['signal'] = signal
dailies_df['hist'] = hist
target_dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']
target_dailies_df
return (target_dailies_df,)
@app.cell
def _(target_dailies_df):
target_dailies_df.reset_index(inplace=True)
target_dailies_df_inc = target_dailies_df[target_dailies_df["Close"] > target_dailies_df["Open"]]
target_dailies_df_dec = target_dailies_df[target_dailies_df["Close"] < target_dailies_df["Open"]]
return target_dailies_df_dec, target_dailies_df_inc
@app.cell
def daily_chart(
target_dailies_df,
target_dailies_df_dec,
target_dailies_df_inc,
):
from bokeh.io import show, output_notebook
from bokeh.layouts import column
from bokeh.plotting import figure
output_notebook()
width = 1200
up_color = "black"
down_color = "grey"
price_figure = figure(width=width, height=400, tools='pan,wheel_zoom,box_zoom,reset')
price_figure.min_border = 0
x_padding = 1
y_padding = 10
price_figure.x_range.bounds = (min(target_dailies_df.index) - x_padding, max(target_dailies_df.index) + x_padding)
price_figure.y_range.bounds = (min(target_dailies_df['Low']) - y_padding, max(target_dailies_df['High']) + y_padding)
price_figure.xaxis.major_label_overrides = {i: date.strftime('%Y-%m-%d') for i, date in zip(target_dailies_df.index, target_dailies_df['trade_date'])}
price_figure.segment(target_dailies_df.index, target_dailies_df['High'], target_dailies_df.index, target_dailies_df['Low'], color='black')
price_figure.vbar(target_dailies_df_inc.index, 0.6, target_dailies_df_inc['Open'], target_dailies_df_inc['Close'], color=up_color)
price_figure.vbar(target_dailies_df_dec.index, 0.6, target_dailies_df_dec['Open'], target_dailies_df_dec['Close'], color=down_color)
price_figure.line(target_dailies_df.index, target_dailies_df['sma30'], color='orange')
price_figure.line(target_dailies_df.index, target_dailies_df['sma60'], color='red')
# 控制图表只能放大不能缩小
macd_figure = figure(width=width, height=200, tools='pan,wheel_zoom,box_zoom,reset')
macd_figure.x_range.bounds = (min(target_dailies_df.index) - x_padding, max(target_dailies_df.index) + x_padding)
macd_figure.y_range.bounds = (min(target_dailies_df['macd']) - y_padding, max(target_dailies_df['macd']) + y_padding)
macd_figure.line(target_dailies_df.index, target_dailies_df['macd'], color='orange')
macd_figure.line(target_dailies_df.index, target_dailies_df['signal'], color='red')
# Add MACD histogram bars for positive and negative values
macd_positive = target_dailies_df[target_dailies_df['macd'] > 0]
macd_negative = target_dailies_df[target_dailies_df['macd'] < 0]
macd_figure.vbar(macd_positive.index, 0.6, 0, macd_positive['macd'], color=up_color)
macd_figure.vbar(macd_negative.index, 0.6, 0, macd_negative['macd'], color=down_color)
# Add zero line
macd_figure.line(target_dailies_df.index, [0] * len(target_dailies_df), color='black', line_dash='dashed')
# show(price_figure)
# show(macd_figure)
show(column(price_figure, macd_figure))
return
if __name__ == "__main__":
app.run()

72
notebook/sql2dataframe.ipynb
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@@ -1,72 +0,0 @@
{
"cells": [
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"import pandas as pd\n",
"\n",
"host = '81.71.3.24'\n",
"port = 6785\n",
"database = 'leopard_dev'\n",
"username = 'leopard'\n",
"password = '9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X'"
],
"id": "1e8d815ee9b8c936"
},
{
"cell_type": "code",
"execution_count": null,
"id": "initial_id",
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import psycopg2\n",
"\n",
"dailies_df = pd.DataFrame()\n",
"\n",
"with psycopg2.connect(host=host, port=port, database=database, user=username, password=password) as connection:\n",
" with connection.cursor() as cursor:\n",
" # language=PostgreSQL\n",
" cursor.execute(\n",
" \"\"\"select trade_date, open, close, high, low, factor\n",
"from leopard_daily daily\n",
" left join leopard_stock stock on stock.id = daily.stock_id\n",
"where stock.code = '000001.SZ'\n",
" and daily.trade_date between '2025-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
"order by daily.trade_date\"\"\"\n",
" )\n",
" rows = cursor.fetchall()\n",
"\n",
" dailies_df = pd.DataFrame.from_records(rows, columns=['trade_date', 'open', 'close', 'high', 'low', 'factor'])\n",
"\n",
"dailies_df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

82
notebook/sqlalchemy.ipynb
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15
pyproject.toml
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@@ -4,13 +4,10 @@ version = "0.1.0"
description = "Stock analysis"
requires-python = ">=3.14"
dependencies = [
"adata>=2.9.5",
"akshare>=1.18.20",
"backtesting~=0.6.5",
"baostock>=0.8.9",
"duckdb>=1.4.4",
"jupyter~=1.1.1",
"jupyter-bokeh>=4.0.5",
"marimo>=0.19.6",
"matplotlib~=3.10.8",
"mplfinance>=0.12.10b0",
"pandas~=2.3.3",
@@ -21,5 +18,13 @@ dependencies = [
"ta-lib>=0.6.8",
"tabulate>=0.9.0",
"tqdm>=4.67.1",
"tushare>=1.4.24",
]
[dependency-groups]
dev = [
"mcp>=1",
"openai>=2.16.0",
"pydantic>=2",
"pydantic-ai>=1.48.0",
"ruff>=0.14.14",
]

94
sql/initial.sql
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CREATE TABLE stock
(
code varchar not null,
name varchar not null,
fullname varchar,
industry varchar,
listed_date date,
market varchar,
exchange varchar,
primary key (code)
);
CREATE TABLE daily
(
code varchar not null,
trade_date date not null,
open double,
close double,
high double,
low double,
previous_close double,
turnover double,
volume integer,
price_change_amount double,
factor double,
primary key (code, trade_date)
);
CREATE TABLE finance_indicator
(
code varchar not null,
year integer not null,
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)
)

2221
uv.lock generated
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