a65adcc2e5
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:
- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
- docx family: kept docx (official, full toolchain) + docx-cn
(GB/T 9704 Chinese official-document constants),
dropped docx_writer (no scripts, name collided with docx)
- humanizer family: kept humanizer-zh (6 zh reference docs),
dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
live credentials
Result: 25 skills, 572 files, ~7.5 MB.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
249 lines
10 KiB
Python
249 lines
10 KiB
Python
#!/usr/bin/env python3
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"""贵州茅台过去一年风险分析报告生成器
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计算风险指标：年化波动率、最大回撤、Sharpe比率、Beta、VaR
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图表1：茅台累计收益率 vs 沪深300（双轴：柱+线）
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图表2：滚动波动率 vs 沪深300收盘价（双轴：面积+线）
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"""
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import sys
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import json
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import numpy as np
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from pathlib import Path
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from datetime import datetime, timedelta
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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import tushare as ts
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import pandas as pd
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from ppt_station.config import settings
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# ============================================================================
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# 配置
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# ============================================================================
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STOCK_CODE = "600519.SH"
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STOCK_NAME = "贵州茅台"
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INDEX_CODE = "000300.SH"
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INDEX_NAME = "沪深300"
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OUTPUT_DIR = Path(__file__).resolve().parent.parent.parent / "output" / "moutai_risk"
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TEMPLATE_PATH = Path(__file__).resolve().parent.parent.parent / "aim" / "aim03.pptx"
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# 过去一年
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END_DATE = datetime.now()
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START_DATE = END_DATE - timedelta(days=365)
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START_STR = START_DATE.strftime("%Y%m%d")
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END_STR = END_DATE.strftime("%Y%m%d")
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YEAR = END_DATE.year
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def fetch_data():
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"""获取茅台和沪深300过去一年日线数据"""
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pro = ts.pro_api(settings.tushare_token)
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print("📊 获取贵州茅台日线数据...", file=sys.stderr)
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df_stock = ts.pro_bar(ts_code=STOCK_CODE, start_date=START_STR, end_date=END_STR, adj="qfq")
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df_stock = df_stock.sort_values("trade_date").reset_index(drop=True)
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df_stock["trade_date"] = pd.to_datetime(df_stock["trade_date"], format="%Y%m%d")
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print("📊 获取沪深300指数数据...", file=sys.stderr)
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df_index = ts.pro_bar(ts_code=INDEX_CODE, start_date=START_STR, end_date=END_STR, asset="I")
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df_index = df_index.sort_values("trade_date").reset_index(drop=True)
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df_index["trade_date"] = pd.to_datetime(df_index["trade_date"], format="%Y%m%d")
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return df_stock, df_index
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def compute_risk_metrics(df_stock, df_index):
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"""计算全部风险指标"""
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# 合并
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stock = df_stock[["trade_date", "close"]].copy()
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stock.columns = ["trade_date", "stock_close"]
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index = df_index[["trade_date", "close"]].copy()
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index.columns = ["trade_date", "index_close"]
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df = pd.merge(stock, index, on="trade_date", how="inner")
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# 日收益率
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df["stock_ret"] = df["stock_close"].pct_change()
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df["index_ret"] = df["index_close"].pct_change()
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df = df.dropna()
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# 年化波动率
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stock_vol = df["stock_ret"].std() * np.sqrt(245)
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index_vol = df["index_ret"].std() * np.sqrt(245)
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# 最大回撤
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cum_stock = (1 + df["stock_ret"]).cumprod()
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running_max = cum_stock.cummax()
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drawdown = (cum_stock - running_max) / running_max
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max_drawdown = drawdown.min()
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# Sharpe（假设无风险利率 2%）
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rf = 0.02
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annual_ret = (df["stock_close"].iloc[-1] / df["stock_close"].iloc[0]) - 1
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sharpe = (annual_ret - rf) / stock_vol if stock_vol > 0 else 0
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# Beta
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cov = np.cov(df["stock_ret"], df["index_ret"])
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beta = cov[0, 1] / cov[1, 1] if cov[1, 1] > 0 else 0
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# VaR (95%)
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var_95 = np.percentile(df["stock_ret"], 5)
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# 累计收益率
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total_return = df["stock_close"].iloc[-1] / df["stock_close"].iloc[0] - 1
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index_return = df["index_close"].iloc[-1] / df["index_close"].iloc[0] - 1
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return {
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"annual_vol": stock_vol,
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"index_vol": index_vol,
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"max_drawdown": max_drawdown,
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"sharpe": sharpe,
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"beta": beta,
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"var_95": var_95,
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"total_return": total_return,
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"index_return": index_return,
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"latest_price": df["stock_close"].iloc[-1],
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"report_date": df["trade_date"].iloc[-1].strftime("%Y-%m-%d"),
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"start_date": df["trade_date"].iloc[0].strftime("%Y-%m-%d"),
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}, df
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def build_chart1_csv(df_stock, df_index):
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"""图表1：茅台累计收益率 vs 沪深300收盘价"""
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stock = df_stock[["trade_date", "close"]].copy()
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stock.columns = ["trade_date", "close_stock"]
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index = df_index[["trade_date", "close"]].copy()
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index.columns = ["trade_date", "close_index"]
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df = pd.merge(stock, index, on="trade_date", how="inner")
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base = df["close_stock"].iloc[0]
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df["茅台累计收益率"] = df["close_stock"] / base - 1
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df["沪深300指数"] = df["close_index"]
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df = df.rename(columns={"trade_date": "日期"})
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return df[["日期", "沪深300指数", "茅台累计收益率"]]
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def build_chart2_csv(df_stock, df_index):
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"""图表2：20日滚动波动率（年化）vs 沪深300收盘价"""
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stock = df_stock[["trade_date", "close"]].copy()
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stock.columns = ["trade_date", "close_stock"]
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index = df_index[["trade_date", "close"]].copy()
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index.columns = ["trade_date", "close_index"]
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df = pd.merge(stock, index, on="trade_date", how="inner")
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df["daily_ret"] = df["close_stock"].pct_change()
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df["20日滚动波动率"] = df["daily_ret"].rolling(20).std() * np.sqrt(245)
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df["沪深300指数"] = df["close_index"]
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df = df.dropna()
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df = df.rename(columns={"trade_date": "日期"})
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return df[["日期", "沪深300指数", "20日滚动波动率"]]
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def main():
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# 1. 获取数据
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df_stock, df_index = fetch_data()
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print(f"✅ 获取到 {len(df_stock)} 条茅台数据, {len(df_index)} 条指数数据", file=sys.stderr)
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# 2. 计算风险指标
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metrics, _ = compute_risk_metrics(df_stock, df_index)
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print(f"📈 年化波动率: {metrics['annual_vol']:.2%}", file=sys.stderr)
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print(f"📉 最大回撤: {metrics['max_drawdown']:.2%}", file=sys.stderr)
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print(f"📊 Sharpe: {metrics['sharpe']:.2f}", file=sys.stderr)
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print(f"📊 Beta: {metrics['beta']:.2f}", file=sys.stderr)
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print(f"📊 VaR(95%): {metrics['var_95']:.2%}", file=sys.stderr)
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# 3. 生成图表 CSV
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chart1_df = build_chart1_csv(df_stock, df_index)
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chart2_df = build_chart2_csv(df_stock, df_index)
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chart1_csv = OUTPUT_DIR / "return_chart.csv"
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chart2_csv = OUTPUT_DIR / "volatility_chart.csv"
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chart1_df.to_csv(chart1_csv, index=False)
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chart2_df.to_csv(chart2_csv, index=False)
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print(f"💾 已保存 CSV: {chart1_csv}, {chart2_csv}", file=sys.stderr)
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# 4. 构建 config.json
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config = {
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"text_data": {
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"标题": f"{STOCK_NAME}过去一年风险分析报告",
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"作者": "PPT-Station 自动生成",
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"日期": metrics["report_date"],
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"标题1": f"{STOCK_NAME}收益与风险概览",
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"标题2": f"{STOCK_NAME}波动率与回撤分析",
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"标题3": f"{STOCK_NAME}风险指标总结",
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"组合名称": f"{STOCK_NAME}（{STOCK_CODE}）",
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"报告日期": metrics["report_date"],
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"成立日期": metrics["start_date"],
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"资产规模": f"最新价 {metrics['latest_price']:.2f} 元",
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"本年收益": f"{metrics['total_return']:.2%}",
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"本年收益金额": f"沪深300: {metrics['index_return']:.2%}",
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"累计收益率": f"波动率 {metrics['annual_vol']:.2%}",
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"累计收益金额": f"最大回撤 {metrics['max_drawdown']:.2%}",
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"平均年化": f"Sharpe {metrics['sharpe']:.2f} | Beta {metrics['beta']:.2f} | VaR95 {metrics['var_95']:.2%}",
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},
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"chart_configs": {
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"当年收益率走势图": {
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"csv_path": "return_chart.csv",
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"categories_col": "日期",
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"series_config": [
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{"key": "沪深300指数", "name": f"{INDEX_NAME}指数(收盘价)", "type": "bar", "axis": "secondary"},
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{"key": "茅台累计收益率", "name": f"{STOCK_NAME}累计收益率（左轴）", "type": "line", "axis": "primary"},
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],
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"style": {"color_scheme": "aim00", "line_width_pt": 2.0, "marker_style": "none"},
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"layout": {
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"title": f"{STOCK_NAME}过去一年累计收益率 vs {INDEX_NAME}",
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"legend": {"position": "top", "font_size_pt": 9, "font_name": "黑体"},
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"value_axis": {"number_format": "0%", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False},
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"secondary_value_axis": {"number_format": "#,##0", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False},
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"date_axis": {"number_format": "yyyy/mm"},
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},
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},
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"组合成立以来收益率走势": {
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"csv_path": "volatility_chart.csv",
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"categories_col": "日期",
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"series_config": [
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{"key": "沪深300指数", "name": f"{INDEX_NAME}指数(收盘价)", "type": "area", "axis": "secondary"},
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{"key": "20日滚动波动率", "name": "20日滚动波动率（年化）", "type": "line", "axis": "primary"},
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],
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"style": {"color_scheme": "aim00", "line_width_pt": 2.0, "marker_style": "none"},
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"layout": {
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"title": f"{STOCK_NAME}20日滚动波动率（年化）",
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"legend": {"position": "top", "font_size_pt": 9, "font_name": "黑体"},
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"value_axis": {"number_format": "0%", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": True},
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"secondary_value_axis": {"number_format": "#,##0", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False},
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"date_axis": {"number_format": "yyyy/mm"},
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},
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},
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},
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}
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config_path = OUTPUT_DIR / "config.json"
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with open(config_path, "w", encoding="utf-8") as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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# 5. 生成 PPT
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print(f"\n🔧 生成 PPT...", file=sys.stderr)
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output_pptx = OUTPUT_DIR / f"moutai_risk_{YEAR}.pptx"
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from ppt_station.template.replacer import TemplateReplacer
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from generate_ppt import build_chart_config
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chart_configs = {}
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for chart_name, chart_def in config["chart_configs"].items():
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print(f" 构建图表: {chart_name}", file=sys.stderr)
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chart_configs[chart_name] = build_chart_config(chart_name, chart_def, OUTPUT_DIR)
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replacer = TemplateReplacer(TEMPLATE_PATH)
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replacer.replace(data=config["text_data"], chart_configs=chart_configs)
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replacer.save(output_pptx)
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print(f"\n✅ 风险分析报告已生成: {output_pptx}", file=sys.stderr)
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print(json.dumps({"status": "ok", "output": str(output_pptx)}))
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if __name__ == "__main__":
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main()
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