#!/usr/bin/env python3 """贵州茅台年度分析报告生成器 独立示例脚本，演示如何使用 tushare + ppt_station 生成完整投资分析报告。 新报告请优先使用 run_job.py + Job JSON 声明式编排。 使用 tushare 获取数据，生成包含复杂双轴图表的 PPT。 """ import sys import json from pathlib import Path from datetime import datetime sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) import tushare as ts import pandas as pd import numpy as np from ppt_station.config import settings # ============================================================================ # 配置 # ============================================================================ STOCK_CODE = "600519.SH" STOCK_NAME = "贵州茅台" INDEX_CODE = "000300.SH" INDEX_NAME = "沪深300" YEAR = 2025 OUTPUT_DIR = Path(__file__).resolve().parent.parent.parent / "output" / "moutai_report" TEMPLATE_PATH = Path(__file__).resolve().parent.parent.parent / "aim" / "aim03.pptx" def fetch_data(): """从 tushare 获取所有需要的数据""" pro = ts.pro_api(settings.tushare_token) print("📊 获取贵州茅台日线数据...", file=sys.stderr) # 当年日线数据 df_stock = ts.pro_bar( ts_code=STOCK_CODE, start_date=f"{YEAR}0101", end_date=f"{YEAR}1231", adj="qfq", ) df_stock = df_stock.sort_values("trade_date").reset_index(drop=True) df_stock["trade_date"] = pd.to_datetime(df_stock["trade_date"], format="%Y%m%d") print("📊 获取沪深300指数数据...", file=sys.stderr) # 对应的沪深300指数（使用 pro_bar + asset='I' 替代 index_daily） df_index = ts.pro_bar( ts_code=INDEX_CODE, start_date=f"{YEAR}0101", end_date=f"{YEAR}1231", asset="I", ) df_index = df_index.sort_values("trade_date").reset_index(drop=True) df_index["trade_date"] = pd.to_datetime(df_index["trade_date"], format="%Y%m%d") print("📊 获取近5年日线数据...", file=sys.stderr) # 近5年数据（成立以来走势） df_stock_5y = ts.pro_bar( ts_code=STOCK_CODE, start_date=f"{YEAR - 4}0101", end_date=f"{YEAR}1231", adj="qfq", ) df_stock_5y = df_stock_5y.sort_values("trade_date").reset_index(drop=True) df_stock_5y["trade_date"] = pd.to_datetime(df_stock_5y["trade_date"], format="%Y%m%d") df_index_5y = ts.pro_bar( ts_code=INDEX_CODE, start_date=f"{YEAR - 4}0101", end_date=f"{YEAR}1231", asset="I", ) df_index_5y = df_index_5y.sort_values("trade_date").reset_index(drop=True) df_index_5y["trade_date"] = pd.to_datetime(df_index_5y["trade_date"], format="%Y%m%d") return df_stock, df_index, df_stock_5y, df_index_5y def compute_ytd_chart(df_stock, df_index): """计算当年收益率走势图数据：茅台收益率 vs 沪深300收盘价""" # 合并数据 df = pd.merge( df_stock[["trade_date", "close", "vol"]], df_index[["trade_date", "close"]], on="trade_date", how="inner", suffixes=("_stock", "_index"), ) # 计算累计收益率 base_stock = df["close_stock"].iloc[0] base_index = df["close_index"].iloc[0] df["茅台累计收益率"] = (df["close_stock"] / base_stock - 1) df["沪深300指数"] = df["close_index"] # 日期列 df = df.rename(columns={"trade_date": "日期"}) return df[["日期", "沪深300指数", "茅台累计收益率"]] def compute_5y_chart(df_stock_5y, df_index_5y): """计算近5年走势数据：茅台累计收益率 + 成交额""" stock = df_stock_5y[["trade_date", "close", "amount"]].copy() stock.columns = ["trade_date", "close_stock", "amount_stock"] index = df_index_5y[["trade_date", "close"]].copy() index.columns = ["trade_date", "close_index"] df = pd.merge(stock, index, on="trade_date", how="inner") # 累计收益率 base_stock = df["close_stock"].iloc[0] df["茅台累计收益率"] = (df["close_stock"] / base_stock - 1) # 成交额（万元） df["成交额(万元)"] = df["amount_stock"] / 10 # tushare amount 单位是千元 df = df.rename(columns={"trade_date": "日期"}) return df[["日期", "茅台累计收益率", "成交额(万元)"]] def compute_stats(df_stock, df_stock_5y): """计算关键统计指标""" # 当年收益率 ytd_return = df_stock["close"].iloc[-1] / df_stock["close"].iloc[0] - 1 # 5年累计收益率 total_return = df_stock_5y["close"].iloc[-1] / df_stock_5y["close"].iloc[0] - 1 # 年化收益率 n_years = len(df_stock_5y) / 245 # 大约交易日数 annualized = (1 + total_return) ** (1 / n_years) - 1 if n_years > 0 else 0 # 最新价 latest_price = df_stock["close"].iloc[-1] # 最新市值（假设总股本约12.56亿股） total_shares = 12.56 # 亿股 market_cap = latest_price * total_shares # 亿元 return { "ytd_return": ytd_return, "total_return": total_return, "annualized": annualized, "latest_price": latest_price, "market_cap": market_cap, "report_date": df_stock["trade_date"].iloc[-1].strftime("%Y-%m-%d"), "start_date": df_stock_5y["trade_date"].iloc[0].strftime("%Y-%m-%d"), } def main(): OUTPUT_DIR.mkdir(parents=True, exist_ok=True) # 获取数据 df_stock, df_index, df_stock_5y, df_index_5y = fetch_data() print(f"✅ 获取到 {len(df_stock)} 条当年数据, {len(df_stock_5y)} 条5年数据", file=sys.stderr) # 计算图表数据 ytd_df = compute_ytd_chart(df_stock, df_index) fivey_df = compute_5y_chart(df_stock_5y, df_index_5y) stats = compute_stats(df_stock, df_stock_5y) print(f"📈 当年收益率: {stats['ytd_return']:.2%}", file=sys.stderr) print(f"📈 5年累计收益率: {stats['total_return']:.2%}", file=sys.stderr) print(f"📈 年化收益率: {stats['annualized']:.2%}", file=sys.stderr) print(f"📈 最新价: {stats['latest_price']:.2f}", file=sys.stderr) # 保存 CSV ytd_csv = OUTPUT_DIR / "ytd_chart.csv" fivey_csv = OUTPUT_DIR / "fivey_chart.csv" ytd_df.to_csv(ytd_csv, index=False) fivey_df.to_csv(fivey_csv, index=False) print(f"💾 已保存 CSV: {ytd_csv}, {fivey_csv}", file=sys.stderr) # 构建 config.json config = { "text_data": { "标题": f"{STOCK_NAME}{YEAR}年度投资分析报告", "作者": "PPT-Station 自动生成", "日期": stats["report_date"], "标题1": f"{STOCK_NAME}股价与估值分析", "标题2": f"{STOCK_NAME}财务与经营分析", "标题3": f"{STOCK_NAME}投资价值总结", "组合名称": f"{STOCK_NAME}（{STOCK_CODE}）", "报告日期": stats["report_date"], "成立日期": stats["start_date"], "资产规模": f"{stats['market_cap']:.0f}亿元", "本年收益": f"{stats['ytd_return']:.2%}", "本年收益金额": f"{stats['latest_price']:.2f}元/股", "累计收益率": f"{stats['total_return']:.2%}", "累计收益金额": f"近5年({YEAR-4}-{YEAR})", "平均年化": f"{stats['annualized']:.2%}", }, "chart_configs": { "当年收益率走势图": { "csv_path": "ytd_chart.csv", "categories_col": "日期", "series_config": [ { "key": "沪深300指数", "name": f"{INDEX_NAME}指数(收盘价)", "type": "bar", "axis": "secondary", }, { "key": "茅台累计收益率", "name": f"{STOCK_NAME}累计收益率（左轴）", "type": "line", "axis": "primary", }, ], "style": { "color_scheme": "aim00", "line_width_pt": 2.0, "marker_style": "none", }, "layout": { "title": f"{STOCK_NAME}{YEAR}年累计收益率走势图", "legend": { "position": "top", "font_size_pt": 9, "font_name": "黑体", }, "value_axis": { "number_format": "0%", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False, }, "secondary_value_axis": { "number_format": "#,##0", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False, }, "date_axis": { "base_unit": "days", "number_format": "yyyy/mm", }, }, }, "组合成立以来收益率走势": { "csv_path": "fivey_chart.csv", "categories_col": "日期", "series_config": [ { "key": "茅台累计收益率", "name": f"{STOCK_NAME}累计收益率", "type": "line", "axis": "primary", }, { "key": "成交额(万元)", "name": "成交额(万元)", "type": "area", "axis": "secondary", }, ], "style": { "color_scheme": "aim00", "line_width_pt": 2.0, "marker_style": "none", }, "layout": { "title": f"{STOCK_NAME}近5年走势（{YEAR-4}-{YEAR}）", "legend": { "position": "bottom", "font_size_pt": 9, "font_name": "黑体", }, "value_axis": { "number_format": "0%", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False, }, "secondary_value_axis": { "number_format": "#,##0", "font_size_pt": 9, "font_name": "黑体", "has_major_gridlines": False, }, "date_axis": { "base_unit": "days", "number_format": "yyyy/mm", }, }, }, }, } config_path = OUTPUT_DIR / "config.json" with open(config_path, "w", encoding="utf-8") as f: json.dump(config, f, ensure_ascii=False, indent=2) print(f"💾 已保存配置: {config_path}", file=sys.stderr) # 调用 generate_ppt output_pptx = OUTPUT_DIR / f"moutai_{YEAR}_report.pptx" print(f"\n🔧 生成 PPT...", file=sys.stderr) from ppt_station.template.replacer import TemplateReplacer from ppt_station.chart_builder.styles import StyleConfig from ppt_station.chart_builder.layout import ( ChartLayoutConfig, LegendConfig, ValueAxisConfig, ) from ppt_station.chart_builder.date_axis import DateAxisConfig from pptx.enum.chart import XL_LEGEND_POSITION # 导入 generate_ppt 的 build_chart_config sys.path.insert(0, str(Path(__file__).resolve().parent)) from generate_ppt import build_chart_config chart_configs = {} for chart_name, chart_def in config["chart_configs"].items(): print(f" 构建图表: {chart_name}", file=sys.stderr) chart_configs[chart_name] = build_chart_config(chart_name, chart_def, OUTPUT_DIR) replacer = TemplateReplacer(TEMPLATE_PATH) replacer.replace(data=config["text_data"], chart_configs=chart_configs) replacer.save(output_pptx) print(f"\n✅ 报告已生成: {output_pptx}", file=sys.stderr) print(json.dumps({"status": "ok", "output": str(output_pptx)})) if __name__ == "__main__": main()