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feat: 新增越南市场数据抓取、分析与报告功能，并优化香港市场数据抓取逻辑及更新部分JP数据。

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xucheng 2026-01-07 21:15:24 +08:00
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33 changed files with 1647 additions and 498 deletions
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15
README.md
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@ -38,14 +38,15 @@
## 如何运行 ## 如何运行
### 参数说明
- `<MARKET>`: 必填参数，指定目标市场。
使用以下命令来运行程序： 使用以下命令来运行程序：
```bash ```bash
python main.py <MARKET> <SYMBOL> python run_fetcher.py <MARKET> <SYMBOL>
``` ```
### 参数说明
- `<MARKET>`: 必填参数，指定目标市场。 - `<MARKET>`: 必填参数，指定目标市场。
- `CN`: 中国A股市场 - `CN`: 中国A股市场
- `HK`: 中国香港市场 - `HK`: 中国香港市场
@ -62,20 +63,20 @@ python main.py <MARKET> <SYMBOL>
- 分析贵州茅台 (A股): - 分析贵州茅台 (A股):
```bash ```bash
python main.py CN 600519.SH python run_fetcher.py CN 600519.SH
``` ```
- 分析苹果公司 (美股): - 分析苹果公司 (美股):
```bash ```bash
python main.py US AAPL python run_fetcher.py US AAPL
``` ```
If running just `python main.py` without arguments, it executes built-in default test cases (Kweichow Moutai and Apple Inc.). If running just `python run_fetcher.py` without arguments, it executes built-in default test cases (Kweichow Moutai and Apple Inc.).
## 深度分析自动化 (Automated Deep Analysis) ## 深度分析自动化 (Automated Deep Analysis)
项目提供了一个名为 `stock_analysis.py` 的脚本，能够全自动完成从数据获取到AI深度分析报告生成的全流程。 项目提供了一个名为 `stock_analysis.py` 的脚本，能够全自动完成从数据获取到AI深度分析报告生成的全流程。
### 功能特点 ### 功能特点
1. **全自动流程**：一键调用 `main.py` 获取数据 -> 识别公司信息 -> 初始化报告 -> 调用 LLM 进行分章节深度分析。 1. **全自动流程**：一键调用 `run_fetcher.py` 获取数据 -> 识别公司信息 -> 初始化报告 -> 调用 LLM 进行分章节深度分析。
2. **AI 驱动**：利用大语言模型（如 Gemini/GPT-4）根据预设的专业提示词（`prompts.yaml`），对公司简介、基本面、内部人动向、看涨/看跌逻辑进行深度解读。 2. **AI 驱动**：利用大语言模型（如 Gemini/GPT-4）根据预设的专业提示词（`prompts.yaml`），对公司简介、基本面、内部人动向、看涨/看跌逻辑进行深度解读。
3. **结构化报告**：生成的 Markdown 报告保存在 `reports/` 目录下，包含详细的文字分析和指向可视化财务图表的链接。 3. **结构化报告**：生成的 Markdown 报告保存在 `reports/` 目录下，包含详细的文字分析和指向可视化财务图表的链接。

16
backend/app/database.py
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@ -26,6 +26,22 @@ async def init_db():
if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower(): if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
print(f"Migration check: {e}") print(f"Migration check: {e}")
# Migration: Add token columns to report_sections
columns_to_add = [
("prompt_tokens", "INTEGER DEFAULT 0"),
("completion_tokens", "INTEGER DEFAULT 0"),
("total_tokens", "INTEGER DEFAULT 0")
]
for col_name, col_type in columns_to_add:
try:
await conn.execute(text(f"ALTER TABLE report_sections ADD COLUMN {col_name} {col_type}"))
print(f"Migration: Added {col_name} to report_sections table")
except Exception as e:
# SQLite error for duplicate column usually contains "duplicate column name"
if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
print(f"Migration check for {col_name}: {e}")
async def get_db(): async def get_db():
async with AsyncSessionLocal() as session: async with AsyncSessionLocal() as session:
yield session yield session

3
backend/app/models.py
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@ -33,6 +33,9 @@ class ReportSection(Base):
report_id: Mapped[int] = mapped_column(ForeignKey("reports.id")) report_id: Mapped[int] = mapped_column(ForeignKey("reports.id"))
section_name: Mapped[str] = mapped_column(String(50)) # e.g. company_profile, fundamental_analysis section_name: Mapped[str] = mapped_column(String(50)) # e.g. company_profile, fundamental_analysis
content: Mapped[str] = mapped_column(Text) # Markdown content content: Mapped[str] = mapped_column(Text) # Markdown content
total_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
prompt_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
completion_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now()) created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
report: Mapped["Report"] = relationship(back_populates="sections") report: Mapped["Report"] = relationship(back_populates="sections")

21
backend/app/services/analysis_service.py
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@ -25,14 +25,21 @@ async def search_stock(query: str, api_key: str, model: str = "gemini-2.0-flash"
4. 例如："茅台" = "贵州茅台酒股份有限公司" (600519.SH) 4. 例如："茅台" = "贵州茅台酒股份有限公司" (600519.SH)
请返回一个 JSON 数组，包含所有匹配的公司。每个对象包含以下字段： 请返回一个 JSON 数组，包含所有匹配的公司。每个对象包含以下字段：
- 'market': 'CN' (中国), 'US' (美国), 'HK' (香港), 或 'JP' (日本) 之一 - 'market': 'CN' (中国), 'US' (美国), 'HK' (香港), 'JP' (日本), 或 'VN' (越南) 之一
- 'symbol': 完整的股票代码 (例如 'AAPL', '600519.SH', '00700.HK', '688778.SH', '2503.T') - 'symbol': 完整的股票代码 (例如 'AAPL', '600519.SH', '00700.HK', '688778.SH', '2503.T', 'SAB.HM')
- 'company_name': 公司的中文简称（如果有的话，优先使用中文；如果只有英文名，则使用英文名） - 'company_name': 公司的中文简称（如果有的话，优先使用中文；如果只有英文名，则使用英文名）
**匹配规则**： **匹配规则**：
- 如果查询词与某公司简称、全称或股票代码完全匹配，返回该公司 - 如果查询词与某公司简称、全称或股票代码完全匹配，返回该公司
- 如果有多个可能的匹配，返回所有相关公司 - 如果有多个可能的匹配，返回所有相关公司
- 如果公司在中国但用户没有指定市场，默认为 CN（上海/深圳/北京交易所） - 如果公司在中国但用户没有指定市场，默认为 CN（上海/深圳/北京交易所）
- **越南股票后缀规则（iFinD数据源）**：
- 胡志明交易所 (HOSE) -> 后缀 **.HM** (例如: VNM.HM, SAB.HM, VCB.HM)
- 河内交易所 (HNX) -> 后缀 **.HN** (例如: PVS.HN, SHS.HN)
- UPCoM 市场 -> iFinD 通常也使用 .HN 或 .HM，或者特定的 UPCoM 后缀，但绝不要使用 .VN (这是 Bloomberg 格式)
- 示例：MCH (Masan Consumer) -> MCH.HN 或 MCH.HM (UPCoM 在 iFinD 中可能归类不一，但 MCH.HN 是常见尝试，MCH.HM 也可以尝试。请根据搜索结果确认 iFinD 使用哪一个。**强烈倾向于使用 .HM 或 .HN，严禁使用 .VN**)
- MCH 具体案例：用户指出应为 MCH.HM (或 MCH.HN)，绝非 MCH.VN。请只返回 .HM 或 .HN。
- 如果不确定是 HM 还是 HN，优先返回 .HM。
- 如果完全没找到匹配，返回 {{ "error": "未找到相关公司" }} - 如果完全没找到匹配，返回 {{ "error": "未找到相关公司" }}
示例响应（单个结果）： 示例响应（单个结果）：
@ -52,9 +59,9 @@ async def search_stock(query: str, api_key: str, model: str = "gemini-2.0-flash"
"company_name": "腾讯控股" "company_name": "腾讯控股"
}}, }},
{{ {{
"market": "US", "market": "VN",
"symbol": "TCEHY", "symbol": "VNM.HM",
"company_name": "Tencent Holdings ADR" "company_name": "Vinamilk"
}} }}
] ]
@ -128,9 +135,9 @@ async def run_analysis_task(report_id: int, market: str, symbol: str, api_key: s
company_name_for_prompt = report.company_name company_name_for_prompt = report.company_name
# 2. Run Main Data Fetching Script (main.py) # 2. Run Main Data Fetching Script (run_fetcher.py)
root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../")) root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../"))
cmd = [sys.executable, "main.py", market, symbol] cmd = [sys.executable, "run_fetcher.py", market, symbol]
print(f"Executing data fetch command: {cmd} in {root_dir}") print(f"Executing data fetch command: {cmd} in {root_dir}")
process = await asyncio.create_subprocess_exec( process = await asyncio.create_subprocess_exec(

31
backend/app/services/llm_engine.py
View File

@ -60,10 +60,22 @@ async def call_llm(api_key: str, model_name: str, system_prompt: str, user_promp
) )
response = await asyncio.to_thread(run_sync) response = await asyncio.to_thread(run_sync)
return response.text usage = response.usage_metadata
prompt_tokens = usage.prompt_token_count if usage else 0
completion_tokens = usage.candidates_token_count if usage else 0
return {
"text": response.text,
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens
}
except Exception as e: except Exception as e:
print(f"API Call Failed: {e}") print(f"API Call Failed: {e}")
return f"\n\nError generating section: {e}\n\n" return {
"text": f"\n\nError generating section: {e}\n\n",
"prompt_tokens": 0,
"completion_tokens": 0
}
async def process_analysis_steps(report_id: int, company_name: str, symbol: str, market: str, db: AsyncSession, api_key: str): async def process_analysis_steps(report_id: int, company_name: str, symbol: str, market: str, db: AsyncSession, api_key: str):
# 1. Load Prompts # 1. Load Prompts
@ -71,7 +83,7 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
prompt_dir = os.path.join(root_dir, "Prompt") prompt_dir = os.path.join(root_dir, "Prompt")
prompts = await load_prompts(db, prompt_dir) prompts = await load_prompts(db, prompt_dir)
# 2. Read Data Context (report.md generated by main.py) # 2. Read Data Context (report.md generated by run_fetcher.py)
base_dir = os.path.join(root_dir, "data", market) base_dir = os.path.join(root_dir, "data", market)
symbol_dir = os.path.join(base_dir, symbol) symbol_dir = os.path.join(base_dir, symbol)
@ -82,7 +94,7 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
data_path = os.path.join(symbol_dir, "report.md") data_path = os.path.join(symbol_dir, "report.md")
if not os.path.exists(data_path): if not os.path.exists(data_path):
# If report.md is missing, maybe main.py failed or output structure changed. # If report.md is missing, maybe run_fetcher.py failed or output structure changed.
# We try to proceed or fail. # We try to proceed or fail.
print(f"Warning: {data_path} not found.") print(f"Warning: {data_path} not found.")
data_context = "No financial data available." data_context = "No financial data available."
@ -127,9 +139,9 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
if key == "bearish_analysis" and csv_context: if key == "bearish_analysis" and csv_context:
current_data_context += csv_context current_data_context += csv_context
content = await call_llm(api_key, model_name, system_content, user_content, current_data_context, enable_search=True) result = await call_llm(api_key, model_name, system_content, user_content, current_data_context, enable_search=True)
return (key, content) return (key, result)
# Run all sections concurrently # Run all sections concurrently
print(f"Starting concurrent analysis with {len(steps)} sections...") print(f"Starting concurrent analysis with {len(steps)} sections...")
@ -139,11 +151,14 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
for result in results: for result in results:
if result is None: if result is None:
continue continue
key, content = result key, result_data = result
section = ReportSection( section = ReportSection(
report_id=report_id, report_id=report_id,
section_name=key, section_name=key,
content=content content=result_data["text"],
prompt_tokens=result_data["prompt_tokens"],
completion_tokens=result_data["completion_tokens"],
total_tokens=result_data["prompt_tokens"] + result_data["completion_tokens"]
) )
db.add(section) db.add(section)

5
data/HK/00700.HK/raw_balance_sheet_raw.csv
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@ -1,5 +0,0 @@
cash_equi_short_term_inve_oas,accou_and_notes_recei_oas,inventories_oas,ppe_net_oas,long_term_inv_and_receiv_oas,goodwill_and_intasset_oas,short_term_debt_oas,short_term_borrowings_oas,account_and_note_payable_oas,advance_from_cust_current_oas,defer_revenue_current_oas,long_term_debt_oas,long_term_borrowings_oas,total_assets_oas,equity_attri_to_companyowner_oas,prepaid_expenses_current_oas,end_date
380444000000.0,51315000000.0,435000000.0,172648000000.0,706700000000.0,215832000000.0,63974000000.0,58577000000.0,143381000000.0,998000000.0,120908000000.0,335632000000.0,322304000000.0,2013310000000.0,1114639000000.0,26893000000.0,20250630
343159000000.0,48203000000.0,440000000.0,133283000000.0,589410000000.0,196127000000.0,58485000000.0,48526000000.0,127335000000.0,1042000000.0,100097000000.0,291004000000.0,277107000000.0,1780995000000.0,973548000000.0,31265000000.0,20241231
379155000000.0,46606000000.0,456000000.0,104458000000.0,460591000000.0,177727000000.0,47691000000.0,25561000000.0,115109000000.0,669000000.0,86168000000.0,309388000000.0,292920000000.0,1577246000000.0,808591000000.0,27824000000.0,20231231
290756000000.0,45467000000.0,2333000000.0,103777000000.0,431451000000.0,161802000000.0,17934000000.0,5981000000.0,102827000000.0,816000000.0,82216000000.0,330761000000.0,312337000000.0,1578131000000.0,721391000000.0,24393000000.0,20221231
1 cash_equi_short_term_inve_oas accou_and_notes_recei_oas inventories_oas ppe_net_oas long_term_inv_and_receiv_oas goodwill_and_intasset_oas short_term_debt_oas short_term_borrowings_oas account_and_note_payable_oas advance_from_cust_current_oas defer_revenue_current_oas long_term_debt_oas long_term_borrowings_oas total_assets_oas equity_attri_to_companyowner_oas prepaid_expenses_current_oas end_date
2 380444000000.0 51315000000.0 435000000.0 172648000000.0 706700000000.0 215832000000.0 63974000000.0 58577000000.0 143381000000.0 998000000.0 120908000000.0 335632000000.0 322304000000.0 2013310000000.0 1114639000000.0 26893000000.0 20250630
3 343159000000.0 48203000000.0 440000000.0 133283000000.0 589410000000.0 196127000000.0 58485000000.0 48526000000.0 127335000000.0 1042000000.0 100097000000.0 291004000000.0 277107000000.0 1780995000000.0 973548000000.0 31265000000.0 20241231
4 379155000000.0 46606000000.0 456000000.0 104458000000.0 460591000000.0 177727000000.0 47691000000.0 25561000000.0 115109000000.0 669000000.0 86168000000.0 309388000000.0 292920000000.0 1577246000000.0 808591000000.0 27824000000.0 20231231
5 290756000000.0 45467000000.0 2333000000.0 103777000000.0 431451000000.0 161802000000.0 17934000000.0 5981000000.0 102827000000.0 816000000.0 82216000000.0 330761000000.0 312337000000.0 1578131000000.0 721391000000.0 24393000000.0 20221231

2
data/HK/00700.HK/raw_basic_info_raw.csv
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@ -1,2 +0,0 @@
corp_cn_name,accounting_date,ipo_date
腾讯控股有限公司,1231,20040616
1 corp_cn_name accounting_date ipo_date
2 腾讯控股有限公司 1231 20040616

5
data/HK/00700.HK/raw_cash_flow_raw.csv
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@ -1,5 +0,0 @@
net_cash_flows_from_oa_oas,purchase_of_ppe_and_ia_oas,dividends_paid_oas,end_date
151265000000.0,57457000000.0,37535000000.0,20250630
258521000000.0,96048000000.0,28859000000.0,20241231
221962000000.0,47407000000.0,20983000000.0,20231231
146091000000.0,50850000000.0,12952000000.0,20221231
1 net_cash_flows_from_oa_oas purchase_of_ppe_and_ia_oas dividends_paid_oas end_date
2 151265000000.0 57457000000.0 37535000000.0 20250630
3 258521000000.0 96048000000.0 28859000000.0 20241231
4 221962000000.0 47407000000.0 20983000000.0 20231231
5 146091000000.0 50850000000.0 12952000000.0 20221231

4
data/HK/00700.HK/raw_dividends_raw.csv
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@ -1,4 +0,0 @@
date_str,dividends
20241231,38104168998.825
20231231,29163521377.441
20221231,20700985117.366
1 date_str dividends
2 20241231 38104168998.825
3 20231231 29163521377.441
4 20221231 20700985117.366

6
data/HK/00700.HK/raw_employee_count_raw.csv
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@ -1,6 +0,0 @@
date_str,employee_count
20261231,111221.0
20251231,111221.0
20241231,110558.0
20231231,105417.0
20221231,108436.0
1 date_str employee_count
2 20261231 111221.0
3 20251231 111221.0
4 20241231 110558.0
5 20231231 105417.0
6 20221231 108436.0

5
data/HK/00700.HK/raw_historical_metrics_raw.csv
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@ -1,5 +0,0 @@
date_str,PE,PB,MarketCap,Price
20250630,0.0,0.0,4204320544234.2,467.83035
20241231,0.0,0.0,3562280981362.7,388.01076
20231231,0.0,0.0,2523103788380.7,266.066192
20221231,0.0,0.0,2854854121108.1,298.35218
1 date_str PE PB MarketCap Price
2 20250630 0.0 0.0 4204320544234.2 467.83035
3 20241231 0.0 0.0 3562280981362.7 388.01076
4 20231231 0.0 0.0 2523103788380.7 266.066192
5 20221231 0.0 0.0 2854854121108.1 298.35218

5
data/HK/00700.HK/raw_income_statement_raw.csv
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@ -1,5 +0,0 @@
revenue_oas,gross_profit_oas,sga_expenses_oas,selling_marketing_expenses_oas,ga_expenses_oas,income_tax_expense_oas,net_income_attri_to_common_sh_oas,operating_income_oas,end_date
364526000000.0,205506000000.0,82861000000.0,17276000000.0,65585000000.0,25068000000.0,103449000000.0,117670000000.0,20250630
660257000000.0,349246000000.0,149149000000.0,36388000000.0,112761000000.0,45018000000.0,194073000000.0,208099000000.0,20241231
609015000000.0,293109000000.0,137736000000.0,34211000000.0,103525000000.0,43276000000.0,115216000000.0,160074000000.0,20231231
554552000000.0,238746000000.0,135925000000.0,29229000000.0,106696000000.0,21516000000.0,188243000000.0,227114000000.0,20221231
1 revenue_oas gross_profit_oas sga_expenses_oas selling_marketing_expenses_oas ga_expenses_oas income_tax_expense_oas net_income_attri_to_common_sh_oas operating_income_oas end_date
2 364526000000.0 205506000000.0 82861000000.0 17276000000.0 65585000000.0 25068000000.0 103449000000.0 117670000000.0 20250630
3 660257000000.0 349246000000.0 149149000000.0 36388000000.0 112761000000.0 45018000000.0 194073000000.0 208099000000.0 20241231
4 609015000000.0 293109000000.0 137736000000.0 34211000000.0 103525000000.0 43276000000.0 115216000000.0 160074000000.0 20231231
5 554552000000.0 238746000000.0 135925000000.0 29229000000.0 106696000000.0 21516000000.0 188243000000.0 227114000000.0 20221231

6
data/HK/00700.HK/raw_repurchases_raw.csv
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@ -1,6 +0,0 @@
date_str,repurchases
20261231,1271296551.4
20251231,80610335058.2
20241231,112003383926.44
20231231,49432707948.16
20221231,33794068650.24
1 date_str repurchases
2 20261231 1271296551.4
3 20251231 80610335058.2
4 20241231 112003383926.44
5 20231231 49432707948.16
6 20221231 33794068650.24

89
data/HK/00700.HK/report.md
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@ -1,89 +0,0 @@
# 腾讯控股有限公司 (00700.HK) - Financial Report
*Report generated on: 2026-01-03*
| 代码 | 简称 | 上市日期 | 年结日 | 市值(亿) | PE | PB | 股息率(%) |
|:---|:---|:---|:---|:---|:---|:---|:---|
| 00700.HK | 腾讯控股有限公司 | 2004-06-16 | 1231 | 42043.21 | 20.32 | 3.77 | 0.89% |
## 主要指标
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| ROE | 9.28% | 19.93% | 14.25% | 26.09% |
| ROA | 5.14% | 10.90% | 7.30% | 11.93% |
| ROCE/ROIC | 6.21% | 12.62% | 10.79% | 16.36% |
| 毛利率 | 56.38% | 52.90% | 48.13% | 43.05% |
| 净利润率 | 28.38% | 29.39% | 18.92% | 33.95% |
| 收入(亿) | 3,645.26 | 6,602.57 | 6,090.15 | 5,545.52 |
| 收入增速 | - | 8.41% | 9.82% | - |
| 净利润(亿) | 1,034.49 | 1,940.73 | 1,152.16 | 1,882.43 |
| 净利润增速 | - | 68.44% | -38.79% | - |
| 经营净现金流(亿) | 1,512.65 | 2,585.21 | 2,219.62 | 1,460.91 |
| 资本开支(亿) | 574.57 | 960.48 | 474.07 | 508.50 |
| 自由现金流(亿) | 938.08 | 1,624.73 | 1,745.55 | 952.41 |
| 分红(亿) | 375.35 | 288.59 | 209.83 | 129.52 |
| 回购(亿) | - | 1,120.03 | 494.33 | 337.94 |
| 总资产(亿) | 20,133.10 | 17,809.95 | 15,772.46 | 15,781.31 |
| 净资产(亿) | 11,146.39 | 9,735.48 | 8,085.91 | 7,213.91 |
| 商誉(亿) | 2,158.32 | 1,961.27 | 1,777.27 | 1,618.02 |
## 费用指标
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| 销售费用率 | 4.74% | 5.51% | 5.62% | 5.27% |
| 管理费用率 | 17.99% | 17.08% | 17.00% | 19.24% |
| SG&A比例 | 22.73% | 22.59% | 22.62% | 24.51% |
| 研发费用率 | - | - | - | - |
| 其他费用率 | 5.27% | 0.91% | 6.59% | -15.40% |
| 折旧费用占比 | - | - | - | - |
| 所得税率 | 19.51% | 18.83% | 27.30% | 10.26% |
## 资产占比
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| 现金占比 | 18.90% | 19.27% | 24.04% | 18.42% |
| 库存占比 | 0.02% | 0.02% | 0.03% | 0.15% |
| 应收款占比 | 2.55% | 2.71% | 2.95% | 2.88% |
| 预付款占比 | 1.34% | 1.76% | 1.76% | 1.55% |
| 固定资产占比 | 8.58% | 7.48% | 6.62% | 6.58% |
| 长期投资占比 | 35.10% | 33.09% | 29.20% | 27.34% |
| 商誉占比 | 10.72% | 11.01% | 11.27% | 10.25% |
| 其他资产占比 | 22.80% | 24.66% | 24.12% | 32.83% |
| 应付款占比 | 7.12% | 7.15% | 7.30% | 6.52% |
| 预收款占比 | 0.00% | 0.00% | 0.00% | 0.00% |
| 短期借款占比 | 6.09% | 6.01% | 4.64% | 1.52% |
| 长期借款占比 | 32.68% | 31.90% | 38.19% | 40.75% |
| 运营资产占比 | -3.22% | -2.66% | -2.55% | -1.94% |
| 有息负债率 | 38.77% | 37.91% | 42.83% | 42.27% |
## 周转能力
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| 存货周转天数 | 0 | 0 | 0 | 2 |
| 应收款周转天数 | 25 | 26 | 27 | 29 |
| 应付款周转天数 | 164 | 149 | 132 | 118 |
| 固定资产周转率 | 4.22 | 4.95 | 5.83 | 5.34 |
| 总资产周转率 | 0.36 | 0.37 | 0.39 | 0.35 |
## 人均效率
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| 员工人数 | - | 110,558 | 105,417 | 108,436 |
| 人均创收(万) | - | 597.20 | 577.72 | 511.41 |
| 人均创利(万) | - | 175.54 | 109.30 | 173.60 |
| 人均薪酬(万) | - | - | - | - |
## 市场表现
| 指标 | 2025H1 | 2024A | 2023A | 2022A |
|:---|--:|--:|--:|--:|
| 股价 | 467.83 | 388.01 | 266.07 | 298.35 |
| 市值(亿) | 42,043 | 35,623 | 25,231 | 28,549 |
| PE | 20.32 | 18.36 | 21.90 | 15.17 |
| PB | 3.77 | 3.66 | 3.12 | 3.96 |
| 股东户数 | - | - | - | - |

4
data/JP/2503.T/raw_balance_sheet_raw.csv
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@ -1,6 +1,6 @@
cash_equi_short_term_inve_oas,accou_and_notes_recei_oas,inventories_oas,ppe_net_oas,long_term_inv_and_receiv_oas,goodwill_and_intasset_oas,short_term_debt_oas,short_term_borrowings_oas,account_and_note_payable_oas,contra_liabilities_current_oas,advance_from_cust_current_oas,defer_revenue_current_oas,long_term_debt_oas,long_term_borrowings_oas,total_assets_oas,equity_attri_to_companyowner_oas,prepaid_expenses_current_oas,end_date cash_equi_short_term_inve_oas,accou_and_notes_recei_oas,inventories_oas,ppe_net_oas,long_term_inv_and_receiv_oas,goodwill_and_intasset_oas,short_term_debt_oas,short_term_borrowings_oas,account_and_note_payable_oas,contra_liabilities_current_oas,advance_from_cust_current_oas,defer_revenue_current_oas,long_term_debt_oas,long_term_borrowings_oas,total_assets_oas,equity_attri_to_companyowner_oas,prepaid_expenses_current_oas,end_date
,,,,,,,,,,,,,,,,,20261231
,,,,,,,,,,,,,,,,,20251231
5899320980.35,22326249202.25,16650772821.2,31263474425.4,4794152048.0,53852622755.049995,3617228537.3,3617228537.3,7518553255.849999,,,,36159437268.15,36159437268.15,155576124604.95,54802733151.25,,20241231 5899320980.35,22326249202.25,16650772821.2,31263474425.4,4794152048.0,53852622755.049995,3617228537.3,3617228537.3,7518553255.849999,,,,36159437268.15,36159437268.15,155576124604.95,54802733151.25,,20241231
7070829794.070001,21350738934.77,16675747687.67,29873189030.72,5307593791.54,34970889368.92,5072156415.77,5072156415.77,6915651724.870001,,,,27998809255.25,27998809255.25,144576837566.65,57062250906.689995,,20231231 7070829794.070001,21350738934.77,16675747687.67,29873189030.72,5307593791.54,34970889368.92,5072156415.77,5072156415.77,6915651724.870001,,,,27998809255.25,27998809255.25,144576837566.65,57062250906.689995,,20231231
5046190177.88,20369212787.27,15183728587.93,29336618624.86,5409547685.4,25662437929.58,5989276634.969999,5989276634.969999,6303080634.48,,,,21383987001.46,21383987001.46,133028563816.29,51281444590.26,,20221231 5046190177.88,20369212787.27,15183728587.93,29336618624.86,5409547685.4,25662437929.58,5989276634.969999,5989276634.969999,6303080634.48,,,,21383987001.46,21383987001.46,133028563816.29,51281444590.26,,20221231
8709165023.76,20609942061.24,13679704695.48,29539550251.08,6497706183.72,25484093179.92,5616320844.24,5616320844.24,5452427104.8,,,,24897794036.4,24897794036.4,136777293387.96,49476819729.48,,20211231
10725243245.05,22595254368.58,13732598542.25,33002436277.529995,6686243673.8,28770955850.0,15747071314.71,15747071314.71,6078069930.14,,,,24889089960.7,24889089960.7,155512580861.81,53026073056.08,,20201231

1 cash_equi_short_term_inve_oas accou_and_notes_recei_oas inventories_oas ppe_net_oas long_term_inv_and_receiv_oas goodwill_and_intasset_oas short_term_debt_oas short_term_borrowings_oas account_and_note_payable_oas contra_liabilities_current_oas advance_from_cust_current_oas defer_revenue_current_oas long_term_debt_oas long_term_borrowings_oas total_assets_oas equity_attri_to_companyowner_oas prepaid_expenses_current_oas end_date
20261231
20251231
2 5899320980.35 22326249202.25 16650772821.2 31263474425.4 4794152048.0 53852622755.049995 3617228537.3 3617228537.3 7518553255.849999 36159437268.15 36159437268.15 155576124604.95 54802733151.25 20241231
3 7070829794.070001 21350738934.77 16675747687.67 29873189030.72 5307593791.54 34970889368.92 5072156415.77 5072156415.77 6915651724.870001 27998809255.25 27998809255.25 144576837566.65 57062250906.689995 20231231
4 5046190177.88 20369212787.27 15183728587.93 29336618624.86 5409547685.4 25662437929.58 5989276634.969999 5989276634.969999 6303080634.48 21383987001.46 21383987001.46 133028563816.29 51281444590.26 20221231
5 8709165023.76 20609942061.24 13679704695.48 29539550251.08 6497706183.72 25484093179.92 5616320844.24 5616320844.24 5452427104.8 24897794036.4 24897794036.4 136777293387.96 49476819729.48 20211231
6 10725243245.05 22595254368.58 13732598542.25 33002436277.529995 6686243673.8 28770955850.0 15747071314.71 15747071314.71 6078069930.14 24889089960.7 24889089960.7 155512580861.81 53026073056.08 20201231

4
data/JP/2503.T/raw_cash_flow_raw.csv
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@ -1,6 +1,6 @@
net_cash_flows_from_oa_oas,purchase_of_ppe_and_ia_oas,dividends_paid_oas,end_date net_cash_flows_from_oa_oas,purchase_of_ppe_and_ia_oas,dividends_paid_oas,end_date
,,,20261231
,,,20251231
11263845394.2,8376036701.2,2704873943.8,20241231 11263845394.2,8376036701.2,2704873943.8,20241231
10238024262.94,5734031186.9,2896993175.0,20231231 10238024262.94,5734031186.9,2896993175.0,20231231
7093529728.46,5153093891.57,2814032263.74,20221231 7093529728.46,5153093891.57,2814032263.74,20221231
12134499912.36,4777098580.2,2998115590.08,20211231
10423243057.93,5882300964.62,3498421765.62,20201231

1 net_cash_flows_from_oa_oas purchase_of_ppe_and_ia_oas dividends_paid_oas end_date
20261231
20251231
2 11263845394.2 8376036701.2 2704873943.8 20241231
3 10238024262.94 5734031186.9 2896993175.0 20231231
4 7093529728.46 5153093891.57 2814032263.74 20221231
5 12134499912.36 4777098580.2 2998115590.08 20211231
6 10423243057.93 5882300964.62 3498421765.62 20201231

4
data/JP/2503.T/raw_historical_metrics_raw.csv
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@ -1,6 +1,6 @@
date_str,PE,PB,MarketCap,Price date_str,PE,PB,MarketCap,Price
20261231,0.0,0.0,95997084050.16,105.02963244
20251231,0.0,0.0,95997084050.16,105.02963244
20241231,0.0,0.0,86865526677.3,95.03886945 20241231,0.0,0.0,86865526677.3,95.03886945
20231231,0.0,0.0,95138465046.76,104.09022434 20231231,0.0,0.0,95138465046.76,104.09022434
20221231,0.0,0.0,96179539188.82,105.22925513 20221231,0.0,0.0,96179539188.82,105.22925513
20211231,0.0,0.0,93409361034.96,102.19842564
20201231,0.0,0.0,0.0,0.0

1 date_str PE PB MarketCap Price
20261231 0.0 0.0 95997084050.16 105.02963244
20251231 0.0 0.0 95997084050.16 105.02963244
2 20241231 0.0 0.0 86865526677.3 95.03886945
3 20231231 0.0 0.0 95138465046.76 104.09022434
4 20221231 0.0 0.0 96179539188.82 105.22925513
5 20211231 0.0 0.0 93409361034.96 102.19842564
6 20201231 0.0 0.0 0.0 0.0

4
data/JP/2503.T/raw_income_statement_raw.csv
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@ -1,6 +1,6 @@
revenue_oas,gross_profit_oas,sga_expenses_oas,selling_marketing_expenses_oas,ga_expenses_oas,rd_expenses_oas,income_tax_expense_oas,net_income_attri_to_common_sh_oas,operating_income_oas,end_date revenue_oas,gross_profit_oas,sga_expenses_oas,selling_marketing_expenses_oas,ga_expenses_oas,rd_expenses_oas,income_tax_expense_oas,net_income_attri_to_common_sh_oas,operating_income_oas,end_date
,,,,,,,,,20261231
,,,,,,,,,20251231
108461428374.25,49442244062.75,34274708414.449997,7858262714.049999,,5382196355.9,2500510225.5,2700142872.7,5813651487.0,20241231 108461428374.25,49442244062.75,34274708414.449997,7858262714.049999,,5382196355.9,2500510225.5,2700142872.7,5813651487.0,20241231
107536033978.57,48541816110.34,34112774799.24,8351653454.849999,,4277221488.55,2348378241.39,5677955475.53,7572185952.06,20231231 107536033978.57,48541816110.34,34112774799.24,8351653454.849999,,4277221488.55,2348378241.39,5677955475.53,7572185952.06,20231231
104102553826.44,47393090179.79,33504032019.72,8687928238.56,,3886365990.93,2491542010.45,5808644417.809999,6070906489.7699995,20221231 104102553826.44,47393090179.79,33504032019.72,8687928238.56,,3886365990.93,2491542010.45,5808644417.809999,6070906489.7699995,20221231
100791329828.4,45529592285.04,32524275468.12,9023230136.88,,3851779537.44,1725974819.16,3308307454.8,3767232058.08,20211231
116952038544.15,50831829234.21,36502312399.16,9503078333.689999,,4078520115.0,1562420984.83,4548656503.45,6507863747.53,20201231

1 revenue_oas gross_profit_oas sga_expenses_oas selling_marketing_expenses_oas ga_expenses_oas rd_expenses_oas income_tax_expense_oas net_income_attri_to_common_sh_oas operating_income_oas end_date
20261231
20251231
2 108461428374.25 49442244062.75 34274708414.449997 7858262714.049999 5382196355.9 2500510225.5 2700142872.7 5813651487.0 20241231
3 107536033978.57 48541816110.34 34112774799.24 8351653454.849999 4277221488.55 2348378241.39 5677955475.53 7572185952.06 20231231
4 104102553826.44 47393090179.79 33504032019.72 8687928238.56 3886365990.93 2491542010.45 5808644417.809999 6070906489.7699995 20221231
5 100791329828.4 45529592285.04 32524275468.12 9023230136.88 3851779537.44 1725974819.16 3308307454.8 3767232058.08 20211231
6 116952038544.15 50831829234.21 36502312399.16 9503078333.689999 4078520115.0 1562420984.83 4548656503.45 6507863747.53 20201231

92
data/JP/2503.T/report.html
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@ -205,7 +205,7 @@
<body> <body>
<div class="report-container"> <div class="report-container">
<h1>麒麟控股株式会社 (2503.T) - Financial Report</h1> <h1>麒麟控股株式会社 (2503.T) - Financial Report</h1>
<p><em>Report generated on: 2026-01-03</em></p> <p><em>Report generated on: 2026-01-07</em></p>
<table class="company-table"> <table class="company-table">
<thead> <thead>
@ -236,67 +236,67 @@
<thead> <thead>
<tr> <tr>
<th>指标</th> <th>指标</th>
<th>2026A</th><th>2025A</th><th>2024A</th><th>2023A</th><th>2022A</th> <th>2024A</th><th>2023A</th><th>2022A</th><th>2021A</th><th>2020A</th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">ROE</td><td>-</td><td>-</td><td>4.93%</td><td>9.95%</td><td>11.33%</td></tr> <tr><td class="metric-name">ROE</td><td>4.93%</td><td>9.95%</td><td>11.33%</td><td>6.69%</td><td>8.58%</td></tr>
<tr><td class="metric-name">ROA</td><td>-</td><td>-</td><td>1.74%</td><td>3.93%</td><td>4.37%</td></tr> <tr><td class="metric-name">ROA</td><td>1.74%</td><td>3.93%</td><td>4.37%</td><td>2.42%</td><td>2.92%</td></tr>
<tr><td class="metric-name">ROCE/ROIC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">ROCE/ROIC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">毛利率</td><td>-</td><td>-</td><td>45.59%</td><td>45.14%</td><td>45.53%</td></tr> <tr><td class="metric-name">毛利率</td><td>45.59%</td><td>45.14%</td><td>45.53%</td><td>45.17%</td><td>43.46%</td></tr>
<tr><td class="metric-name">净利润率</td><td>-</td><td>-</td><td>2.49%</td><td>5.28%</td><td>5.58%</td></tr> <tr><td class="metric-name">净利润率</td><td>2.49%</td><td>5.28%</td><td>5.58%</td><td>3.28%</td><td>3.89%</td></tr>
<tr><td class="metric-name">收入(亿)</td><td>-</td><td>-</td><td>1,084.61</td><td>1,075.36</td><td>1,041.03</td></tr> <tr><td class="metric-name">收入(亿)</td><td>1,084.61</td><td>1,075.36</td><td>1,041.03</td><td>1,007.91</td><td>1,169.52</td></tr>
<tr><td class="metric-name">收入增速</td><td>-</td><td>-</td><td>0.86%</td><td>3.30%</td><td>-</td></tr> <tr><td class="metric-name">收入增速</td><td>0.86%</td><td>3.30%</td><td>3.29%</td><td>-13.82%</td><td>-</td></tr>
<tr><td class="metric-name">净利润(亿)</td><td>-</td><td>-</td><td>27.00</td><td>56.78</td><td>58.09</td></tr> <tr><td class="metric-name">净利润(亿)</td><td>27.00</td><td>56.78</td><td>58.09</td><td>33.08</td><td>45.49</td></tr>
<tr><td class="metric-name">净利润增速</td><td>-</td><td>-</td><td>-52.45%</td><td>-2.25%</td><td>-</td></tr> <tr><td class="metric-name">净利润增速</td><td>-52.45%</td><td>-2.25%</td><td>75.58%</td><td>-27.27%</td><td>-</td></tr>
<tr><td class="metric-name">经营净现金流(亿)</td><td>-</td><td>-</td><td>112.64</td><td>102.38</td><td>70.94</td></tr> <tr><td class="metric-name">经营净现金流(亿)</td><td>112.64</td><td>102.38</td><td>70.94</td><td>121.34</td><td>104.23</td></tr>
<tr><td class="metric-name">资本开支(亿)</td><td>-</td><td>-</td><td>83.76</td><td>57.34</td><td>51.53</td></tr> <tr><td class="metric-name">资本开支(亿)</td><td>83.76</td><td>57.34</td><td>51.53</td><td>47.77</td><td>58.82</td></tr>
<tr><td class="metric-name">自由现金流(亿)</td><td>-</td><td>-</td><td>28.88</td><td>45.04</td><td>19.40</td></tr> <tr><td class="metric-name">自由现金流(亿)</td><td>28.88</td><td>45.04</td><td>19.40</td><td>73.57</td><td>45.41</td></tr>
<tr><td class="metric-name">分红(亿)</td><td>-</td><td>-</td><td>27.05</td><td>28.97</td><td>28.14</td></tr> <tr><td class="metric-name">分红(亿)</td><td>27.05</td><td>28.97</td><td>28.14</td><td>29.98</td><td>34.98</td></tr>
<tr><td class="metric-name">回购(亿)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">回购(亿)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">总资产(亿)</td><td>-</td><td>-</td><td>1,555.76</td><td>1,445.77</td><td>1,330.29</td></tr> <tr><td class="metric-name">总资产(亿)</td><td>1,555.76</td><td>1,445.77</td><td>1,330.29</td><td>1,367.77</td><td>1,555.13</td></tr>
<tr><td class="metric-name">净资产(亿)</td><td>-</td><td>-</td><td>548.03</td><td>570.62</td><td>512.81</td></tr> <tr><td class="metric-name">净资产(亿)</td><td>548.03</td><td>570.62</td><td>512.81</td><td>494.77</td><td>530.26</td></tr>
<tr><td class="metric-name">商誉(亿)</td><td>-</td><td>-</td><td>538.53</td><td>349.71</td><td>256.62</td></tr> <tr><td class="metric-name">商誉(亿)</td><td>538.53</td><td>349.71</td><td>256.62</td><td>254.84</td><td>287.71</td></tr>
<tr class="section-row"><td class="section-label">费用指标</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">费用指标</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">销售费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">销售费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">管理费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">管理费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">SG&A比例</td><td>-</td><td>-</td><td>31.60%</td><td>31.72%</td><td>32.18%</td></tr> <tr><td class="metric-name">SG&A比例</td><td>31.60%</td><td>31.72%</td><td>32.18%</td><td>32.27%</td><td>31.21%</td></tr>
<tr><td class="metric-name">研发费用率</td><td>-</td><td>-</td><td>4.96%</td><td>3.98%</td><td>3.73%</td></tr> <tr><td class="metric-name">研发费用率</td><td>4.96%</td><td>3.98%</td><td>3.73%</td><td>3.82%</td><td>3.49%</td></tr>
<tr><td class="metric-name">其他费用率</td><td>-</td><td>-</td><td>6.53%</td><td>4.16%</td><td>4.03%</td></tr> <tr><td class="metric-name">其他费用率</td><td>6.53%</td><td>4.16%</td><td>4.03%</td><td>5.80%</td><td>4.88%</td></tr>
<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">折旧费用占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">所得税率</td><td>-</td><td>-</td><td>48.08%</td><td>29.26%</td><td>30.02%</td></tr> <tr><td class="metric-name">所得税率</td><td>48.08%</td><td>29.26%</td><td>30.02%</td><td>34.28%</td><td>25.57%</td></tr>
<tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">现金占比</td><td>-</td><td>-</td><td>3.79%</td><td>4.89%</td><td>3.79%</td></tr> <tr><td class="metric-name">现金占比</td><td>3.79%</td><td>4.89%</td><td>3.79%</td><td>6.37%</td><td>6.90%</td></tr>
<tr><td class="metric-name">库存占比</td><td>-</td><td>-</td><td>10.70%</td><td>11.53%</td><td>11.41%</td></tr> <tr><td class="metric-name">库存占比</td><td>10.70%</td><td>11.53%</td><td>11.41%</td><td>10.00%</td><td>8.83%</td></tr>
<tr><td class="metric-name">应收款占比</td><td>-</td><td>-</td><td>14.35%</td><td>14.77%</td><td>15.31%</td></tr> <tr><td class="metric-name">应收款占比</td><td>14.35%</td><td>14.77%</td><td>15.31%</td><td>15.07%</td><td>14.53%</td></tr>
<tr><td class="metric-name">预付款占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">预付款占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">固定资产占比</td><td>-</td><td>-</td><td>20.10%</td><td>20.66%</td><td>22.05%</td></tr> <tr><td class="metric-name">固定资产占比</td><td>20.10%</td><td>20.66%</td><td>22.05%</td><td>21.60%</td><td>21.22%</td></tr>
<tr><td class="metric-name">长期投资占比</td><td>-</td><td>-</td><td>3.08%</td><td>3.67%</td><td>4.07%</td></tr> <tr><td class="metric-name">长期投资占比</td><td>3.08%</td><td>3.67%</td><td>4.07%</td><td>4.75%</td><td>4.30%</td></tr>
<tr><td class="metric-name">商誉占比</td><td>-</td><td>-</td><td>34.61%</td><td>24.19%</td><td>19.29%</td></tr> <tr><td class="metric-name">商誉占比</td><td>34.61%</td><td>24.19%</td><td>19.29%</td><td>18.63%</td><td>18.50%</td></tr>
<tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>100.00%</td><td>100.00%</td><td>13.36%</td><td>20.29%</td><td>24.07%</td></tr> <tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>13.36%</td><td>20.29%</td><td>24.07%</td><td>23.58%</td><td>25.72%</td></tr>
<tr><td class="metric-name">应付款占比</td><td>-</td><td>-</td><td>4.83%</td><td>4.78%</td><td>4.74%</td></tr> <tr><td class="metric-name">应付款占比</td><td>4.83%</td><td>4.78%</td><td>4.74%</td><td>3.99%</td><td>3.91%</td></tr>
<tr><td class="metric-name">预收款占比</td><td>-</td><td>-</td><td>0.00%</td><td>0.00%</td><td>0.00%</td></tr> <tr><td class="metric-name">预收款占比</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td></tr>
<tr><td class="metric-name">短期借款占比</td><td>-</td><td>-</td><td>2.33%</td><td>3.51%</td><td>4.50%</td></tr> <tr><td class="metric-name">短期借款占比</td><td>2.33%</td><td>3.51%</td><td>4.50%</td><td>4.11%</td><td>10.13%</td></tr>
<tr><td class="metric-name">长期借款占比</td><td>-</td><td>-</td><td>46.48%</td><td>38.73%</td><td>32.15%</td></tr> <tr><td class="metric-name">长期借款占比</td><td>46.48%</td><td>38.73%</td><td>32.15%</td><td>36.41%</td><td>32.01%</td></tr>
<tr><td class="metric-name">运营资产占比</td><td>0.00%</td><td>0.00%</td><td>20.22%</td><td>21.52%</td><td>21.99%</td></tr> <tr><td class="metric-name">运营资产占比</td><td>20.22%</td><td>21.52%</td><td>21.99%</td><td>21.08%</td><td>19.45%</td></tr>
<tr><td class="metric-name">有息负债率</td><td>-</td><td>-</td><td>48.81%</td><td>42.24%</td><td>36.65%</td></tr> <tr><td class="metric-name">有息负债率</td><td>48.81%</td><td>42.24%</td><td>36.65%</td><td>40.51%</td><td>42.14%</td></tr>
<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">存货周转天数</td><td>-</td><td>-</td><td>102</td><td>103</td><td>97</td></tr> <tr><td class="metric-name">存货周转天数</td><td>102</td><td>103</td><td>97</td><td>90</td><td>75</td></tr>
<tr><td class="metric-name">应收款周转天数</td><td>-</td><td>-</td><td>75</td><td>72</td><td>71</td></tr> <tr><td class="metric-name">应收款周转天数</td><td>75</td><td>72</td><td>71</td><td>74</td><td>70</td></tr>
<tr><td class="metric-name">应付款周转天数</td><td>-</td><td>-</td><td>46</td><td>42</td><td>40</td></tr> <tr><td class="metric-name">应付款周转天数</td><td>46</td><td>42</td><td>40</td><td>36</td><td>33</td></tr>
<tr><td class="metric-name">固定资产周转率</td><td>-</td><td>-</td><td>3.47</td><td>3.60</td><td>3.55</td></tr> <tr><td class="metric-name">固定资产周转率</td><td>3.47</td><td>3.60</td><td>3.55</td><td>3.41</td><td>3.54</td></tr>
<tr><td class="metric-name">总资产周转率</td><td>-</td><td>-</td><td>0.70</td><td>0.74</td><td>0.78</td></tr> <tr><td class="metric-name">总资产周转率</td><td>0.70</td><td>0.74</td><td>0.78</td><td>0.74</td><td>0.75</td></tr>
<tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">员工人数</td><td>31,934</td><td>31,934</td><td>31,934</td><td>30,538</td><td>29,515</td></tr> <tr><td class="metric-name">员工人数</td><td>31,934</td><td>30,538</td><td>29,515</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">人均创收(万)</td><td>-</td><td>-</td><td>339.64</td><td>352.14</td><td>352.71</td></tr> <tr><td class="metric-name">人均创收(万)</td><td>339.64</td><td>352.14</td><td>352.71</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">人均创利(万)</td><td>-</td><td>-</td><td>8.46</td><td>18.59</td><td>19.68</td></tr> <tr><td class="metric-name">人均创利(万)</td><td>8.46</td><td>18.59</td><td>19.68</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="5"></td></tr> <tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="5"></td></tr>
<tr><td class="metric-name">股价</td><td>105.03</td><td>105.03</td><td>95.04</td><td>104.09</td><td>105.23</td></tr> <tr><td class="metric-name">股价</td><td>95.04</td><td>104.09</td><td>105.23</td><td>102.20</td><td>0.00</td></tr>
<tr><td class="metric-name">市值(亿)</td><td>959.97</td><td>959.97</td><td>868.66</td><td>951.38</td><td>961.80</td></tr> <tr><td class="metric-name">市值(亿)</td><td>868.66</td><td>951.38</td><td>961.80</td><td>934.09</td><td>0.00</td></tr>
<tr><td class="metric-name">PE</td><td>-</td><td>-</td><td>32.17</td><td>16.76</td><td>16.56</td></tr> <tr><td class="metric-name">PE</td><td>32.17</td><td>16.76</td><td>16.56</td><td>28.23</td><td>0.00</td></tr>
<tr><td class="metric-name">PB</td><td>-</td><td>-</td><td>1.59</td><td>1.67</td><td>1.88</td></tr> <tr><td class="metric-name">PB</td><td>1.59</td><td>1.67</td><td>1.88</td><td>1.89</td><td>0.00</td></tr>
<tr><td class="metric-name">股东户数</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr> <tr><td class="metric-name">股东户数</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
</tbody> </tbody>
</table> </table>

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data/JP/2503.T/report.md
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# 麒麟控股株式会社 (2503.T) - Financial Report # 麒麟控股株式会社 (2503.T) - Financial Report
*Report generated on: 2026-01-03* *Report generated on: 2026-01-07*
| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) | | 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |
|:---|:---|:---|:---|:---|:---| |:---|:---|:---|:---|:---|:---|
@ -7,83 +7,83 @@
## 主要指标 ## 主要指标
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| ROE | - | - | 4.93% | 9.95% | 11.33% | | ROE | 4.93% | 9.95% | 11.33% | 6.69% | 8.58% |
| ROA | - | - | 1.74% | 3.93% | 4.37% | | ROA | 1.74% | 3.93% | 4.37% | 2.42% | 2.92% |
| ROCE/ROIC | - | - | - | - | - | | ROCE/ROIC | - | - | - | - | - |
| 毛利率 | - | - | 45.59% | 45.14% | 45.53% | | 毛利率 | 45.59% | 45.14% | 45.53% | 45.17% | 43.46% |
| 净利润率 | - | - | 2.49% | 5.28% | 5.58% | | 净利润率 | 2.49% | 5.28% | 5.58% | 3.28% | 3.89% |
| 收入(亿) | - | - | 1,084.61 | 1,075.36 | 1,041.03 | | 收入(亿) | 1,084.61 | 1,075.36 | 1,041.03 | 1,007.91 | 1,169.52 |
| 收入增速 | - | - | 0.86% | 3.30% | - | | 收入增速 | 0.86% | 3.30% | 3.29% | -13.82% | - |
| 净利润(亿) | - | - | 27.00 | 56.78 | 58.09 | | 净利润(亿) | 27.00 | 56.78 | 58.09 | 33.08 | 45.49 |
| 净利润增速 | - | - | -52.45% | -2.25% | - | | 净利润增速 | -52.45% | -2.25% | 75.58% | -27.27% | - |
| 经营净现金流(亿) | - | - | 112.64 | 102.38 | 70.94 | | 经营净现金流(亿) | 112.64 | 102.38 | 70.94 | 121.34 | 104.23 |
| 资本开支(亿) | - | - | 83.76 | 57.34 | 51.53 | | 资本开支(亿) | 83.76 | 57.34 | 51.53 | 47.77 | 58.82 |
| 自由现金流(亿) | - | - | 28.88 | 45.04 | 19.40 | | 自由现金流(亿) | 28.88 | 45.04 | 19.40 | 73.57 | 45.41 |
| 分红(亿) | - | - | 27.05 | 28.97 | 28.14 | | 分红(亿) | 27.05 | 28.97 | 28.14 | 29.98 | 34.98 |
| 回购(亿) | - | - | - | - | - | | 回购(亿) | - | - | - | - | - |
| 总资产(亿) | - | - | 1,555.76 | 1,445.77 | 1,330.29 | | 总资产(亿) | 1,555.76 | 1,445.77 | 1,330.29 | 1,367.77 | 1,555.13 |
| 净资产(亿) | - | - | 548.03 | 570.62 | 512.81 | | 净资产(亿) | 548.03 | 570.62 | 512.81 | 494.77 | 530.26 |
| 商誉(亿) | - | - | 538.53 | 349.71 | 256.62 | | 商誉(亿) | 538.53 | 349.71 | 256.62 | 254.84 | 287.71 |
## 费用指标 ## 费用指标
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| 销售费用率 | - | - | - | - | - | | 销售费用率 | - | - | - | - | - |
| 管理费用率 | - | - | - | - | - | | 管理费用率 | - | - | - | - | - |
| SG&A比例 | - | - | 31.60% | 31.72% | 32.18% | | SG&A比例 | 31.60% | 31.72% | 32.18% | 32.27% | 31.21% |
| 研发费用率 | - | - | 4.96% | 3.98% | 3.73% | | 研发费用率 | 4.96% | 3.98% | 3.73% | 3.82% | 3.49% |
| 其他费用率 | - | - | 6.53% | 4.16% | 4.03% | | 其他费用率 | 6.53% | 4.16% | 4.03% | 5.80% | 4.88% |
| 折旧费用占比 | - | - | - | - | - | | 折旧费用占比 | - | - | - | - | - |
| 所得税率 | - | - | 48.08% | 29.26% | 30.02% | | 所得税率 | 48.08% | 29.26% | 30.02% | 34.28% | 25.57% |
## 资产占比 ## 资产占比
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| 现金占比 | - | - | 3.79% | 4.89% | 3.79% | | 现金占比 | 3.79% | 4.89% | 3.79% | 6.37% | 6.90% |
| 库存占比 | - | - | 10.70% | 11.53% | 11.41% | | 库存占比 | 10.70% | 11.53% | 11.41% | 10.00% | 8.83% |
| 应收款占比 | - | - | 14.35% | 14.77% | 15.31% | | 应收款占比 | 14.35% | 14.77% | 15.31% | 15.07% | 14.53% |
| 预付款占比 | - | - | - | - | - | | 预付款占比 | - | - | - | - | - |
| 固定资产占比 | - | - | 20.10% | 20.66% | 22.05% | | 固定资产占比 | 20.10% | 20.66% | 22.05% | 21.60% | 21.22% |
| 长期投资占比 | - | - | 3.08% | 3.67% | 4.07% | | 长期投资占比 | 3.08% | 3.67% | 4.07% | 4.75% | 4.30% |
| 商誉占比 | - | - | 34.61% | 24.19% | 19.29% | | 商誉占比 | 34.61% | 24.19% | 19.29% | 18.63% | 18.50% |
| 其他资产占比 | 100.00% | 100.00% | 13.36% | 20.29% | 24.07% | | 其他资产占比 | 13.36% | 20.29% | 24.07% | 23.58% | 25.72% |
| 应付款占比 | - | - | 4.83% | 4.78% | 4.74% | | 应付款占比 | 4.83% | 4.78% | 4.74% | 3.99% | 3.91% |
| 预收款占比 | - | - | 0.00% | 0.00% | 0.00% | | 预收款占比 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
| 短期借款占比 | - | - | 2.33% | 3.51% | 4.50% | | 短期借款占比 | 2.33% | 3.51% | 4.50% | 4.11% | 10.13% |
| 长期借款占比 | - | - | 46.48% | 38.73% | 32.15% | | 长期借款占比 | 46.48% | 38.73% | 32.15% | 36.41% | 32.01% |
| 运营资产占比 | 0.00% | 0.00% | 20.22% | 21.52% | 21.99% | | 运营资产占比 | 20.22% | 21.52% | 21.99% | 21.08% | 19.45% |
| 有息负债率 | - | - | 48.81% | 42.24% | 36.65% | | 有息负债率 | 48.81% | 42.24% | 36.65% | 40.51% | 42.14% |
## 周转能力 ## 周转能力
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| 存货周转天数 | - | - | 102 | 103 | 97 | | 存货周转天数 | 102 | 103 | 97 | 90 | 75 |
| 应收款周转天数 | - | - | 75 | 72 | 71 | | 应收款周转天数 | 75 | 72 | 71 | 74 | 70 |
| 应付款周转天数 | - | - | 46 | 42 | 40 | | 应付款周转天数 | 46 | 42 | 40 | 36 | 33 |
| 固定资产周转率 | - | - | 3.47 | 3.60 | 3.55 | | 固定资产周转率 | 3.47 | 3.60 | 3.55 | 3.41 | 3.54 |
| 总资产周转率 | - | - | 0.70 | 0.74 | 0.78 | | 总资产周转率 | 0.70 | 0.74 | 0.78 | 0.74 | 0.75 |
## 人均效率 ## 人均效率
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| 员工人数 | 31,934 | 31,934 | 31,934 | 30,538 | 29,515 | | 员工人数 | 31,934 | 30,538 | 29,515 | - | - |
| 人均创收(万) | - | - | 339.64 | 352.14 | 352.71 | | 人均创收(万) | 339.64 | 352.14 | 352.71 | - | - |
| 人均创利(万) | - | - | 8.46 | 18.59 | 19.68 | | 人均创利(万) | 8.46 | 18.59 | 19.68 | - | - |
| 人均薪酬(万) | - | - | - | - | - | | 人均薪酬(万) | - | - | - | - | - |
## 市场表现 ## 市场表现
| 指标 | 2026A | 2025A | 2024A | 2023A | 2022A | | 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
|:---|--:|--:|--:|--:|--:| |:---|--:|--:|--:|--:|--:|
| 股价 | 105.03 | 105.03 | 95.04 | 104.09 | 105.23 | | 股价 | 95.04 | 104.09 | 105.23 | 102.20 | 0.00 |
| 市值(亿) | 959.97 | 959.97 | 868.66 | 951.38 | 961.80 | | 市值(亿) | 868.66 | 951.38 | 961.80 | 934.09 | 0.00 |
| PE | - | - | 32.17 | 16.76 | 16.56 | | PE | 32.17 | 16.76 | 16.56 | 28.23 | 0.00 |
| PB | - | - | 1.59 | 1.67 | 1.88 | | PB | 1.59 | 1.67 | 1.88 | 1.89 | 0.00 |
| 股东户数 | - | - | - | - | - | | 股东户数 | - | - | - | - | - |

69
frontend/src/app/analysis/[id]/page.tsx
View File

@ -124,21 +124,21 @@ export default function AnalysisPage({ params }: { params: Promise<{ id: string
<Tabs defaultValue="financial_data" className="space-y-4"> <Tabs defaultValue="financial_data" className="space-y-4">
<TabsList className="bg-muted/50 p-1 flex-wrap h-auto"> <TabsList className="bg-muted/50 p-1 flex-wrap h-auto">
<TabsTrigger value="financial_data">财务数据</TabsTrigger> <TabsTrigger value="financial_data">财务数据</TabsTrigger>
{report.sections?.map((s: any) => {
const nameMap: Record<string, string> = { {[
'company_profile': '公司简介', { id: 'company_profile', label: '公司简介' },
'fundamental_analysis': '基本面分析', { id: 'fundamental_analysis', label: '基本面分析' },
'insider_analysis': '内部人士分析', { id: 'insider_analysis', label: '内部人士分析' },
'bullish_analysis': '看涨分析', { id: 'bullish_analysis', label: '看涨分析' },
'bearish_analysis': '看跌分析' { id: 'bearish_analysis', label: '看跌分析' }
}; ].map((section) => (
return ( <div key={section.id} className="flex items-center">
<TabsTrigger key={s.section_name} value={s.section_name} className="capitalize"> <div className="h-4 w-[1px] bg-border mx-1" />
{nameMap[s.section_name] || s.section_name.replace(/_/g, " ")} <TabsTrigger value={section.id} className="capitalize">
{section.label}
</TabsTrigger> </TabsTrigger>
); </div>
})} ))}
{report.sections?.length === 0 && report.status !== "in_progress" && <TabsTrigger value="empty" disabled>无内容</TabsTrigger>}
</TabsList> </TabsList>
<TabsContent value="financial_data" className="min-h-[500px]"> <TabsContent value="financial_data" className="min-h-[500px]">
@ -152,15 +152,38 @@ export default function AnalysisPage({ params }: { params: Promise<{ id: string
</Card> </Card>
</TabsContent> </TabsContent>
{report.sections?.map((s: any) => ( {[
<TabsContent key={s.section_name} value={s.section_name} className="min-h-[500px]"> { id: 'company_profile', label: '公司简介' },
<Card> { id: 'fundamental_analysis', label: '基本面分析' },
<CardContent className="p-6 md:p-8"> { id: 'insider_analysis', label: '内部人士分析' },
<MarkdownRenderer content={s.content} /> { id: 'bullish_analysis', label: '看涨分析' },
</CardContent> { id: 'bearish_analysis', label: '看跌分析' }
</Card> ].map((section) => {
</TabsContent> const sectionData = report.sections?.find((s: any) => s.section_name === section.id);
))} return (
<TabsContent key={section.id} value={section.id} className="min-h-[500px]">
<Card>
<CardContent className="p-6 md:p-8">
{sectionData ? (
<MarkdownRenderer content={sectionData.content} />
) : (
<div className="flex flex-col items-center justify-center py-20 text-muted-foreground">
{report.status === "in_progress" || report.status === "pending" ? (
<>
<Loader2 className="h-10 w-10 animate-spin mb-4 text-primary" />
<p>AI 正在撰写{section.label}...</p>
<p className="text-sm mt-2 opacity-75">请稍候，全面分析通常需要 1 分钟左右</p>
</>
) : (
<p>该部分暂无内容</p>
)}
</div>
)}
</CardContent>
</Card>
</TabsContent>
);
})}
</Tabs> </Tabs>
</div> </div>
) )

2
frontend/src/app/page.tsx
View File

@ -7,7 +7,7 @@ export default function Home() {
<div className="flex flex-col gap-4"> <div className="flex flex-col gap-4">
<h1 className="text-3xl font-bold tracking-tight">股票分析</h1> <h1 className="text-3xl font-bold tracking-tight">股票分析</h1>
<p className="text-muted-foreground"> <p className="text-muted-foreground">
输入公司名称或股票代码，开始全面的AI驱动的分析。支持中国、香港、美国、日本的公司分析。 输入公司名称或股票代码，开始全面的AI驱动的分析。支持中国、香港、美国、日本、越南的公司分析。
</p> </p>
</div> </div>

6
main.py → run_fetcher.py
View File

@ -25,6 +25,10 @@ def get_strategy(market, stock_code, tushare_token=None, av_key=None):
from strategies.jp_strategy import JP_Strategy from strategies.jp_strategy import JP_Strategy
ifind_token = os.getenv('IFIND_REFRESH_TOKEN') ifind_token = os.getenv('IFIND_REFRESH_TOKEN')
return JP_Strategy(stock_code, ifind_token) return JP_Strategy(stock_code, ifind_token)
elif market == 'VN':
from strategies.vn_strategy import VN_Strategy
ifind_token = os.getenv('IFIND_REFRESH_TOKEN')
return VN_Strategy(stock_code, ifind_token)
else: else:
raise ValueError(f"Unsupported market: {market}") raise ValueError(f"Unsupported market: {market}")
@ -39,7 +43,7 @@ def main():
strategy = get_strategy(market, symbol, tushare_token, av_key) strategy = get_strategy(market, symbol, tushare_token, av_key)
strategy.execute() strategy.execute()
else: else:
print("Usage: python main.py <MARKET> <SYMBOL>") print("Usage: python run_fetcher.py <MARKET> <SYMBOL>")
print("Running default test cases:") print("Running default test cases:")
# Test CN # Test CN

116
src/analysis/vn_analyzer.py Normal file
View File

@ -0,0 +1,116 @@
from .cn_analyzer import CN_Analyzer
import pandas as pd
class VN_Analyzer(CN_Analyzer):
def __init__(self):
super().__init__()
self.market = 'VN'
self.mapping = {
'income': {
'revenue': 'revenue',
'net_income': 'net_income',
'gross_profit': 'gross_profit',
'total_profit': 'total_profit',
'sga_exp': 'sga_exp'
},
'balance': {
'total_equity': 'total_equity',
'total_assets': 'total_assets',
'total_liabilities': 'total_liabilities',
'current_assets': 'current_assets',
'current_liabilities': 'current_liabilities',
'cash': 'cash',
'receivables': 'receivables',
'inventory': 'inventory',
'fixed_assets': 'fixed_assets',
'goodwill': 'goodwill',
'short_term_debt': 'short_term_debt',
'long_term_debt': 'long_term_debt'
},
'cashflow': {
'ocf': 'ocf',
'capex': 'capex',
'dividends': 'dividends'
}
}
def _post_process_columns(self, df, type):
if market_type := self.mapping.get(type):
for col in market_type.values():
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors='coerce')
df = super()._post_process_columns(df, type)
if type == 'balance':
if 'long_term_investments' in df.columns:
df['lt_invest'] = df['long_term_investments']
if 'long_term_debt' not in df.columns: df['long_term_debt'] = 0
if 'long_term_borrowings' in df.columns:
df['long_term_debt'] = df['long_term_debt'].fillna(0) + df['long_term_borrowings'].fillna(0)
return df
def calculate_indicators(self, df_merged, market_metrics, historical_metrics):
if 'revenue' in df_merged.columns and 'gross_profit' in df_merged.columns:
df_merged['cogs'] = df_merged['revenue'] - df_merged['gross_profit']
df_merged = super().calculate_indicators(df_merged, market_metrics, historical_metrics)
has_sga = False
if 'sga_exp' in df_merged.columns and 'revenue' in df_merged.columns:
if df_merged['sga_exp'].notna().any() and (df_merged['sga_exp'] != 0).any():
df_merged['SgaRatio'] = self._safe_div(df_merged['sga_exp'], df_merged['revenue'])
has_sga = True
if not has_sga:
sga_sum = 0
if 'selling_exp' in df_merged.columns: sga_sum = sga_sum + df_merged['selling_exp'].fillna(0)
if 'admin_exp' in df_merged.columns: sga_sum = sga_sum + df_merged['admin_exp'].fillna(0)
if 'revenue' in df_merged.columns:
df_merged['SgaRatio'] = self._safe_div(sga_sum, df_merged['revenue'])
if 'income_tax' in df_merged.columns and 'net_income' in df_merged.columns:
ebt_approx = df_merged['net_income'] + df_merged['income_tax']
df_merged['TaxRate'] = self._safe_div(df_merged['income_tax'], ebt_approx)
if 'GrossMargin' in df_merged.columns and 'NetMargin' in df_merged.columns:
other_ratio = df_merged['GrossMargin'] - df_merged['NetMargin']
if 'SgaRatio' in df_merged.columns:
other_ratio = other_ratio - df_merged['SgaRatio'].fillna(0)
if 'RDRatio' in df_merged.columns:
other_ratio = other_ratio - df_merged['RDRatio'].fillna(0)
df_merged['OtherExpenseRatio'] = other_ratio
if 'MarketCap' in df_merged.columns:
if 'net_income' in df_merged.columns:
calculated_pe = self._safe_div(df_merged['MarketCap'], df_merged['net_income'])
if 'PE' not in df_merged.columns:
df_merged['PE'] = calculated_pe
else:
cond_pe = (df_merged['PE'] != 0) & df_merged['PE'].notna()
df_merged['PE'] = df_merged['PE'].where(cond_pe, calculated_pe)
if 'total_equity' in df_merged.columns:
calculated_pb = self._safe_div(df_merged['MarketCap'], df_merged['total_equity'])
if 'PB' not in df_merged.columns:
df_merged['PB'] = calculated_pb
else:
cond_pb = (df_merged['PB'] != 0) & df_merged['PB'].notna()
df_merged['PB'] = df_merged['PB'].where(cond_pb, calculated_pb)
if 'dividends' in df_merged.columns:
calculated_yield = self._safe_div(df_merged['dividends'], df_merged['MarketCap']) * 100
if 'DividendYield' not in df_merged.columns:
df_merged['DividendYield'] = calculated_yield
else:
df_merged['DividendYield'] = df_merged['DividendYield'].fillna(calculated_yield)
if 'employee_count' in df_merged.columns:
df_merged['Employees'] = df_merged['employee_count']
if 'revenue' in df_merged.columns:
df_merged['RevenuePerEmp'] = self._safe_div(df_merged['revenue'], df_merged['employee_count'])
if 'net_income' in df_merged.columns:
df_merged['ProfitPerEmp'] = self._safe_div(df_merged['net_income'], df_merged['employee_count'])
return df_merged

9
src/fetchers/factory.py
View File

@ -31,5 +31,14 @@ class FetcherFactory:
raise ValueError("iFinD Refresh Token is required for JP market") raise ValueError("iFinD Refresh Token is required for JP market")
from .jp_fetcher import JpFetcher from .jp_fetcher import JpFetcher
return JpFetcher(ifind_token) return JpFetcher(ifind_token)
elif market == 'VN':
ifind_token = kwargs.get('ifind_refresh_token')
if not ifind_token:
import os
ifind_token = os.getenv('IFIND_REFRESH_TOKEN')
if not ifind_token:
raise ValueError("iFinD Refresh Token is required for VN market")
from .vn_fetcher import VnFetcher
return VnFetcher(ifind_token)
else: else:
raise ValueError(f"Unsupported market: {market}") raise ValueError(f"Unsupported market: {market}")

458
src/fetchers/hk_fetcher.py
View File

@ -74,8 +74,10 @@ class HkFetcher(DataFetcher):
if not res: if not res:
return pd.DataFrame() return pd.DataFrame()
if res.get("errorcode") != 0: # Default to 0 if not present (for lenient mocking) or check properly
print(f"iFinD API Error: {res.get('errmsg')} (code: {res.get('errorcode')})") error_code = res.get("errorcode", 0)
if error_code != 0:
print(f"iFinD API Error: {res.get('errmsg')} (code: {error_code})")
return pd.DataFrame() return pd.DataFrame()
tables = res.get("tables", []) tables = res.get("tables", [])
@ -142,42 +144,61 @@ class HkFetcher(DataFetcher):
def _fetch_financial_data_annual(self, symbol: str, indicator_configs: list) -> pd.DataFrame: def _fetch_financial_data_annual(self, symbol: str, indicator_configs: list) -> pd.DataFrame:
"""通用获取历年财务数据 (HKD 为主，但 iFinD 支持转 CNY)""" """通用获取历年财务数据 (HKD 为主，但 iFinD 支持转 CNY)"""
code = self._get_ifind_code(symbol) code = self._get_ifind_code(symbol)
basic_info = self._fetch_basic_info(symbol)
# HK stocks don't always use the same acc_date, but we can try to fetch recent years
current_year = int(time.strftime("%Y")) current_year = int(time.strftime("%Y"))
# 1. First, determine the most recent valid year by trying backwards from current year
last_valid_year = None
# Try up to 3 years back to find the latest available report
# e.g., in Jan 2026, try 2026 -> fail, 2025 -> success
for offset in range(3):
test_year = current_year - offset
test_date = f"{test_year}1231"
# Use the first indicator to test availability
first_indicator = indicator_configs[0]
params = {
"codes": code,
"indipara": [
{"indicator": first_indicator["indicator"], "indiparams": [test_date, first_indicator.get("type", "1"), "CNY"]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
# Check for non-null values
valid_val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
if pd.notna(valid_val) and valid_val != 0:
last_valid_year = test_year
break
if last_valid_year is None:
# Fallback to current year if nothing found (will likely return empty/zeros, but keeps logic flowing)
last_valid_year = current_year
# 2. Fetch 5 years starting from the last valid year
all_dfs = [] all_dfs = []
# HK stocks often report semi-annually or annually. Let's fetch recent reporting periods.
# Instead of guessing dates, we can use "reporting_period" but iFinD's basic_data_service
# often works better with explicit dates if we want annuals.
# Alternatively, we can fetch multiple periods.
for i in range(5): for i in range(5):
target_year = current_year - i target_year = last_valid_year - i
# Try 1231 as primary guess for annual target_date = f"{target_year}1231"
for month_day in ["1231", "0331", "0630", "0930"]:
target_date = f"{target_year}{month_day}" params = {
params = { "codes": code,
"codes": code, "indipara": [
"indipara": [ {"indicator": item["indicator"], "indiparams": [target_date, item.get("type", "1"), "CNY"]}
{"indicator": item["indicator"], "indiparams": [target_date, item.get("type", "1"), "CNY"]} for item in indicator_configs
for item in indicator_configs ]
] }
} res = self.cli.post("basic_data_service", params)
res = self.cli.post("basic_data_service", params) df = self._parse_ifind_tables(res)
df = self._parse_ifind_tables(res)
if not df.empty: if not df.empty:
# Check if it's mostly empty data valid_cols = [c for c in df.columns if c not in ['end_date', 'date']]
valid_cols = [c for c in df.columns if c not in ['end_date', 'date']] if not df[valid_cols].isnull().all().all():
if not df[valid_cols].isnull().all().all(): df['end_date'] = target_date
df['end_date'] = target_date df = df.dropna(axis=1, how='all')
# Drop columns that are entirely NA to prevent FutureWarning in pd.concat all_dfs.append(df)
df = df.dropna(axis=1, how='all')
all_dfs.append(df)
# If we found data for this year, maybe we don't need to try other months?
# Actually some companies changed their fiscal year.
break
if not all_dfs: if not all_dfs:
return pd.DataFrame() return pd.DataFrame()
@ -186,17 +207,59 @@ class HkFetcher(DataFetcher):
def get_income_statement(self, symbol: str) -> pd.DataFrame: def get_income_statement(self, symbol: str) -> pd.DataFrame:
indicators = [ indicators = [
{"indicator": "revenue_oas"}, {"indicator": "total_oi"},
{"indicator": "gross_profit_oas"}, {"indicator": "prime_oi"},
{"indicator": "sga_expenses_oas"}, {"indicator": "other_oi"},
{"indicator": "selling_marketing_expenses_oas"}, {"indicator": "operating_cost"},
{"indicator": "ga_expenses_oas"}, {"indicator": "operating_expense"},
{"indicator": "rd_expenses_oas"}, {"indicator": "operating_fee"},
{"indicator": "income_tax_expense_oas"}, {"indicator": "p_depreciation_and_amortization"},
{"indicator": "net_income_attri_to_common_sh_oas"}, {"indicator": "gross_profit"},
{"indicator": "operating_income_oas"}, {"indicator": "sales_ad_and_ga"},
{"indicator": "ebit_oas"}, {"indicator": "rad_cost"},
{"indicator": "depreciation_and_amortization_oas"} {"indicator": "sales_fee"},
{"indicator": "financial_expense"},
{"indicator": "sales_income"},
{"indicator": "sales_cost"},
{"indicator": "other_income"},
{"indicator": "manage_fee"},
{"indicator": "deprec_and_amorti"},
{"indicator": "total_other_opearting_expense"},
{"indicator": "p_total_cost"},
{"indicator": "operating_profit"},
{"indicator": "total_gal"},
{"indicator": "interest_income"},
{"indicator": "interest_net_pay"},
{"indicator": "interest_expense"},
{"indicator": "income_from_asso_and_joint"},
{"indicator": "other_gal_effct_profit_pre_tax"},
{"indicator": "conti_op_before_tax"},
{"indicator": "profit_before_noncurrent_items"},
{"indicator": "profit_and_loss_of_noncurrent_items"},
{"indicator": "profit_before_tax"},
{"indicator": "income_tax"},
{"indicator": "profit_after_tax"},
{"indicator": "minoritygal"},
{"indicator": "continue_operate_net_profit"},
{"indicator": "noncontinue_operate_net_profit"},
{"indicator": "other_special_items"},
{"indicator": "ni_attr_to_cs"},
{"indicator": "np_atms"},
{"indicator": "preferred_divid_and_other_adjust"},
{"indicator": "oci"},
{"indicator": "total_oci"},
{"indicator": "oci_from_parent"},
{"indicator": "oci_from_minority"},
{"indicator": "invest_property_fv_chg"},
{"indicator": "operating_amt"},
{"indicator": "oi_si"},
{"indicator": "operating_premium_profit_si"},
{"indicator": "to_toallied_corp_perf"},
{"indicator": "to_joint_control_entity_perf"},
{"indicator": "pre_tax_profit_si"},
{"indicator": "after_tax_profit_si"},
{"indicator": "profit_attrbt_to_nonholders"},
{"indicator": "total_income_atncs"}
] ]
df = self._fetch_financial_data_annual(symbol, indicators) df = self._fetch_financial_data_annual(symbol, indicators)
@ -204,20 +267,28 @@ class HkFetcher(DataFetcher):
self._save_raw_data(df, symbol, "income_statement_raw") self._save_raw_data(df, symbol, "income_statement_raw")
rename_map = { rename_map = {
'revenue_oas': 'revenue', 'total_oi': 'revenue',
'gross_profit_oas': 'gross_profit', 'operating_amt': 'turnover', # Backup for revenue
'sga_expenses_oas': 'sga_exp', 'gross_profit': 'gross_profit',
'selling_marketing_expenses_oas': 'selling_marketing_exp', 'sales_ad_and_ga': 'sga_exp',
'ga_expenses_oas': 'ga_exp', 'sales_fee': 'selling_marketing_exp',
'rd_expenses_oas': 'rd_exp', 'manage_fee': 'ga_exp',
'income_tax_expense_oas': 'income_tax', 'rad_cost': 'rd_exp',
'net_income_attri_to_common_sh_oas': 'net_income', 'income_tax': 'income_tax',
'operating_income_oas': 'operating_profit', 'ni_attr_to_cs': 'net_income',
'ebit_oas': 'ebit', 'operating_profit': 'operating_profit',
'depreciation_and_amortization_oas': 'depreciation' 'depreciation': 'depreciation',
'deprec_and_amorti': 'depreciation', # Backup
'p_depreciation_and_amortization': 'depreciation' # Another backup
} }
df_filtered = df.rename(columns=rename_map) df_filtered = df.rename(columns=rename_map)
# Calculate EBIT if not present but operating_profit is there
if 'ebit' not in df_filtered.columns and 'operating_profit' in df_filtered.columns:
# Simple approximation: Operating Profit is often used as EBIT
df_filtered['ebit'] = df_filtered['operating_profit']
for col in df_filtered.columns: for col in df_filtered.columns:
if col not in ['date', 'end_date']: if col not in ['date', 'end_date']:
df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce') df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
@ -226,23 +297,96 @@ class HkFetcher(DataFetcher):
def get_balance_sheet(self, symbol: str) -> pd.DataFrame: def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
indicators = [ indicators = [
{"indicator": "cash_equi_short_term_inve_oas"}, {"indicator": "cce"},
{"indicator": "accou_and_notes_recei_oas"}, {"indicator": "st_investment"},
{"indicator": "inventories_oas"}, {"indicator": "total_cash"},
{"indicator": "ppe_net_oas"}, {"indicator": "account_receivable"},
{"indicator": "long_term_inv_and_receiv_oas"}, {"indicator": "tradable_fnncl_asset"},
{"indicator": "goodwill_and_intasset_oas"}, {"indicator": "derivative_fnncl_assets"},
{"indicator": "short_term_debt_oas"}, {"indicator": "restriv_fund"},
{"indicator": "short_term_borrowings_oas"}, {"indicator": "other_short_term_investment"},
{"indicator": "account_and_note_payable_oas"}, {"indicator": "ar_nr"},
{"indicator": "contra_liabilities_current_oas"}, {"indicator": "total_ar"},
{"indicator": "advance_from_cust_current_oas"}, {"indicator": "or"},
{"indicator": "defer_revenue_current_oas"}, {"indicator": "inventory"},
{"indicator": "long_term_debt_oas"}, {"indicator": "flow_assets_dit"},
{"indicator": "long_term_borrowings_oas"}, {"indicator": "pre_payment"},
{"indicator": "total_assets_oas"}, {"indicator": "other_cunrrent_assets_si"},
{"indicator": "equity_attri_to_companyowner_oas"}, {"indicator": "other_ca"},
{"indicator": "prepaid_expenses_current_oas"} {"indicator": "total_ca"},
{"indicator": "receivables_from_allied_corp"},
{"indicator": "current_assets_si"},
{"indicator": "prepay_deposits_etc"},
{"indicator": "receivables_from_jce"},
{"indicator": "receivables_from_ac"},
{"indicator": "recoverable_tax"},
{"indicator": "total_fixed_assets"},
{"indicator": "depreciation"},
{"indicator": "equity_and_lt_invest"},
{"indicator": "net_fixed_assets"},
{"indicator": "invest_property"},
{"indicator": "equity_investment"},
{"indicator": "investment_in_associate"},
{"indicator": "investment_in_joints"},
{"indicator": "held_to_maturity_invest"},
{"indicator": "goodwill_and_intangible_asset"},
{"indicator": "intangible_assets"},
{"indicator": "accum_amortized"},
{"indicator": "noncurrent_assets_dit"},
{"indicator": "other_noncurrent_assets_si"},
{"indicator": "dt_assets"},
{"indicator": "total_noncurrent_assets"},
{"indicator": "total_assets"},
{"indicator": "ac_equity"},
{"indicator": "lease_prepay"},
{"indicator": "noncurrent_assets_si"},
{"indicator": "st_lt_current_loan"},
{"indicator": "trade_financial_lia"},
{"indicator": "derivative_financial_lia"},
{"indicator": "ap_np"},
{"indicator": "accounts_payable"},
{"indicator": "advance_payment"},
{"indicator": "st_debt"},
{"indicator": "contra_liab"},
{"indicator": "tax_payable"},
{"indicator": "accrued_liab"},
{"indicator": "flow_debt_deferred_income"},
{"indicator": "other_cl"},
{"indicator": "other_cunrrent_liab_si"},
{"indicator": "total_cl"},
{"indicator": "accrued_expenses_etc"},
{"indicator": "money_payable_toac"},
{"indicator": "joint_control_entity_payable"},
{"indicator": "payable_to_associated_corp"},
{"indicator": "lt_debt"},
{"indicator": "long_term_loan"},
{"indicator": "other_noncurrent_liabi"},
{"indicator": "deferred_tax_liability"},
{"indicator": "ncl_deferred_income"},
{"indicator": "other_noncurrent_liab_si"},
{"indicator": "noncurrent_liab_si"},
{"indicator": "total_noncurrent_liab"},
{"indicator": "total_liab"},
{"indicator": "common_shares"},
{"indicator": "capital_reserve"},
{"indicator": "equity_premium"},
{"indicator": "treasury_stock"},
{"indicator": "accumgal"},
{"indicator": "equity_atsopc_sbi"},
{"indicator": "preferred_stock"},
{"indicator": "perpetual_debt"},
{"indicator": "reserve"},
{"indicator": "other_reserves"},
{"indicator": "retained_earnings"},
{"indicator": "oci_bs"},
{"indicator": "total_common_equity"},
{"indicator": "equity_belong_to_parent"},
{"indicator": "minority_interests"},
{"indicator": "other_equity_si"},
{"indicator": "total_equity"},
{"indicator": "total_lib_and_equity"},
{"indicator": "equity_si"},
{"indicator": "equity_atncs"}
] ]
df = self._fetch_financial_data_annual(symbol, indicators) df = self._fetch_financial_data_annual(symbol, indicators)
@ -250,30 +394,39 @@ class HkFetcher(DataFetcher):
self._save_raw_data(df, symbol, "balance_sheet_raw") self._save_raw_data(df, symbol, "balance_sheet_raw")
rename_map = { rename_map = {
'cash_equi_short_term_inve_oas': 'cash', 'cce': 'cash',
'accou_and_notes_recei_oas': 'receivables', 'ar_nr': 'receivables',
'inventories_oas': 'inventory', 'inventory': 'inventory',
'ppe_net_oas': 'fixed_assets', 'net_fixed_assets': 'fixed_assets',
'long_term_inv_and_receiv_oas': 'long_term_investments', 'equity_and_lt_invest': 'long_term_investments',
'goodwill_and_intasset_oas': 'goodwill', 'goodwill_and_intangible_asset': 'goodwill',
'short_term_debt_oas': 'short_term_debt', 'st_debt': 'short_term_debt',
'short_term_borrowings_oas': 'short_term_borrowings', 'st_lt_current_loan': 'short_term_borrowings',
'account_and_note_payable_oas': 'accounts_payable', 'ap_np': 'accounts_payable',
'contra_liabilities_current_oas': 'contract_liabilities', 'contra_liab': 'contract_liabilities',
'advance_from_cust_current_oas': 'advances_from_customers', 'advance_payment': 'advances_from_customers',
'defer_revenue_current_oas': 'deferred_revenue', 'flow_debt_deferred_income': 'deferred_revenue',
'long_term_debt_oas': 'long_term_debt', 'lt_debt': 'long_term_debt',
'long_term_borrowings_oas': 'long_term_borrowings', 'long_term_loan': 'long_term_borrowings',
'total_assets_oas': 'total_assets', 'total_assets': 'total_assets',
'equity_attri_to_companyowner_oas': 'total_equity', 'equity_belong_to_parent': 'total_equity',
'prepaid_expenses_current_oas': 'prepayment' 'pre_payment': 'prepayment'
} }
df_filtered = df.rename(columns=rename_map) df_filtered = df.rename(columns=rename_map)
# Deduplicate columns just in case
df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
if 'total_liabilities' not in df_filtered.columns or df_filtered['total_liabilities'].isnull().all(): if 'total_liabilities' not in df_filtered.columns or df_filtered['total_liabilities'].isnull().all():
if 'total_assets' in df_filtered.columns and 'total_equity' in df_filtered.columns: if 'total_liab' in df_filtered.columns:
df_filtered['total_liabilities'] = df_filtered['total_liab']
elif 'total_assets' in df_filtered.columns and 'total_equity' in df_filtered.columns:
df_filtered['total_liabilities'] = df_filtered['total_assets'] - df_filtered['total_equity'] df_filtered['total_liabilities'] = df_filtered['total_assets'] - df_filtered['total_equity']
# Deduplicate again in case total_liabilities logic added a dupe (unlikely)
df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
for col in df_filtered.columns: for col in df_filtered.columns:
if col not in ['date', 'end_date']: if col not in ['date', 'end_date']:
df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce') df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
@ -282,9 +435,17 @@ class HkFetcher(DataFetcher):
def get_cash_flow(self, symbol: str) -> pd.DataFrame: def get_cash_flow(self, symbol: str) -> pd.DataFrame:
indicators = [ indicators = [
{"indicator": "net_cash_flows_from_oa_oas"}, {"indicator": "ni"},
{"indicator": "purchase_of_ppe_and_ia_oas"}, {"indicator": "depreciation_and_amortization"},
{"indicator": "dividends_paid_oas"} {"indicator": "operating_capital_change"},
{"indicator": "ncf_from_oa"},
{"indicator": "capital_cost"},
{"indicator": "invest_buy"},
{"indicator": "ncf_from_ia"},
{"indicator": "increase_in_share_capital"},
{"indicator": "decrease_in_share_capital"},
{"indicator": "total_dividends_paid"},
{"indicator": "ncf_from_fa"}
] ]
df = self._fetch_financial_data_annual(symbol, indicators) df = self._fetch_financial_data_annual(symbol, indicators)
@ -292,9 +453,9 @@ class HkFetcher(DataFetcher):
self._save_raw_data(df, symbol, "cash_flow_raw") self._save_raw_data(df, symbol, "cash_flow_raw")
rename_map = { rename_map = {
'net_cash_flows_from_oa_oas': 'ocf', 'ncf_from_oa': 'ocf',
'purchase_of_ppe_and_ia_oas': 'capex', 'capital_cost': 'capex',
'dividends_paid_oas': 'dividends' 'total_dividends_paid': 'dividends'
} }
df_filtered = df.rename(columns=rename_map) df_filtered = df.rename(columns=rename_map)
@ -482,3 +643,104 @@ class HkFetcher(DataFetcher):
df_emp = pd.DataFrame(results) df_emp = pd.DataFrame(results)
self._save_raw_data(df_emp, symbol, "employee_count_raw") self._save_raw_data(df_emp, symbol, "employee_count_raw")
return df_emp return df_emp
def get_financial_ratios(self, symbol: str) -> pd.DataFrame:
"""获取官方计算的财务指标（比率、周转天数等）"""
code = self._get_ifind_code(symbol)
current_year = int(time.strftime("%Y"))
# 1. Determine the latest valid year
last_valid_year = None
for offset in range(3):
test_year = current_year - offset
# Try getting ROE as a proxy for data availability
test_date = f"{test_year}1231"
params = {
"codes": code,
"indipara": [{"indicator": "roe", "indiparams": [test_date]}]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
if pd.notna(val) and val != 0:
last_valid_year = test_year
break
if last_valid_year is None:
last_valid_year = current_year
all_dfs = []
# 2. Fetch 5 years starting from last valid year
for i in range(5):
target_year = last_valid_year - i
date_str = f"{target_year}1231"
year_str = str(target_year)
indipara = []
# 1. 人均指标 (参数: Year, "100")
for key in ["salary_pp", "revenue_pp", "profit_pp"]:
indipara.append({"indicator": key, "indiparams": [year_str, "100"]})
# 2. 财务比率与周转率 (参数: Date YYYYMMDD)
ratio_keys = [
"roe", "roa", "roic",
"sales_fee_to_or", "manage_fee_to_revenue", "rad_expense_to_total_income",
"operating_revenue_yoy", "np_atsopc_yoy",
"ibdebt_ratio_asset_base",
"inventory_turnover_days", "receivable_turnover_days", "accounts_payable_turnover_days",
"fixed_asset_turnover_ratio", "total_capital_turnover"
]
for key in ratio_keys:
indipara.append({"indicator": key, "indiparams": [date_str]})
params = {
"codes": code,
"indipara": indipara
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
if 'end_date' not in df.columns:
df['end_date'] = date_str
# Filter out columns that are all NaN
df = df.dropna(axis=1, how='all')
# Identify if we have meaningful data (at least one valid metric)
valid_cols = [c for c in df.columns if c not in ['end_date', 'date', 'code', 'thscode']]
if not df[valid_cols].isnull().all().all():
all_dfs.append(df)
if not all_dfs:
return pd.DataFrame()
combined = pd.concat(all_dfs, ignore_index=True)
self._save_raw_data(combined, symbol, "financial_ratios_raw")
rename_map = {
"salary_pp": "salary_per_employee",
"revenue_pp": "revenue_per_employee",
"profit_pp": "profit_per_employee",
"sales_fee_to_or": "selling_expense_ratio",
"manage_fee_to_revenue": "admin_expense_ratio",
"rad_expense_to_total_income": "rd_expense_ratio",
"operating_revenue_yoy": "revenue_growth",
"np_atsopc_yoy": "net_profit_growth",
"ibdebt_ratio_asset_base": "interest_bearing_debt_ratio",
"fixed_asset_turnover_ratio": "fixed_asset_turnover",
"total_capital_turnover": "total_asset_turnover"
}
df_final = combined.rename(columns=rename_map)
for col in df_final.columns:
if col not in ['date', 'end_date']:
df_final[col] = pd.to_numeric(df_final[col], errors='coerce')
return self._filter_data(df_final)

34
src/fetchers/jp_fetcher.py
View File

@ -151,11 +151,41 @@ class JpFetcher(DataFetcher):
acc_date = basic_info.get("accounting_date", "1231") acc_date = basic_info.get("accounting_date", "1231")
current_year = int(time.strftime("%Y")) current_year = int(time.strftime("%Y"))
# 1. First, determine the most recent valid year by trying backwards from current year
last_valid_year = None
# Try up to 3 years back to find the latest available report
for offset in range(3):
test_year = current_year - offset
test_date = f"{test_year}{acc_date}"
# Use the first indicator to test availability
first_indicator = indicator_configs[0]
params = {
"codes": code,
"indipara": [
{"indicator": first_indicator["indicator"], "indiparams": [test_date, first_indicator.get("type", "1"), "CNY"]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
# Check for non-null values
valid_val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
if pd.notna(valid_val) and valid_val != 0:
last_valid_year = test_year
break
if last_valid_year is None:
last_valid_year = current_year
all_dfs = [] all_dfs = []
# 获取最近 5 年的数据，精准定位会计年结日 # 2. Fetch 5 years starting from the last valid year
for i in range(5): for i in range(5):
target_year = current_year - i target_year = last_valid_year - i
target_date = f"{target_year}{acc_date}" target_date = f"{target_year}{acc_date}"
params = { params = {

9
src/fetchers/us_fetcher.py
View File

@ -48,6 +48,15 @@ class UsFetcher(DataFetcher):
df_annual = pd.DataFrame(data["annualReports"]) df_annual = pd.DataFrame(data["annualReports"])
if "fiscalDateEnding" in df_annual.columns: if "fiscalDateEnding" in df_annual.columns:
df_annual = df_annual.sort_values("fiscalDateEnding", ascending=False) df_annual = df_annual.sort_values("fiscalDateEnding", ascending=False)
# Dynamic year filtering: Find the latest report with valid data and take surrounding 5 years
# For Alpha Vantage, data is already sorted by date descending.
# We simply check for the first row with non-None values in critical columns if possible,
# but usually AV returns valid blocks. We'll just take the top 5.
# Unlike iFinD, AV returns a list of available reports, so we don't need to probe year by year.
# Keep top 5 latest entries
df_annual = df_annual.head(5)
else: else:
print(f"Error fetching {function} for {symbol}: {data}") print(f"Error fetching {function} for {symbol}: {data}")
return pd.DataFrame() return pd.DataFrame()

474
src/fetchers/vn_fetcher.py Normal file
View File

@ -0,0 +1,474 @@
import pandas as pd
import os
import time
from .base import DataFetcher
from .ifind_client import IFindClient
from storage.file_io import DataStorage
class VnFetcher(DataFetcher):
def __init__(self, api_key: str):
# api_key is the iFinD Refresh Token
super().__init__(api_key)
self.cli = IFindClient(refresh_token=api_key)
self.storage = DataStorage()
self._basic_info_cache = {}
def _get_ifind_code(self, symbol: str) -> str:
# Vietnam stocks usually have 3 letter codes.
# We assume the user provides the correct code (e.g. VNM, or VNM.VN).
# We can add simple logic: if it's 3 letters, maybe append nothing?
# iFinD codes often need suffix. But without documentation, safest is to pass through.
return symbol
def _fetch_basic_info(self, symbol: str) -> dict:
"""获取公司的基本信息：中文名称、会计年结日、上市日期"""
code = self._get_ifind_code(symbol)
if code in self._basic_info_cache:
return self._basic_info_cache[code]
params = {
"codes": code,
"indipara": [
{"indicator": "corp_cn_name", "indiparams": []},
{"indicator": "accounting_date", "indiparams": []},
{"indicator": "ipo_date", "indiparams": []}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
self._save_raw_data(df, symbol, "basic_info_raw")
info = {
"name": "",
"accounting_date": "1231", # Default 12-31
"ipo_date": ""
}
if not df.empty:
row = df.iloc[0]
info["name"] = str(row.get("corp_cn_name", ""))
acc_date = str(row.get("accounting_date", "1231")).replace("-", "").replace("/", "")
if acc_date:
info["accounting_date"] = acc_date
info["ipo_date"] = str(row.get("ipo_date", "")).replace("-", "").replace("/", "")
self._basic_info_cache[code] = info
return info
def _save_raw_data(self, data: any, symbol: str, name: str):
if data is None:
return
if isinstance(data, dict):
df = pd.DataFrame([data])
else:
df = data
self.storage.save_data(df, 'VN', symbol, f"raw_{name}")
def _parse_ifind_tables(self, res: dict) -> pd.DataFrame:
"""通用解析 iFinD 返回结果的 tables 结构为 DataFrame"""
if not res:
return pd.DataFrame()
if res.get("errorcode") != 0:
print(f"iFinD API Error: {res.get('errmsg')} (code: {res.get('errorcode')})")
return pd.DataFrame()
tables = res.get("tables", [])
if not tables:
# print("iFinD API Warning: No tables found in response.")
return pd.DataFrame()
table_info = tables[0]
table_data = table_info.get("table", {})
times = table_info.get("time", [])
if not table_data:
return pd.DataFrame()
processed_table_data = {}
for k, v in table_data.items():
if not isinstance(v, list):
processed_table_data[k] = [v]
else:
processed_table_data[k] = v
df = pd.DataFrame(processed_table_data)
if times and len(times) == len(df):
df['end_date'] = [str(t).replace('-', '').replace('/', '').split(' ')[0] for t in times]
elif times and len(df) == 1:
df['end_date'] = str(times[0]).replace('-', '').replace('/', '').split(' ')[0]
if 'end_date' not in df.columns:
for col in ['time', 'date', 'trade_date', 'REPORT_DATE']:
if col in df.columns:
df['end_date'] = df[col].astype(str).str.replace('-', '').str.replace('/', '').str.split(' ').str[0]
break
return df
def _filter_data(self, df: pd.DataFrame) -> pd.DataFrame:
if df.empty or 'end_date' not in df.columns:
return df
df = df.sort_values(by='end_date', ascending=False)
df = df.drop_duplicates(subset=['end_date'], keep='first')
if df.empty:
return df
latest_record = df.iloc[[0]]
try:
latest_date_str = str(latest_record['end_date'].values[0])
last_year_date_str = str(int(latest_date_str) - 10000)
comparable_record = df[df['end_date'].astype(str) == last_year_date_str]
except:
comparable_record = pd.DataFrame()
# VN usually ends in 1231
is_annual = df['end_date'].astype(str).str.endswith('1231')
annual_records = df[is_annual]
combined = pd.concat([latest_record, comparable_record, annual_records])
combined = combined.drop_duplicates(subset=['end_date'])
combined = combined.sort_values(by='end_date', ascending=False)
return combined
def _fetch_financial_data_annual(self, symbol: str, indicator_configs: list) -> pd.DataFrame:
code = self._get_ifind_code(symbol)
basic_info = self._fetch_basic_info(symbol)
acc_date = basic_info.get("accounting_date", "1231")
current_year = int(time.strftime("%Y"))
last_valid_year = None
for offset in range(3):
test_year = current_year - offset
test_date = f"{test_year}{acc_date}"
first_indicator = indicator_configs[0]
params = {
"codes": code,
"indipara": [
{"indicator": first_indicator["indicator"], "indiparams": [test_date, first_indicator.get("type", "1"), "CNY"]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
valid_val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
if pd.notna(valid_val) and valid_val != 0:
last_valid_year = test_year
break
if last_valid_year is None:
last_valid_year = current_year
all_dfs = []
for i in range(5):
target_year = last_valid_year - i
target_date = f"{target_year}{acc_date}"
params = {
"codes": code,
"indipara": [
{"indicator": item["indicator"], "indiparams": [target_date, item.get("type", "1"), "CNY"]}
for item in indicator_configs
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty:
df['end_date'] = target_date
all_dfs.append(df)
if not all_dfs:
return pd.DataFrame()
all_dfs = [d for d in all_dfs if not d.empty and not d.isna().all().all()]
if not all_dfs:
return pd.DataFrame()
return pd.concat(all_dfs, ignore_index=True)
def get_income_statement(self, symbol: str) -> pd.DataFrame:
indicators = [
{"indicator": "revenue_oas"},
{"indicator": "gross_profit_oas"},
{"indicator": "sga_expenses_oas"},
{"indicator": "selling_marketing_expenses_oas"},
{"indicator": "ga_expenses_oas"},
{"indicator": "rd_expenses_oas"},
{"indicator": "income_tax_expense_oas"},
{"indicator": "net_income_attri_to_common_sh_oas"},
{"indicator": "operating_income_oas"}
]
df = self._fetch_financial_data_annual(symbol, indicators)
if df.empty: return df
self._save_raw_data(df, symbol, "income_statement_raw")
rename_map = {
'revenue_oas': 'revenue',
'gross_profit_oas': 'gross_profit',
'sga_expenses_oas': 'sga_exp',
'selling_marketing_expenses_oas': 'selling_marketing_exp',
'ga_expenses_oas': 'ga_exp',
'rd_expenses_oas': 'rd_exp',
'income_tax_expense_oas': 'income_tax',
'net_income_attri_to_common_sh_oas': 'net_income',
'operating_income_oas': 'operating_profit'
}
df_filtered = df.rename(columns=rename_map)
for col in df_filtered.columns:
if col not in ['date', 'end_date']:
df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
return self._filter_data(df_filtered)
def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
indicators = [
{"indicator": "cash_equi_short_term_inve_oas"},
{"indicator": "accou_and_notes_recei_oas"},
{"indicator": "inventories_oas"},
{"indicator": "ppe_net_oas"},
{"indicator": "long_term_inv_and_receiv_oas"},
{"indicator": "goodwill_and_intasset_oas"},
{"indicator": "short_term_debt_oas"},
{"indicator": "short_term_borrowings_oas"},
{"indicator": "account_and_note_payable_oas"},
{"indicator": "contra_liabilities_current_oas"},
{"indicator": "advance_from_cust_current_oas"},
{"indicator": "defer_revenue_current_oas"},
{"indicator": "long_term_debt_oas"},
{"indicator": "long_term_borrowings_oas"},
{"indicator": "total_assets_oas"},
{"indicator": "equity_attri_to_companyowner_oas"},
{"indicator": "prepaid_expenses_current_oas"}
]
df = self._fetch_financial_data_annual(symbol, indicators)
if df.empty: return df
self._save_raw_data(df, symbol, "balance_sheet_raw")
rename_map = {
'cash_equi_short_term_inve_oas': 'cash',
'accou_and_notes_recei_oas': 'receivables',
'inventories_oas': 'inventory',
'ppe_net_oas': 'fixed_assets',
'long_term_inv_and_receiv_oas': 'long_term_investments',
'goodwill_and_intasset_oas': 'goodwill',
'short_term_debt_oas': 'short_term_debt',
'short_term_borrowings_oas': 'short_term_borrowings',
'account_and_note_payable_oas': 'accounts_payable',
'contra_liabilities_current_oas': 'contract_liabilities',
'advance_from_cust_current_oas': 'advances_from_customers',
'defer_revenue_current_oas': 'deferred_revenue',
'long_term_debt_oas': 'long_term_debt',
'long_term_borrowings_oas': 'long_term_borrowings',
'total_assets_oas': 'total_assets',
'equity_attri_to_companyowner_oas': 'total_equity',
'prepaid_expenses_current_oas': 'prepayment'
}
df_filtered = df.rename(columns=rename_map)
if 'total_liabilities' not in df_filtered.columns or df_filtered['total_liabilities'].isnull().all():
if 'total_assets' in df_filtered.columns and 'total_equity' in df_filtered.columns:
df_filtered['total_liabilities'] = df_filtered['total_assets'] - df_filtered['total_equity']
for col in df_filtered.columns:
if col not in ['date', 'end_date']:
df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
return self._filter_data(df_filtered)
def get_cash_flow(self, symbol: str) -> pd.DataFrame:
indicators = [
{"indicator": "net_cash_flows_from_oa_oas"},
{"indicator": "purchase_of_ppe_and_ia_oas"},
{"indicator": "dividends_paid_oas"}
]
df = self._fetch_financial_data_annual(symbol, indicators)
if df.empty: return df
self._save_raw_data(df, symbol, "cash_flow_raw")
rename_map = {
'net_cash_flows_from_oa_oas': 'ocf',
'purchase_of_ppe_and_ia_oas': 'capex',
'dividends_paid_oas': 'dividends'
}
df_filtered = df.rename(columns=rename_map)
for col in df_filtered.columns:
if col not in ['date', 'end_date']:
df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
if 'capex' in df_filtered.columns:
df_filtered['capex'] = df_filtered['capex'].abs()
return self._filter_data(df_filtered)
def get_market_metrics(self, symbol: str) -> dict:
basic_info = self._fetch_basic_info(symbol)
metrics = {
"name": basic_info.get("name", ""),
"list_date": basic_info.get("ipo_date", "")
}
return metrics
def get_historical_metrics(self, symbol: str, dates: list) -> pd.DataFrame:
code = self._get_ifind_code(symbol)
if not dates: return pd.DataFrame()
results = []
for d in dates:
d_str = str(d).replace('-', '').replace('/', '')
fmt_d = f"{d_str[:4]}-{d_str[4:6]}-{d_str[6:]}" if len(d_str) == 8 else d_str
params = {
"codes": code,
"startdate": fmt_d,
"enddate": fmt_d,
"functionpara": {"Interval": "D", "Days": "Alldays", "Fill": "Previous"},
"indipara": [
{"indicator": "pre_close", "indiparams": ["", "0", "CNY"]},
{"indicator": "market_value", "indiparams": ["", "CNY"]}
]
}
res = self.cli.post("date_sequence", params)
df_seq = self._parse_ifind_tables(res)
metrics = {'date_str': d_str, 'PE': 0.0, 'PB': 0.0, 'MarketCap': 0.0, 'Price': 0.0}
if not df_seq.empty:
match = df_seq[df_seq['end_date'] <= d_str].tail(1) if 'end_date' in df_seq.columns else df_seq.tail(1)
if not match.empty:
if 'pre_close' in match.columns:
metrics['Price'] = float(match['pre_close'].iloc[0] or 0.0)
if 'market_value' in match.columns:
metrics['MarketCap'] = float(match['market_value'].iloc[0] or 0.0)
results.append(metrics)
df_hist = pd.DataFrame(results)
self._save_raw_data(df_hist, symbol, "historical_metrics_raw")
return df_hist
def get_dividends(self, symbol: str) -> pd.DataFrame:
code = self._get_ifind_code(symbol)
basic_info = self._fetch_basic_info(symbol)
acc_date = basic_info.get("accounting_date", "1231")
current_year = int(time.strftime("%Y"))
results = []
for i in range(5):
year_str = str(current_year - i)
params = {
"codes": code,
"indipara": [
{"indicator": "annual_cum_dividend", "indiparams": [year_str, "CNY"]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty and 'annual_cum_dividend' in df.columns:
val = df['annual_cum_dividend'].iloc[0]
if pd.notna(val) and val != 0:
results.append({
'date_str': f"{year_str}{acc_date}",
'dividends': float(val)
})
if not results:
return pd.DataFrame()
df_div = pd.DataFrame(results)
self._save_raw_data(df_div, symbol, "dividends_raw")
return df_div
def get_repurchases(self, symbol: str) -> pd.DataFrame:
code = self._get_ifind_code(symbol)
basic_info = self._fetch_basic_info(symbol)
acc_date = basic_info.get("accounting_date", "1231")
mm = acc_date[:2]
dd = acc_date[2:]
fmt_mm_dd = f"{mm}-{dd}"
current_year = int(time.strftime("%Y"))
results = []
for i in range(5):
target_year = current_year - i
start_date = f"{target_year - 1}-{fmt_mm_dd}"
end_date = f"{target_year}-{fmt_mm_dd}"
params = {
"codes": code,
"indipara": [
{"indicator": "repur_num_new", "indiparams": [start_date, end_date, "1"]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty and 'repur_num_new' in df.columns:
val = df['repur_num_new'].iloc[0]
if pd.notna(val) and val != 0:
results.append({
'date_str': f"{target_year}{acc_date}",
'repurchases': float(val)
})
if not results:
return pd.DataFrame()
df_repur = pd.DataFrame(results)
self._save_raw_data(df_repur, symbol, "repurchases_raw")
return df_repur
def get_employee_count(self, symbol: str) -> pd.DataFrame:
code = self._get_ifind_code(symbol)
basic_info = self._fetch_basic_info(symbol)
acc_date = basic_info.get("accounting_date", "1231")
mm = acc_date[:2]
dd = acc_date[2:]
current_year = int(time.strftime("%Y"))
results = []
for i in range(5):
target_year = current_year - i
target_date = f"{target_year}-{mm}-{dd}"
params = {
"codes": code,
"indipara": [
{"indicator": "staff_num", "indiparams": [target_date]}
]
}
res = self.cli.post("basic_data_service", params)
df = self._parse_ifind_tables(res)
if not df.empty and 'staff_num' in df.columns:
val = df['staff_num'].iloc[0]
if pd.notna(val) and val != 0:
results.append({
'date_str': f"{target_year}{acc_date}",
'employee_count': float(val)
})
if not results:
return pd.DataFrame()
df_emp = pd.DataFrame(results)
self._save_raw_data(df_emp, symbol, "employee_count_raw")
return df_emp

30
src/reporting/jp_report_generator.py
View File

@ -15,18 +15,18 @@ class JP_ReportGenerator(BaseReporter):
('ROIC', 'ROCE/ROIC', 'percent'), ('ROIC', 'ROCE/ROIC', 'percent'),
('GrossMargin', '毛利率', 'percent'), ('GrossMargin', '毛利率', 'percent'),
('NetMargin', '净利润率', 'percent'), ('NetMargin', '净利润率', 'percent'),
('revenue', '收入(亿)', 'currency_yi'), ('revenue', '收入(亿 CNY)', 'currency_yi'),
('RevenueGrowth', '收入增速', 'percent_color'), ('RevenueGrowth', '收入增速', 'percent_color'),
('net_income', '净利润(亿)', 'currency_yi'), ('net_income', '净利润(亿 CNY)', 'currency_yi'),
('NetIncomeGrowth', '净利润增速', 'percent_color'), ('NetIncomeGrowth', '净利润增速', 'percent_color'),
('ocf', '经营净现金流(亿)', 'currency_yi_color'), ('ocf', '经营净现金流(亿 CNY)', 'currency_yi_color'),
('Capex', '资本开支(亿)', 'currency_yi'), ('Capex', '资本开支(亿 CNY)', 'currency_yi'),
('FCF', '自由现金流(亿)', 'currency_yi_compare'), ('FCF', '自由现金流(亿 CNY)', 'currency_yi_compare'),
('dividends', '分红(亿)', 'currency_yi'), ('dividends', '分红(亿 CNY)', 'currency_yi'),
('repurchases', '回购(亿)', 'currency_yi'), ('repurchases', '回购(亿 CNY)', 'currency_yi'),
('total_assets', '总资产(亿)', 'currency_yi'), ('total_assets', '总资产(亿 CNY)', 'currency_yi'),
('total_equity', '净资产(亿)', 'currency_yi'), ('total_equity', '净资产(亿 CNY)', 'currency_yi'),
('goodwill', '商誉(亿)', 'currency_yi') ('goodwill', '商誉(亿 CNY)', 'currency_yi')
], ],
"费用指标": [ "费用指标": [
('SellingRatio', '销售费用率', 'percent'), ('SellingRatio', '销售费用率', 'percent'),
@ -62,13 +62,13 @@ class JP_ReportGenerator(BaseReporter):
], ],
"人均效率": [ "人均效率": [
('Employees', '员工人数', 'int'), ('Employees', '员工人数', 'int'),
('RevenuePerEmp', '人均创收(万)', 'currency_wan'), ('RevenuePerEmp', '人均创收(万 CNY)', 'currency_wan'),
('ProfitPerEmp', '人均创利(万)', 'currency_wan'), ('ProfitPerEmp', '人均创利(万 CNY)', 'currency_wan'),
('AvgWage', '人均薪酬(万)', 'currency_wan'), ('AvgWage', '人均薪酬(万 CNY)', 'currency_wan'),
], ],
"市场表现": [ "市场表现": [
('Price', '股价', 'float'), ('Price', '股价 (CNY)', 'float'),
('MarketCap', '市值(亿)', 'currency_yi_market'), ('MarketCap', '市值(亿 CNY)', 'currency_yi_market'),
('PE', 'PE', 'float'), ('PE', 'PE', 'float'),
('PB', 'PB', 'float'), ('PB', 'PB', 'float'),
('Shareholders', '股东户数', 'int'), ('Shareholders', '股东户数', 'int'),

334
data/HK/00700.HK/report.html → src/reporting/vn_report_generator.py
View File

@ -1,9 +1,233 @@
from .base_generator import BaseReporter
import pandas as pd
import datetime
import os
import markdown
class VN_ReportGenerator(BaseReporter):
def __init__(self):
super().__init__()
self.indicators = {
"主要指标": [
('ROE', 'ROE', 'percent'),
('ROA', 'ROA', 'percent'),
('ROIC', 'ROCE/ROIC', 'percent'),
('GrossMargin', '毛利率', 'percent'),
('NetMargin', '净利润率', 'percent'),
('revenue', '收入(亿 CNY)', 'currency_yi'),
('RevenueGrowth', '收入增速', 'percent_color'),
('net_income', '净利润(亿 CNY)', 'currency_yi'),
('NetIncomeGrowth', '净利润增速', 'percent_color'),
('ocf', '经营净现金流(亿 CNY)', 'currency_yi_color'),
('Capex', '资本开支(亿 CNY)', 'currency_yi'),
('FCF', '自由现金流(亿 CNY)', 'currency_yi_compare'),
('dividends', '分红(亿 CNY)', 'currency_yi'),
('repurchases', '回购(亿 CNY)', 'currency_yi'),
('total_assets', '总资产(亿 CNY)', 'currency_yi'),
('total_equity', '净资产(亿 CNY)', 'currency_yi'),
('goodwill', '商誉(亿 CNY)', 'currency_yi')
],
"费用指标": [
('SellingRatio', '销售费用率', 'percent'),
('AdminRatio', '管理费用率', 'percent'),
('SgaRatio', 'SG&A比例', 'percent'),
('RDRatio', '研发费用率', 'percent'),
('OtherExpenseRatio', '其他费用率', 'percent'),
('DepreciationRatio', '折旧费用占比', 'percent'),
('TaxRate', '所得税率', 'percent'),
],
"资产占比": [
('CashRatio', '现金占比', 'percent_alert_30'),
('InventoryRatio', '库存占比', 'percent'),
('ReceivablesRatio', '应收款占比', 'percent'),
('PrepaymentRatio', '预付款占比', 'percent'),
('FixedAssetsRatio', '固定资产占比', 'percent'),
('LongTermInvestmentRatio', '长期投资占比', 'percent'),
('GoodwillRatio', '商誉占比', 'percent'),
('OtherAssetsRatio', '其他资产占比', 'percent'),
('PayablesRatio', '应付款占比', 'percent'),
('AdvanceReceiptsRatio', '预收款占比', 'percent'),
('ShortTermDebtRatio', '短期借款占比', 'percent'),
('LongTermDebtRatio', '长期借款占比', 'percent'),
('OperatingAssetsRatio', '运营资产占比', 'percent'),
('InterestBearingDebtRatio', '有息负债率', 'percent'),
],
"周转能力": [
('InventoryDays', '存货周转天数', 'int'),
('ReceivablesDays', '应收款周转天数', 'int_alert_90'),
('PayablesDays', '应付款周转天数', 'int'),
('FixedAssetsTurnover', '固定资产周转率', 'float'),
('TotalAssetTurnover', '总资产周转率', 'float'),
],
"人均效率": [
('Employees', '员工人数', 'int'),
('RevenuePerEmp', '人均创收(万 CNY)', 'currency_wan'),
('ProfitPerEmp', '人均创利(万 CNY)', 'currency_wan'),
('AvgWage', '人均薪酬(万 CNY)', 'currency_wan'),
],
"市场表现": [
('Price', '股价 (CNY)', 'float'),
('MarketCap', '市值(亿 CNY)', 'currency_yi_market'),
('PE', 'PE', 'float'),
('PB', 'PB', 'float'),
('Shareholders', '股东户数', 'int'),
]
}
def _preprocess_data(self, df, market):
df = super()._preprocess_data(df, market)
if not df.empty:
dates = pd.to_datetime(df['date_str'], format='%Y%m%d')
latest_year = dates.dt.year.max()
is_dec = dates.dt.month == 12
is_latest = df.index == df.index[0]
df = df[is_dec | is_latest]
return df
def _format_period_label(self, date_value):
if pd.isna(date_value):
return "-"
date_str = str(date_value)
if len(date_str) != 8:
return date_str
year = date_str[:4]
try:
return f"{year}A"
except ValueError:
return f"{year}A"
def _get_headers(self, df):
return [self._format_period_label(date_value) for date_value in df['date_str']]
def _generate_md_company_info(self, symbol, metrics, market):
today_str = datetime.date.today().strftime("%Y-%m-%d")
name = metrics.get('name', '')
raw_list_date = metrics.get('list_date', '')
if isinstance(raw_list_date, str) and len(raw_list_date) == 8:
list_date = f"{raw_list_date[:4]}-{raw_list_date[4:6]}-{raw_list_date[6:]}"
else:
list_date = raw_list_date
pe = metrics.get('pe', 0) or 0
pb = metrics.get('pb', 0) or 0
div = metrics.get('dividend_yield', 0) or 0
md = []
md.append(f"# {name} ({symbol}) - Financial Report")
md.append(f"*Report generated on: {today_str}*\n")
md.append("| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |")
md.append("|:---|:---|:---|:---|:---|:---|")
md.append(f"| {symbol} | {name} | {list_date} | {pe:.2f} | {pb:.2f} | {div:.2f}% |")
return "\n".join(md)
def generate_report(self, df_analysis, symbol, market, metrics, output_dir):
md_content = self._generate_markdown_content(df_analysis, market, symbol, metrics)
os.makedirs(output_dir, exist_ok=True)
md_path = os.path.join(output_dir, "report.md")
with open(md_path, "w", encoding='utf-8') as f:
f.write(md_content)
df_for_html = df_analysis.copy() if isinstance(df_analysis, pd.DataFrame) else pd.DataFrame()
if not df_for_html.empty:
df_for_html = self._preprocess_data(df_for_html, market)
headers = self._get_headers(df_for_html)
else:
headers = []
html_content = self._build_html_content(symbol, metrics, headers, df_for_html)
final_html = self.to_html(symbol, html_content)
html_path = os.path.join(output_dir, "report.html")
with open(html_path, "w", encoding='utf-8') as f:
f.write(final_html)
def _build_html_content(self, symbol, metrics, headers, df):
today_str = datetime.date.today().strftime("%Y-%m-%d")
name = metrics.get('name') or symbol
raw_list_date = metrics.get('list_date', '')
if isinstance(raw_list_date, str) and len(raw_list_date) == 8:
list_date = f"{raw_list_date[:4]}-{raw_list_date[4:6]}-{raw_list_date[6:]}"
else:
list_date = raw_list_date or "-"
pe = metrics.get('pe', 0) or 0
pb = metrics.get('pb', 0) or 0
div = metrics.get('dividend_yield', 0) or 0
company_table = f"""
<table class="company-table">
<thead>
<tr>
<th>代码</th>
<th>简称</th>
<th>上市日期</th>
<th>PE</th>
<th>PB</th>
<th>股息率(%)</th>
</tr>
</thead>
<tbody>
<tr>
<td>{symbol}</td>
<td>{name}</td>
<td>{list_date}</td>
<td>{pe:.2f}</td>
<td>{pb:.2f}</td>
<td>{div:.2f}%</td>
</tr>
</tbody>
</table>
"""
if df is None or df.empty or not headers:
metrics_table = "<p class=\"no-data\">暂无可用财务指标</p>"
else:
header_cells = "".join([f"<th>{header}</th>" for header in headers])
data_column_count = max(len(headers), 1)
rows_html = []
for group_name, items in self.indicators.items():
rows_html.append(
f"<tr class=\"section-row\">"
f"<td class=\"section-label\">{group_name}</td>"
f"<td class=\"section-spacer\" colspan=\"{data_column_count}\"></td>"
"</tr>"
)
for key, label, fmt_type in items:
value_cells = [f"<td class=\"metric-name\">{label}</td>"]
for _, row_series in df.iterrows():
value_cells.append(f"<td>{self._format_value(row_series.get(key), fmt_type)}</td>")
row_class = "other-assets-row" if key == 'OtherAssetsRatio' else ""
if row_class:
rows_html.append(f"<tr class=\"{row_class}\">{''.join(value_cells)}</tr>")
else:
rows_html.append(f"<tr>{''.join(value_cells)}</tr>")
rows_markup = "\n".join(rows_html)
metrics_table = f"""
<table class="metrics-table" data-table="metrics" data-scrollable="true">
<thead>
<tr>
<th>指标</th>
{header_cells}
</tr>
</thead>
<tbody>
{rows_markup}
</tbody>
</table>
"""
html_sections = [
f"<h1>{name} ({symbol}) - Financial Report</h1>",
f"<p><em>Report generated on: {today_str}</em></p>",
company_table,
'<div class="table-gap"></div>',
metrics_table
]
return "\n".join(html_sections)
def to_html(self, symbol, html_content):
styled_html = '''
<html> <html>
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1" />
<title>00700.HK Financial Report</title> <title>{symbol} Financial Report</title>
<style> <style>
:root { :root {
--bg: #f5f6fa; --bg: #f5f6fa;
@ -204,107 +428,7 @@
</head> </head>
<body> <body>
<div class="report-container"> <div class="report-container">
<h1>腾讯控股有限公司 (00700.HK) - Financial Report</h1> {html_content}
<p><em>Report generated on: 2026-01-03</em></p>
<table class="company-table">
<thead>
<tr>
<th>代码</th>
<th>简称</th>
<th>上市日期</th>
<th>年结日</th>
<th>市值(亿)</th>
<th>PE</th>
<th>PB</th>
<th>股息率(%)</th>
</tr>
</thead>
<tbody>
<tr>
<td>00700.HK</td>
<td>腾讯控股有限公司</td>
<td>2004-06-16</td>
<td>1231</td>
<td>42043.21</td>
<td>20.32</td>
<td>3.77</td>
<td>0.89%</td>
</tr>
</tbody>
</table>
<div class="table-gap"></div>
<table class="metrics-table" data-table="metrics" data-scrollable="true">
<thead>
<tr>
<th>指标</th>
<th>2025H1</th><th>2024A</th><th>2023A</th><th>2022A</th>
</tr>
</thead>
<tbody>
<tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">ROE</td><td>9.28%</td><td>19.93%</td><td>14.25%</td><td>26.09%</td></tr>
<tr><td class="metric-name">ROA</td><td>5.14%</td><td>10.90%</td><td>7.30%</td><td>11.93%</td></tr>
<tr><td class="metric-name">ROCE/ROIC</td><td>6.21%</td><td>12.62%</td><td>10.79%</td><td>16.36%</td></tr>
<tr><td class="metric-name">毛利率</td><td>56.38%</td><td>52.90%</td><td>48.13%</td><td>43.05%</td></tr>
<tr><td class="metric-name">净利润率</td><td>28.38%</td><td>29.39%</td><td>18.92%</td><td>33.95%</td></tr>
<tr><td class="metric-name">收入(亿)</td><td>3,645.26</td><td>6,602.57</td><td>6,090.15</td><td>5,545.52</td></tr>
<tr><td class="metric-name">收入增速</td><td>-</td><td>8.41%</td><td>9.82%</td><td>-</td></tr>
<tr><td class="metric-name">净利润(亿)</td><td>1,034.49</td><td>1,940.73</td><td>1,152.16</td><td>1,882.43</td></tr>
<tr><td class="metric-name">净利润增速</td><td>-</td><td>68.44%</td><td>-38.79%</td><td>-</td></tr>
<tr><td class="metric-name">经营净现金流(亿)</td><td>1,512.65</td><td>2,585.21</td><td>2,219.62</td><td>1,460.91</td></tr>
<tr><td class="metric-name">资本开支(亿)</td><td>574.57</td><td>960.48</td><td>474.07</td><td>508.50</td></tr>
<tr><td class="metric-name">自由现金流(亿)</td><td>938.08</td><td>1,624.73</td><td>1,745.55</td><td>952.41</td></tr>
<tr><td class="metric-name">分红(亿)</td><td>375.35</td><td>288.59</td><td>209.83</td><td>129.52</td></tr>
<tr><td class="metric-name">回购(亿)</td><td>-</td><td>1,120.03</td><td>494.33</td><td>337.94</td></tr>
<tr><td class="metric-name">总资产(亿)</td><td>20,133.10</td><td>17,809.95</td><td>15,772.46</td><td>15,781.31</td></tr>
<tr><td class="metric-name">净资产(亿)</td><td>11,146.39</td><td>9,735.48</td><td>8,085.91</td><td>7,213.91</td></tr>
<tr><td class="metric-name">商誉(亿)</td><td>2,158.32</td><td>1,961.27</td><td>1,777.27</td><td>1,618.02</td></tr>
<tr class="section-row"><td class="section-label">费用指标</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">销售费用率</td><td>4.74%</td><td>5.51%</td><td>5.62%</td><td>5.27%</td></tr>
<tr><td class="metric-name">管理费用率</td><td>17.99%</td><td>17.08%</td><td>17.00%</td><td>19.24%</td></tr>
<tr><td class="metric-name">SG&A比例</td><td>22.73%</td><td>22.59%</td><td>22.62%</td><td>24.51%</td></tr>
<tr><td class="metric-name">研发费用率</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">其他费用率</td><td>5.27%</td><td>0.91%</td><td>6.59%</td><td>-15.40%</td></tr>
<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr><td class="metric-name">所得税率</td><td>19.51%</td><td>18.83%</td><td>27.30%</td><td>10.26%</td></tr>
<tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">现金占比</td><td>18.90%</td><td>19.27%</td><td>24.04%</td><td>18.42%</td></tr>
<tr><td class="metric-name">库存占比</td><td>0.02%</td><td>0.02%</td><td>0.03%</td><td>0.15%</td></tr>
<tr><td class="metric-name">应收款占比</td><td>2.55%</td><td>2.71%</td><td>2.95%</td><td>2.88%</td></tr>
<tr><td class="metric-name">预付款占比</td><td>1.34%</td><td>1.76%</td><td>1.76%</td><td>1.55%</td></tr>
<tr><td class="metric-name">固定资产占比</td><td>8.58%</td><td>7.48%</td><td>6.62%</td><td>6.58%</td></tr>
<tr><td class="metric-name">长期投资占比</td><td>35.10%</td><td>33.09%</td><td>29.20%</td><td>27.34%</td></tr>
<tr><td class="metric-name">商誉占比</td><td>10.72%</td><td>11.01%</td><td>11.27%</td><td>10.25%</td></tr>
<tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>22.80%</td><td>24.66%</td><td>24.12%</td><td>32.83%</td></tr>
<tr><td class="metric-name">应付款占比</td><td>7.12%</td><td>7.15%</td><td>7.30%</td><td>6.52%</td></tr>
<tr><td class="metric-name">预收款占比</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td></tr>
<tr><td class="metric-name">短期借款占比</td><td>6.09%</td><td>6.01%</td><td>4.64%</td><td>1.52%</td></tr>
<tr><td class="metric-name">长期借款占比</td><td>32.68%</td><td>31.90%</td><td>38.19%</td><td>40.75%</td></tr>
<tr><td class="metric-name">运营资产占比</td><td>-3.22%</td><td>-2.66%</td><td>-2.55%</td><td>-1.94%</td></tr>
<tr><td class="metric-name">有息负债率</td><td>38.77%</td><td>37.91%</td><td>42.83%</td><td>42.27%</td></tr>
<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">存货周转天数</td><td>0</td><td>0</td><td>0</td><td>2</td></tr>
<tr><td class="metric-name">应收款周转天数</td><td>25</td><td>26</td><td>27</td><td>29</td></tr>
<tr><td class="metric-name">应付款周转天数</td><td>164</td><td>149</td><td>132</td><td>118</td></tr>
<tr><td class="metric-name">固定资产周转率</td><td>4.22</td><td>4.95</td><td>5.83</td><td>5.34</td></tr>
<tr><td class="metric-name">总资产周转率</td><td>0.36</td><td>0.37</td><td>0.39</td><td>0.35</td></tr>
<tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">员工人数</td><td>-</td><td>110,558</td><td>105,417</td><td>108,436</td></tr>
<tr><td class="metric-name">人均创收(万)</td><td>-</td><td>597.20</td><td>577.72</td><td>511.41</td></tr>
<tr><td class="metric-name">人均创利(万)</td><td>-</td><td>175.54</td><td>109.30</td><td>173.60</td></tr>
<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
<tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="4"></td></tr>
<tr><td class="metric-name">股价</td><td>467.83</td><td>388.01</td><td>266.07</td><td>298.35</td></tr>
<tr><td class="metric-name">市值(亿)</td><td>42,043</td><td>35,623</td><td>25,231</td><td>28,549</td></tr>
<tr><td class="metric-name">PE</td><td>20.32</td><td>18.36</td><td>21.90</td><td>15.17</td></tr>
<tr><td class="metric-name">PB</td><td>3.77</td><td>3.66</td><td>3.12</td><td>3.96</td></tr>
<tr><td class="metric-name">股东户数</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
</tbody>
</table>
</div> </div>
<script> <script>
document.addEventListener('DOMContentLoaded', function() { document.addEventListener('DOMContentLoaded', function() {
@ -384,7 +508,7 @@
'应收款周转天数': (cell) => { '应收款周转天数': (cell) => {
const value = parseValue(cell.textContent); const value = parseValue(cell.textContent);
if (value !== null && value > 90) { if (value !== null && value > 90) {
cell.classList.add('bg-red', 'font-red'); cell.classList.add('bg-red', 'font-red');
} }
}, },
'现金占比': highlightIfOverThirtyPercent, '现金占比': highlightIfOverThirtyPercent,
@ -446,4 +570,6 @@
</script> </script>
</body> </body>
</html> </html>
'''
final_html = styled_html.replace('{symbol}', symbol).replace('{html_content}', html_content)
return final_html

76
src/strategies/vn_strategy.py Normal file
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@ -0,0 +1,76 @@
from .base_strategy import BaseStrategy
from fetchers.factory import FetcherFactory
from analysis.vn_analyzer import VN_Analyzer
from reporting.vn_report_generator import VN_ReportGenerator
from storage.file_io import DataStorage
import os
import pandas as pd
class VN_Strategy(BaseStrategy):
def __init__(self, stock_code, ifind_refresh_token):
super().__init__(stock_code)
self.refresh_token = ifind_refresh_token
self.fetcher = FetcherFactory.get_fetcher('VN', ifind_refresh_token=self.refresh_token)
self.analyzer = VN_Analyzer()
self.reporter = VN_ReportGenerator()
self.storage = DataStorage()
self.raw_data = {}
self.analysis_result = None
def fetch_data(self):
print(f"Fetching data for VN market, stock: {self.stock_code}")
# Fetch Financial Statements
self.raw_data['income'] = self.fetcher.get_income_statement(self.stock_code)
self.raw_data['balance'] = self.fetcher.get_balance_sheet(self.stock_code)
self.raw_data['cashflow'] = self.fetcher.get_cash_flow(self.stock_code)
# Rename 'end_date' to 'date' for analyzer compatibility
for key in ['income', 'balance', 'cashflow']:
if not self.raw_data[key].empty and 'end_date' in self.raw_data[key].columns:
self.raw_data[key] = self.raw_data[key].rename(columns={'end_date': 'date'})
# Fetch Market Metrics (Real-time and static)
self.raw_data['metrics'] = self.fetcher.get_market_metrics(self.stock_code)
# Fetch Historical Metrics (Price and Market Cap)
dates = []
if not self.raw_data['income'].empty and 'date' in self.raw_data['income'].columns:
dates = self.raw_data['income']['date'].tolist()
self.raw_data['historical_metrics'] = self.fetcher.get_historical_metrics(self.stock_code, dates)
# Fetch Dividends
self.raw_data['dividends'] = self.fetcher.get_dividends(self.stock_code)
# Fetch Repurchases
self.raw_data['repurchases'] = self.fetcher.get_repurchases(self.stock_code)
# Fetch Employee Count
self.raw_data['employee_count'] = self.fetcher.get_employee_count(self.stock_code)
def analyze_data(self):
print(f"Analyzing data for VN market, stock: {self.stock_code}")
self.analysis_result = self.analyzer.process_data(
self.raw_data['income'],
self.raw_data['balance'],
self.raw_data['cashflow'],
self.raw_data['metrics'],
self.raw_data.get('historical_metrics'),
self.raw_data.get('dividends'),
self.raw_data.get('repurchases'),
self.raw_data.get('employee_count')
)
def generate_report(self):
print(f"Generating report for VN market, stock: {self.stock_code}")
if self.analysis_result is not None and not self.analysis_result.empty:
output_dir = os.path.join("data", 'VN', self.stock_code)
self.reporter.generate_report(
df_analysis=self.analysis_result,
symbol=self.stock_code,
market='VN',
metrics=self.raw_data['metrics'],
output_dir=output_dir
)
else:
print("No analysis result to generate report.")

105
test_hk_fetcher_logic.py Normal file
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@ -0,0 +1,105 @@
import os
import sys
import pandas as pd
from unittest.mock import MagicMock
from src.fetchers.hk_fetcher import HkFetcher
# Mock the IFindClient to avoid actual network requests and credentials
class MockIFindClient:
def __init__(self, refresh_token):
pass
def post(self, endpoint, params):
# Simulate data availability logic
# If querying for 20261231, return empty
# If querying for 20251231, return data
# Extract date from params
date = "unknown"
if "indipara" in params:
for item in params["indipara"]:
if "indiparams" in item and len(item["indiparams"]) > 0:
param0 = item["indiparams"][0]
if len(param0) == 8: # YYYYMMDD
date = param0
break
# Test Case 1: Detect year logic
if "20261231" in date:
return {"tables": [{"time": [], "table": {}}]} # Empty
if "20251231" in date:
return {
"tables": [{
"time": ["2025-12-31"],
"table": {
"revenue_oas": [1000],
"roe": [15.5],
"total_oi": [5000]
}
}]
}
if "20241231" in date:
return {
"tables": [{
"time": ["2024-12-31"],
"table": {
"revenue_oas": [900],
"roe": [14.0],
"total_oi": [4000]
}
}]
}
return {"tables": []}
def test_hk_fetcher_year_detection():
print("Testing HK Fetcher Year Detection Logic...")
# Mock time.strftime to return 2026
import time
original_strftime = time.strftime
time.strftime = MagicMock(return_value="2026")
try:
fetcher = HkFetcher("fake_token")
# Replace the client with our mock
fetcher.cli = MockIFindClient("fake_token")
# 1. Test get_income_statement logic
print("\nTesting _fetch_financial_data_annual (via income statement)...")
# We expect it to try 2026 (fail), then 2025 (succeed), then fetch 2025-2021
df_income = fetcher.get_income_statement("0700.HK")
if not df_income.empty:
dates = df_income['end_date'].tolist()
print(f"Fetched Income Statement Dates: {dates}")
if "20251231" in dates and "20261231" not in dates:
print("PASS: Correctly anchored to 2025 instead of 2026.")
else:
print(f"FAIL: Logic incorrect. Dates found: {dates}")
else:
print("FAIL: No data returned.")
# 2. Test get_financial_ratios logic
print("\nTesting get_financial_ratios...")
df_ratios = fetcher.get_financial_ratios("0700.HK")
if not df_ratios.empty:
dates = df_ratios['end_date'].tolist()
print(f"Fetched Ratios Dates: {dates}")
if "20251231" in dates and "20261231" not in dates:
print("PASS: Correctly anchored to 2025 instead of 2026.")
else:
print(f"FAIL: Logic incorrect. Dates found: {dates}")
else:
print("FAIL: No data returned.")
finally:
# Restore time
time.strftime = original_strftime
if __name__ == "__main__":
test_hk_fetcher_year_detection()