388 lines
13 KiB
Python
388 lines
13 KiB
Python
# -*- coding: utf-8 -*-
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"""
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FinMind 新聞資料模組
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用於獲取台灣股市相關新聞和公告
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API 文檔：https://finmind.github.io/
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主要資料集：
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- TaiwanStockNews: 台股相關新聞（主要新聞來源）
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- TaiwanStockDividendResult: 除權息公告
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- TaiwanStockMonthRevenue: 月營收公告
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- TaiwanStockInstitutionalInvestorsBuySell: 法人買賣超
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"""
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import json
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from datetime import datetime, timedelta
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from typing import Optional
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import pandas as pd
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from .finmind_common import (
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_make_api_request,
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format_date,
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get_default_start_date,
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normalize_stock_id,
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FinMindError,
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FinMindDataNotFoundError,
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format_output,
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)
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def get_news(
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ticker: str,
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start_date: str,
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end_date: str,
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use_toon: bool = True
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) -> str:
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"""
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獲取台灣股市相關新聞資訊。
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使用 FinMind 的 TaiwanStockNews 資料集獲取真正的台股新聞。
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資料區間：2019-04-01 ~ now
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返回欄位：
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- date: 新聞日期
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- stock_id: 股票代碼
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- description: 新聞內容
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- link: 新聞連結
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- source: 新聞來源
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- title: 新聞標題
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Args:
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ticker: 股票代碼（例如 "2330"）
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start_date: 開始日期
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end_date: 結束日期
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use_toon: 是否使用 toon 格式（保留參數）
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Returns:
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str: JSON 格式的新聞資訊
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"""
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ticker = normalize_stock_id(ticker)
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news_items = []
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# 1. 獲取真正的新聞（TaiwanStockNews）
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# 注意：TaiwanStockNews 只支援 start_date，不支援 end_date
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try:
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import requests
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import os
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url = "https://api.finmindtrade.com/api/v4/data"
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token = os.getenv("FINMIND_API_TOKEN") or os.getenv("FINMIND_API_KEY")
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headers = {"Authorization": f"Bearer {token}"} if token else {}
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params = {
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"dataset": "TaiwanStockNews",
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"data_id": ticker,
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"start_date": start_date,
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}
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response = requests.get(url, headers=headers, params=params, timeout=30)
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response.raise_for_status()
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news_response = response.json()
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if "data" in news_response and news_response["data"]:
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# 過濾日期範圍內的新聞
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filtered_news = []
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for item in news_response["data"]:
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news_date = item.get("date", "")[:10] # 只取日期部分
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if news_date and start_date <= news_date <= end_date:
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filtered_news.append(item)
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# 取最近 20 筆新聞
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for item in filtered_news[-20:]:
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news_items.append({
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"title": item.get("title", "無標題"),
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"date": item.get("date", "")[:10], # 只保留日期部分
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"type": "news",
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"summary": "", # TaiwanStockNews 沒有 description 欄位
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"link": item.get("link", ""),
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"source": item.get("source", "FinMind")
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})
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except FinMindError as e:
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print(f"獲取新聞時發生錯誤: {e}")
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# 2. 如果沒有新聞或新聞太少，補充其他資訊
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if len(news_items) < 5:
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# 補充股利公告
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try:
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dividend_response = _make_api_request(
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dataset="TaiwanStockDividendResult",
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data_id=ticker,
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start_date=start_date,
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end_date=end_date
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)
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if "data" in dividend_response and dividend_response["data"]:
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for item in dividend_response["data"][:3]:
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news_items.append({
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"title": f"{ticker} 除權息公告",
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"date": item.get("date", ""),
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"type": "dividend",
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"summary": f"除權息日：{item.get('date', '')}，"
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f"參考價：{item.get('reference_price', 'N/A')}，"
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f"股利合計：{item.get('stock_and_cache_dividend', 'N/A')}",
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"link": "",
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"source": "FinMind"
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})
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except FinMindError:
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pass
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# 補充月營收公告
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try:
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revenue_response = _make_api_request(
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dataset="TaiwanStockMonthRevenue",
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data_id=ticker,
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start_date=start_date,
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end_date=end_date
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)
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if "data" in revenue_response and revenue_response["data"]:
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for item in revenue_response["data"][-3:]:
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revenue = item.get("revenue", 0)
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if isinstance(revenue, (int, float)):
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revenue_str = f"{revenue:,.0f}"
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else:
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revenue_str = str(revenue)
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news_items.append({
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"title": f"{ticker} 月營收公告",
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"date": item.get("date", ""),
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"type": "revenue",
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"summary": f"{item.get('revenue_year', '')}年{item.get('revenue_month', '')}月營收：{revenue_str}",
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"link": "",
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"source": "FinMind"
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})
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except FinMindError:
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pass
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# 按日期排序（最新的在前）
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news_items.sort(key=lambda x: x.get("date", ""), reverse=True)
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# 統計新聞類型
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news_count = len([n for n in news_items if n.get("type") == "news"])
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other_count = len(news_items) - news_count
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result = {
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"stock_id": ticker,
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"items": len(news_items),
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"news_count": news_count,
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"note": f"包含 {news_count} 則新聞" + (f"和 {other_count} 則公司公告" if other_count > 0 else ""),
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"feed": news_items[:15] # 限制最多 15 筆
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}
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return format_output(result, use_toon)
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def get_global_news(
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curr_date: str,
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look_back_days: int = 7
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) -> str:
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"""
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獲取台灣股市整體市場新聞/動態。
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注意：FinMind 不提供全球新聞 API，
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本函式透過市場整體指標提供替代資訊。
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Args:
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curr_date: 當前日期
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look_back_days: 回溯天數
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Returns:
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str: JSON 格式的市場動態
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"""
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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start_date_dt = curr_date_dt - timedelta(days=look_back_days)
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start_date = format_date(start_date_dt)
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end_date = format_date(curr_date_dt)
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market_news = []
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# 1. 獲取整體市場融資融券
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try:
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margin_response = _make_api_request(
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dataset="TaiwanStockTotalMarginPurchaseShortSale",
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start_date=start_date,
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end_date=end_date
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)
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if "data" in margin_response and margin_response["data"]:
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for item in margin_response["data"][-3:]: # 取最近 3 筆
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market_news.append({
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"title": "台股整體融資融券動態",
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"date": item.get("date", ""),
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"type": "margin_total",
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"summary": f"{item.get('name', '')}：今日餘額 {item.get('TodayBalance', 'N/A'):,}，"
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f"增減 {item.get('TodayBalance', 0) - item.get('YesBalance', 0):+,}",
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"source": "FinMind"
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})
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except FinMindError:
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pass
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# 2. 獲取整體三大法人買賣超
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try:
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institutional_response = _make_api_request(
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dataset="TaiwanStockTotalInstitutionalInvestors",
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start_date=start_date,
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end_date=end_date
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)
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if "data" in institutional_response and institutional_response["data"]:
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df = pd.DataFrame(institutional_response["data"])
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dates = sorted(df["date"].unique(), reverse=True)[:3]
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for date in dates:
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day_data = df[df["date"] == date]
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summary_parts = []
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for _, row in day_data.iterrows():
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name = row.get("name", "")
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buy = row.get("buy", 0)
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sell = row.get("sell", 0)
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net = buy - sell
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if isinstance(net, (int, float)):
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summary_parts.append(f"{name} {net/100000000:+,.2f} 億")
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if summary_parts:
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market_news.append({
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"title": "台股三大法人買賣超",
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"date": date,
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"type": "institutional_total",
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"summary": "，".join(summary_parts),
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"source": "FinMind"
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})
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except FinMindError:
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pass
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# 按日期排序
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market_news.sort(key=lambda x: x.get("date", ""), reverse=True)
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result = {
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"market": "Taiwan",
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"items": len(market_news),
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"note": "FinMind 不提供新聞 API，此為市場整體動態資訊",
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"feed": market_news
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}
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return format_output(result)
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def get_insider_sentiment(ticker: str, curr_date: str) -> str:
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"""
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獲取內部人交易情緒（透過法人買賣超資料模擬）。
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Args:
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ticker: 股票代碼
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curr_date: 當前日期
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Returns:
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str: JSON 格式的情緒分析
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"""
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ticker = normalize_stock_id(ticker)
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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start_date_dt = curr_date_dt - timedelta(days=30)
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start_date = format_date(start_date_dt)
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try:
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response = _make_api_request(
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dataset="TaiwanStockInstitutionalInvestorsBuySell",
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data_id=ticker,
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start_date=start_date,
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end_date=curr_date
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)
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if "data" in response and response["data"]:
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df = pd.DataFrame(response["data"])
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# 計算整體買賣超趨勢
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total_buy = df["buy"].sum()
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total_sell = df["sell"].sum()
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net = total_buy - total_sell
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# 判斷情緒
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if net > 0:
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sentiment = "正面"
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sentiment_score = min(1.0, net / (total_buy + total_sell) * 2) if (total_buy + total_sell) > 0 else 0
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else:
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sentiment = "負面"
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sentiment_score = max(-1.0, net / (total_buy + total_sell) * 2) if (total_buy + total_sell) > 0 else 0
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result = {
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"stock_id": ticker,
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"period": f"{start_date} ~ {curr_date}",
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"sentiment": sentiment,
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"sentiment_score": round(sentiment_score, 3),
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"total_buy": int(total_buy),
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"total_sell": int(total_sell),
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"net": int(net),
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"note": "基於法人買賣超資料計算的情緒指標"
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}
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return format_output(result)
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else:
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return format_output({
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"stock_id": ticker,
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"error": "查無資料"
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})
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except FinMindError as e:
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return format_output({
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"stock_id": ticker,
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"error": str(e)
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})
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def get_insider_transactions(symbol: str) -> str:
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"""
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獲取內部人交易資訊。
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注意：FinMind 未提供內部人交易 API，
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本函式透過法人買賣超資料提供類似資訊。
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Args:
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symbol: 股票代碼
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Returns:
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str: JSON 格式的交易資訊
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"""
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symbol = normalize_stock_id(symbol)
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end_date = format_date(datetime.now())
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start_date = get_default_start_date(years_back=0) # 最近 1 個月
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start_date_dt = datetime.now() - timedelta(days=30)
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start_date = format_date(start_date_dt)
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try:
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response = _make_api_request(
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dataset="TaiwanStockInstitutionalInvestorsBuySell",
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data_id=symbol,
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start_date=start_date,
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end_date=end_date
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)
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if "data" in response and response["data"]:
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# 只保留最近 15 筆
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data = response["data"][-15:]
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result = {
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"stock_id": symbol,
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"data_type": "institutional_trading",
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"note": "FinMind 不提供內部人交易 API，此為法人買賣超資料",
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"data": data
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}
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return format_output(result)
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else:
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return format_output({
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"stock_id": symbol,
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"data": [],
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"message": "查無資料"
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})
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except FinMindError as e:
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return format_output({
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"error": str(e),
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"stock_id": symbol
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})
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