diff --git a/AGENTS.md b/AGENTS.md index 97a109b..d656f6e 100755 --- a/AGENTS.md +++ b/AGENTS.md @@ -84,3 +84,9 @@ - 截断续写：报告长导致 finish_reason=length 时，拼接已有内容并向 messages 追加"继续"指令让模型续写（最多3次），truncated 仅在续写后仍截断时为 true；call_llm 读超时 300s（续写携带全部上下文） - 快照入库防护：_capture_market_snapshot 校验 indices 非空且涨跌统计不全 0，fuyao 失败时跳过入库，防止空快照污染环比 - 调试注意：独立脚本跑 backend 代码必须显式 `load_dotenv("/path/to/repo/.env")`——fuyao_apikey 在仓库根目录 .env（uvicorn 靠 --env-file 参数加载），backend/.env 只有 MX keys；且 stdin 脚本里 load_dotenv() 无参调用会因 frame 断言报错，须显式传路径 + +- 第三档呈现：报告页按 `## ` 二级标题拆分为多张卡片渲染（`splitReport`），首卡含 # 标题+定调引用块；表格单元格数字按 A股惯例红涨绿跌（`colorizeChildren` 只给带 +/- 号的数字着色）；标题栏显示"总第 N 期"（`issue_number`，latest 接口用 COUNT(trade_date<=) 子查询计算） +- 海外指数/国内期货：`fetch_global_markets` 走 push2delay 的 ulist.np（海外 secid `100.NDX` 等）+ clist（期货 fs `m:8/113/142/114/115`，只取名称含"主连/主力合约"且排除"次主连"，按成交额降序取前12）；接入 dashboard `globalMarkets` 字段 +- ⚠️ openteam 网关对 LLM 单请求有约 120s 硬超时（超时返回 502 或空 SSE），GLM reasoning 长报告一次生成必死。解法：`call_llm` 全流式（SSE）+ `collect_ai_analysis` 分三段生成（REPORT_PARTS，每段约1200-1600字，各重试3次带退避，失败占位不阻塞）；工具轮拿到数据后立即 break 进分段（再问一轮只会空转120s）；`thinking:{type:disabled}` 参数网关返回400不可用 +- 工具结果必须瘦身：`get_active_core_stocks` 只保留出现次数前25只+题材前5（全量145KB会撑爆上下文）；get_market_dashboard 全量约13KB可接受 +- glm-5.3-flash 空返回特征：SSE 200 但只有 reasoning_content 无 content/tool_calls/finish_reason（可能流满120s被掐），按空轮次处理重试即可 diff --git a/backend/routes/ai_analysis.py b/backend/routes/ai_analysis.py index f6ca617..1ff66fa 100644 --- a/backend/routes/ai_analysis.py +++ b/backend/routes/ai_analysis.py @@ -43,8 +43,11 @@ def _can_trigger(trade_date: str) -> tuple[bool, str]: async def get_latest_report(): conn = get_connection() try: + # issue_number：按报告日期序数作为总期号 row = conn.execute( - "SELECT * FROM ai_reports ORDER BY trade_date DESC, id DESC LIMIT 1" + """SELECT r.*, + (SELECT COUNT(*) FROM ai_reports WHERE trade_date <= r.trade_date) AS issue_number + FROM ai_reports r ORDER BY r.trade_date DESC, r.id DESC LIMIT 1""" ).fetchone() if not row: raise HTTPException(status_code=404, detail="暂无分析报告") diff --git a/backend/routes/market_dashboard.py b/backend/routes/market_dashboard.py index f871451..714b0cd 100644 --- a/backend/routes/market_dashboard.py +++ b/backend/routes/market_dashboard.py @@ -165,6 +165,7 @@ async def _build_dashboard() -> dict: skyrocket_data, sector_flow_data, margin_data, + global_markets_data, ) = await asyncio.gather( fuyao_client.hot_stock_list("day"), fuyao_client.dragon_tiger_list("all"), @@ -173,6 +174,7 @@ async def _build_dashboard() -> dict: fuyao_client.skyrocket_list("day"), market_extra.fetch_sector_fund_flow(), market_extra.fetch_margin_summary(), + market_extra.fetch_global_markets(), return_exceptions=True, ) except Exception: @@ -183,6 +185,7 @@ async def _build_dashboard() -> dict: skyrocket_data = {} sector_flow_data = None margin_data = None + global_markets_data = None # ── 解析指数 ── indices = [] @@ -565,6 +568,7 @@ async def _build_dashboard() -> dict: "sectorStrength": sector_strength[:31], "conceptStrength": concept_strength[:10], "sectorFundFlow": sector_flow_data if isinstance(sector_flow_data, dict) else None, + "globalMarkets": global_markets_data if isinstance(global_markets_data, dict) else None, "events": events, "limitLadder": limit_ladder, "updateTime": datetime.now(BJT).strftime("%Y-%m-%d %H:%M:%S"), diff --git a/backend/services/ai_service.py b/backend/services/ai_service.py index a733d38..b73de82 100644 --- a/backend/services/ai_service.py +++ b/backend/services/ai_service.py @@ -7,6 +7,7 @@ 4. 保存报告到数据库 """ +import asyncio import json import re import traceback @@ -29,7 +30,7 @@ SYSTEM_PROMPT = """你是一位专业的A股市场分析师，擅长从数据中 - 风险提示，每次推荐都需说明风险点 可用工具： -- get_market_dashboard: 获取市场整体数据（指数/涨跌统计/市场温度/连板梯队/行业强度/板块资金流/两融/事件情报） +- get_market_dashboard: 获取市场整体数据（指数/涨跌统计/市场温度/连板梯队/行业强度/板块资金流/两融/海外指数与国内期货/事件情报） - get_theme_history: 获取指定日期的题材涨幅排行 - get_active_core_stocks: 获取核心股追踪数据（10日涨幅矩阵+所属题材） - get_stock_quote: 获取个股实时行情 @@ -48,7 +49,7 @@ DAILY_ANALYSIS_PROMPT = """请对 {trade_date} 的A股市场进行收盘分析 {prev_snapshot_section} 请先调用以下工具获取数据： -1. get_market_dashboard - 获取市场整体数据（含涨跌统计、市场温度、连板梯队 limitLadder、板块资金流 sectorFundFlow、两融） +1. get_market_dashboard - 获取市场整体数据（含涨跌统计、市场温度、连板梯队 limitLadder、板块资金流 sectorFundFlow、两融、海外与期货 globalMarkets） 2. get_theme_history(date="{trade_date}") - 获取今日题材涨幅 3. get_active_core_stocks - 获取核心股数据 4. get_news(limit=30) - 获取今日财经快讯 @@ -64,59 +65,101 @@ DAILY_ANALYSIS_PROMPT = """请对 {trade_date} 的A股市场进行收盘分析 4. 板块资金面必须引用 get_market_dashboard 返回的 sectorFundFlow：行业主力净流入TOP3、净流出TOP3、概念净流入TOP3（单位亿元），结合题材分析说明资金动向。 -5. 重要消息面必须基于 get_news 返回的快讯整理：挑5-8条对次日盘面影响最大的消息，每条格式为"【分类】一句话新闻 —— 一句影响解读"（分类用：宏观/政策/行业/公司/海外）；快讯中若没有某方面的重要消息，如实说明，严禁编造工具中不存在的新闻。 +5. 海外市场与国内期货必须引用 get_market_dashboard 返回的 globalMarkets：overseas 为海外主要指数（纳斯达克/道琼斯/标普500/恒生/日经/富时），futures 为国内期货主力合约（按成交额降序，已含价格与涨跌幅）；点评与A股关联度高的品种（股指期货、原油、贵金属、黑色系），数据缺失则如实说明。 -6. 适当使用表格展示数据对比。 +6. 重要消息面必须基于 get_news 返回的快讯整理：挑5-8条对次日盘面影响最大的消息，每条格式为"【分类】一句话新闻 —— 一句影响解读"（分类用：宏观/政策/行业/公司/海外）；快讯中若没有某方面的重要消息，如实说明，严禁编造工具中不存在的新闻。 -然后基于数据生成报告，结构如下： +7. 适当使用表格展示数据对比。 -## 一、市场总览 -- 主要指数表现（上证、深证、创业板、科创50） -- 涨跌家数统计（含涨停/跌停/炸板率，须环比） -- 市场温度评估 - -## 二、题材热点分析 -- 今日涨幅前5题材 -- 持续活跃的题材 -- 新兴热点题材 -- 明显退潮的题材（警示） -- 板块主力资金流（按格式规则4引用数据） - -## 三、核心股追踪 -- 连板梯队分析（按格式规则3完整呈现） -- 核心股表现 -- 龙头股辨识 - -## 四、关注方向 -- 明日值得关注的题材方向 -- 潜在的交易机会 - -## 五、下个交易日建议 -- 明日大盘预判（支撑/压力位） -- 建议关注的题材方向（2-3个） -- 建议关注的核心股（附理由） -- 操作策略（仓位建议、买卖时机） -- 需要规避的方向 - -## 六、重要消息面 -- 基于 get_news 快讯整理（按格式规则5） - -## 七、风险提示 -- 需要警惕的风险因素 -- 操作建议 +然后基于数据生成报告。报告共八章，将由系统分三次调用完成，每次调用只负责其中一部分，具体写作指令由后续消息给出。 请用 Markdown 格式输出，适当使用表格展示数据对比。""" +# 分段生成指令：网关对单次 LLM 请求有约120s硬超时，整篇报告一次生成必被掐断， +# 故拆为三段（每段约1200-1600字），各自独立调用后拼接 +REPORT_PARTS = [ + """现在写报告的【第1部分】，只输出这一部分，直接输出 Markdown，不要任何开场白或说明： +1. 以 `# {title} A股收盘分析报告` 一级标题开头 +2. 标题后第一行输出定调引用块，格式严格为：`> 今日定调：<80字内核心结论，含1-2个关键数字>` +3. 写 `## 一、市场总览`（指数表格、涨跌统计须环比、市场温度）与 `## 二、题材热点分析`（涨幅前5、持续活跃、新兴热点、退潮警示、板块主力资金流TOP3） +全文控制在1600字以内。""", + """现在写报告的【第2部分】，只输出这一部分，直接输出 Markdown，不要重复之前内容： +- `## 三、核心股追踪`（连板梯队完整表格：层级/股票/涨停原因/封单，首板挑3-5只人气股点评；核心股表现；龙头辨识） +- `## 四、关注方向`（明日题材方向、潜在交易机会） +全文控制在1300字以内。""", + """现在写报告的【第3部分】，只输出这一部分，直接输出 Markdown，不要重复之前内容： +- `## 五、下个交易日建议`（大盘预判、题材方向、核心股、仓位策略、规避方向） +- `## 六、重要消息面`（5-8条，格式【分类】新闻——影响解读） +- `## 七、海外市场与国内期货`（点评对次日A股的影响） +- `## 八、风险提示` +全文控制在1900字以内。""", +] + # 报告头部的"今日定调"引用行，保存时提取为 summary _TONE_LINE_RE = re.compile(r"^>\s*今日定调[：:]\s*(.+)$", re.MULTILINE) +async def _consume_sse(resp: httpx.Response) -> dict: + """消费 OpenAI 兼容 SSE 流，拼装为与非流式响应相同的结构""" + content_parts: list[str] = [] + finish_reason = "" + usage: dict = {} + # tool_calls 按 index 拼装（流式下 arguments 分片到达） + tool_acc: dict[int, dict] = {} + + async for line in resp.aiter_lines(): + if not line.startswith("data:"): + continue + data = line[5:].strip() + if not data or data == "[DONE]": + continue + try: + chunk = json.loads(data) + except json.JSONDecodeError: + continue + if chunk.get("usage"): + usage = chunk["usage"] + choices = chunk.get("choices") or [] + if not choices: + continue + delta = choices[0].get("delta") or {} + if delta.get("content"): + content_parts.append(delta["content"]) + for tc in delta.get("tool_calls") or []: + idx = tc.get("index", 0) + slot = tool_acc.setdefault(idx, {"id": "", "type": "function", + "function": {"name": "", "arguments": ""}}) + if tc.get("id"): + slot["id"] = tc["id"] + fn = tc.get("function") or {} + if fn.get("name"): + slot["function"]["name"] += fn["name"] + if fn.get("arguments"): + slot["function"]["arguments"] += fn["arguments"] + if choices[0].get("finish_reason"): + finish_reason = choices[0]["finish_reason"] + + message: dict = {"role": "assistant", "content": "".join(content_parts) or None} + if tool_acc: + message["tool_calls"] = [ + {"id": tool_acc[i]["id"], "type": "function", + "function": tool_acc[i]["function"]} + for i in sorted(tool_acc) + ] + return {"choices": [{"message": message, "finish_reason": finish_reason}], "usage": usage} + + async def call_llm(messages: list, tools: list = None) -> dict: - """调用 OpenAI 兼容 API""" + """调用 OpenAI 兼容 API（流式）。 + + 必须用 stream：网关对非流式请求有约120s的代理超时，长生成会被 502 掐断； + 流式下字节持续到达不会被判定超时。返回结构与非流式一致。 + """ async with httpx.AsyncClient() as client: payload = { "model": AI_MODEL, "messages": messages, + "stream": True, } if AI_MAX_TOKENS is not None: payload["max_tokens"] = AI_MAX_TOKENS @@ -126,17 +169,40 @@ async def call_llm(messages: list, tools: list = None) -> dict: payload["tools"] = tools payload["tool_choice"] = "auto" - resp = await client.post( - f"{AI_API_BASE}/chat/completions", - headers={ - "Authorization": f"Bearer {AI_API_KEY}", - "Content-Type": "application/json", - }, - json=payload, - timeout=300, # 续写调用携带全部上下文且输出很长，需要较宽的读超时 - ) - resp.raise_for_status() - return resp.json() + max_attempts = 4 + for attempt in range(1, max_attempts + 1): + try: + async with client.stream( + "POST", + f"{AI_API_BASE}/chat/completions", + headers={ + "Authorization": f"Bearer {AI_API_KEY}", + "Content-Type": "application/json", + "Accept": "text/event-stream", + }, + json=payload, + timeout=600, + ) as resp: + if resp.status_code == 429 and attempt < max_attempts: + wait = min(30 * attempt, 90) + print(f"[ai-service] LLM 429 限流，{wait}s 后重试（第 {attempt}/{max_attempts - 1} 次）") + await asyncio.sleep(wait) + continue + if resp.status_code >= 500 and attempt < max_attempts: + print(f"[ai-service] LLM {resp.status_code}，{min(15 * attempt, 60)}s 后重试") + await asyncio.sleep(min(15 * attempt, 60)) + continue + resp.raise_for_status() + return await _consume_sse(resp) + except httpx.TransportError as e: + # 网络层错误（超时/断连）也值得重试 + if attempt < max_attempts: + wait = min(15 * attempt, 60) + print(f"[ai-service] LLM 网络错误（{type(e).__name__}），{wait}s 后重试") + await asyncio.sleep(wait) + continue + raise + raise RuntimeError("LLM 调用重试次数耗尽") async def collect_ai_analysis(trade_date: str) -> dict: @@ -162,12 +228,11 @@ async def collect_ai_analysis(trade_date: str) -> dict: )} ] + # ── 阶段一：工具轮（只获取数据，模型若直接开写报告则丢弃，由阶段二重写） ── tools_used = [] total_tokens = 0 - final_content = "" was_truncated = False - continuations = 0 - max_rounds = 30 # 安全上限，正常分析约 3-8 轮 + max_rounds = 8 for i in range(max_rounds): response = await call_llm(messages, tools=TOOLS) @@ -175,44 +240,60 @@ async def collect_ai_analysis(trade_date: str) -> dict: choice = response["choices"][0] message = choice["message"] - messages.append(message) - finish_reason = choice.get("finish_reason", "") - print(f"[ai-service] round {i}: finish={finish_reason}, " + print(f"[ai-service] tool round {i}: finish={finish_reason}, " f"content_len={len(message.get('content') or '')}, " f"tool_calls={len(message.get('tool_calls') or [])}") - if finish_reason == "stop": - final_content += message.get("content") or "" - break - - if finish_reason == "length": - # 单次输出上限截断：拼接已有内容并让模型续写（最多3次） - final_content += message.get("content") or "" - if continuations < 3: - continuations += 1 - print(f"[ai-service] 响应被截断，第 {continuations} 次续写") - messages.append({ - "role": "user", - "content": "报告输出被截断了。请从截断处无缝续写剩余内容：直接接着写，不要重复已输出的部分，也不要重新输出标题。", - }) - continue - was_truncated = True - print("[ai-service] 警告：多次续写后仍被截断") - break - - if finish_reason == "tool_calls": - for tool_call in message.get("tool_calls", []): + if finish_reason == "tool_calls" and message.get("tool_calls"): + messages.append(message) + for tool_call in message["tool_calls"]: func_name = tool_call["function"]["name"] func_args = json.loads(tool_call["function"]["arguments"]) tools_used.append(func_name) - result = await execute_tool(func_name, func_args) messages.append({ "role": "tool", "tool_call_id": tool_call["id"], "content": result }) + break # 工具已齐，立即进入分段写作（再问一轮模型只会空转120s） + + # 非工具轮（模型直接开写/空返回）：只要有工具结果就直接进入分段写作； + # 一轮工具都没拿到则重试 + if tools_used: + break + print(f"[ai-service] 未获取到工具数据，重试（{i + 1}/{max_rounds}）") + await asyncio.sleep(5) + + if not tools_used: + raise RuntimeError("工具数据获取失败，无法生成报告") + + # ── 阶段二：分段生成报告（绕开网关单请求约120s硬超时） ── + final_content = "" + for part_idx, part_prompt in enumerate(REPORT_PARTS): + part_content = "" + for attempt, backoff in ((1, 0), (2, 10), (3, 30)): + if backoff: + await asyncio.sleep(backoff) + part_messages = messages + [{ + "role": "user", + "content": part_prompt.format(title=trade_date) if part_idx == 0 else part_prompt, + }] + response = await call_llm(part_messages) + total_tokens += response.get("usage", {}).get("total_tokens", 0) + choice = response["choices"][0] + part_content = choice["message"].get("content") or "" + finish = choice.get("finish_reason", "") + print(f"[ai-service] part {part_idx + 1} attempt {attempt}: finish={finish}, len={len(part_content)}") + if part_content and finish in ("stop", "length"): + break + print(f"[ai-service] part {part_idx + 1} 生成异常，重试") + if not part_content: + was_truncated = True + print(f"[ai-service] 警告：part {part_idx + 1} 三次尝试均失败") + part_content = f"\n\n> （第{part_idx + 1}部分生成失败，请稍后重新生成）\n" + final_content += (final_content and "\n\n" or "") + part_content summary = _extract_summary(final_content) report_id = _save_report(trade_date, final_content, summary, tools_used, total_tokens) diff --git a/backend/services/ai_tools.py b/backend/services/ai_tools.py index 0b21087..9546807 100644 --- a/backend/services/ai_tools.py +++ b/backend/services/ai_tools.py @@ -16,7 +16,7 @@ TOOLS = [ "type": "function", "function": { "name": "get_market_dashboard", - "description": "获取A股市场看板数据，包含主要指数行情、全市场涨跌统计（涨跌家数/涨停/跌停/炸板率/成交额）、市场温度评分与竞价信号、行业强度榜、概念热度、完整连板梯队（limitLadder字段，含涨停原因与封单金额）、事件情报（热门股/龙虎榜/飙升/异动）", + "description": "获取A股市场看板数据，包含主要指数行情、全市场涨跌统计（涨跌家数/涨停/跌停/炸板率/成交额）、市场温度评分与竞价信号、行业强度榜、概念热度、板块主力资金流、两融余额、海外主要指数与国内期货主力合约（globalMarkets字段）、完整连板梯队（limitLadder字段，含涨停原因与封单金额）、事件情报（热门股/龙虎榜/飙升/异动）", "parameters": {"type": "object", "properties": {}, "required": []} } }, @@ -184,9 +184,9 @@ async def _get_active_core_stocks() -> str: if s["lastAppear"] is None or r["trade_date"] > s["lastAppear"]: s["lastAppear"] = r["trade_date"] - stocks = list(stock_days.values()) - stocks.sort(key=lambda x: x.get("lastAppear") or "", reverse=True) - stocks.sort(key=lambda x: -x["appearCount"]) + # 只保留最近10日中出现次数最多的前25只（全量可达145KB，会把上下文撑爆） + stocks = sorted(stock_days.values(), key=lambda x: -x["appearCount"])[:25] + keep_codes = {s["stockCode"] for s in stocks} themes_rows = conn.execute( f"""SELECT stock_code, theme_code, theme_name FROM daily_core_stock_themes @@ -195,10 +195,12 @@ async def _get_active_core_stocks() -> str: ).fetchall() themes_by_stock = {} for t in themes_rows: + if t["stock_code"] not in keep_codes: + continue per = themes_by_stock.setdefault(t["stock_code"], {}) per.setdefault(t["theme_code"], {"theme_code": t["theme_code"], "theme_name": t["theme_name"]}) for s in stocks: - s["themes"] = list(themes_by_stock.get(s["stockCode"], {}).values()) + s["themes"] = list(themes_by_stock.get(s["stockCode"], {}).values())[:5] latest = dates[-1] if dates else None for s in stocks: diff --git a/backend/services/market_extra.py b/backend/services/market_extra.py index eeb3fdd..85fdbba 100644 --- a/backend/services/market_extra.py +++ b/backend/services/market_extra.py @@ -86,6 +86,73 @@ async def _fetch_flow_boards(client: httpx.AsyncClient, fs: str, po: int, pz: in return boards +_GLOBAL_INDICES = "100.NDX,100.DJIA,100.SPX,100.HSI,100.N225,100.FTSE" +_FUT_MARKETS = ("m:8", "m:113", "m:142", "m:114", "m:115") # 中金所/上期所/上期能源/大商所/郑商所 + + +async def fetch_global_markets() -> dict | None: + """海外主要指数 + 国内期货主力合约；失败返回 None""" + try: + async with httpx.AsyncClient(timeout=_TIMEOUT, headers={"User-Agent": _UA}) as client: + overseas, futures = await asyncio.gather( + _fetch_overseas_indices(client), + _fetch_futures_main(client), + ) + if not overseas and not futures: + return None + return {"overseas": overseas, "futures": futures} + except Exception: + return None + + +async def _fetch_overseas_indices(client: httpx.AsyncClient) -> list[dict]: + resp = await client.get( + "https://push2delay.eastmoney.com/api/qt/ulist.np/get", + params={"secids": _GLOBAL_INDICES, "fields": "f12,f14,f2,f3", "fltt": 2, "invt": 2}, + ) + resp.raise_for_status() + diff = (resp.json().get("data") or {}).get("diff") or [] + return [ + {"name": d.get("f14", ""), "price": d.get("f2"), "changePct": d.get("f3")} + for d in diff + ] + + +async def _fetch_futures_main(client: httpx.AsyncClient) -> list[dict]: + """国内期货主力合约（名称含"主连/主力合约"），按成交额降序取前12""" + import re + + results: list[dict] = [] + for fs in _FUT_MARKETS: + try: + resp = await client.get( + "https://push2delay.eastmoney.com/api/qt/clist/get", + params={"fid": "f6", "po": 1, "pz": 200, "pn": 1, "np": 1, + "fltt": 2, "invt": 2, "fs": fs, "fields": "f12,f14,f2,f3,f6"}, + ) + resp.raise_for_status() + diff = (resp.json().get("data") or {}).get("diff") or [] + except Exception: + continue + for d in diff: + name = d.get("f14") or "" + if "主连" not in name and "主力合约" not in name: + continue + if "次主连" in name: # 次主力合约，排除 + continue + amount = d.get("f6") + if not isinstance(amount, (int, float)): + continue + results.append({ + "name": re.sub(r"(主连|主力合约)$", "", name), + "price": d.get("f2"), + "changePct": d.get("f3"), + "amountYi": round(amount / 1e8, 1), + }) + results.sort(key=lambda x: -x["amountYi"]) + return results[:12] + + async def fetch_sector_fund_flow() -> dict | None: """板块主力资金流排行：行业净流入/净流出 TOP6 + 概念净流入 TOP6；失败返回 None""" try: diff --git a/src/lib/ai-analysis-api.ts b/src/lib/ai-analysis-api.ts index dbcd4e2..ecbd884 100644 --- a/src/lib/ai-analysis-api.ts +++ b/src/lib/ai-analysis-api.ts @@ -15,6 +15,7 @@ export interface AiReport { model: string; tokens_used: number; generation_count?: number; + issue_number?: number; created_at: string; updated_at?: string | null; } diff --git a/src/routes/ai-analysis.tsx b/src/routes/ai-analysis.tsx index 4b727e0..9d31320 100644 --- a/src/routes/ai-analysis.tsx +++ b/src/routes/ai-analysis.tsx @@ -4,6 +4,7 @@ import { ArrowLeft, Clock, Loader2, AlertCircle, Wrench, Calendar } from "lucide import { useQuery } from "@tanstack/react-query"; import Markdown from "react-markdown"; import remarkGfm from "remark-gfm"; +import type { Components } from "react-markdown"; import { Mermaid } from "../components/Mermaid"; import { fetchAiLatestReport } from "../lib/ai-analysis-api"; import { Card, CardContent } from "../components/ui/card"; @@ -12,6 +13,51 @@ export const Route = createFileRoute("/ai-analysis")({ component: AiAnalysisPage, }); +/** A股惯例红涨绿跌：只给带显式 +/- 号的数字着色（+2.3% / -1.2亿 / +56万...），无符号数字语义不明保持默认 */ +const SIGNED_NUM_RE = /([+-]\d+(?:\.\d+)?(?:%|亿|万亿|万)?)/g; + +function colorizeText(text: string): React.ReactNode[] { + return text.split(SIGNED_NUM_RE).map((part, i) => { + if (part.startsWith("+") || part.startsWith("-")) { + const val = parseFloat(part); + if (!Number.isNaN(val) && val !== 0) { + if (val > 0) return {part}; + return {part}; + } + } + return {part}; + }); +} + +function colorizeChildren(children: React.ReactNode): React.ReactNode { + return React.Children.map(children, (child) => { + if (typeof child === "string") return colorizeText(child); + if (React.isValidElement(child)) { + const kids = (child.props as { children?: React.ReactNode }).children; + if (kids != null) { + return React.cloneElement( + child as React.ReactElement<{ children?: React.ReactNode }>, + {}, + colorizeChildren(kids), + ); + } + } + return child; + }); +} + +/** 按 "## " 二级标题拆分报告为多张卡片；首段（# 标题 + 定调引用块）单独一张 */ +function splitReport(content: string): { intro: string; sections: { title: string; body: string }[] } { + const parts = content.split(/\n(?=## )/); + const sections = parts.slice(1).map((p) => { + const nl = p.indexOf("\n"); + const title = (nl === -1 ? p : p.slice(0, nl)).replace(/^##\s*/, "").trim(); + const body = nl === -1 ? "" : p.slice(nl + 1).trim(); + return { title, body }; + }); + return { intro: parts[0].trim(), sections }; +} + function AiAnalysisPage() { const { data: report, isLoading, isError } = useQuery({ queryKey: ["ai-report-latest"], @@ -19,6 +65,30 @@ function AiAnalysisPage() { retry: false, }); + const mdComponents: Components = { + table({ children, ...props }) { + return ( +
+ {children}
+
+ ); + }, + td({ children, ...props }) { + return {colorizeChildren(children)}; + }, + code({ className, children, ...props }) { + const match = /language-(\w+)/.exec(className || ""); + if (match && match[1] === "mermaid") { + return ; + } + return ( + + {children} + + ); + }, + }; + return (
@@ -30,6 +100,9 @@ function AiAnalysisPage() {

AI 收盘分析 + {report?.issue_number ? ( + 总第 {report.issue_number} 期 + ) : null}

@@ -81,44 +154,45 @@ function AiAnalysisPage() {
)} - {/* Report Content */} - - - - {children}
- - ); - }, - code({ className, children, ...props }) { - const match = /language-(\w+)/.exec(className || ""); - if (match && match[1] === "mermaid") { - return ; - } - return ( - - {children} - - ); - }, - }} - > - {report.content} - - - - - {/* Disclaimer */} -

- 本报告由 AI 生成，仅供参考，不构成投资建议 -

+ {/* 报告正文：按 ## 章节分卡片渲染 */} + {(() => { + const { intro, sections } = splitReport(report.content); + return ( +
+ {intro && ( + + + + {intro} + + + + )} + {sections.map((sec) => ( + +
+

{sec.title}

+
+ + + {sec.body} + + + + ))} +
+ ); + })()} )} + + {/* Disclaimer */} +

+ 本报告由 AI 生成，仅供参考，不构成投资建议 +

); } + +export default AiAnalysisPage;