From f3f43a5a5dfb1bc0d2e1d6e89c99f51b2389f2c4 Mon Sep 17 00:00:00 2001 From: aaron <> Date: Wed, 22 Apr 2026 11:56:23 +0800 Subject: [PATCH] 1 --- .../app/__pycache__/config.cpython-313.pyc | Bin 4502 -> 5561 bytes backend/app/analysis/sector_realtime.py | 93 ++++++++++++++ .../api/__pycache__/market.cpython-313.pyc | Bin 11547 -> 11653 bytes .../recommendations.cpython-313.pyc | Bin 8709 -> 8872 bytes .../api/__pycache__/sectors.cpython-313.pyc | Bin 9417 -> 6911 bytes .../api/__pycache__/stocks.cpython-313.pyc | Bin 32329 -> 32872 bytes backend/app/api/market.py | 5 +- backend/app/api/recommendations.py | 9 +- backend/app/api/sectors.py | 116 +++--------------- backend/app/api/stocks.py | 21 +++- backend/app/config.py | 24 ++++ .../data/__pycache__/models.cpython-313.pyc | Bin 8465 -> 9181 bytes backend/app/data/models.py | 15 +++ .../__pycache__/recommender.cpython-313.pyc | Bin 38175 -> 38219 bytes .../__pycache__/screener.cpython-313.pyc | Bin 58246 -> 60096 bytes backend/app/engine/recommender.py | 1 + backend/app/engine/screener.py | 73 ++++++++--- .../llm/__pycache__/prompts.cpython-313.pyc | Bin 6690 -> 7090 bytes backend/app/llm/batch_screener.py | 112 +++++++++++++---- backend/app/llm/daily_review.py | 8 +- backend/app/llm/prompts.py | 55 +++++---- backend/app/llm/strategy_board.py | 109 ++++++++++++++-- backend/astock.db | Bin 2797568 -> 2797568 bytes frontend/.next/app-build-manifest.json | 5 - frontend/.next/server/app-paths-manifest.json | 3 +- frontend/src/app/(auth)/dashboard/page.tsx | 90 ++++++++++---- frontend/src/app/(auth)/diagnose/page.tsx | 46 ++++++- .../src/app/(auth)/recommendations/page.tsx | 55 +++++++-- frontend/src/app/(auth)/sectors/page.tsx | 53 +++++--- frontend/src/app/(auth)/stock/[code]/page.tsx | 106 +++++++++++----- frontend/src/components/sector-heatmap.tsx | 15 ++- frontend/src/components/stock-card.tsx | 82 ++++++------- frontend/src/lib/api.ts | 12 ++ 33 files changed, 787 insertions(+), 321 deletions(-) create mode 100644 backend/app/analysis/sector_realtime.py diff --git a/backend/app/__pycache__/config.cpython-313.pyc b/backend/app/__pycache__/config.cpython-313.pyc index 08737201510cc1acba09a8324d06eab61ffb15b2..de9c77422d339dee09620a383c774f01d75336ae 100644 GIT binary patch delta 1089 zcmZ`%Z%k5A6u%Ealm~*d53C8jrmk2;Qdyg6TT7VLvOn0CX|cD!(qXmxaIIE?%{DdB z8k&@*m8j*1w0}lG@wL9;_@Ha85Bc7M`l3zO=gx(4w(4y6oO|y%=XcJ%zjLSH&8D0s zl}dp?K9naLM9u9SE%o5`hD%1PypG->vC?Z%qZBKxIaXyIZIx`1BLp7#4yjc|JdG4q zos}5W&mQfOQ>qv0tZ0_@3_<%XGT3CmTCyYGw`_~#Shnw|dX2k;N(U4ogRHp(_Q`0k zFfhaiJH=P8#r|obE5rwHChoQI;Z7m&MC>0G2Zt9T?KL2W4pmevL^|d@*RcdLaV1d) zKwZf!swrwJvKwUhFuB`L6e*66fclulxp2L!p~-zh5>*Jwg9PdNPjp^SS!dbZtOx7jT2q|fHcRiGp?3!>*O4YZAQj-O{I0uds@Zb>BN9vxYZ3D3uD*Cp+Tm)nLEc~2O|!8#5+@I2tY_i z{d~|1ouxZih;%`zbaUxG6x)rA;C;cri~bKnc>O#P?pc9w6O5jTTu<+S|MFiXTg+GW%;r;}O C*>MQ~ delta 126 zcmdm~Jx!VKGcPX}0}#l3yO-I`JCRR bool: + """东方财富板块名与 Tushare 板块名模糊匹配。""" + em_clean = em_name.rstrip("行业").rstrip("板块").rstrip("概念").strip() + ts_clean = ts_name.rstrip("行业").rstrip("板块").rstrip("概念").strip() + if em_clean == ts_clean: + return True + short, long = (em_clean, ts_clean) if len(em_clean) <= len(ts_clean) else (ts_clean, em_clean) + return short in long + + +def _apply_empty_overlay(sector: SectorInfo) -> SectorInfo: + sector.realtime_pct_change = None + sector.realtime_limit_up_count = None + sector.realtime_amount = None + sector.realtime_turnover_rate = None + sector.realtime_up_count = None + sector.realtime_down_count = None + sector.leading_stocks_realtime = [] + sector.is_realtime = False + sector.data_mode = "daily_snapshot" + return sector + + +async def enrich_sectors_with_realtime(sectors: list[SectorInfo]) -> list[SectorInfo]: + """按需为板块快照追加实时字段并重排。""" + if not sectors: + return sectors + + latest_trade_date = sectors[0].trade_date or tushare_client.get_latest_trade_date() + if not should_prefer_realtime_today(latest_trade_date): + return [_apply_empty_overlay(sector) for sector in sectors] + + try: + em_sectors = await get_sector_realtime_ranking() + except Exception: + logger.warning("东方财富板块实时数据获取失败，回退到日级快照") + return [_apply_empty_overlay(sector) for sector in sectors] + + if not em_sectors: + return [_apply_empty_overlay(sector) for sector in sectors] + + em_name_map = {item["sector_name"]: item for item in em_sectors} + matched = 0 + + for sector in sectors: + em_data = em_name_map.get(sector.sector_name) + if not em_data: + for item in em_sectors: + if _match_sector_name(item["sector_name"], sector.sector_name): + em_data = item + break + + if not em_data: + _apply_empty_overlay(sector) + continue + + matched += 1 + sector.realtime_pct_change = float(em_data.get("pct_change", 0) or 0) + sector.realtime_limit_up_count = None + sector.realtime_amount = round(float(em_data.get("amount", 0) or 0) / 10000, 2) + sector.realtime_turnover_rate = float(em_data.get("turnover_rate", 0) or 0) + sector.realtime_up_count = int(em_data.get("up_count", 0) or 0) + sector.realtime_down_count = int(em_data.get("down_count", 0) or 0) + sector.leading_stocks_realtime = [] + if em_data.get("leading_stock_name"): + sector.leading_stocks_realtime = [{ + "ts_code": em_data.get("leading_stock_code", ""), + "name": em_data.get("leading_stock_name", ""), + "pct_chg": float(em_data.get("leading_stock_pct", 0) or 0), + "amount": 0, + }] + sector.is_realtime = True + sector.data_mode = "realtime_overlay" + + logger.info("板块实时覆盖: %s/%s 匹配成功", matched, len(sectors)) + sectors.sort(key=lambda s: (s.realtime_pct_change if s.realtime_pct_change is not None else s.pct_change), reverse=True) + return sectors diff --git a/backend/app/api/__pycache__/market.cpython-313.pyc b/backend/app/api/__pycache__/market.cpython-313.pyc index cd8d0984bcea38a22af608c8f17b261c7406741a..40b70f23224a83cdfe329b8255424e9b05b2b6f9 100644 GIT binary patch delta 1744 zcmZWpYitx%6rMXfv%7umqmS*hyR`dgX}5*80*ew$u~CVF7Qz-Gw6dA*+`__kr=FQs z_<@fqC=Z2l6BA?fk5MCuF|tbVhlw%K-=HR#iP8AWHj#t`;vWC 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zw3uOVl4)&Fe_`tHDKT|X!I=O_ZQ%{qN$x8NF@?Zu0osV6Z<$f$?_%!#E@KEQDw2>C zKJf~c(eRWQP73E}^`=fqABJ@cR)ScGQFR3MUaJ>FTM^<|i-)Glk6&nPj`;;d0rEVCQDF!%xA>v$UMx zjK*!2?pT4(i}+d5_r912vOOJ_f-`1KQMyn|w2ZjU&)a3{lxTSo;u}Z%AdLIOLEOHW zk9I^iiE%&KtdO^!^l~@Ew*@W!|EBNj<;$lgU=>HV0p&I>lqeF#Lv9;GW;R$|p@u?B z4aBF|j>ei;D2Ok>OxTjH+?AzC{oa@F9Q$bS(C2&pW9zxE?jMuBw+_Gi?b(qFa!>Y+ zzR#a0-jn|t{ZYUB!=01E+ApVKXlevXB8kNm&_-~qqVVo^y_*_S z0qpWobP_Saco5BmD1kihLY`S@Gh;C#Cq(FoZ+Vq1;fR#>jS76l9hSSw2}_mNxSZC! z9pFpVS7A@}2^O@~tODnQ9$7l3$@>vGVN>XPd~z~`rC{2X zLT)n-y+wUvbP2+5*cB9I(Y}4Zt<|A_TAsHlp*>1$PI(?+K_c-k>KT11@*zkPlbE_X zW8wl bool: - """东方财富板块名与 Tushare 板块名模糊匹配 - - 东方财富用"酿酒行业"，Tushare 可能叫"白酒"； - 东方财富用"汽车整车"，Tushare 可能叫"汽车"。 - 用包含匹配（短名在长名中）或尾部去掉"行业"后完全匹配。 - """ - # 去掉常见后缀再做比较 - em_clean = em_name.rstrip("行业").rstrip("板块").rstrip("概念").strip() - ts_clean = ts_name.rstrip("行业").rstrip("板块").rstrip("概念").strip() - if em_clean == ts_clean: - return True - # 短名包含在长名中 - short, long = (em_clean, ts_clean) if len(em_clean) <= len(ts_clean) else (ts_clean, em_clean) - return short in long - - -async def _enrich_sectors_realtime(sectors_data: list[dict]) -> list[dict]: - """盘中时，用东方财富实时板块数据补充涨幅和涨停数 - - 一次请求替代之前腾讯批量获取数千只成分股的方式。 - """ - if not is_market_session(): - for s in sectors_data: - s["realtime_pct_change"] = None - s["realtime_limit_up_count"] = None - s["is_realtime"] = False - return sectors_data - - # 从东方财富获取实时板块排名（1次 HTTP 请求） - try: - em_sectors = await get_sector_realtime_ranking() - except Exception: - logger.warning("东方财富板块实时数据获取失败，回退到日级数据") - for s in sectors_data: - s["realtime_pct_change"] = None - s["realtime_limit_up_count"] = None - s["is_realtime"] = False - return sectors_data - - if not em_sectors: - for s in sectors_data: - s["realtime_pct_change"] = None - s["realtime_limit_up_count"] = None - s["is_realtime"] = False - return sectors_data - - # 构建东方财富板块名查找表（用于匹配） - em_name_map = {s["sector_name"]: s for s in em_sectors} - - matched = 0 - for s in sectors_data: - ts_name = s["sector_name"] - # 尝试匹配：先精确，再模糊 - em_data = em_name_map.get(ts_name) - if not em_data: - # 模糊匹配 - for em_s in em_sectors: - if _match_sector_name(em_s["sector_name"], ts_name): - em_data = em_s - break - - if em_data: - matched += 1 - s["realtime_pct_change"] = em_data["pct_change"] - s["is_realtime"] = True - # 涨停家数仍保留 Tushare 数据（东方财富此字段不可用） - s["realtime_limit_up_count"] = None - # 更新领涨股（东方财富直接提供） - if em_data.get("leading_stock_name"): - s["leading_stocks_realtime"] = [ - { - "ts_code": em_data.get("leading_stock_code", ""), - "name": em_data.get("leading_stock_name", ""), - "pct_chg": em_data.get("leading_stock_pct", 0), - "amount": 0, - } - ] - else: - s["realtime_pct_change"] = None - s["realtime_limit_up_count"] = None - s["is_realtime"] = False - - logger.info(f"板块实时数据: {matched}/{len(sectors_data)} 匹配成功") - - # 盘中按实时涨幅重新排序 - sectors_data.sort(key=lambda s: s.get("realtime_pct_change") or s.get("pct_change") or 0, reverse=True) - - return sectors_data - - @router.get("/hot") async def get_hot_sectors(limit: int = 10): """获取热门板块排名（盘中自动补充实时数据）""" sectors = await get_latest_sectors() + sectors = await enrich_sectors_with_realtime(sectors) + trade_date = sectors[0].trade_date if sectors else "" + sectors_data = [ { "sector_code": s.sector_code, @@ -125,11 +33,25 @@ async def get_hot_sectors(limit: int = 10): "pct_trend": s.pct_trend, "turnover_avg": s.turnover_avg, "main_force_ratio": s.main_force_ratio, + "trade_date": trade_date, + "realtime_pct_change": s.realtime_pct_change, + "realtime_limit_up_count": s.realtime_limit_up_count, + "realtime_amount": s.realtime_amount, + "realtime_turnover_rate": s.realtime_turnover_rate, + "realtime_up_count": s.realtime_up_count, + "realtime_down_count": s.realtime_down_count, + "leading_stocks_realtime": s.leading_stocks_realtime, + "is_realtime": s.is_realtime, + "data_mode": s.data_mode, } for s in sectors[:limit] ] - sectors_data = await _enrich_sectors_realtime(sectors_data) + realtime_enabled = any(s.get("is_realtime") for s in sectors_data) + mode = "realtime_overlay" if realtime_enabled else "daily_snapshot" + for s in sectors_data: + s["data_mode"] = mode + s["structure_trade_date"] = trade_date return sectors_data diff --git a/backend/app/api/stocks.py b/backend/app/api/stocks.py index 24f68413..54a15e28 100644 --- a/backend/app/api/stocks.py +++ b/backend/app/api/stocks.py @@ -513,10 +513,10 @@ async def diagnose_stock(ts_code: str, mode: str = Query("entry")): pass mode_instruction_map = { - "entry": "这是建仓前诊断。重点判断是否值得纳入操作或重点关注，强调触发条件和失效条件。", - "holding": "这是持仓复核。重点判断原有逻辑是否仍成立，是否该继续持有、减仓或退出。", - "review": "这是回撤复盘。重点分析问题出在个股、板块还是市场环境，并给出修正建议。", - "tracking": "这是继续跟踪。重点判断是否保留在观察池、何时升级为可操作或何时移除。", + "entry": "这是建仓前诊断。必须明确当前是可操作、重点关注、观察还是回避，并给出触发与失效边界。", + "holding": "这是持仓复核。必须回答逻辑是否还成立，当前更适合持有、减仓、退出还是继续观察。", + "review": "这是回撤复盘。重点拆清问题来自市场、板块还是个股执行，并提出下一轮修正动作。", + "tracking": "这是继续跟踪。必须说明保留理由、升级条件和移除条件，避免空泛表述。", } mode_label_map = { "entry": "建仓前诊断", @@ -551,6 +551,8 @@ async def diagnose_stock(ts_code: str, mode: str = Query("entry")): 3. 趋势评分是推荐体系的技术面核心分数（均线排列40+高低点结构35+MA20方向25=满分100），辅助信号计数仅供参考不参与主评分。 4. 位置安全评分高(>80)表示股价处于相对低位，低(<40)表示可能追高。 5. 板块信息和推荐体系信息优先级高于单一技术指标。 +6. 先给结论和动作，再解释原因；不要先铺陈背景再拖到最后才下结论。 +7. 如果证据不足，也要明确给出“观察”或“回避”，不能写成模糊建议。 {freshness_note} 请严格按以下 Markdown 结构输出，不要写成泛泛长文： @@ -558,18 +560,21 @@ async def diagnose_stock(ts_code: str, mode: str = Query("entry")): ## 当前结论 - 结论: 只能从「可操作 / 重点关注 / 观察 / 回避」中选一个 - 一句话判断: 用一句话解释为什么 +- 当前动作: 只能从「执行 / 等确认 / 继续跟踪 / 暂不参与」中选一个 - 适配模式: 说明更适合启动试错、分歧回流、趋势跟随还是只观察 ## 核心逻辑 - 市场环境: 当前大盘和风格是否支持这只票 - 板块位置: 所属板块是主线、次主线还是观察线 - 个股角色: 龙头 / 跟风 / 独立逻辑 / 非核心 +- 关键证据: 只提最重要的两到三条证据，不要抄原始数据 ## 执行动作 - 触发条件: 什么情况下才可以行动 - 失效条件: 什么情况下放弃 - 仓位建议: 用低 / 中 / 高 或百分比表达 - 适合谁: 适合激进试错、低吸等待、还是不适合参与 +- 跟踪重点: 下一交易时段最该盯住什么 ## 风险清单 - 风险1: @@ -579,11 +584,15 @@ async def diagnose_stock(ts_code: str, mode: str = Query("entry")): ## 复盘问题 - 如果后续走势不符合预期，优先检查哪两个问题 +## 会诊纪要 +- 用两到三句话总结本次会诊，不要写成长文，不要复制前面的条目 + 要求： - 结论必须明确，不能模糊两可 - 少写形容词，多写交易判断 - 不要重复原始数据 -- 文字保持简洁，避免旧式研报语气""" +- 文字保持简洁，避免旧式研报语气 +- 每个条目尽量一句话说清，不要堆砌长段落""" # ── SSE 流式返回 ── async def _stream_diagnosis(): @@ -593,7 +602,7 @@ async def diagnose_stock(ts_code: str, mode: str = Query("entry")): stream = await client.chat.completions.create( model=settings.deepseek_model, messages=[ - {"role": "system", "content": "你是A股AI投研作战台中的个股会诊模块。你的职责不是写传统长文研报，而是基于市场环境、板块地位、推荐体系评分和跟踪结果，输出可执行、结构化的交易会诊意见。回复必须使用Markdown，结论明确，强调触发条件、失效条件、仓位和风险。"}, + {"role": "system", "content": "你是A股AI投研作战台中的个股会诊模块。你输出的是交易会诊单，不是传统研报。必须先给明确结论，再给执行动作、风险边界和跟踪重点。回复必须使用Markdown，结构严格、结论清晰、语言简短，禁止空泛抒情。"}, {"role": "user", "content": user_msg}, ], max_tokens=1500, diff --git a/backend/app/config.py b/backend/app/config.py index 5d89e409..69c1fe05 100644 --- a/backend/app/config.py +++ b/backend/app/config.py @@ -105,3 +105,27 @@ def is_pre_close() -> bool: now = datetime.now(ZoneInfo("Asia/Shanghai")) t = now.hour * 100 + now.minute return 1500 <= t <= 1530 + + +def today_trade_date() -> str: + """返回上海时区下的今天日期（YYYYMMDD）。""" + from zoneinfo import ZoneInfo + return datetime.now(ZoneInfo("Asia/Shanghai")).strftime("%Y%m%d") + + +def should_prefer_realtime_today(latest_trade_date: str | None = None) -> bool: + """是否应该优先使用“今天”的实时数据。 + + 规则： + 1. 非交易日直接 False + 2. 交易日内（含午休、收盘后）如果 Tushare 最新交易日还不是今天，则优先实时 + 3. 即便 Tushare 最新交易日已经是今天，只要仍处于 15:30 前的延迟窗口，也优先实时 + """ + if not is_market_session() and not is_pre_close(): + return False + + today = today_trade_date() + if latest_trade_date and latest_trade_date != today: + return True + + return is_market_session() or is_pre_close() diff --git a/backend/app/data/__pycache__/models.cpython-313.pyc b/backend/app/data/__pycache__/models.cpython-313.pyc index 60b466d92c7018c4a44cc53d0dda3c287ba40101..8adf4b9edf7d0138a3601c0eae2e3a07f9830217 100644 GIT binary patch delta 2164 zcmZ9NZERCj7{`0zcHO$ZuCHs?t-#(|CsT(3O*A-QumJ<R;?KAHk9?u)p~E3dq52*Rgj+tY+|mYJK13twun`B8lp#t zt%m3_H17t@ZG`gCG#!TLC(lEkmj#lo3GdSu3hXnqV6uHp^BY>{dcSfaV2Gi0F%dMh 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z%NS;pvc$*qX6~V)RjHar<14~rPeGp)zRp9QfrZ#;%L;TQbP}KXwld$bN&P@tw)@jrqck_8_w$G1P-l-Pz}jxa*In|l{-Idx+>vNx;pNo2 ROFq^^emOYw&LtnT!oO4}F_HiP diff --git a/backend/app/data/models.py b/backend/app/data/models.py index ea4c85a0..30102021 100644 --- a/backend/app/data/models.py +++ b/backend/app/data/models.py @@ -56,6 +56,7 @@ class CapitalFlow(BaseModel): class SectorInfo(BaseModel): sector_code: str sector_name: str + trade_date: str = "" pct_change: float = 0 # 涨跌幅 % capital_inflow: float = 0 # 主力净流入（万元，原始数据来自Tushare亿元×10000） limit_up_count: int = 0 # 涨停数 @@ -70,6 +71,15 @@ class SectorInfo(BaseModel): pct_trend: list[float] = [] # 近5日涨跌幅趋势 turnover_avg: float = 0 # 板块平均换手率 main_force_ratio: float = 0 # 主力资金占比（主力净流入/总成交额） + realtime_pct_change: float | None = None + realtime_limit_up_count: int | None = None + realtime_amount: float | None = None + realtime_turnover_rate: float | None = None + realtime_up_count: int | None = None + realtime_down_count: int | None = None + leading_stocks_realtime: list[dict] = [] + is_realtime: bool = False + data_mode: str = "daily_snapshot" class MarketTemperature(BaseModel): @@ -157,11 +167,16 @@ class StrategySectorFocus(BaseModel): heat_score: float = 0 pct_change: float = 0 limit_up_count: int = 0 + turnover_rate: float = 0 + up_count: int = 0 + down_count: int = 0 + data_mode: str = "daily_snapshot" view: str = "" class StrategyBoard(BaseModel): trade_date: str + data_mode: str = "daily_snapshot" market_regime: str risk_level: str action_bias: str diff --git a/backend/app/engine/__pycache__/recommender.cpython-313.pyc b/backend/app/engine/__pycache__/recommender.cpython-313.pyc index 1820ec7a44b420706a078b91597975369495f50f..3c5a1ee20647bb939636dd7b1a83142731b27376 100644 GIT binary patch delta 526 zcmbQgis|$!Cf?7yyj%=GVCV26^Z!QPC^kmL&3$aE8o9-Qg3Unuxrt>mf2{uGqK-1g z@Xaqf*cc}_cZxD{P5xLZ$;drfu}fz10vGYg3SBId3%Wc&G^4=e!Y{h7T>F>de3?eeqEv8s}V&NC#`_2mH zO}Cg}@%u~2WZsF9_4bS{eC~=2lNjCX*(NgsS<@8V*_pR9x-+rua5V)okLmINMUPoB 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zao&`1MU4cMRFSb8m!2}EU(xb*h!CH0MaN0K5FT~Kz)7Q^(cf)7rB3)ZoVT08{BK_V JD^a1n{|CW06M6su diff --git a/backend/app/engine/recommender.py b/backend/app/engine/recommender.py index db61dd8f..86c3b0f0 100644 --- a/backend/app/engine/recommender.py +++ b/backend/app/engine/recommender.py @@ -817,6 +817,7 @@ async def _load_sectors_from_db() -> list[SectorInfo]: sectors.append(SectorInfo( sector_code=r["sector_code"], sector_name=r["sector_name"], + trade_date=r.get("trade_date") or "", pct_change=r["pct_change"] or 0, capital_inflow=r["capital_inflow"] or 0, limit_up_count=r["limit_up_count"] or 0, diff --git a/backend/app/engine/screener.py b/backend/app/engine/screener.py index 6ee74363..46a56749 100644 --- a/backend/app/engine/screener.py +++ b/backend/app/engine/screener.py @@ -29,7 +29,8 @@ from app.analysis.trend_scanner import scan_trend_breakout from app.analysis.signals import generate_signals from app.analysis.intraday import intraday_market_temperature, intraday_filter_stocks, intraday_sector_scan from app.data.models import MarketTemperature, SectorInfo, TechnicalSignal, Recommendation -from app.config import settings, is_trading_hours, is_market_session +from app.config import settings, is_trading_hours, is_market_session, should_prefer_realtime_today +from app.data.tushare_client import tushare_client from app.llm.strategy_selector import select_strategy_profile logger = logging.getLogger(__name__) @@ -45,7 +46,8 @@ async def run_screening(trade_date: str = None) -> dict: "scan_mode": "intraday" | "post_market", } """ - intraday = is_market_session() + latest_trade_date = tushare_client.get_latest_trade_date() + intraday = should_prefer_realtime_today(latest_trade_date) scan_mode = "intraday" if intraday else "post_market" logger.info(f"=== 筛选模式: {'盘中实时' if intraday else '盘后'} ===") @@ -695,32 +697,60 @@ async def _build_recommendations( ) llm_results = await analyze_candidates_individually(llm_top, market_summary) - # 综合量化 + LLM 判断 + # 综合规则边界 + LLM 最终裁决 for rec in recommendations: llm_data = llm_results.get(rec.ts_code) if llm_data: rec.llm_analysis = llm_data.get("analysis", "") + rec.llm_score = float(llm_data.get("conviction", 0) or 0) - # LLM 信号强度转换为分数调整 - # 调整幅度温和，保底不低于 60，避免推荐被过滤掉 - strength = llm_data.get("strength", "中") - llm_signal = llm_data.get("signal", "HOLD") + verdict = llm_data.get("verdict", "watch") + action_plan = llm_data.get("action_plan", "") + conviction = float(llm_data.get("conviction", 6) or 6) + ai_score = conviction * 10 - if llm_signal == "SKIP": - # 降分但保底 60，排序靠后但不消失 - rec.score = max(60, round(rec.score - 15, 1)) - elif llm_signal == "HOLD": - rec.score = max(60, round(rec.score - 5, 1)) - elif llm_signal == "BUY" and strength == "强": - rec.score = round(rec.score + 10, 1) - elif llm_signal == "BUY" and strength == "中": - rec.score = round(rec.score + 5, 1) - else: # BUY + 弱 - pass # 不调整 + if verdict == "execute": + rec.score = round(rec.score * 0.4 + ai_score * 0.6 + 4, 1) + elif verdict == "watch": + rec.score = round(rec.score * 0.5 + ai_score * 0.5 - 2, 1) + else: # skip + rec.score = round(rec.score * 0.45 + ai_score * 0.35 - 18, 1) + + if verdict == "skip": + rec.signal = "HOLD" + rec.action_plan = "观察" + rec.lifecycle_status = "candidate" + if not rec.risk_note: + rec.risk_note = llm_data.get("risk_flag", "") or rec.risk_note + else: + if action_plan in {"可操作", "重点关注", "观察"}: + rec.action_plan = action_plan + elif verdict == "execute": + rec.action_plan = "可操作" + else: + rec.action_plan = "重点关注" + + rec.signal = "BUY" if verdict == "execute" else "HOLD" + if rec.action_plan == "可操作": + rec.lifecycle_status = "actionable" + elif rec.action_plan == "重点关注": + rec.lifecycle_status = "candidate" + + if llm_data.get("timing"): + rec.entry_timing = llm_data["timing"] + + if llm_data.get("trigger_condition"): + rec.trigger_condition = llm_data["trigger_condition"] + if llm_data.get("invalidation_condition"): + rec.invalidation_condition = llm_data["invalidation_condition"] + if llm_data.get("position_pct") is not None: + rec.suggested_position_pct = float(llm_data["position_pct"] or 0) + if llm_data.get("risk_flag"): + rec.risk_note = llm_data["risk_flag"] rec.level = _score_to_level(rec.score) - # 用 LLM 给出的价格替代硬编码价格 + # 用 LLM 给出的价格替代结构化规则价格 if llm_data.get("entry_price"): rec.entry_price = llm_data["entry_price"] if llm_data.get("target_price"): @@ -728,6 +758,11 @@ async def _build_recommendations( if llm_data.get("stop_loss"): rec.stop_loss = llm_data["stop_loss"] + # LLM 明确 skip 的标的，从推荐前列剔除 + recommendations = [ + rec for rec in recommendations + if not (rec.llm_score is not None and rec.llm_score <= 4 and rec.action_plan == "观察" and rec.score < strategy_profile.min_score) + ] recommendations.sort(key=lambda r: r.score, reverse=True) recommendations = recommendations[:settings.top_stock_count] logger.info(f"LLM 逐股分析完成, 综合评分后保留 {len(recommendations)} 只") diff --git a/backend/app/llm/__pycache__/prompts.cpython-313.pyc 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100644 --- a/backend/app/llm/batch_screener.py +++ b/backend/app/llm/batch_screener.py @@ -5,6 +5,7 @@ """ import asyncio +import json import logging import re @@ -21,11 +22,17 @@ async def analyze_single_stock(candidate: dict, market_summary: str) -> dict: market_summary: 市场环境摘要 返回: { - "signal": "BUY"/"HOLD"/"SKIP", - "strength": "强"/"中"/"弱", + "verdict": "execute"/"watch"/"skip", + "action_plan": "可操作"/"重点关注"/"观察", + "conviction": int, + "timing": str, "entry_price": float or None, "target_price": float or None, "stop_loss": float or None, + "trigger_condition": str, + "invalidation_condition": str, + "position_pct": int, + "risk_flag": str, "analysis": str, } """ @@ -36,7 +43,7 @@ async def analyze_single_stock(candidate: dict, market_summary: str) -> dict: stock_text = f"""\ 股票: {candidate['name']}({candidate['ts_code']}) 板块: {candidate.get('sector', '未知')} -量化评分: {candidate.get('quant_score', 0)}/100 +规则参考分: {candidate.get('quant_score', 0)}/100 位置安全: {candidate.get('position_score', 50)}/100 当前价: {candidate.get('current_price', '未知')}""" @@ -56,9 +63,10 @@ async def analyze_single_stock(candidate: dict, market_summary: str) -> dict: { "role": "system", "content": ( - "你是一位专业的A股趋势交易分析师，专注于中短线（1-5日）交易。" - "你根据技术分析结论独立判断入场时机，给出具体的买卖价格建议。" - "不要被量化评分束缚，给出你真实的判断。" + "你是一位A股短线交易裁决员。" + "你的任务是决定这只股票今天是否该进入推荐前列，以及应该归入可操作、重点关注还是观察。" + "不要复述数据，不要写成长文，不要被规则参考分绑架。" + "必须返回合法JSON。" ), }, {"role": "user", "content": user_msg}, @@ -72,69 +80,131 @@ async def analyze_single_stock(candidate: dict, market_summary: str) -> dict: except Exception as e: logger.error(f"LLM 分析 {candidate.get('ts_code')} 失败: {e}") return { - "signal": "HOLD", - "strength": "弱", + "verdict": "watch", + "action_plan": "重点关注", + "conviction": 4, + "timing": "", "entry_price": None, "target_price": None, "stop_loss": None, + "trigger_condition": "", + "invalidation_condition": "", + "position_pct": 0, + "risk_flag": "AI 裁决暂不可用", "analysis": "AI分析暂不可用", } def _parse_single_response(text: str) -> dict: """解析单只股票的 LLM 返回""" - # 提取信号 + data = _extract_json_object(text) + if data: + verdict = str(data.get("verdict", "watch")).strip().lower() + if verdict not in {"execute", "watch", "skip"}: + verdict = "watch" + + action_plan = str(data.get("action_plan", "")).strip() + if action_plan not in {"可操作", "重点关注", "观察"}: + action_plan = {"execute": "可操作", "watch": "重点关注", "skip": "观察"}[verdict] + + conviction = _clamp_int(data.get("conviction"), minimum=1, maximum=10, default=6) + position_pct = _clamp_int(data.get("position_pct"), minimum=0, maximum=35, default=0) + + return { + "verdict": verdict, + "action_plan": action_plan, + "conviction": conviction, + "timing": str(data.get("timing", "")).strip(), + "entry_price": _parse_float(data.get("entry_price")), + "target_price": _parse_float(data.get("target_price")), + "stop_loss": _parse_float(data.get("stop_loss")), + "trigger_condition": str(data.get("trigger_condition", "")).strip(), + "invalidation_condition": str(data.get("invalidation_condition", "")).strip(), + "position_pct": position_pct, + "analysis": str(data.get("analysis", "")).strip() or "暂无分析", + "risk_flag": str(data.get("risk_flag", "")).strip(), + } + + # 兼容旧格式 signal = "HOLD" signal_match = re.search(r"信号[:\s]*(BUY|HOLD|SKIP)", text) if signal_match: signal = signal_match.group(1) - # 提取信号强度 + verdict = "execute" if signal == "BUY" else "skip" if signal == "SKIP" else "watch" strength = "中" strength_match = re.search(r"信号强度[:\s]*(强|中|弱)", text) if strength_match: strength = strength_match.group(1) + conviction = {"强": 8, "中": 6, "弱": 4}.get(strength, 6) - # 提取买入价 entry_price = None entry_match = re.search(r"买入价[:\s]*(\d+(?:\.\d+)?)", text) if entry_match: entry_price = float(entry_match.group(1)) - - # 提取止盈价 target_price = None target_match = re.search(r"止盈价[:\s]*(\d+(?:\.\d+)?)", text) if target_match: target_price = float(target_match.group(1)) - - # 提取止损价 stop_loss = None stop_match = re.search(r"止损价[:\s]*(\d+(?:\.\d+)?)", text) if stop_match: stop_loss = float(stop_match.group(1)) - - # 提取分析 analysis = "" analysis_match = re.search(r"分析[:\s]*(.+)", text, re.DOTALL) if analysis_match: analysis = analysis_match.group(1).strip() return { - "signal": signal, - "strength": strength, + "verdict": verdict, + "action_plan": {"execute": "可操作", "watch": "重点关注", "skip": "观察"}[verdict], + "conviction": conviction, + "timing": "", "entry_price": entry_price, "target_price": target_price, "stop_loss": stop_loss, + "trigger_condition": "", + "invalidation_condition": "", + "position_pct": 20 if verdict == "execute" else 0, "analysis": analysis or "暂无分析", + "risk_flag": "", } +def _extract_json_object(text: str) -> dict | None: + match = re.search(r"\{[\s\S]*\}", text) + if not match: + return None + try: + parsed = json.loads(match.group(0)) + return parsed if isinstance(parsed, dict) else None + except Exception: + return None + + +def _parse_float(value) -> float | None: + try: + if value in (None, "", 0, "0"): + return None + return float(value) + except Exception: + return None + + +def _clamp_int(value, minimum: int, maximum: int, default: int) -> int: + try: + parsed = int(round(float(value))) + except Exception: + return default + return max(minimum, min(maximum, parsed)) + + async def analyze_candidates_individually( candidates: list[dict], market_summary: str, max_concurrent: int = 3 ) -> dict[str, dict]: """对候选股票逐个做 LLM 分析（控制并发数） - 返回: {ts_code: {"signal", "strength", "entry_price", ...}} + 返回: {ts_code: {"verdict", "action_plan", "conviction", "entry_price", ...}} """ if not settings.deepseek_api_key or not candidates: return {} @@ -149,7 +219,7 @@ async def analyze_candidates_individually( result = await analyze_single_stock(c, market_summary) logger.info( f"LLM 结果: {c.get('name', ts_code)} → " - f"信号={result['signal']} 强度={result['strength']} " + f"裁决={result['verdict']} 计划={result['action_plan']} 置信度={result['conviction']} " f"买入={result.get('entry_price')} 止盈={result.get('target_price')} " f"止损={result.get('stop_loss')}" ) diff --git a/backend/app/llm/daily_review.py b/backend/app/llm/daily_review.py index b927b58e..7172001d 100644 --- a/backend/app/llm/daily_review.py +++ b/backend/app/llm/daily_review.py @@ -2,7 +2,7 @@ import logging -from app.config import settings +from app.config import settings, today_trade_date logger = logging.getLogger(__name__) @@ -13,7 +13,8 @@ async def generate_review() -> dict: from app.data import tencent_client from app.engine.recommender import get_latest_recommendations, get_latest_sectors - trade_date = tushare_client.get_latest_trade_date() + latest_trade_date = tushare_client.get_latest_trade_date() + trade_date = today_trade_date() # 收集市场数据 result = await get_latest_recommendations() @@ -45,7 +46,7 @@ async def generate_review() -> dict: index_summary += f"{name_map[code]}: {d.get('price', 0):.2f} ({d.get('pct_chg', 0):+.2f}%), " sector_summary = "热门板块: " + "、".join( - f"{s.sector_name}({s.pct_change:+.1f}%)" for s in sectors[:5] + f"{s.sector_name}({(getattr(s, 'realtime_pct_change', None) or s.pct_change):+.1f}%)" for s in sectors[:5] ) rec_summary = "" @@ -58,6 +59,7 @@ async def generate_review() -> dict: user_msg = f"""请根据以下数据生成今日A股市场复盘报告（中文）： 日期: {trade_date} +Tushare 最新交易日: {latest_trade_date} {market_summary} {index_summary} {sector_summary} diff --git a/backend/app/llm/prompts.py b/backend/app/llm/prompts.py index 38c16cea..700934d4 100644 --- a/backend/app/llm/prompts.py +++ b/backend/app/llm/prompts.py @@ -122,32 +122,37 @@ ANALYSIS_USER_TEMPLATE = """\ # ── AI 逐股筛选 Prompt ── SINGLE_STOCK_ANALYSIS_PROMPT = """\ -你是一位专业的A股趋势交易分析师，专注于中短线交易（持仓1-5个交易日）。 +你是一位A股中短线交易裁决员，不是量化打分解释器。你的职责是基于给定的市场、板块、量价和位置结论，决定这只股票今天应不应该进入推荐池前列，以及应该归入什么动作级别。 -量化系统已通过多轮筛选认定该股票具备投资价值，请你基于以下技术分析结论，独立判断入场时机。 +你的原则： +1. 量化分数只是参考，不是最终答案 +2. 如果板块地位、量价质量、位置或时机不匹配，可以直接否决高分股 +3. 如果股票具备明确触发与失效边界，即使量化分不是最高，也可以提升优先级 +4. 输出的是交易裁决单，不是研报 -你的任务： -1. 综合趋势、量价、技术指标和位置，判断当前是否适合介入 -2. 如果适合介入，给出具体的买入价位、止盈价和止损价 -3. 评估信号强度 +请严格输出 JSON，不要输出 Markdown，不要添加多余解释。字段如下： +{ + "verdict": "execute | watch | skip", + "action_plan": "可操作 | 重点关注 | 观察", + "conviction": 1-10, + "timing": "一句话描述当下最合适的处理方式", + "entry_price": 0, + "target_price": 0, + "stop_loss": 0, + "trigger_condition": "一句话", + "invalidation_condition": "一句话", + "position_pct": 0, + "analysis": "2-4句话，说明为什么给这个裁决", + "risk_flag": "一句话说明最大风险" +} -注意： -- 你看到的是量化系统对K线和技术指标的分析结论，不是原始数据 -- 请独立判断，不要被量化评分影响太多 -- 止盈空间通常3-8%，止损空间通常3-5% -- 中短线交易，重点关注1-5日的走势 +裁决标准： +- execute: 今天具备执行条件或非常接近执行条件，可以进入推荐前列 +- watch: 逻辑还在，但需要等待确认，不应直接作为首选执行标的 +- skip: 当前不适合进入推荐前列，宁可错过也不主动参与 -请严格按以下格式输出： - -信号: BUY/HOLD/SKIP -信号强度: 强/中/弱 -买入价: XX.XX（建议入场价位，基于当前价微调） -止盈价: XX.XX（目标价位，给出合理空间） -止损价: XX.XX（跌破此价离场） -分析: 3-5句话，说明核心逻辑、入场理由和主要风险 - -说明： -- BUY: 看好，建议在买入价附近介入 -- HOLD: 观望，等待更好的时机 -- SKIP: 不看好，风险大于收益 -- 信号强度"强"表示把握较大，"弱"表示不确定性较高""" +补充要求： +- conviction 必须是 1-10 的整数 +- position_pct 返回 0-35 的整数；如果不适合参与，就返回 0 +- 没有把握时优先给 watch 或 skip +- trigger_condition 和 invalidation_condition 必须可执行，不能写空话""" diff --git a/backend/app/llm/strategy_board.py b/backend/app/llm/strategy_board.py index 3f9eb0bb..532cf6e2 100644 --- a/backend/app/llm/strategy_board.py +++ b/backend/app/llm/strategy_board.py @@ -6,7 +6,8 @@ import logging -from app.config import settings +from app.analysis.sector_realtime import enrich_sectors_with_realtime +from app.config import settings, should_prefer_realtime_today, today_trade_date from app.data.models import ( MarketTemperature, Recommendation, @@ -19,6 +20,35 @@ from app.data.models import ( logger = logging.getLogger(__name__) +def _sector_pct(sector: SectorInfo) -> float: + return float(sector.realtime_pct_change if sector.realtime_pct_change is not None else sector.pct_change) + + +def _sector_turnover(sector: SectorInfo) -> float: + return float(sector.realtime_turnover_rate if sector.realtime_turnover_rate is not None else sector.turnover_avg) + + +def _sector_up_count(sector: SectorInfo) -> int: + return int(sector.realtime_up_count if sector.realtime_up_count is not None else 0) + + +def _sector_down_count(sector: SectorInfo) -> int: + return int(sector.realtime_down_count if sector.realtime_down_count is not None else 0) + + +def _sector_breadth(sector: SectorInfo) -> int: + return _sector_up_count(sector) - _sector_down_count(sector) + + +def _sector_strength_score(sector: SectorInfo) -> float: + strength = _sector_pct(sector) * 12 + strength += min(max(_sector_breadth(sector), -30), 30) * 0.6 + strength += min(_sector_turnover(sector), 12) * 1.5 + strength += min(sector.limit_up_count, 8) * 2.5 + strength += min(sector.days_continuous, 5) * 1.5 + return round(strength, 1) + + async def build_strategy_board(include_llm: bool = False) -> dict: """生成今日市场作战面板。""" from app.engine.recommender import ( @@ -31,6 +61,7 @@ async def build_strategy_board(include_llm: bool = False) -> dict: market_temp = latest.get("market_temp") recommendations = latest.get("recommendations", []) sectors = await get_latest_sectors() + sectors = await enrich_sectors_with_realtime(sectors) performance = await get_performance_stats() from app.llm.strategy_iteration import build_strategy_iteration_report iteration_report = await build_strategy_iteration_report(limit=50, include_llm=include_llm) @@ -59,11 +90,18 @@ def _build_rule_board( performance: dict, ) -> StrategyBoard: temp = market_temp.temperature if market_temp else 0 - trade_date = market_temp.trade_date if market_temp else "" + raw_trade_date = market_temp.trade_date if market_temp else "" + prefer_realtime = should_prefer_realtime_today(raw_trade_date) + trade_date = today_trade_date() if prefer_realtime else raw_trade_date + data_mode = "realtime_today" if prefer_realtime else "daily_snapshot" market_regime, risk_level, action_bias, position_suggestion = _classify_market(temp, market_temp) actionable = [r for r in recommendations if r.action_plan == "可操作"] watch = [r for r in recommendations if r.action_plan == "重点关注"] + strong_sectors = [ + s for s in sectors[:5] + if _sector_pct(s) >= 1.5 and (_sector_breadth(s) > 0 or s.limit_up_count >= 2) + ] avg_score = ( round(sum(r.score for r in recommendations) / len(recommendations), 1) if recommendations else 0 @@ -74,11 +112,13 @@ def _build_rule_board( watch_sectors = [_sector_focus(s) for s in sectors[:5]] avoid_rules = _build_avoid_rules(temp, sectors, recommendations) iteration_notes = _build_iteration_notes(performance, recommendations) + mode_prefix = "今日实时视角：" if prefer_realtime else "" summary = ( - f"{market_regime}，风险等级{risk_level}。" + f"{mode_prefix}{market_regime}，风险等级{risk_level}。" f"当前 {len(recommendations)} 只入选，其中 {len(actionable)} 只可操作、" f"{len(watch)} 只重点关注，平均分 {avg_score}。" + f"{'主线活跃板块 ' + ' / '.join(s.sector_name for s in strong_sectors[:3]) + '。' if strong_sectors else '板块尚未形成强共振，优先等确认。'}" ) metrics = { @@ -94,6 +134,7 @@ def _build_rule_board( return StrategyBoard( trade_date=trade_date, + data_mode=data_mode, market_regime=market_regime, risk_level=risk_level, action_bias=action_bias, @@ -125,11 +166,17 @@ def _classify_market( def _choose_strategy_mode( temp: float, sectors: list[SectorInfo], recommendations: list[Recommendation] ) -> str: - early_mid = [s for s in sectors[:5] if s.stage in ("early", "mid")] - if temp >= 60 and early_mid: - return "主线突破 + 回踩确认" - if temp >= 45: - return "精选回踩，降低追高" + top = sectors[:5] + strong = [s for s in top if _sector_pct(s) >= 2 and _sector_breadth(s) > 0] + active = [s for s in top if _sector_pct(s) >= 1 and (_sector_turnover(s) >= 2 or s.limit_up_count >= 2)] + end_stage = [s for s in top if s.stage == "end" and _sector_pct(s) > 0] + + if temp >= 65 and len(strong) >= 2: + return "顺主线做强势确认" + if temp >= 55 and active: + return "围绕强板块做回踩确认" + if temp < 45 or end_stage: + return "缩仓观察，回避后排追高" if recommendations: return "观察池跟踪，等待触发" return "防守观察" @@ -159,16 +206,37 @@ def _build_strategy_focus( if sectors: main = sectors[0] + sector_pct = _sector_pct(main) + breadth = _sector_breadth(main) + turnover = _sector_turnover(main) focus.append(StrategyFocus( label=f"{main.sector_name} 主线跟踪", - description=f"热度 {main.heat_score}，阶段 {main.stage}，优先确认资金是否延续。", + description=( + f"当前涨幅 {sector_pct:+.2f}% ，广度 {breadth:+d}，换手 {turnover:.1f}% ，" + f"阶段 {main.stage}，优先确认资金是否延续。" + ), )) + if len(sectors) > 1: + runner_up = sectors[1] + focus.append(StrategyFocus( + label=f"{runner_up.sector_name} 轮动监控", + description=( + f"强度分 {_sector_strength_score(runner_up)}，" + f"若涨幅继续扩大且广度转强，可切入今日第二梯队。" + ), + )) + if temp < 45: focus.append(StrategyFocus( label="防守优先", description="市场温度不足，推荐只作为观察池，不宜扩大仓位。", )) + elif sectors and _sector_pct(sectors[0]) < 1: + focus.append(StrategyFocus( + label="等一致性强化", + description="板块领涨幅度仍有限，优先等主线扩散和个股触发后再出手。", + )) return focus @@ -185,9 +253,19 @@ def _sector_focus(sector: SectorInfo) -> StrategySectorFocus: sector_name=sector.sector_name, stage=sector.stage, heat_score=sector.heat_score, - pct_change=sector.pct_change, + pct_change=_sector_pct(sector), limit_up_count=sector.limit_up_count, - view=stage_view, + turnover_rate=_sector_turnover(sector), + up_count=_sector_up_count(sector), + down_count=_sector_down_count(sector), + data_mode=sector.data_mode, + view=( + f"{stage_view}；当前涨幅 {_sector_pct(sector):+.2f}%" + + ( + f"，上涨/下跌 {_sector_up_count(sector)}/{_sector_down_count(sector)}" + if sector.is_realtime else "" + ) + ), ) @@ -195,10 +273,17 @@ def _build_avoid_rules( temp: float, sectors: list[SectorInfo], recommendations: list[Recommendation] ) -> list[str]: rules = [] + top = sectors[:5] if temp < 45: rules.append("市场温度低于45时，不追突破首日，只等次日确认或回踩。") - if any(s.stage == "end" for s in sectors[:5]): + if any(s.stage == "end" for s in top): rules.append("板块进入末期时，降低同板块追高标的权重。") + if top and _sector_pct(top[0]) < 1: + rules.append("主线板块涨幅不足1%时，不把局部异动当成全面进攻信号。") + if any(_sector_breadth(s) < 0 for s in top[:3] if s.is_realtime): + rules.append("板块涨幅与广度背离时，优先回避后排补涨和冲高追单。") + if any(_sector_turnover(s) > 8 and s.stage in ("late", "end") for s in top): + rules.append("高换手叠加板块后期阶段时，防止情绪过热后的快速分歧。") if any(r.position_score < 35 for r in recommendations): rules.append("位置安全分低于35的标的，只观察不主动追入。") if not rules: diff --git a/backend/astock.db b/backend/astock.db index 31ffc2d15593d63624abff6a3595d7544766d392..ccf34c11fdc21489c4b8c6a0f39849e420a2d53d 100644 GIT binary patch delta 6361 zcmbtY4OCNSn$Ar^0t5&V0t5_y67Ic-F$oaBV{5e5pBaBZv1;W{t(_Sit?r)boYp!L z6p23=Fvu%{6%+*jKn6=tK%%pAy4`l$y1Ua(TW8yz;U?j%XVp{NJ#%JfyYF}JO@OAe z=S(-}y!pcSUGDpR&-?Se&s+1w6{-fMF(vw-L?XQ^kw_wOJk-P#QU?dBud1n*!Km0l zbv#ZUFUK#(Hu9?qzm9Bh{cBng)#Qpwzbwx)FEG2${daa7<+_xv4kLdS41PIxh;pT7 zWhwpmTKu?HW@U;?d$W>Lo>i+Pda0D6D5WybVzO9G`4&sWyOCQ`#W_}e)c)u@b&aFPd6S_WsIZGGWyJ;kBDPwV$SrBI|OG88#V7>WW# zi4u+yff9+LLWx3&Mu|aDqr{?UP-daTp~RzTQ4&xRQIb$}D9I=(D6>)YD5)rEDCzFK zPcxdgL`oGOOI4BhblaTO770+VLnQN|9XW{ha04-o64Os16fayu@1+QZHy$%phGi-Q(C1`XL4I2xzT4vyBgeVAZr|C%K<&v+ 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z?~z)?IVtmvc6$1Bf0bTFDX0F$*|6gp)I>9;>6y8X6jR42k52Coo$c!wb-G#XL9T$7}Bw?{mGa$o2dHXY-@3XWRK#z$K@1 zqgSy|fWqiS>k%bbUXk*N@D$F3Ly_kGNXs~EGm#W9y^Nf|ic~Cja=GE>FLwV}1ctGW z@O{E?>I^~;TV2Jmc%%<+vU7vn{;>Z@==gZH4KH%wsW=}Pud+ka3+Z!!a$#7YMW{Sod=ejl7|ss&C$3oTKeaD@=hDF+ducnUwv&eiCrTV}bpdUN=7&Gr zdSP*}Z4EtJd~*Vx+?t-4!cXWpA>qV{(^dG;WN6Pw{*tB3aOPqgYC)!=GoiPQ$$&bp zU14QLB?yg57&h;1RCh~O1%qa6bQe^}c-tdL!LyIkNVeTX30KR=E1+vYU!LDV%0Tue UZ4_r^&2*?$)ti~)n=OU^1JT?iL;wH) diff --git a/frontend/.next/app-build-manifest.json b/frontend/.next/app-build-manifest.json index bce6a549..ef123f7d 100644 --- a/frontend/.next/app-build-manifest.json +++ b/frontend/.next/app-build-manifest.json @@ -70,11 +70,6 @@ "static/chunks/webpack.js", "static/chunks/main-app.js", "static/chunks/app/(public)/page.js" - ], - "/_not-found/page": [ - "static/chunks/webpack.js", - "static/chunks/main-app.js", - "static/chunks/app/_not-found/page.js" ] } } \ No newline at end of file diff --git a/frontend/.next/server/app-paths-manifest.json b/frontend/.next/server/app-paths-manifest.json index 32c12767..21ce9557 100644 --- a/frontend/.next/server/app-paths-manifest.json +++ b/frontend/.next/server/app-paths-manifest.json @@ -1,7 +1,6 @@ { - "/_not-found/page": "app/_not-found/page.js", - "/(auth)/dashboard/page": "app/(auth)/dashboard/page.js", "/(auth)/stock/[code]/page": "app/(auth)/stock/[code]/page.js", + "/(auth)/dashboard/page": "app/(auth)/dashboard/page.js", "/(auth)/chat/page": "app/(auth)/chat/page.js", "/(public)/login/page": "app/(public)/login/page.js", "/(public)/page": "app/(public)/page.js" diff --git a/frontend/src/app/(auth)/dashboard/page.tsx b/frontend/src/app/(auth)/dashboard/page.tsx index 6c39b3a5..548bc231 100644 --- a/frontend/src/app/(auth)/dashboard/page.tsx +++ b/frontend/src/app/(auth)/dashboard/page.tsx @@ -350,18 +350,34 @@ function MissionControl({ const risks = board?.avoid_rules?.length ? board.avoid_rules : ["等待市场状态和推荐结果更新后生成风险约束。"]; const primaryQueue = (actionable.length ? actionable : watch).slice(0, 3); const laneTitle = actionable.length ? "优先执行" : watch.length ? "候选观察" : "等待信号"; - const strategyName = strategyProfile?.name ?? board?.recommended_mode ?? "待定"; - const strategyHint = strategyProfile?.description ?? "系统判断今天更适合采用的出手方式"; - const riskText = risks.slice(0, 2).join(" / "); + const strategyName = strategyProfile?.name && strategyProfile.name !== "当前推荐策略" + ? strategyProfile.name + : board?.recommended_mode ?? "待定"; + const strategyHint = strategyProfile?.description ?? "结合市场状态与主线强弱得出的出手方式"; + const isRealtimeBoard = board?.data_mode === "realtime_today"; + const topSectorEvidence = topSectors.map((sector) => ({ + key: sector.sector_code, + name: sector.sector_name, + pct: sector.realtime_pct_change ?? sector.pct_change, + breadth: sector.realtime_up_count != null && sector.realtime_down_count != null + ? `${sector.realtime_up_count}/${sector.realtime_down_count}` + : null, + turnover: sector.realtime_turnover_rate, + })); return ( -
+
-
-
+
+
今日结论 + {isRealtimeBoard && ( + + 今日实时 + + )} {board?.generated_by === "rules+llm" && ( AI增强 )} @@ -371,14 +387,19 @@ function MissionControl({
-
+

{board?.market_regime ?? "等待市场状态"}

-

+

{board?.summary ?? "系统尚未生成今日作战结论。触发扫描后，将基于市场温度、板块主线、推荐生命周期和策略复盘生成操作框架。"}

+ {board?.trade_date && ( +
+ {isRealtimeBoard ? `分析日期 ${board.trade_date} · 今日实时优先` : `数据日期 ${board.trade_date}`} +
+ )}
@@ -387,23 +408,32 @@ function MissionControl({
-
+
- sector.sector_name).join(" / ") : "暂无"} extra={riskText || "暂无风险约束"} /> +
-
- {topSectors.length ? ( - topSectors.map((sector) => ( - - {sector.sector_name} - = 0 ? "text-red-400" : "text-emerald-400"}`}> - {sector.pct_change > 0 ? "+" : ""}{sector.pct_change.toFixed(2)}% - - - )) +
+
主线证据
+ {topSectorEvidence.length ? ( +
+ {topSectorEvidence.map((sector) => ( +
+
+ {sector.name} + = 0 ? "text-red-400" : "text-emerald-400"}`}> + {sector.pct > 0 ? "+" : ""}{sector.pct.toFixed(2)}% + +
+
+ {sector.breadth ? 涨跌 {sector.breadth} : null} + {sector.turnover != null ? 换手 {sector.turnover.toFixed(1)}% : null} +
+
+ ))} +
) : ( 暂无主线板块 )} @@ -443,8 +473,8 @@ function CompactDecision({ label, value, extra }: { label: string; value: string return (
{label}
-
{value}
- {extra ?
{extra}
: null} +
{value}
+ {extra ?
{extra}
: null}
); } @@ -455,6 +485,7 @@ function CompactMissionStock({ rec }: { rec: LatestResult["recommendations"][num "重点关注": "bg-amber-500/15 text-amber-400 border-amber-500/20", "观察": "bg-surface-3 text-text-muted border-border-default", }; + const aiConviction = rec.llm_score != null ? Math.round(rec.llm_score) : null; return (
-
{rec.score}
-
参考
+ {aiConviction != null ? ( + <> +
AI {aiConviction}/10
+
{rec.action_plan ?? "观察"}
+ + ) : ( + <> +
{rec.action_plan ?? "观察"}
+
参考分 {Math.round(rec.score)}
+ + )}
- {rec.trigger_condition ?? rec.reasons?.[0] ?? "等待触发条件确认"} + {rec.trigger_condition ?? rec.entry_timing ?? rec.reasons?.[0] ?? "等待触发条件确认"}
); diff --git a/frontend/src/app/(auth)/diagnose/page.tsx b/frontend/src/app/(auth)/diagnose/page.tsx index ee79a8e1..78ec2259 100644 --- a/frontend/src/app/(auth)/diagnose/page.tsx +++ b/frontend/src/app/(auth)/diagnose/page.tsx @@ -301,7 +301,7 @@ export default function DiagnosePage() {
正在分析中...
-
收集行情数据、技术指标、资金流向并生成分析报告
+
收集行情、板块和推荐归档，生成本次会诊结论
)} @@ -345,6 +345,15 @@ export default function DiagnosePage() {
+
+
会诊摘要
+
+ + + + +
+
@@ -506,3 +515,38 @@ function buildDiagnosisRisk(thesis: StockThesisResponse | null, loading: boolean } return "当前没有明确风险备注"; } + +function buildDiagnosisAction( + thesis: StockThesisResponse | null, + mode: "entry" | "holding" | "review" | "tracking", +) { + if (thesis?.recommendation?.action_plan === "可操作") { + return "仅在触发条件成立时执行，不提前交易。"; + } + if (thesis?.recommendation?.action_plan === "重点关注") { + return "继续跟踪板块和个股强度，等确认后再做动作。"; + } + if (mode === "holding") { + return "优先核对持仓逻辑、失效条件和风险暴露。"; + } + if (mode === "review") { + return "先定位问题出在市场环境、板块节奏还是个股执行。"; + } + return "当前以观察和补充证据为主。"; +} + +function buildDiagnosisNextStep( + thesis: StockThesisResponse | null, + mode: "entry" | "holding" | "review" | "tracking", +) { + if (thesis?.recommendation?.trigger_condition) { + return `重点盯住：${thesis.recommendation.trigger_condition}`; + } + if (mode === "tracking") { + return "继续观察是否进入可操作或重点关注状态。"; + } + if (mode === "holding") { + return "检查是否需要减仓、止损或继续持有。"; + } + return "结合下一个交易时段的量价和板块变化再判断。"; +} diff --git a/frontend/src/app/(auth)/recommendations/page.tsx b/frontend/src/app/(auth)/recommendations/page.tsx index 2cd2d374..af10af7d 100644 --- a/frontend/src/app/(auth)/recommendations/page.tsx +++ b/frontend/src/app/(auth)/recommendations/page.tsx @@ -158,6 +158,27 @@ export default function RecommendationsPage() {
+
+
Recommendation Logic
+
+ + + +
+
+ {opsStatus && (
@@ -333,8 +354,6 @@ export default function RecommendationsPage() { {/* Mobile stats inline after date */}
{filter === "all" ? group.count : filtered.length}只 - · - 参考{group.avg_score} {group.buy_count > 0 && ( <> · @@ -346,12 +365,6 @@ export default function RecommendationsPage() { {/* Desktop stats */}
-
- 参考均值 - - {group.avg_score} - -
买入 @@ -505,9 +518,9 @@ function RecommendationCommandCenter({
Recommendation Ops
-

推荐不是排行榜，是执行管线

+

推荐闭环

- 这里把 AI 选股结果拆成四个动作池：能操作的先盯触发，未确认的继续等待，已推荐的持续跟踪，结束后的样本进入策略迭代。 + 先看可操作和重点关注，再看跟踪结果，结束样本自动回流到策略迭代。

@@ -539,6 +552,28 @@ function RecommendationCommandCenter({ ); } +function GlossaryCard({ + label, + value, + description, +}: { + label: string; + value: string; + description: string; +}) { + return ( +
+
+
{label}
+
+ {value} +
+
+
{description}
+
+ ); +} + function PipelineMetric({ label, value, tone }: { label: string; value: number; tone?: "red" | "amber" | "cyan" }) { const color = tone === "red" ? "text-red-400" : tone === "amber" ? "text-amber-400" : tone === "cyan" ? "text-cyan-400" : "text-text-primary"; return ( diff --git a/frontend/src/app/(auth)/sectors/page.tsx b/frontend/src/app/(auth)/sectors/page.tsx index 3d370dcd..15cdc91c 100644 --- a/frontend/src/app/(auth)/sectors/page.tsx +++ b/frontend/src/app/(auth)/sectors/page.tsx @@ -110,6 +110,10 @@ function SectorDetailCard({ sector, index, factorScores }: { const displayPct = sector.realtime_pct_change ?? sector.pct_change; const isUp = displayPct > 0; const displayLimitUp = sector.realtime_limit_up_count ?? sector.limit_up_count; + const displayAmount = sector.realtime_amount ?? sector.capital_inflow; + const displayTurnover = sector.realtime_turnover_rate ?? sector.turnover_avg ?? 0; + const displayUpCount = sector.realtime_up_count; + const displayDownCount = sector.realtime_down_count; const leaders = sector.is_realtime ? (sector.leading_stocks_realtime?.length ? sector.leading_stocks_realtime : sector.leading_stocks) : sector.leading_stocks; @@ -186,24 +190,36 @@ function SectorDetailCard({ sector, index, factorScores }: { {/* Metrics row - 4 columns */}
-
资金净流入
-
0 ? "text-red-400" : "text-emerald-400"}`}> - {sector.capital_inflow > 0 ? "+" : ""}{formatNumber(sector.capital_inflow)} +
{sector.is_realtime ? "实时成交额" : "资金净流入"}
+
0 ? "text-red-400" : "text-emerald-400"}`}> + {displayAmount > 0 ? "+" : ""}{formatNumber(displayAmount)}
-
涨停股
-
- {displayLimitUp} 只 -
+
{sector.is_realtime ? "上涨/下跌" : "涨停股"}
+ {sector.is_realtime && displayUpCount != null && displayDownCount != null ? ( +
+ {displayUpCount} / {displayDownCount} +
+ ) : ( +
+ {displayLimitUp} 只 +
+ )}
-
主力占比
-
30 ? "text-amber-400" : mainForceRatio < 0 ? "text-red-400" : "text-text-secondary" - }`}> - {mainForceRatio.toFixed(1)}% -
+
{sector.is_realtime ? "实时换手" : "主力占比"}
+ {sector.is_realtime ? ( +
+ {displayTurnover.toFixed(1)}% +
+ ) : ( +
30 ? "text-amber-400" : mainForceRatio < 0 ? "text-red-400" : "text-text-secondary" + }`}> + {mainForceRatio.toFixed(1)}% +
+ )}
热度评分
@@ -344,6 +360,8 @@ export default function SectorsPage() { ); const hasRealtime = sectors.some((s) => s.is_realtime); + const structureTradeDate = sectors[0]?.structure_trade_date || sectors[0]?.trade_date || ""; + const dataMode = sectors[0]?.data_mode || "daily_snapshot"; const loadRotation = useCallback(async () => { try { @@ -405,8 +423,15 @@ export default function SectorsPage() {

板块主线

判断当前主线、板块阶段、资金持续性和领涨股强度 - {hasRealtime && · 实时数据} + {hasRealtime && · 盘中实时优先}

+ {hasRealtime && dataMode === "realtime_overlay" && ( +

+ 盘中模式下，涨幅、成交额、上涨/下跌家数与领涨股为实时覆盖；阶段、资金连续性等结构字段仍基于 + {structureTradeDate || "最近交易日"} + 的板块快照。 +

+ )}
{!sectors.length ? ( diff --git a/frontend/src/app/(auth)/stock/[code]/page.tsx b/frontend/src/app/(auth)/stock/[code]/page.tsx index 6f598793..60f6fbf6 100644 --- a/frontend/src/app/(auth)/stock/[code]/page.tsx +++ b/frontend/src/app/(auth)/stock/[code]/page.tsx @@ -162,6 +162,7 @@ export default function StockDetailPage() { const latestTracking = thesis?.latest_tracking; const latestFlow = capitalFlow.length > 0 ? capitalFlow[capitalFlow.length - 1] : null; const pageName = recommendation?.name || thesis?.name || quote?.name || code; + const aiConviction = recommendation?.llm_score != null ? Math.round(recommendation.llm_score) : null; return ( @@ -181,7 +182,7 @@ export default function StockDetailPage() {
- Stock Thesis + 个股作战卡 {recommendation?.action_plan ? ( {recommendation.action_plan} @@ -215,6 +216,12 @@ export default function StockDetailPage() {
{thesis?.data_freshness.message ?? "加载中"}
+
+ + + + +
推荐 {formatDateTime(thesis?.data_freshness.recommendation_created_at)} 跟踪 {thesis?.data_freshness.tracking_date || "暂无"} @@ -237,7 +244,7 @@ export default function StockDetailPage() {
- +
@@ -289,28 +296,54 @@ function PlanCard({ }) { return (
-
- - {recommendation?.llm_score != null ? ( - AI {recommendation.llm_score}/10 - ) : null} +
+ + {recommendation?.llm_score != null ? ( + AI 置信 {Math.round(recommendation.llm_score)}/10 + ) : null}
+ {recommendation ? : null} {trackingNote ? : null}
); } -function EvidenceCard({ recommendation }: { recommendation: RecommendationData | null | undefined }) { +function EvidenceCard({ + recommendation, + quote, + signals, +}: { + recommendation: RecommendationData | null | undefined; + quote: QuoteData | null; + signals: StockSignals | null; +}) { const reasons = recommendation?.reasons ?? []; + const evidenceChips = [ + recommendation?.entry_signal_type ? `信号 ${signalTypeLabel(recommendation.entry_signal_type)}` : null, + quote?.pct_chg != null ? `涨幅 ${quote.pct_chg > 0 ? "+" : ""}${quote.pct_chg.toFixed(2)}%` : null, + quote?.turnover_rate != null ? `换手 ${quote.turnover_rate.toFixed(2)}%` : null, + signals?.ma_bullish ? "均线多头" : null, + signals?.volume_breakout ? "放量突破" : null, + signals?.pullback_support ? "回踩支撑" : null, + ].filter(Boolean) as string[]; return (
- + + {evidenceChips.length ? ( +
+ {evidenceChips.slice(0, 6).map((item) => ( + + {item} + + ))} +
+ ) : null}
{reasons.length ? reasons.map((reason, index) => (
@@ -381,7 +414,7 @@ function TrackingCard({ tracking }: { tracking: StockThesisResponse["latest_trac function QuoteSnapshot({ quote, evidenceLoaded }: { quote: QuoteData | null; evidenceLoaded: boolean }) { return (
- + {quote ? ( <>
@@ -419,14 +452,14 @@ function SignalSnapshot({ }) { return (
- + {signals ? ( <> -
- - - - +
+ + + +
@@ -515,22 +548,6 @@ function TrackingMetric({ label, value }: { label: string; value: number | null ); } -function DimensionScore({ label, value }: { label: string; value: number }) { - const width = Math.max(0, Math.min(value, 100)); - const gradientClass = value >= 70 ? "score-bar-gradient-high" : value >= 50 ? "score-bar-gradient-mid" : "score-bar-gradient-low"; - return ( -
-
- {label} - {value.toFixed(0)} -
-
-
-
-
- ); -} - function SignalFlag({ label, active }: { label: string; active: boolean }) { return (
@@ -606,3 +623,28 @@ function formatFlowAmount(val: number): string { if (absVal >= 10000) return (val / 10000).toFixed(1) + "亿"; return val.toFixed(0) + "万"; } + +function signalTypeLabel(signalType?: string) { + const map: Record = { + breakout: "突破", + pullback: "回踩", + launch: "启动", + reversal: "反转", + breakout_confirm: "突破确认", + }; + return map[signalType || ""] ?? "观察"; +} + +function signalBias(signals: StockSignals, recScore: RecScore | null) { + const trend = recScore?.technical_score ?? signals.trend_score; + if (trend >= 75 && signals.volume_breakout) return "偏强，可等确认"; + if (trend >= 60) return "中性偏强"; + if (signals.pullback_support) return "等待支撑确认"; + return "仍需观察"; +} + +function positionComment(positionScore: number) { + if (positionScore >= 75) return "位置相对安全"; + if (positionScore >= 50) return "位置中性"; + return "位置偏高，防追高"; +} diff --git a/frontend/src/components/sector-heatmap.tsx b/frontend/src/components/sector-heatmap.tsx index 78bec50b..fec95c76 100644 --- a/frontend/src/components/sector-heatmap.tsx +++ b/frontend/src/components/sector-heatmap.tsx @@ -38,6 +38,10 @@ export default function SectorHeatmap({ sectors }: { sectors: SectorData[] }) { const isTop3 = index < 3; // 盘中使用实时涨停数 const displayLimitUp = s.realtime_limit_up_count ?? s.limit_up_count; + const displayAmount = s.realtime_amount ?? s.capital_inflow; + const displayBreadth = s.realtime_up_count != null && s.realtime_down_count != null + ? `${s.realtime_up_count}/${s.realtime_down_count}` + : null; // Bar width based on score relative to max const barWidth = `${Math.max(intensity * 100, 15)}%`; @@ -85,14 +89,19 @@ export default function SectorHeatmap({ sectors }: { sectors: SectorData[] }) {
- 0 ? "text-red-400" : "text-emerald-400"}`}> - {s.capital_inflow > 0 ? "+" : ""} - {formatNumber(s.capital_inflow)} + 0 ? "text-red-400" : "text-emerald-400"}`}> + {displayAmount > 0 ? "+" : ""} + {formatNumber(displayAmount)} {displayPct > 0 ? "+" : ""} {displayPct.toFixed(2)}% + {displayBreadth && ( + + {displayBreadth} + + )} {/* Heat score pill */} = { @@ -39,6 +40,9 @@ export default function StockCard({ rec }: { rec: RecommendationData }) { "重点关注": "等待确认，不提前交易", "观察": "只记录，不主动出手", }; + const evidence = [rec.reasons?.[0], rec.entry_timing, rec.data_freshness] + .filter(Boolean) + .slice(0, 2) as string[]; return (
@@ -70,12 +74,13 @@ export default function StockCard({ rec }: { rec: RecommendationData }) {
-
- AI 决策 -
-
- {rec.action_plan ?? "观察"} -
+
结论
+
{rec.action_plan ?? "观察"}
+ {aiConviction != null ? ( +
+ AI {aiConviction}/10 +
+ ) : null}
@@ -108,6 +113,15 @@ export default function StockCard({ rec }: { rec: RecommendationData }) { {rec.review_after_days}日复盘 ) : null} + {aiConviction != null ? ( + + AI置信 {aiConviction}/10 + + ) : rec.score ? ( + + 参考分 {rec.score.toFixed(0)} + + ) : null} {rec.level} @@ -115,20 +129,25 @@ export default function StockCard({ rec }: { rec: RecommendationData }) {
)} -
-
-
证据维度
- - 参考 {rec.score.toFixed(0)} - + {evidence.length > 0 && ( +
+
核心证据
+
+ {evidence.map((item, index) => ( +
+ + {item} +
+ ))} +
+
+ 规则供需 {Math.round(rec.supply_demand_score ?? 0)} + 规则形态 {Math.round(rec.price_action_score ?? 0)} + 规则趋势 {Math.round(rec.technical_score ?? 0)} + 规则位置 {Math.round(rec.position_score ?? 50)} +
-
- - - - -
-
+ )} {/* Price reference */} {rec.entry_price && ( @@ -193,10 +212,10 @@ export default function StockCard({ rec }: { rec: RecommendationData }) {
- 推演记录在详情页归档 - {rec.llm_score != null && ( + 详细推演在详情页归档 + {aiConviction != null && ( - AI {rec.llm_score}/10 + AI {aiConviction}/10 )}
@@ -215,25 +234,6 @@ export default function StockCard({ rec }: { rec: RecommendationData }) { ); } -function ScoreBar({ label, value, weight }: { label: string; value: number; weight?: string }) { - const width = Math.min(value, 100); - const gradientClass = value >= 70 ? "score-bar-gradient-high" : value >= 50 ? "score-bar-gradient-mid" : "score-bar-gradient-low"; - return ( -
-
- {label}{weight ? {weight} : null} - {value.toFixed(0)} -
-
-
-
-
- ); -} - function TrackingMetric({ label, value }: { label: string; value: number | null }) { const num = value ?? 0; const color = num > 0 ? "text-red-400" : num < 0 ? "text-emerald-400" : "text-text-secondary"; diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index a385523a..2c88df1f 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -174,6 +174,7 @@ export interface SectorData { sector_code: string; sector_name: string; pct_change: number; + trade_date?: string; capital_inflow: number; limit_up_count: number; days_continuous: number; @@ -187,7 +188,13 @@ export interface SectorData { main_force_ratio?: number; realtime_pct_change?: number | null; realtime_limit_up_count?: number | null; + realtime_amount?: number | null; + realtime_turnover_rate?: number | null; + realtime_up_count?: number | null; + realtime_down_count?: number | null; is_realtime?: boolean; + data_mode?: "realtime_overlay" | "daily_snapshot"; + structure_trade_date?: string; } export interface LatestResult { @@ -272,6 +279,10 @@ export interface StrategySectorFocus { heat_score: number; pct_change: number; limit_up_count: number; + turnover_rate?: number; + up_count?: number; + down_count?: number; + data_mode?: string; view: string; } @@ -307,6 +318,7 @@ export interface StrategyIterationReport { export interface StrategyBoard { trade_date: string; + data_mode?: string; market_regime: string; risk_level: string; action_bias: string;