355 lines
14 KiB
Python
355 lines
14 KiB
Python
"""AI 概念轮动分析 — 从概念涨幅排名矩阵提炼主线/新晋/退潮信号。
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数据来源:
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- rps_rotation.build_rps_rotation: 概念涨幅排名矩阵 (N 日 × ~387 概念)
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- market_overview_builder.build_market_overview: 大盘背景 (指数/情绪/涨停)
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架构 (复刻 market_recap):
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预计算轮动信号 → 拼装 prompt → stream_ai_text 流式调用 → NDJSON 协议输出
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协议事件: meta(摘要) / delta(文本片段) / error / done
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"""
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from __future__ import annotations
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import json
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import logging
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import math
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from collections.abc import AsyncIterator
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logger = logging.getLogger(__name__)
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# ================================================================
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# System Prompt — 轮动策略师人格 + 固定章节模板
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# ================================================================
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_SYSTEM_PROMPT = """你是一位专注 A 股题材轮动的资深策略师,拥有 12 年一线实战经验,擅长从概念板块的**涨幅排名矩阵**中识别主力资金脉络,区分机构主导的持续性主线与游资驱动的脉冲式轮动,产出可直接指导题材跟踪与节奏把握的轮动分析。
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## 输出规范
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用 **Markdown** 格式输出,严格遵循以下结构。不要输出任何 JSON 或代码块,直接输出 Markdown 正文。
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### 1. 🎯 主线研判(2-3 句)
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点名当前最核心的 1-2 条主线题材(连续多日霸榜的强势概念),用一句话概括其逻辑(政策/产业/业绩/事件驱动),并判断是**主升期/加速期/扩散期/见顶期**。结尾用【主线强度:强 / 中 / 弱】定性。
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### 2. 🆕 新晋强势
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列出排名快速跃升的概念(从榜单中后段冲进前列的),逐个给出:
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- 概念名 + 近 N 日排名变化(如 `45→20→8`)
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- 涨幅加速度(连日递增 = 趋势加强)
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- 可能的驱动逻辑(从板块属性推断,不要编造具体消息)
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- 判断是**主力切入**还是**消息脉冲**
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### 3. 📉 退潮预警
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列出从高位明显滑落的概念(连续排名下滑或涨幅骤降),逐个给出:
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- 概念名 + 排名下滑轨迹
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- 退潮性质(高位分歧/资金撤离/补跌)
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- 是否扩散风险
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### 4. 🏛️ 机构主线 vs 🎰 游资轮动
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基于排名稳定性区分两类资金行为:
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- **机构主线**:排名标准差小、长期稳居前列的概念 → 持续性判断、是否可作底仓方向
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- **游资轮动**:排名剧烈波动、脉冲式冲高的概念 → 短线节奏提示、追高风险
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给出当前市场**整体轮动节奏**(快轮动/慢轮动/主线聚焦)的判断。
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### 5. 🌐 结合大盘
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结合提供的大盘数据(指数涨跌/情绪/涨停数),判断:
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- 当前大盘环境对题材轮动是助力还是阻力
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- 情绪温度与轮动节奏的匹配度(如情绪冰点但题材活跃 = 抱团;情绪火热但轮动快 = 末段)
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### 6. 🎯 操作建议
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- **跟踪方向**:主线延续 + 新晋确认的概念
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- **规避方向**:明确退潮 + 高位脉冲的概念
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- **节奏提示**:当前适合追高 / 低吸 / 观望,及切换信号(如"主线概念连续 2 日跌出前 10 则确认退潮")
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### 7. ⚠️ 风险提示
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列出需要盯的风险(如主线断层、情绪与轮动背离、成交萎缩)。末尾附一行:
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"> ⚠️ 本报告由 AI 基于公开行情数据生成,仅供参考,不构成任何投资建议。交易有风险,入市需谨慎。"
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## 分析准则(务必遵守)
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0. **只输出结论,不输出思考过程**:禁止复述你的分析步骤。不要写"我先看...""基于上述数据我认为"——直接给结论。
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1. **数据说话**:每个判断引用具体排名/涨幅数值,严禁空泛套话("强势"必须改成"连续 4 日稳居前 5,均涨 +4.2%")。
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2. **诚实中立**:数据不支持的结论就直言"信号不足,暂无法判断",不要硬凑。
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3. **区分资金性质**:这是本分析的核心价值——机构 vs 游资的判断必须基于排名稳定性(标准差),不要凭感觉。
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4. **不重复数字**:正文负责解读信号含义,不要照抄罗列已提供的全部原始数据。
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5. **简明实战**:总字数 1000-1800 字,重在可执行。
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6. **客观推断**:若无明确消息,从量价异动推断可能逻辑并给结论,不要标注"[推断]"或编造具体新闻。
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现在请基于下方概念轮动数据进行分析。"""
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# ================================================================
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# 预计算: 把排名矩阵转成结构化轮动信号
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# ================================================================
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# 每类信号最多取多少个概念喂给 AI (控制 token)
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_TOP_N = 8
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def _compute_rotation_signals(dates: list[str], columns: dict) -> dict:
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"""从概念涨幅排名矩阵计算轮动信号。
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Args:
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dates: 日期列表 (最新在最前, 与 columns key 一致)
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columns: {日期: [[概念, 涨幅], ...]} 每列各自降序
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Returns:
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{
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"persistent_leaders": [...], # 连续多日稳居前列 (主线)
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"rising": [...], # 排名快速跃升 (新晋)
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"fading": [...], # 从高位滑落 (退潮)
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"institutional": [...], # 排名稳定 (机构特征)
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"hot_money": [...], # 排名波动大 (游资特征)
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}
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每项含: concept, ranks (按 dates 顺序), pcts, avg_rank, rank_std
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ranks 时间方向: ranks[0] = 最早日, ranks[-1] = 最新日 (已反转, 左老右新)
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"""
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if not dates or not columns:
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return {}
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# 按时间正序 (左老右新) 处理
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dates_asc = list(reversed(dates))
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# 收集每个概念在各日期的 (排名, 涨幅)。排名 = 该日在列中的索引 + 1。
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concept_data: dict[str, list[tuple[int, float]]] = {}
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for d in dates_asc:
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col = columns.get(d) or []
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for idx, (name, pct) in enumerate(col):
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concept_data.setdefault(name, []).append((idx + 1, pct))
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n_dates = len(dates_asc)
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def _stats(ranks_pcts: list[tuple[int, float]]) -> dict:
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ranks = [r for r, _ in ranks_pcts]
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pcts = [p for _, p in ranks_pcts]
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avg = sum(ranks) / len(ranks) if ranks else 0
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var = sum((r - avg) ** 2 for r in ranks) / len(ranks) if ranks else 0
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return {
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"ranks": ranks,
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"pcts": [round(p, 4) for p in pcts],
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"avg_rank": round(avg, 1),
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"rank_std": round(math.sqrt(var), 1),
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}
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persistent: list[dict] = []
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rising: list[dict] = []
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fading: list[dict] = []
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institutional: list[dict] = []
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hot_money: list[dict] = []
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for concept, rp in concept_data.items():
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# 缺失日补 (大排名, 0 涨幅) 保持时间轴对齐
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if len(rp) < n_dates:
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rp = rp + [(999, 0.0)] * (n_dates - len(rp))
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s = _stats(rp)
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s["concept"] = concept
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ranks = s["ranks"]
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latest_rank = ranks[-1]
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earliest_rank = ranks[0]
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# 最近 3 日 (不足则全部) 均排名, 判断近期强度
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recent = ranks[-min(3, len(ranks)):]
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recent_avg = sum(recent) / len(recent)
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# 主线: 近期稳居前 10
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if recent_avg <= 10 and latest_rank <= 10:
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persistent.append(s)
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# 新晋: 早期排名靠后(>30), 最新冲进前 20, 跃升幅度大
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jump = earliest_rank - latest_rank
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if earliest_rank > 30 and latest_rank <= 20 and jump >= 20:
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rising.append(s)
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# 退潮: 早期排名靠前(<=10), 最新滑落到 30 外
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drop = latest_rank - earliest_rank
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if earliest_rank <= 10 and latest_rank > 30 and drop >= 20:
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fading.append(s)
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# 机构: 排名标准差小且平均排名靠前 (稳定强势)
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if s["rank_std"] <= 5 and s["avg_rank"] <= 20:
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institutional.append(s)
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# 游资: 排名标准差大 (波动剧烈)
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if s["rank_std"] >= 20:
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hot_money.append(s)
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# 排序: 主线按近期排名升序; 新晋按跃升幅度降序; 退潮按跌幅降序
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persistent.sort(key=lambda x: x["avg_rank"])
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rising.sort(key=lambda x: x["ranks"][0] - x["ranks"][-1], reverse=True)
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fading.sort(key=lambda x: x["ranks"][-1] - x["ranks"][0], reverse=True)
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institutional.sort(key=lambda x: (x["rank_std"], x["avg_rank"]))
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hot_money.sort(key=lambda x: x["rank_std"], reverse=True)
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return {
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"persistent_leaders": persistent[:_TOP_N],
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"rising": rising[:_TOP_N],
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"fading": fading[:_TOP_N],
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"institutional": institutional[:_TOP_N],
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"hot_money": hot_money[:_TOP_N],
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}
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# ================================================================
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# Prompt 构建
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# ================================================================
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def _fmt_pct(v) -> str:
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if v is None:
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return "—"
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return f"{v*100:+.2f}%"
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def _build_market_block(overview: dict) -> str:
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"""大盘背景精简块 (复用 market_overview 已算好的字段)。"""
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indices = overview.get("indices") or []
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emo = overview.get("emotion") or {}
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lim = overview.get("limit") or {}
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amt = overview.get("amount") or {}
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idx_lines = []
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for idx in indices[:4]:
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name = idx.get("name") or idx.get("symbol") or "?"
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chg = idx.get("change_pct")
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idx_lines.append(f"{name} {_fmt_pct(chg)}")
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idx_str = " / ".join(idx_lines) or "指数缺失"
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total_amount = (amt.get("total") or 0) / 1e8 # 元 → 亿
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return (
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f"- 指数: {idx_str}\n"
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f"- 情绪: {emo.get('score', 50)} ({emo.get('label', '—')})\n"
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f"- 涨停/炸板/跌停: {lim.get('limit_up', 0)} / {lim.get('broken', 0)} / {lim.get('limit_down', 0)}"
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f" (最高连板 {lim.get('max_boards', 0)})\n"
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f"- 两市成交额: {total_amount:.0f} 亿元"
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)
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def _build_signal_block(title: str, items: list[dict]) -> str:
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"""轮动信号块: 把预计算的概念信号转成紧凑文本。"""
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if not items:
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return f"### {title}\n(本类无明显信号)"
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lines = [f"### {title}"]
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for it in items:
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ranks_str = "→".join(str(r) if r < 999 else "—" for r in it["ranks"])
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avg_pct = sum(it["pcts"]) / len(it["pcts"]) if it["pcts"] else 0
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lines.append(
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f"- {it['concept']}: 排名 {ranks_str} | 均排名 {it['avg_rank']} "
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f"| 排名波动σ {it['rank_std']} | 区间均涨 {_fmt_pct(avg_pct)}"
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)
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return "\n".join(lines)
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def _build_user_prompt(signals: dict, overview: dict, days: int, dates: list[str], focus: str) -> str:
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"""组装 user 消息: 大盘背景 + 轮动信号 + focus。"""
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dates_asc = list(reversed(dates))
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date_range = f"{dates_asc[0]} ~ {dates_asc[-1]}" if dates_asc else "—"
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parts = [
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f"# 概念涨幅轮动数据 (最近 {days} 个交易日: {date_range})",
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"",
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"## 大盘背景",
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_build_market_block(overview),
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"",
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"## 轮动信号 (排名时间方向: 左→右 = 旧→新, 排名越小越强)",
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"",
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_build_signal_block("🎯 主线 (连续霸榜)", signals.get("persistent_leaders", [])),
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"",
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_build_signal_block("🆕 新晋强势 (排名跃升)", signals.get("rising", [])),
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"",
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_build_signal_block("📉 退潮预警 (高位滑落)", signals.get("fading", [])),
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"",
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_build_signal_block("🏛️ 机构特征 (排名稳定)", signals.get("institutional", [])),
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"",
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_build_signal_block("🎰 游资特征 (排名波动大)", signals.get("hot_money", [])),
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]
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if focus.strip():
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parts.extend(["", f"## 用户关注点\n{focus.strip()}"])
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return "\n".join(parts)
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def _build_summary(signals: dict) -> str:
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"""meta 事件的摘要 (前端可立即展示)。"""
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leaders = signals.get("persistent_leaders", [])
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rising = signals.get("rising", [])
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fading = signals.get("fading", [])
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leader_names = "、".join(it["concept"] for it in leaders[:3]) or "暂无明确主线"
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return f"主线: {leader_names} | 新晋 {len(rising)} | 退潮 {len(fading)}"
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# ================================================================
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# 流式主入口
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# ================================================================
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async def analyze_rotation_stream(
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repo,
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days: int = 12,
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focus: str = "",
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) -> AsyncIterator[str]:
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"""流式概念轮动分析: yield 出每个 NDJSON 事件。
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Args:
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repo: KlineRepository (必填)。
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days: 分析最近 N 个交易日 (7-30)。
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focus: 用户追加的关注点。
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"""
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from app.services.rps_rotation import build_rps_rotation
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from app.services.market_overview_builder import build_market_overview
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# 1. 取轮动矩阵
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rotation = build_rps_rotation(repo, days)
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dates = rotation.get("dates") or []
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columns = rotation.get("columns") or {}
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if not dates or not columns:
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yield json.dumps({
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"type": "error",
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"message": "暂无概念轮动数据,请先在「概念分析」页获取概念数据源",
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}, ensure_ascii=False)
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return
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# 2. 预计算轮动信号
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signals = _compute_rotation_signals(dates, columns)
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# 3. 大盘背景 (失败不阻断, 降级为空)
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try:
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overview = build_market_overview(repo)
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except Exception as e: # noqa: BLE001
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logger.warning("rotation analyze: 大盘背景获取失败, 降级为空: %s", e)
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overview = {}
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# 4. meta 事件
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yield json.dumps({
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"type": "meta",
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"days": days,
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"summary": _build_summary(signals),
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}, ensure_ascii=False)
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# 5. 构建 prompt + 流式调用 LLM
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try:
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from app.services.ai_provider import stream_ai_text, ai_configured
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if not ai_configured():
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yield json.dumps({
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"type": "error",
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"message": "AI 未配置,请在「设置」页填写 API Key 与接口地址",
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}, ensure_ascii=False)
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return
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user_prompt = _build_user_prompt(signals, overview, days, dates, focus)
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async for delta in stream_ai_text(
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[
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{"role": "system", "content": _SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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],
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temperature=0.5,
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max_tokens=4000,
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):
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yield json.dumps({"type": "delta", "content": delta}, ensure_ascii=False)
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except Exception as e: # noqa: BLE001
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logger.exception("AI concept rotation analyze failed: %s", e)
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yield json.dumps({"type": "error", "message": f"AI 轮动分析失败: {e}"}, ensure_ascii=False)
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yield json.dumps({"type": "done"}, ensure_ascii=False)
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