update
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sam
parent
d4d5351c66
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@ -24,12 +24,21 @@ def department_prompt(settings: "DepartmentSettings", context: "DepartmentContex
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market_lines = "\n".join(
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f"- {key}: {value}" for key, value in sorted(context.market_snapshot.items())
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)
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scope_lines = "\n".join(f"- {item}" for item in settings.data_scope)
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role_description = settings.description.strip()
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role_instruction = settings.prompt.strip()
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instructions = f"""
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部门名称：{settings.title}
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股票代码：{context.ts_code}
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交易日：{context.trade_date}
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角色说明：{role_description or '未配置，默认沿用部门名称所代表的研究职责。'}
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职责指令：{role_instruction or '在保持部门风格的前提下，结合可用数据做出审慎判断。'}
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【可用数据范围】
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{scope_lines or '- 使用系统提供的全部上下文，必要时指出仍需的额外数据。'}
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【核心特征】
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{feature_lines or '- (无)'}
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@ -5,7 +5,7 @@ from dataclasses import dataclass, field
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import json
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import os
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from pathlib import Path
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from typing import Dict, List, Optional
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from typing import Dict, Iterable, List, Optional
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def _default_root() -> Path:
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@ -186,19 +186,102 @@ class DepartmentSettings:
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title: str
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description: str = ""
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weight: float = 1.0
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data_scope: List[str] = field(default_factory=list)
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prompt: str = ""
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llm: LLMConfig = field(default_factory=LLMConfig)
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def _default_departments() -> Dict[str, DepartmentSettings]:
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presets = [
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("momentum", "动量策略部门"),
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("value", "价值评估部门"),
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("news", "新闻情绪部门"),
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("liquidity", "流动性评估部门"),
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("macro", "宏观研究部门"),
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("risk", "风险控制部门"),
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{
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"code": "momentum",
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"title": "动量策略部门",
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"description": "跟踪价格动量与量价共振，评估短线趋势延续的概率。",
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"data_scope": [
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"daily.close",
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"daily.open",
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"daily_basic.turnover_rate",
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"factors.mom_20",
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"factors.mom_60",
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],
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"prompt": "你主导动量风格研究，关注价格与成交量的加速变化，需在保持纪律的前提下判定短期多空倾向。",
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},
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{
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"code": "value",
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"title": "价值评估部门",
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"description": "衡量估值水平与盈利质量，为中期配置提供性价比判断。",
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"data_scope": [
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"daily_basic.pe",
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"daily_basic.pb",
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"daily_basic.roe",
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"fundamental.growth",
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],
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"prompt": "你负责价值与质量评估，应结合估值分位、盈利持续性及安全边际给出配置建议。",
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},
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{
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"code": "news",
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"title": "新闻情绪部门",
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"description": "监控舆情热度与事件影响，识别情绪驱动的短期风险与机会。",
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"data_scope": [
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"news.sentiment_index",
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"news.heat_score",
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"events.latest_headlines",
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],
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"prompt": "你专注新闻和事件驱动，应评估正负面舆情对标的短线波动的可能影响。",
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},
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{
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"code": "liquidity",
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"title": "流动性评估部门",
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"description": "衡量成交活跃度与交易成本，控制进出场的实现可能性。",
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"data_scope": [
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"daily_basic.volume_ratio",
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"daily_basic.turnover_rate_f",
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"market.spread_estimate",
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],
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"prompt": "你负责评估该标的的流动性与滑点风险，需要提出可执行的仓位调整建议。",
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},
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{
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"code": "macro",
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"title": "宏观研究部门",
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"description": "追踪宏观与行业景气度，为行业配置和风险偏好提供参考。",
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"data_scope": [
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"macro.industry_heat",
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"macro.liquidity_cycle",
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"index.performance_peers",
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],
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"prompt": "你负责宏观与行业研判，应结合宏观周期、行业景气与相对强弱给出方向性意见。",
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},
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{
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"code": "risk",
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"title": "风险控制部门",
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"description": "监控极端风险、合规与交易限制，必要时行使否决。",
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"data_scope": [
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"market.limit_flags",
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"portfolio.position",
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"risk.alerts",
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],
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"prompt": "你负责风险控制，应识别停牌、涨跌停、持仓约束等因素，必要时提出减仓或观望建议。",
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},
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]
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return {code: DepartmentSettings(code=code, title=title) for code, title in presets}
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return {
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item["code"]: DepartmentSettings(
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code=item["code"],
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title=item["title"],
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description=item.get("description", ""),
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data_scope=list(item.get("data_scope", [])),
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prompt=item.get("prompt", ""),
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)
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for item in presets
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}
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def _normalize_data_scope(raw: object) -> List[str]:
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if isinstance(raw, str):
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tokens = raw.replace(";", "\n").replace(",", "\n").splitlines()
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return [token.strip() for token in tokens if token.strip()]
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if isinstance(raw, Iterable) and not isinstance(raw, (bytes, bytearray, str)):
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return [str(item).strip() for item in raw if str(item).strip()]
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return []
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@dataclass
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@ -388,6 +471,8 @@ def _load_from_file(cfg: AppConfig) -> None:
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title = data.get("title") or code
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description = data.get("description") or ""
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weight = float(data.get("weight", 1.0))
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prompt_text = str(data.get("prompt") or "")
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data_scope = _normalize_data_scope(data.get("data_scope"))
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llm_cfg = LLMConfig()
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route_name = data.get("llm_route")
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resolved_cfg = None
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@ -420,6 +505,8 @@ def _load_from_file(cfg: AppConfig) -> None:
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title=title,
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description=description,
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weight=weight,
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data_scope=data_scope,
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prompt=prompt_text,
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llm=resolved_cfg,
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)
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if new_departments:
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@ -451,6 +538,8 @@ def save_config(cfg: AppConfig | None = None) -> None:
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"title": dept.title,
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"description": dept.description,
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"weight": dept.weight,
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"data_scope": list(dept.data_scope),
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"prompt": dept.prompt,
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"llm": {
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"strategy": dept.llm.strategy if dept.llm.strategy in ALLOWED_LLM_STRATEGIES else "single",
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"majority_threshold": dept.llm.majority_threshold,
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