Import 9 alphaear finance skills
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
This commit is contained in:
Kunthawat Greethong
381
skills/alphaear-predictor/scripts/schema/isq_template.py
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381
skills/alphaear-predictor/scripts/schema/isq_template.py
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"""
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ISQ (Investment Signal Quality) 评估框架 Template
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统一定义 ISQ 的各个维度、评分标准、和使用方法。
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支持默认 template 和自定义 template。
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"""
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from typing import Dict, List, Any, Optional
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from pydantic import BaseModel, Field
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from enum import Enum
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from pathlib import Path
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import json
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class ISQDimension(str, Enum):
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"""ISQ 评估维度"""
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SENTIMENT = "sentiment" # 情绪/走势方向
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CONFIDENCE = "confidence" # 确定性/可信度
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INTENSITY = "intensity" # 强度/影响量级
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EXPECTATION_GAP = "expectation_gap" # 预期差/市场认知差
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TIMELINESS = "timeliness" # 时效性/窗口紧迫度
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TRANSMISSION = "transmission" # 逻辑传导清晰度
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class ISQDimensionSpec(BaseModel):
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"""ISQ 单个维度的定义规范"""
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name: str = Field(..., description="维度名称")
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key: str = Field(..., description="维度键名")
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description: str = Field(..., description="维度描述")
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range_type: str = Field(default="0-1", description="取值范围 (0-1 或 1-5 等)")
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scale_factor: float = Field(default=1.0, description="显示时的缩放因子")
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examples: Dict[str, str] = Field(default_factory=dict, description="不同分值的示例解释")
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visualization_color: Optional[str] = Field(default=None, description="可视化颜色")
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class ISQTemplate(BaseModel):
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"""ISQ 评估框架 Template"""
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template_id: str = Field(..., description="模板 ID")
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template_name: str = Field(..., description="模板名称")
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description: str = Field(..., description="模板描述")
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# 核心维度定义
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dimensions: Dict[str, ISQDimensionSpec] = Field(..., description="维度定义字典")
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# 评分指导
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scoring_guide: str = Field(..., description="评分指导说明")
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# 应用场景
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applicable_scenarios: List[str] = Field(default_factory=list, description="适用场景")
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# 聚合算法
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aggregation_method: str = Field(default="weighted_average", description="聚合方法 (weighted_average, product 等)")
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dimension_weights: Dict[str, float] = Field(default_factory=dict, description="维度权重")
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class ISQScore(BaseModel):
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"""单个信号的 ISQ 评分结果"""
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signal_id: str = Field(..., description="信号 ID")
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template_id: str = Field(..., description="使用的模板 ID")
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# 各维度评分
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scores: Dict[str, float] = Field(..., description="各维度评分")
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# 总分
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overall_score: float = Field(..., description="综合评分")
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# 评分理由
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rationale: Dict[str, str] = Field(default_factory=dict, description="各维度评分理由")
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# 时间戳
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timestamp: str = Field(..., description="评分时间")
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# =====================================================
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# 默认 Template
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# =====================================================
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DEFAULT_ISQ_TEMPLATE = ISQTemplate(
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template_id="default_isq_v1",
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template_name="标准投资信号质量评估框架 (ISQ v1.0)",
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description="AlphaEar 默认的 ISQ 评估框架，用于标准化评估投资信号的质量维度",
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dimensions={
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"sentiment": ISQDimensionSpec(
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name="情绪/走势",
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key="sentiment",
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description="基础情绪偏向和市场走势判断",
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range_type="-1.0 到 1.0",
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scale_factor=1.0,
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examples={
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"-1.0": "极度悲观/极度看空",
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"-0.5": "明显看空",
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"0.0": "中性/没有明确方向",
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"0.5": "明显看多",
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"1.0": "极度乐观/极度看多"
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},
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visualization_color="#ef4444" # 红色表示负面，绿色表示正面
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),
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"confidence": ISQDimensionSpec(
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name="确定性",
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key="confidence",
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description="信号的可信度和确定性程度",
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range_type="0.0 到 1.0",
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scale_factor=1.0,
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examples={
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"0.0-0.3": "信息来源不可靠/传言多/逻辑推导牵强",
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"0.3-0.6": "信息相对可靠/有一定逻辑/但仍有不确定性",
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"0.6-0.8": "信息来源权威/逻辑清晰/高度可信",
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"0.8-1.0": "官方确认/数据明确/完全确定"
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},
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visualization_color="#3b82f6" # 蓝色
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),
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"intensity": ISQDimensionSpec(
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name="强度/影响量级",
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key="intensity",
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description="信号对相关板块/个股的潜在影响程度",
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range_type="1 到 5",
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scale_factor=20.0, # 用于雷达图缩放 (5 -> 100)
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examples={
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"1": "影响微弱，可能被市场忽略",
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"2": "小幅影响，短期可能有波动",
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"3": "中等影响，值得重点关注",
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"4": "强烈影响，可能成为市场焦点",
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"5": "极强影响，市场预期明显变化"
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},
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visualization_color="#f97316" # 橙色
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),
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"expectation_gap": ISQDimensionSpec(
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name="预期差",
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key="expectation_gap",
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description="市场预期与现实之间的差距",
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range_type="0.0 到 1.0",
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scale_factor=1.0,
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examples={
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"0.0-0.2": "市场充分认知，预期差小",
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"0.2-0.5": "市场部分认知，存在一定预期差",
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"0.5-0.8": "市场认知不足，预期差较大，存在博弈空间",
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"0.8-1.0": "市场严重低估/高估，巨大预期差"
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},
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visualization_color="#22c55e" # 绿色
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),
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"timeliness": ISQDimensionSpec(
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name="时效性",
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key="timeliness",
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description="信号的时间窗口紧迫度",
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range_type="0.0 到 1.0",
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scale_factor=1.0,
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examples={
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"0.0-0.2": "长期信号，反应窗口 > 3 月",
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"0.2-0.5": "中期信号，反应窗口 1-3 月",
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"0.5-0.8": "短期信号，反应窗口 1 周 - 1 月",
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"0.8-1.0": "超短期信号，反应窗口 < 1 周（需立即行动）"
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},
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visualization_color="#a855f7" # 紫色
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),
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},
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scoring_guide="""
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### ISQ 评分指导 (Investment Signal Quality)
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ISQ 框架用于多维度评估投资信号的质量。每个信号由 5 个维度组成：
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1. **情绪 (Sentiment)**: -1.0 到 1.0，表示看空(-)/中性(0)/看多(+)
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2. **确定性 (Confidence)**: 0.0 到 1.0，数值越高越确定
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3. **强度 (Intensity)**: 1 到 5，数值越高影响越大
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4. **预期差 (Expectation Gap)**: 0.0 到 1.0，市场预期与现实的差距
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5. **时效性 (Timeliness)**: 0.0 到 1.0，反应窗口的紧迫程度
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### 综合评分算法
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综合评分 = 确定性 × 0.35 + 强度/5 × 0.30 + 预期差 × 0.20 + 时效性 × 0.15
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范围: 0.0 到 1.0
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- 0.0-0.3: 信号质量较差，不建议跟进
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- 0.3-0.6: 信号质量一般，可作参考
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- 0.6-0.8: 信号质量良好，值得跟进
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- 0.8-1.0: 信号质量优异，强烈推荐
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### 评分时的注意事项
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- **不要混淆方向和强度**：情绪可以是看空，但确定性和强度仍可能很高
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- **预期差往往是 Alpha 来源**：高预期差 + 高确定性 = 最佳博弈机会
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- **考虑时间成本**：长期信号需要更高的确定性才值得跟进
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- **数据为王**：所有评分必须有具体数据支撑
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""",
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applicable_scenarios=[
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"上市公司基本面变化分析",
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"产业政策与监管事件评估",
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"地缘政治与宏观经济影响",
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"技术进步与产业升级",
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"突发事件与应急响应"
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],
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aggregation_method="weighted_average",
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dimension_weights={
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"confidence": 0.35,
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"intensity": 0.30,
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"expectation_gap": 0.20,
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"timeliness": 0.15
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}
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)
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# =====================================================
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# ISQ Template 管理系统
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# =====================================================
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class ISQTemplateManager:
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"""ISQ Template 管理器"""
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def __init__(self):
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self.templates: Dict[str, ISQTemplate] = {
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DEFAULT_ISQ_TEMPLATE.template_id: DEFAULT_ISQ_TEMPLATE
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}
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def register_template(self, template: ISQTemplate) -> None:
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"""注册新的 template"""
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self.templates[template.template_id] = template
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def register_template_dict(self, template_dict: Dict[str, Any]) -> ISQTemplate:
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"""从 dict 注册模板，返回实例。"""
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tpl = ISQTemplate(**template_dict)
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self.register_template(tpl)
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return tpl
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def get_template(self, template_id: str) -> ISQTemplate:
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"""获取指定 template"""
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if template_id not in self.templates:
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return DEFAULT_ISQ_TEMPLATE
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return self.templates[template_id]
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def list_templates(self) -> List[Dict[str, str]]:
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"""列出所有可用 template"""
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return [
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{
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"id": t.template_id,
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"name": t.template_name,
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"description": t.description,
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"dimensions": list(t.dimensions.keys())
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}
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for t in self.templates.values()
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]
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def get_dimension(self, template_id: str, dimension_key: str) -> ISQDimensionSpec:
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"""获取指定 template 的某个维度定义"""
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template = self.get_template(template_id)
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return template.dimensions.get(dimension_key)
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def get_scoring_prompt(self, template_id: str) -> str:
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"""获取用于 LLM 的评分 prompt"""
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template = self.get_template(template_id)
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dimensions_desc = "\n".join([
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f"- **{d.name} ({d.key})**\n"
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f" 范围: {d.range_type}\n"
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f" 说明: {d.description}\n"
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f" 示例: {', '.join(f'{k}={v}' for k, v in list(d.examples.items())[:3])}"
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for d in template.dimensions.values()
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])
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return f"""
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### ISQ 评估指导 ({template.template_name})
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使用以下 {len(template.dimensions)} 个维度评估信号质量：
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{dimensions_desc}
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### 评分标准
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{template.scoring_guide}
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### 输出格式 (JSON)
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请输出以下 JSON 格式的评分结果：
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{{
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"sentiment": <float>,
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"confidence": <float>,
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"intensity": <int>,
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"expectation_gap": <float>,
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"timeliness": <float>,
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"rationale": {{
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"sentiment": "评分理由",
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"confidence": "评分理由",
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"intensity": "评分理由",
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"expectation_gap": "评分理由",
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"timeliness": "评分理由"
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}}
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}}
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"""
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# 全局 template 管理器实例
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isq_template_manager = ISQTemplateManager()
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# =====================================================
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# 配置加载
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# =====================================================
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def load_templates_from_config(config_path: Optional[str] = None) -> None:
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"""从配置目录加载所有 JSON 模板文件，未找到则跳过，不影响默认模板。
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支持单个 JSON 文件或目录（目录下的所有 .json 文件）。
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"""
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if config_path:
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path = Path(config_path)
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else:
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# 默认目录：config/isq_templates/
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# __file__ = src/schema/isq_template.py
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# parent = src/schema, parent.parent = src, parent.parent.parent = 项目根目录
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path = Path(__file__).resolve().parent.parent.parent / "config"
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if not path.exists():
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return
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# 如果是目录，扫描所有 .json 文件
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if path.is_dir():
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json_files = list(path.glob("*.json"))
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else:
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json_files = [path]
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for json_file in json_files:
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try:
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data = json.loads(json_file.read_text(encoding="utf-8"))
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# 如果是单个模板对象，转为列表
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if isinstance(data, dict):
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templates = [data]
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elif isinstance(data, list):
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templates = data
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else:
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continue
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# 注册所有模板
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for tpl_dict in templates:
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if not isinstance(tpl_dict, dict):
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continue
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try:
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isq_template_manager.register_template_dict(tpl_dict)
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except Exception:
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# 忽略单个模板的加载错误，继续其他模板
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continue
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except Exception:
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# JSON 解析失败，跳过该文件
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continue
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# 在模块加载时自动尝试加载配置模板
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load_templates_from_config()
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# =====================================================
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# 便利函数
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# =====================================================
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def get_isq_template(template_id: str = "default_isq_v1") -> ISQTemplate:
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"""获取 ISQ template"""
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return isq_template_manager.get_template(template_id)
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def get_isq_scoring_prompt(template_id: str = "default_isq_v1") -> str:
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"""获取用于 LLM 的 ISQ 评分 prompt"""
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return isq_template_manager.get_scoring_prompt(template_id)
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def calculate_isq_overall_score(scores: Dict[str, float], template_id: str = "default_isq_v1") -> float:
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"""计算 ISQ 综合评分"""
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template = get_isq_template(template_id)
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overall = 0.0
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for dim_key, weight in template.dimension_weights.items():
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if dim_key in scores:
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score = scores[dim_key]
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# 处理强度维度的特殊缩放 (1-5 -> 0-1)
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if dim_key == "intensity":
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score = score / 5.0
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overall += score * weight
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return min(1.0, max(0.0, overall)) # 限制在 0-1 之间
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100
skills/alphaear-predictor/scripts/schema/models.py
Normal file
100
skills/alphaear-predictor/scripts/schema/models.py
Normal file
@@ -0,0 +1,100 @@
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from pydantic import BaseModel, Field
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from typing import List, Optional, Dict, Any
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from datetime import datetime
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class TransmissionNode(BaseModel):
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node_name: str = Field(..., description="产业链节点名称")
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impact_type: str = Field(..., description="利好/利空/中性")
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logic: str = Field(..., description="该节点的传导逻辑")
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class IntentAnalysis(BaseModel):
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keywords: List[str] = Field(..., description="核心实体、事件或概念关键词")
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search_queries: List[str] = Field(..., description="优化后的搜索引擎查询词")
|
||||
is_specific_event: bool = Field(..., description="是否查询特定突发事件")
|
||||
time_range: str = Field(..., description="时间范围 (recent/all/specific_date)")
|
||||
intent_summary: str = Field(..., description="一句话意图描述")
|
||||
|
||||
class FilterResult(BaseModel):
|
||||
"""LLM 筛选结果 - 快速判断是否有有效信号"""
|
||||
has_valid_signals: bool = Field(..., description="列表中是否包含有效的金融信号")
|
||||
selected_ids: List[int] = Field(default_factory=list, description="筛选出的有效信号 ID 列表")
|
||||
themes: List[str] = Field(default_factory=list, description="信号涉及的主题")
|
||||
reason: Optional[str] = Field(default=None, description="如果无有效信号，说明原因")
|
||||
|
||||
class InvestmentSignal(BaseModel):
|
||||
# 核心元数据
|
||||
signal_id: str = Field(default="unknown_sig", description="唯一信号 ID")
|
||||
title: str = Field(..., description="信号标题")
|
||||
summary: str = Field(default="暂无摘要分析", description="100 字核心观点快报")
|
||||
reasoning: str = Field(default="", description="详细的推演逻辑和理由")
|
||||
|
||||
# 逻辑传导 (ISQ Key 1)
|
||||
transmission_chain: List[TransmissionNode] = Field(default_factory=list, description="产业链传导逻辑链条")
|
||||
|
||||
# 信号质量 (ISQ Key 2) - 来自 isq_template.DEFAULT_ISQ_TEMPLATE
|
||||
# 参考: src/schema/isq_template.py 的 DEFAULT_ISQ_TEMPLATE 定义
|
||||
sentiment_score: float = Field(default=0.0, description="[ISQ] 情绪/走势 (-1.0=极度看空 ~ 0.0=中性 ~ 1.0=极度看多)")
|
||||
confidence: float = Field(default=0.5, description="[ISQ] 确定性 (0.0=不可信 ~ 1.0=完全确定)")
|
||||
intensity: int = Field(default=3, description="[ISQ] 强度/影响量级 (1=微弱 ~ 5=极强)")
|
||||
expectation_gap: float = Field(default=0.5, description="[ISQ] 预期差/博弈空间 (0.0=充分定价 ~ 1.0=巨大预期差)")
|
||||
timeliness: float = Field(default=0.8, description="[ISQ] 时效性 (0.0=长期 ~ 1.0=超短期)")
|
||||
|
||||
# 预测与博弈 (ISQ Key 3)
|
||||
expected_horizon: str = Field(default="T+N", description="预期的反应时窗 (如: T+0, T+3, Long-term)")
|
||||
price_in_status: str = Field(default="未知", description="市场预期消化程度 (未定价/部分定价/充分定价)")
|
||||
|
||||
# 关联实体
|
||||
impact_tickers: List[Dict[str, Any]] = Field(default_factory=list, description="受影响的代码列表及其权重")
|
||||
industry_tags: List[str] = Field(default_factory=list, description="关联行业标签")
|
||||
|
||||
# 溯源
|
||||
sources: List[Dict[str, str]] = Field(default_factory=list, description="来源详情 (包含 title, url, source_name)")
|
||||
|
||||
class ResearchContext(BaseModel):
|
||||
"""研究员搜集的背景信息结构"""
|
||||
raw_signal: str = Field(..., description="原始信号内容")
|
||||
tickers_found: List[Dict[str, Any]] = Field(default_factory=list, description="找到的相关标的及其基本面/股价信息")
|
||||
industry_background: str = Field(..., description="行业背景及产业链现状")
|
||||
latest_developments: List[str] = Field(default_factory=list, description="相关事件的最新进展")
|
||||
key_risks: List[str] = Field(default_factory=list, description="潜在风险点")
|
||||
search_results_summary: str = Field(..., description="搜索结果的综合摘要")
|
||||
|
||||
class ScanContext(BaseModel):
|
||||
"""扫描员搜集的原始数据结构"""
|
||||
hot_topics: List[str] = Field(..., description="当前市场热点话题")
|
||||
news_summaries: List[Dict[str, Any]] = Field(..., description="关键新闻摘要列表")
|
||||
market_data: Dict[str, Any] = Field(default_factory=dict, description="相关的市场行情数据")
|
||||
sentiment_overview: str = Field(..., description="整体市场情绪概览")
|
||||
raw_data_summary: str = Field(..., description="原始数据的综合摘要")
|
||||
|
||||
class SignalCluster(BaseModel):
|
||||
theme_title: str = Field(..., description="主题名称")
|
||||
signal_ids: List[int] = Field(..., description="包含的信号 ID 列表")
|
||||
rationale: str = Field(..., description="聚类理由")
|
||||
|
||||
class ClusterContext(BaseModel):
|
||||
"""信号聚类结果结构"""
|
||||
clusters: List[SignalCluster] = Field(..., description="聚类列表")
|
||||
|
||||
class KLinePoint(BaseModel):
|
||||
date: str = Field(..., description="日期")
|
||||
open: float = Field(..., description="开盘价")
|
||||
high: float = Field(..., description="最高价")
|
||||
low: float = Field(..., description="最低价")
|
||||
close: float = Field(..., description="收盘价")
|
||||
volume: float = Field(..., description="成交量")
|
||||
|
||||
class ForecastResult(BaseModel):
|
||||
ticker: str = Field(..., description="股票代码")
|
||||
base_forecast: List[KLinePoint] = Field(default_factory=list, description="Kronos 模型原始预测")
|
||||
adjusted_forecast: List[KLinePoint] = Field(default_factory=list, description="LLM 调整后的预测")
|
||||
rationale: str = Field(default="", description="预测调整理由及逻辑说明")
|
||||
timestamp: str = Field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d %H:%M:%S"), description="生成时间")
|
||||
|
||||
class InvestmentReport(BaseModel):
|
||||
overall_sentiment: str = Field(..., description="整体市场情绪评价")
|
||||
market_entropy: float = Field(..., description="市场分歧度 (0-1, 1代表极高分歧)")
|
||||
signals: List[InvestmentSignal] = Field(..., description="深度解析的投资信号列表")
|
||||
forecasts: List[ForecastResult] = Field(default_factory=list, description="相关标的的预测结果")
|
||||
timestamp: str = Field(..., description="报告生成时间")
|
||||
meta_info: Optional[Dict[str, Any]] = Field(default_factory=dict, description="其他元数据")
|
||||
Reference in New Issue
Block a user