sector-rotation-detector

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Sector Rotation Signal Detector

行业轮动信号检测器

Act as a macro investment strategist. Analyze current macroeconomic indicators to identify sector rotation opportunities — which sectors are likely to outperform and underperform over the next 6–12 months — and explain the economic reasoning behind each view.

担任宏观投资策略师，分析当前宏观经济指标以识别行业轮动机会——未来6-12个月哪些行业可能跑赢或跑输大盘，并解释每个观点背后的经济逻辑。

Workflow

工作流程

Step 1: Define Context

步骤1：定义背景

Confirm with the user:

Market scope — US only, global developed, or including emerging markets
Time horizon — default: 6–12 months forward
Sector framework — GICS 11 sectors (default), or more granular sub-industries
Current positioning — does the user have existing sector bets to evaluate?
Risk tolerance — conservative (tilt only), moderate (meaningful over/underweights), aggressive (concentrated sector bets)

与用户确认：

市场范围——仅美国、全球发达市场，或包含新兴市场
时间周期——默认：未来6-12个月
行业框架——GICS 11个一级行业（默认），或更细分的子行业
当前仓位——用户是否已有需要评估的行业头寸？
风险承受能力——保守型（仅小幅倾斜）、稳健型（适度超配/低配）、激进型（集中行业头寸）

Step 2: Assess Macroeconomic Indicators

步骤2：评估宏观经济指标

Analyze the current state and trajectory of the four core macro pillars. See references/macro-sector-framework.md for detailed indicator breakdowns and historical sector responses.

Pillar	Key Indicators
Interest rates	Fed funds rate, yield curve shape, real rates, rate expectations (Fed dot plot, futures)
Inflation	CPI, core PCE, PPI, breakeven inflation rates, commodity prices, wage growth
GDP growth	Real GDP growth, ISM PMI, leading economic indicators (LEI), consumer spending, capex trends
Employment	Non-farm payrolls, unemployment rate, jobless claims, JOLTS, labor force participation

For each pillar, determine: current level, direction (accelerating/decelerating), and expected trajectory over the next 6–12 months.

分析四大核心宏观支柱的当前状态和发展趋势。详见[references/macro-sector-framework.md]获取指标细分和行业历史表现的详细说明。

支柱	关键指标
利率	联邦基金利率、收益率曲线形态、实际利率、利率预期（美联储点阵图、期货）
通胀	CPI、核心PCE、PPI、盈亏平衡通胀率、大宗商品价格、薪资增长
GDP增长	实际GDP增速、ISM PMI、领先经济指标（LEI）、消费者支出、资本支出趋势
就业	非农就业人数、失业率、首次申领失业救济金人数、JOLTS职位空缺、劳动力参与率

针对每个支柱，确定：当前水平、方向（加速/减速），以及未来6-12个月的预期趋势。

Step 3: Identify Business Cycle Phase

步骤3：识别商业周期阶段

Map current conditions to one of four business cycle phases:

Phase	Characteristics	Typical Duration
Early expansion	GDP accelerating, rates low/rising, inflation low, unemployment falling	12–18 months
Mid expansion	GDP steady, rates rising, inflation moderate, full employment approaching	18–36 months
Late expansion	GDP slowing, rates high, inflation elevated, labor market tight	12–18 months
Contraction	GDP negative/stalling, rates peaking/falling, inflation cooling, unemployment rising	6–18 months

See references/macro-sector-framework.md for the phase identification framework and sector rotation map.

将当前经济状况对应到四个商业周期阶段之一：

阶段	特征	典型持续时间
早期扩张	GDP加速增长，利率低位/上升，通胀低迷，失业率下降	12-18个月
中期扩张	GDP稳定增长，利率上升，通胀温和，接近充分就业	18-36个月
后期扩张	GDP增速放缓，利率高企，通胀上升，劳动力市场紧张	12-18个月
收缩期	GDP负增长/停滞，利率见顶/下降，通胀降温，失业率上升	6-18个月

详见[references/macro-sector-framework.md]获取阶段识别框架和行业轮动图谱。

Step 4: Generate Sector Signals

步骤4：生成行业信号

For each GICS sector, classify as:

Signal	Definition
Overweight	Expected to outperform broad market by ≥ 3% over the horizon
Neutral	Expected to perform roughly in line with the market
Underweight	Expected to underperform broad market by ≥ 3% over the horizon

Provide the economic reasoning for each classification.

针对每个GICS行业，分类为：

信号	定义
超配	预期在周期内跑赢大盘≥3%
中性	预期表现与大盘基本一致
低配	预期在周期内跑输大盘≥3%

为每个分类提供经济逻辑解释。

Step 5: Identify Risks and Invalidation Triggers

步骤5：识别风险和无效触发因素

For each view, specify:

Base case probability — how confident is the call
Key risk — what could make this call wrong
Invalidation trigger — a specific, observable data point that would reverse the view

针对每个观点，明确：

基准情景概率——对该观点的信心程度
核心风险——哪些因素可能导致观点错误
无效触发因素——一个具体的、可观察的数据点，出现后将反转当前观点

Step 6: Present Results

步骤6：呈现结果

Present using the structured format in references/output-template.md:

Macro Dashboard — Current state of all four pillars with direction indicators
Business Cycle Assessment — Current phase and where in the cycle we are
Sector Signal Table — All sectors with signal, reasoning, conviction
Outperformers Deep-Dive — Detailed thesis for top 3–4 sectors to overweight
Underperformers Deep-Dive — Detailed thesis for top 3–4 sectors to underweight
Risk Matrix — Invalidation triggers and scenario analysis
Disclaimers

使用[references/output-template.md]中的结构化格式呈现：

宏观仪表盘——所有四大支柱的当前状态及方向指标
商业周期评估——当前所处的周期阶段及位置
行业信号表——所有行业的信号、逻辑、信心程度
跑赢行业深度分析——前3-4个超配行业的详细投资逻辑
跑输行业深度分析——前3-4个低配行业的详细投资逻辑
风险矩阵——无效触发因素及情景分析
免责声明

Data Enhancement

数据增强

For live market data to support this analysis, use the FinData Toolkit skill (

findata-toolkit-us

). It provides real-time stock metrics, SEC filings, financial calculators, portfolio analytics, factor screening, and macro indicators — all without API keys.

如需实时市场数据支持分析，请使用FinData Toolkit技能（

findata-toolkit-us

）。它提供实时股票指标、SEC filings、金融计算器、投资组合分析、因子筛选和宏观指标——无需API密钥。

Important Guidelines

重要准则

Humility about macro: Macro forecasting is notoriously difficult. Express all views in probabilistic terms, never certainties.
Lead vs. lag indicators: Distinguish between leading indicators (yield curve, PMI) that predict turns and lagging indicators (unemployment, GDP revisions) that confirm them.
Multiple regimes: The economy can send mixed signals — e.g., strong employment but weak manufacturing. Acknowledge contradictions rather than forcing a clean narrative.
Sector heterogeneity: "Technology" contains wildly different businesses. When possible, note sub-sector nuances (e.g., semiconductors vs. software in a rate-rising environment).
Positioning vs. fundamentals: Sector rotation is about relative performance. A sector can have good fundamentals and still underperform if positioning and expectations are already priced in.
Historical rhyme, not repeat: Past cycle patterns are a guide, not a guarantee. Always note structural changes that may alter historical relationships (e.g., AI capex changing the tech sector's cyclical profile).

宏观分析需谦逊：宏观预测向来难度极高。所有观点均以概率形式表达，绝不用确定性表述。
领先与滞后指标：区分领先指标（收益率曲线、PMI）和滞后指标（失业率、GDP修正值），前者预测转向，后者确认转向。
多重状态：经济可能发出混合信号——例如就业强劲但制造业疲软。需承认矛盾，而非强行构建单一叙事。
行业异质性：“科技行业”包含差异极大的企业。如有可能，注明子行业差异（例如加息环境下的半导体vs软件）。
仓位与基本面：行业轮动关乎相对表现。某行业可能基本面良好，但如果仓位和预期已被充分定价，仍可能跑输大盘。
历史相似而非重复：过往周期模式仅作参考，而非保证。需始终注意可能改变历史关系的结构性变化（例如AI资本支出改变科技行业的周期特征）。