exposure-coach

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Chinese

Exposure Coach

Overview

概述

Exposure Coach synthesizes outputs from market-breadth-analyzer, uptrend-analyzer, macro-regime-detector, market-top-detector, ftd-detector, theme-detector, sector-analyst, and institutional-flow-tracker into a unified control-plane decision. The skill answers the solo trader's core question: "How much capital should I commit to equities right now?" before any individual stock analysis begins.

Exposure Coach 整合 market-breadth-analyzer、uptrend-analyzer、macro-regime-detector、market-top-detector、ftd-detector、theme-detector、sector-analyst 和 institutional-flow-tracker 的输出结果，形成统一的决策控制平面。该工具旨在解答独立交易者的核心问题："当前我应该将多少资金投入股市？"——这一步应在进行个股分析前完成。

When to Use

适用场景

Before initiating any new stock positions to determine appropriate capital commitment
At the start of each trading week to calibrate portfolio exposure
When multiple market signals conflict and a unified posture is needed
After significant macro or market events to reassess exposure ceiling
When transitioning between market regimes (broadening, concentration, contraction)

在建立任何新股票仓位前，确定合适的资金投入比例
每个交易周开始时，校准投资组合的敞口水平
当多个市场信号存在冲突，需要统一的市场态势判断时
重大宏观或市场事件发生后，重新评估敞口上限
市场状态切换时（如从集中化转为扩散化、收缩等）

Prerequisites

前置要求

Python 3.9+
FMP API key (set
```
FMP_API_KEY
```
environment variable) for institutional-flow-tracker data
Input JSON files from upstream skills (see Workflow Step 1)
Standard library +
```
argparse
```
,
```
json
```
,
```
datetime
```

Python 3.9及以上版本
FMP API密钥（需设置
```
FMP_API_KEY
```
环境变量），用于获取institutional-flow-tracker的数据
上游工具生成的输入JSON文件（见工作流步骤1）
标准库 +
```
argparse
```
、
```
json
```
、
```
datetime
```
模块

Workflow

工作流

Step 1: Gather Upstream Skill Outputs

步骤1：收集上游工具输出

Collect the most recent JSON outputs from integrated skills. Each file provides a specific signal dimension:

Skill	Output File Pattern	Signal Provided
market-breadth-analyzer	`breadth_*.json`	Advance/decline ratios, new highs/lows
uptrend-analyzer	`uptrend_*.json`	Uptrend participation percentage
macro-regime-detector	`regime_*.json`	Current regime (Concentration, Broadening, etc.)
market-top-detector	`top_risk_*.json`	Distribution day count, top probability score
ftd-detector	`ftd_*.json`	Failure-to-deliver anomalies
theme-detector	`theme_*.json`	Active investment themes and rotation
sector-analyst	`sector_*.json`	Sector performance rankings
institutional-flow-tracker	`institutional_*.json`	Net institutional buying/selling

收集各整合工具最新的JSON输出文件，每个文件提供特定维度的信号：

工具	输出文件格式	提供的信号
market-breadth-analyzer	`breadth_*.json`	涨跌家数比、创新高/新低个股数量
uptrend-analyzer	`uptrend_*.json`	上涨趋势参与度百分比
macro-regime-detector	`regime_*.json`	当前市场状态（集中化、扩散化等）
market-top-detector	`top_risk_*.json`	派发日数量、顶部概率评分
ftd-detector	`ftd_*.json`	未交付（Failure-to-Deliver）异常情况
theme-detector	`theme_*.json`	活跃投资主题及风格切换
sector-analyst	`sector_*.json`	行业表现排名
institutional-flow-tracker	`institutional_*.json`	机构资金净买入/卖出情况

Step 2: Run Exposure Scoring Engine

步骤2：运行敞口评分引擎

Execute the exposure scoring script with paths to upstream outputs:

bash

python3 skills/exposure-coach/scripts/calculate_exposure.py \
  --breadth reports/breadth_latest.json \
  --uptrend reports/uptrend_latest.json \
  --regime reports/regime_latest.json \
  --top-risk reports/top_risk_latest.json \
  --ftd reports/ftd_latest.json \
  --theme reports/theme_latest.json \
  --sector reports/sector_latest.json \
  --institutional reports/institutional_latest.json \
  --output-dir reports/

The script accepts partial inputs; missing files reduce confidence but do not block execution.

执行敞口评分脚本，并传入上游输出文件的路径：

bash

python3 skills/exposure-coach/scripts/calculate_exposure.py \
  --breadth reports/breadth_latest.json \
  --uptrend reports/uptrend_latest.json \
  --regime reports/regime_latest.json \
  --top-risk reports/top_risk_latest.json \
  --ftd reports/ftd_latest.json \
  --theme reports/theme_latest.json \
  --sector reports/sector_latest.json \
  --institutional reports/institutional_latest.json \
  --output-dir reports/

脚本支持部分输入；若缺少文件，会降低结果置信度，但不会终止执行。

Step 3: Interpret the Market Posture Summary

步骤3：解读市场态势总结

Review the generated posture report containing:

Exposure Ceiling -- Maximum recommended equity allocation (0-100%)
Bias Direction -- Growth vs Value tilt based on regime and flow
Participation Assessment -- Broad (healthy) vs Narrow (fragile) market
Action Recommendation -- NEW_ENTRY_ALLOWED, REDUCE_ONLY, or CASH_PRIORITY
Confidence Level -- HIGH, MEDIUM, or LOW based on input completeness

查看生成的态势报告，内容包括：

敞口上限 —— 建议的最高股票配置比例（0-100%）
风格偏好 —— 基于市场状态和资金流向的成长与价值风格倾向
参与度评估 —— 广度（健康）或窄度（脆弱）市场
行动建议 —— NEW_ENTRY_ALLOWED、REDUCE_ONLY 或 CASH_PRIORITY
置信度等级 —— HIGH、MEDIUM 或 LOW，基于输入文件的完整性

Step 4: Apply Exposure Guidance

步骤4：应用敞口指导建议

Map the posture recommendation to portfolio actions:

Recommendation	Action
NEW_ENTRY_ALLOWED	Proceed with stock-level analysis and new positions
REDUCE_ONLY	No new entries; trim existing positions on strength
CASH_PRIORITY	Raise cash aggressively; avoid all new commitments

将态势建议映射到投资组合操作：

建议	操作
NEW_ENTRY_ALLOWED	继续进行个股分析并建立新仓位
REDUCE_ONLY	不新增仓位；在行情走强时减持现有仓位
CASH_PRIORITY	大幅增加现金持有；避免所有新的资金投入

Output Format

输出格式

JSON Report

JSON报告

json

{
  "schema_version": "1.0",
  "generated_at": "2026-03-16T07:00:00Z",
  "exposure_ceiling_pct": 70,
  "bias": "GROWTH",
  "participation": "BROAD",
  "recommendation": "NEW_ENTRY_ALLOWED",
  "confidence": "HIGH",
  "component_scores": {
    "breadth_score": 65,
    "uptrend_score": 72,
    "regime_score": 80,
    "top_risk_score": 25,
    "ftd_score": 10,
    "theme_score": 68,
    "sector_score": 70,
    "institutional_score": 75
  },
  "inputs_provided": ["breadth", "uptrend", "regime", "top_risk"],
  "inputs_missing": ["ftd", "theme", "sector", "institutional"],
  "rationale": "Broad participation with low top risk supports elevated exposure."
}

json

{
  "schema_version": "1.0",
  "generated_at": "2026-03-16T07:00:00Z",
  "exposure_ceiling_pct": 70,
  "bias": "GROWTH",
  "participation": "BROAD",
  "recommendation": "NEW_ENTRY_ALLOWED",
  "confidence": "HIGH",
  "component_scores": {
    "breadth_score": 65,
    "uptrend_score": 72,
    "regime_score": 80,
    "top_risk_score": 25,
    "ftd_score": 10,
    "theme_score": 68,
    "sector_score": 70,
    "institutional_score": 75
  },
  "inputs_provided": ["breadth", "uptrend", "regime", "top_risk"],
  "inputs_missing": ["ftd", "theme", "sector", "institutional"],
  "rationale": "Broad participation with low top risk supports elevated exposure."
}

Markdown Report

Markdown报告

The markdown report provides a one-page summary suitable for quick review:

markdown

undefined

Markdown报告为一页式总结，便于快速查看：

markdown

undefined

Market Posture Summary

Date: 2026-03-16 | Confidence: HIGH

Exposure Ceiling: 70%

Dimension	Score	Status
Breadth	65	Healthy
Uptrend Participation	72%	Broad
Regime	Broadening	Favorable
Top Risk	25	Low

Dimension	Score	Status
Breadth	65	Healthy
Uptrend Participation	72%	Broad
Regime	Broadening	Favorable
Top Risk	25	Low

Recommendation: NEW_ENTRY_ALLOWED

Bias: Growth > Value Participation: Broad (healthy internals)

Rationale

Broad participation with low distribution day count supports elevated equity exposure. New positions allowed within the 70% ceiling.


Reports are saved to `reports/` with filenames `exposure_posture_YYYY-MM-DD_HHMMSS.{json,md}`.

Broad participation with low distribution day count supports elevated equity exposure. New positions allowed within the 70% ceiling.


报告将保存至`reports/`目录，文件名为`exposure_posture_YYYY-MM-DD_HHMMSS.{json,md}`。

Resources

资源

```
scripts/calculate_exposure.py
```
-- Main orchestrator that scores and synthesizes inputs
```
references/exposure_framework.md
```
-- Scoring rules and threshold definitions
```
references/regime_exposure_map.md
```
-- Regime-to-exposure ceiling mappings

```
scripts/calculate_exposure.py
```
—— 主协调脚本，负责对输入进行评分与整合
```
references/exposure_framework.md
```
—— 评分规则与阈值定义
```
references/regime_exposure_map.md
```
—— 市场状态与敞口上限的对应关系

Key Principles

核心原则

Safety First -- Default to lower exposure when inputs are incomplete or conflicting
Regime Alignment -- Let macro regime set the baseline; breadth adjusts within bounds
Actionable Output -- Always produce a clear recommendation, not just data aggregation

安全优先 —— 当输入不完整或存在冲突时，默认采用较低敞口建议
状态匹配 —— 以宏观市场状态为基准，通过市场广度在基准范围内调整敞口
输出可执行 —— 始终给出明确的建议，而非仅进行数据汇总