volume-price-reversal

volume-price-reversal is a skill for Claude Code, Codex from duolongworld/AI_Renaissance. It costs 74 tokens per session (17,451 once invoked), scanned A, original, Apache-2.0.

A financial-analysis method for finding possible short-term reversals by comparing unusual price movements with unusual trading volume.

In plain words
What is it for?
Use it to assess short-term overbought or oversold conditions, post-news reversal possibilities, and potential timing for reducing or starting a position.
Why use it?
It helps identify when a stock, market sector, or index may have moved too far and could move back toward its recent average. The analysis has specific data requirements and situations where its result should be treated as neutral or reviewed by a person.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to assess short-term overbought or oversold conditions, post-news reversal possibilities, and potential timing for reducing or starting a position.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/duolongworld/ai_renaissance/volume_price_reversal
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add duolongworld/AI_Renaissance --skill volume_price_reversal
Clone the repo
git clone --depth 1 https://github.com/duolongworld/AI_Renaissance

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for volume-price-reversal

README.md
[![agentmods](https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/volume_price_reversal/github.svg)](https://agentmods.dev/skills/duolongworld/ai_renaissance/volume_price_reversal)
Your own site
<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/volume_price_reversal"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/volume_price_reversal/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for volume-price-reversal

Your own site · 80×15
<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/volume_price_reversal"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/volume_price_reversal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 17,451 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.17451
Opus 5 $0.00037 $0.08725
Sonnet 5 $0.00015 $0.03490
Haiku 4.5 $0.00007 $0.01745

Measured 13d ago against content hash 1c380c36c172, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

volume-price-reversal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (runtime.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/technical/volume_price_reversal/SKILL.md · 733 lines

How it starts

The opening of the file, as written. The whole thing — 733 lines — stays where its author put it; the contents beside it link to each section on GitHub.

量价背离与反转信号识别

理论来源:Goldman Sachs QIS STR 因子家族,结合 Campbell-Grossman-Wang (1993)、Lee-Swaminathan (2000)、Gervais-Kaniel-Mingelgrin (2001) 等学术经典整理。


1. 适用范围

所属小组:专家2组(指标)

适用任务:

  • 个股、板块、指数的短期量价背离识别(顶背离 / 底背离 / 量价齐升后乏力 / 量价齐跌后衰竭)
  • 事件驱动后的反转概率判断(财报公告、研报调评、指数调仓)
  • 持仓股短期超涨减仓时机 / 目标股低位建仓时机判断
  • 适合时间周期:521 个交易日(短期),A 股建议优先取 510 日

边界说明

  • 强趋势牛市(过去 3 个月指数涨幅 > 20% 且仍加速)中,动量主导,反转策略失效,输出 neutral

  • 极端流动性枯竭阶段(市场出现系统性危机、全面踩踏)中,价格冲击不回归,输出 neutral

  • A 股以下场景不适用,输出 neutral 并标记 needs_human_review: true

    • 政策强刺激期(如单日指数涨超 3%、全面利好政策出台)
    • 游资题材股 / 妖股(连续涨停且无基本面支撑)
    • ST 股 / 退市风险股(±5% 涨跌停 + 基本面恶化)
    • 北交所股票(流动性差,须严格执行 §4.4 过滤器三 Amihud 和过滤器四绝对成交额双重检查;不达标即输出 neutral
    • 重大节假日前后 2 个交易日(春节 / 国庆资金季节性扰动)
  • 成交量数据缺失或历史数据不足 60 日时,需降低 confidence 并标记人工复核


2. 输入材料

必填输入

  • 标的:股票代码 / 板块名称 / 指数代码
  • 时间范围:分析窗口(至少包含过去 60 个交易日行情数据)
  • 核心行情数据:
    • 日线收盘价序列(用于计算 STR 和形态判断)
    • 日线最高价 / 最低价(用于识别上影线 / 下影线形态)
    • 日线成交量序列(用于计算 VolSurp 和均量比较)
    • 换手率序列(用于换手率分层,至少 20 日)
  • 数据来源:行情数据(market_data),来自东方财富 / 同花顺 / Wind 等

可选输入

  • RSI 指标数值(RSI6 / RSI12 / RSI24),用于复合判断规则
  • 主力资金净流入数据(超大单净额),来自资金流数据(fund_flow)
  • A 股涨停板信息(是否涨停、封板时间、打开次数)
  • A 股集合竞价价格(9:25 竞价价格 vs 昨收比较)
  • 融资余额变化(连续 N 日净增 / 净减)
  • 板块 / 行业成交量均值(用于板块超涨超跌判断)

缺失处理

  • 缺少成交量数据 → 无法执行任何量价判断,输出 direction: "neutral"confidence 降至 0.2,meta.needs_human_review: truemeta.uncertainties 写明「缺少成交量数据,无法完成量价分析」
  • 缺少换手率数据 → 跳过换手率分层,confidence 降低 0.1,在 meta.uncertainties 说明「换手率分层未执行,信号权重未调整」
  • 历史数据不足 60 日 → confidence 降至 0.3,meta.needs_human_review: true
  • 缺少 RSI 数据 → 仅执行量价形态判断,跳过 RSI 复合判断规则;meta.uncertainties 写明「RSI 缺失,复合信号不完整」
  • 可选输入缺失 → 继续分析,但在 meta.uncertainties 中说明对信号覆盖度的影响

3. 分析步骤

  1. 确认分析对象与市场环境:检查标的所属市场(A 股主板 / 科创板 / 创业板 / 北交所 / 港股 / 美股),判断是否处于强趋势牛市或极端流动性枯竭,若是则直接输出 neutral
  2. 检查数据完整性:核对必填输入是否齐全;缺失时按缺失处理规则降低 confidence 或输出 neutral
  3. 计算量价核心指标
    • 计算 STR(横截面短期反转得分)
    • 计算 VolSurp(异常成交量 Z 分数)
    • 计算换手率分位,确定分层系数
    • 计算 Amihud ILLIQ,判断流动性是否可执行
    • 计算绝对成交额(当日 + 过去 20 日均值),对照 §4.4 过滤器四阈值表,确定是降权 ×0.5 还是强制 neutral
  4. A 股涨跌停前置过滤(若标的为 A 股):检查当日是否涨停一字板/跌停一字板/未开板封死,若是则跳过形态一/二的顶/底背离判断,转入 §4.7 涨停板规则。
  5. 识别量价形态:依据第 4 节判断规则,匹配以下四种形态中的一种或多种:顶背离 / 底背离 / 量价齐升后乏力 / 量价齐跌后衰竭;形态三/四须按 §4.2 长上下影线精确定义(三条件全成立)判定,并区分是否为十字星犹豫形态。
  6. 执行 RSI + 空间位置三维度复合验证:按 §4.3 复合判断三维度(量价 + 动量 + 空间)规则,校验信号是否撞到关键压力位/支撑位;空间未确认的反转信号显著降权并写入 meta.uncertainties
  7. 执行 A 股特有规则(若标的为 A 股):按第 4.7 节处理涨停板特有形态、T+1 执行约束、参数阈值调整。
  8. 确定 direction / confidence / risk_level:汇总所有信号,按第 4 节规则映射,对照 §4.4 指标解读速查表逐项累加 confidence 调整量。
  9. 整理 evidence、key_findings 和命中逻辑说明
    • 记录触发规则的指标值、数据来源、对比基准;
    • 对每个被命中"强制约束等级"的指标(如 STR 中度/强烈、VolSurp 爆量/巨量/缩量/地量、换手率高/极端/低、影线标准/极端、流动性差、绝对成交额不足/长期不足等),必须从 §4.4 速查表的「命中逻辑说明」列原文引用对应一句话,写入:
      • 标准 JSON 的 reasoning 字段(多指标命中时按"指标名 → 命中逻辑"句式拼接为一段)
      • 标准 JSON 的 meta.evidence[].note 字段(每条 evidence 对应一个指标,note 写该指标命中等级 + 命中逻辑说明)
      • §5.2 Markdown 摘要中该指标行的「→ 意味着:……」子项
    • 命中"平淡/正常/中等/容量充足/非长影线"等中性等级时不需要写命中逻辑说明,避免输出冗余。
  10. 输出结果
  • Agent 系统调用:按第 5 节格式输出标准 JSON,供 Signal.from_dict() 解析;
  • 人工 / Cursor 直接调用:按第 5.2 节格式输出 Markdown 摘要,省略 JSON,提升可读性。
  • 不确定调用场景时,优先输出 JSON

Read the full file on GitHub · 733 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 733 lines · 74 tokens per session scan A 1c380c36c172

Subscribe to this mod's changes

volume-price-reversal is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 15d ago), licensed Apache-2.0. It adds 74 tokens to every session and 17,451 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

sector-rotation

An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.

HKUDS/Vibe-Trading · 39 tokens

strategy-pivot-designer

Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

tradermonty/claude-trading-skills · 28 tokens

twitter-reader

Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…

himself65/finance-skills · 161 tokens

chenhao-limit-up

A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.

questflowai/investorskills · 44 tokens

furusato

A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.

kazukinagata/shinkoku · 102 tokens

reading-receipt

An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.

kazukinagata/shinkoku · 64 tokens