Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
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.
npx skills add HKUDS/Vibe-Trading --skill earnings-forecastgit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote 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.
[](https://agentmods.dev/skills/hkuds/vibe-trading/earnings-forecast)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/earnings-forecast"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/earnings-forecast/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.
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/earnings-forecast"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/earnings-forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.02435 |
| Opus 5 | $0.00024 | $0.01218 |
| Sonnet 5 | $0.00010 | $0.00487 |
| Haiku 4.5 | $0.00005 | $0.00244 |
Grade A, and why
earnings-forecast 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 11d ago.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- earnings-forecast — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
盈利预测与一致预期
概述
围绕企业盈利预测和市场一致预期偏差构建交易信号。核心逻辑：股价短期由盈利预期差驱动，捕捉「预期差」比预测绝对盈利更有价值。两条主线：① 自主预测 vs 一致预期对比寻找偏差；② 跟踪分析师预期修正动量。
核心概念
1. 自上而下预测法（Top-Down）
预测链条：
GDP增速预测 → 行业增加值增速 → 行业收入增速 → 龙头公司收入增速 → 利润率假设 → EPS预测
A股实战示例（以白酒行业为例）：
| 层级 | 指标 | 预测逻辑 |
|---|---|---|
| 宏观 | GDP +5.0% | 消费占GDP比重65%，消费增速约+6% |
| 行业 | 白酒收入 +8% | 高端白酒量价齐升，结构升级 |
| 公司 | 贵州茅台(600519.SH) | 出厂价+10%，销量+2%，收入约+12% |
| 盈利 | 净利润率55% | 提价传导，费用率稳定 |
| EPS | 约62元 | 净利润/总股本 |
适用场景： 行业beta判断、大盘盈利周期定位、宏观策略配合
2. 自下而上预测法（Bottom-Up）
收入拆解三板斧：
# 方法1：量价拆解
revenue = volume * price
# 例：中国神华(601088.SH) = 煤炭销量(亿吨) × 煤价(元/吨) + 电力收入
# 方法2：客户/产品拆解
revenue = sum(segment_revenue for segment in business_lines)
# 例：美的集团(000333.SZ) = 暖通空调 + 消费电器 + 机器人及自动化
# 方法3：门店/用户拆解
revenue = stores * revenue_per_store # 或 users * ARPU
# 例：海底捞(6862.HK) = 门店数 × 翻台率 × 客单价 × 营业天数
利润率假设关键点：
- 毛利率：原材料成本占比变动、产品结构升级
- 费用率：规模效应（收入增、费用率降）、研发投入变动
- 税率：高新技术企业15% vs 普通25%，是否有税收优惠到期
3. 标准化未预期盈利（SUE）
公式：
SUE = (actual_EPS - consensus_EPS) / std(actual_EPS - consensus_EPS)
# consensus_EPS = 分析师一致预期EPS（取中位数）
# std = 过去8个季度预测偏差的标准差
信号阈值（A股实证参考）：
| SUE范围 | 含义 | 交易动作 |
|---|---|---|
| SUE > +2.0 | 大幅超预期 | 强买入信号 |
| SUE +1.0~+2.0 | 温和超预期 | 买入信号 |
| SUE -1.0~+1.0 | 符合预期 | 无信号 |
| SUE -2.0~-1.0 | 温和低于预期 | 卖出信号 |
| SUE < -2.0 | 大幅低于预期 | 强卖出信号 |
4. 盈余公告后漂移（PEAD）
现象： 业绩公告后，超预期方向的股价漂移可持续30-60个交易日。
A股PEAD策略实现：
# 策略逻辑
# 1. 业绩公告日（年报4/30前，中报8/31前，季报各截止日）
# 2. 计算SUE
# 3. SUE > +1.5 的股票买入持有 40 个交易日
# 4. SUE < -1.5 的股票卖出/做空（如果可以）
# 关键参数
holding_period = 40 # 持有交易日数
sue_threshold = 1.5 # SUE阈值
max_positions = 10 # 最大持仓数
rebalance_on = "earnings_date" # 在业绩公告日调仓
A股PEAD注意事项：
- A股做空受限（融券），PEAD策略通常只做多头
- 业绩预告（1月底/7月中旬）比正式报告更早，抢先反应
- 年报4/30截止，集中在4月发布，信息拥挤期需分散
5. 分析师预期修正动量
三个关键指标：
# 1. 预期修正比率（ERM）
ERM = (上调家数 - 下调家数) / 总覆盖家数
# ERM > 0.3 = 正面动量, ERM < -0.3 = 负面动量
# 2. 预期变化幅度
eps_change_pct = (new_consensus - old_consensus_30d_ago) / abs(old_consensus_30d_ago)
# 变化 > +5% = 显著上调
# 3. 预期离散度
dispersion = std(all_analyst_EPS) / mean(all_analyst_EPS)
# 离散度 > 0.3 = 分歧大, 不确定性高
# 离散度 < 0.1 = 共识强, 确定性高
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.
- 11d ago First seen · 200 lines · 48 tokens per session scan A 87eb84ea6dff
earnings-forecast is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 2,435 once invoked, about $0.0002 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.
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