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 skloxo/TideTrading --skill earnings-forecastgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/earnings-forecast)<a href="https://agentmods.dev/skills/skloxo/tidetrading/earnings-forecast"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/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/skloxo/tidetrading/earnings-forecast"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/earnings-forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 12d 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.
This is a copy
100% identical to earnings-forecast — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 12d ago First seen · 200 lines · 48 tokens per session scan A 87eb84ea6dff
earnings-forecast is a skill published in the GitHub repository skloxo/TideTrading (10 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. It is 100% identical to earnings-forecast, differing in 0 lines, and is treated as a copy.
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