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 aAAaqwq/AGI-Super-Team --skill alpha101git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/alpha101)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/alpha101"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/alpha101/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/aaaaqwq/agi-super-team/alpha101"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/alpha101.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00114 | $0.01102 |
| Opus 5 | $0.00057 | $0.00551 |
| Sonnet 5 | $0.00023 | $0.00220 |
| Haiku 4.5 | $0.00011 | $0.00110 |
Grade A, and why
alpha101 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 9d 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.
What it actually says
Alpha101 — WorldQuant 101 Formulaic Alphas
概述
Zura Kakushadze 论文《101 Formulaic Alphas》(arXiv:1601.00991) 的完整Python实现。 101个真实量化交易alpha因子，公式即代码。
论文关键数据
- 平均持仓期: 0.6-6.4天
- 平均两两相关性: 15.9%
- 收益与波动率强相关: R ~ σ^0.76
文件结构
skills/alpha101/
├── SKILL.md ← 本文件
├── scripts/
│ ├── alpha101.py ← 101个因子函数 + 基础函数库
│ ├── compute_ic.py ← 因子IC/IR计算
│ └── backtest_alpha.py ← 单因子回测
└── references/
└── paper_notes.md ← 论文笔记与函数定义
使用方式
1. 因子计算
from scripts.alpha101 import compute_alphas, alpha101
# 输入: date x ticker DataFrame
data = {
'open': df_open, 'close': df_close, 'high': df_high, 'low': df_low,
'volume': df_vol, 'vwap': df_vwap, 'returns': df_returns
}
# 计算所有可用因子
alphas = compute_alphas(data) # dict of alpha_name -> DataFrame
# 单独计算
from scripts.alpha101 import alpha101
a101 = alpha101(df_open, df_close, df_high, df_low)
2. 因子IC测试
python scripts/compute_ic.py --data <path> --output results/
3. 单因子回测
python scripts/backtest_alpha.py --alpha 101 --data <path>
因子分类
| 类别 | 因子 | 输入 |
|---|---|---|
| 纯价量 | #1-#47, #49-#55, #60, #61, #71-#74, #84, #88, #101 | OHLCV + VWAP |
| 行业中性化 | #48, #56, #58-#59, #63, #67, #69-#70, #76, #79-#82, #87, #89-#91, #93, #97, #100 | + 行业分类 |
| 复杂参数 | #57-#99 | 非整数窗口, 混合权重 |
基础函数速查
rank(x) 截面排名 [0,1]
delay(x,d) d天前的值
delta(x,d) 当期 - d天前
correlation(x,y,d) d天滚动相关
scale(x,a=1) 缩放使sum(abs(x))=a
decay_linear(x,d) 线性衰减加权均值
ts_min/ts_max(x,d) 滚动最小/最大
ts_rank(x,d) 时间序列排名
ts_sum/ts_std(x,d) 滚动求和/标准差
adv{d} d天平均成交额
IndNeutralize(x,ind) 行业中性化
回测注意事项
- 交易成本: 论文因子扣除cost后Sharpe才是真Sharpe
- 过拟合: 101个因子中部分可能已衰减，需样本外验证
- 市场适配:
- A股: T+1限制，持仓期需调整
- 加密: 24/7，日频→小时频需改窗口参数
- Polymarket: 流动性低，部分因子不适用
- 行业因子: 需要行业分类映射，加密市场可用板块替代
决策框架
因子计算 → IC/IR筛选(>0.03) → 样本外验证 → 组合构建(低相关等权) → 回测扣费 → 实盘
What ships with it
4 files 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.
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.
- 9d ago First seen · 110 lines · 114 tokens per session scan A 7d3e2eb9cff7
alpha101 is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed 2d ago), licensed MIT. It adds 114 tokens to every session and 1,102 once invoked, about $0.0006 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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