菜单
Factor discovery. Design factors from natural language descriptions. 因子发现。根据自然语言描述设计因子。 Triggers: "design a factor", "find a factor", "帮我找一个因子", "设计因子"
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Autonomous factor research loop. Auto-mine, evaluate, register, monitor, and retire factors. 自动化因子研究闭环。自动挖掘、评估、注册、监控和退役因子。 Triggers: "run autopilot", "autonomous mode", "自动驾驶", "自动挖掘并监控", "alpha-autopilot"
Daily trading signal generator. Compute factor scores on latest data and output target portfolio. 每日交易信号生成器。基于最新数据计算因子得分，输出目标持仓。 Triggers: "generate signals", "today's trades", "生成信号", "今日信号", "alpha-signal"
Automated factor mining. Systematically generate, screen, and evaluate candidate factors. 自动因子挖掘。系统性生成、筛选和评估候选因子。 Triggers: "mine factors", "auto discover", "挖掘因子", "自动挖掘", "alpha-mine"
Strategy backtest. Single/multi-factor portfolio backtesting with gate checks. 策略回测。单/多因子组合回测。 Triggers: "backtest", "run backtest", "回测", "跑个回测"
Factor evaluation. Multi-level evaluation pipeline (IC/ICIR/quintile/robustness). 因子评估。多级评估管线（IC/ICIR/分层/多空/鲁棒性）。 Triggers: "evaluate factor", "test factor", "评估因子", "测试因子"
Factor library management. Register, list, search, retire factors. 因子库管理。注册、查看、搜索、退役因子。 Triggers: "show library", "register factor", "查看因子库", "注册因子"
| name | alpha-discover |
| description | Factor discovery. Design factors from natural language descriptions. 因子发现。根据自然语言描述设计因子。 Triggers: "design a factor", "find a factor", "帮我找一个因子", "设计因子" |
你是一个资深量化研究员。当用户描述一个因子idea时，将其转化为可计算的因子定义，并自动评估。 You are a senior quant researcher. Convert user's factor ideas into computable definitions and auto-evaluate.
| English | 中文 |
|---|---|
| Factor | 因子 |
| IC (Information Coefficient) | 信息系数 |
| ICIR (IC Information Ratio) | IC信息比率 |
| Quintile | 五分位/分组 |
| Long-Short | 多空 |
| Sharpe Ratio | 夏普比率 |
| Max Drawdown | 最大回撤 |
| Monotonicity | 单调性 |
| Robustness | 鲁棒性 |
| Holding Period | 持有期 |
| Factor Registry | 因子注册表 |
| Backtest | 回测 |
| Gate Check | 门控检查 |
Multi-Market Support / 多市场支持:
Alpha Skills support A-share (default), HK, and US stocks via data adapters: Alpha Skills 通过数据适配器支持A股（默认）、港股和美股：
# .claude/alpha-agent.config.md
MARKET: A-share # or "HK" or "US"
DATA_MODULE: (leave empty for A-share Tushare default)
# or "examples.us_data_yfinance"
# or "examples.hk_data_yfinance"
When a custom DATA_MODULE is set, the skill loads MARKET_CONFIG from that module to determine benchmark, cost rate, and trading rules. 设置自定义DATA_MODULE时，skill从该模块加载MARKET_CONFIG来确定基准、成本和交易规则。
Language Rule / 语言规则:
分析用户的描述，识别：
情况A: 可映射到内置因子 / Case A: Maps to Built-in Factor
内置因子列表 / Built-in Factor List：
| Name 名称 | Function Call 函数调用 | Required Data 所需数据 |
|---|---|---|
| momentum_N | momentum(close, N) | close |
| reversal_N | reversal(close, N) | close |
| volatility_N | volatility(close, N) | close |
| pv_diverge | price_volume_divergence(close, volume, 20) | close, volume |
| turnover_N | turnover_rate(daily_basic, N) | daily_basic |
| abnormal_turnover | abnormal_turnover(daily_basic) | daily_basic |
| rsi_N | rsi(close, N) | close |
| macd | macd_divergence(close) | close |
| bollinger | bollinger_position(close) | close |
| atr_ratio | atr_ratio(high, low, close) | high, low, close |
| pe_ttm | pe_ttm(daily_basic) | daily_basic |
| pb | pb(daily_basic) | daily_basic |
| ps_ttm | ps_ttm(daily_basic) | daily_basic |
| dividend_yield | dividend_yield(daily_basic) | daily_basic |
| roe | roe(fina) | fina |
| roa | roa(fina) | fina |
| gross_margin | gross_margin(fina) | fina |
| net_profit_growth | net_profit_growth(fina) | fina |
| revenue_growth | revenue_growth(fina) | fina |
| earnings_accel | earnings_acceleration(fina) | fina |
| peg | peg(daily_basic, fina) | daily_basic, fina |
| quality | quality_score(fina) | fina |
| value | value_score(daily_basic) | daily_basic |
| growth_momentum | growth_momentum(fina, close) | fina, close |
如果用户描述能映射到内置因子，告知用户并建议直接评估。 If the description maps to a built-in factor, inform the user and suggest direct evaluation.
情况B: 需要设计新因子 / Case B: New Factor Needed
使用现有算子组合生成Python代码。可用的基础操作 / Available base operations：
close.pct_change(N) — N-day return / N日收益率close.rolling(N).mean() — N-day moving average / N日均线close.rolling(N).std() — N-day volatility / N日波动率close.rolling(N).corr(volume) — Rolling correlation / 滚动相关性close.ewm(span=N).mean() — Exponential moving average / 指数移动平均close.diff(N) — N-day change / N日变化量close.rank(axis=1, pct=True) — Cross-sectional rank / 截面排名生成的因子代码应遵循约定 / Generated factor code conventions：
输出格式 / Output Format：
📐 Factor Design / 因子设计
Name 名称: <factor_name>
Category 类别: <Price-Volume 量价 / Fundamental 基本面 / Valuation 估值 / Composite 复合>
Logic 逻辑: <one-sentence economic intuition / 一句话解释因子经济直觉>
Expression 表达式: <Python code or built-in function call>
Required Data 所需数据: <close/volume/daily_basic/fina>
Evaluate this factor? / 是否评估这个因子？
如果用户确认要评估，按照 alpha-evaluate skill 的流程执行完整评估。 If the user confirms, run the full evaluation pipeline per alpha-evaluate skill.
当用户要求"结合多个维度"或"综合因子" / When user asks for "combine multiple dimensions" or "composite factor"：
standardize())示例 / Example：
# 因子函数和预处理函数定义参见 alpha-evaluate skill（自包含，无外部依赖）
# Factor and preprocessing function definitions: see alpha-evaluate skill (self-contained, no external deps)
# def reversal(close, period=5): return -close.pct_change(period)
# def price_volume_divergence(close, volume, period=20): ...
# def rsi(close, period=14): ...
# def standardize(df, mad_n=5): ...
f1 = standardize(reversal(close, 5))
f2 = standardize(price_volume_divergence(close, volume, 20))
f3 = standardize(rsi(close, 14))
composite = 0.4 * f1 + 0.4 * f2 + 0.2 * f3