القائمة
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"
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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