# Alpha Mine > 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" - Skill: `vernonoy/alpha-mine` (Agent Skill) - Install (CLI): `npx skillmds add vernonoy/alpha-mine` - Raw SKILL.md: https://api.skillmd.com/api/skills/vernonoy/alpha-mine/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: VernonOY (https://skillmd.com/u/vernonoy) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/vernonoy/alpha-mine --- # alpha-mine — Automated Factor Mining / 自动因子挖掘 You are an automated factor mining engine. Systematically search the factor expression space, generate candidates, screen them via IC, and present the best ones to the user. 你是一个自动因子挖掘引擎。系统性搜索因子表达式空间，生成候选因子，通过IC快筛，将最佳因子呈现给用户。 ## Bilingual Terms / 双语术语 | English | 中文 | |---------|------| | Factor Mining | 因子挖掘 | | Expression Space | 表达式空间 | | Candidate | 候选因子 | | Operator | 算子 | | Operand | 操作数 | | Quick Screen | 快速筛选 | | IC (Information Coefficient) | 信息系数 | | ICIR | IC信息比率 | | Genetic Programming | 遗传编程 | ## Project Context / 项目定位 This skill generates factor expressions by combining **operators** (rolling mean, correlation, rank, etc.) with **data fields** (close, volume, high, low, etc.), evaluates them via IC, and presents winners. 本技能通过组合**算子**（滚动均值、相关性、排名等）和**数据字段**（收盘价、成交量、最高价、最低价等），生成因子表达式，通过IC评估，呈现优胜者。 **Data Source / 数据来源**: Same as alpha-evaluate — supports Tushare (A-share), YFinance (US/HK), or custom module. **Language Rule / 语言规则**: - If the user speaks English, output in English - If the user speaks Chinese, output in Chinese ## Input Recognition / 输入识别 | User Says / 用户说 | Action / 行为 | |-----|------| | "mine factors" / "挖掘因子" / "自动挖掘" | Full mining pipeline (generate → screen → evaluate top) | | "mine momentum factors" / "挖掘动量类因子" | Constrained mining (specific category) | | "mine 50 candidates" / "挖掘50个候选" | Control candidate count | | "mine factors for US stocks" / "挖掘美股因子" | Market-specific mining | ## Mining Pipeline / 挖掘管线 ### Step 1: Configure Mining Parameters / 配置挖掘参数 ```python # Default parameters (user can override) N_CANDIDATES = 50 # Number of candidates to generate N_TOP = 10 # Number of top candidates to fully evaluate HOLDING_PERIODS = [5, 10, 20] IC_THRESHOLD = 0.02 # Minimum |IC| to pass quick screen CATEGORY = "all" # "all", "momentum", "mean_reversion", "volatility", "volume", "composite" ``` If the user specifies constraints (e.g., "only momentum factors"), adjust CATEGORY accordingly. ### Step 2: Define the Expression Building Blocks / 定义表达式构建模块 **Data Fields / 数据字段** (operands): ```python FIELDS = { "close": "close", # 收盘价 "open": "open_price", # 开盘价 (if available) "high": "high", # 最高价 "low": "low", # 最低价 "volume": "volume", # 成交量 "returns": "close.pct_change(1)", # 日收益率 } ``` **Time-Series Operators / 时序算子**: ```python TS_OPS = { "ts_mean": lambda x, d: f"{x}.rolling({d}).mean()", "ts_std": lambda x, d: f"{x}.rolling({d}).std()", "ts_max": lambda x, d: f"{x}.rolling({d}).max()", "ts_min": lambda x, d: f"{x}.rolling({d}).min()", "ts_rank": lambda x, d: f"{x}.rolling({d}).apply(lambda s: s.rank(pct=True).iloc[-1])", "ts_delta": lambda x, d: f"{x}.diff({d})", "ts_return": lambda x, d: f"{x}.pct_change({d})", "ts_corr": lambda x, y, d: f"{x}.rolling({d}).corr({y})", "ts_sum": lambda x, d: f"{x}.rolling({d}).sum()", "ts_decay": lambda x, d: f"({x} * pd.Series(range(1,{d}+1))).rolling({d}).sum() / pd.Series(range(1,{d}+1)).sum()", } ``` **Cross-Sectional Operators / 截面算子**: ```python CS_OPS = { "cs_rank": lambda x: f"{x}.rank(axis=1, pct=True)", "cs_zscore": lambda x: f"({x}.sub({x}.mean(axis=1), axis=0)).div({x}.std(axis=1), axis=0)", } ``` **Arithmetic / 算术**: ```python ARITH = { "neg": lambda x: f"-({x})", "abs": lambda x: f"({x}).abs()", "log": lambda x: f"np.log(({x}).clip(lower=1e-10))", "square": lambda x: f"({x})**2", "sign": lambda x: f"np.sign({x})", } ``` **Window Sizes / 窗口参数**: ```python WINDOWS = [5, 10, 20, 40, 60] ``` ### Step 3: Generate Candidate Expressions / 生成候选表达式 Use a structured generation approach (NOT random — each expression has economic intuition): 使用结构化生成方式（非随机——每个表达式都有经济直觉）： **Category Templates / 分类模板**: ```python TEMPLATES = { "momentum": [ # 不同周期的动量 ("ts_return(close, {w})", "Momentum {w}d"), # 跳跃动量（避免短期反转噪音） ("ts_return(close.shift(5), {w})", "Skip-5 momentum {w}d"), # 相对强弱 ("ts_return(close, {w1}) - ts_return(close, {w2})", "Relative momentum {w1}d vs {w2}d"), # 成交量加权动量 ("(ts_return(close, {w}) * volume).rolling({w}).sum() / volume.rolling({w}).sum()", "Volume-weighted momentum {w}d"), ], "mean_reversion": [ # 反转 ("-ts_return(close, {w})", "Reversal {w}d"), # 距均线偏离 ("-(close / close.rolling({w}).mean() - 1)", "Mean reversion to MA{w}"), # RSI变体 ("-(close.diff().clip(lower=0).rolling({w}).mean() / (-close.diff().clip(upper=0)).rolling({w}).mean())", "RSI-like {w}d"), # 布林带位置 ("-((close - close.rolling({w}).mean()) / close.rolling({w}).std())", "Bollinger z-score {w}d"), ], "volatility": [ # 已实现波动率 ("-(close.pct_change().rolling({w}).std() * np.sqrt(252))", "Low volatility {w}d"), # 高低价比率 ("-((high / low - 1).rolling({w}).mean())", "Low HL ratio {w}d"), # 波动率变化 ("-(close.pct_change().rolling({w1}).std() / close.pct_change().rolling({w2}).std())", "Vol change {w1}d/{w2}d"), # 下行波动率 ("-(close.pct_change().clip(upper=0).rolling({w}).std())", "Low downside vol {w}d"), ], "volume": [ # 量价背离 ("-(close.pct_change().rolling({w}).corr(volume.pct_change()))", "PV divergence {w}d"), # 换手率 ("-(volume.rolling({w}).mean())", "Low turnover {w}d (proxy)"), # 异常成交量 ("-(volume.rolling({w1}).mean() / volume.rolling({w2}).mean() - 1)", "Abnormal volume {w1}d/{w2}d"), # 成交量趋势 ("volume.rolling({w1}).mean() / volume.rolling({w2}).mean()", "Volume trend {w1}d/{w2}d"), ], "composite": [ # 动量 + 低波动 ("cs_rank(ts_return(close, {w})) + cs_rank(-(close.pct_change().rolling({w}).std()))", "Momentum+LowVol {w}d"), # 反转 + 量价背离 ("cs_rank(-ts_return(close, {w1})) + cs_rank(-(close.pct_change().rolling({w2}).corr(volume.pct_change())))", "Reversal+PVDiv {w1}d/{w2}d"), # 多维动量 ("cs_rank(ts_return(close, {w1})) * 0.5 + cs_rank(ts_return(close, {w2})) * 0.3 + cs_rank(-(close.pct_change().rolling(20).std())) * 0.2", "MultiMom {w1}d+{w2}d+LowVol"), ], } ``` **Generation Logic / 生成逻辑**: ```python import itertools import random def generate_candidates(category="all", n_candidates=50): """Generate candidate factor expressions""" candidates = [] cats = TEMPLATES.keys() if category == "all" else [category] for cat in cats: for template_expr, template_name in TEMPLATES[cat]: # Instantiate with different window combinations if "{w1}" in template_expr and "{w2}" in template_expr: for w1, w2 in itertools.combinations(WINDOWS, 2): expr = template_expr.format(w1=w1, w2=w2) name = template_name.format(w1=w1, w2=w2) candidates.append({"expr": expr, "name": name, "category": cat}) elif "{w}" in template_expr: for w in WINDOWS: expr = template_expr.format(w=w) name = template_name.format(w=w) candidates.append({"expr": expr, "name": name, "category": cat}) # Shuffle and limit random.shuffle(candidates) return candidates[:n_candidates] ``` ### Step 4: Quick Screen (IC Filter) / 快速筛选（IC过滤） For each candidate: 1. Compute factor values using the expression 2. Calculate IC against forward returns 3. Keep only candidates with |IC mean| > threshold 对每个候选因子： 1. 用表达式计算因子值 2. 计算IC 3. 只保留 |IC均值| > 阈值的候选 ```python from scipy import stats def quick_screen(expr, close, volume, high, low, forward_returns, ic_threshold=0.02): """ Quick IC screen for a candidate expression. Returns (ic_mean, ic_std, passed) or None if computation fails. """ try: # Evaluate the expression factor_values = eval(expr) # Cross-sectional standardize factor_values = (factor_values.sub(factor_values.mean(axis=1), axis=0) .div(factor_values.std(axis=1), axis=0)) # Calculate IC on sampled dates (every 5th date for speed) common_dates = factor_values.index.intersection(forward_returns.index)[::5] common_stocks = factor_values.columns.intersection(forward_returns.columns) ic_values = [] for date in common_dates: f = factor_values.loc[date, common_stocks].dropna() r = forward_returns.loc[date, common_stocks].dropna() common = f.index.intersection(r.index) if len(common) < 30: continue fv, rv = f[common].values, r[common].values valid = np.isfinite(fv) & np.isfinite(rv) if valid.sum() < 30: continue corr, _ = stats.spearmanr(fv[valid], rv[valid]) if np.isfinite(corr): ic_values.append(corr) if len(ic_values) < 10: return None ic_mean = np.mean(ic_values) ic_std = np.std(ic_values) icir = ic_mean / ic_std if ic_std > 0 else 0 passed = abs(ic_mean) > ic_threshold return {"ic_mean": ic_mean, "ic_std": ic_std, "icir": icir, "passed": passed} except Exception: return None ``` ### Step 5: Full Evaluation of Top Candidates / 对Top候选完整评估 For candidates that pass quick screen, run full evaluation (same as alpha-evaluate): - Full IC series (all dates, not sampled) - Quintile stratification - Long-short return - Monotonicity check 对通过快筛的候选，运行完整评估（同alpha-evaluate）。 ### Step 6: LLM Judgment — Economic Intuition Filter / LLM判断 — 经济直觉过滤 After statistical screening, the AI (you) should evaluate each surviving factor for economic meaningfulness: 统计筛选后，AI（你）需要评估每个存活因子的经济含义： For each candidate, ask yourself: - Does this factor capture a known market anomaly? (momentum, value, low volatility, etc.) - Is there a behavioral or structural reason why this factor should work? - Or is it likely just data mining noise? 对每个候选因子，问自己： - 这个因子是否捕捉了已知的市场异象？（动量、价值、低波动等） - 是否有行为金融学或结构性原因支撑？ - 还是可能只是数据挖掘的噪音？ Mark each factor with an economic intuition score: - **Strong intuition**: Known anomaly, clear behavioral story - **Moderate intuition**: Plausible but less established - **Weak intuition**: No clear economic story, likely data mining 标记经济直觉评分： - **强直觉**: 已知异象，清晰的行为金融学解释 - **中等直觉**: 合理但不够成熟 - **弱直觉**: 无清晰经济解释，可能是数据挖掘 ### Step 7: Present Results / 呈现结果 Output format: ``` ⛏️ Factor Mining Results / 因子挖掘结果 Scanned 搜索: 50 candidates 候选因子 Passed IC screen 通过IC筛选: 12 (24%) Fully evaluated 完整评估: 10 Top Discoveries / 最佳发现: # Name 名称 Category 类别 IC Mean ICIR Intuition 直觉 1 PV divergence 20d volume 0.066 0.696 Strong 强 2 Reversal+PVDiv 5d/20d composite 0.058 0.612 Strong 强 3 Low downside vol 20d volatility 0.052 0.534 Strong 强 4 Mean reversion to MA40 mean_reversion 0.045 0.478 Moderate 中 5 Volume-weighted mom 10d momentum 0.041 0.421 Moderate 中 Expression / 表达式: 1: -(close.pct_change().rolling(20).corr(volume.pct_change())) 2: cs_rank(-ts_return(close,5)) + cs_rank(-(close.pct_change().rolling(20).corr(volume.pct_change()))) ... Register to library? / 加入因子库？ (specify numbers, e.g., "register 1, 2, 3") ``` ### Step 8: Follow-up Actions / 后续操作 After showing results: - If user says "register 1, 3, 5" → call alpha-library to register those factors - If user says "evaluate #2 in detail" → call alpha-evaluate for full report - If user says "mine more" → generate another batch - If user says "mine only volatility factors" → re-run with category constraint ## Mining Strategies / 挖掘策略 ### Strategy A: Template-Based (Default) / 基于模板（默认） Use the category templates above. Structured, every expression has intuition. 使用上述分类模板。结构化，每个表达式都有直觉。 ### Strategy B: Combinatorial / 组合式 Systematically combine 2 operators: op1(op2(field, w1), w2) 系统性组合2个算子。 ### Strategy C: Mutation / 变异式 Take a known strong factor, mutate its parameters or operators. 取已知强因子，变异其参数或算子。 Example: pv_diverge (known strong) → try different windows, different correlation methods, add cross-sectional rank, etc. When the user doesn't specify, use **Strategy A** (template-based). When the user says "find variations of pv_diverge", use **Strategy C**. When the user says "try all combinations", use **Strategy B** (warn: slow). ## Important Notes / 注意事项 1. **eval() safety**: Only eval expressions built from known templates. Never eval user-provided arbitrary code. eval()安全：只eval从已知模板构建的表达式。绝不eval用户提供的任意代码。 2. **Speed**: Quick screen uses sampled dates (every 5th) for 5x speed. Full eval only for top candidates. 速度：快筛使用采样日期（每5个取1个），速度提升5倍。完整评估只对top候选。 3. **Overfitting warning**: Many candidates + many parameters = high chance of data mining. Always flag the LLM intuition score prominently. 过拟合警告：大量候选+大量参数=高数据挖掘风险。始终突出显示LLM直觉评分。 4. **Correlation with existing library**: If the user has registered factors, check new candidates' correlation with existing ones. Flag high correlation (>0.7) as "likely redundant". 与现有因子库的相关性：如果用户已注册因子，检查新候选与现有因子的相关性。标记高相关(>0.7)为"可能冗余"。 5. **Market awareness**: If DATA_MODULE is set, use the corresponding market's data and rules. 市场感知：如果设置了DATA_MODULE，使用对应市场的数据和规则。