# Alpha Backtest > 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" - Skill: `vernonoy/alpha-backtest` (Agent Skill) - Install (CLI): `npx skillmds add vernonoy/alpha-backtest` - Raw SKILL.md: https://api.skillmd.com/api/skills/vernonoy/alpha-backtest/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-backtest --- # alpha-backtest — Strategy Backtest / 策略回测 你是一个量化策略回测工程师。当用户要求回测时，构建因子选股策略并使用BacktestEngine运行回测。 You are a quant strategy backtest engineer. Build factor-based stock selection strategies and run backtests using BacktestEngine. ## Bilingual Terms / 双语术语 | 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 | ## 项目定位 / Project Context - 数据 Data: `data_cache/` (已缓存Parquet cached Parquet) - 配置 Config: `.claude/alpha-agent.config.md` (门控指标等 gate metrics etc.) - 输出 Output: `output/` 目录 directory **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： ```markdown # .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**: - If the user speaks English, output in English - If the user speaks Chinese, output in Chinese - Table headers always show both languages: "IC Mean IC均值" ## 输入识别 / Input Recognition 1. **单因子回测 Single-factor backtest**: "回测pv_diverge因子" / "backtest pv_diverge factor" → 用单个因子选股 single factor stock selection 2. **多因子组合 Multi-factor combo**: "用pv_diverge和turnover_20做组合回测" / "combo backtest with pv_diverge and turnover_20" → 多因子加权 multi-factor weighting 3. **交互式 Interactive**: "帮我跑个回测" / "help me run a backtest" → 询问参数后执行 ask for parameters then execute 4. **从因子库选取 From registry**: "用因子库里最强的3个因子回测" / "backtest with top 3 factors from library" → 从registry读取 read from registry ## 执行流程 / Execution Pipeline ### Step 1: 确定参数 / Determine Parameters 从用户输入和配置文件确定 / Determine from user input and config： - **因子列表 Factor list**: 哪些因子参与 which factors（名称列表 name list） - **权重方案 Weight scheme**: 等权 equal weight（默认 default）/ ICIR加权 ICIR-weighted / 用户指定 user-specified - **回测区间 Backtest period**: 默认 default 2022-01-01 ~ 2025-12-31 - **IS/OOS切分 IS/OOS split**: 默认 default IS至 until 2024-12-31，OOS从 from 2025-01-01 - **调仓频率 Rebalance frequency**: 默认 default 20个交易日 trading days（月频 monthly） - **持仓数量 Holdings count**: 默认 default 15只 stocks - **是否择时 Market timing**: 默认开启 default on（MA20/MA60） - **市值过滤 Market cap filter**: 从配置读取 read from config - **交易成本 Transaction cost**: 从配置读取 read from config（默认 default 0.003） 如果用户没有明确指定，使用默认值并告知。 If user doesn't specify, use defaults and inform. ### Market-Aware Trading Rules / 市场感知交易规则 Skill根据 MARKET_CONFIG 自动应用对应的交易规则： Skill automatically applies trading rules based on MARKET_CONFIG: | Rule / 规则 | A-share A股 | HK 港股 | US 美股 | |-------------|-------------|---------|---------| | Price Limit 涨跌停 | ±10% | None 无 | None 无 | | T+N | T+1 | T+0 | T+0 | | Round-trip Cost 双边成本 | 0.3% | 0.2% | 0.1% | | Min Trade Unit 最小单位 | 100 shares | 100+ | 1 share | | Stamp Duty 印花税 | 0.1% (sell) | 0.13% | 0 | 从 DATA_MODULE 的 MARKET_CONFIG 自动读取这些字段，无需用户手动指定。 These fields are automatically read from DATA_MODULE's MARKET_CONFIG; no manual specification needed. ### Step 2: 数据加载与预处理 / Data Loading & Preprocessing 同 alpha-evaluate 的数据加载流程（加载缓存 → pivot → 前复权 → 过滤）。 Same as alpha-evaluate data loading pipeline (load cache → pivot → forward-adjust → filter). ### Step 3: 计算因子并生成信号 / Compute Factors & Generate Signals ```python import pandas as pd import numpy as np # ── 因子计算函数（自包含）/ Factor functions (self-contained) ── # 定义所需因子函数（参见 alpha-evaluate skill 中的完整实现） # Define required factor functions (see alpha-evaluate skill for full implementations) def momentum(close, period=20): return close.pct_change(period) def reversal(close, period=5): return -close.pct_change(period) def volatility(close, period=20): return -(close.pct_change().rolling(period).std() * np.sqrt(252)) def price_volume_divergence(close, volume, period=20): price_ret = close.pct_change() vol_ret = volume.pct_change() result = pd.DataFrame(index=close.index, columns=close.columns, dtype=float) for col in close.columns: if col in volume.columns: result[col] = price_ret[col].rolling(period).corr(vol_ret[col]) return -result def rsi(close, period=14): delta = close.diff() gain = delta.clip(lower=0).rolling(period).mean() loss = (-delta.clip(upper=0)).rolling(period).mean() rs = gain / loss return -(100 - 100 / (1 + rs) - 50) def turnover_rate(daily_basic_df, period=20): df = daily_basic_df[["ts_code","trade_date","turnover_rate_f"]].copy() df["trade_date"] = pd.to_datetime(df["trade_date"], format="%Y%m%d") pivot = df.pivot_table(index="trade_date", columns="ts_code", values="turnover_rate_f") return -pivot.rolling(period).mean() # ... 其他因子按同样模式定义 / other factors defined following the same pattern ... # AI应根据用户指定的因子名称，参考 alpha-evaluate skill 中的实现模式现场编写 # AI should write factor code on-the-fly based on the patterns in alpha-evaluate skill # ── 预处理函数 / Preprocessing functions ── def winsorize_mad(df, n=5): median = df.median(axis=1) mad = df.sub(median, axis=0).abs().median(axis=1) upper = median + n * 1.4826 * mad lower = median - n * 1.4826 * mad return df.clip(lower, upper, axis=0) def zscore_cross_section(df): return df.sub(df.mean(axis=1), axis=0).div(df.std(axis=1), axis=0) def standardize(df, mad_n=5): return zscore_cross_section(winsorize_mad(df, n=mad_n)) # ── 计算各因子 / Compute each factor ── factor_dfs = {} for name in factor_names: factor_dfs[name] = standardize(<对应因子函数 corresponding function>(...)) # 复合得分 / Composite score composite = None for name, weight in weights.items(): ranked = factor_dfs[name].rank(axis=1, pct=True) if composite is None: composite = ranked * weight else: common_dates = composite.index.intersection(ranked.index) common_stocks = composite.columns.intersection(ranked.columns) composite = composite.loc[common_dates, common_stocks].fillna(0.5) * (1 - weight) + \ ranked.loc[common_dates, common_stocks].fillna(0.5) * weight # 择时信号（可选）/ Market timing signal (optional) if use_timing: benchmark_close = ... # 沪深300 CSI 300 ma_fast = benchmark_close.rolling(20).mean() ma_slow = benchmark_close.rolling(60).mean() ratio = (ma_fast - ma_slow) / ma_slow # ratio > 0.02 满仓 full position, -0.02~0.02 半仓 half position, < -0.02 空仓 empty # 生成调仓信号 / Generate rebalance signals trading_days = close.index mask = (trading_days >= start_date) & (trading_days <= end_date) bt_days = trading_days[mask] rebal_dates = bt_days[::rebal_freq] signals = {} for date in rebal_dates: if use_timing and ratio.get(date, 0) < -0.02: continue # 空仓 empty position scores = composite.loc[date].dropna() # 根据市场规则过滤 / Filter by market rules if market_config.get("price_limit") is not None: limit = market_config["price_limit"] # 如 0.1 for A股 daily_ret = close.pct_change() if date in daily_ret.index: limit_up = daily_ret.loc[date] > limit * 0.95 # 留5%余量 scores = scores[~scores.index.isin(limit_up[limit_up].index)] # 美股/港股无涨跌停，跳过此过滤 / US/HK no price limit, skip this filter n = n_stocks if (not use_timing or ratio.get(date, 0) > 0.02) else n_stocks // 2 top = scores.nlargest(n) # 按得分加权 / Weight by score w = top / top.sum() signals[date] = w.to_dict() ``` ### Step 4: 运行回测 / Run Backtest ```python def simple_backtest(close, signals, cost_rate=0.003): """ 简易回测引擎 / Simple backtest engine close: DataFrame (index=日期, columns=股票) signals: dict {date: {stock: weight}} 或 {date: [stock_list]} cost_rate: 双边交易成本 round-trip transaction cost 返回 Returns: nav Series, metrics dict """ # 如果signals的value是list，转为等权 / Convert list to equal weight for dt in signals: if isinstance(signals[dt], list): n = len(signals[dt]) signals[dt] = {s: 1.0/n for s in signals[dt]} if n > 0 else {} trading_days = close.index.sort_values() signal_dates = sorted(signals.keys()) nav_values, nav_dates = [], [] current_weights = {} current_nav = 1.0 daily_ret = close.pct_change() for i, today in enumerate(trading_days): if today < signal_dates[0]: continue # 持仓漂移 / Portfolio drift if current_weights and i > 0: port_ret = sum(w * (daily_ret.at[today, s] if s in daily_ret.columns and pd.notna(daily_ret.at[today, s]) else 0) for s, w in current_weights.items()) current_nav *= (1 + port_ret) # 调仓 / Rebalance if today in signals and signals[today]: new_w = signals[today] total = sum(new_w.values()) if total > 0: new_w = {k: v/total for k, v in new_w.items()} turnover = sum(abs(new_w.get(s, 0) - current_weights.get(s, 0)) for s in set(new_w) | set(current_weights)) current_nav *= (1 - turnover * cost_rate / 2) current_weights = new_w nav_values.append(current_nav) nav_dates.append(today) nav = pd.Series(nav_values, index=pd.DatetimeIndex(nav_dates)) # 计算指标 / Compute metrics daily_r = nav.pct_change().dropna() n_years = len(daily_r) / 252 total_ret = nav.iloc[-1] / nav.iloc[0] - 1 ann_ret = (1 + total_ret) ** (1/n_years) - 1 if n_years > 0 else 0 ann_vol = daily_r.std() * np.sqrt(252) sharpe = (ann_ret - 0.025) / ann_vol if ann_vol > 0 else 0 cummax = nav.cummax() max_dd = ((nav - cummax) / cummax).min() monthly = nav.resample("ME").last().pct_change().dropna() win_rate = (monthly > 0).mean() if len(monthly) > 0 else 0 profit_months = monthly[monthly > 0].sum() loss_months = monthly[monthly < 0].sum() profit_factor = abs(profit_months / loss_months) if abs(loss_months) > 1e-12 else float("inf") return nav, { "annual_return": ann_ret, "sharpe": sharpe, "max_drawdown": max_dd, "monthly_win_rate": win_rate, "profit_factor": profit_factor, "total_return": total_ret, "annual_vol": ann_vol, } # ── 运行回测 / Run backtest ── # 基准 / Benchmark idx = index_data.copy() idx["trade_date"] = pd.to_datetime(idx["trade_date"], format="%Y%m%d") benchmark = idx.set_index("trade_date")["close"].sort_index() nav, metrics = simple_backtest(close, signals, cost_rate=cost_rate) ``` ### Step 5: 门控检查 / Gate Check ```python # 从配置读取门控阈值 / Read gate thresholds from config # 使用 metrics dict 中的指标进行门控检查 / Use metrics dict for gate checks gate_pass = { "sharpe >= 1.0": metrics["sharpe"] >= 1.0, "max_dd >= -25%": metrics["max_drawdown"] >= -0.25, "profit_factor >= 1.0": metrics["profit_factor"] >= 1.0, "monthly_wr >= 55%": metrics["monthly_win_rate"] >= 0.55, } ``` ### Step 6: IS/OOS对比 / IS/OOS Comparison 分别在IS和OOS区间运行回测，计算Sharpe衰减 / Run backtest on IS and OOS separately, compute Sharpe decay： ```python is_signals = {d: s for d, s in signals.items() if d <= is_end} oos_signals = {d: s for d, s in signals.items() if d >= oos_start} is_nav, is_metrics = simple_backtest(close, is_signals, cost_rate=cost_rate) oos_nav, oos_metrics = simple_backtest(close, oos_signals, cost_rate=cost_rate) # Sharpe衰减 / Sharpe decay sharpe_decay = 1 - oos_metrics["sharpe"] / is_metrics["sharpe"] if is_metrics["sharpe"] != 0 else float("nan") ``` ### Step 7: 生成报告 / Generate Report ```python import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams["font.sans-serif"] = ["SimHei", "Arial Unicode MS", "DejaVu Sans"] matplotlib.rcParams["axes.unicode_minus"] = False # 用matplotlib生成回测报告图表 / Generate backtest report charts with matplotlib: # 子图1 Subplot1: 策略净值 vs 基准净值 Strategy NAV vs Benchmark NAV (line chart) # 子图2 Subplot2: 回撤曲线 Drawdown curve (filled area chart) # 子图3 Subplot3: 月度收益热力图 Monthly return heatmap # 子图4 Subplot4: 滚动Sharpe Rolling Sharpe (line chart, 60-day window) fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle("Backtest Report: MultiFactorStrategy") # Plot NAV, drawdown, monthly returns, rolling Sharpe # ... (AI writes the specific plotting code based on nav and metrics) plt.tight_layout() save_path = os.path.join(OUTPUT_DIR, "backtest_report.png") fig.savefig(save_path, dpi=150, bbox_inches="tight") plt.close() ``` ### Step 8: 输出结果 / Output Results ``` 📈 Backtest Results / 回测结果: Factors 因子: ×weight + ×weight + ... Period 区间: YYYY-MM-DD ~ YYYY-MM-DD | Rebalance 调仓: N days/日 | Holdings 持仓: N stocks/只 IS Period IS期间 OOS Period OOS期间 Full Period 全区间 Annual Return 年化收益 xx.xx% xx.xx% xx.xx% Sharpe x.xxx x.xxx x.xxx MaxDD 最大回撤 -xx.xx% -xx.xx% -xx.xx% PF (Profit Factor) x.xxx x.xxx x.xxx Monthly WR 月度胜率 xx.x% xx.x% xx.x% Calmar x.xxx x.xxx x.xxx Sharpe Decay Sharpe衰减: xx.x% (IS→OOS) Gate Check 门控检查: ✓/✗ Sharpe ≥ 1.0 → x.xxx ✓/✗ MaxDD ≥ -25% → -xx.xx% ✓/✗ PF ≥ 1.0 → x.xxx ✓/✗ Monthly WR 月度胜率 ≥ 55% → xx.x% ✓/✗ Max Consec Loss Months 最大连续亏损月 ≤ 4 → N months/个月 Report chart 报告图表: output/backtest_report.png ``` ## 注意事项 / Notes 1. 多因子权重归一化 Multi-factor weight normalization：确保权重之和=1.0 ensure weights sum to 1.0 2. 择时逻辑 Market timing：空仓日不生成信号 no signals on empty days，BacktestEngine在无信号期间保持现金 holds cash when no signals 3. 涨停过滤 Limit-up filter：A股涨幅>9.5%的股票无法买入 A-share stocks with >9.5% gain cannot be bought 4. 如果因子需要daily_basic或fina数据但缺失，提示用户 / If factor needs daily_basic or fina data but missing, inform user 5. 回测时间较长时告知用户 Inform user for long backtests "Running backtest, ~1-2 min... / 正在回测，预计1-2分钟..." 6. 门控阈值从 Gate thresholds from .claude/alpha-agent.config.md 读取 read