diff --git a/backend/app/backtest/factor.py b/backend/app/backtest/factor.py index 0c375b0..2b48e39 100644 --- a/backend/app/backtest/factor.py +++ b/backend/app/backtest/factor.py @@ -205,6 +205,9 @@ class FactorBacktestService: elapsed_ms=round((time.perf_counter() - t0) * 1000, 1), ) + # P1: 预计算共享下期收益 (仅依赖 close/date/symbol), 避免每个因子重复 shift/调仓日 JOIN。 + panel = self._attach_shared_next_return(panel, config) + metadata = {item["id"]: item for item in FACTOR_COLUMNS} items: list[FactorBatchItem] = [] for factor_name in factor_names: @@ -294,6 +297,39 @@ class FactorBacktestService: panel = self._compute_missing_factors(panel, missing) return panel + @staticmethod + def _attach_shared_next_return( + panel: pl.DataFrame, config: FactorBatchConfig, + ) -> pl.DataFrame: + """对 [start, end] 内 close 有效序列计算一次 _next_return, 复用给批次内每个因子。 + + _next_return 仅依赖 (symbol, date, close, rebalance), 与具体因子无关; 因子空值 + 集中在预热期前缀, 过滤后剩余序列无内部空洞, 故与 _evaluate_panel 内逐因子在 + 过滤后面板上重算的结果完全等价。 + """ + if "_next_return" in panel.columns: + return panel + base = ( + panel.filter((pl.col("date") >= config.start) & (pl.col("date") <= config.end)) + .filter(pl.col("close").is_not_null() & (pl.col("close") > 0)) + .select(["symbol", "date", "close"]) + .sort(["symbol", "date"]) + ) + if base.is_empty(): + return panel.with_columns(pl.lit(None).cast(pl.Float64).alias("_next_return")) + if config.rebalance == "daily": + base = base.with_columns( + (pl.col("close").shift(-1).over("symbol") / pl.col("close") - 1) + .alias("_next_return") + ) + else: + base = FactorBacktestService._calc_period_return(base, config.rebalance) + return panel.join( + base.select(["symbol", "date", "_next_return"]), + on=["symbol", "date"], + how="left", + ) + def _evaluate_panel( self, source_panel: pl.DataFrame, @@ -309,7 +345,14 @@ class FactorBacktestService: return _err(f"因子列 '{factor_col}' 不存在于 enriched 数据中, 且无法从基础行情计算") if "close" not in source_panel.columns: return _err("enriched 数据缺少收盘价 close") - panel = source_panel.select(["symbol", "date", "close", factor_col]) + + # 批量模式由 run_batch 预计算 _next_return 并随 source_panel 传入, 直接复用; + # 单因子 run() 路径未预计算, 仍按原逻辑在此计算。 + select_cols = ["symbol", "date", "close", factor_col] + precomputed_return = "_next_return" in source_panel.columns + if precomputed_return: + select_cols.append("_next_return") + panel = source_panel.select(select_cols) panel = panel.filter((pl.col("date") >= config.start) & (pl.col("date") <= config.end)) # 过滤有效行 @@ -326,16 +369,17 @@ class FactorBacktestService: n_symbols = panel["symbol"].n_unique() n_dates = panel["date"].n_unique() - # 计算下期收益 - # 根据调仓频率计算不同周期的 forward return - if config.rebalance == "daily": - panel = panel.with_columns( - (pl.col("close").shift(-1).over("symbol") / pl.col("close") - 1) - .alias("_next_return") - ) - else: - # weekly/monthly: 计算到下个调仓日的收益 - panel = self._calc_period_return(panel, config.rebalance) + # 计算下期收益 — _next_return 仅依赖 (symbol, date, close, rebalance), 与因子无关; + # 因子空值集中在预热期前缀, 过滤后剩余序列无内部空洞, 故预计算与逐因子重算结果等价。 + if not precomputed_return: + if config.rebalance == "daily": + panel = panel.with_columns( + (pl.col("close").shift(-1).over("symbol") / pl.col("close") - 1) + .alias("_next_return") + ) + else: + # weekly/monthly: 计算到下个调仓日的收益 + panel = self._calc_period_return(panel, config.rebalance) # ── 1. IC 分析 ── ic_df = self._calc_ic(panel, factor_col) @@ -578,16 +622,16 @@ class FactorBacktestService: group_cols = sorted([c for c in pivot.columns if c != "date"], key=FactorBacktestService._group_sort_key) - # 累乘净值曲线 + # 向量化累乘净值: null 视为 0 收益 (净值不变); 累乘保持全精度, 输出时 round(4), + # 等价于原 dict 累乘 `nav_values[c] *= (1+ret); entry[c] = round(nav_values[c], 4)`。 + nav_df = pivot.with_columns( + [(1.0 + pl.col(c).fill_null(0.0)).cum_prod().alias(c) for c in group_cols] + ) result: list[dict] = [] - nav_values: dict[str, float] = {c: 1.0 for c in group_cols} - - for row in pivot.iter_rows(named=True): + for row in nav_df.iter_rows(named=True): entry: dict = {"date": str(row["date"])[:10]} for c in group_cols: - ret = float(row[c]) if row[c] is not None else 0.0 - nav_values[c] *= (1 + ret) - entry[c] = round(nav_values[c], 4) + entry[c] = round(float(row[c]), 4) result.append(entry) return result @@ -608,36 +652,33 @@ class FactorBacktestService: ) n_days = max((end - start).days, 1) years = n_days / 365.25 + # 夏普 — 年化系数必须匹配 group_nav 的调仓频率 (每个净值点 = 一个调仓周期收益); + # 周/月频收益若乘 √252 会把 Sharpe 高估 √(252/期数) 倍 (月频 ≈4.6x, 周频 ≈2.2x)。 + _ann = {"daily": 252, "weekly": 52, "monthly": 12}.get(rebalance, 252) stats = [] for i, c in enumerate(group_cols): values = [r[c] for r in group_nav if r.get(c) is not None] if not values: continue - total_return = values[-1] - 1.0 - annual_return = (values[-1]) ** (1 / max(years, 0.01)) - 1 if values[-1] > 0 else 0.0 + arr = np.asarray(values, dtype=np.float64) + last = float(arr[-1]) + total_return = last - 1.0 + annual_return = last ** (1 / max(years, 0.01)) - 1 if last > 0 else 0.0 - # 最大回撤 - peak = 1.0 - max_dd = 0.0 - for v in values: - peak = max(peak, v) - dd = (v - peak) / peak - max_dd = min(max_dd, dd) + # 最大回撤 (向量化): 峰值 = max(1.0, 历史最高), 与原 peak 初值 1.0 的逐行 max 一致 + peak = np.maximum(np.maximum.accumulate(arr), 1.0) + max_dd = float(np.min((arr - peak) / peak)) - # 日收益序列 - daily_rets = [] - for j in range(1, len(values)): - if values[j - 1] > 0: - daily_rets.append(values[j] / values[j - 1] - 1) - - # 夏普 — 年化系数必须匹配 group_nav 的调仓频率 (每个净值点 = 一个调仓周期收益); - # 周/月频收益若乘 √252 会把 Sharpe 高估 √(252/期数) 倍 (月频 ≈4.6x, 周频 ≈2.2x)。 - if daily_rets: - arr = np.array(daily_rets) - _ann = {"daily": 252, "weekly": 52, "monthly": 12}.get(rebalance, 252) - sharpe = float(np.mean(arr) / np.std(arr)) * np.sqrt(_ann) if np.std(arr) > 0 else 0.0 - win_rate = float(np.mean(arr > 0)) + # 周期收益序列 (向量化): nav[t]/nav[t-1] - 1, 仅保留 nav[t-1] > 0 的样本 + prev = arr[:-1] + with np.errstate(divide="ignore", invalid="ignore"): + rets = arr[1:] / prev - 1.0 + rets = rets[prev > 0] + if rets.size: + std = float(np.std(rets)) + sharpe = float(np.mean(rets) / std) * np.sqrt(_ann) if std > 0 else 0.0 + win_rate = float(np.mean(rets > 0)) else: sharpe = 0.0 win_rate = 0.0 @@ -674,38 +715,36 @@ class FactorBacktestService: top_col = group_cols[-1] # Q5 (最高) bottom_col = group_cols[0] # Q1 (最低) - # 独立计算 top 和 bottom 的日收益，然后合成 - ls_value = 1.0 - prev_top = 1.0 - prev_bot = 1.0 - peak = 1.0 - max_dd = 0.0 - ls_nav: list[dict] = [] + # 向量化: 各组净值 (null 视为 1.0), 前置 1.0 作为初值 prev_top/prev_bot, + # 等价于原逐行 prev_top/prev_bot 初始 1.0 的累乘逻辑。 + top = np.array( + [r[top_col] if r.get(top_col) is not None else 1.0 for r in group_nav], + dtype=np.float64, + ) + bot = np.array( + [r[bottom_col] if r.get(bottom_col) is not None else 1.0 for r in group_nav], + dtype=np.float64, + ) + prev_top = np.concatenate(([1.0], top[:-1])) + prev_bot = np.concatenate(([1.0], bot[:-1])) - for row in group_nav: - top_nav = float(row.get(top_col, 1.0)) if row.get(top_col) is not None else 1.0 - bot_nav = float(row.get(bottom_col, 1.0)) if row.get(bottom_col) is not None else 1.0 + # 分组收益; prev <= 0 时按原逻辑置 0 (做多 top, 做空 bottom = 取反) + with np.errstate(divide="ignore", invalid="ignore"): + top_ret = np.where(prev_top > 0, top / prev_top - 1.0, 0.0) + bot_ret = np.where(prev_bot > 0, bot / prev_bot - 1.0, 0.0) + ls_ret = (top_ret - bot_ret) / 2.0 # 各分配 50% 资金 + ls_value = np.cumprod(1.0 + ls_ret) - # top 组收益 (做多) - top_ret = (top_nav / prev_top - 1) if prev_top > 0 else 0.0 - # bottom 组收益 (做空 = 取反) - bot_ret = -(bot_nav / prev_bot - 1) if prev_bot > 0 else 0.0 - # 多空组合收益 - ls_ret = (top_ret + bot_ret) / 2 # 各分配 50% 资金 - ls_value *= (1 + ls_ret) + # 最大回撤: 峰值 = max(1.0, 历史最高), 与原 peak 初值 1.0 一致 + peak = np.maximum(np.maximum.accumulate(ls_value), 1.0) + max_dd = float(np.min((ls_value - peak) / peak)) - prev_top = top_nav - prev_bot = bot_nav - - peak = max(peak, ls_value) - dd = (ls_value - peak) / peak if peak > 0 else 0.0 - max_dd = min(max_dd, dd) - - ls_nav.append({"date": row["date"], "value": round(ls_value, 4)}) - - total_ret = ls_value - 1.0 + ls_nav = [ + {"date": group_nav[k]["date"], "value": round(float(ls_value[k]), 4)} + for k in range(len(group_nav)) + ] ls_stats = { - "total_return": round(total_ret, 4), + "total_return": round(float(ls_value[-1]) - 1.0, 4), "max_drawdown": round(max_dd, 4), "top_group": top_col, "bottom_group": bottom_col,