# ===================================================================== # 量化策略流派 · 官方教学示例（横截面动量 / Cross-sectional Momentum） # 策略：按月调仓，做多过去 N 日收益最高的「赢家」组合、做空最低的「输家」 # 说明：本代码为社区教学示例，由站点整理发布，仅供学习交流。 # 示例代码由站点提供，站点不对代码的安全性、合规性与收益 # 表现作任何明示或默示担保；据此交易风险自负。 # 依赖：numpy（pip install numpy） # 运行：python cross_sectional_momentum.py # ===================================================================== import numpy as np def gen_assets(n_assets=20, n_days=756, seed=11): """生成多资产收益：共同市场因子 + 个股漂移 + 部分序列相关（动量）。""" rng = np.random.default_rng(seed) drift = rng.normal(0.0003, 0.0008, n_assets) market = rng.normal(0, 0.008, n_days) rets = np.zeros((n_days, n_assets)) for a in range(n_assets): ar = rng.uniform(-0.04, 0.08) # 部分资产带自相关 prev = 0.0 for t in range(n_days): prev = ar * prev rets[t, a] = drift[a] + 0.6 * market[t] + prev + rng.normal(0, 0.012) prices = np.exp(np.cumsum(rets, axis=0)) return prices def backtest(prices, look=126, hold=21, top=0.3, cost=0.0005): n_days, n_assets = prices.shape pos = np.zeros_like(prices) for start in range(0, n_days - hold, hold): end = start + look if end >= n_days: break mom = prices[end - 1] / prices[start] - 1.0 # 过去 look 日收益 order = np.argsort(mom) k = int(n_assets * top) w = 1.0 / (2 * k) for a in order[-k:]: pos[start:start + hold, a] = w # 赢家做多 for a in order[:k]: pos[start:start + hold, a] = -w # 输家做空 asset_ret = np.zeros_like(prices) asset_ret[1:] = prices[1:] / prices[:-1] - 1 port = np.sum(pos * asset_ret, axis=1) chg = np.abs(np.diff(pos, axis=0)).sum(axis=1) # 调仓换手成本 port[1:] -= chg * cost eq = np.cumprod(1 + port) sharpe = port[1:].mean() / (port[1:].std() + 1e-9) * np.sqrt(252) mdd = (eq - np.maximum.accumulate(eq)).min() return dict(total=eq[-1] - 1, sharpe=sharpe, mdd=mdd) if __name__ == "__main__": px = gen_assets() m = backtest(px) print("横截面动量（赢家-输家组合）回测") print(f" 资产数 / 交易日 : {px.shape[1]} / {px.shape[0]}") print(f" 累计收益 : {m['total'] * 100:8.1f}%") print(f" 年化夏普 : {m['sharpe']:8.2f}") print(f" 最大回撤 : {m['mdd'] * 100:8.1f}%")