diff --git a/czsc/traders/weight_backtest.py b/czsc/traders/weight_backtest.py index 826dccca1..bcfe72136 100644 --- a/czsc/traders/weight_backtest.py +++ b/czsc/traders/weight_backtest.py @@ -15,6 +15,8 @@ from typing import Union, AnyStr, Callable from multiprocessing import cpu_count from concurrent.futures import ProcessPoolExecutor + +import czsc from czsc.traders.base import CzscTrader from czsc.utils.io import save_json from czsc.utils.stats import daily_performance, evaluate_pairs @@ -226,9 +228,13 @@ class WeightBacktest: """持仓权重回测 飞书文档：https://s0cqcxuy3p.feishu.cn/wiki/Pf1fw1woQi4iJikbKJmcYToznxb + + 更新日志： + + - V240627: 增加dailys属性，品种每日的交易信息 """ - version = "V231126" + version = "V240627" def __init__(self, dfw, digits=2, **kwargs) -> None: """持仓权重回测 @@ -278,6 +284,7 @@ def __init__(self, dfw, digits=2, **kwargs) -> None: self.dfw["weight"] = self.dfw["weight"].astype("float").round(digits) self.symbols = list(self.dfw["symbol"].unique().tolist()) default_n_jobs = min(cpu_count() // 2, len(self.symbols)) + self._dailys = None self.results = self.backtest(n_jobs=kwargs.get("n_jobs", default_n_jobs)) @property @@ -290,6 +297,46 @@ def daily_return(self) -> pd.DataFrame: """品种等权费后日收益率""" return self.results.get("品种等权日收益", pd.DataFrame()) + @property + def dailys(self) -> pd.DataFrame: + """品种每日的交易信息 + + columns = ['date', 'symbol', 'edge', 'return', 'cost', 'n1b', 'turnover'] + + 其中: + date 交易日， + symbol 合约代码， + n1b 品种每日收益率， + edge 策略每日收益率， + return 策略每日收益率减去交易成本后的真实收益， + cost 交易成本 + turnover 当日的单边换手率 + """ + return self._dailys.copy() if self._dailys is not None else pd.DataFrame() + + @property + def alpha(self) -> pd.DataFrame: + """策略超额收益 + + columns = ['date', '策略', '基准', '超额'] + """ + if self._dailys is None: + return pd.DataFrame() + df1 = self._dailys.groupby("date").agg({"return": "mean", "n1b": "mean"}) + df1["alpha"] = df1["return"] - df1["n1b"] + df1.rename(columns={"return": "策略", "n1b": "基准", "alpha": "超额"}, inplace=True) + df1 = df1.reset_index() + return df1 + + @property + def alpha_stats(self): + """策略超额收益统计""" + df = self.alpha.copy() + stats = czsc.daily_performance(df["超额"].to_list()) + stats["开始日期"] = df["date"].min().strftime("%Y-%m-%d") + stats["结束日期"] = df["date"].max().strftime("%Y-%m-%d") + return stats + def get_symbol_daily(self, symbol): """获取某个合约的每日收益率 @@ -301,19 +348,21 @@ def get_symbol_daily(self, symbol): 4. 计算每条数据扣除手续费后的收益（edge_post_fee）：收益减去手续费。 5. 根据日期进行分组，并对每组进行求和操作，得到每日的总收益、总扣除手续费后的收益和总手续费。 6. 重置索引，并将交易标的符号添加到DataFrame中。 - 7. 重命名列名，将'edge_post_fee'列改为'return'，将'dt'列改为'date'。 + 7. 重命名列名，将'edge_post_fee'列改为 return，将'dt'列改为 date。 8. 选择需要的列，并返回包含日期、交易标的、收益、扣除手续费后的收益和手续费的DataFrame。 :param symbol: str，合约代码 :return: pd.DataFrame，品种每日收益率， - columns = ['date', 'symbol', 'edge', 'return', 'cost'] + columns = ['date', 'symbol', 'edge', 'return', 'cost', 'n1b'] 其中 - date 为交易日， - symbol 为合约代码， - edge 为每日收益率， - return 为每日收益率减去交易成本后的真实收益， - cost 为交易成本 + date 交易日， + symbol 合约代码， + n1b 品种每日收益率， + edge 策略每日收益率， + return 策略每日收益率减去交易成本后的真实收益， + cost 交易成本 + turnover 当日的单边换手率 数据样例如下： @@ -328,13 +377,19 @@ def get_symbol_daily(self, symbol): ========== ======== ============ ============ ======= """ dfs = self.dfw[self.dfw["symbol"] == symbol].copy() - dfs["edge"] = dfs["weight"] * (dfs["price"].shift(-1) / dfs["price"] - 1) - dfs["cost"] = abs(dfs["weight"].shift(1) - dfs["weight"]) * self.fee_rate + dfs["n1b"] = dfs["price"].shift(-1) / dfs["price"] - 1 + dfs["edge"] = dfs["weight"] * dfs["n1b"] + dfs["turnover"] = abs(dfs["weight"].shift(1) - dfs["weight"]) + dfs["cost"] = dfs["turnover"] * self.fee_rate dfs["edge_post_fee"] = dfs["edge"] - dfs["cost"] - daily = dfs.groupby(dfs["dt"].dt.date).agg({"edge": "sum", "edge_post_fee": "sum", "cost": "sum"}).reset_index() + daily = ( + dfs.groupby(dfs["dt"].dt.date) + .agg({"edge": "sum", "edge_post_fee": "sum", "cost": "sum", "n1b": "sum", "turnover": "sum"}) + .reset_index() + ) daily["symbol"] = symbol daily.rename(columns={"edge_post_fee": "return", "dt": "date"}, inplace=True) - daily = daily[["date", "symbol", "edge", "return", "cost"]] + daily = daily[["date", "symbol", "n1b", "edge", "return", "cost", "turnover"]].copy() return daily def get_symbol_pairs(self, symbol): @@ -485,6 +540,8 @@ def backtest(self, n_jobs=1): ): res[symbol] = res_symbol + self._dailys = pd.concat([v["daily"] for k, v in res.items() if k in symbols], ignore_index=True) + dret = pd.concat([v["daily"] for k, v in res.items() if k in symbols], ignore_index=True) dret = pd.pivot_table(dret, index="date", columns="symbol", values="return").fillna(0) dret["total"] = dret[list(res.keys())].mean(axis=1) diff --git a/examples/test_offline/test_weight_backtest.py b/examples/test_offline/test_weight_backtest.py index 1657ebbd3..0b8fd1d78 100644 --- a/examples/test_offline/test_weight_backtest.py +++ b/examples/test_offline/test_weight_backtest.py @@ -1,10 +1,11 @@ import sys + sys.path.insert(0, ".") sys.path.insert(0, "..") import czsc import pandas as pd -assert czsc.WeightBacktest.version == "V231126" +assert czsc.WeightBacktest.version == "V240627" def run_by_weights(): @@ -12,11 +13,16 @@ def run_by_weights(): dfw = pd.read_feather(r"C:\Users\zengb\Downloads\weight_example.feather") wb = czsc.WeightBacktest(dfw, digits=1, fee_rate=0.0002, n_jobs=1) # wb = czsc.WeightBacktest(dfw, digits=1, fee_rate=0.0002) + dailys = wb.dailys + + # 计算等权组合的超额 + df1 = dailys.groupby("date").agg({"return": "mean", "n1b": "mean"}) + df1["alpha"] = df1["return"] - df1["n1b"] # ------------------------------------------------------------------------------------ # 查看绩效评价 # ------------------------------------------------------------------------------------ - print(wb.results['绩效评价']) + print(wb.results["绩效评价"]) # {'开始日期': '20170103', # '结束日期': '20230731', # '年化': 0.093, # 品种等权之后的年化收益率 @@ -41,5 +47,5 @@ def run_by_weights(): wb.report(res_path=r"C:\Users\zengb\Desktop\231005\weight_example") -if __name__ == '__main__': +if __name__ == "__main__": run_by_weights()