From f467afcf5e5b42941b3da5f16fa8c279f544fad7 Mon Sep 17 00:00:00 2001 From: "vn.py" Date: Tue, 19 Feb 2019 15:18:20 +0800 Subject: [PATCH] [Mod]set all statistics to 0 if no trade in backtesting --- vnpy/app/cta_strategy/backtesting.py | 122 ++++++++++++++++----------- 1 file changed, 75 insertions(+), 47 deletions(-) diff --git a/vnpy/app/cta_strategy/backtesting.py b/vnpy/app/cta_strategy/backtesting.py index a8664662..5e7224ea 100644 --- a/vnpy/app/cta_strategy/backtesting.py +++ b/vnpy/app/cta_strategy/backtesting.py @@ -300,56 +300,81 @@ class BacktestingEngine: if not df: df = self.daily_df - - # Calculate balance related time series data - df["balance"] = df["net_pnl"].cumsum() + self.capital - df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0) - df["highlevel"] = ( - df["balance"].rolling( - min_periods=1, window=len(df), center=False).max() - ) - df["drawdown"] = df["balance"] - df["highlevel"] - df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100 - - # Calculate statistics value - start_date = df.index[0] - end_date = df.index[-1] - - total_days = len(df) - profit_days = len(df[df["net_pnl"] > 0]) - loss_days = len(df[df["net_pnl"] < 0]) - - end_balance = df["balance"].iloc[-1] - max_drawdown = df["drawdown"].min() - max_ddpercent = df["ddpercent"].min() - - total_net_pnl = df["net_pnl"].sum() - daily_net_pnl = total_net_pnl / total_days - - total_commission = df["commission"].sum() - daily_commission = total_commission / total_days - - total_slippage = df["slippage"].sum() - daily_slippage = total_slippage / total_days - - total_turnover = df["turnover"].sum() - daily_turnover = total_turnover / total_days - - total_trade_count = df["trade_count"].sum() - daily_trade_count = total_trade_count / total_days - - total_return = (end_balance / self.capital - 1) * 100 - annual_return = total_return / total_days * 240 - daily_return = df["return"].mean() * 100 - return_std = df["return"].std() * 100 - - if return_std: - sharpe_ratio = daily_return / return_std * np.sqrt(240) - else: + + if df is None: + # Set all statistics to 0 if no trade. + start_date = "" + end_date = "" + total_days = 0 + profit_days = 0 + loss_days = 0 + end_balance = 0 + max_drawdown = 0 + max_ddpercent = 0 + total_net_pnl = 0 + daily_net_pnl = 0 + total_commission = 0 + daily_commission = 0 + total_slippage = 0 + daily_slippage = 0 + total_turnover = 0 + daily_turnover = 0 + total_trade_count = 0 + daily_trade_count = 0 + total_return = 0 + annual_return = 0 + daily_return = 0 + return_std = 0 sharpe_ratio = 0 + else: + # Calculate balance related time series data + df["balance"] = df["net_pnl"].cumsum() + self.capital + df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0) + df["highlevel"] = ( + df["balance"].rolling( + min_periods=1, window=len(df), center=False).max() + ) + df["drawdown"] = df["balance"] - df["highlevel"] + df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100 + + # Calculate statistics value + start_date = df.index[0] + end_date = df.index[-1] + + total_days = len(df) + profit_days = len(df[df["net_pnl"] > 0]) + loss_days = len(df[df["net_pnl"] < 0]) + + end_balance = df["balance"].iloc[-1] + max_drawdown = df["drawdown"].min() + max_ddpercent = df["ddpercent"].min() + + total_net_pnl = df["net_pnl"].sum() + daily_net_pnl = total_net_pnl / total_days + + total_commission = df["commission"].sum() + daily_commission = total_commission / total_days + + total_slippage = df["slippage"].sum() + daily_slippage = total_slippage / total_days + + total_turnover = df["turnover"].sum() + daily_turnover = total_turnover / total_days + + total_trade_count = df["trade_count"].sum() + daily_trade_count = total_trade_count / total_days + + total_return = (end_balance / self.capital - 1) * 100 + annual_return = total_return / total_days * 240 + daily_return = df["return"].mean() * 100 + return_std = df["return"].std() * 100 + + if return_std: + sharpe_ratio = daily_return / return_std * np.sqrt(240) + else: + sharpe_ratio = 0 # Output - # 输出统计结果 self.output("-" * 30) self.output(f"首个交易日：\t{start_date}") self.output(f"最后交易日：\t{end_date}") @@ -414,6 +439,9 @@ class BacktestingEngine: """""" if not df: df = self.daily_df + + if df is None: + return plt.figure(figsize=(10, 16))