[Mod]移除ctaBacktesting中对pandas的依赖
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vn.py
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8af3355553
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145d5215e7
@ -955,6 +955,7 @@ class BacktestingEngine(object):
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resultList.sort(reverse=True, key=lambda result:result[1])
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return resultList
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#----------------------------------------------------------------------
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def outputOptimizeResult(self, resultList):
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self.output('-' * 30)
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self.output(u'优化结果：')
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@ -998,62 +999,67 @@ class BacktestingEngine(object):
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dailyResult.calculatePnl(openPosition, self.size, self.rate, self.slippage )
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openPosition = dailyResult.closePosition
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# 生成DataFrame
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resultDict = {k:[] for k in dailyResult.__dict__.keys()}
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for dailyResult in self.dailyResultDict.values():
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for k, v in dailyResult.__dict__.items():
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resultDict[k].append(v)
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resultDf = pd.DataFrame.from_dict(resultDict)
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# 计算衍生数据
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resultDf = resultDf.set_index('date')
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return resultDf
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#----------------------------------------------------------------------
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def calculateDailyStatistics(self, df):
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def calculateDailyStatistics(self, annualDays=240):
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"""计算按日统计的结果"""
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df['balance'] = df['netPnl'].cumsum() + self.capital
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df['return'] = (np.log(df['balance']) - np.log(df['balance'].shift(1))).fillna(0)
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df['highlevel'] = df['balance'].rolling(min_periods=1,window=len(df),center=False).max()
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df['drawdown'] = df['balance'] - df['highlevel']
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df['ddPercent'] = df['drawdown'] / df['highlevel'] * 100
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dateList = self.dailyResultDict.keys()
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resultList = self.dailyResultDict.values()
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# 计算统计结果
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startDate = df.index[0]
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endDate = df.index[-1]
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startDate = dateList[0]
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endDate = dateList[-1]
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totalDays = len(dateList)
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totalDays = len(df)
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profitDays = len(df[df['netPnl']>0])
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lossDays = len(df[df['netPnl']<0])
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profitDays = 0
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lossDays = 0
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endBalance = self.capital
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highlevel = self.capital
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totalNetPnl = 0
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totalTurnover = 0
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totalCommission = 0
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totalSlippage = 0
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totalTradeCount = 0
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endBalance = df['balance'].iloc[-1]
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maxDrawdown = df['drawdown'].min()
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maxDdPercent = df['ddPercent'].min()
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netPnlList = []
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balanceList = []
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highlevelList = []
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drawdownList = []
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ddPercentList = []
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returnList = []
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totalNetPnl = df['netPnl'].sum()
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dailyNetPnl = totalNetPnl / totalDays
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for result in resultList:
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if result.netPnl > 0:
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profitDays += 1
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elif result.netPnl < 0:
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lossDays += 1
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netPnlList.append(result.netPnl)
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totalCommission = df['commission'].sum()
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dailyCommission = totalCommission / totalDays
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prevBalance = endBalance
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endBalance += result.netPnl
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balanceList.append(endBalance)
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returnList.append(endBalance/prevBalance - 1)
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totalSlippage = df['slippage'].sum()
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dailySlippage = totalSlippage / totalDays
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highlevel = max(highlevel, endBalance)
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highlevelList.append(highlevel)
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totalTurnover = df['turnover'].sum()
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dailyTurnover = totalTurnover / totalDays
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drawdown = endBalance - highlevel
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drawdownList.append(drawdown)
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ddPercentList.append(drawdown/highlevel*100)
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totalTradeCount = df['tradeCount'].sum()
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dailyTradeCount = totalTradeCount / totalDays
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totalTurnover += result.turnover
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totalCommission += result.commission
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totalSlippage += result.slippage
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totalTradeCount += result.tradeCount
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totalNetPnl += result.netPnl
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maxDrawdown = min(drawdownList)
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maxDdPercent = min(ddPercentList)
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totalReturn = (endBalance / self.capital - 1) * 100
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annualizedReturn = totalReturn / totalDays * 240
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dailyReturn = df['return'].mean() * 100
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returnStd = df['return'].std() * 100
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dailyReturn = np.mean(returnList) * 100
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annualizedReturn = dailyReturn * annualDays
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returnStd = np.std(returnList) * 100
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if returnStd:
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sharpeRatio = dailyReturn / returnStd * np.sqrt(240)
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sharpeRatio = dailyReturn / returnStd * np.sqrt(annualDays)
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else:
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sharpeRatio = 0
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@ -1068,15 +1074,15 @@ class BacktestingEngine(object):
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'maxDrawdown': maxDrawdown,
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'maxDdPercent': maxDdPercent,
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'totalNetPnl': totalNetPnl,
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'dailyNetPnl': dailyNetPnl,
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'dailyNetPnl': totalNetPnl/totalDays,
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'totalCommission': totalCommission,
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'dailyCommission': dailyCommission,
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'dailyCommission': totalCommission/totalDays,
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'totalSlippage': totalSlippage,
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'dailySlippage': dailySlippage,
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'dailySlippage': totalSlippage/totalDays,
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'totalTurnover': totalTurnover,
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'dailyTurnover': dailyTurnover,
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'dailyTurnover': totalTurnover/totalDays,
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'totalTradeCount': totalTradeCount,
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'dailyTradeCount': dailyTradeCount,
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'dailyTradeCount': totalTradeCount/totalDays,
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'totalReturn': totalReturn,
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'annualizedReturn': annualizedReturn,
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'dailyReturn': dailyReturn,
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@ -1084,14 +1090,23 @@ class BacktestingEngine(object):
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'sharpeRatio': sharpeRatio
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}
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return df, result
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d = {}
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d['balance'] = balanceList
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d['return'] = returnList
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d['highLevel'] = highlevelList
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d['drawdown'] = drawdownList
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d['ddPercent'] = ddPercentList
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d['date'] = dateList
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d['netPnl'] = netPnlList
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return d, result
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#----------------------------------------------------------------------
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def showDailyResult(self, df=None, result=None):
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def showDailyResult(self, d=None, result=None):
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"""显示按日统计的交易结果"""
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if df is None:
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df = self.calculateDailyResult()
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df, result = self.calculateDailyStatistics(df)
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if d is None:
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self.calculateDailyResult()
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d, result = self.calculateDailyStatistics()
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# 输出统计结果
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self.output('-' * 30)
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@ -1131,19 +1146,19 @@ class BacktestingEngine(object):
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pBalance = plt.subplot(4, 1, 1)
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pBalance.set_title('Balance')
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df['balance'].plot(legend=True)
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plt.plot(d['date'], d['balance'])
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pDrawdown = plt.subplot(4, 1, 2)
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pDrawdown.set_title('Drawdown')
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pDrawdown.fill_between(range(len(df)), df['drawdown'].values)
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pDrawdown.fill_between(range(len(d['drawdown'])), d['drawdown'])
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pPnl = plt.subplot(4, 1, 3)
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pPnl.set_title('Daily Pnl')
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df['netPnl'].plot(kind='bar', legend=False, grid=False, xticks=[])
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plt.bar(range(len(d['drawdown'])), d['netPnl'])
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pKDE = plt.subplot(4, 1, 4)
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pKDE.set_title('Daily Pnl Distribution')
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df['netPnl'].hist(bins=50)
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plt.hist(d['netPnl'], bins=50)
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plt.show()
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