量化回测：backtrader回测封装代码
main
# -*- coding:utf-8 -*- #正常显示画图时出现的中文和负号 from pylab import mpl mpl.rcParams['font.sans-serif']=['SimHei'] import backtrader as bt import numpy as np from testpkg import backtest #策略 class GridStrategy(bt.Strategy): params = ( ("printlog", True), ("top", 4.2), ("buttom", 3.5), ) def __init__(self): self.mid = (self.p.top + self.p.buttom) / 2.0 # 百分比区间计算 # 这里多1/2，是因为arange函数是左闭右开区间。 perc_level = [x for x in np.arange(1 + 0.02 * 5, 1 - 0.02 * 5 - 0.02 / 2, -0.02)] # 价格区间 # print(self.mid) self.price_levels = [self.mid * x for x in perc_level] # 记录上一次穿越的网格 self.last_price_index = None # 总手续费 self.comm = 0.0 def next(self): # print(self.last_price_index) # 开仓 if self.last_price_index == None: # print("b", len(self.price_levels)) for i in range(len(self.price_levels)): price = self.data.close[0] # print("c", i, price, self.price_levels[i][0]) if self.data.close[0] > self.price_levels[i]: self.last_price_index = i self.order_target_percent(target=i / (len(self.price_levels) - 1)) print("a") return # 调仓 else: signal = False while True: upper = None lower = None if self.last_price_index > 0: upper = self.price_levels[self.last_price_index - 1] if self.last_price_index < len(self.price_levels) - 1: lower = self.price_levels[self.last_price_index + 1] # 还不是最轻仓，继续涨，再卖一档 if upper != None and self.data.close > upper: self.last_price_index = self.last_price_index - 1 signal = True continue # 还不是最重仓，继续跌，再买一档 if lower != None and self.data.close < lower: self.last_price_index = self.last_price_index + 1 signal = True continue break if signal: self.long_short = None self.order_target_percent(target=self.last_price_index / (len(self.price_levels) - 1)) # 输出交易记录 def log(self, txt, dt=None, doprint=False): if self.params.printlog or doprint: dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def notify_order(self, order): # 有交易提交/被接受，啥也不做 if order.status in [order.Submitted, order.Accepted]: return # 交易完成，报告结果 if order.status in [order.Completed]: if order.isbuy(): self.log( '执行买入, 价格: %.2f, 成本: %.2f, 手续费 %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.comm += order.executed.comm elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log("交易失败") self.order = None else: self.log( '执行卖出, 价格: %.2f, 成本: %.2f, 手续费 %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.comm += order.executed.comm # 输出手续费 def stop(self): self.log("手续费:%.2f 成本比例:%.5f" % (self.comm, self.comm / self.broker.getvalue())) if __name__ == "__main__": start = "2018-01-01" end = "2020-07-05" name = ["300etf"] code = ["510300"] backtest = backtest.BackTest(GridStrategy, start, end, code, name, 100000) result = backtest.run() # backtest.output() print(result)
backtest
# coding:utf-8 # 量化交易回测类 import backtrader as bt import backtrader.analyzers as btay import tushare as ts import os import pandas as pd import datetime import matplotlib.pyplot as plt import empyrical as ey import math import tushare as ts import numpy as np from scipy import stats from backtrader.utils.py3 import map from backtrader import Analyzer, TimeFrame from backtrader.mathsupport import average, standarddev from backtrader.analyzers import AnnualReturn import operator from pathlib import Path # 回测类 class BackTest: def __init__(self, strategy, start, end, code, name, cash=0.01, commission=0.0003, benchmarkCode="510300", bDraw=True): self.__cerebro = None self.__strategy = strategy self.__start = start self.__end = end self.__code = code self.__name = name self.__result = None self.__commission = commission self.__initcash = cash self.__backtestResult = pd.Series() self.__returns = pd.Series() self.__benchmarkCode = benchmarkCode self.__benchReturns = pd.Series() self.__benchFeed = None self.__bDraw = bDraw self.__start_date = None self.__end_date = None self._init() # 执行回测 def run(self): self.__backtestResult["期初账户总值"] = self.getValue() self.__results = self.__cerebro.run() self.__backtestResult["期末账户总值"] = self.getValue() self._Result() if self.__bDraw == True: self._drawResult() self.__returns = self._timeReturns(self.__results) self.__benchReturns = self._getBenchmarkReturns(self.__results) self._riskAnaly(self.__returns, self.__benchReturns, self.__backtestResult) return self.getResult() # 获取账户总价值 def getValue(self): return self.__cerebro.broker.getvalue() # 获取回测指标 def getResult(self): return self.__backtestResult # 获取策略及基准策略收益率的序列 def getReturns(self): return self.__returns, self.__benchReturns # 执行参数优化的回测 def optRun(self, *args, **kwargs): self._optStrategy(*args, **kwargs) results = self.__cerebro.run() if len(kwargs) == 1: testResults = self._optResult(results, **kwargs) elif len(kwargs) > 1: testResults = self._optResultMore(results, **kwargs) self._init() return testResults # 输出回测结果 def output(self): print("夏普比例:", self.__results[0].analyzers.sharpe.get_analysis()["sharperatio"]) print("年化收益率:", self.__results[0].analyzers.AR.get_analysis()) print("最大回撤:%.2f，最大回撤周期%d" % (self.__results[0].analyzers.DD.get_analysis().max.drawdown, self.__results[0].analyzers.DD.get_analysis().max.len)) print("总收益率:%.2f" % (self.__results[0].analyzers.RE.get_analysis()["rtot"])) # self.__results[0].analyzers.TA.pprint() # 进行参数优化 def _optStrategy(self, *args, **kwargs): self.__cerebro = bt.Cerebro(maxcpus=1) self.__cerebro.optstrategy(self.__strategy, *args, **kwargs) self._createDataFeeds() self._settingCerebro() # 真正进行初始化的地方 def _init(self): self.__cerebro = bt.Cerebro() self.__cerebro.addstrategy(self.__strategy) self._createDataFeeds() self._settingCerebro() # 设置cerebro def _settingCerebro(self): # 添加回撤观察器 self.__cerebro.addobserver(bt.observers.DrawDown) # 添加基准观察器 self.__cerebro.addobserver(bt.observers.Benchmark, data=self.__benchFeed, timeframe=bt.TimeFrame.NoTimeFrame) # 设置手续费 self.__cerebro.broker.setcommission(commission=self.__commission) # 设置初始资金 self.__cerebro.broker.setcash(self.__initcash) # 添加分析对象 self.__cerebro.addanalyzer(btay.SharpeRatio, _name="sharpe", riskfreerate=0.02, stddev_sample=True, annualize=True) self.__cerebro.addanalyzer(btay.AnnualReturn, _name="AR") self.__cerebro.addanalyzer(btay.DrawDown, _name="DD") self.__cerebro.addanalyzer(btay.Returns, _name="RE") self.__cerebro.addanalyzer(btay.TradeAnalyzer, _name="TA") self.__cerebro.addanalyzer(btay.TimeReturn, _name="TR") self.__cerebro.addanalyzer(btay.TimeReturn, _name="TR_Bench", data=self.__benchFeed) self.__cerebro.addanalyzer(btay.SQN, _name="SQN") # 建立数据源 def _createDataFeeds(self): # 建立回测数据源 for i in range(len(self.__code)): dataFeed = self._createDataFeedsProcess(self.__code[i], self.__name[i]) self.__cerebro.adddata(dataFeed, name=self.__name[i]) self.__benchFeed = self._createDataFeedsProcess(self.__benchmarkCode, "benchMark") self.__cerebro.adddata(self.__benchFeed, name="benchMark") # 建立数据源的具体过程 def _createDataFeedsProcess(self, code, name): df_data = self._getData(code) start_date = list(map(int, self.__start.split("-"))) end_date = list(map(int, self.__end.split("-"))) self.__start_date = datetime.datetime(start_date[0], start_date[1], start_date[2]) self.__end_date = datetime.datetime(end_date[0], end_date[1], end_date[2]) dataFeed = bt.feeds.PandasData(dataname=df_data, name=name, fromdate=datetime.datetime(start_date[0], start_date[1], start_date[2]), todate=datetime.datetime(end_date[0], end_date[1], end_date[2])) return dataFeed # 计算胜率信息 def _winInfo(self, trade_info, result): total_trade_num = trade_info["total"]["total"] if total_trade_num > 1: win_num = trade_info["won"]["total"] lost_num = trade_info["lost"]["total"] result["交易次数"] = total_trade_num result["胜率"] = win_num / total_trade_num result["败率"] = lost_num / total_trade_num # 根据SQN值对策略做出评估 # 按照backtrader文档写的 def _judgeBySQN(self, sqn): result = None if sqn >= 1.6 and sqn <= 1.9: result = "低于平均" elif sqn > 1.9 and sqn <= 2.4: result = "平均水平" elif sqn > 2.4 and sqn <= 2.9: result = "良好" elif sqn > 2.9 and sqn <= 5.0: result = "优秀" elif sqn > 5.0 and sqn <= 6.9: result = "卓越" elif sqn > 6.9: result = "大神?" else: result = "很差" self.__backtestResult["策略评价(根据SQN)"] = result return result # 计算并保存回测结果指标 def _Result(self): self.__backtestResult["账户总额"] = self.getValue() self.__backtestResult["总收益率"] = self.__results[0].analyzers.RE.get_analysis()["rtot"] self.__backtestResult["年化收益率"] = self.__results[0].analyzers.RE.get_analysis()["rnorm"] # self.__backtestResult["交易成本"] = self.__cerebro.strats[0].getCommission() self.__backtestResult["夏普比率"] = self.__results[0].analyzers.sharpe.get_analysis()["sharperatio"] self.__backtestResult["最大回撤"] = self.__results[0].analyzers.DD.get_analysis().max.drawdown self.__backtestResult["最大回撤期间"] = self.__results[0].analyzers.DD.get_analysis().max.len self.__backtestResult["SQN"] = self.__results[0].analyzers.SQN.get_analysis()["sqn"] self._judgeBySQN(self.__backtestResult["SQN"]) # 计算胜率信息 trade_info = self.__results[0].analyzers.TA.get_analysis() self._winInfo(trade_info, self.__backtestResult) # 取得优化参数时的指标结果 def _getOptAnalysis(self, result): temp = dict() temp["总收益率"] = result[0].analyzers.RE.get_analysis()["rtot"] temp["年化收益率"] = result[0].analyzers.RE.get_analysis()["rnorm"] temp["夏普比率"] = result[0].analyzers.sharpe.get_analysis()["sharperatio"] temp["最大回撤"] = result[0].analyzers.DD.get_analysis().max.drawdown temp["最大回撤期间"] = result[0].analyzers.DD.get_analysis().max.len sqn = result[0].analyzers.SQN.get_analysis()["sqn"] temp["SQN"] = sqn temp["策略评价(根据SQN)"] = self._judgeBySQN(sqn) trade_info = self.__results[0].analyzers.TA.get_analysis() self._winInfo(trade_info, temp) return temp # 在优化多个参数时计算并保存回测结果 def _optResultMore(self, results, **kwargs): testResults = pd.DataFrame() i = 0 for key in kwargs: for value in kwargs[key]: temp = self._getOptAnalysis(results[i]) temp["参数名"] = key temp["参数值"] = value returns = self._timeReturns(results[i]) benchReturns = self._getBenchmarkReturns(results[i]) self._riskAnaly(returns, benchReturns, temp) testResults = testResults.append(temp, ignore_index=True) # testResults.set_index(["参数值"], inplace = True) return testResults # 在优化参数时计算并保存回测结果 def _optResult(self, results, **kwargs): testResults = pd.DataFrame() params = [] for k, v in kwargs.items(): for t in v: params.append(t) i = 0 for result in results: temp = self._getOptAnalysis(result) temp["参数名"] = k temp["参数值"] = params[i] i += 1 returns = self._timeReturns(result) benchReturns = self._getBenchmarkReturns(result) self._riskAnaly(returns, benchReturns, temp) testResults = testResults.append(temp, ignore_index=True) # testResults.set_index(["参数值"], inplace = True) return testResults # 计算收益率序列 def _timeReturns(self, result): return pd.Series(result[0].analyzers.TR.get_analysis()) # 运行基准策略，获取基准收益值 def _getBenchmarkReturns(self, result): return pd.Series(result[0].analyzers.TR_Bench.get_analysis()) # 分析策略的风险指标 def _riskAnaly(self, returns, benchReturns, results): risk = riskAnalyzer(returns, benchReturns) result = risk.run() results["阿尔法"] = result["阿尔法"] results["贝塔"] = result["贝塔"] results["信息比例"] = result["信息比例"] results["策略波动率"] = result["策略波动率"] results["欧米伽"] = result["欧米伽"] # self.__backtestResult["夏普值"] = result["夏普值"] results["sortino"] = result["sortino"] results["calmar"] = result["calmar"] # 回测结果绘图 def _drawResult(self): self.__cerebro.plot(numfigs=2) figname = type(self).__name__ + ".png" plt.savefig(figname) # 获取数据 def _getData(self, code): filename = code + ".csv" path = "./data/" # 如果数据目录不存在，创建目录 if not os.path.exists(path): os.makedirs(path) # 已有数据文件，直接读取数据 if os.path.exists(path + filename): df = pd.read_csv(path + filename) else: # 没有数据文件，用tushare下载 df = ts.get_k_data(code, autype="qfq", start=self.__start, end=self.__end) df.to_csv(path + filename) df.index = pd.to_datetime(df.date) df['openinterest'] = 0 df = df[['open', 'high', 'low', 'close', 'volume', 'openinterest']] return df # 用empyrical库计算风险指标 class riskAnalyzer: def __init__(self, returns, benchReturns, riskFreeRate=0.02): self.__returns = returns self.__benchReturns = benchReturns self.__risk_free = riskFreeRate self.__alpha = 0.0 self.__beta = 0.0 self.__info = 0.0 self.__vola = 0.0 self.__omega = 0.0 self.__sharpe = 0.0 self.__sortino = 0.0 self.__calmar = 0.0 def run(self): # 计算各指标 self._alpha_beta() self._info() self._vola() self._omega() self._sharpe() self._sortino() self._calmar() result = pd.Series(dtype="float64") result["阿尔法"] = self.__alpha result["贝塔"] = self.__beta result["信息比例"] = self.__info result["策略波动率"] = self.__vola result["欧米伽"] = self.__omega result["夏普值"] = self.__sharpe result["sortino"] = self.__sortino result["calmar"] = self.__calmar return result def _alpha_beta(self): self.__alpha, self.__beta = ey.alpha_beta(returns=self.__returns, factor_returns=self.__benchReturns, risk_free=self.__risk_free, annualization=1) def _info(self): self.__info = ey.excess_sharpe(returns=self.__returns, factor_returns=self.__benchReturns) def _vola(self): self.__vola = ey.annual_volatility(self.__returns, period='daily') def _omega(self): self.__omega = ey.omega_ratio(returns=self.__returns, risk_free=self.__risk_free) def _sharpe(self): self.__sharpe = ey.sharpe_ratio(returns=self.__returns, annualization=1) def _sortino(self): self.__sortino = ey.sortino_ratio(returns=self.__returns) def _calmar(self): self.__calmar = ey.calmar_ratio(returns=self.__returns) # 测试函数 def test(): # 构造测试数据 returns = pd.Series( index=pd.date_range("2017-03-10", "2017-03-19"), data=(-0.012143, 0.045350, 0.030957, 0.004902, 0.002341, -0.02103, 0.00148, 0.004820, -0.00023, 0.01201)) print(returns) benchmark_returns = pd.Series( index=pd.date_range("2017-03-10", "2017-03-19"), data=(-0.031940, 0.025350, -0.020957, -0.000902, 0.007341, -0.01103, 0.00248, 0.008820, -0.00123, 0.01091)) print(benchmark_returns) # 计算累积收益率 creturns = ey.cum_returns(returns) print("累积收益率\n", creturns) risk = riskAnalyzer(returns, benchmark_returns, riskFreeRate=0.01) results = risk.run() print(results) # 直接调用empyrical试试 alpha = ey.alpha(returns=returns, factor_returns=benchmark_returns, risk_free=0.01) calmar = ey.calmar_ratio(returns) print(alpha, calmar) # 自己计算阿尔法值 annual_return = ey.annual_return(returns) annual_bench = ey.annual_return(benchmark_returns) print(annual_return, annual_bench) alpha2 = (annual_return - 0.01) - results["贝塔"] * (annual_bench - 0.01) print(alpha2) # 自己计算阿尔法贝塔 def get_return(code, startdate, endate): df = ts.get_k_data(code, ktype="D", autype="qfq", start=startdate, end=endate) p1 = np.array(df.close[1:]) p0 = np.array(df.close[:-1]) logret = np.log(p1 / p0) rate = pd.DataFrame() rate[code] = logret rate.index = df["date"][1:] return rate def alpha_beta(code, startdate, endate): mkt_ret = get_return("sh", startdate, endate) stock_ret = get_return(code, startdate, endate) df = pd.merge(mkt_ret, stock_ret, left_index=True, right_index=True) x = df.iloc[:, 0] y = df.iloc[:, 1] beta, alpha, r_value, p_value, std_err = stats.linregress(x, y) return (alpha, beta) def stocks_alpha_beta(stocks, startdate, endate): df = pd.DataFrame() alpha = [] beta = [] for code in stocks.values(): a, b = alpha_beta(code, startdate, endate) alpha.append(float("%.4f" % a)) beta.append(float("%.4f" % b)) df["alpha"] = alpha df["beta"] = beta df.index = stocks.keys() return df startdate = "2017-01-01" endate = "2018-11-09" stocks = {'中国平安': '601318', '格力电器': '000651', '招商银行': '600036', '恒生电子': '600570', '中信证券': '600030', '贵州茅台': '600519'} results = stocks_alpha_beta(stocks, startdate, endate) print("自己计算结果") print(results) # 用empyrical计算 def stocks_alpha_beta2(stocks, startdate, endate): df = pd.DataFrame() alpha = [] beta = [] for code in stocks.values(): a, b = empyrical_alpha_beta(code, startdate, endate) alpha.append(float("%.4f" % a)) beta.append(float("%.4f" % b)) df["alpha"] = alpha df["beta"] = beta df.index = stocks.keys() return df def empyrical_alpha_beta(code, startdate, endate): mkt_ret = get_return("sh", startdate, endate) stock_ret = get_return(code, startdate, endate) alpha, beta = ey.alpha_beta(returns=stock_ret, factor_returns=mkt_ret, annualization=1) return (alpha, beta) results2 = stocks_alpha_beta2(stocks, startdate, endate) print("empyrical计算结果") print(results2) print(results2["alpha"] / results["alpha"]) # 测试夏普值的计算 def testSharpe(): # 读取数据 stock_data = pd.read_csv("stock_data.csv", parse_dates=["Date"], index_col=["Date"]).dropna() benchmark_data = pd.read_csv("benchmark_data.csv", parse_dates=["Date"], index_col=["Date"]).dropna() # 了解数据 print("Stocks\n") print(stock_data.info()) print(stock_data.head()) print("\nBenchmarks\n") print(benchmark_data.info()) print(benchmark_data.head()) # 输出统计量 print(stock_data.describe()) print(benchmark_data.describe()) # 计算每日回报率 stock_returns = stock_data.pct_change() print(stock_returns.describe()) sp_returns = benchmark_data.pct_change() print(sp_returns.describe()) # 每日超额回报 excess_returns = pd.DataFrame() risk_free = 0.04 / 252.0 excess_returns["Amazon"] = stock_returns["Amazon"] - risk_free excess_returns["Facebook"] = stock_returns["Facebook"] - risk_free print(excess_returns.describe()) # 超额回报的均值 avg_excess_return = excess_returns.mean() print(avg_excess_return) # 超额回报的标准差 std_excess_return = excess_returns.std() print(std_excess_return) # 计算夏普比率 # 日夏普比率 daily_sharpe_ratio = avg_excess_return.div(std_excess_return) # 年化夏普比率 annual_factor = np.sqrt(252) annual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor) print("年化夏普比率\n", annual_sharpe_ratio) # 用empyrical算 sharpe = pd.DataFrame() a = ey.sharpe_ratio(stock_returns["Amazon"], risk_free=risk_free) # , annualization = 252) b = ey.sharpe_ratio(stock_returns["Facebook"], risk_free=risk_free) print("empyrical计算结果") print(a, b) print(a / annual_sharpe_ratio["Amazon"], b / annual_sharpe_ratio["Facebook"]) if __name__ == "__main__": # testpkg() testSharpe()
