商务网站建设推荐,公司网页制作报价,怎样做网站备案,网站程序如何上传Backtrader 文档学习-Platform Concepts
1.开始之前
导入backtrader #xff0c;以及backtrader 的指示器、数据反馈的模块 。
import backtrader as bt
import backtrader.indicators as btind
import backtrader.feeds as btfeeds看看btind模块下有什么方法和属性#x…Backtrader 文档学习-Platform Concepts
1.开始之前
导入backtrader 以及backtrader 的指示器、数据反馈的模块 。
import backtrader as bt
import backtrader.indicators as btind
import backtrader.feeds as btfeeds看看btind模块下有什么方法和属性
obj_str
for i in dir(btind):if i[:1] ! _ :obj_str i ,
print(obj_str)指示器中的指标函数很多方法属性如下
ADX,ADXR,AO,APO,ATR,AbsPriceOsc,AbsolutePriceOscillator,AccDeOsc,AccelerationDecelerationOscillator,Accum,AdaptiveMovingAverage,AdaptiveMovingAverageEnvelope,AdaptiveMovingAverageOsc,AdaptiveMovingAverageOscillator,All,AllN,And,Any,AnyN,ApplyN,ArithmeticMean,AroonDown,AroonIndicator,AroonOsc,AroonOscillator,AroonUp,AroonUpDown,AroonUpDownOsc,AroonUpDownOscillator,Average,AverageDirectionalMovementIndex,AverageDirectionalMovementIndexRating,AverageTrueRange,AverageWeighted,AwesomeOsc,AwesomeOscillator,BBands,BaseApplyN,BollingerBands,BollingerBandsPct,CCI,Cmp,CmpEx,CointN,CommodityChannelIndex,CrossDown,CrossOver,CrossUp,CumSum,CumulativeSum,DEMA,DEMAEnvelope,DEMAOsc,DEMAOscillator,DI,DM,DMA,DMAEnvelope,DMAOsc,DMAOscillator,DMI,DPO,DV2,DemarkPivotPoint,DetrendedPriceOscillator,DicksonMA,DicksonMAEnvelope,DicksonMAOsc,DicksonMAOscillator,DicksonMovingAverage,DicksonMovingAverageEnvelope,DicksonMovingAverageOsc,DicksonMovingAverageOscillator,DirectionalIndicator,DirectionalMovement,DirectionalMovementIndex,DivByZero,DivZeroByZero,DoubleExponentialMovingAverage,DoubleExponentialMovingAverageEnvelope,DoubleExponentialMovingAverageOsc,DoubleExponentialMovingAverageOscillator,DownDay,DownDayBool,DownMove,EC,ECEnvelope,ECOsc,ECOscillator,EMA,EMAEnvelope,EMAOsc,EMAOscillator,Envelope,EnvelopeMixIn,ErrorCorrecting,ErrorCorrectingEnvelope,ErrorCorrectingOsc,ErrorCorrectingOscillator,ExpSmoothing,ExpSmoothingDynamic,ExponentialMovingAverage,ExponentialMovingAverageEnvelope,ExponentialMovingAverageOsc,ExponentialMovingAverageOscillator,ExponentialSmoothing,ExponentialSmoothingDynamic,FibonacciPivotPoint,FindFirstIndex,FindFirstIndexHighest,FindFirstIndexLowest,FindLastIndex,FindLastIndexHighest,FindLastIndexLowest,HMA,HMAEnvelope,HMAOsc,HMAOscillator,HeikinAshi,Highest,HullMA,HullMAEnvelope,HullMAOsc,HullMAOscillator,HullMovingAverage,HullMovingAverageEnvelope,HullMovingAverageOsc,HullMovingAverageOscillator,Hurst,HurstExponent,Ichimoku,If,Indicator,KAMA,KAMAEnvelope,KAMAOsc,KAMAOscillator,KST,KnowSureThing,LAGF,LRSI,LaguerreFilter,LaguerreRSI,LineActions,List,Logic,Lowest,MACD,MACDHisto,MACDHistogram,MAXINT,Max,MaxN,Mean,MeanDev,MeanDeviation,MetaMovAvBase,Min,MinN,MinusDI,MinusDirectionalIndicator,ModifiedMovingAverage,ModifiedMovingAverageEnvelope,ModifiedMovingAverageOsc,ModifiedMovingAverageOscillator,Momentum,MomentumOsc,MomentumOscillator,MovAv,MovingAverage,MovingAverageAdaptive,MovingAverageAdaptiveEnvelope,MovingAverageAdaptiveOsc,MovingAverageAdaptiveOscillator,MovingAverageBase,MovingAverageDoubleExponential,MovingAverageDoubleExponentialEnvelope,MovingAverageDoubleExponentialOsc,MovingAverageDoubleExponentialOscillator,MovingAverageExponential,MovingAverageExponentialEnvelope,MovingAverageExponentialOsc,MovingAverageExponentialOscillator,MovingAverageSimple,MovingAverageSimpleEnvelope,MovingAverageSimpleOsc,MovingAverageSimpleOscillator,MovingAverageSmoothed,MovingAverageSmoothedEnvelope,MovingAverageSmoothedOsc,MovingAverageSmoothedOscillator,MovingAverageTripleExponential,MovingAverageTripleExponentialEnvelope,MovingAverageTripleExponentialOsc,MovingAverageTripleExponentialOscillator,MovingAverageWeighted,MovingAverageWeightedEnvelope,MovingAverageWeightedOsc,MovingAverageWeightedOscillator,MovingAverageWilder,MovingAverageWilderEnvelope,MovingAverageWilderOsc,MovingAverageWilderOscillator,MultiLogic,MultiLogicReduce,NZD,NonZeroDifference,OLS_BetaN,OLS_Slope_InterceptN,OLS_TransformationN,OperationN,Or,Oscillator,OscillatorMixIn,PGO,PPO,PPOShort,PSAR,ParabolicSAR,PctChange,PctRank,PercPriceOsc,PercPriceOscShort,PercentChange,PercentRank,PercentagePriceOscillator,PercentagePriceOscillatorShort,PeriodN,PivotPoint,PlusDI,PlusDirectionalIndicator,PrettyGoodOsc,PrettyGoodOscillator,PriceOsc,PriceOscillator,RMI,ROC,ROC100,RSI,RSI_Cutler,RSI_EMA,RSI_SMA,RSI_SMMA,RSI_Safe,RSI_Wilder,RateOfChange,RateOfChange100,Reduce,ReduceN,RelativeMomentumIndex,RelativeStrengthIndex,SMA,SMAEnvelope,SMAOsc,SMAOscillator,SMMA,SMMAEnvelope,SMMAOsc,SMMAOscillator,SimpleMovingAverage,SimpleMovingAverageEnvelope,SimpleMovingAverageOsc,SimpleMovingAverageOscillator,SmoothedMovingAverage,SmoothedMovingAverageEnvelope,SmoothedMovingAverageOsc,SmoothedMovingAverageOscillator,StandardDeviation,StdDev,Stochastic,StochasticFast,StochasticFull,StochasticSlow,Sum,SumN,TEMA,TEMAEnvelope,TEMAOsc,TEMAOscillator,TR,TRIX,TSI,TripleExponentialMovingAverage,TripleExponentialMovingAverageEnvelope,TripleExponentialMovingAverageOsc,TripleExponentialMovingAverageOscillator,Trix,TrixSignal,TrueHigh,TrueLow,TrueRange,TrueStrengthIndicator,UltimateOscillator,UpDay,UpDayBool,UpMove,Vortex,WMA,WMAEnvelope,WMAOsc,WMAOscillator,WeightedAverage,WeightedMovingAverage,WeightedMovingAverageEnvelope,WeightedMovingAverageOsc,WeightedMovingAverageOscillator,WilderMA,WilderMAEnvelope,WilderMAOsc,WilderMAOscillator,WilliamsAD,WilliamsR,ZLEMA,ZLEMAEnvelope,ZLEMAOsc,ZLEMAOscillator,ZLInd,ZLIndEnvelope,ZLIndOsc,ZLIndOscillator,ZLIndicator,ZLIndicatorEnvelope,ZLIndicatorOsc,ZLIndicatorOscillator,ZeroLagEma,ZeroLagEmaEnvelope,ZeroLagEmaOsc,ZeroLagEmaOscillator,ZeroLagExponentialMovingAverage,ZeroLagExponentialMovingAverageEnvelope,ZeroLagExponentialMovingAverageOsc,ZeroLagExponentialMovingAverageOscillator,ZeroLagIndicator,ZeroLagIndicatorEnvelope,ZeroLagIndicatorOsc,ZeroLagIndicatorOscillator,absolute_import,accdecoscillator,alias,aroon,atr,awesomeoscillator,basicops,bollinger,bt,cci,cmp,contrib,crossover,dema,deviation,directionalmove,division,dma,dpo,dv2,ema,envelope,functools,haD,haDelta,hadelta,heikinashi,hma,hurst,ichimoku,kama,kst,linename,lrsi,mabase,macd,map,math,module,momentum,movav,movname,newaliases,newcls,newclsdct,newclsdoc,newclsname,ols,operator,oscillator,percentchange,percentrank,pivotpoint,prettygoodoscillator,priceoscillator,print_function,psar,range,rmi,rsi,sma,smma,stochastic,suffix,sys,trix,tsi,ultimateoscillator,unicode_literals,williams,with_metaclass,wma,zlema,zlind,也可以直接通过bt导入下面的模块feeds indicators
import backtrader as bt
thefeed bt.feeds.OneOfTheFeeds(...)
theind bt.indicators.SimpleMovingAverage(...)2.数据读入-无处不在
所有的策略都基于数据平台端用户不用考虑如何接收数据。 Data Feeds are automagically provided member variables to the strategy in the form of an array and shortcuts to the array positions 数据源是以数组的形式或者对数组位置快捷访问的方式提供给strategy策略作为成员变量使用的。 这句话需要好好理解。
数组的形式 self.datas[0].close 数组位置快捷访问self.data0.close
class MyStrategy(bt.Strategy):params dict(period20)def __init__(self):sma btind.SimpleMovingAverage(self.datas[0], periodself.params.period)print(sma)# Keep a reference to the close line in the data[0] dataseriesself.dataclose self.datas[0].closeself.dataclosetest self.data0.closeprint(-* 20,close,- * 20 )print(self.dataclose[0])print(self.dataclosetest[0]) 执行结果 sma 的数据类型backtrader.indicators.sma.SimpleMovingAverage
backtrader.indicators.sma.SimpleMovingAverage object at 0x7f616bd7c820
-------------------- close --------------------
133.01
133.01 Data Feeds get added to the platform and they will show up inside the strategy in the sequential order in which they were added to the system. SQL语句按日期排序不论是正序还是逆序desc 到BackTrader中都是按时间倒序排列[0]是最后的日期。
3.快捷访问数据
self.datas数组项可以通过自动成员变量直接访问
self.data targets self.datas[0]
self.dataX targets self.datas[X]下面好好测试一下BackTrader的数据访问数据之间的关系。
#class backtrader.linebuffer.LineBuffer
print(type(cerebro.datas[0].close))#class backtrader.feeds.pandafeed.PandasData
print(type(cerebro.datas[0]))#class list
print(type(cerebro.datas))# (close, low, high, open, volume, openinterest, datetime)
print(cerebro.datas[0].getlinealiases())结果如下
class backtrader.linebuffer.LineBuffer
class backtrader.feeds.pandafeed.PandasData
class list
(close, low, high, open, volume, openinterest, datetime)说明
1、cerebro.datas数据集的类型是 class ‘list’ 对应载入cerebro的股票数据可以是多个股票就是股票的集合。2、cerebro.datas[0]是class ‘backtrader.feeds.pandafeed.PandasData’其实与dataframe类似包括以下列(‘close’, ‘low’, ‘high’, ‘open’, ‘volume’, ‘openinterest’, ‘datetime’)的集合。 -3、cerebro.datas[0].close就是dataframe的一列数据BackTrader称之为Line class ‘backtrader.linebuffer.LineBuffer’ 。4、cerebro.datas[0].close[0] 就是访问到数据的元素实际数据值。5、cerebro.datas[0]都是按时间顺序逆序的第一个元素index是0 下一个是-1 以此类推。6、时间序列必须用内置方法处理
print(cerebro.datas[0].datetime[0])
print(pd.to_datetime(cerebro.datas[0].datetime[0],units))
print(bt.num2date(cerebro.datas[0].datetime[0]))结果如下
737424.0
1970-01-09 12:50:24
2019-12-31 00:00:00直接访问datetime列 是浮点数。 开始用pd.to_datetime 函数转换的时间是1970年迷惑很长时间。
重要必须用自带的函数转换时间 bt.num2date
4.默认数据载入
class MyStrategy(bt.Strategy):params dict(period20)def __init__(self):sma btind.SimpleMovingAverage(periodself.params.period)...self.data has been completely removed from the invocation of SimpleMovingAverage. If this is done, the indicator (in this case the SimpleMovingAverage) receives the first data of the object in which is being created (the Strategy), which is self.data (aka self.data0 or self.datas[0]) 默认使用的self是什么 如果只有一个数据源cerebro只载入一个股票数据不用显式指定系统会缺省使用self.datas[0]self.dataself.data0self.datas[0] 也就是第一个加入的数据源 。所以说self.data 就没有显式出现在调用SMA指示器 。 如果有多个载入数据还是注明好清晰可读。
5. 一切都是数据载入
class MyStrategy(bt.Strategy):#定义策略全局参数params dict(period120, period225, period310, period4)def __init__(self):# 使用SMA指示器载入datas[0]使用参数1sma1 btind.SimpleMovingAverage(self.datas[0], periodself.p.period1)# This 2nd Moving Average operates using sma1 as data# 使用第一个SMA指示器数据再次作为SMA的数据载入使用参数2sma2 btind.SimpleMovingAverage(sma1, periodself.p.period2)# New data created via arithmetic operation# 随意的计算 something sma2 - sma1 self.data.close# This 3rd Moving Average operates using something as data# 随意计算的结果作为第三次SMA数据载入使用参数3sma3 btind.SimpleMovingAverage(something, periodself.p.period3)# Comparison operators work too ...# 比较后取大值 有点疑问 greater sma3 sma1# Pointless Moving Average of True/False values but valid# This 4th Moving Average operates using greater as data# 比较后结果第四次载入SMA指示器使用参数4sma3 btind.SimpleMovingAverage(greater, periodself.p.period4)...总而言之所有的数据转换变化后都可以作为数据载入再次进行运算。
6. 参数
在backtrader中所有的类都按照如下方法来使用参数
1、声明一个带有缺省值的参数作为类的一个属性元组或者字典结构。 2、关键字类型参数kwargs会扫描匹配的参数将值赋值给对应的参数完成后从kwargs删除。 3、这些参数都可以在类实例中通过访问成员变量self.params可以简写为self.p来使用。 使用元组 tuple
class MyStrategy(bt.Strategy):params ((period, 20),)def __init__(self):sma btind.SimpleMovingAverage(self.data, periodself.p.period)使用字典dict
class MyStrategy(bt.Strategy):params dict(period20)def __init__(self):sma btind.SimpleMovingAverage(self.data, periodself.p.period)7.Lines 关键的概念
从使用用户通常是strategy的角度来说就是包括一个或多个line这些line是一系列的数据在图中可以形成一条线line。line是数组不是pd.series 没有index 。
class MyStrategy(bt.Strategy):params dict(period20)def __init__(self):self.movav btind.SimpleMovingAverage(self.data, periodself.p.period)def next(self):if self.movav.lines.sma[0] self.data.lines.close[0]:print(round(self.movav.lines.sma[0],2),self.data.lines.close[0],Simple Moving Average is greater than the closing price)
执行结果
83.34 74.4 Simple Moving Average is greater than the closing price
83.16 75.1 Simple Moving Average is greater than the closing price
83.01 76.6 Simple Moving Average is greater than the closing price
82.92 80.1 Simple Moving Average is greater than the closing price
82.82 79.64 Simple Moving Average is greater than the closing price
82.69 78.52 Simple Moving Average is greater than the closing price
82.36 76.1 Simple Moving Average is greater than the closing price
81.87 74.28 Simple Moving Average is greater than the closing price
... ... self.movav:这个是一个SimpleMovingAverage的指标indicator本身就包含一个具有lines属性的sma。这里特殊注意一下计算SimpleMovingAverage使用的self.data没有指定具体Line的话,缺省用的是close价进行计算。
简单的方法访问lines xxx.lines 可以简化为 xxx.l xxx.lines.name可以简化为xxx.lines_name 一些复杂的对象也可以通过如下方法访问 self.data_name 等于 self.data.lines.name 如果有多个变量的话也可以self.data1_name 替代self.data1.lines.name
此外Line的名字也可以通过如下方式访问 self.data.close and self.movav.sma class MyStrategy(bt.Strategy):params dict(period20)def __init__(self):self.movav btind.SimpleMovingAverage(self.data, periodself.p.period)def next(self):# 数据集0 收盘价在110和120if (self.datas[0].close[0] 110.0) (self.datas[0].close[0] 120.0):print(bt.num2date(self.datas[0].datetime[0]),self.datas[0].close[0] 110.0)# 数据集1 收盘价在110和120 if self.datas[1].close[0] 9.0:print(bt.num2date(self.datas[1].datetime[0]),self.datas[1].close[0] 9.0)# 数据集SMA 收盘价在88和90if (self.movav.lines.sma[0] 88) (self.movav.sma[0] 90) :print(round(self.movav.lines.sma[0],2),self.movav.lines.sma[0] 88)print(self.movav.getlinealiases())#print(cerebro.datas[0].getlinealiases())结果如下
2018-04-18 00:00:00 self.datas[1].close[0] 9.0
2018-04-24 00:00:00 self.datas[1].close[0] 9.0
2018-04-25 00:00:00 self.datas[1].close[0] 9.0
2018-04-26 00:00:00 self.datas[1].close[0] 9.0
2019-04-09 00:00:00 self.datas[0].close[0] 110.0
88.78 self.movav.lines.sma[0] 88
(sma,)
89.64 self.movav.lines.sma[0] 88
(sma,)
2019-06-20 00:00:00 self.datas[0].close[0] 110.0
2019-06-21 00:00:00 self.datas[0].close[0] 110.0
2019-06-24 00:00:00 self.datas[0].close[0] 110.0
2019-06-25 00:00:00 self.datas[0].close[0] 110.0
2019-06-26 00:00:00 self.datas[0].close[0] 110.0
2019-06-27 00:00:00 self.datas[0].close[0] 110.0
2019-06-28 00:00:00 self.datas[0].close[0] 110.0
2019-07-24 00:00:00 self.datas[0].close[0] 110.0
2019-08-01 00:00:00 self.datas[0].close[0] 110.0
2019-08-02 00:00:00 self.datas[0].close[0] 110.0
2019-08-05 00:00:00 self.datas[0].close[0] 110.0
2019-08-07 00:00:00 self.datas[0].close[0] 110.0
2019-08-09 00:00:00 self.datas[0].close[0] 110.0需要好好说明一下
简写示例 if (self.movav.lines.sma[0] 88) (self.movav.sma[0] 90) : 多数据源
两种方式引用 self.movav.lines.sma[0] self.movav.sma[0]
cerebro载入两个股票数据。 特别注意调用datas 有s self.datas[0].close[0] self.datas[0].close[0] self.datas[0].close[0]
SMA指示器调用后是数组没有日期字段 开始也想增加打印日期 print(bt.num2date(self.movav.datetime[0]) 无日期 只有一列
print(self.movav.getlinealiases())
(sma,)未完待续
8.访问Lines
data btfeeds.BacktraderCSVData(datanamemydata.csv)...class MyStrategy(bt.Strategy):...def next(self):if self.data.close[0] 30.0:...两个表达式等价 if self.data.close[0] 30.0: if self.data.lines.close[0] 30.0: 9. Lines长度
Lines是随时变化的run的时候next不断改变Lines的长度在数据载入策略指示器应用中需要测量Lines的长度。 两个函数len和buflen之间的区别
len已处理了Linesbuflen为数据加载Lines的总数 buflen是 还是载入的两个数据集验证一下
class MyStrategy1(bt.Strategy):params dict(period20)def __init__(self):self.movav0 btind.SimpleMovingAverage(self.data0, periodself.p.period)self.movav1 btind.SimpleMovingAverage(self.data1, periodself.p.period)print(self.p.period:,self.p.period)def next(self):print(self.datas[0].lines.buflen(),self.datas[0].lines.buflen())print(self.datas[1].buflen(),self.datas[1].buflen())print(len(self.datas[0]),len(self.datas[0]))print(len(self.datas[1]),len(self.datas[1]))cerebro.addstrategy(MyStrategy1,period30)# Run over everything
cerebro.run()
结果如下
self.p.period: 30
self.p.period: 30
self.datas[0].lines.buflen() 244
self.datas[1].buflen() 244
len(self.datas[0]) 30
len(self.datas[1]) 30
self.datas[0].lines.buflen() 244
self.datas[1].buflen() 244
len(self.datas[0]) 31
len(self.datas[1]) 31
... ...
len(self.datas[0]) 243
len(self.datas[1]) 243
self.datas[0].lines.buflen() 244
self.datas[1].buflen() 244
len(self.datas[0]) 244
len(self.datas[1]) 244分析说明
self.datas[0].lines.buflen() 和self.datas[1].buflen() 一样可以忽略lines对象。 print(self.datas[0].lines.buflen(),self.datas[0].lines.buflen())print(self.datas[1].buflen(),self.datas[1].buflen())在init中print(‘self.p.period:’,self.p.period)执行了两次因为每次加载策略的时候不同的数据集都执行init() 。print(‘self.p.period:’,self.p.period) 打印值是30 非默认的20 。buflen()是lines的方法
10.Lines和参数的继承
支持多重继承继承基类的参数如果多个基类定义相同的参数则使用继承列表中最后一个类的默认值如果在子类中重新定义了相同的参数则新的默认值将接管基类的默认值
lines还有什么方法属性
class MyStrategy2(bt.Strategy):params dict(period20)def __init__(self):self.movav0 btind.SimpleMovingAverage(self.data0, periodself.p.period)self.movav1 btind.SimpleMovingAverage(self.data1, periodself.p.period)print(self.p.period:,self.p.period)obj_str for i in dir(self.datas[0].lines):if i[:1] ! _ :obj_str i , print(obj_str)def next(self):passcerebro.addstrategy(MyStrategy2,period30)# Run over everything
cerebro.run()
结果如下
self.p.period: 30
advance,backwards,buflen,close,datetime,extend,extrasize,forward,fullsize,get,getlinealiases,high,home,itersize,lines,low,open,openinterest,reset,rewind,size,volume,11.索引0和-1
重点 0指的是系统当前正在处理的数据而不是第一个数据。 strategy类只会进行取值操作而indicator只会进行赋值操作。 在backtrader中-1指的是当前处理数据索引为0的上一个数据。
def next(self):if self.data.close[0] self.data.close[-1]:print(Closing price is higher today)以当前值0为基准上一个值索引-1上上一个值为-2还可以继续-3-4…
12.切片
BackTrader的切片和Python的概念不同 用Python的list切片从头到尾的切片在BackTrader不能用。
myslice self.my_sma[0:] # slice from the beginning til the end
myslice self.my_sma[0:-1] # slice from the beginning til the end按逆序也不行
myslice cerebro.datas[0].close[-1:-3] # from last value backwards to the 3rd last value
myslice cerebro.datas[0].close[-3:-1] # from last value backwards to the 3rd last valueBackTrader的切片用函数
myslice self.my_sma.get(ago0, size1) # default values shown测试一下
cerebro.datas[1].close.get(ago0, size3)
结果
array(d, [5.82, 5.85, 5.78])遍历处理日期
for i in cerebro.datas[1].datetime.get(ago0, size3) :print(bt.num2date(i))
结果2019-12-27 00:00:00
2019-12-30 00:00:00
2019-12-31 00:00:00 数组是支持Python的切片
cerebro.datas[1].datetime.get(ago0, size5)[0:3]
结果
array(d, [737418.0, 737419.0, 737420.0])需要注意返回值的顺序 最左的值对应离ago最远的值最右的是ago索引对应的值。 例如如下是返回5最新的个值不包括正在处理的值通过下图理解一下
13.Lines 的索引延迟
文档上的有点小瑕疵 self.movav btind.SimpleMovingAverage(self.data, periodself.p.period) 定义变量应该是 self.sma
class MyStrategy4(bt.Strategy):params dict(period20)def __init__(self):self.sma btind.SimpleMovingAverage(self.datas[1], periodself.p.period)self.cmpval self.datas[1].close(-1) self.smadef next(self):if self.cmpval[0]:print(self.cmpval[0],self.sma[0],self.datas[1].close[0],self.datas[1].close[-1])cerebro.addstrategy(MyStrategy4)# Run over everything
cerebro.run() 分析说明 关键是close(-1) 是圆括号不是取值的方括号 对象类型是backtrader.linebuffer._LineDelay object at 0x7f616a25e1c0 self.datas[1].close(-1) 重新简化测试
class MyStrategy5(bt.Strategy):params dict(period20)def __init__(self):self.cmpval self.datas[1].close(-1) def next(self):print(self.datas[1].close[0],self.cmpval[0])cerebro.addstrategy(MyStrategy5)# Run over everything
cerebro.run()
结果 第一列第一个 第二列第二个以此类推
5.12 5.03
5.49 5.12
5.79 5.49
5.79 5.79
5.8 5.79
5.84 5.814. Lines 耦合
应用场景 耦合主要用于将时间窗口不同的两个line建立关系。比如 不同时间窗口的数据源具有不同的长度indcator在使用这些数据的时候会复制这个长度。 股票的日线数据每年大约250个bar对应250个工作日 股票的周线数据,每年大约52个bar对应52周
原文 The reader could imagine a date comparison taking place in the background to find out a day - week correspondence, but: Indicators are just mathematical formulas and have no datetime information They know nothing about the environment, just that if the data provides enough values, a calculation can take place. 读者可以想象一个日期比较的场景找出日和星期的通信 但是 指示器可以调整数学公式没有不考虑日期信息 读者对于场景一无所知如能够提供足够的数据就可以计算 。
测试一下效果
class MyStrategy6(bt.Strategy):params dict(period20)def __init__(self):# data0 是日线数据self.sma0 btind.SMA(self.data1.close, period5) # 5 日线的平均# data1 是周线数据self.sma1 btind.SMA(self.data1.close, period5) # 5 周线的平均self.couplesma self.sma1()self.buysig self.sma0 self.couplesma#print(type(sma1())) # class backtrader.metabase.LinesCoupler_14#print(self.sma1.get(ago0, size5)) # class backtrader.indicators.sma.SMAdef next(self):print(sma1:,self.sma1.get(ago0,size len(self.sma1)))print(self.couplesma:,self.couplesma.get(ago0,size len(self.sma1)))if self.buysig[0] :print(self.buysig[0],日均线大于周均线)# 初始化一个空的cerebor
cerebro declare_cerebar()
# 加入策略
cerebro.addstrategy(MyStrategy6)# Run over everything
cerebro.run()
sma1通过加一个括号()将数据适配到每日的时间窗口 程序做了调整说明如下
新的数据类型 #print(type(sma1())) # class ‘backtrader.metabase.LinesCoupler_14’ sma1()就是耦合数据类型class backtrader.metabase.LinesCoupler 是一个新的数据类型 self.couplesma也应该是class backtrader.metabase.LinesCoupler 没有触发判断语句 按说明补齐长度应该能够进行比较有是1的值。 输出结果报错超出IndexError: array index out of range
日线的bar远远大于周线的bar做SMA 都用5个周期做效果一样还是应该长度不一致。
改日再看看是数据问题还是没有理解 line耦合的概念
15. 自然构造 操作符
第一步用操作符创建对象
在Indicators指标和strategy策略的初始化阶段init函数通过操作符创建对象 。
class MyStrategy7(bt.Strategy):def __init__(self):sma btind.SimpleMovingAverage(self.data, period20)close_over_sma self.data.close smasma_dist_to_high self.data.high - smasma_dist_small sma_dist_to_high 3.5# Unfortunately and cannot be overridden in Python being# a language construct and not an operator and thus a# function has to be provided by the platform to emulate itsell_sig bt.And(close_over_sma, sma_dist_small)print(sell_sig,type(sell_sig))print(close_over_sma,type(close_over_sma),sma_dist_small,type(sma_dist_small))cerebro declare_cerebar()# 加入策略
cerebro.addstrategy(MyStrategy7)
cerebro.run()
结果
sell_sig class backtrader.functions.And
close_over_sma class backtrader.linebuffer.LinesOperation sma_dist_small class backtrader.linebuffer.LinesOperation还有class ‘backtrader.functions.And’数据类型
第二步用操作符创建对象
if self.sma 30.0: 比较的是self.sma[0] to 30.0 (第一个Line 的当前值) if self.sma self.data.close: 比较 self.sma[0] 和self.data.close[0]。
class MyStrategy7(bt.Strategy):def __init__(self):self.sma sma btind.SimpleMovingAverage(self.data0, period20)close_over_sma self.data0.close smaself.sma_dist_to_high self.data0.high - smasma_dist_small self.sma_dist_to_high 3.5print(type(sma),type(close_over_sma))print(type(self.sma_dist_to_high),type(self.data0.high))# Unfortunately and cannot be overridden in Python being# a language construct and not an operator and thus a# function has to be provided by the platform to emulate itself.sell_sig bt.And(close_over_sma, sma_dist_small)def next(self):# Although this does not seem like an operator it actually is# in the sense that the object is being tested for a True/False# responseif self.sma 30.0:print(sma is greater than 30.0!,self.sma[0])if self.sma self.data.close:print(sma is above the close price!,self.sma,self.data.close,self.data.close[0])if self.sell_sig: # if sell_sig True: would also be validprint(sell sig is True!,self.sell_sig[0])else:print(sell sig is False!,self.sell_sig[0])if self.sma_dist_to_high 5.0:print(distance from sma to hig is greater than 5.0!,self.sma_dist_to_high[0]) self.sma_dist_to_high self.data0.high - sma 说明 LineBuffer 类型 减 SimpleMovingAverage 类型 结果是 LinesOperation 类型也是数值 有点迷惑
self.sma_dist_to_high class ‘backtrader.linebuffer.LinesOperation’ self.data0.high class ‘backtrader.linebuffer.LineBuffer’ sma class ‘backtrader.indicators.sma.SimpleMovingAverage’
结果
class backtrader.indicators.sma.SimpleMovingAverage class backtrader.linebuffer.LinesOperation
class backtrader.linebuffer.LinesOperation class backtrader.linebuffer.LineBuffer
sma is greater than 30.0! 52.5345
sell sig is False! 0.0
distance from sma to hig is greater than 5.0! 6.045499999999997
sma is greater than 30.0! 52.9015
sell sig is False! 0.0
... ...
不可重写的操作符/函数
如下操作符/函数以及对应重写的函数。
操作符:
and - Andor - Or
逻辑控制:
if - If
函数:
any - Anyall - Allcmp - Cmpmax - Maxmin - Minsum - SumSum实际上是使用math.fsum作为底层操作因为backtrader应用中需要处理大量浮点数据而普通的sum函数精度会有影响。reduce - Reduce
操作符/函数针对迭代器运行。迭代器中的元素可以是常规的Python数值类型int、float等也可以是Lines的对象。 bt.And示例
class MyStrategy(bt.Strategy):def __init__(self):sma1 btind.SMA(self.data.close, period15)self.buysig bt.And(sma1 self.data.close, sma1 self.data.high)def next(self):if self.buysig[0]:pass # do something herebt.If示例
class MyStrategy(bt.Strategy):def __init__(self):sma1 btind.SMA(self.data.close, period15)high_or_low bt.If(sma1 self.data.close, self.data.low, self.data.high)sma2 btind.SMA(high_or_low, period15)
