Where are indicators used?
Looking back at Backtrader's main functional modules and backtesting process (see: Backtrader is here!), it can be found that the calculation and use of indicators are only involved when writing a strategy strategy, and it is the __init__() and next( ) method is the most involved.
It is recommended to calculate metrics ahead of time in __init__()
The __init__() function in Strategy will only be called once at the very beginning of the backtesting process,
while next() will be called multiple times in sequence each trading day, so in order to improve the efficiency of backtesting, it is recommended to first call __init__ Calculate the indicators (even trading signals) at one time in (),
and then call the calculated indicators in next(), which can effectively avoid the repeated calculation of indicators and improve the running speed of backtesting. It is recommended to follow the principle of "__init__() is responsible for indicator calculation, and next() is responsible for indicator calling".
import backtrader.indicators as btind # 导入策略分析模块
class MyStrategy(bt.Strategy):
# 先在 __init__ 中提前算好指标
def __init__(self):
sma1 = btind.SimpleMovingAverage(self.data)
ema1 = btind.ExponentialMovingAverage()
close_over_sma = self.data.close > sma1
close_over_ema = self.data.close > ema1
sma_ema_diff = sma1 - ema1
# 生成交易信号
buy_sig = bt.And(close_over_sma, close_over_ema, sma_ema_diff > 0)
# 在 next 中直接调用计算好的指标
def next(self):
if buy_sig:
self.buy()Various shorthand forms when calculating indicators
class TestStrategy(bt.Strategy):
def __init__(self):
# 最简方式：直接省略指向的数据集
self.sma1 = btind.SimpleMovingAverage(period=5)
# 只指定第一个数据表格
self.sma2 = btind.SMA(self.data, period=5)
# 指定第一个数据表格的close 线
self.sma3 = btind.SMA(self.data.close, period=5)
# 完整写法
self.sma4 = btind.SMA(self.datas[0].lines[0], period=5)
# 指标函数也支持简写 SimpleMovingAverage → SMA
def next(self):
# 提取当前时间点
print('datetime', self.datas[0].datetime.date(0))
# 打印当日、昨日、前日的均线
print('sma1',self.sma1.get(ago=0, size=3))
print('sma2',self.sma2.get(ago=0, size=3))
print('sma3',self.sma3.get(ago=0, size=3))
print('sma4',self.sma4.get(ago=0, size=3))
cerebro = bt.Cerebro()
st_date = datetime.datetime(2019,1,2)
end_date = datetime.datetime(2021,1,28)
datafeed1 = bt.feeds.PandasData(dataname=data1, fromdate=st_date, todate=end_date)
cerebro.adddata(datafeed1, name='600466.SH')
datafeed2 = bt.feeds.PandasData(dataname=data2, fromdate=st_date, todate=end_date)
cerebro.adddata(datafeed2, name='603228.SH')
cerebro.addstrategy(TestStrategy)
rasult = cerebro.run()Various shorthand forms
when calling the indicator When calling the indicator, the indexing and slicing operations of the line will be involved. In order to make the operation easier,
when calling the indicator value at the current moment in next(), you can omit the index [0]: that is, in next() , self.sma5[0] ↔ self.sma5, self.data.close[0] ↔ self.data.close, etc. are equivalent,
omitting the short form of [0] self.sma5, self.data. close, etc. all point to the current value by default, and the current value is automatically indexed
class TestStrategy(bt.Strategy):
def __init__(self):
self.sma5 = btind.SimpleMovingAverage(period=5) # 5日均线
self.sma10 = btind.SimpleMovingAverage(period=10) # 10日均线
self.buy_sig = self.sma5 > self.sma10 # 5日均线上穿10日均线
def next(self):
# 提取当前时间点
print('datetime', self.datas[0].datetime.date(0))
# 打印当前值
print('close', self.data.close[0], self.data.close)
print('sma5', self.sma5[0], self.sma5)
print('sma10', self.sma10[0], self.sma10)
print('buy_sig', self.buy_sig[0], self.buy_sig)
# 比较收盘价与均线的大小
if self.data.close > self.sma5:
print('------收盘价上穿5日均线------')
if self.data.close[0] > self.sma10:
print('------收盘价上穿10日均线------')
if self.buy_sig:
print('------ buy ------')
cerebro = bt.Cerebro()
st_date = datetime.datetime(2019,1,2)
end_date = datetime.datetime(2021,1,28)
datafeed1 = bt.feeds.PandasData(dataname=data1, fromdate=st_date, todate=end_date)
cerebro.adddata(datafeed1, name='600466.SH')
cerebro.addstrategy(TestStrategy)
rasult = cerebro.run()
class TestStrategy(bt.Strategy):
def __init__(self):
self.sma5 = btind.SimpleMovingAverage(period=5) # 5日均线
self.sma10 = btind.SimpleMovingAverage(period=10) # 10日均线
# bt.And 中所有条件都满足时返回 1；有一个条件不满足就返回 0
self.And = bt.And(self.data>self.sma5, self.data>self.sma10, self.sma5>self.sma10)
# bt.Or 中有一个条件满足时就返回 1；所有条件都不满足时返回 0
self.Or = bt.Or(self.data>self.sma5, self.data>self.sma10, self.sma5>self.sma10)
# bt.If(a, b, c) 如果满足条件 a，就返回 b，否则返回 c
self.If = bt.If(self.data>self.sma5,1000, 5000)
# bt.All,同 bt.And
self.All = bt.All(self.data>self.sma5, self.data>self.sma10, self.sma5>self.sma10)
# bt.Any，同 bt.Or
self.Any = bt.Any(self.data>self.sma5, self.data>self.sma10, self.sma5>self.sma10)
# bt.Max，返回同一时刻所有指标中的最大值
self.Max = bt.Max(self.data, self.sma10, self.sma5)
# bt.Min，返回同一时刻所有指标中的最小值
self.Min = bt.Min(self.data, self.sma10, self.sma5)
# bt.Sum，对同一时刻所有指标进行求和
self.Sum = bt.Sum(self.data, self.sma10, self.sma5)
# bt.Cmp(a,b), 如果 a>b ，返回 1；否则返回 -1
self.Cmp = bt.Cmp(self.data, self.sma5)
def next(self):
print('---------- datetime',self.data.datetime.date(0), '------------------')
print('close:', self.data[0], 'ma5:', self.sma5[0], 'ma10:', self.sma10[0])
print('close>ma5',self.data>self.sma5, 'close>ma10',self.data>self.sma10, 'ma5>ma10', self.sma5>self.sma10)
print('self.And', self.And[0], self.data>self.sma5 and self.data>self.sma10 and self.sma5>self.sma10)
print('self.Or', self.Or[0], self.data>self.sma5 or self.data>self.sma10 or self.sma5>self.sma10)
print('self.If', self.If[0], 1000 if self.data>self.sma5 else 5000)
print('self.All',self.All[0], self.data>self.sma5 and self.data>self.sma10 and self.sma5>self.sma10)
print('self.Any', self.Any[0], self.data>self.sma5 or self.data>self.sma10 or self.sma5>self.sma10)
print('self.Max',self.Max[0], max([self.data[0], self.sma10[0], self.sma5[0]]))
print('self.Min', self.Min[0], min([self.data[0], self.sma10[0], self.sma5[0]]))
print('self.Sum', self.Sum[0], sum([self.data[0], self.sma10[0], self.sma5[0]]))
print('self.Cmp', self.Cmp[0], 1 if self.data>self.sma5 else -1)
cerebro = bt.Cerebro()
st_date = datetime.datetime(2019,1,2)
ed_date = datetime.datetime(2021,1,28)
datafeed1 = bt.feeds.PandasData(dataname=data1, fromdate=st_date, todate=ed_date)
cerebro.adddata(datafeed1, name='600466.SH')
cerebro.addstrategy(TestStrategy)
rasult = cerebro.run()
How to align indicators with different periods
Usually, the data of the same cycle is operated, such as daily market data calculation returns various daily indicators, weekly market data calculation returns various weekly indicators, ..., market data and indicators Periods are aligned, and times are aligned.
But sometimes there are situations in which data of different cycles are manipulated, such as comparing the daily market with the monthly indicators. The daily market has data every day, while the monthly indicators have only one per month, and there are no two data in terms of time. aligned, as follows:
You can use the "( )" syntax operation to align data of different periods. The alignment direction is "alignment from large periods to small periods".
You can select a line in the indicator object to align, or you can align the entire indicator object.
When using this syntax, set runonce in cerebro.run() to False to achieve alignment:
# self.data0 是日度行情、self.data1 是月度行情
self.month = btind.xxx(self.data1) # 计算返回的 self.month 指标也是月度的
# 选择指标对象中的第一条 line 进行对齐
self.sellsignal = self.data0.close < self.month.lines[0]()
# 对齐整个指标对象
self.month_ = self.month()
self.signal = self.data0.close < self.month_.lines[0]
cerebro.run(runonce=False)