BackTrader 中文文档（十五）（2）https://developer.aliyun.com/article/1505364
该代码
from __future__ import (absolute_import, division, print_function, unicode_literals) import argparse import datetime import backtrader as bt class BaseStrategy(bt.Strategy): params = dict( fast_ma=10, slow_ma=20, ) def __init__(self): # omitting a data implies self.datas[0] (aka self.data and self.data0) fast_ma = bt.ind.EMA(period=self.p.fast_ma) slow_ma = bt.ind.EMA(period=self.p.slow_ma) # our entry point self.crossup = bt.ind.CrossUp(fast_ma, slow_ma) class ManualStopOrStopTrail(BaseStrategy): params = dict( stop_loss=0.02, # price is 2% less than the entry point trail=False, ) def notify_order(self, order): if not order.status == order.Completed: return # discard any other notification if not self.position: # we left the market print('SELL@price: {:.2f}'.format(order.executed.price)) return # We have entered the market print('BUY @price: {:.2f}'.format(order.executed.price)) if not self.p.trail: stop_price = order.executed.price * (1.0 - self.p.stop_loss) self.sell(exectype=bt.Order.Stop, price=stop_price) else: self.sell(exectype=bt.Order.StopTrail, trailamount=self.p.trail) def next(self): if not self.position and self.crossup > 0: # not in the market and signal triggered self.buy() class ManualStopOrStopTrailCheat(BaseStrategy): params = dict( stop_loss=0.02, # price is 2% less than the entry point trail=False, ) def __init__(self): super().__init__() self.broker.set_coc(True) def notify_order(self, order): if not order.status == order.Completed: return # discard any other notification if not self.position: # we left the market print('SELL@price: {:.2f}'.format(order.executed.price)) return # We have entered the market print('BUY @price: {:.2f}'.format(order.executed.price)) def next(self): if not self.position and self.crossup > 0: # not in the market and signal triggered self.buy() if not self.p.trail: stop_price = self.data.close[0] * (1.0 - self.p.stop_loss) self.sell(exectype=bt.Order.Stop, price=stop_price) else: self.sell(exectype=bt.Order.StopTrail, trailamount=self.p.trail) class AutoStopOrStopTrail(BaseStrategy): params = dict( stop_loss=0.02, # price is 2% less than the entry point trail=False, buy_limit=False, ) buy_order = None # default value for a potential buy_order def notify_order(self, order): if order.status == order.Cancelled: print('CANCEL@price: {:.2f} {}'.format( order.executed.price, 'buy' if order.isbuy() else 'sell')) return if not order.status == order.Completed: return # discard any other notification if not self.position: # we left the market print('SELL@price: {:.2f}'.format(order.executed.price)) return # We have entered the market print('BUY @price: {:.2f}'.format(order.executed.price)) def next(self): if not self.position and self.crossup > 0: if self.buy_order: # something was pending self.cancel(self.buy_order) # not in the market and signal triggered if not self.p.buy_limit: self.buy_order = self.buy(transmit=False) else: price = self.data.close[0] * (1.0 - self.p.buy_limit) # transmit = False ... await child order before transmission self.buy_order = self.buy(price=price, exectype=bt.Order.Limit, transmit=False) # Setting parent=buy_order ... sends both together if not self.p.trail: stop_price = self.data.close[0] * (1.0 - self.p.stop_loss) self.sell(exectype=bt.Order.Stop, price=stop_price, parent=self.buy_order) else: self.sell(exectype=bt.Order.StopTrail, trailamount=self.p.trail, parent=self.buy_order) APPROACHES = dict( manual=ManualStopOrStopTrail, manualcheat=ManualStopOrStopTrailCheat, auto=AutoStopOrStopTrail, ) def runstrat(args=None): args = parse_args(args) cerebro = bt.Cerebro() # Data feed kwargs kwargs = dict() # Parse from/to-date dtfmt, tmfmt = '%Y-%m-%d', 'T%H:%M:%S' for a, d in ((getattr(args, x), x) for x in ['fromdate', 'todate']): if a: strpfmt = dtfmt + tmfmt * ('T' in a) kwargs[d] = datetime.datetime.strptime(a, strpfmt) data0 = bt.feeds.BacktraderCSVData(dataname=args.data0, **kwargs) cerebro.adddata(data0) # Broker cerebro.broker = bt.brokers.BackBroker(**eval('dict(' + args.broker + ')')) # Sizer cerebro.addsizer(bt.sizers.FixedSize, **eval('dict(' + args.sizer + ')')) # Strategy StClass = APPROACHES[args.approach] cerebro.addstrategy(StClass, **eval('dict(' + args.strat + ')')) # Execute cerebro.run(**eval('dict(' + args.cerebro + ')')) if args.plot: # Plot if requested to cerebro.plot(**eval('dict(' + args.plot + ')')) def parse_args(pargs=None): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=( 'Stop-Loss Approaches' ) ) parser.add_argument('--data0', default='../../datas/2005-2006-day-001.txt', required=False, help='Data to read in') # Strategy to choose parser.add_argument('approach', choices=APPROACHES.keys(), help='Stop approach to use') # Defaults for dates parser.add_argument('--fromdate', required=False, default='', help='Date[time] in YYYY-MM-DD[THH:MM:SS] format') parser.add_argument('--todate', required=False, default='', help='Date[time] in YYYY-MM-DD[THH:MM:SS] format') parser.add_argument('--cerebro', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--broker', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--sizer', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--strat', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--plot', required=False, default='', nargs='?', const='{}', metavar='kwargs', help='kwargs in key=value format') return parser.parse_args(pargs) if __name__ == '__main__': runstrat()
开发递归指标（带种子）
原文：
www.backtrader.com/blog/posts/2018-01-27-recursive-indicators/recursive-indicator/
backtrader 的最初目标之一是：
- 能够快速原型化指标以测试新想法
它不必是一个完美的指标，但能够快速轻松地开发它们确实有所帮助。为了确认设计是正确的，backtrader标准武器库中的第一个指标之一是指数移动平均线（又称EMA），根据定义是：递归。
注意
小知识：您可能会想象第一个指标是简单移动平均线
由于在backtrader 社区中发布了如何开发递归指标的问题，让我们快速开发一个ExponentialMovingAverage指标。
像这样的递归指标
- 它使用先前的值来计算当前值
您可以在Wikipedia - 指数移动平均线中看到数学示例
如果您足够勇敢读完所有内容，您会发现期间用于计算指数平滑。我们将使用它。
为了解决计算第一个值的难题，行业决定使用前period个值的简单平均值。
作为杠杆，我们将使用bt.indicators.PeriodN，它：
- 已经定义了一个
period参数 - 通知框架关于最终用户使用的实际
period
在此处查看其定义：文档 - 指标参考
然后让我们开发我们的EMA
import backtrader as bt class EMA(bt.indicators.PeriodN): params = {'period': 30} # even if defined, we can redefine the default value lines = ('ema',) # our output line def __init__(self): self.alpha = 2.0 / (1.0 + self.p.period) # period -> exp smoothing factor def nextstart(self): # calculate here the seed value self.lines.ema[0] = sum(self.data.get(size=self.p.period)) / self.p.period def next(self): ema1 = self.lines.ema[-1] # previous EMA value self.lines.ema[0] = ema1 * (1.0 - self.alpha) + self.data[0] * self.alpha
几乎比说更容易。关键在于在nextstart中提供种子值，其中
- 当指标的最小预热期满足时将被调用一次。
与next相反，它将为系统传递的每个新数据值调用
nextstart的默认实现只是将工作委托给next，对于大多数指标（例如简单移动平均线）来说，这是正确的做法。但在这种情况下，重写并提供种子值是关键。
将其与数据一起绘制
作为一个移动平均线，如果指标绘制在计算平均值的数据的同一轴上会很好。因为我们从PeriodN继承了绘图的默认值（在文档中查看）：
subplot=True
这当然意味着我们的指标将创建一个subplot（图表上的另一个轴）。这可以很容易地被覆盖。
import backtrader as bt class EMA(bt.indicators.PeriodN): plot = dict(subplot=False)
完成。如果您想控制更多绘图选项，请查看文档 - 绘图
祝你好运！
2017
道琼斯工业平均指数 10 天连涨
原文：
www.backtrader.com/blog/posts/2017-08-08-dow-10-day-streak/dow-10-day-streak/
这已经成为新闻了。道琼斯工业平均指数创下了历史新高，已经连续上涨了 10 天，创下了 9 次历史新高。例如：
许多人肯定已经注意到道琼斯正在经历这样的连涨，文章只是告诉我们这已成为主流。但是一些问题出现了：
- 这是正常还是非凡的？
- 之后会发生什么？
让我们通过启动 backtrader 来回答这些问题，通过制定一个 分析器 来做到这一点：分析情况并回答问题（有关代码，请参见下文）
我们的样本数据包含5923个交易日。让我们看看当前的连续上涨日在哪里。
这是正常还是非凡的？
执行我们的代码显示，连续10天这样的日子至少是非凡的，如果不是特别的话。
$ ./updaystreaks.py --data 099I-DJI --upstreak hilo=True count rank upstreak upleg upleg % drawdown rel drawdown 1987-01-02 1 1 13 219.069946 0.116193 0.017616 0.171407 2017-02-09 2 2 12 822.109375 0.041074 0.001875 0.047548 1970-11-19 3 2 12 66.900024 0.088986 0.010321 0.127055 1929-06-20 4 2 12 32.000000 0.101716 0.031134 0.340625 1991-12-18 5 3 11 315.100098 0.109167 0.011113 0.113614 1955-01-18 6 3 11 22.200012 0.057290 0.014334 0.265765 2017-07-25 7 4 10 622.289062 0.028949 NaN NaN 2013-03-01 8 4 10 488.959961 0.034801 0.008102 0.240919 1996-11-04 9 4 10 348.839844 0.058148 0.004792 0.087605 1973-07-16 10 4 10 53.600037 0.060695 0.095935 1.686565 1959-11-17 11 4 10 31.599976 0.049945 0.011216 0.237342 1959-06-24 12 4 10 36.200012 0.057680 0.020649 0.381215 1955-08-23 13 4 10 25.400024 0.056344 0.008772 0.165353 1933-03-03 14 4 10 12.600002 0.250497 0.142415 0.730158 1920-12-29 15 4 10 8.099998 0.119118 0.022339 0.209876 2016-07-08 16 5 9 778.378906 0.043688 0.016552 0.396003 1996-05-08 17 5 9 334.369629 0.061755 0.002442 0.041990 1989-07-03 18 5 9 141.890137 0.058804 0.007179 0.129677 1968-04-23 19 5 9 38.000000 0.043123 0.070535 1.736842 1967-04-13 20 5 9 49.700012 0.059061 0.006593 0.118713 1967-01-03 21 5 9 55.799988 0.071603 0.006321 0.094982 1965-01-22 22 5 9 18.500000 0.020838 0.031326 1.540541 1964-03-06 23 5 9 19.600037 0.024506 0.016127 0.678570 1955-06-15 24 5 9 12.399994 0.028343 0.005537 0.201613 1955-04-05 25 5 9 16.299988 0.039553 0.010465 0.276074 1954-09-01 26 5 9 18.599976 0.055822 0.009325 0.177419 1945-04-06 27 5 9 9.000000 0.058140 0.008526 0.155555 1929-02-18 28 5 9 21.800018 0.072812 0.086005 1.279815 1921-10-18 29 5 9 4.300003 0.061871 0.008130 0.139536
当前的连涨，尚未结束，排名（并列）第 4。请注意：
- 连涨时期的上涨幅度在连涨 10 天或更长时间时最小
- 三个连续上涨日为 9 天的日子在百分比方面略小，分别是 1955 年、1964 年和 1965 年
- 今年有另一个长达 12 天的排名第二的连涨
之后会发生什么？
即使表格已经显示了持续上涨日结束后的回撤和相对回撤（从上涨日开始计算，因此可能 > 100%），但最好通过视觉方式来回答问题。
图表很快就会显示出来：
- 这样的长连涨似乎表明了强劲，不会真的预期到有大的回撤作为反应
但是等等！！！
在排名第 1 和第 2 的 remarkable 日期中，我们有：
count rank upstreak upleg upleg % drawdown rel drawdown 1987-01-02 1 1 13 219.069946 0.116193 0.017616 0.171407 2017-02-09 2 2 12 822.109375 0.041074 0.001875 0.047548 1970-11-19 3 2 12 66.900024 0.088986 0.010321 0.127055 1929-06-20 4 2 12 32.000000 0.101716 0.031134 0.340625 ...
的确，因为1987和1929后来确实有非常大的熊市。但是并不是连涨结束后立即发生，如统计数据所示：相对回撤没有超过 100%，因此新高跟随了那些连涨结束后。
代码
from __future__ import (absolute_import, division, print_function, unicode_literals) import argparse import collections import datetime import itertools import matplotlib.pyplot as plt import pandas as pd import backtrader as bt class UpStreak(bt.Analyzer): params = dict( sep=',', hilo=False, ) def __init__(self): self.upday = bt.ind.UpDayBool() self.curdt = None # streak start date self.incs = dict() # upleg in points self.pincs = dict() # upleg in percentage self.close0 = dict() # starting price for upleg self.peaks = collections.deque() # endng price for upleg self.ddown = dict() # absolute drawdowns self.ddownrel = dict() # relative drawdown (% of upleg retraced) self.rets = collections.defaultdict(int) # holds main results def next(self): curclose = self.data.close[0] lastclose = self.data.close[-1] self.peaks.append((None, None)) while True: dt, peak = self.peaks.popleft() if dt is None: break # all elements seen if peak > curclose: # peak not overdone, update drawdown ddown = 1.0 - curclose / peak self.ddown[dt] = max(self.ddown[dt], ddown) self.peaks.append((dt, peak)) # not done yet inc = self.incs[dt] fall = peak - curclose ddownrel = fall / inc self.ddownrel[dt] = max(self.ddownrel[dt], ddownrel) if self.upday: if self.curdt is None: # streak begins self.curdt = self.strategy.datetime.date() if self.p.hilo: lastclose = self.data.low[-1] self.close0[self.curdt] = lastclose self.incs[self.curdt] = inc = curclose - self.close0[self.curdt] self.pincs[self.curdt] = inc / self.close0[self.curdt] self.rets[self.curdt] += 1 # update current streak else: if self.curdt is not None: # streak ends if self.p.hilo: lastclose = self.data.high[-1] inc = self.incs[self.curdt] fall = lastclose - curclose self.ddownrel[self.curdt] = fall / inc self.ddown[self.curdt] = 1.0 - curclose / lastclose self.peaks.append((self.curdt, lastclose)) self.curdt = None def stop(self): s = sorted( self.rets.items(), reverse=True, key=lambda item: (item[1], item[0]) ) # keep it in dict format self.rets = collections.OrderedDict(s) self.s = collections.OrderedDict(s) self.headers = [ 'date', 'count', 'rank', 'upstreak', 'upleg', 'upleg %', 'drawdown', 'rel drawdown', ] i = 0 count = itertools.count(1) last = float('inf') for dt, streak in self.s.items(): if streak < last: i += 1 last = streak ddown = self.ddown.get(dt, None) ddownrel = self.ddownrel.get(dt, None) inc = self.incs.get(dt, None) pinc = self.pincs.get(dt, None) self.s[dt] = [ next(count), i, streak, inc, pinc, ddown, ddownrel ] def get_dataframe(self): return pd.DataFrame.from_items( self.s.items(), orient='index', columns=self.headers[1:], # skip index ) def print_ranking(self): i = 0 last = float('inf') print(self.p.sep.join(self.headers)) for dt, items in self.s.items(): print( self.p.sep.join( str(x) for x in itertools.chain([dt], items) ) ) def runstrat(args=None): args = parse_args(args) cerebro = bt.Cerebro() kwargs = dict() # Data feed kwargs # Parse from/to-date dtfmt, tmfmt = '%Y-%m-%d', 'T%H:%M:%S' for a, d in ((getattr(args, x), x) for x in ['fromdate', 'todate']): if a: strpfmt = dtfmt + tmfmt * ('T' in a) kwargs[d] = datetime.datetime.strptime(a, strpfmt) fromdate = kwargs.get('fromdate', datetime.date.min) store = bt.stores.VChartFile() data = store.getdata(dataname=args.data, **kwargs) cerebro.adddata(data) cerebro.addanalyzer(UpStreak, **eval('dict(' + args.upstreak + ')')) result = cerebro.run() st0 = result[0] a = st0.analyzers.upstreak # Plot some things # pd.set_option('display.max_columns', 500) pd.set_option('display.expand_frame_repr', False) df = a.get_dataframe() up = df['upstreak'] up9 = df[up >= 9] print(up9) up7 = df[up >= 7] x = up7['upstreak'] y = up7['rel drawdown'] * 100.0 plt.scatter(x, y) plt.ylabel('% Relative Drawdown') plt.xlabel('Updays streak') plt.title('DJI Relative Drawdown after N consecutive UpDays') plt.show() # Plot some things y = up7['drawdown'] * 100.0 plt.ylabel('% Absolute Drawdown') plt.xlabel('Updays streak') plt.title('DJI Drawdown after N consecutive UpDays') plt.scatter(x, y) plt.show() def parse_args(pargs=None): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=( 'UpDayStreaks' ) ) parser.add_argument('--data', default='', required=True, help='Data Ticker') parser.add_argument('--fromdate', required=False, default='', help='Date[time] in YYYY-MM-DD[THH:MM:SS] format') parser.add_argument('--todate', required=False, default='', help='Date[time] in YYYY-MM-DD[THH:MM:SS] format') parser.add_argument('--cerebro', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--upstreak', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--strat', required=False, default='', metavar='kwargs', help='kwargs in key=value format') parser.add_argument('--plot', required=False, default='', nargs='?', const='{}', metavar='kwargs', help='kwargs in key=value format') return parser.parse_args(pargs) if __name__ == '__main__': runstrat()
BackTrader 中文文档（十五）（4）https://developer.aliyun.com/article/1505366
