Indicators/Strategies/Analyzers

• A

  How to plot close price +/- ATR
  • alexgiul  

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  In order to have something automatically plotted in bt it should be data feed, indicator or observer. So you may want to develop an indicator or observer. Docs - Indicator Development DOcs - Observers and Statistics On the other side, formula you are using will not give you a picture shown, it will be only a channel moving together with the close price. It will never cross the price. Hope you understand it and this formula is only first step. Some time ago in order to develop similar picture I developed stop loss/take profit observer. Here is the link, hope it might help Stop loss/Take profit observer
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  Analyzers stats when datas have only trading days
  • exu  

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  @exu said in Analyzers stats when datas have only trading days: just checking on how the built-in analyzers deal with that... I fail to understand what the built-in analyzers have to deal with. They have de-facto default values (the ones mostly used by the industry/people/call-it-X) which you can customize to your liking.
• T

  Set minimum trade time / don't sell until certain amount of time has elapsed
  • tw00000  

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  If you issue it several times in a row before the broker has had a chance to close a position, of course.
• M

  Best practice for date range for Emini Backtest
  • msfinedu  

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  Updating the thread for people facing the same problem. This is directly related to future contracts. IBPy looks for m_includedExpired to be true to dish out expire futures minutes data. A possible solution to circumvent this is provided by backtrader here https://community.backtrader.com/topic/1533/extract-historical-expired-futures-contract-data-from-ib/2
• A

  sharpe ratio wrong?
  • Alain  

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  Sorry but you have, even if your intent was other. There is a huge difference between a thread title like this: Question with regards to the SharpeRatio calculation and this: sharpe ratio wrong? By writing your own calculation to compare results you had already concluded that the results provided by backtrader weren't right, but not that you were not using the analyzer properly. It's not that I believe your post is ill-intentioned, but you had already concluded.
• 

  Zigzag Indicator return "maximum recursion depth exceeded while calling a Python object"
  • yogi wahyu  

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  Hi. Zigzag is the indicator, not a strategy. You should inherit from bt.Strategy and pass this class to cerebro. import backtrader as bt from custom.zigzag import ZigZag class Futures(bt.Strategy): params = ( ('period', 20), ) def __init__(self): self.zigzag = ZigZag() cerebro = bt.Cerebro() data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) cerebro.addstrategy(Futures) cerebro.run() cerebro.plot()
• 

  Hidden Markov Model - Create Indicator & use in strategy
  • Amw_775  

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  You should take 2 (or 3) steps back and develop a simple indicator. From the documentation - Docs - Indicator Development (you should read the entire doc) def next(self): self.lines.dummyline[0] = max(0.0, self.params.value) Now look for that in your code.
• C

  More Backtrader way to calculate covariance matrix
  • cnimativ  

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  @cnimativ said in More Backtrader way to calculate covariance matrix: My current implementation uses a counter to wait out the lookback period and then runs all calculations in the def next() method. You should be using the addminperiod API or subclassing from the indicator PeriodN. @cnimativ said in More Backtrader way to calculate covariance matrix: While it 'works', what is the more backtrader way of doing the same thing? You calculate something in next using numpy. What would you be looking for? @cnimativ said in More Backtrader way to calculate covariance matrix: But how do I access the value of N periods of indicators, run calculations, and then construct cross-asset indicators? You are already using get @cnimativ said in More Backtrader way to calculate covariance matrix: And would it be possible to have, say, an indicator that contains multidimensional matrices? Not a clue what you mean. Indicator have lines. You may of course store anything in a member attribute.
• 

  Plotting divergences
  • pepelepew25  

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  No, there is no such facility.
• 

  Zigzag indicator
  • chuka  

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  @backtrader oke, Thans for helping me, and it work complete. :) this is my fully code: from datetime import datetime import backtrader as bt import math %matplotlib notebook class ZigZag(bt.ind.PeriodN): ''' Identifies Peaks/Troughs of a timeseries ''' lines = ( 'trend', 'last_pivot_t', 'last_pivot_x', 'last_pivot_ago', 'zigzag_peak', 'zigzag_valley', 'zigzag', 'last_zigzag', ) # Fancy plotting name # plotlines = dict(logreturn=dict(_name='log_ret')) plotinfo = dict( subplot=False, plotlinelabels=True, plotlinevalues=True, plotvaluetags=True, ) plotlines = dict( trend=dict(marker='', markersize=0.0, ls='', _plotskip=True), last_pivot_t=dict(marker='', markersize=0.0, ls='', _plotskip=True), last_pivot_x=dict(marker='', markersize=0.0, ls='', _plotskip=True), last_pivot_ago=dict(marker='', markersize=0.0, ls='', _plotskip=True), zigzag_peak=dict(marker='v', markersize=4.0, color='red', fillstyle='full', ls=''), zigzag_valley=dict(marker='^', markersize=4.0, color='red', fillstyle='full', ls=''), zigzag=dict(_name='zigzag', color='blue', ls='-', _skipnan=True), last_zigzag=dict(_name='last_zigzag', color='blue', ls='--', _skipnan=True), ) # update value to standard for Moving Averages params = ( ('period', 2), ('up_retrace', 0.1), ('dn_retrace', 0.1), ('bardist', 0.015), # distance to max/min in absolute perc ) def __init__(self): super(ZigZag, self).__init__() if not self.p.up_retrace: raise ValueError('Upward retracement should not be zero.') if not self.p.dn_retrace: raise ValueError('Downward retracement should not be zero.') if self.p.up_retrace < 0: self.p.up_retrace = -self.p.up_retrace if self.p.dn_retrace > 0: self.p.dn_retrace = -self.p.dn_retrace self.p.up_retrace = self.p.up_retrace / 100 self.p.dn_retrace = self.p.dn_retrace / 100 self.missing_val = float('nan') def prenext(self): self.lines.trend[0] = 0 self.lines.last_pivot_t[0] = 0 self.lines.last_pivot_x[0] = self.data[0] self.lines.last_pivot_ago[0] = 0 self.lines.zigzag_peak[0] = self.missing_val self.lines.zigzag_valley[0] = self.missing_val self.lines.zigzag[0] = self.missing_val self.lines.last_zigzag[0] = self.missing_val def next(self): data = self.data trend = self.lines.trend last_pivot_t = self.lines.last_pivot_t last_pivot_x = self.lines.last_pivot_x last_pivot_ago = self.lines.last_pivot_ago zigzag_peak = self.lines.zigzag_peak zigzag_valley = self.lines.zigzag_valley zigzag = self.lines.zigzag last_zigzag = self.lines.last_zigzag x = data[0] r = x / last_pivot_x[-1] - 1 curr_idx = len(data) - 1 trend[0] = trend[-1] last_pivot_x[0] = last_pivot_x[-1] last_pivot_t[0] = last_pivot_t[-1] last_pivot_ago[0] = curr_idx - last_pivot_t[0] zigzag_peak[0] = self.missing_val zigzag_valley[0] = self.missing_val zigzag[0] = self.missing_val last_zigzag[0] = x if trend[-1] == 0: if r >= self.p.up_retrace: piv = last_pivot_x[0] * (1 - self.p.bardist) zigzag_valley[-int(last_pivot_ago[0])] = piv zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0] trend[0] = 1 last_pivot_x[0] = x last_pivot_t[0] = curr_idx elif r <= self.p.dn_retrace: piv = last_pivot_x[0] * (1 + self.p.bardist) zigzag_peak[-int(last_pivot_ago[0])] = piv zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0] trend[0] = -1 last_pivot_x[0] = x last_pivot_t[0] = curr_idx elif trend[-1] == -1: if r >= self.p.up_retrace: piv = last_pivot_x[0] * (1 - self.p.bardist) zigzag_valley[-int(last_pivot_ago[0])] = piv zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0] trend[0] = 1 last_pivot_x[0] = x last_pivot_t[0] = curr_idx elif x < last_pivot_x[-1]: last_pivot_x[0] = x last_pivot_t[0] = curr_idx elif trend[-1] == 1: if r <= self.p.dn_retrace: piv = last_pivot_x[0] * (1 + self.p.bardist) zigzag_peak[-int(last_pivot_ago[0])] = piv zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0] trend[0] = -1 last_pivot_x[0] = x last_pivot_t[0] = curr_idx elif x > last_pivot_x[-1]: last_pivot_t[0] = curr_idx last_pivot_x[0] = x idx = 1 while idx < len(self.zigzag) and math.isnan(zigzag[-idx]): last_zigzag[-idx] = self.missing_val idx += 1 if idx < len(self.data): last_zigzag[-idx] = zigzag[-idx] class SmaCross(bt.SignalStrategy): params = (('pfast', 10), ('pslow', 30),) def init(self): sma1, sma2 = bt.ind.SMA(period=self.p.pfast), bt.ind.SMA(period=self.p.pslow) self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(sma1, sma2)) cerebro = bt.Cerebro() data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) cerebro.addstrategy(SmaCross) cerebro.addindicator(ZigZag) cerebro.run() cerebro.plot(style='candlestick')
• F

  Multi timeframe indicator plotted twice and in wrong timeframe
  • fino  

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  @backtrader said in Multi timeframe indicator plotted twice and in wrong timeframe: Use coupling: Docs - Mixing Timeframes in Indicators This functionality allows to calculate an indicator on a timeframe an automatically apply the indicator to another lower timeframe. I tried coupling the D1 indicator with the following code as suggested by the guide, but I still get the same error: ValueError: x and y must have same first dimension, but have shapes (54,) and (6,) class LevelsDisplay(bt.Strategy): params = dict(period=30) def __init__(self): # self.data1 = D1 Timeframe levels = Levels(self.data1, period=self.p.period) self.levels = levels() self.levels.plotinfo.subplot = False self.levels.plotinfo.plotmaster = self.data0 I do not see any other places in the Strategy where I could couple. The guide says See how the () is executed with no arguments (in the background a None is being supplied). The following is happening: pivotpoint.s1() is returning an internal LinesCoupler object which follows the rhythm of the larger scope. This coupler fills itself with the latest delivered value from the real s1 (starting with a default value of NaN) How does the Cerebro know which timeframe is the "the larger scope"? Does it analyze all the added data? BTW the Mixing Timeframes guide is where I started and it does not mention plotmaster at all. Maybe it could be useful for newbies if it was somehow mentioned. Just a suggestion.
• 

  Indicator - High since most recent Low
  • sickboy_76au  

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  Thanks that worked .... I had an issue with kicking off the calc at the right time as this was an indicator on another indicator which is an average of another average, so had to find a way to delay the start. but the below is not very pretty but it worked. I think it might be better in a new indicator class but I kept getting a variety of errors with that and my python isn't good enough. Thanks for the help. def __init__(self): #keep a reference to the close self.dataclose = self.datas[0].close self.dataopen = self.datas[0].open self.datahi = self.datas[0].high self.datalo = self.datas[0].low self.datadate = self.datas[0].datetime self.dmi = btind.DirectionalMovement(period = self.params.fastdays) self.dyn_adx_hi = [0,] self.dyn_adx_lo = [0,] def next(self): if len(self.dmi.adx) > (self.params.fastdays)*3+1 and len(self.dmi.adx) <=(self.params.fastdays)*3+2: self.dyn_adx_hi[0] = self.dmi.adx[-1] self.dyn_adx_lo[0] = self.dmi.adx[-1] if len(self.dmi.adx) > (self.params.fastdays)*3+2: if self.dmi.adx[0] > self.dyn_adx_hi[-1] or self.dmi.adx[0] >= self.dmi.adx[-1]: self.dyn_adx_hi[0] = self.dmi.adx[0] self.dyn_adx_lo[0] = self.dyn_adx_lo[-1] elif self.dmi.adx[0] < self.dyn_adx_lo[-1] or self.dmi.adx[0] < self.dmi.adx[-1]: self.dyn_adx_lo[0] = self.dmi.adx[0] self.dyn_adx_hi[0] = self.dyn_adx_hi[-1]
• 

  Problem with Transactions analyzer
  analyzer incorrect • • adrtod  

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  I see. Thanks @backtrader for the answer and for the amazing work!
• A

  Backetest step by step
  • arikenig  

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  Thanks!!
• 

  Initiating Leverage and Margin
  • Robin Dhillon  

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  If you post code, you should have a look at what's right above the title of the post For code/output blocks: Use ``` (aka backtick or grave accent) in a single line before and after the block. See: http://commonmark.org/help/ Because if you don't, code is basically non-readable. You say 2x orders are rejected, but how leverage is configured is not known. What you exactly do is unclear, but you probably want to see this discussion (there are some others you can quickly google). https://community.backtrader.com/topic/1508/leverage-doesn-t-affect-returns-in-backtesting/
• D

  When plotting the EMA12 and EMA26, indicate when the two EMA cross
  • data-analyst  

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  You can also use the MA_CrossOver strategy class in the bt. Strategy parent. Just change the init attributes to accommodate for the EMA function. https://github.com/backtrader/backtrader/blob/master/backtrader/strategies/sma_crossover.py
• 

  Executing Sell and Buy orders of an asset
  • Robin Dhillon  

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  @backtrader I tried executing the trade with what you mentioned above, but I got the best execution using the bracket orders with a risk method controlling the sizes. Really appreciate the timely response, Thank you.
• D

  broker cash and value on separate scales in the plot
  • dongiulio  

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  You can always separate them. Use stdstats=False in cerebro Docs - Cerebro And add the observers your like. There are separate observers for Broker and Value. Docs - Observers and Statistics Docs - Observer Reference
• C

  custom indicators
  • chaitanya goud  

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  See Docs - Indicator Development Medium - Custom Indicator Development in Python with backtrader
• S

  Help with Volume-Weigthed Moving Average (VWMA) indicator needed
  • Søren Pallesen  

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  @søren-pallesen Hi, yes, among many other items I've created a rolling VWAP for backtrader which I've found useful. And as a challenge to myself I did it in four actual lines of code. If you use this you need to pay me by shipping me a box of your local delicacies (although not dried fish or lutefisk). Investopedia has some good entries on VWAP and MVWAP as I recall. ''' Author: B. Bradford MIT License Copyright (c) B. Bradford Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. That they contact me for shipping information for the purpose of sending a local delicacy of their choice native to whatever region they are domiciled. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. class VolumeWeightedAveragePrice(bt.Indicator): plotinfo = dict(subplot=False) params = (('period', 30), ) alias = ('VWAP', 'VolumeWeightedAveragePrice',) lines = ('VWAP',) plotlines = dict(VWAP=dict(alpha=0.50, linestyle='-.', linewidth=2.0)) def __init__(self): # Before super to ensure mixins (right-hand side in subclassing) # can see the assignment operation and operate on the line cumvol = bt.ind.SumN(self.data.volume, period = self.p.period) typprice = ((self.data.close + self.data.high + self.data.low)/3) * self.data.volume cumtypprice = bt.ind.SumN(typprice, period=self.p.period) self.lines[0] = cumtypprice / cumvol super(VolumeWeightedAveragePrice, self).__init__()