@ThatBlokeDave Thank you. Will give it a try

Most assets have prices well above 0 and hence

if self.data.close > -20

will probably always evaluate to True (the asset in use here could have prices below 0 of course) and therefore it will sell

Additionally, the RSI oscillates between 0 and 100, so this

if self.rsi < -80

is for sure never going to evaluate to True, i.e.: it will never buy

If there is also no control of whether one is in the market, sell orders will be continuously issued.

The code by ab_trader respects the original but controls the position but controls the position. The final outcome will probably be:

One sell No buy ever

@RandyT said in datas time indication off and .resampledata():

Where are we at with ability to .resampledata() from IB to larger timeframes? It was my most recent understanding that we are better off creating separate feed requests rather than resample bt.timeframe.Minutes to bt.timeframe.Days in this case.

Not very far away. Too many other things in the backlog. It is still better, due to how backfilling is handled, to create a separate data feed.

It also would probably be more appropriate for me to be using .replaydata() here as I realize as I type this that I am not going to get a 15:55 bar on the daily feeds, nor will I have updated indicator data to look at at 15:55 without .replaydata(). Is .replaydata() working? I seem to remember that either we should not mix .replaydata() with use of .resampledata() or some question if it was working.

The too many corner cases that the core code tries to catch is the key. Your combinations are stretching the limits of what actually can be done within reasonable limits with live feeds. Synchronizing frozen data feeds was easy and an easy route was chosen for it, rather than putting a more complex mechanism in place.

It's a matter of trial and error. Unfortunately.

A rework of the platform (probably with bcolz although still undecided)

Thanks once again for your patience ...

The real patience is on your side, by checking the unchecked and things that even been committed and not yet tested (you find the bugs even before they are there)

Thanks everyone!

My hack above worked today, so I will take your much more elegant bit of python and replace a few lines. My python foo is still a bit green... Thanks!

Thanks for advice and overall help. It was easy to build and use indicator.

Final result (contains references to external code - main project):

import backtrader as bt from backtrader import num2date from main.lab.data.earning_dates import EarningDates class EarningIndicator(bt.Indicator): params = ( ("earning", None), ("skip_before", 1), ("skip_after", 2), ) lines = ("earning_period",) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.skip_days = [] a = EarningDates(0, self.params.earning, self.params.skip_before, self.params.skip_after) self.skip_days = a.get_skip_dates() def next(self): current_date = num2date(self.data.datetime[0]).date() if current_date in self.skip_days: self.lines.earning_period[0] = 1.0 else: self.lines.earning_period[0] = 0

New positions can be opened closed between T+26 and T+100, but nothing related to expiring option. It doesn't get rolled or anything. I have continuous option prices(until expiration of course).

Figuring/carrying expiration date is not my main concern. I am wondering how to sell at the correct value when it expires, last price of the option doesn't necessarily indicate correct settlement value.

At each next do I check if it is past expiration for all open positions and then close accordingly ? How will the trade go thru if there is no data (because it's past expiration) ?

See Blog - Plotting Date Ranges

Reconsidering this Indicator, it should probably be called something else like Study, Analysis, Post-Analysis, Forensics ...

The reason is that the actual produced value is being stored at in the past -3 (or -self.p.period // 2) instead of at 0.

For an analysis with frozen data the code indicates where the swing points are, but as a practical indicator, it should point out at position 0 that something happened which has to be acted upon ... now.

UPDATE: SOLVED @backtrader could you please delete this post?

Need help from python experts with strategy parameters.
I have a strategy defined as:

class MyStrategy(bt.Strategy): params = (('param1', None), ('param2', None), ('param3', None), ) def __init(self)___: .... def next(self): ....

Also I have function defined for single backtest with strategy_params argument as list:

def single_backtest(strategy_params): cerebro = bt.Cerebro() cerebro.addstrategy(MasterStrategy, ?????) cerebro.adddata(data) strats = cerebro.run() return cerebro

And function call for single backtest as follows:

if __name__ == '__main__': single_run_params = [10, 10, 10] cerebro = single_backtest(single_run_params)

How this strategy_params list can be transformed (if necessary) and passed to .addstrategy() method? So in the future I just add/remove parameters in the strategy and change list in the main.

Check out also this thread where a discussion on the same topic on access by name has also started: Community - Multi-asset ranking and rebalancing

That's probably no code from notify_trade (or else a lot is missing), because you are checking an order from what seems code from a sample notify_order

Two different orders are being processed which may or may not be related at all. Since the price and size components are unknown, it is difficult to evaluate what it's actually being delivered and what the expectation would be.

That's something you know and need not necessarily be true for other use cases. The code is written in a generic way which can also be applied during for example prenext

@jaikumarm The trade chart is possible today with the standard cerebro.plot() function if you have the appropriate analyzers configured.

For reference if anyone has a similar issue and comes here through google, here is the cleaned up working version

from __future__ import (absolute_import, division, print_function, unicode_literals) import argparse import datetime import backtrader as bt import backtrader.feeds as btfeeds import backtrader.indicators as btind class SMAStrategy(bt.Strategy): params = ( ('period', 10), ('onlydaily', False), ) def __init__(self): self.sma_small_tf = btind.SMA(self.data, period=self.p.period) if not self.p.onlydaily: self.sma_large_tf = btind.SMA(self.data1, period=self.p.period) def nextstart(self): print('--------------------------------------------------') print('nextstart called with len', len(self)) print('--------------------------------------------------') super(SMAStrategy, self).nextstart() def runstrat(): args = parse_args() # Create a cerebro entity cerebro = bt.Cerebro(stdstats=False) # Add a strategy if not args.indicators: cerebro.addstrategy(bt.Strategy) else: cerebro.addstrategy( SMAStrategy, # args for the strategy period=args.period, onlydaily=args.onlydaily, ) tframes = dict(minutes=bt.TimeFrame.Minutes,daily=bt.TimeFrame.Days, weekly=bt.TimeFrame.Weeks, monthly=bt.TimeFrame.Months) # Get the dates from the args fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d') todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d') # Load the Data datapath = args.dataname or '../../datas/2006-day-001.txt' data = bt.feeds.GenericCSVData( dataname=datapath, nullvalue=0.0, dtformat=('%m/%d/%Y'), tmformat=('%H:%M:%S'), timeframe = tframes[args.ltf_resample], compression = args.ltf_comp, fromdate=fromdate, todate=todate, datetime=0, time=1, high=3, low=4, open=2, close=5, volume=6, openinterest=-1 #-1 means not used ) cerebro.resampledata(data, timeframe=tframes[args.ltf_resample], compression=args.ltf_comp) # Handy dictionary for the argument timeframe conversion # Resample the data data2 = bt.feeds.GenericCSVData( dataname=datapath, nullvalue=0.0, timeframe = tframes[args.htf_resample], compression = args.htf_comp, dtformat=('%m/%d/%Y'), tmformat=('%H:%M:%S'), fromdate=fromdate, todate=todate, datetime=0, time=1, high=3, low=4, open=2, close=5, volume=6, openinterest=-1 #-1 means not used ) cerebro.resampledata(data2, timeframe=tframes[args.htf_resample], compression=args.htf_comp) # Run over everything cerebro.run() # Plot the result cerebro.plot(style='bar') def parse_args(): parser = argparse.ArgumentParser( description='Multitimeframe test') parser.add_argument('--dataname', default='', required=False, help='File Data to Load') parser.add_argument('--timeframe', default='weekly', required=False, choices=['daily', 'weekly', 'monhtly'], help='Timeframe to resample to') parser.add_argument('--indicators', action='store_true', help='Wether to apply Strategy with indicators') parser.add_argument('--ltf_comp', default=1, type=int, help='Compression setting for lower time frame resampling') parser.add_argument('--ltf_resample', required=True, help='resample data for LTF. Valid\ intervals:\n monthly\n weekly\n daily\n minutes') parser.add_argument('--htf_comp', default=1, type=int, help='Compression setting for higher time frame resampling') parser.add_argument('--htf_resample', required=True, help='resample data for HTF. Valid\ intervals:\n monthly\n weekly\n daily\n minutes') parser.add_argument('--period', default=10, required=False, type=int, help='Period to apply to indicator') parser.add_argument('--fromdate', '-f', required=True, help='Starting date in YYYY-MM-DD format') parser.add_argument('--todate', '-t', required=True, help='Ending date in YYYY-MM-DD format') return parser.parse_args() if __name__ == '__main__': runstrat()

Thanks @backtrader, yes I understand the definition of prenext() as your quotes above show.. but they don't show how to use it to manage differing timeframes :-)

The sample code looks like it is useful though! It appears all I need after all is this?!

def prenext(self): self.next()

@RandyT said in .resampledata() skips data:

The issue is seen when I backfill static data and for some reason, when using the .resampledata() method, the system is dropping every other bar. This is identical to another issue we were seeing when I was trying to .resampledata() from the InfluxDB feed. In that case, .adddata() produced all bars. .resampledata() dropped every other bar.

This issue was due to not having specified the sessionend for the data, which collided with the algorithm in charge of knowing when the day has come to an end. This was also the reason to add the default sessionend to, at least, all CSV data feeds, which has uncovered that some users were even not actually specifying the incoming timeframe, which was confusing the resampling code.

perfect, so this is it:

class CustomDataLoader(btfeeds.PandasData): lines = ('TOTAL_SCORE','Beta',) params = ( ('openinterest',None), #None= column not present ('TOTAL_SCORE',-1), ('Beta',-1) ) datafields = btfeeds.PandasData.datafields + (['TOTAL_SCORE','Beta'])

One of the main purposes of setting up the community was to have things kept here as a reference for other users to see, rather than (for example) having things scattered across closed tickets in GitHub or as comments in a separate platform for the blog.

As such the best way to seek answers probably is:

Put the code here (use the COMPOSE button to see how to best paste, but the syntax is standard markdown)

Paste the chart which shows the many buys and sells, given it has been produced

The default chart has a plot of the evolution of cash and the total portfolio value

There is no need real need to paste the data unless it may be running for example backwards (like it happened to one user) or you want to show an specific amount of data describing a use case (for example breaking resampling)

Bonus question: How do you an equity curve on a separate chart ?

Blog - BTFD (Buy The F... Dip) shows how to do it by unplotting the data feeds and using only observers

So I can loop through all my data in the Next..

self.datas is an array and you can loop over it.

How do I plot the portfolio performance? instead of backtrader printing 1000 different charts...

Depends on what you have defined as performance for your portfolio. There is for example a TimeReturn observer which is intended to plot exactly what the name implies.

If you want to plot other performance indicator, you will need to define it as an indicator or observer

The default is to plot the evolution of the cash and the actual portfolio value which should give a quick indication as to where the system is

And only a single chart will be printed per strategy.

Is there an integrated solution to do this in Backtrader?

There are several samples, blog and documentation pages. See for example:

Docs - Benchmarking

Here the performance is defined as the comparison against another asset.