@mac0 said in Calling data by date:

Side question: is there a way to compute TimeReturn on each ticker without having to call addanalyzer(TimeReturn, data=cerebro.datas[x]) for each one of them?

No.

@mac0 said in Calling data by date:

I manually cross-checked the number and they seem fine for all dates but the last , i.e. 31st of January 2011. To get to the result given by the analyzer I need to compute the result from the close of 31/12/2010 to the close of 28/01/2011. Why the last day isn't taken into account? I cheat-on-open so everything should be after the close.

It seems your data would end on 2011-01-28. The analyzer doesn't know your data ends there ... and the end of the montha and/or next month has not been seen. It can only calculate it when stopping (stop method) and you can therefore only see that in the corresponding strategy stop method.

@Paska-Houso said in A question about next():

@beejaysea said in A question about next():

self.log('{}: Order ref: {} / Type {} @ {} / Status {}'.format( self.data.datetime.date(0),

You are not logging the dates related to the order statuses. You are logging the actual time at which things are being seen. And this should let you easily answer the question posed above

@Paska-Houso said in A question about next():

How do you get an order submitted, accepted and executed on the same date?

Simply: you are logging things at the same point in time, but not logging the time at which they happened. This is what @ab_trader suggested as the most obvious explanation for the apparent mismatch in prices and execution.The mismatch happens because you compare the order price with the wrong prices.

Okay. I have changed a few things around, and I think I see what's going on here, and where I need to make changes.

Thank you.

@Michael-Scofield: the docs have the same version as the package.

What is missing?

Thank you very much for all your help, much appreciated!

@blonc said in passing data to strategy via an *args:

the bellow issue:

And what's exactly the issue?

That's a well defined and documented behavior of the platform. It has also been discussed before by people who wanted to access data before all warm-up periods were fulfilled and worked using prenext and adding multiple checks.

I encountered the same issue.

python: 3.8.3
backtrader: 1.9.76.123
matplotlib: 3.3.1

you can either downgrade to matplotlib 3.2.2 or refer this pull request to fix it yourself:
https://github.com/mementum/backtrader/pull/418

@backtrader said in Best Approach for Live-Trading With Cheat on Open:

So it seems that I have to supply the --replay parameter in oandav20test.py.

I don't know. What you need is a data source which is tick based. You said you wanted to be notified of each price change. There should be no need to replay anything for that data feed.

Hi,

with samples/ibtest/ibtest.py I did some tests with Interactive Brokers livetrading. These are the results:

ibtest.py --data0 EUR.USD-CASH-IDEALPRO --timeframe Seconds --compression 30 --port 7497 --no-rightedge

***** DATA NOTIF: LIVE LIVE, Data0, 0962, 736801.8864360515, 2018-04-17T17:16:28.074845, 1.23699, 1.23699, 1.23699, 1.23699, 0, 0, 1.23703 LIVE, Data0, 0963, 736801.8866241798, 2018-04-17T17:16:44.329133, 1.237, 1.237, 1.237, 1.237, 0, 0, 1.237046 LIVE, Data0, 0964, 736801.8866485074, 2018-04-17T17:16:46.431042, 1.2371, 1.2371, 1.2371, 1.2371, 0, 0, 1.237034 LIVE, Data0, 0965, 736801.8866486517, 2018-04-17T17:16:46.443504, 1.237, 1.237, 1.237, 1.237, 0, 0, 1.237002 LIVE, Data0, 0966, 736801.886811497, 2018-04-17T17:17:00.513344, 1.23694, 1.23694, 1.23694, 1.23694, 0, 0, 1.237006 LIVE, Data0, 0967, 736801.8868541777, 2018-04-17T17:17:04.200952, 1.23693, 1.23693, 1.23693, 1.23693, 0, 0, 1.236994 LIVE, Data0, 0968, 736801.8869536915, 2018-04-17T17:17:12.798944, 1.23694, 1.23694, 1.23694, 1.23694, 0, 0, 1.236982 LIVE, Data0, 0969, 736801.8871321745, 2018-04-17T17:17:28.219872, 1.23691, 1.23691, 1.23691, 1.23691, 0, 0, 1.236944 LIVE, Data0, 0970, 736801.8871946137, 2018-04-17T17:17:33.614623, 1.23699, 1.23699, 1.23699, 1.23699, 0, 0, 1.236942 LIVE, Data0, 0971, 736801.8871946996, 2018-04-17T17:17:33.622046, 1.23698, 1.23698, 1.23698, 1.23698, 0, 0, 1.23695 LIVE, Data0, 0972, 736801.8871947962, 2018-04-17T17:17:33.630394, 1.23699, 1.23699, 1.23699, 1.23699, 0, 0, 1.236962 LIVE, Data0, 0973, 736801.8872731669, 2018-04-17T17:17:40.401621, 1.23698, 1.23698, 1.23698, 1.23698, 0, 0, 1.23697 LIVE, Data0, 0974, 736801.8872752264, 2018-04-17T17:17:40.579562, 1.23699, 1.23699, 1.23699, 1.23699, 0, 0, 1.236986 LIVE, Data0, 0975, 736801.8874047493, 2018-04-17T17:17:51.770338, 1.237, 1.237, 1.237, 1.237, 0, 0, 1.236988

ibtest.py --data0 EUR.USD-CASH-IDEALPRO --timeframe Seconds --compression 30 --port 7497 --resample

***** DATA NOTIF: LIVE LIVE, Data0, 0962, 736801.8892361111, 2018-04-17T17:20:30.000000, 1.23689, 1.23706, 1.23689, 1.23701, 0.0, 0, 1.2369620000000001 LIVE, Data0, 0963, 736801.8895833333, 2018-04-17T17:21:00.000000, 1.23702, 1.23703, 1.237, 1.23702, 0.0, 0, 1.23696 LIVE, Data0, 0964, 736801.8899305556, 2018-04-17T17:21:30.000000, 1.23701, 1.23707, 1.23699, 1.23707, 0.0, 0, 1.236974 LIVE, Data0, 0965, 736801.8902777778, 2018-04-17T17:22:00.000000, 1.23708, 1.23708, 1.23696, 1.23702, 0.0, 0, 1.237004

ibtest.py --data0 EUR.USD-CASH-IDEALPRO --timeframe Seconds --compression 30 --port 7497 --replay

***** DATA NOTIF: LIVE LIVE, Data0, 0962, 736801.8905911663, 2018-04-17T17:22:27.076771, 1.23694, 1.23694, 1.23694, 1.23694, 0.0, 0, 1.2369999999999999 LIVE, Data0, 0962, 736801.890625, 2018-04-17T17:22:30.000000, 1.23694, 1.23694, 1.23694, 1.23694, 0.0, 0, 1.2369999999999999 LIVE, Data0, 0963, 736801.8908331305, 2018-04-17T17:22:47.982471, 1.23693, 1.23693, 1.23693, 1.23693, 0.0, 0, 1.236982 LIVE, Data0, 0963, 736801.8908331448, 2018-04-17T17:22:47.983708, 1.23693, 1.23696, 1.23693, 1.23696, 0.0, 0, 1.236988 LIVE, Data0, 0963, 736801.8908638399, 2018-04-17T17:22:50.635766, 1.23693, 1.23698, 1.23693, 1.23698, 0.0, 0, 1.236992 LIVE, Data0, 0963, 736801.8908640771, 2018-04-17T17:22:50.656264, 1.23693, 1.23699, 1.23693, 1.23699, 0.0, 0, 1.236994 LIVE, Data0, 0963, 736801.8908658416, 2018-04-17T17:22:50.808718, 1.23693, 1.237, 1.23693, 1.237, 0.0, 0, 1.236996 LIVE, Data0, 0963, 736801.8908685311, 2018-04-17T17:22:51.041084, 1.23693, 1.23701, 1.23693, 1.23701, 0.0, 0, 1.236998 LIVE, Data0, 0963, 736801.8908700076, 2018-04-17T17:22:51.168654, 1.23693, 1.23702, 1.23693, 1.23702, 0.0, 0, 1.2369999999999999 LIVE, Data0, 0963, 736801.8908825091, 2018-04-17T17:22:52.248782, 1.23693, 1.23702, 1.23693, 1.23701, 0.0, 0, 1.236998 LIVE, Data0, 0963, 736801.890961202, 2018-04-17T17:22:59.047850, 1.23693, 1.23702, 1.23693, 1.23702, 0.0, 0, 1.2369999999999999 LIVE, Data0, 0963, 736801.8909623256, 2018-04-17T17:22:59.144933, 1.23693, 1.23704, 1.23693, 1.23704, 0.0, 0, 1.237004 LIVE, Data0, 0963, 736801.8909629615, 2018-04-17T17:22:59.199871, 1.23693, 1.23705, 1.23693, 1.23705, 0.0, 0, 1.2370059999999998 LIVE, Data0, 0963, 736801.890965801, 2018-04-17T17:22:59.445203, 1.23693, 1.23705, 1.23693, 1.23704, 0.0, 0, 1.237004 LIVE, Data0, 0963, 736801.8909722222, 2018-04-17T17:23:00.000000, 1.23693, 1.23705, 1.23693, 1.23704, 0.0, 0, 1.237004 LIVE, Data0, 0964, 736801.8909756911, 2018-04-17T17:23:00.299714, 1.23705, 1.23705, 1.23705, 1.23705, 0, 0, 1.2369999999999999 LIVE, Data0, 0964, 736801.8909989573, 2018-04-17T17:23:02.309913, 1.23705, 1.23705, 1.23704, 1.23704, 0.0, 0, 1.236998 LIVE, Data0, 0964, 736801.8910004616, 2018-04-17T17:23:02.439886, 1.23705, 1.23705, 1.23703, 1.23703, 0.0, 0, 1.236996

It seems to me, that the --replay parameter really delivers what I need:

tick based during live streaming after each candlestick timeframe, len(self.data0) increases by one and also the last call to next() for that block seems to comprise consistent values for OHLC for the whole candlestick.

The logic cannot be elsewhere but next, because it is what will be called on a regular basis.

The difference between creating and operating with indicators is explained here: Docs - Platform Concepts - Stage 1 and Stage 2

You may also look at the lifecycle of the Strategy: Docs - Strategy

It appears that somehow I fixed this in the code, and this behavior stopped happening.

I'm not exactly sure what I did, unfortunately, but here's some stuff I added that seemed to help get clarity and prevent orders from getting placed on top of each other

if len(self._orders) > 1: if datetime - bt.num2date(self._orders[-1].created.dt) < timedelta(minutes=90): print("Orders placed too recently, let's not place more ") self.recent_or_open_orders = 1 else: self.recent_or_open_orders = None elif len(self._orderspending) > 0: self.recent_or_open_orders = 1 print("Current time: {}".format(datetime)) for order in self._orderspending: print("Order {} created at {}".format(order.ref, bt.num2date(order.created.dt))) else: self.recent_or_open_orders = None pos = self.getposition(data).size print(self.recent_or_open_orders) print(pos) if self.recent_or_open_orders == None and not pos: # no market / no orders / no recent orders

@backtrader said in Matching of stopLimit orders:

@sandro-böhme said in Matching of stopLimit orders:

Somehow from the notify methods I assumed that everything will be handled asynchronously but it doesn't

That's a huge mistake. You may think it's an advantage short-term ... but it isn't. The notify_xxx do simply let you know how things have happened. They imply nothing.

That was indeed a big mistake. Thanks for your support @backtrader!

Your original question is understood. It doesn't matter if you ask in in tradingview or in Hawaii.

The answer is already above, but it seems you fail to understand it.

@han-chen said in Stop loss and profit taker orders, which price get hit first? (add example, previous the questions was confusing):

so do you know which price it hits first? 10.5 or 11?

FIFO order execution. Order introduction in the system is not given. No answer is possible.

@han-chen said in Stop loss and profit taker orders, which price get hit first? (add example, previous the questions was confusing):

It could have been opened at 10.8 and go down to 10, go up to 12, then close down at 10.8, this will hit the stop.
Or it could have been opened at 10.8 and go up to 12, go down to 10, then go up and close at 10.8, this will hit the profit target.

Fantastic ... you have perfectly identified and diagnosed your problem: 4-hour bars don't have the resolution you need for the small price differences which you use for your stop-loss/take-profit orders. If the 4-hour bars are the one giving you the signals, you need a smaller timeframe for actual order execution.

Anyone giving you any other answer is giving you false and wrong advice.

Many other people experience the same problem with daily bars and believe that cheat-on-close and/or cheat-on-open is the solution, which it isn't.

Thank you for your answer,

The first thing I apologize, I think I did not know how to put the code well.

The second my code is not secret, its only a test. I put only the backtest running parts because there are too many lines.

I read the documentation before post and had no problem hiding the imported data directly. My problem is that I don't know how to hide daily resampled data in the chart. Perhaps i lose something but I really can't find anything about it. Here I show everything:

from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt # The backtrader platform import backtrader.feeds as btfeeds # For load CSV data. from backtrader import Indicator, CmpEx # For indicators import backtrader.indicators as btind # For indicators import datetime # For datetime objects from datetime import time import matplotlib # For ploting # Symbols to load symbol_list = ['EURUSD','GBPUSD','USDJPY'] # List for storage the loaded datas data_list = [] # Load the data feeds for symbol in symbol_list: # 1 minute data (Each tick for analyze the strategy)_______________________________________________________ data0 = btfeeds.GenericCSVData( dataname='{}_GMT+0_2018-2019_M1.csv'.format(symbol), fromdate=datetime.datetime(2018, 1, 1), todate=datetime.datetime(2018, 1, 31), #(2018, 1, 31) nullvalue=0.0, dtformat=('%d.%m.%Y %H:%M:%S'), datetime=0, open=1, high=2, low=3, close=4, volume=-1, openinterest=-1, timeframe=bt.TimeFrame.Minutes, compression=1, plot = True ) data_list.append(data0) # 30 minute data (Pullback checking and plotting)__________________________________________________________ data1 = btfeeds.GenericCSVData( dataname='{}_GMT+0_2018-2019_M30.csv'.format(symbol), fromdate=datetime.datetime(2018, 1, 1), todate=datetime.datetime(2018, 1, 31), nullvalue=0.0, dtformat=('%d.%m.%Y %H:%M:%S'), datetime=0, open=1, high=2, low=3, close=4, volume=-1, openinterest=-1, timeframe=bt.TimeFrame.Minutes, compression=30, plot = False ) data_list.append(data1) # 1 Day data (For pivot points indicator)_______________________________________________________________ data2 = btfeeds.GenericCSVData( dataname='{}_GMT+0_2018-2019_D1.csv'.format(symbol), fromdate=datetime.datetime(2018, 1, 1), todate=datetime.datetime(2018, 1, 31), nullvalue=0.0, dtformat=('%d.%m.%Y %H:%M:%S'), datetime=0, open=1, high=2, low=3, close=4, volume=-1, openinterest=-1, timeframe=bt.TimeFrame.Days, compression=1, plot = False ) data_list.append(data2) # Assign data feed variables data_eurusd_m1 = data_list[0] data_eurusd_m30 = data_list[1] data_eurusd_d1 = data_list[2] data_gbpusd_m1 = data_list[3] data_gbpusd_m30 = data_list[4] data_gbpusd_d1 = data_list[5] data_usdjpy_m1 = data_list[6] data_usdjpy_m30 = data_list[7] data_usdjpy_d1 = data_list[8] class PivotPoints_V8_Indicator(Indicator): lines = ('PP', 'R1', 'S1', 'R2', 'S2', 'R3', 'S3') #plotinfo = dict(subplot=False) plotinfo = dict(subplot=False) params = (('_autoplot', True),) # attempt to plot on real target data def _plotinit(self): # Try to plot to the actual timeframe master if self.p._autoplot: if hasattr(self.data, 'data'): self.plotinfo.plotmaster = self.data.data #self.plotinfo.plotmaster = self.data.datas[1] # The second Data (M30) # The indicator plots on resampled data* def __init__(self): h = self.data.high # current high l = self.data.low # current low c = self.data.close # current close self.lines.PP = PP = (h + l + c) / 3.0 self.lines.R1 = R1 = 2.0 * PP - l self.lines.S1 = S1 = 2.0 * PP - h self.lines.R2 = R2 = (PP - S1) + R1 self.lines.S2 = S2 = PP - (R1 - S1) self.lines.R3 = (PP - S2) + R2 self.lines.S3 = PP - (R2 - S2) #super(PivotPoint, self).__init__() # enable coopertive inheritance if self.p._autoplot: self.plotinfo.plot = False # disable own plotting self() # Coupler to follow real object def time_in_range(start, end, x): """Return true if x is in the range [start, end]""" if start <= end: return start <= x <= end else: return start <= x or x <= end # The Strategy class PIVOTPOINTS_V8(bt.Strategy): # datas[0] = EURUSD #... # datas[3] = GBPUSD #... # datas[6] = USDJPY #... def log(self, txt, dt=None): ''' Logging function for this strategy''' dt = dt or self.datas[0].datetime.datetime(0) print('%s, %s' % (dt.strftime('%d-%m-%Y %H:%M:%S'), txt)) def __init__(self): # Strategy symbol self.symbol = 'EURUSD.r' #,'GBPUSD.r','USDJPY.r' # Indicator self.pivotindicator = PivotPoints_V8_Indicator(self.datas[3]) # the resampled data #self.pivotindicator.plotinfo.plotlinelabels = True # Data references self.M1_datetime = self.datas[0].datetime self.M1_close = self.datas[0].close self.M30_close = self.datas[1].close self.D1_close = self.datas[2].close self.D1_high = self.datas[2].high self.D1_low = self.datas[2].low # Strategy variables self.pullback_size = 70 self.pullback_candles = 10 #(M30 Candles!) self.op_counters = [0,0,0,0,0,0,0,0,0,0,0,0,0,0] self.send_distance = 70 #(Points) self.open_distance = 20 #(Points) self.lot = 1.5 #(1 lot = 100.000 unitary units) self.stoploss = 100 self.takeprofit = 100 # Keep a reference to the orders #self.order = None def notify_order(self, order): #date = self.data.datetime.datetime().date() date = self.data.datetime.datetime() if order.status == order.Accepted: print('-'*32,' NOTIFY ORDER ','-'*32) print('Order Accepted') print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format( date, order.status, order.ref, order.size, 'NA' if not order.price else round(order.price,5) )) if order.status == order.Completed: print('-'*32,' NOTIFY ORDER ','-'*32) print('Order Completed') print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format( date, order.status, order.ref, order.size, 'NA' if not order.price else round(order.price,5) )) print('Created: {} Price: {} Size: {}'.format(bt.num2date(order.created.dt), order.created.price,order.created.size)) print('-'*80) if order.status == order.Canceled: print('-'*32,' NOTIFY ORDER ','-'*32) print('Order Canceled') print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format( date, order.status, order.ref, order.size, 'NA' if not order.price else round(order.price,5) )) if order.status == order.Rejected: print('-'*32,' NOTIFY ORDER ','-'*32) print('WARNING! Order Rejected') print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format( date, order.status, order.ref, order.size, 'NA' if not order.price else round(order.price,5) )) print('-'*80) def notify_trade(self, trade): date = self.data.datetime.datetime() if trade.isclosed: print('-'*32,' NOTIFY TRADE ','-'*32) print('{}, Close Price: {}, Profit, Gross {}, Net {}'.format( date, trade.price, round(trade.pnl,2), round(trade.pnlcomm,2))) print('-'*80) def next(self): #//////// Trading Time ////////////////////////////////////////////////////////////// start = time(4,0,0) #4,0,0 end = time (20,50,0) #20,50,0 now = self.M1_datetime.time() weekday = self.datas[0].datetime.datetime(0).weekday() if time_in_range(start, end, now) and weekday in [1,2,3,4]: #(Monday=0, Tueasday=1...Sunday=6) # --- Get Data ---------------------------------------------------------------------- # Pivot Points values PP = self.pivotindicator.lines.PP[0] R1 = self.pivotindicator.lines.R1[0] S1 = self.pivotindicator.lines.S1[0] R2 = self.pivotindicator.lines.R2[0] S2 = self.pivotindicator.lines.S2[0] R3 = self.pivotindicator.lines.R3[0] S3 = self.pivotindicator.lines.S3[0] # List of pivot point levels for send orders iteration (Buy and Sell each level) pp_levels = [PP,PP,R1,R1,S1,S1,R2,R2,S2,S2,R3,R3,S3,S3] # Price (M1 Close, Backtrader cant handle tick data for Bid and Ask prices) price = self.M1_close[0] # Point value if 'JPY' in self.symbol: point = 0.001 else: point = 0.00001 # Pullbacks periods_back = self.pullback_candles #(M30 Candles) back_rates = self.M1_close.get(size=periods_back, ago=0) highest = max(back_rates) lowest = min(back_rates) pullback_buy = (highest - price) / point > self.pullback_size pullback_sell = (price - lowest) / point > self.pullback_size # --- Send Orders --------------------------------------------------------------- # Check if any entry condition has been met for i in range(len(self.op_counters)): # BUY iterations on pivot levels if i in [0,2,4,6,8,10,12]: if price > pp_levels[i] and price < (pp_levels[i] + self.send_distance * point) and pullback_buy == True and self.op_counters[i] == 0 : price = pp_levels[i]+(self.open_distance*point) # Price of the limit order sl = price - (self.stoploss*point) tp =price + (self.takeprofit*point) buy_order = self.buy_bracket(limitprice=tp, price=price, stopprice=sl, exectype=bt.Order.Limit, size= self.lot*100000) # Update ticket # Update counter self.op_counters[i] +=1 # SELL iterations on pivot levels elif i in [1,3,5,7,9,11,13]: if price < pp_levels[i] and price > (pp_levels[i] - self.send_distance * point) and pullback_sell == True and self.op_counters[i] == 0 : price = pp_levels[i]-(self.open_distance*point) # Price of the limit order sl = price + (self.stoploss*point) tp =price - (self.takeprofit*point) sell_order = self.sell_bracket(limitprice=tp, price=price, stopprice=sl, exectype=bt.Order.Limit, size= self.lot*100000) # Update ticket # Update counter self.op_counters[i] +=1 # --- Orders Managment ----------------------------------------------------------- else: #//////// Out of trading Time //////////////////////////////////////////////////// # --- Reset ---------------------------------------------------------------------- self.op_counters = [0,0,0,0,0,0,0,0,0,0,0,0,0,0] # --- Close all ---------------------------------------------------------------------- if self.position: self.close() #self.close(data= self.datas[0]) if __name__ == '__main__': # Instantiate the Cerebro engine cerebro = bt.Cerebro(stdstats=False) # remove the standard observers # Add a strategy cerebro.addstrategy(PIVOTPOINTS_V8) # Add the Data Feeds to Cerebro cerebro.adddata(data_eurusd_m1) cerebro.adddata(data_eurusd_m30) cerebro.adddata(data_eurusd_d1) #cerebro.adddata(data_gbpusd_m1) #cerebro.adddata(data_gbpusd_m30) #cerebro.adddata(data_gbpusd_d1) #cerebro.adddata(data_usdjpy_m1) #cerebro.adddata(data_usdjpy_m30) #cerebro.adddata(data_usdjpy_d1) # Resampled data for the indicator cerebro.resampledata(data_eurusd_m1, timeframe=bt.TimeFrame.Days) # Set our desired cash start cerebro.broker.setcash(100000.0) cerebro.broker.set_checksubmit(False) # Set the commission - 25 monetary units per 1 lot (1 lot = 100.000 monetary units), per buy/sell = 0.0125%, total= 0.025% cerebro.broker.setcommission(commission=0.000125) # Add Analyzers #cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='TradeAnalyzer') #cerebro.addanalyzer(bt.analyzers.Returns, _name='Returns') #cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='TimeReturn') #cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions') #cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='PositionsValue') #cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown') #cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn') # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything (loop over the data) results = cerebro.run() strat = results[0] # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) cerebro.plot(style='candle', numfigs=1, barup ='lime', bardown ='red', valuetags=False)

@kriku I am trying to replicate the same scenario for Indian stock . I have a paper account with IB.

from _datetime import datetime import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function for this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() ibstore = bt.stores.IBStore(host='127.0.0.1', port=4002, clientId=7777777) #ibstore = bt.stores.IBStore(host='127.0.0.1', port=4002, clientId=6666666) data = ibstore.getdata(dataname="RELIANCE-STK-SMART-IND", historical=True, fromdate=datetime(2019, 1, 1), todate=datetime(2019, 8, 1), timeframe=bt.TimeFrame.Minutes, compression=5) cerebro.adddata(data) cerebro.addstrategy(TestStrategy) cerebro.broker.setcash(100000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

But this is not giving me any data. Getting below output :

Starting Portfolio Value: 100000.00 Final Portfolio Value: 100000.00

I have the permission enabled in IB to share the live account data with paper account. Am i missing something ?

Seems like pandas data feed doesn't have dtformat parameter, it relates to csv data feed.

Blog - Fund tracking

@run-out You are correct. Needed to go back to the drawing board as a result. Thank you.

Does this have anything to do with the "short interest" indicator? I'm trying to figure out how to create it in backtrader but I don't know if what this post is about is the same thing.

@richard-o-regan Good afternoon. I just joined BackTrader. Long-time futures trader; technical. Years ago with aid of programmers created own linux-based backtesting and then real time trading. Now looking for back testing platform. Found QuantConnect on IB site, but others recommended Backtrader. I will be working with programmer, but based on number of years experience backtesting tens of thousands of iterations, what we did was to rather than merely take output, and rearrange, place all output into a large spd sheet. That included say all 500 iterations for a test, along with all the indiv vars, and columns such as you suggest, % pft tdes, etc. However, with spd sheet, could easily sort on columns, as well as sub-sorts. Regards,

The idea is always to have a generic model, like it is the case with the timezones, in which you may pass pytz instances or something that, more or less, complies to the interface of pytz.

A similar approach will be taken.

The problem is difficult to comprehend, because things seem to work like they:

Snippet1

self.b_bands = bt.ind.BollingerBands() self.rsi = bt.ind.RelativeStrengthIndex(period=self.params.rsi, upperband=70, lowerband=30)

Two (2) indicators are declared in the __init__ method of the Strategy and two (2) indicators show up in the char

BollingerBands which defaults to plotting along the data (because the values from this indicator are meant to be in line with the value) RSI which defaults to be plotted on a subplot because it has fixed values ranging from 0 to 100

Snippet 2

# self.b_bands = bt.ind.BollingerBands() self.rsi = bt.ind.RelativeStrengthIndex(period=self.params.rsi, upperband=70, lowerband=30) self.bb = BBandsIndicator(mode=self.params.mode)

And again two (2) indicators there. The story of RSI is already known.

BBandsIndicator is a direct descendent of Indicator and like most indicators the default is to plot on a subplot

Both charts are ok