@gerry You can do it both ways. Using notify_order you are setting up the stop trail after being notified of the first order completing. If you use Bracket it does that job for you.

As I analyzed further, I have an issue with every day's first candle in 125 mins chart. Please refer to the below screenshot, two 125 mins(4th and 5th candles) have formed in the second day's first 30 mins interval.

bt_chart1.png

Strategy code

class SmaCross(bt.Strategy): # list of parameters which are configurable for the strategy params = dict( pfast=10, # period for the fast moving average pslow=30 # period for the slow moving average ) def __init__(self): self.pp = bt.ind.PivotPoint(self.data1) sma1 = bt.ind.SMA(period=self.p.pfast) # fast moving average sma2 = bt.ind.SMA(period=self.p.pslow) # slow moving average # To set the stop price self.atr = bt.indicators.ATR(self.data0, period=20) self.crossover = self.data0.close > self.pp.r1() # crossover signal def next(self): if not self.position: # not in the market if self.crossover > 0: # if fast crosses slow to the upside self.order = self.buy() pdist = self.atr[0] * 1.0 self.pstop = self.data.close[0] - pdist self.log('high- %.2f low - %.2f close - %.2f' % (self.data1.high[0],self.data1.low[0],self.data1.close[0])) else: # in the market pclose = self.data.close[0] pstop = self.pstop if pclose < pstop: self.close() # stop met - get out else: pdist = self.atr[0] * 1.0 # Update only if greater than self.pstop = max(pstop, pclose - pdist)

@ab_trader Thank you for your link to the documents about slices! I was trying to find the minimum of a slice of indicator data and got nothing but errors. It is working now!

@run-out Thank you!

@run-out
Thanks a million, this definately helped me!!!

@tjmille2 said in Historical testing with IB:

Interactive Brokers data feed

IB doesn't have a ready made backtesting/replay tool. Basically, you have to download historical data and run it through your strategy. IB don't offer a real backtesting environment for your Python API.

@doomdaam Sorry I made a mistake. It is not supposed to trade ^TNX at all only VO.

^TNX is the rente sma.

@vladisld Thanks!!, I need to change my font size...

@vladisld Thanks for the reply!

I've adapted your method and and set all property attributes of the position (including size, price, datetime, adjbase, upopened and upclosed) saved from a previous run inside the start() function of an analyser, and it works just the way I wanted. This helps a lot! Cheers!

@thatblokedave it works good if the data source is csv,but what happen if the datasource is real time data,may be the return data0 and data1 is not equal

@jaezilla Hi thanks for your reply! I have taken a similar approach. I calculate the values in strategy and then record them in an customised observer. However, doing so doesn't record the margin on the fly, so you might be making trades when you actually have insufficient funds.

This however does have an upside, in my opinion. It gives you more freedom when you test out strategies. You can simply check if you've been margin-called post-backtesting and estimate the amount of leverage you have left with your margins, instead of discovering on the fly that your strategy is constraint by your margin.

@bigchappa

Backtrader works only when resampling interval is divided by session duration. meaning if session duration is 8 hours, one can resample them to 8 1h candles. I think your last 1 h candle will have only 30 minutes. So BT may not resample the way you expect.

@kjiessar In that analyzer, self.rets does not track trades, it tracks entries which is a list of position size, price, data name and proceeds. Additionally, the position already aggregates multiple executions for multiple trades in a given data name (in notify_order), so there isn't an issue here using datetime as a key. It simply gives you a time series record of all position metrics at each bar in the analysis for all data names.

Here is the full function for context:

def next(self): # super(Transactions, self).next() # let dtkey update entries = [] for i, dname in self._idnames: pos = self._positions.get(dname, None) if pos is not None: size, price = pos.size, pos.price if size: entries.append([size, price, i, dname, -size * price]) if entries: self.rets[self.strategy.datetime.datetime()] = entries self._positions.clear()