@backtrader
My understanding is that self.close will close the entry order. From seeing the code at the link I attached, I assumed that attached bracket orders (SL and/or TP) had to still be cancelled manually (one of them, at least).
Commenting out both the parts of code you highlighted, results in running, and accurate looking, backtest. Could you clarify whether self.close will cancel an attached child order or whether we need to do it manually? Have I set up my ordering correctly?
@run-out
Hey, thank you for that. I plan on transferring one of my factor models over soon so that will be very useful as a start/reference point!
To be honest, I think it's more trouble with opening/closing the bracket orders, and keeping track of those orders. I just want something that I can trust there is no "funny business" going on where it is entering where it shouldn't, orders/trades are being accounted for etc. The "realest" possible backtest. :)
@backtrader
Ok, I see that now. That is the same way you have shown it to be done in the link I pointed out?
Can you give me an example of how to cleanly handle ordering and orders for a multi-data, long and short strategy? Or at least point me in the direction of getting me closer to that.
Thanks in advance.
Hi there,
For a few months now I have been piecing together different parts of a framework with backtrader to handle multiple datas and be able to trade both long and short. However, I keep running into an error. Firstly, I will summarise what the code should achieve, post the code, and then post the error. Any help in helping me get this strategy off the ground would be much appreciated!
Target:
For each data (1D timeframe) in the trading universe:
-> Is it a long environment or a short environment?
If long environment and not in trade already:
Does it meet conditions to take a long?
-> If yes, order with or without tp
-> If no, do nothing
If long environment and in a trade:
Does it meet condition to close trade?
-> If yes, close trade
-> If no, leave trade open / do nothing
Exactly vice versa for the short side logic.
Here is the code I currently have to try to achieve the above:
note: BackTrader is the folder where I keep my own creations, such as indicators.
import backtrader as bt
import math
from BackTrader import indicators
class NHL(bt.Strategy):
params = (
('use_tp', True),
('tp_percent', 12),
('ma_fast_len', 10),
('ma_slow_len', 100),
('ind1_period', 80)
)
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):
self.name = 'Multi_Long_Short'
self.inds = dict()
self.trades = dict()
self.orders = dict()
self.cash = self.broker.get_value()
self.risk = 0.01
self.onePlot = False
for i, d in enumerate(self.datas):
plot_ind1 = True
plot_vstop1 = True
plot_vstop2 = False
self.inds[d] = dict()
self.inds[d]['ind1'] = indicators.Ind1(d, plot=plot_ind1, n=self.p.ind1_period)
self.inds[d]['vstop1'] = indicators.Vol_Stop(d, mult=1, plot=plot_vstop1)
self.inds[d]['vstop2'] = indicators.Vol_Stop(d, mult=2, plot=plot_vstop2)
use_sma = False
if use_sma:
self.inds[d]['fast_ma'] = bt.indicators.SMA(d, period=self.p.ma_fast_len)
self.inds[d]['slow_ma'] = bt.indicators.SMA(d, period=self.p.ma_slow_len)
else:
self.inds[d]['fast_ma'] = bt.indicators.ExponentialMovingAverage(d, period=self.p.ma_fast_len)
self.inds[d]['slow_ma'] = bt.indicators.ExponentialMovingAverage(d, period=self.p.ma_slow_len)
if i > 0:
if self.onePlot:
d.plotinfo.plotmaster = self.datas[0]
def notify_order(self, order):
order_name = order.info.name
d = order.data
data_name = d._name
order_price = order.executed.price
if order.status in [order.Submitted, order.Accepted]:
return
if order.status in [order.Completed]:
self.log('%s, %s, %s, Price: %.5f' % (data_name, self.name, order_name, order_price))
elif order.status in [order.Canceled]:
# if not order.info.name in ['Long SL', 'Short SL', 'Long Entry', 'Short Entry', 'Long TP', 'Short TP']:
self.log('Order Canceled')
elif order.status in [order.Margin, order.Rejected]:
self.log('Order Margin/Rejected')
if not order.alive():
orders = self.orders[d]
idx = orders.index(order)
orders[idx] = None
if all(x is None for x in orders):
orders[:] = []
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f, PORTFOLIO %.2f' %
(trade.pnl, trade.pnlcomm, self.broker.get_value()))
def next(self):
for i, d in enumerate(self.datas):
name = d._name
close = d.close[0]
close1 = d.close[-1]
high = d.high[0]
low = d.low[0]
self.log('{}: {}'.format(name, close))
ind1_1 = self.inds[d]['ind1'].lines.ind1_1[0]
ind1_2 = self.inds[d]['ind1'].lines.ind1_2[0]
ma_fast = self.inds[d]['fast_ma'][0]
ma_slow = self.inds[d]['slow_ma'][0]
vstop1 = self.inds[d]['vstop1'][0]
vstop2 = self.inds[d]['vstop2'][0]
long_environment = ma_fast > ma_slow
short_environment = ma_fast < ma_slow
go_long = close >= ind1_1
go_short = close <= ind1_2
close_long = close < ma_fast
close_short = close > ma_fast
position = self.getposition(d).size
r_percent = self.p.tp_percent / 100
if not position and not self.orders.get(d, None):
if long_environment and go_long:
sl = vstop2 if (close > vstop2) else (low - (close*0.01))
qty = math.floor(self.cash * self.risk / abs(close - sl))
qty = qty if (qty > 0) else 1 # Just in case
tp = close + (close*r_percent)
if self.p.use_tp:
# Open Long with TP
o1 = self.buy(data=d, price=close, size=qty, exectype=bt.Order.Limit, transmit=False)
o2 = self.sell(data=d, price=sl, size=o1.size, exectype=bt.Order.Stop, parent=o1, transmit=False)
o3 = self.sell(data=d, price=tp, size=o1.size, exectype=bt.Order.Limit, parent=o1, transmit=True)
o1.addinfo(name='Long Entry')
o2.addinfo(name='Long SL')
o3.addinfo(name='Long TP')
self.orders[d] = [o1, o2, o3]
else:
# Open Long without TP
o1 = self.buy(data=d, price=close, size=qty, exectype=bt.Order.Limit, transmit=False)
o2 = self.sell(data=d, price=sl, size=o1.size, exectype=bt.Order.Stop, parent=o1, transmit=True)
o1.addinfo(name='Long Entry')
o2.addinfo(name='Long SL')
self.orders[d] = [o1, o2]
elif short_environment and go_short:
sl = vstop2 if (close < vstop2) else high + (close*0.01)
qty = math.floor(self.cash * self.risk / abs(close - sl))
qty = qty if (qty > 0) else 1 # Just in case
tp = close - (close*r_percent)
if self.p.use_tp:
# Open Short with TP
o1 = self.sell(data=d, price=close, size=qty, exectype=bt.Order.Limit, transmit=False)
o2 = self.buy(data=d, price=sl, size=o1.size, exectype=bt.Order.Stop, parent=o1, transmit=False)
o3 = self.buy(data=d, price=tp, size=o1.size, exectype=bt.Order.Limit, parent=o1, transmit=True)
o1.addinfo(name='Short Entry')
o2.addinfo(name='Short SL')
o3.addinfo(name='Short TP')
self.orders[d] = [o1, o2, o3]
else:
# Open Short without TP
o1 = self.sell(data=d, price=close, size=qty, exectype=bt.Order.Limit, transmit=False)
o2 = self.buy(data=d, price=sl, size=o1.size, exectype=bt.Order.Stop, parent=o1, transmit=True)
o1.addinfo(name='Short Entry')
o2.addinfo(name='Short SL')
self.orders[d] = [o1, o2]
elif position:
if (position > 0) and close_long:
# Close Long
long_close = self.close(data=d)
if long_close:
long_close.addinfo(name='Close Long')
self.orders[d].append(long_close)
self.cancel(self.orders[d][1])
elif (position < 0) and close_short:
# Close Short
short_close = self.close(data=d)
if short_close:
short_close.addinfo(name='Close Short')
self.orders[d].append(short_close)
self.cancel(self.orders[d][1])
The above has been pieced together mainly through docs, examples, forum posts, etc.
Here is an example of the error that I am hitting at the moment:
Traceback (most recent call last):
File "/Users/.../Desktop/Python Projects/AlgoTrading/.../backtester.py", line 170, in <module>
run_strat()
File "/Users/.../Desktop/Python Projects/AlgoTrading/.../backtester.py", line 88, in run_strat
run = cerebro.run(stdstats=False, tradehistory=True)
File "/anaconda3/lib/python3.7/site-packages/backtrader/cerebro.py", line 1127, in run
runstrat = self.runstrategies(iterstrat)
File "/anaconda3/lib/python3.7/site-packages/backtrader/cerebro.py", line 1293, in runstrategies
self._runonce(runstrats)
File "/anaconda3/lib/python3.7/site-packages/backtrader/cerebro.py", line 1695, in _runonce
strat._oncepost(dt0)
File "/anaconda3/lib/python3.7/site-packages/backtrader/strategy.py", line 309, in _oncepost
self.next()
File "/Users/.../Desktop/Python Projects/AlgoTrading/.../LT_2.py", line 120, in next
self.cancel(self.orders[d][1])
IndexError: list index out of range
I am struggling to see where it is going wrong in achieving what I have in mind, the ordering and "orderbook" are implemented almost identically to:
https://www.backtrader.com/blog/posts/2017-04-09-multi-example/multi-example/
I believe what I have is very close to being fully functional for my purposes - can anyone help me get past this error and/or achieve what I would like in-full?
Please let me know if I need to provide more code.
Thanks in advance!
@backtrader
Sorry to bring up an old thread, but I'm still having issues with one aspect of the above.
I would like to access the returns information of each data within the strategy. I don't think this is currently possible - could you help me with this?
Thanks in advance!
@run-out
Thanks for your input! Unfortunately, I don't know ahead of time which assets I would like to plot.
I would like to access something rank-able, such as returns of each asset, and make plotting decisions based on that (top-performing, mean, bottom-performing - for example).
Any ideas or tips on achieving that?
Hi there,
I work with a large universe (50+) and when backtesting, plotting the results is an unseeable mess.
I was wondering if it is possible to filter the results and to only plot certain data that meet the filter - a simple example would be the top 3 and bottom 3 performing assets in the backtest.
Any help would be much appreciated!
@backtrader said in Multi-Data Indicator implementation help.:
Before I read the messages. See please the article below and why that isn't a good idea.
- Articles - Momentum Strategy - Next and PreNext
Will do, thank you!
If you could respond to the questions as well, that would be amazing! :)
Sorry for the relentless stream of replies, haha. I have solved the problem, with multiple data (assets).
import backtrader as bt
import numpy as np
class MQN(bt.Indicator):
lines = ('mqn', 'ma',)
params = (
('len', 35),
('ma_len', 20),
)
def __init__(self):
self.addminperiod(self.p.len+1)
def next(self):
for i, d in enumerate(self.datas):
data = d.get(size=self.p.len+1)
changes = []
for i in range(1, len(data)):
changes.append(data[-i] - data[-i-1])
if len(changes) == self.p.len:
avg_chng = np.average(changes)
stdev = np.std(changes)
sqroot = np.sqrt(self.p.len)
mqn = sqroot * (avg_chng/stdev)
self.l.mqn[0] = mqn
ma = self.l.mqn.get(size=self.p.ma_len)
self.l.ma[0] = np.average(ma)
Calling this in my strategy, and passing in the specified data (asset) does the trick and plots each separate indicator, with its respective SMA.
I hope my documentation of progress here has/will be useful to some.
My final questions are:
Is the above code the most efficient/cleanest/best way to go about handling the operations I'm doing? As mentioned earlier, I have several other indicators, similar in nature to this one, so I would like to make sure I really nail the right way before making my way through programming all of them.
Is there a way/hack to eliminate the "instantiation lag" that occurs with indicators in BT? Is it possible to pass in data from before the start of the backtest so that any indicators are already fully instantiated at t=0?
Managed to get something running!
import backtrader as bt
import numpy as np
class MQN4(bt.Indicator):
lines = ('mqn', 'ma',)
params = (
('len', 35),
('ma_len', 20),
)
def __init__(self):
self.addminperiod(self.p.len+1)
def next(self):
d = self.data.get(size=self.p.len+1)
changes = []
for i in range(1, len(d)):
changes.append(d[-i] - d[-i-1])
avg_chng = sum(changes) / self.p.len
stdev = np.std(changes)
sqroot = (self.p.len ** 0.5)
mqn = sqroot * (avg_chng/stdev)
self.l.mqn[0] = mqn
# ma = bt.ind.SMA(self.l.mqn, period=self.p.ma_len)
# self.l.ma[0] = ma
This gives me what I would like! For one asset. How could I modify (or utilise!) 'lines' so that I can have a separate output on each asset? i.e. N separately computed indicators for N assets.
Furthermore, is there any way to remove the "gap" at the beginning, i.e. remove the buffering of the indicator? Is it possible to feed through (t-x) data but begin the backtest at (t)? Or any other way?