Code: import bt

data = bt.get('spy, agg', start='2010-01-01')

s = bt.Strategy('s1', [bt.algos.RunMonthly(), bt.algos.SelectAll(), bt.algos.WeighEqually(), bt.algos.Rebalance()])

test = bt.Backtest(s, data) res = bt.run(test)

AttributeError Traceback (most recent call last) in () 8 bt.algos.Rebalance()]) 9 ---> 10 test = bt.Backtest(s, data) 11 res = bt.run(test)

C:\Users****\Anaconda\lib\site-packages\bt\backtest.pyc in init(self, strategy, data, name, initial_capital, commissions, integer_positions) 120 integer_positions=True): 121 --> 122 if data.columns.duplicated().any(): 123 cols = data.columns[data.columns.duplicated().tolist()].tolist() 124 raise Exception(

AttributeError: 'Index' object has no attribute 'duplicated'

The example SMA Crossover Strategy in the document failed, and the error log is below:

AttributeError: 'Series' object has no attribute 'items'

I Think this is because the WeighTarget does not assign an dict to target.temp['weights'], so I just repacle target.temp['weights'] = w.dropna() with {w.index[i]:w.values[i] for i in range(len(w.index))} maybe there will be a better way.

Hi, I'm a newbie to python, but I'm really interested in using your bt project for backtesting. I tried to follow the instructions --> install Anaconda for Mac (I used the 64bit version, Python 3.4). Everything installs fine and I can use Anaconda's python dist via terminal.

Then, I try to import bt but I get this error:

import bt Traceback (most recent call last): File "", line 1, in File "//anaconda/lib/python3.4/site-packages/bt/init.py", line 1, in import core ImportError: No module named 'core'

What is the core module? I can't find it.

Thanks!

When I run this test, This function test_strategybase_tree_rebalance_level2 failed because of the nan value of c1.weight.

BTW, I port bt and ffn to python3 and run the test with nose, so maybe the port is not so perfect which lead to this bug.

I have used systemic investor toolbox (SIT) package in R and its very good for backtesting. In that package creating signal for long, short or long-short strategy is very easy.

#long only strategy
signal<-ifelse(RSI3 < 30 & CCI20 > -290 & CCI20 < -100 & DEMA10c > -40 & DEMA10c < -20,1,NA)

#short only strategy
signal<-ifelse(RSI3 >30 & CCI20 > -290 & CCI20 > -100 & DEMA10c < -40 & DEMA10c > -20,-1,NA)

#long and short strategy
signal<-ifelse(RSI3 < 30 & CCI20 > -290 & CCI20 < -100 & DEMA10c > -40 & DEMA10c < -20,1,-1)

and then pass the signal to a backtest function

data$weight[] = NA
data$weight[] = signal
models$result = bt.run.share(data, clean.signal=T, trade.summary = TRUE)

It would be easier if bt also has similar simplified functions.

Hi Phillippe, First of all thanks for creating a great package such as BT. I am running into some small hiatus here. Everything seems okay, and I think the data is in Dataframe and it could be shown in a graph. But when I am trying to run Backtest I get an exception message as follow: "

Exception: Cannot allocate capital to 0 because price is 0 as of 2020-06-25 00:00:00`

"

any help would be highly appreciated.

Thanks.

`

Construct the Signal and plotting it

EMA_short = talib.EMA(price_data['tsla'], timeperiod=10).to_frame() EMA_long = talib.EMA(price_data['tsla'], timeperiod=40).to_frame()

signal = EMA_long.copy() signal[EMA_long.isnull()] = 0

signal[EMA_short > EMA_long] = 1 signal[EMA_short < EMA_long] = -1

#Plot the signal, price and MAs combined_df = bt.merge(signal, price_data, EMA_short, EMA_long) combined_df.columns = ['Signal', 'Price', 'EMA_short', 'EMA_long']

combined_df.plot(secondary_y=['Signal'])`

the plot looks okay but I am unable to use bt.run

`

Backtest

#Define the strategy bt_strategy = bt.Strategy('EMA_crossover', [bt.algos.WeighTarget(signal), bt.algos.Rebalance()])

#Create the backtest and run it bt_backtest = bt.Backtest(bt_strategy, price_data) bt_result = bt.run(bt_backtest)

#Plot the backtest result bt_result.plot(title='Backtest result') `

`

Return Error

Exception Traceback (most recent call last) in 6 #Create the backtest and run it 7 bt_backtest = bt.Backtest(bt_strategy, price_data) ----> 8 bt_result = bt.run(bt_backtest) 9 10 #Plot the backtest result

~/opt/anaconda3/lib/python3.8/site-packages/bt/backtest.py in run(*backtests) 26 # run each backtest 27 for bkt in backtests: ---> 28 bkt.run() 29 30 return Result(*backtests)

~/opt/anaconda3/lib/python3.8/site-packages/bt/backtest.py in run(self) 238 239 if not self.strategy.bankrupt: --> 240 self.strategy.run() 241 # need update after to save weights, values and such 242 self.strategy.update(dt)

~/opt/anaconda3/lib/python3.8/site-packages/bt/core.cpython-38-darwin.so in bt.core.Strategy.run()

~/opt/anaconda3/lib/python3.8/site-packages/bt/core.cpython-38-darwin.so in bt.core.AlgoStack.call()

~/opt/anaconda3/lib/python3.8/site-packages/bt/algos.py in call(self, target) 1810 # Turn off updating while we rebalance each child 1811 for item in iteritems(targets): -> 1812 target.rebalance(item[1], child=item[0], base=base, update=False) 1813 1814 # Now update

~/opt/anaconda3/lib/python3.8/site-packages/bt/core.cpython-38-darwin.so in bt.core.StrategyBase.rebalance()

~/opt/anaconda3/lib/python3.8/site-packages/bt/core.cpython-38-darwin.so in bt.core.SecurityBase.allocate()

Exception: Cannot allocate capital to 0 because price is 0 as of 2020-06-25 00:00:00`

Hi, i'm having a issue with to install bt on jupyter notebook. On Google Colab i'm installing without any problem at all. I found the same issue in this session 2 years ago, i did the same steps, but the error persists.

Hi I tried to run the simple example from the doc:

import bt
import pandas as pd

# fetch some data
data = bt.get('spy,agg', start='2010-01-01')
print(data.tail())

s = bt.Strategy('s1', [bt.algos.RunMonthly(),
                       bt.algos.SelectAll(),
                       bt.algos.WeighEqually(),
                       bt.algos.Rebalance()])

# create a backtest and run it
test = bt.Backtest(s, data)
res = bt.run(test)

# first let's see an equity curve
res.plot()

But no graph appears as indicated in the manual. I just get:

<AxesSubplot:title={'center':'Equity Progression'}>

Then with

res.plot_histogram()

I get an error message:

AttributeError: 'Rectangle' object has no property 'normed'

and with:

res.plot_security_weights()

Nothing happens.

Any suggestion ? Thx.

This is odd, because I had installed it successfully before in Anaconda on this machine. But after a fresh Anaconda install, ffn is fine but not bt. Here is what it tells me:

failed to build bt

ERROR: Command errored out with exit status 1:
 command: 'E:\Anaconda3\envs\finance\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\ajryounger\\AppData\\Local\\Temp\\pip-install-p1ipp9lw\\bt\\setup.py'"'"'; __file__='"'"'C:\\Users\\ajryounger\\AppData\\Local\\Temp\\pip-install-p1ipp9lw\\bt\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\ajryounger\AppData\Local\Temp\pip-record-048hfjfz\install-record.txt' --single-version-externally-managed --compile --install-headers 'E:\Anaconda3\envs\finance\Include\bt'
     cwd: C:\Users\ajryounger\AppData\Local\Temp\pip-install-p1ipp9lw\bt\
Complete output (13 lines):
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\bt
copying bt\algos.py -> build\lib.win-amd64-3.7\bt
copying bt\backtest.py -> build\lib.win-amd64-3.7\bt
copying bt\core.py -> build\lib.win-amd64-3.7\bt
copying bt\__init__.py -> build\lib.win-amd64-3.7\bt
running build_ext
building 'bt.core' extension
error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
----------------------------------------

ERROR: Command errored out with exit status 1: 'E:\Anaconda3\envs\finance\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\ajryounger\AppData\Local\Temp\pip-install-p1ipp9lw\bt\setup.py'"'"'; file='"'"'C:\Users\ajryounger\AppData\Local\Temp\pip-install-p1ipp9lw\bt\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record 'C:\Users\ajryounger\AppData\Local\Temp\pip-record-048hfjfz\install-record.txt' --single-version-externally-managed --compile --install-headers 'E:\Anaconda3\envs\finance\Include\bt' Check the logs for full command output.

I am not sure that I **really ** need to install an obsolete MS VC++! I have every one from the 2008 to the 2015-19 versions but not 2014 for whatever reasons.

It's not fatal, because what I really use is ffn, but I though I would let you know. I really do appreciate both libraries and have had students in my financial theory course use bt in the past.

sincerely a. younger

Thank you guys for creating this tool, it has been very useful so far in my testing. I apologize if this is not the right place to be asking this question, but I'm running into an issue.

Testing out a simple strategy of 1.0 weight on a single equity, I'm able to produce results that broadly match a simple returns calculation (price.pct_change().cumsum()). I believe the difference in results is due to re-balancing, but when I try to run the test without the Rebalance algo, I get float division by zero error:

<ipython-input-53-c33459b5d55d> in <module>()
----> 1 res = bt.run(t)

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\bt\backtest.pyc in run(*backtests)
     28         bkt.run()
     29
---> 30     return Result(*backtests)
     31
     32

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\bt\backtest.pyc in __init__(self, *backtests)
    315     def __init__(self, *backtests):
    316         tmp = [pd.DataFrame({x.name: x.strategy.prices}) for x in backtests]
--> 317         super(Result, self).__init__(*tmp)
    318         self.backtest_list = backtests
    319         self.backtests = {x.name: x for x in backtests}

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in __init__(self, *prices)
    644         self._end = self._prices.index[-1]
    645         # calculate stats for entire series
--> 646         self._update(self._prices)
    647
    648     def __getitem__(self, key):

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in _update(self, data)
    653
    654     def _update(self, data):
--> 655         self._calculate(data)
    656         # lookback returns dataframe
    657         self.lookback_returns = pd.DataFrame(

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in _calculate(self, data)
    666         for c in data.columns:
    667             prc = data[c]
--> 668             self[c] = PerformanceStats(prc)
    669
    670     def _stats(self):

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in __init__(self, prices, rf)
     61         self.rf = rf
     62
---> 63         self._update(self.prices)
     64
     65     def set_riskfree_rate(self, rf):

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in _update(self, obj)
     80     def _update(self, obj):
     81         # calc
---> 82         self._calculate(obj)
     83
     84         # update derived structure

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in _calculate(self, obj)
    188         self.daily_mean = r.mean() * 252
    189         self.daily_vol = r.std() * np.sqrt(252)
--> 190         self.daily_sharpe = r.calc_sharpe(rf=self.rf, nperiods=252)
    191         self.daily_sortino = calc_sortino_ratio(r, rf=self.rf, nperiods=252)
    192         self.best_day = r.max()

C:\Users\dyang\AppData\Local\Continuum\Anaconda2\lib\site-packages\ffn\core.pyc in calc_sharpe(returns, rf, nperiod
s, annualize)
   1181
   1182     er = returns.to_excess_returns(rf, nperiods=nperiods)
-> 1183     res = er.mean() / er.std()
   1184
   1185     if annualize:

ZeroDivisionError: float division by zero

I am using the same weights algo as shown in the SMA Cross example:

class WeighTarget(bt.Algo):

	def __init__(self, target_weights):
		self.tw = target_weights

	def __call__(self, target):
		if target.now in self.tw.index:
			w = self.tw.loc[target.now]

			target.temp['weights'] = w.dropna()

		return True

Again, apologies if this is the wrong place to ask, 99% chance I'm just missing something, 1% chance this is a real "Issue"

There is a problem in rebalancing implementation (rebalance() and its sister method). The cash target code goes into a infinite recursive loop if target weights are negative (short positions). (_I've also seen it happen even in a portfolio of positive weights with a commission function which may be causing cash to dip below)

"Exception: Potentially infinite loop detected. This occured while trying to reduce the amount of shares purchased to respect the outlay <= amount rule. This is most likely due to a commission function that outputs a commission that is greater than the amount of cash a short sale can raise."

############# test.py ############### import pandas as pd import bt import datetime as dt import random

date_span = pd.DatetimeIndex(start='10/1/2017', end='10/11/2017', freq='B') numper = len(date_span.values) comms = 0.01

data = [[10,15,20,25,30,35,40,45], [10,10,10,10,20,20,20,20], [20,20,20,30,30,30,40,40], [20,10,20,10,20,10,20,10]] data = [[row[i] for row in data] for i in range(len(data[0]))] #Transpose price = pd.DataFrame(data=data, index=date_span) price.columns = ['a', 'b', 'c', 'd'] #price = price[['a', 'b']]

sig1 = pd.DataFrame(price['a'] >= price['b'] +10, columns=['a']) sig2 = pd.DataFrame(price['a'] < price['b'] +10, columns=['b']) signal = sig1.join(sig2)

signal1 = price.diff(1) > 0 signal2 = price.diff(1) < 0

tw = price.copy() tw.set_value(tw.index,tw.columns,0) # Initialize Set everything to 0

tw[signal1] = -1.0 tw[signal2] = 1.0

s1 = bt.Strategy('long_short', [ bt.algos.WeighTarget(tw), bt.algos.RunDaily(), bt.algos.Rebalance()])

####now we create the Backtest , commissions=(lambda q, p: abs(p * q) * comms) t = bt.Backtest(s1, price, initial_capital=1000000, commissions=(lambda q, p: abs(p * q) * comms) )

####and let's run it! res = bt.run(t) ########################

I would like to execute a long on a set of security and short in a index, like S&P. The short should be the same size of the long position.

Does anyone has a sample of that? Is that possible?

Max Drawdown(MDD) of the .display() may be Daily MDD. But for most asset allocations, the MDD is based on a monthly basis. I hope monthly MDD would be added to result of the backtest.

I am backtesting some momentum strategies with bt package. As you know, momentum strategies require a longer period of data than the trading period. So I added bt.algos.RunAfterDate to my strategies.

I expected bt.backtest.Result should be chaged when bt.algos.RunAfterDate was added. But Start date of Result was still first date of the data. I doubt the accuracy of the CAGR value. Because CAGR is mostly affected by the trading period.

I hope this part will be fixed.

# Pseudocode
# When first date of the data is 2000-01-01 ...
my_strategy = [
    ...
    bt.algos.RunAfterDate("2001-01-01")
    ...
]

# Result of my backtest
Stat     my_strategy
-----    --------------
Start    2000-01-01       # not 2001-01-01
...
CAGR     7.25             # derived based on data period or trading period?

see the '' signal = data > sma '' in example, but if i have a indicator, when indicator < 10, i buy it; indicator > 50, sold it. how to implement this example.

I'm trying to pass a negative weight list using the "WeighTarget" method, but it won't work. When I call "get_security_weights", the weight does not stand at -100%, it keeps moving (backtesting in only one security).

When I set the "WeighTarget" to 100%, it works just fine.

Anyone has a clue why short selling is not working?