atr (and others) indicators not working with resample

[ironhak]

Hi, I'm using minute level data:

Dataframe sample

                              Open     High      Low    Close
Date                                                         
2020-01-01 22:02:00+00:00  1.32463  1.32464  1.32462  1.32463
2020-01-01 22:03:00+00:00  1.32463  1.32466  1.32462  1.32466
2020-01-01 22:04:00+00:00  1.32466  1.32466  1.32463  1.32463
2020-01-01 22:05:00+00:00  1.32465  1.32466  1.32462  1.32462
2020-01-01 22:06:00+00:00  1.32462  1.32470  1.32462  1.32463
...                            ...      ...      ...      ...
2020-01-29 23:55:00+00:00  1.30208  1.30208  1.30208  1.30208
2020-01-29 23:56:00+00:00  1.30207  1.30208  1.30207  1.30208
2020-01-29 23:57:00+00:00  1.30208  1.30208  1.30208  1.30208
2020-01-29 23:58:00+00:00  1.30208  1.30208  1.30203  1.30203
2020-01-29 23:59:00+00:00  1.30202  1.30207  1.30202  1.30207

I need the average daily range, so I thought I could just resample the atr to daily frequency. So I followed the documentation:

def init(self):
        # Average daily range
        self.adr = resample_apply('D', ta.atr, self.data.High, self.data.Low, self.data.Close)

Error output:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~/my_strat/.venv/lib/python3.12/site-packages/backtesting/backtesting.py:150, in Strategy.I(self, func, name, plot, overlay, color, scatter, *args, **kwargs)
    149 try:
--> 150     value = func(*args, **kwargs)
    151 except Exception as e:

File ~/my_strat/.venv/lib/python3.12/site-packages/backtesting/lib.py:322, in resample_apply.<locals>.wrap_func(resampled, *args, **kwargs)
    321 # Resample back to data index
--> 322 if not isinstance(result.index, pd.DatetimeIndex):
    323     result.index = resampled.index

AttributeError: 'numpy.ndarray' object has no attribute 'index'

The above exception was the direct cause of the following exception:

RuntimeError                              Traceback (most recent call last)
Cell In[205], line 73
     70 bt = Backtest(data, smr_01, margin = 1/100)
     71 # import time
     72 # start_time = time.time()
---> 73 stats = bt.run()
     74 # end_time = time.time()
     75 
     76 # time_taken = end_time - start_time
   (...)     82 # print(f"Number of candlesticks: {num_candlesticks}")
     83 # print(f"Candlesticks per second: {candlesticks_per_second}")
     84 bt.plot()

File ~/my_strat/.venv/lib/python3.12/site-packages/backtesting/backtesting.py:1296, in Backtest.run(self, **kwargs)
   1293 broker: _Broker = self._broker(data=data)
   1294 strategy: Strategy = self._strategy(broker, data, kwargs)
-> 1296 strategy.init()
   1297 data._update()  # Strategy.init might have changed/added to data.df
   1299 # Indicators used in Strategy.next()

Cell In[205], line 39, in smr_01.init(self)
     37 self.range_25, self.range_50, self.range_75 = self.I(range_levels, self.daily_high, self.daily_low, overlay=True)
     38 # Average daily range
---> 39 self.adr = resample_apply('D', ta.atr, self.data.High, self.data.Low, self.data.Close)
     40 # self.adr = resample_apply('D', ta.sma, self.data.Close, 14)#, self.data.Low, self.data.Close)
     42 self.adr2 = self.I(average_daily_range, self.data.df)

File ~/my_strat/.venv/lib/python3.12/site-packages/backtesting/lib.py:330, in resample_apply(rule, func, series, agg, *args, **kwargs)
    326     return result
    328 wrap_func.__name__ = func.__name__
--> 330 array = strategy_I(wrap_func, resampled, *args, **kwargs)
    331 return array

File ~/my_strat/.venv/lib/python3.12/site-packages/backtesting/backtesting.py:152, in Strategy.I(self, func, name, plot, overlay, color, scatter, *args, **kwargs)
    150     value = func(*args, **kwargs)
    151 except Exception as e:
--> 152     raise RuntimeError(f'Indicator "{name}" error. See traceback above.') from e
    154 if isinstance(value, pd.DataFrame):
    155     value = value.values.T

RuntimeError: Indicator "atr(H[D],L,C)" error. See traceback above.

However I don't get any error with:

self.sma = resample_apply('D', ta.sma, self.data.Close, 14)

So, again following the docs, I tried doing it myself:

def average_daily_range(df, period):

    df_resampled = df.resample('D', label='right').agg({'High': 'max', 'Low': 'min', 'Close': 'last'})
    print(df_resampled)
    df_resampled.dropna()
    atr = ta.atr(df_resampled['High'], df_resampled['Low'], df_resampled['Close'], period)
    atr = atr.reindex(df.index).ffill()
    return atr

class smr_01(Strategy):

    def init(self):
        # Average daily range
        # self.adr = resample_apply('D', ta.atr, self.data.High, self.data.Low, self.data.Close)
        self.sma = resample_apply('D', ta.sma, self.data.Close, 14)
        self.adr = self.I(average_daily_range, self.data.df, 14)

Resampled df:

                              High      Low    Close
Date                                                
2020-01-02 00:00:00+00:00  1.32608  1.32457  1.32497
2020-01-03 00:00:00+00:00  1.32661  1.31152  1.31467
2020-01-04 00:00:00+00:00  1.31600  1.30531  1.30787
2020-01-05 00:00:00+00:00      NaN      NaN      NaN
2020-01-06 00:00:00+00:00  1.30855  1.30633  1.30768
2020-01-07 00:00:00+00:00  1.31785  1.30638  1.31711
2020-01-08 00:00:00+00:00  1.32120  1.30948  1.31134
2020-01-09 00:00:00+00:00  1.31694  1.30799  1.31051
2020-01-10 00:00:00+00:00  1.31233  1.30126  1.30691
2020-01-11 00:00:00+00:00  1.30968  1.30422  1.30569
2020-01-12 00:00:00+00:00      NaN      NaN      NaN
2020-01-13 00:00:00+00:00  1.30441  1.30287  1.30432
2020-01-14 00:00:00+00:00  1.30450  1.29608  1.29859
2020-01-15 00:00:00+00:00  1.30329  1.29542  1.30211
2020-01-16 00:00:00+00:00  1.30582  1.29850  1.30392
2020-01-17 00:00:00+00:00  1.30828  1.30252  1.30760
2020-01-18 00:00:00+00:00  1.31184  1.30050  1.30058
2020-01-19 00:00:00+00:00      NaN      NaN      NaN
2020-01-20 00:00:00+00:00  1.30071  1.29915  1.30051
2020-01-21 00:00:00+00:00  1.30132  1.29617  1.30035
2020-01-22 00:00:00+00:00  1.30831  1.29952  1.30451
2020-01-23 00:00:00+00:00  1.31525  1.30343  1.31435
2020-01-24 00:00:00+00:00  1.31508  1.30966  1.31186
2020-01-25 00:00:00+00:00  1.31739  1.30565  1.30701
2020-01-26 00:00:00+00:00      NaN      NaN      NaN
2020-01-27 00:00:00+00:00  1.30799  1.30606  1.30606
2020-01-28 00:00:00+00:00  1.31050  1.30395  1.30588
2020-01-29 00:00:00+00:00  1.30649  1.29752  1.30231
2020-01-30 00:00:00+00:00  1.30273  1.29892  1.30207

Now I have a working ATR resampled to daily... But there is a problem, as you may have noticed both sma and atr are resampled daily with a period of 14:

As you see the ATR start on 20 jan 2020 at 12:00, while the SMA start on 17 jan 2020 at 12:00. So am I doing something wrong or is the library that should be updated?

Packages version:

Package                 Version
----------------------- -----------
asttokens               3.0.0
backtesting             0.6.3
bokeh                   3.6.3
comm                    0.2.2
contourpy               1.3.1
cycler                  0.12.1
debugpy                 1.8.13
decorator               5.2.1
executing               2.2.0
fonttools               4.56.0
ipykernel               6.29.5
ipython                 9.0.0
ipython-pygments-lexers 1.1.1
jedi                    0.19.2
jinja2                  3.1.6
jupyter-client          8.6.3
jupyter-core            5.7.2
kiwisolver              1.4.8
markupsafe              3.0.2
matplotlib              3.10.1
matplotlib-inline       0.1.7
mplfinance              0.12.10b0
nest-asyncio            1.6.0
numpy                   2.2.3
packaging               24.2
pandas                  2.2.3
pandas-ta               0.3.14b0
parso                   0.8.4
pexpect                 4.9.0
pillow                  11.1.0
platformdirs            4.3.6
prompt-toolkit          3.0.50
psutil                  7.0.0
ptyprocess              0.7.0
pure-eval               0.2.3
pygments                2.19.1
pyparsing               3.2.1
python-dateutil         2.9.0.post0
pytz                    2025.1
pyyaml                  6.0.2
pyzmq                   26.2.1
setuptools              76.0.0
six                     1.17.0
stack-data              0.6.3
tornado                 6.4.2
traitlets               5.14.3
tzdata                  2025.1
wcwidth                 0.2.13
xyzservices             2025.1.0

[ironhak]

Just to add:

On the left is atr applied to daily data, on the right is the data on the left converted back to minute data, i.e. the following command that I found in the docs:

atr = atr.reindex(df.index).ffill()

As you see the daily atr starts on 18 Jan while the one sampled back to 1m data starts on 20 Jan... tough the values are the same.

[ironhak]

According to docs:

Notice label='right'. If it were set to 'left' (default), the strategy would exhibit look-ahead bias.
But doing:

data2 = data.resample('D',label='right').agg({'Open': 'first', 
                                 'High': 'max', 
                                 'Low': 'min', 
                                 'Close': 'last'})#.dropna()

print(data2)

Gives as output

Open     High      Low    Close
Date                                          
2020-01-02  1.32463  1.32608  1.32457  1.32497
2020-01-03  1.32497  1.32661  1.31152  1.31467
2020-01-04  1.31466  1.31600  1.30531  1.30787 --> Error, no data for this day (saturday)
2020-01-05      NaN      NaN      NaN      NaN
2020-01-06  1.30808  1.30855  1.30633  1.30768
2020-01-07  1.30767  1.31785  1.30638  1.31711
2020-01-08  1.31708  1.32120  1.30948  1.31134
2020-01-09  1.31135  1.31694  1.30799  1.31051
2020-01-10  1.31047  1.31233  1.30126  1.30691
2020-01-11  1.30691  1.30968  1.30422  1.30569 --> Error, no data for this day (saturday)
2020-01-12      NaN      NaN      NaN      NaN
2020-01-13  1.30347  1.30441  1.30287  1.30432
2020-01-14  1.30432  1.30450  1.29608  1.29859
2020-01-15  1.29858  1.30329  1.29542  1.30211
2020-01-16  1.30211  1.30582  1.29850  1.30392
2020-01-17  1.30395  1.30828  1.30252  1.30760
2020-01-18  1.30758  1.31184  1.30050  1.30058 --> Error, no data for this day (saturday)
2020-01-19      NaN      NaN      NaN      NaN
2020-01-20  1.29915  1.30071  1.29915  1.30051
2020-01-21  1.30052  1.30132  1.29617  1.30035
2020-01-22  1.30037  1.30831  1.29952  1.30451
2020-01-23  1.30451  1.31525  1.30343  1.31435
2020-01-24  1.31435  1.31508  1.30966  1.31186
2020-01-25  1.31185  1.31739  1.30565  1.30701 --> Error, no data for this day (saturday)
2020-01-26      NaN      NaN      NaN      NaN
2020-01-27  1.30799  1.30799  1.30606  1.30606
2020-01-28  1.30606  1.31050  1.30395  1.30588
2020-01-29  1.30588  1.30649  1.29752  1.30231
2020-01-30  1.30230  1.30273  1.29892  1.30207

Indeed if I check:

print(data[data.index.date == pd.to_datetime('2020-01-04').date()])

Empty DataFrame
Columns: [Open, High, Low, Close]
Index: []

Instead if I don't specify the label

```python
data2 = data.resample('D').agg({'Open': 'first', 
                                 'High': 'max', 
                                 'Low': 'min', 
                                 'Close': 'last'}).dropna()

Everything works good, and also:

now the sma indicator and atr strart on the same day, which is the intended result. Hence the final resampled atr function would be:

def average_daily_range(df, period):
    # df_resampled = df.resample('D', label='right').agg({'High': 'max', 'Low': 'min', 'Close': 'last'})
    df_resampled = df.resample('D').agg({'High': 'max', 'Low': 'min', 'Close': 'last'})
    df_resampled.dropna(inplace=True)
    atr = ta.atr(df_resampled['High'], df_resampled['Low'], df_resampled['Close'], period)
    atr = atr.reindex(data.index).ffill()

    return atr

So there are two problems that emerged from this issue:

1. Why the ta.atr function doesn't work with resample_apply?
2. Why according to documentation I must use label=right even tough doing so produces a bad result not properly aligned with original data?

[kernc]

1. Why the ta.atr function doesn't work with resample_apply?

AttributeError: 'numpy.ndarray' object has no attribute 'index'

Seems to break here:

backtesting.py/backtesting/lib.py

Lines 319 to 328 in b1a869c

	result = func(resampled, *args, **kwargs)
	if not isinstance(result, pd.DataFrame) and not isinstance(result, pd.Series):
	result = np.asarray(result)
	if result.ndim == 1:
	result = pd.Series(result, name=resampled.name)
	elif result.ndim == 2:
	result = pd.DataFrame(result.T)
	# Resample back to data index
	if not isinstance(result.index, pd.DatetimeIndex):
	result.index = resampled.index

Following the logic, I think the inner branch is missing a trailing else: raise ... clause.
What (shape) does your ta.atr(self.data.High, self.data.Low, self.data.Close) actually return?

---

2. Why according to documentation I must use label=right even tough doing so produces a bad result not properly aligned with original data?

Use of label='right' ensures the labeled bin consists only of data of the preceding period, e.g. when there is no data on a Saturday, the value for Sunday is empty/nan. This prevents look-ahead bias where value at time t inadvertently incorporates future values.

[ironhak]

ta.atr comes from pandas-ta library. ATR is just a single column, like ta.sma.

Use of label='right' ensures the labeled bin consists only of data of the preceding period, e.g. when there is no data on a Saturday, the value for Sunday is empty/nan. This prevents look-ahead bias where value at time t inadvertently incorporates future values.

Yes but on my data there should be data in Sunday. In my minute data (forex) there is data from Sunday 22:00 to Friday 23:00. So the fact that the resampled version using label = 'Right' leaves Sunday empty and Saturday populated is indeed wrong and not consistent with original data. Am I missing something here?

Thanks for your kind response and for this amazing library.

[kernc]

ta.atr comes from pandas-ta library. ATR is just a single column

What its shape, ndim? It looks like this condition holds: ta.atr(h, l, c).ndim not in (1, 2), whereas it shouldn't!

Just to add: ...

On the left is atr applied to daily data, on the right is the data on the left converted back to minute data, i.e. the following command that I found in the docs:

atr = atr.reindex(df.index).ffill()

Note, the lib actually does:

backtesting.py/backtesting/lib.py

Lines 329 to 330 in b1a869c

	result = result.reindex(index=series.index.union(resampled.index),
	method='ffill').reindex(series.index)

Does this help?

[ironhak]

Hello, so:

atr = ta.atr(data.High, data.Low, data.Close)
print(type(atr))
print(atr.shape)

Produces:

<class 'pandas.core.series.Series'>
(31669,)

So why doesn't it work if I do: self.adr = resample_apply('D', ta.atr, self.data.High, self.data.Low, self.data.Close)?

Also the problem about label='right' persists.

[ironhak]

Doing more tests... Resampling candlestick data from 1min to 5min using suggested label='right:

    data5m = data.resample('5min', label='right').agg({
        "Open": "first",
        "High": "max",
        "Low": "min",
        "Close": "last",
    })

Produces:

As you see the end result is a candlestick that starts at 22:05 but contains datas from 22:00 to 22:04. This is wrong because the index refers to the Open datetime, hence the row 22:05 in a 5min timeframe should have:

• Open: market price at 22:05:00
• High: highest market price from 22:05:00 to 22:09:59
• Low: lowest market price from 22:05:00 to 22:09:59
• Close: market price at 22:09:59.

Indeed, if I don't use label='right', i.e.:

    data5m = data.resample('5min').agg({
        "Open": "first",
        "High": "max",
        "Low": "min",
        "Close": "last",
    })

I obtain the intended outcome:

So the resample_apply function:

• Should avoid using label='right' (?)
  • If so, also the documentation snippet should be changed.
• Should be fixed in order to work with indicators like ta.atr.

Edit: I double checked with TradingView charts and they are identical, proving even further we don't need `label='right'`

Resampled data chart:

Tradingview:

[kernc]

As you see the end result is a candlestick that starts at 22:05 but contains datas from 22:00 to 22:04. This is wrong

Thanks for the illustrative example!

You don't learn that the supposed 22:00:00 bar closed at 1.20032 until 22:04:00 bar closes! I don't know what labeling your data source uses, but plotting that info anytime before the complete end of bar 22:04:00 would introduce look-ahead bias.

Likewise, applying a simple passthrough function:

from backtesting import Strategy, Backtest
from backtesting.test import EURUSD

class S(Strategy):
    def init(self):
        resample_apply('1d', lambda x: x, self.data.Close, color='blue')
    def next(self):
        pass

bt = Backtest(EURUSD, S)
_ = bt.run()
bt.plot()

you can see it uses previous complete bar's value as the current value. Had it used the current bar's (potentially incomplete) value, this would introduce look-ahead bias and would redraw / repaint / mislead, like TradingView does.

This part of the issue is "works-as-planned" / wontfix.

---

2. Should be fixed in order to work with indicators like ta.atr.

AttributeError: 'numpy.ndarray' object has no attribute 'index'

Please provide the following output:

>>> atr = ta.atr(h, l, c)
>>> atr.__class__.__mro__
>>> atr
>>> np.ndim(atr)

[ironhak]

Dear kernc, yes I see your point of view. I always used the TradingView and MT4/5 way of labeling data, always been aware of repainting and learned to account for it on my testings.

You don't learn that the supposed 22:00:00 bar closed at 1.20032 until 22:04:00 bar closes!

Yes, the idea is that the Open price is fixed and High, Low, Close changes every new tick until the candle closes. I see that other data providers, like Bloomberg, label the data on the close.

This part of the issue is "works-as-planned" / wontfix.

That's perfectly fine, I'll account for this. Thank's for taking time to explain :)

---

Please provide the following output:

atr = ta.atr(data.High, data.Low, data.Close)
print(atr.__class__.__mro__)
print(atr)
print(np.ndim(atr))

(<class 'pandas.core.series.Series'>, <class 'pandas.core.base.IndexOpsMixin'>, <class 'pandas.core.arraylike.OpsMixin'>, <class 'pandas.core.generic.NDFrame'>, <class 'pandas.core.base.PandasObject'>, <class 'pandas.core.accessor.DirNamesMixin'>, <class 'pandas.core.indexing.IndexingMixin'>, <class 'object'>)
Date
2020-01-01 22:02:00         NaN
2020-01-01 22:03:00         NaN
2020-01-01 22:04:00         NaN
2020-01-01 22:05:00         NaN
2020-01-01 22:06:00         NaN
                         ...   
2020-04-30 23:55:00    0.000128
2020-04-30 23:56:00    0.000122
2020-04-30 23:57:00    0.000125
2020-04-30 23:58:00    0.000127
2020-04-30 23:59:00    0.000125
Name: ATRr_14, Length: 123800, dtype: float64
1

[kernc]

Well, that's confusing. If the result object is already a Series, there's no way I see for it to crash with AttributeError: 'numpy.ndarray' object has no attribute 'index' ... 🤔

[ironhak]

Well... If you manage to find some time you can manage to try for yourself...

pip install pandas-ta

import pandas_ta as ta
from backtesting import Strategy
import backtesting as bt
from backtesting.lib import resample_apply

# Import some minute-level data

class myStrat(Strategy):
  def init(self):
    self.atr = resample_apply('D', ta.atr, self.data.High, self.data.Low, self.data.Close)

...

Sorry if the snippet is not 100% accurate, I'm on my phone. Anyway I'm sure you get what I'm trying to say.