@support that is really useful! thank you very much!

Thank you! I am looking at the indicator (TAs) documentation right now

You can get the fast stochastic indicator or k with:

stoch_k = qnta.stochastic_k(high, low, close, 14)

To get k and d for both, the fast and slow stochastic indicator:

stoch_fast_k, stoch_fast_d = qnta.stochastic(high, low, close, 14)
stoch_slow_k, stoch_slow_d = qnta.slow_stochastic(high, low, close, 14)

as Investopedia and Wikipedia mentioned, the indicator should be in range of 0-100,
I tried the following

def strategy(data):
    high = data.sel(field="high")
    low = data.sel(field="low")
    close = data.sel(field="close")
    is_liquid = data.sel(field='is_liquid')
    stoch_k = qnta.stochastic_k(high, low, close, 14)
    
    if stoch_k>30 :
        weights = 1
    elif stoch_k<80 :
        weights = -1
    else :
        weights = 0
    
    weights = weights / 10.0
    weights = weights * is_liquid
    return weights

but get the following error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-33-b67077d72c3d> in <module>
     39     strategy= strategy,
     40     analyze= True,
---> 41     build_plots= True
     42 )

~/book/qnt/backtester.py in backtest(competition_type, strategy, load_data, lookback_period, test_period, start_date, end_date, window, step, analyze, build_plots, collect_all_states)
    288     if is_submitted() and args_count > 1:
    289         state = qnstate.read()
--> 290     result = strategy_wrap(data, state)
    291     result, state = unpack_result(result)
    292 

~/book/qnt/backtester.py in <lambda>(d, s)
    268 
    269     args_count = len(inspect.getfullargspec(strategy).args)
--> 270     strategy_wrap = (lambda d, s: strategy(d)) if args_count < 2 else strategy
    271 
    272     # ---

<ipython-input-33-b67077d72c3d> in strategy(data)
     19     stoch_k = qnta.stochastic_k(high, low, close, 14)
     20 
---> 21     if stoch_k>30 :
     22         weights = 1
     23     elif stoch_k<80 :

/usr/local/lib/python3.7/site-packages/xarray/core/common.py in __bool__(self)
    127 
    128     def __bool__(self: Any) -> bool:
--> 129         return bool(self.values)
    130 
    131     def __float__(self: Any) -> float:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I had tried to follow the documentation. and develop a really easy strategy to test out the function of the platform. it simply do a long position when volume in this hour>volume in previous hour but get the following error

import xarray as xr

import qnt.ta as qnta
import qnt.backtester as qnbt
import qnt.data as qndata



def load_hourdata(period):
    return qndata.crypto.load_data(tail = 365 * 5)



def strategy(data):
    crypto_data = qndata.crypto.load_data( tail = 365 * 5)
    BTC_vol = crypto_data.sel(field = 'vol').sel(asset = 'BTC')
    BTC_past_vol= qnta.shift(BTC_vol, periods=1)
    
    weights  = xr.where(BTC_vol > BTC_past_vol, 1, -1) # 1 - long position (**buy**), -1 - short position (**sell**)
    return weights



weights = qnbt.backtest(
    competition_type= "crypto_daily_long_short",
    load_data= load_data,
    lookback_period= 365*4,
    start_date= "2014-01-01",
    strategy= strategy,
    analyze= True,
    build_plots= True
)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-19-dcf17b33384f> in <module>
     29     strategy= strategy,
     30     analyze= True,
---> 31     build_plots= True
     32 )

~/book/qnt/backtester.py in backtest(competition_type, strategy, load_data, lookback_period, test_period, start_date, end_date, window, step, analyze, build_plots, collect_all_states)
    293     log_info("Load data for cleanup...")
    294     data = qndata.load_data_by_type(competition_type, assets=result.asset.values.tolist(), tail=60)
--> 295     result = qnout.clean(result, data)
    296     result.name = competition_type
    297     log_info("Write result...")

~/book/qnt/output.py in clean(output, data, kind, debug)
     66         # uniq asset fix
     67         val,idx = np.unique(output.asset, return_index=True)
---> 68         output = output.isel(asset=idx)
     69 
     70         if single_day:

/usr/local/lib/python3.7/site-packages/xarray/core/dataarray.py in isel(self, indexers, drop, missing_dims, **indexers_kwargs)
   1145         # lists, or zero or one-dimensional np.ndarray's
   1146 
-> 1147         variable = self._variable.isel(indexers, missing_dims=missing_dims)
   1148 
   1149         coords = {}

/usr/local/lib/python3.7/site-packages/xarray/core/variable.py in isel(self, indexers, missing_dims, **indexers_kwargs)
   1130         indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel")
   1131 
-> 1132         indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims)
   1133 
   1134         key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)

/usr/local/lib/python3.7/site-packages/xarray/core/utils.py in drop_dims_from_indexers(indexers, dims, missing_dims)
    838         if invalid:
    839             raise ValueError(
--> 840                 f"Dimensions {invalid} do not exist. Expected one or more of {dims}"
    841             )
    842 

ValueError: Dimensions {'asset'} do not exist. Expected one or more of ('time',)