### use your regime_trade(Stockdata, param_2=0.15) as helper function


def strategy(data):
# param data: the data returned from load_data function, xarray.DataArray structure. 
    Stockdata = ... # prepare data for regime_trade input, like you did for single pass
    trades = {}
    for j in logopenmod.keys():
        trades[j] = regime_trade(Stockdata[j].iloc[:,3], 0.15)
    pd_signals = pd.concat(trades.values(), axis=1)
    xr_signals = xr.DataArray(pd_signals, dims=('time', 'asset'))
    is_liquid = data.sel(field="is_liquid") # assume that "stocks" is exactly the same as data is
    return xr_signals * is_liquid
    

Try it with Multi-pass, and change the name in competition_type. Set the start date as below:

weights = qnbk.backtest(
  competition_type = "stocks_nasdaq100",
  load_data = load_data, # if omitted it loads data by competition_type
  lookback_period = 365,
  start_date = "2006-01-01", # set start_date 
  strategy = strategy,
  analyze = True,
)

Regards

series = signals_df.squeeze().rename(asset_name)
return series

After putting it to trades = dict(), you can concatenate trades.values(), which will create pandas.DataFrame of signals by assets:

pd_signals = pd.concat(trades.values(), axis=1)

Then, convert it to xarray.DataArray and pass it as weights to backtester.

import xarray as xr

xr_signals = xr.DataArray(pd_signals, dims=('time', 'asset'))

Hello

An example using pandas
One can work with pandas DataFrames at intermediate steps and at the end convert them to xarray data structures:

def get_price_pct_change(prices):
    prices_pandas = prices.to_pandas()
    assets = data.coords["asset"].values
    for asset in assets:
        prices_pandas[asset] = prices_pandas[asset].pct_change()
    return prices_pandas

prices = data.sel(field="close") * 1.0
prices_pct_change = get_price_pct_change(prices).unstack().to_xarray()