Hello !
I'd like to know how to load 2 kind of data such as 'stocks' and 'index' in order to work with Multi-backtesting_ml.

def load_data(period):
    stocks = qndata.stocks.load_ndx_data(tail=period)
    index = qndata.index.load_data(tail=period)
    
    return stocks, index

weights = qnbt.backtest_ml(
    load_data                     = load_data,
    train                         = train_model,
    predict                       = predict_weights,
    train_period                  =  15 *365,  # the data length for training in calendar days
    retrain_interval              = 1 *365,  # how often we have to retrain models (calendar days)
    retrain_interval_after_submit = 1,        # how often retrain models after submission during evaluation (calendar days)
    predict_each_day              = False,    # Is it necessary to call prediction for every day during backtesting?
                                              # Set it to True if you suspect that get_features is looking forward.
    competition_type              = "stocks_nasdaq100",  # competition type
    lookback_period               = 365,                 # how many calendar days are needed by the predict function to generate the output
    start_date                    = "2006-01-01",        # backtest start date
    analyze                       = True,
    build_plots                   = True  # do you need the chart?
)

What should I do ?

Best regards,

@support
Thanks a lot !

Dear Help,

I have an warning statement.

`WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

I tried to use a virtual environment but, I couldn't .

What should I do ?

Best regards.

@support
Thanks for your reply !
Are you planning to hold long-short stocks ?

Hello,

I'm looking forward to joining the next contest.
What is the next theme ?

Best regards,

@support

Concerning my previous question, I wonder if there was some progress. It would be very helpful if I can implement my strategy in the Google Colaboratory environment. I look forward to hearing from you.

@vyacheslav_b

Thanks for your help !

I have an error below.

What is 'state' ?
What should I give positional argument ?

Best regards,

Hello !
I'd like to know how to load 2 kind of data such as 'stocks' and 'index' in order to work with Multi-backtesting_ml.

def load_data(period):
    stocks = qndata.stocks.load_ndx_data(tail=period)
    index = qndata.index.load_data(tail=period)
    
    return stocks, index

weights = qnbt.backtest_ml(
    load_data                     = load_data,
    train                         = train_model,
    predict                       = predict_weights,
    train_period                  =  15 *365,  # the data length for training in calendar days
    retrain_interval              = 1 *365,  # how often we have to retrain models (calendar days)
    retrain_interval_after_submit = 1,        # how often retrain models after submission during evaluation (calendar days)
    predict_each_day              = False,    # Is it necessary to call prediction for every day during backtesting?
                                              # Set it to True if you suspect that get_features is looking forward.
    competition_type              = "stocks_nasdaq100",  # competition type
    lookback_period               = 365,                 # how many calendar days are needed by the predict function to generate the output
    start_date                    = "2006-01-01",        # backtest start date
    analyze                       = True,
    build_plots                   = True  # do you need the chart?
)

What should I do ?

Best regards,

Hello !

I'd like to rewrite this code for Multi-pass Backtesting to pass the competition filter.
What should I do ?

def pair_trading(data):
    pair = ['NAS:FWLT', 'NAS:PCAR']
    close = data.sel(field='close')
    price = close.sel(asset=pair).transpose('time', 'asset').fillna(0) #欠損値処理
    modelCoint = VECM(price.values, k_ar_diff=0, deterministic='na')
    res = modelCoint.fit()
    pairs_value = res.beta[0]*price.sel(asset=pair[0])+ res.beta[1]*price.sel(asset=pair[1]) #ポートフォリオ価値の作成
    diff = pairs_value.shift(time=-1) - pairs_value
    threshold = diff.std(dim='time')*2 #閾値の設定
    signal = np.sign(diff)
    
    weights = xr.zeros_like(close) #ポートフォリオウェイトの初期化

    for t in data.time:
        if (weights.sel(time=t)==[0,0]).any() and pairs_value.sel(time=t) >= threshold:
            weights.loc[dict(time=t)] = [1,-1]
            
        if (weights.sel(time=t)==[0,0]).any() and pairs_value.sel(time=t) <= -threshold:
            weights.loc[dict(time=t)] = [-1,1]
    
        if (weights.sel(time=t)==[1,-1]).any() and signal.sel(time=t) < 0:
            weights.loc[dict(time=t)] = [0,0]
            
        if (weights.sel(time=t)==[-1,1]).any() and signal.sel(time=t) >= 0:
            weights.loc[dict(time=t)] = [0,0]

    return weights

Best regards

@support
Thanks !

I tried adding the codes below

! sudo apt-get install python3.7 python3.7-distutils python3-pip
! sudo apt-get update -y
! sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
! sudo update-alternatives --config python3 <<< '2'
! sudo python -m pip install --upgrade --force-reinstall pip

but, I can not import 'qnt' packages.

What shoud I do ?