import xarray as xr

import qnt.backtester as qnbt
import qnt.data as qndata
import numpy as np
import pandas as pd
import logging


def load_data(period):
    return qndata.cryptofutures.load_data(tail=period)


def predict_weights(market_data):

    def get_ml_model():
        # you can use any machine learning model
        from sklearn.linear_model import RidgeClassifier
        model = RidgeClassifier(random_state=18)
        return model

    def get_features_dict(data):
        def get_features_for(asset_name):
            data_for_instrument = data.copy(True).sel(asset=[asset_name])

            # Feature 1
            price = data_for_instrument.sel(field="close").ffill('time').bfill('time').fillna(0)  # fill NaN
            price_df = price.to_dataframe()

            # Feature 2
            vol = data_for_instrument.sel(field="vol").ffill('time').bfill('time').fillna(0)  # fill NaN
            vol_df = vol.to_dataframe()

            # Merge dataframes
            for_result = pd.merge(price_df, vol_df, on='time')
            for_result = for_result.drop(['field_x', 'field_y'], axis=1)

            return for_result

        features_all_assets = {}

        asset_all = data.asset.to_pandas().to_list()
        for asset in asset_all:
            features_all_assets[asset] = get_features_for(asset)

        return features_all_assets

    def get_target_classes(data):
        # for classifiers, you need to set classes
        # if 1 then the price will rise tomorrow

        price_current = data.sel(field="close").dropna('time')  # rm NaN
        price_future = price_current.shift(time=-1).dropna('time')

        class_positive = 1
        class_negative = 0

        target_is_price_up = xr.where(price_future > price_current, class_positive, class_negative)
        return target_is_price_up.to_pandas()

    data = market_data.copy(True)

    asset_name_all = data.coords['asset'].values
    features_all_df = get_features_dict(data)
    target_all_df = get_target_classes(data)

    predict_weights_next_day_df = data.sel(field="close").isel(time=-1).to_pandas()

    for asset_name in asset_name_all:
        target_for_learn_df = target_all_df[asset_name]
        feature_for_learn_df = features_all_df[asset_name][:-1]  # last value reserved for prediction

        # align features and targets
        target_for_learn_df, feature_for_learn_df = target_for_learn_df.align(feature_for_learn_df, axis=0,
                                                                              join='inner')

        model = get_ml_model()
        try:
            model.fit(feature_for_learn_df.values, target_for_learn_df)

            feature_for_predict_df = features_all_df[asset_name][-1:]

            predict = model.predict(feature_for_predict_df.values)
            predict_weights_next_day_df[asset_name] = predict
        except:
            logging.exception("model failed")
            # if there is exception, return zero values
            return xr.zeros_like(data.isel(field=0, time=0))

    return predict_weights_next_day_df.to_xarray()


weights = qnbt.backtest(
    competition_type="cryptofutures",
    load_data=load_data,
    lookback_period=18,
    start_date='2014-01-01',
    strategy=predict_weights,
    analyze=True,
    build_plots=True
)

Here is an example with indicators (Sharpe Ratio = 0.8)

 def get_features_for(asset_name):
    data_for_instrument = data.copy(True).sel(asset=[asset_name])

    # Feature 1
    price = data_for_instrument.sel(field="close")
    price = qnt.ta.roc(price, 1)
    price = price.ffill('time').bfill('time').fillna(0)
    price_df = price.to_pandas()

    # Feature 2
    vol = data_for_instrument.sel(field="vol")
    vol = vol.ffill('time').bfill('time').fillna(0)  # fill NaN
    vol_df = vol.to_pandas()

    # Merge dataframes
    for_result = pd.merge(price_df, vol_df, on='time')

    return for_result

def get_features(data):
        # let's come up with features for machine learning
        # take the logarithm of closing prices
        def remove_trend(prices_pandas_):
            prices_pandas = prices_pandas_.copy(True)
            assets = prices_pandas.columns
            print(assets)
            for asset in assets:
                print(prices_pandas[asset])
                prices_pandas[asset] = np.log(prices_pandas[asset])
            return prices_pandas
        
        # Feature 1
        price = data.sel(field="close").ffill('time').bfill('time').fillna(0) # fill NaN
        price_df = price.to_dataframe()
        
        # Feature 2
        vol = data.sel(field="vol").ffill('time').bfill('time').fillna(0) # fill NaN
        vol_df = vol.to_dataframe()
        
        # Merge dataframes
        for_result = pd.merge(price_df, vol_df, on='time')
        for_result = for_result.drop(['field_x', 'field_y'], axis=1)
            
        features_no_trend_df = remove_trend(for_result)
        return features_no_trend_df

Can you help with some code as to what you are suggesting?
Thanks

1. Can you help pls with an example on how to include more than one feature, such as from the fields (OHLCV)?
2. And also from the qnt.ta library?
   I am running into a problem converting the feature set to pandas when there are more than one features.

price = data.sel(field="close").ffill('time').bfill('time').fillna(0) # fill NaN
        for_result = price.to_pandas()

Thank you.

Ok, I'm inspired that this is doable for me.
Btw, thanks to everyone on your team for responding to my support requests & helping me understand the Quantiacs platform in a few short weeks.

Yes, you can continue. The system saves a copy when you submit the strategy.

I hope this one works.
I can keep working on getting a higher Sharpe Ratio, and update the strategy, right?
Thanks.

This strategy correlates with the examples.

The cofactor(correlation factor) must be lower than 0.9 or the Shape Ratio of your strategy must be higher (for the last 3 years).

Try to use the other features: volume, ROC(rate of change), or other technical indicators.

Regards.