hmm_market_behavior_handle_data_catalyst.py

def handle_data(context, data):
    current_date = get_datetime().date()
    current_time = get_datetime().time()

    # Just one time in a day (first minute)
    if current_time.hour == 0 and current_time.minute == 0 and current_time.second == 0:
        prices = pd.DataFrame()
        volumes = pd.DataFrame()

        try:
            prices = data.history(context.asset,
                fields = 'price',
                bar_count = context.n_periods,
                frequency = context.tf)

            volumes = data.history(context.asset,
                fields = 'volume',
                bar_count = context.n_periods,
                frequency = context.tf)
        except:
            print('NO DATA')

        if prices.shape[0] == context.n_periods and volumes.shape[0] == context.n_periods:
            features = pd.DataFrame()
            features['price'] = prices
            features['volume'] = volumes
            features['last_return'] = features['price'].pct_change()
            features['std_normalized'] = features['price'].rolling(context.std_period).apply(std_normalized)
            features['ma_ratio'] = features['price'].rolling(context.ma_period).apply(ma_ratio)
            features['price_deviation'] = features['price'].rolling(context.price_deviation_period).apply(values_deviation)
            features['volume_deviation'] = features['volume'].rolling(context.volume_deviation_period).apply(values_deviation)

            state = context.random_states[0]
            if features.dropna().shape[0] == (context.n_periods - context.ma_period + 1):
                state = int(context.model.predict(features[context.cols_features].dropna())[-1])
            else:
                print('PROBLEM: features dataframe is too small')

            print('State on ' + str(current_date) + ' ' + str(current_time) + ': ' + str(state))
        
            print('Amount on ' + str(current_date) + ' ' + str(current_time) + ': ' + str(context.portfolio.positions[context.asset].amount))
            print(prices.dropna())
            print(volumes.dropna())

            if context.portfolio.positions[context.asset].amount <= 0 and state in context.long_states:
                print('LONG on ' + str(current_date) + ' ' + str(current_time))
                order_target_percent(context.asset, 1.0 * context.leverage)
                context.best_price_ts = data.current(context.asset, 'close')

            if context.portfolio.positions[context.asset].amount != 0 and state in context.random_states:
                print('CLOSE on ' + str(current_date) + ' ' + str(current_time))
                order_target_percent(context.asset, 0.0)

            if context.portfolio.positions[context.asset].amount >= 0 and state in context.short_states:
                print('SHORT on ' + str(current_date) + ' ' + str(current_time))
                order_target_percent(context.asset, -1.0 * context.leverage)   
                context.best_price_ts = data.current(context.asset, 'close')   

            record(price = prices[-1],
                state = state,
                amount = context.portfolio.positions[context.asset].amount)