def _calculate_arima(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Calculate ARIMA(5,1,0) forecast for next period."""
        try:
            close_prices = data["close"].dropna()

            if len(close_prices) < 50:  # Minimum data for ARIMA
                return {
                    "arima_forecast": None,
                    "arima_direction": "INSUFFICIENT_DATA",
                    "arima_confidence": 0.0,
                }

            # Fit ARIMA model
            model = ARIMA(close_prices, order=ARIMA_ORDER)
            fitted_model = model.fit()

            # Forecast next period
            forecast = fitted_model.forecast(steps=1)
            forecast_value = float(forecast.iloc[0])
            current_price = float(close_prices.iloc[-1])

            # Determine direction
            if forecast_value > current_price * 1.001:  # >0.1% increase
                direction = "BULLISH"
            elif forecast_value < current_price * 0.999:  # >0.1% decrease
                direction = "BEARISH"
            else:
                direction = "NEUTRAL"

            # Calculate confidence based on forecast magnitude
            pct_change = abs((forecast_value - current_price) / current_price)
            confidence = min(pct_change * 100, 1.0)  # Cap at 1.0

            return {
                "arima_forecast": forecast_value,
                "arima_direction": direction,
                "arima_confidence": confidence,
            }

        except Exception as e:
            return {
                "arima_forecast": None,
                "arima_direction": f"ERROR: {str(e)[:50]}",
                "arima_confidence": 0.0,
            }