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Re: ARIMA Package for Zorro [Re: qin] #489439 05/27/26 19:54 05/27/26 19:54
Joined: Sep 2017 Posts: 304 TipmyPip OP Senior Member
TipmyPip OP Senior Member Joined: Sep 2017 Posts: 304	Dear Qin, I have found for you a nice video that examples for quant development, It is very interesting, and you don't need to try all the ideas at once, but you can start with small steps. https://youtu.be/G57FAI-dkXs?si=0YhmlMeB0me3CGK8 Last edited by TipmyPip; 05/27/26 20:10. ZorroGPT - https://bit.ly/3Gbsm4S

ARIMA Package for Zorro (64bit) [Re: TipmyPip] #489440 05/27/26 22:41 05/27/26 22:41
Joined: Sep 2017 Posts: 304 TipmyPip OP Senior Member
TipmyPip OP Senior Member Joined: Sep 2017 Posts: 304	64bit version of AutoArima Package for Zorro AutoArima 64bit 1. Memory and Pointer Management Functions (13) aa_alloc_vars aa_clear_arima_work aa_copy_vars aa_free_vars aa_prepare_arima_work aa_shift_vars aa_zero_vars copy_arima_model free_arima_model free_arima_work init_arima_model init_arima_work reset_arima_model 2. Basic Math and Array Utilities (13) aa_argmax aa_argmin aa_correlation aa_covariance aa_demean aa_max aa_mean aa_min aa_normalize_minmax aa_standardize aa_stddev aa_sum aa_variance 3. Input Validation and Data Cleaning (11) aa_clip_outliers aa_count_invalid_values aa_fill_missing_forward aa_fill_missing_mean aa_has_invalid_values aa_limit_returns aa_remove_invalid_values aa_replace_zero_prices aa_validate_price_series aa_validate_series aa_winsorize_series 4. Transformations and Differencing (15) aa_boxcox_lambda aa_boxcox_transform aa_difference_once aa_difference_series aa_difference_twice aa_inverse_boxcox_transform aa_inverse_difference aa_inverse_difference_path aa_inverse_log_transform aa_inverse_return_forecast aa_inverse_seasonal_difference aa_log_transform aa_return_transform aa_seasonal_difference_once aa_seasonal_difference_series 5. Stationarity and Differencing Selection (16) aa_adf_pvalue_approx aa_adf_statistic aa_adf_test aa_calculate_d aa_calculate_D aa_is_stationary aa_kpss_pvalue_approx aa_kpss_statistic aa_kpss_test aa_ndiffs aa_ndiffs_heuristic aa_nsdiffs aa_nsdiffs_heuristic aa_pp_pvalue_approx aa_pp_statistic aa_pp_test 6. Autocorrelation and Partial Autocorrelation (10) aa_acf aa_acf_cutoff_lag aa_autocorrelation aa_autocovariance aa_initial_ar_from_pacf aa_initial_ma_from_acf aa_levinson_durbin aa_pacf aa_pacf_cutoff_lag aa_yule_walker 7. AR / MA Validity and Stability (9) aa_ar_root_modulus aa_clamp_coefficients aa_coefficients_are_valid aa_enforce_invertibility aa_enforce_stationarity aa_is_invertible_ma aa_is_stationary_ar aa_ma_root_modulus aa_min_root_modulus 8. ARMA / ARIMA / SARIMA Fitting Functions (7) aa_arima_fit aa_arma_fit aa_css_fit aa_exact_mle_fit aa_kalman_loglikelihood aa_mle_fit aa_sarima_fit 9. Optimizer Functions (14) aa_adam_fit aa_bfgs_fit aa_check_convergence aa_compute_gradient aa_compute_hessian aa_gradient_descent_fit aa_gradient_norm aa_lbfgs_fit aa_nelder_mead_fit aa_optimizer_report aa_set_optimizer_defaults aa_set_optimizer_learning_rate aa_set_optimizer_max_iter aa_set_optimizer_tolerance 10. Likelihood and Model Scoring (10) aa_aic_score aa_aicc_score aa_aicc_score_general aa_bic_score aa_compare_ic aa_conditional_loglikelihood aa_exact_loglikelihood aa_hqic_score aa_loglikelihood aa_model_score 11. AutoARIMA Search Functions (11) aa_auto_arima_search aa_candidate_exists aa_expand_candidate_models aa_fallback_model aa_grid_search_arima aa_init_candidate aa_select_best_model aa_set_candidate aa_stepwise_auto_arima aa_try_neighbor_models aa_validate_candidate_model 12. Seasonal ARIMA / SARIMA Functions (7) aa_auto_sarima_search aa_detect_seasonal_period aa_sarima_forecast_multi_step aa_sarima_forecast_one_step aa_seasonal_acf_score aa_seasonal_strength aa_stepwise_auto_sarima 13. ARIMAX / SARIMAX Functions (8) aa_arimax_fit aa_arimax_forecast aa_prepare_exogenous_matrix aa_regression_fit aa_regression_predict aa_sarimax_fit aa_sarimax_forecast aa_validate_exogenous_data 14. Forecasting Functions (9) aa_backtransform_forecast aa_bias_adjusted_backtransform aa_forecast_bands aa_forecast_confidence_interval aa_forecast_multi_step aa_forecast_one_step aa_forecast_standard_error aa_forecast_variance aa_integrate_forecast 15. Residual Diagnostics (21) aa_arch_lm_pvalue aa_arch_lm_stat aa_arch_lm_test aa_box_pierce_pvalue aa_box_pierce_stat aa_box_pierce_test aa_compute_residuals aa_diagnostic_report aa_durbin_watson_test aa_jarque_bera_pvalue aa_jarque_bera_stat aa_jarque_bera_test aa_ljung_box_pvalue aa_ljung_box_stat aa_ljung_box_test aa_residual_acf aa_residual_mean aa_residual_normality_check aa_residual_stddev aa_residual_variance aa_residual_white_noise_check 16. Model Reporting and File I/O (8) aa_load_model_from_file aa_print_coefficients aa_print_forecast_report aa_print_model_summary aa_print_residual_diagnostics aa_save_diagnostics_to_file aa_save_forecast_to_file aa_save_model_to_file 17. Model Caching and Reuse (7) aa_cache_best_model aa_clear_model_cache aa_init_model_cache aa_load_cached_model aa_refit_best_model aa_reuse_previous_model aa_should_refit_model 18. Walk-Forward and Forecast Evaluation (8) aa_directional_accuracy aa_forecast_error_mae aa_forecast_error_mape aa_forecast_error_mse aa_forecast_error_rmse aa_rolling_forecast_test aa_train_test_split aa_walk_forward_arima 19. Trading Signal Functions (9) aa_backtest_forecast_signal aa_forecast_edge aa_forecast_return aa_forecast_zscore aa_position_size_from_confidence aa_position_size_from_forecast aa_signal_from_confidence_band aa_signal_from_directional_accuracy aa_signal_from_forecast 20. Zorro Integration Functions (6) aa_zorro_forecast_current_asset aa_zorro_get_close_series aa_zorro_plot_forecast aa_zorro_plot_forecast_bands aa_zorro_print_model aa_zorro_trade_from_forecast 21. Current AutoARIMA Compatibility Functions (7) aa_prepare_auto_arima_work auto_arima_forecast auto_arima_forecast_with_work free_auto_arima_result free_auto_arima_work init_auto_arima_result init_auto_arima_work Last edited by TipmyPip; 05/27/26 22:42. ZorroGPT - https://bit.ly/3Gbsm4S

EUR/USD SMA + ARIMA forecast [Re: TipmyPip] #489441
05/28/26 01:43 05/28/26 01:43

Joined: Sep 2017
Posts: 304

TipmyPip

OP
Senior Member

TipmyPip

OP
Senior Member

Joined: Sep 2017
Posts: 304

This Zorro lite-C script plots a EUR/USD daily chart that combines a traditional SMA indicator with an external ARIMA forecast. It imports selected functions from AutoAri32.dll and defines a minimal AUTO_ARIMA_RESULT structure so the DLL can return the selected ARIMA order, convergence state, forecast value, and AICc score. During each run, the script loads EUR/USD closing prices, calculates a 20-period simple moving average, and skips execution until the LookBack period is complete. The ARIMA model uses the previous 120 completed closing prices to generate a one-step forecast. Before forecasting, the price series is validated, and the result is checked for convergence, invalid values, and a realistic EUR/USD range. If ARIMA fails, the script safely falls back to the previous close. It then plots price, SMA20, ARIMA forecast, and forecast error in pips, while printing diagnostic model statistics to the log.

Code

// Arima01.c
// EUR/USD SMA + ARIMA forecast
// Requires AutoAri32.dll in the same folder as this script.

// Minimal copy of AUTO_ARIMA_RESULT from aa_arima_types.h.
// Field names can be local; order and types must match the DLL.
typedef struct
{
    int p;
    int d;
    int q;
    int converged;

    var sse;
    var aicc;
    var forecast;

    var* ar;
    var* ma;

    int arCap;
    int maCap;

} AUTO_ARIMA_RESULT;

#define PRAGMA_API init_auto_arima_result;AutoAri32!init_auto_arima_result
#define PRAGMA_API free_auto_arima_result;AutoAri32!free_auto_arima_result
#define PRAGMA_API auto_arima_forecast;AutoAri32!auto_arima_forecast
#define PRAGMA_API aa_validate_price_series;AutoAri32!aa_validate_price_series

void init_auto_arima_result(AUTO_ARIMA_RESULT* R);
void free_auto_arima_result(AUTO_ARIMA_RESULT* R);
int auto_arima_forecast(vars Close,int N,var TickSize,int MaxP,int MaxQ,AUTO_ARIMA_RESULT* Result);
int aa_validate_price_series(vars Close,int N);

#define ARIMA_WINDOW 120

function run()
{
    set(PLOTNOW,LOGFILE);

    BarPeriod = 1440;
    LookBack = ARIMA_WINDOW + 50;
    MaxBars = 800;

    PlotWidth = 1200;
    PlotHeight1 = 600;
    PlotBars = 300;

    asset("EUR/USD");

    vars Price = series(priceClose());

    var SMA20 = SMA(Price,20);

    if(is(LOOKBACK))
        return;

    var Forecast = Price[1]; // safe fallback
    int Status = 0;

    AUTO_ARIMA_RESULT R;
    init_auto_arima_result(&R);

    // The library documents price-series validation before model use.
    // It checks positive prices, invalid values, and enough samples.
    if(aa_validate_price_series(Price+1,ARIMA_WINDOW))
    {
        Status = auto_arima_forecast(Price+1,ARIMA_WINDOW,PIP,3,3,&R);

        if(Status and R.converged and !invalid(R.forecast))
        {
            if(R.forecast > 0.5 and R.forecast < 2.0)
                Forecast = R.forecast;
        }
    }

    var ErrorPips = (Price[0] - Forecast) / PIP;

    plot("Close",Price[0],MAIN|LINE,BLUE);
    plot("SMA20",SMA20,MAIN|LINE,RED);
    plot("ARIMA",Forecast,MAIN|LINE,GREEN);
    plot("ErrPips",ErrorPips,NEW|LINE,BLACK);

    printf(
        "\nBar %i Close %.5f SMA20 %.5f ARIMA %.5f Err %.2f pips Status %i p=%i d=%i q=%i AICc %.4f",
        Bar,
        Price[0],
        SMA20,
        Forecast,
        ErrorPips,
        Status,
        R.p,
        R.d,
        R.q,
        R.aicc
    );

    free_auto_arima_result(&R);
}

ZorroGPT - https://bit.ly/3Gbsm4S

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