// src/screens/ai_quant_lab/QuantModulePanel_Statsmodels.cpp // // Statsmodels panel — 6 econometric analyses (OLS, ARIMA, stationarity, // ACF/PACF, Granger causality, descriptive). Mirrors the GS Quant / Functime // pattern. Extracted from QuantModulePanel.cpp to keep that file maintainable. #include "screens/ai_quant_lab/QuantModulePanel.h" #include "screens/ai_quant_lab/QuantModulePanel_Common.h" #include "screens/ai_quant_lab/QuantModulePanel_GsHelpers.h" #include "screens/ai_quant_lab/QuantModulePanel_Styles.h" #include "services/ai_quant_lab/AIQuantLabService.h" #include "ui/theme/Theme.h" #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace fincept::screens { using namespace fincept::services::quant; using namespace fincept::screens::quant_styles; using namespace fincept::screens::quant_common; using namespace fincept::screens::quant_gs_helpers; // ═══════════════════════════════════════════════════════════════════════════════ // STATSMODELS PANEL — 6 econometric analyses (OLS, ARIMA, stationarity, // ACF/PACF, Granger causality, descriptive). Mirrors the GS Quant / Functime // pattern. // ═══════════════════════════════════════════════════════════════════════════════ QWidget* QuantModulePanel::build_statsmodels_panel() { auto* w = new QWidget(this); auto* vl = new QVBoxLayout(w); vl->setContentsMargins(16, 16, 16, 16); vl->setSpacing(12); auto* tabs = new QTabWidget(w); tabs->setStyleSheet(tab_ss(module_.color.name())); tabs->setSizePolicy(QSizePolicy::Expanding, QSizePolicy::Maximum); // Same LOAD SAMPLE helper shape as functime auto add_sample_btn = [this](QLineEdit* edit, QWidget* parent, unsigned seed, const QString& tip, bool walk = false) -> QPushButton* { auto* btn = new QPushButton(tr("LOAD SAMPLE"), parent); btn->setCursor(Qt::PointingHandCursor); btn->setFixedHeight(22); btn->setToolTip(tip); btn->setStyleSheet( QString("QPushButton { color:%1; background:transparent; border:1px solid %2;" "padding:0 8px; font-size:9px; font-weight:700; letter-spacing:0.5px; border-radius:2px; }" "QPushButton:hover { color:%3; border-color:%3; background:rgba(255,255,255,0.04); }") .arg(ui::colors::TEXT_TERTIARY(), ui::colors::BORDER_DIM(), module_.color.name())); QPointer guard(edit); connect(btn, &QPushButton::clicked, this, [guard, seed, walk]() { if (guard) guard->setText(walk ? sample_random_walk(seed) : sample_series(seed)); }); return btn; }; // ── OLS Regression ─────────────────────────────────────────────────────── auto* ols = new QWidget(this); auto* oll = new QVBoxLayout(ols); oll->setContentsMargins(12, 12, 12, 12); oll->setSpacing(8); auto* ols_y = new QLineEdit(ols); ols_y->setPlaceholderText(tr("Dependent variable y (>= 10 values)")); ols_y->setStyleSheet(input_ss()); text_inputs_["sm_ols_y"] = ols_y; oll->addWidget(build_input_row(tr("y (Dependent)"), ols_y, ols)); oll->addWidget(add_sample_btn(ols_y, ols, 71, tr("100-pt synthetic series (level ~50)"))); auto* ols_x = new QLineEdit(ols); ols_x->setPlaceholderText( tr("Regressor x (single column, same length as y). For multi-feature use the JSON 2D form.")); ols_x->setStyleSheet(input_ss()); text_inputs_["sm_ols_x"] = ols_x; oll->addWidget(build_input_row(tr("x (Regressor)"), ols_x, ols)); oll->addWidget(add_sample_btn(ols_x, ols, 72, tr("100-pt synthetic regressor"))); auto* ols_const = new QComboBox(ols); ols_const->addItems({"true", "false"}); ols_const->setStyleSheet(combo_ss()); combo_inputs_["sm_ols_const"] = ols_const; oll->addWidget(build_input_row(tr("Add Constant"), ols_const, ols)); auto* ols_run = make_run_button(tr("RUN OLS REGRESSION"), ols); connect(ols_run, &QPushButton::clicked, this, [this]() { QJsonArray y, x; QString bad; if (!parse_doubles(text_inputs_["sm_ols_y"]->text(), y, &bad)) { display_error(tr("y: '%1' is not numeric.").arg(bad)); return; } if (!parse_doubles(text_inputs_["sm_ols_x"]->text(), x, &bad)) { display_error(tr("x: '%1' is not numeric.").arg(bad)); return; } if (y.size() < 10) { display_error(tr("OLS needs at least 10 observations; you provided %1.").arg(y.size())); return; } if (y.size() != x.size()) { display_error(tr("y (%1) and x (%2) must have the same length.").arg(y.size()).arg(x.size())); return; } show_loading(tr("Fitting OLS on %1 obs...").arg(y.size())); QJsonObject params; params["y"] = y; params["x"] = x; params["add_constant"] = combo_inputs_["sm_ols_const"]->currentText() == "true"; AIQuantLabService::instance().sm_ols(params); }); oll->addWidget(ols_run); oll->addStretch(); tabs->addTab(ols, tr("OLS Regression")); // ── ARIMA ──────────────────────────────────────────────────────────────── auto* ar = new QWidget(this); auto* arl = new QVBoxLayout(ar); arl->setContentsMargins(12, 12, 12, 12); arl->setSpacing(8); auto* ar_vals = new QLineEdit(ar); ar_vals->setPlaceholderText(tr("Time series values (>= 30)")); ar_vals->setStyleSheet(input_ss()); text_inputs_["sm_ar_values"] = ar_vals; arl->addWidget(build_input_row(tr("Series Values"), ar_vals, ar)); arl->addWidget(add_sample_btn(ar_vals, ar, 81, tr("200-pt synthetic series with seasonality"))); auto* ar_p = new QSpinBox(ar); ar_p->setRange(0, 10); ar_p->setValue(1); ar_p->setStyleSheet(spinbox_ss()); int_inputs_["sm_ar_p"] = ar_p; arl->addWidget(build_input_row(tr("AR Order p"), ar_p, ar)); auto* ar_d = new QSpinBox(ar); ar_d->setRange(0, 3); ar_d->setValue(1); ar_d->setStyleSheet(spinbox_ss()); int_inputs_["sm_ar_d"] = ar_d; arl->addWidget(build_input_row(tr("Differencing d"), ar_d, ar)); auto* ar_q = new QSpinBox(ar); ar_q->setRange(0, 10); ar_q->setValue(1); ar_q->setStyleSheet(spinbox_ss()); int_inputs_["sm_ar_q"] = ar_q; arl->addWidget(build_input_row(tr("MA Order q"), ar_q, ar)); auto* ar_h = new QSpinBox(ar); ar_h->setRange(1, 365); ar_h->setValue(14); ar_h->setStyleSheet(spinbox_ss()); int_inputs_["sm_ar_horizon"] = ar_h; arl->addWidget(build_input_row(tr("Forecast Horizon"), ar_h, ar)); auto* ar_run = make_run_button(tr("FIT ARIMA + FORECAST"), ar); connect(ar_run, &QPushButton::clicked, this, [this]() { QJsonArray vals; QString bad; if (!parse_doubles(text_inputs_["sm_ar_values"]->text(), vals, &bad)) { display_error(tr("Series Values: '%1' is not numeric.").arg(bad)); return; } if (vals.size() < 30) { display_error(tr("ARIMA needs at least 30 observations; you provided %1.").arg(vals.size())); return; } const int p = int_inputs_["sm_ar_p"]->value(); const int d = int_inputs_["sm_ar_d"]->value(); const int q = int_inputs_["sm_ar_q"]->value(); show_loading(tr("Fitting ARIMA(%1,%2,%3) on %4 obs and projecting %5 steps...") .arg(p) .arg(d) .arg(q) .arg(vals.size()) .arg(int_inputs_["sm_ar_horizon"]->value())); QJsonObject params; params["values"] = vals; params["p"] = p; params["d"] = d; params["q"] = q; params["horizon"] = int_inputs_["sm_ar_horizon"]->value(); AIQuantLabService::instance().sm_arima(params); }); arl->addWidget(ar_run); arl->addStretch(); tabs->addTab(ar, tr("ARIMA")); // ── Stationarity Tests (single d, full ADF + KPSS detail) ──────────────── auto* st = new QWidget(this); auto* stl = new QVBoxLayout(st); stl->setContentsMargins(12, 12, 12, 12); stl->setSpacing(8); auto* st_vals = new QLineEdit(st); st_vals->setPlaceholderText(tr("Time series values (>= 30)")); st_vals->setStyleSheet(input_ss()); text_inputs_["sm_st_values"] = st_vals; stl->addWidget(build_input_row(tr("Series Values"), st_vals, st)); stl->addWidget(add_sample_btn(st_vals, st, 91, tr("150-pt random walk with drift"), true)); auto* st_d = new QSpinBox(st); st_d->setRange(0, 3); st_d->setValue(0); st_d->setStyleSheet(spinbox_ss()); int_inputs_["sm_st_d"] = st_d; stl->addWidget(build_input_row(tr("Differencing Order d"), st_d, st)); auto* st_adf = new QComboBox(st); st_adf->addItems({"c", "ct", "ctt", "n"}); st_adf->setToolTip( tr("c = constant, ct = constant + trend, ctt = constant + trend + quadratic trend, n = no constant")); st_adf->setStyleSheet(combo_ss()); combo_inputs_["sm_st_adf_reg"] = st_adf; stl->addWidget(build_input_row(tr("ADF Regression Type"), st_adf, st)); auto* st_kpss = new QComboBox(st); st_kpss->addItems({"c", "ct"}); st_kpss->setToolTip(tr("c = constant only, ct = constant + trend")); st_kpss->setStyleSheet(combo_ss()); combo_inputs_["sm_st_kpss_reg"] = st_kpss; stl->addWidget(build_input_row(tr("KPSS Regression Type"), st_kpss, st)); auto* st_run = make_run_button(tr("RUN ADF + KPSS"), st); connect(st_run, &QPushButton::clicked, this, [this]() { QJsonArray vals; QString bad; if (!parse_doubles(text_inputs_["sm_st_values"]->text(), vals, &bad)) { display_error(tr("Series Values: '%1' is not numeric.").arg(bad)); return; } if (vals.size() < 30) { display_error(tr("Stationarity tests need at least 30 observations; you provided %1.").arg(vals.size())); return; } show_loading(tr("Running ADF + KPSS at d=%1...").arg(int_inputs_["sm_st_d"]->value())); QJsonObject params; params["values"] = vals; params["d"] = int_inputs_["sm_st_d"]->value(); params["adf_regression"] = combo_inputs_["sm_st_adf_reg"]->currentText(); params["kpss_regression"] = combo_inputs_["sm_st_kpss_reg"]->currentText(); AIQuantLabService::instance().sm_stationarity_tests(params); }); stl->addWidget(st_run); stl->addStretch(); tabs->addTab(st, tr("Stationarity")); // ── ACF / PACF ─────────────────────────────────────────────────────────── auto* ap = new QWidget(this); auto* apl = new QVBoxLayout(ap); apl->setContentsMargins(12, 12, 12, 12); apl->setSpacing(8); auto* ap_vals = new QLineEdit(ap); ap_vals->setPlaceholderText(tr("Time series values (>= 20). Used for ARIMA(p,q) order selection.")); ap_vals->setStyleSheet(input_ss()); text_inputs_["sm_ap_values"] = ap_vals; apl->addWidget(build_input_row(tr("Series Values"), ap_vals, ap)); apl->addWidget(add_sample_btn(ap_vals, ap, 101, tr("200-pt synthetic series with seasonality"))); auto* ap_lags = new QSpinBox(ap); ap_lags->setRange(2, 100); ap_lags->setValue(20); ap_lags->setStyleSheet(spinbox_ss()); int_inputs_["sm_ap_nlags"] = ap_lags; apl->addWidget(build_input_row(tr("Number of Lags"), ap_lags, ap)); auto* ap_run = make_run_button(tr("COMPUTE ACF + PACF"), ap); connect(ap_run, &QPushButton::clicked, this, [this]() { QJsonArray vals; QString bad; if (!parse_doubles(text_inputs_["sm_ap_values"]->text(), vals, &bad)) { display_error(tr("Series Values: '%1' is not numeric.").arg(bad)); return; } if (vals.size() < 20) { display_error(tr("ACF/PACF needs at least 20 observations; you provided %1.").arg(vals.size())); return; } show_loading(tr("Computing ACF + PACF up to lag %1...").arg(int_inputs_["sm_ap_nlags"]->value())); QJsonObject params; params["values"] = vals; params["nlags"] = int_inputs_["sm_ap_nlags"]->value(); AIQuantLabService::instance().sm_acf_pacf(params); }); apl->addWidget(ap_run); apl->addStretch(); tabs->addTab(ap, tr("ACF / PACF")); // ── Granger Causality ──────────────────────────────────────────────────── auto* gc = new QWidget(this); auto* gcl = new QVBoxLayout(gc); gcl->setContentsMargins(12, 12, 12, 12); gcl->setSpacing(8); auto* gc_y = new QLineEdit(gc); gc_y->setPlaceholderText(tr("Effect series y (the one we ask: 'is this caused by x?')")); gc_y->setStyleSheet(input_ss()); text_inputs_["sm_gc_y"] = gc_y; gcl->addWidget(build_input_row(tr("y (Effect)"), gc_y, gc)); gcl->addWidget(add_sample_btn(gc_y, gc, 111, tr("200-pt synthetic effect series"))); auto* gc_x = new QLineEdit(gc); gc_x->setPlaceholderText(tr("Potential cause series x (same length as y)")); gc_x->setStyleSheet(input_ss()); text_inputs_["sm_gc_x"] = gc_x; gcl->addWidget(build_input_row(tr("x (Potential Cause)"), gc_x, gc)); gcl->addWidget(add_sample_btn(gc_x, gc, 112, tr("200-pt synthetic candidate cause"))); auto* gc_lag = new QSpinBox(gc); gc_lag->setRange(1, 20); gc_lag->setValue(4); gc_lag->setStyleSheet(spinbox_ss()); int_inputs_["sm_gc_max_lag"] = gc_lag; gcl->addWidget(build_input_row(tr("Max Lag to Test"), gc_lag, gc)); auto* gc_hint = new QLabel( tr("H₀: x does NOT Granger-cause y. p < 0.05 ⇒ x carries predictive information about y at that lag."), gc); gc_hint->setWordWrap(true); gc_hint->setStyleSheet(QString("color:%1; font-size:10px;").arg(ui::colors::TEXT_TERTIARY())); gcl->addWidget(gc_hint); auto* gc_run = make_run_button(tr("TEST GRANGER CAUSALITY"), gc); connect(gc_run, &QPushButton::clicked, this, [this]() { QJsonArray y, x; QString bad; if (!parse_doubles(text_inputs_["sm_gc_y"]->text(), y, &bad)) { display_error(tr("y: '%1' is not numeric.").arg(bad)); return; } if (!parse_doubles(text_inputs_["sm_gc_x"]->text(), x, &bad)) { display_error(tr("x: '%1' is not numeric.").arg(bad)); return; } if (y.size() < 30 || x.size() < 30) { display_error(tr("Granger test needs at least 30 observations in both series.")); return; } if (y.size() != x.size()) { display_error(tr("y (%1) and x (%2) must have the same length.").arg(y.size()).arg(x.size())); return; } show_loading(tr("Testing Granger causality up to lag %1...").arg(int_inputs_["sm_gc_max_lag"]->value())); QJsonObject params; params["y"] = y; params["x"] = x; params["max_lag"] = int_inputs_["sm_gc_max_lag"]->value(); AIQuantLabService::instance().sm_granger_causality(params); }); gcl->addWidget(gc_run); gcl->addStretch(); tabs->addTab(gc, tr("Granger Causality")); // ── Descriptive + Normality ────────────────────────────────────────────── auto* de = new QWidget(this); auto* del = new QVBoxLayout(de); del->setContentsMargins(12, 12, 12, 12); del->setSpacing(8); auto* de_vals = new QLineEdit(de); de_vals->setPlaceholderText(tr("Numeric values (>= 8). Includes Jarque-Bera + Shapiro-Wilk normality tests.")); de_vals->setStyleSheet(input_ss()); text_inputs_["sm_de_values"] = de_vals; del->addWidget(build_input_row(tr("Values"), de_vals, de)); del->addWidget(add_sample_btn(de_vals, de, 121, tr("200-pt synthetic series"))); auto* de_run = make_run_button(tr("DESCRIBE + TEST NORMALITY"), de); connect(de_run, &QPushButton::clicked, this, [this]() { QJsonArray vals; QString bad; if (!parse_doubles(text_inputs_["sm_de_values"]->text(), vals, &bad)) { display_error(tr("Values: '%1' is not numeric.").arg(bad)); return; } if (vals.size() < 8) { display_error(tr("Need at least 8 values; you provided %1.").arg(vals.size())); return; } show_loading(tr("Computing descriptive statistics + normality on %1 values...").arg(vals.size())); QJsonObject params; params["values"] = vals; AIQuantLabService::instance().sm_descriptive(params); }); del->addWidget(de_run); del->addStretch(); tabs->addTab(de, tr("Descriptive")); vl->addWidget(tabs); auto* rc = new QWidget(w); results_layout_ = new QVBoxLayout(rc); results_layout_->setContentsMargins(0, 8, 0, 0); results_layout_->setSpacing(8); vl->addWidget(rc); vl->addStretch(); return w; } // ── STATSMODELS: rich card-based display per command ──────────────────────── void QuantModulePanel::display_statsmodels_result(const QString& command, const QJsonObject& payload) { clear_results(); if (!payload.value("success").toBool(false)) { const QString err = payload.value("error").toString(tr("Unknown error")); const QString kind = payload.value("error_kind").toString(); const QString prefix = kind == "validation" ? tr("Input error: ") : kind == "runtime" ? tr("Computation failed: ") : QString(); display_error(prefix + err); return; } const QJsonObject d = payload.value("data").toObject(); const QString accent = module_.color.name(); // Header strip — same shape as gs_quant / functime QString header_text = command.toUpper(); header_text.replace('_', ' '); if (d.contains("n_observations")) header_text += tr(" | %1 OBS").arg(d.value("n_observations").toInt()); results_layout_->addWidget(gs_section_header(header_text, accent)); // ── 1. CHECK STATUS ────────────────────────────────────────────────────── if (command == "check_status") { auto bool_card = [this](const QString& label, bool ok) { return gs_make_card(label, ok ? tr("AVAILABLE") : tr("MISSING"), this, ok ? ui::colors::POSITIVE() : ui::colors::NEGATIVE()); }; QList deps = { bool_card(tr("STATSMODELS"), d.value("statsmodels").toBool()), bool_card(tr("SCIPY"), d.value("scipy").toBool()), gs_make_card(tr("BACKEND"), d.value("backend").toString().toUpper(), this), gs_make_card(tr("OPS"), QString::number(d.value("ops_available").toArray().size()), this, ui::colors::INFO()), }; results_layout_->addWidget(gs_card_row(deps, this)); const auto ops = d.value("ops_available").toArray(); QStringList names; for (const auto& v : ops) names << v.toString(); auto* lbl = new QLabel(tr("Operations: %1").arg(names.join(", "))); lbl->setWordWrap(true); lbl->setStyleSheet(QString("color:%1; font-size:10px; font-family:'Courier New';" "padding:8px 10px; background:%2; border:1px solid %3;") .arg(ui::colors::TEXT_SECONDARY(), ui::colors::BG_SURFACE(), ui::colors::BORDER_DIM())); results_layout_->addWidget(lbl); status_label_->setText(tr("Statsmodels backend ready")); return; } // ── 2. OLS ─────────────────────────────────────────────────────────────── if (command == "ols") { const double r2 = d.value("r_squared").toDouble(); const double adj = d.value("adj_r_squared").toDouble(); const double f_stat = d.value("f_statistic").toDouble(); const double f_p = d.value("f_p_value").toDouble(); const double dw = d.value("durbin_watson").toDouble(); const double bp_p = d.value("breusch_pagan_p").toDouble(); auto* hdr = new QLabel(tr("OLS | %1 OBS | %2 FEATURES%3") .arg(d.value("n_observations").toInt()) .arg(d.value("n_features").toInt()) .arg(d.value("has_constant").toBool() ? tr(" + CONSTANT") : QString())); hdr->setStyleSheet(QString("color:%1; font-size:11px; font-family:'Courier New'; font-weight:700;" "padding:8px 10px; background:%2; border:1px solid %3;") .arg(ui::colors::TEXT_PRIMARY(), ui::colors::BG_SURFACE(), ui::colors::BORDER_DIM())); results_layout_->addWidget(hdr); QList fit = { gs_make_card(tr("R²"), gs_fmt_num(r2, 4), this, r2 >= 0.7 ? ui::colors::POSITIVE() : r2 >= 0.3 ? ui::colors::WARNING() : ui::colors::NEGATIVE()), gs_make_card(tr("ADJ R²"), gs_fmt_num(adj, 4), this), gs_make_card(tr("F STATISTIC"), gs_fmt_num(f_stat, 2), this), gs_make_card(tr("F p-VALUE"), gs_fmt_num(f_p, 4), this, f_p < 0.05 ? ui::colors::POSITIVE() : ui::colors::WARNING()), }; results_layout_->addWidget(gs_card_row(fit, this)); QList ic = { gs_make_card(tr("AIC"), gs_fmt_num(d.value("aic").toDouble(), 2), this), gs_make_card(tr("BIC"), gs_fmt_num(d.value("bic").toDouble(), 2), this), gs_make_card(tr("LOG-LIK"), gs_fmt_num(d.value("log_likelihood").toDouble(), 2), this), gs_make_card(tr("RESID SE"), gs_fmt_num(d.value("residual_std_error").toDouble(), 4), this), }; results_layout_->addWidget(gs_card_row(ic, this)); const QString dw_col = (dw >= 1.5 && dw <= 2.5) ? ui::colors::POSITIVE() : ui::colors::WARNING(); QList diag = { gs_make_card(tr("DURBIN-WATSON"), gs_fmt_num(dw, 3), this, dw_col), gs_make_card(tr("AUTOCORR."), d.value("dw_interpretation").toString().toUpper(), this, dw_col), gs_make_card(tr("BREUSCH-PAGAN p"), gs_fmt_num(bp_p, 4), this), gs_make_card(tr("HOMOSCEDASTIC"), d.value("homoscedastic_5pct").toBool() ? tr("YES") : tr("NO"), this, d.value("homoscedastic_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::WARNING()), }; results_layout_->addWidget(gs_card_row(diag, this)); // Coefficient table const QJsonArray coefs = d.value("coefficients").toArray(); if (!coefs.isEmpty()) { auto* table = new QTableWidget(coefs.size(), 7, this); table->setHorizontalHeaderLabels({tr("Term"), tr("Estimate"), tr("Std. Error"), tr("t"), tr("p"), tr("95% CI Lower"), tr("95% CI Upper")}); table->verticalHeader()->setVisible(false); table->setEditTriggers(QAbstractItemView::NoEditTriggers); table->setSelectionMode(QAbstractItemView::SingleSelection); table->horizontalHeader()->setSectionResizeMode(QHeaderView::Stretch); table->setStyleSheet(table_ss()); table->setMaximumHeight(qMin(coefs.size() * 26 + 40, 320)); for (int i = 0; i < coefs.size(); ++i) { const auto c = coefs[i].toObject(); table->setItem(i, 0, new QTableWidgetItem(c.value("name").toString())); auto* est = new QTableWidgetItem(QString::number(c.value("estimate").toDouble(), 'f', 4)); est->setForeground(QColor(gs_pos_neg_color(c.value("estimate").toDouble()))); est->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); table->setItem(i, 1, est); auto add_num = [&](int col, double v, int dec, const QString& col_color = {}) { auto* it = new QTableWidgetItem(QString::number(v, 'f', dec)); it->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); if (!col_color.isEmpty()) it->setForeground(QColor(col_color)); table->setItem(i, col, it); }; add_num(2, c.value("std_error").toDouble(), 4); add_num(3, c.value("t_stat").toDouble(), 3); const double pv = c.value("p_value").toDouble(); add_num(4, pv, 4, c.value("significant_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY()); add_num(5, c.value("ci_lower").toDouble(), 4); add_num(6, c.value("ci_upper").toDouble(), 4); table->setRowHeight(i, 24); } results_layout_->addWidget(table); } status_label_->setText(tr("R²=%1 Adj=%2 F=%3 (p=%4)") .arg(r2, 0, 'f', 4) .arg(adj, 0, 'f', 4) .arg(f_stat, 0, 'f', 2) .arg(f_p, 0, 'g', 3)); return; } // ── 3. ARIMA ───────────────────────────────────────────────────────────── if (command == "arima") { const QJsonArray order = d.value("order").toArray(); const int p = order.size() > 0 ? order[0].toInt() : 0; const int d_ord = order.size() > 1 ? order[1].toInt() : 0; const int q = order.size() > 2 ? order[2].toInt() : 0; const int horizon = d.value("horizon").toInt(); const double aic = d.value("aic").toDouble(); const double bic = d.value("bic").toDouble(); const double r2 = d.value("in_sample_r2").toDouble(); const double lb_p = d.value("ljung_box_lag10_p").toDouble(); const bool whitenoise = d.value("ljung_box_residuals_white_noise").toBool(); const double last_actual = d.value("last_actual").toDouble(); const double first_fc = d.value("first_forecast").toDouble(); const double last_fc = d.value("last_forecast").toDouble(); auto* hdr = new QLabel(tr("ARIMA(%1,%2,%3) | HORIZON %4 STEPS | %5 OBS") .arg(p) .arg(d_ord) .arg(q) .arg(horizon) .arg(d.value("n_observations").toInt())); hdr->setStyleSheet(QString("color:%1; font-size:11px; font-family:'Courier New'; font-weight:700;" "padding:8px 10px; background:%2; border:1px solid %3;") .arg(ui::colors::TEXT_PRIMARY(), ui::colors::BG_SURFACE(), ui::colors::BORDER_DIM())); results_layout_->addWidget(hdr); QList fit = { gs_make_card(tr("AIC"), gs_fmt_num(aic, 2), this), gs_make_card(tr("BIC"), gs_fmt_num(bic, 2), this), gs_make_card(tr("HQIC"), gs_fmt_num(d.value("hqic").toDouble(), 2), this), gs_make_card(tr("IN-SAMPLE R²"), gs_fmt_num(r2, 4), this, r2 >= 0.7 ? ui::colors::POSITIVE() : r2 >= 0.3 ? ui::colors::WARNING() : ui::colors::NEGATIVE()), }; results_layout_->addWidget(gs_card_row(fit, this)); QList resid = { gs_make_card(tr("LB(10) p-VALUE"), gs_fmt_num(lb_p, 4), this, whitenoise ? ui::colors::POSITIVE() : ui::colors::WARNING()), gs_make_card(tr("RESID WHITE NOISE"), whitenoise ? tr("YES") : tr("NO"), this, whitenoise ? ui::colors::POSITIVE() : ui::colors::WARNING()), gs_make_card(tr("RESID MEAN"), gs_fmt_num(d.value("residual_mean").toDouble(), 4), this), gs_make_card(tr("RESID σ"), gs_fmt_num(d.value("residual_std").toDouble(), 4), this), }; results_layout_->addWidget(gs_card_row(resid, this)); const double horizon_drift = last_fc - last_actual; QList proj = { gs_make_card(tr("LAST ACTUAL"), gs_fmt_num(last_actual, 4), this), gs_make_card(tr("FIRST FORECAST"), gs_fmt_num(first_fc, 4), this, gs_pos_neg_color(first_fc - last_actual)), gs_make_card(tr("LAST FORECAST"), gs_fmt_num(last_fc, 4), this, gs_pos_neg_color(horizon_drift)), gs_make_card(tr("HORIZON Δ"), gs_fmt_num(horizon_drift, 4), this, gs_pos_neg_color(horizon_drift)), }; results_layout_->addWidget(gs_card_row(proj, this)); // Forecast table with 95% bands const QJsonArray forecast = d.value("forecast").toArray(); if (!forecast.isEmpty()) { auto* table = new QTableWidget(forecast.size(), 4, this); table->setHorizontalHeaderLabels({tr("Step"), tr("Lower 95%"), tr("Point"), tr("Upper 95%")}); table->verticalHeader()->setVisible(false); table->setEditTriggers(QAbstractItemView::NoEditTriggers); table->setSelectionMode(QAbstractItemView::NoSelection); table->horizontalHeader()->setSectionResizeMode(QHeaderView::Stretch); table->setStyleSheet(table_ss()); table->setMaximumHeight(qMin(forecast.size() * 22 + 32, 280)); for (int i = 0; i < forecast.size(); ++i) { const auto o = forecast[i].toObject(); table->setItem(i, 0, new QTableWidgetItem(QString::number(o.value("step").toInt()))); auto* lo = new QTableWidgetItem(QString::number(o.value("lower_95").toDouble(), 'f', 4)); lo->setForeground(QColor(ui::colors::NEGATIVE())); lo->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); table->setItem(i, 1, lo); auto* pt = new QTableWidgetItem(QString::number(o.value("point").toDouble(), 'f', 4)); pt->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); table->setItem(i, 2, pt); auto* up = new QTableWidgetItem(QString::number(o.value("upper_95").toDouble(), 'f', 4)); up->setForeground(QColor(ui::colors::POSITIVE())); up->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); table->setItem(i, 3, up); table->setRowHeight(i, 22); } results_layout_->addWidget(table); } status_label_->setText(tr("ARIMA(%1,%2,%3) — AIC=%4 R²=%5 LB(10) p=%6") .arg(p) .arg(d_ord) .arg(q) .arg(aic, 0, 'f', 1) .arg(r2, 0, 'f', 3) .arg(lb_p, 0, 'f', 3)); return; } // ── 4. STATIONARITY TESTS ──────────────────────────────────────────────── if (command == "stationarity_tests") { const QString verdict = d.value("verdict").toString(); const int d_ord = d.value("differencing_order").toInt(); auto* hdr = new QLabel(tr("VERDICT: %1 | DIFFERENCING d = %2").arg(verdict).arg(d_ord)); const bool stationary = verdict.contains("STATIONARY") && !verdict.contains("NON-"); hdr->setStyleSheet(QString("color:%1; font-size:11px; font-family:'Courier New'; font-weight:700;" "padding:8px 10px; background:%2; border-left:3px solid %3;") .arg(ui::colors::TEXT_PRIMARY(), ui::colors::BG_SURFACE(), stationary ? ui::colors::POSITIVE() : ui::colors::NEGATIVE())); results_layout_->addWidget(hdr); const QJsonObject adf = d.value("adf").toObject(); const QJsonObject kp = d.value("kpss").toObject(); // ADF row auto* adf_lbl = new QLabel(tr("ADF (H₀: unit root → non-stationary)")); adf_lbl->setStyleSheet(QString("color:%1; font-size:10px; font-weight:700; padding:4px 0 0 2px;") .arg(ui::colors::TEXT_TERTIARY())); results_layout_->addWidget(adf_lbl); QList adf_row = { gs_make_card(tr("STATISTIC"), gs_fmt_num(adf.value("statistic").toDouble(), 4), this), gs_make_card(tr("p-VALUE"), gs_fmt_num(adf.value("p_value").toDouble(), 4), this, adf.value("stationary_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::WARNING()), gs_make_card(tr("VERDICT @ 5%"), adf.value("stationary_5pct").toBool() ? tr("STATIONARY") : tr("NON-STATIONARY"), this, adf.value("stationary_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::NEGATIVE()), gs_make_card(tr("LAGS USED"), QString::number(adf.value("lags_used").toInt()), this), }; results_layout_->addWidget(gs_card_row(adf_row, this)); QList adf_crit = { gs_make_card(tr("ADF CRIT 1%"), gs_fmt_num(adf.value("critical_1pct").toDouble(), 4), this), gs_make_card(tr("ADF CRIT 5%"), gs_fmt_num(adf.value("critical_5pct").toDouble(), 4), this), gs_make_card(tr("ADF CRIT 10%"), gs_fmt_num(adf.value("critical_10pct").toDouble(), 4), this), gs_make_card(tr("REGRESSION"), adf.value("regression").toString().toUpper(), this), }; results_layout_->addWidget(gs_card_row(adf_crit, this)); // KPSS row auto* kpss_lbl = new QLabel(tr("KPSS (H₀: stationary)")); kpss_lbl->setStyleSheet(QString("color:%1; font-size:10px; font-weight:700; padding:4px 0 0 2px;") .arg(ui::colors::TEXT_TERTIARY())); results_layout_->addWidget(kpss_lbl); QList kp_row = { gs_make_card(tr("STATISTIC"), gs_fmt_num(kp.value("statistic").toDouble(), 4), this), gs_make_card(tr("p-VALUE"), gs_fmt_num(kp.value("p_value").toDouble(), 4), this, kp.value("stationary_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::WARNING()), gs_make_card(tr("VERDICT @ 5%"), kp.value("stationary_5pct").toBool() ? tr("STATIONARY") : tr("NON-STATIONARY"), this, kp.value("stationary_5pct").toBool() ? ui::colors::POSITIVE() : ui::colors::NEGATIVE()), gs_make_card(tr("LAGS USED"), QString::number(kp.value("lags_used").toInt()), this), }; results_layout_->addWidget(gs_card_row(kp_row, this)); QList kp_crit = { gs_make_card(tr("KPSS CRIT 1%"), gs_fmt_num(kp.value("critical_1pct").toDouble(), 4), this), gs_make_card(tr("KPSS CRIT 5%"), gs_fmt_num(kp.value("critical_5pct").toDouble(), 4), this), gs_make_card(tr("KPSS CRIT 10%"), gs_fmt_num(kp.value("critical_10pct").toDouble(), 4), this), gs_make_card(tr("REGRESSION"), kp.value("regression").toString().toUpper(), this), }; results_layout_->addWidget(gs_card_row(kp_crit, this)); status_label_->setText(verdict); return; } // ── 5. ACF / PACF ─────────────────────────────────────────────────────── if (command == "acf_pacf") { const int p_sug = d.value("suggested_arima_p").toInt(); const int q_sug = d.value("suggested_arima_q").toInt(); const double band = d.value("significance_band_95pct").toDouble(); auto* hdr = new QLabel(tr("LAGS %1 | SIGNIFICANCE BAND ±%2 | SUGGESTED ARIMA(p=%3, q=%4)") .arg(d.value("nlags").toInt()) .arg(band, 0, 'f', 4) .arg(p_sug) .arg(q_sug)); hdr->setStyleSheet(QString("color:%1; font-size:11px; font-family:'Courier New'; font-weight:700;" "padding:8px 10px; background:%2; border-left:3px solid %3;") .arg(ui::colors::TEXT_PRIMARY(), ui::colors::BG_SURFACE(), accent)); results_layout_->addWidget(hdr); QList sum = { gs_make_card(tr("SIG. ACF LAGS"), QString::number(d.value("n_significant_acf_lags").toInt()), this), gs_make_card(tr("SIG. PACF LAGS"), QString::number(d.value("n_significant_pacf_lags").toInt()), this), gs_make_card(tr("SUGGESTED p"), QString::number(p_sug), this, ui::colors::INFO()), gs_make_card(tr("SUGGESTED q"), QString::number(q_sug), this, ui::colors::INFO()), }; results_layout_->addWidget(gs_card_row(sum, this)); // Lag table const QJsonArray lags = d.value("lags").toArray(); if (!lags.isEmpty()) { auto* table = new QTableWidget(lags.size(), 5, this); table->setHorizontalHeaderLabels({tr("Lag"), tr("ACF"), tr("ACF Sig."), tr("PACF"), tr("PACF Sig.")}); table->verticalHeader()->setVisible(false); table->setEditTriggers(QAbstractItemView::NoEditTriggers); table->setSelectionMode(QAbstractItemView::NoSelection); table->horizontalHeader()->setSectionResizeMode(QHeaderView::Stretch); table->setStyleSheet(table_ss()); table->setMaximumHeight(qMin(lags.size() * 22 + 32, 360)); for (int i = 0; i < lags.size(); ++i) { const auto o = lags[i].toObject(); table->setItem(i, 0, new QTableWidgetItem(QString::number(o.value("lag").toInt()))); auto* a = new QTableWidgetItem(QString::number(o.value("acf").toDouble(), 'f', 4)); a->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); a->setForeground( QColor(o.value("acf_significant").toBool() ? ui::colors::POSITIVE() : ui::colors::TEXT_PRIMARY())); table->setItem(i, 1, a); table->setItem(i, 2, new QTableWidgetItem(o.value("acf_significant").toBool() ? "✓" : "—")); auto* p = new QTableWidgetItem(QString::number(o.value("pacf").toDouble(), 'f', 4)); p->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); p->setForeground( QColor(o.value("pacf_significant").toBool() ? ui::colors::POSITIVE() : ui::colors::TEXT_PRIMARY())); table->setItem(i, 3, p); table->setItem(i, 4, new QTableWidgetItem(o.value("pacf_significant").toBool() ? "✓" : "—")); table->setRowHeight(i, 22); } results_layout_->addWidget(table); } status_label_->setText(tr("Suggested ARIMA(p=%1, q=%2)").arg(p_sug).arg(q_sug)); return; } // ── 6. GRANGER CAUSALITY ──────────────────────────────────────────────── if (command == "granger_causality") { const bool any_causal = d.value("x_causes_y_at_any_lag").toBool(); const QJsonArray causal_lags = d.value("causal_lags").toArray(); auto* hdr = new QLabel(tr("MAX LAG TESTED: %1 | %2") .arg(d.value("max_lag").toInt()) .arg(d.value("interpretation").toString())); hdr->setWordWrap(true); hdr->setStyleSheet(QString("color:%1; font-size:11px; font-family:'Courier New'; font-weight:700;" "padding:8px 10px; background:%2; border-left:3px solid %3;") .arg(ui::colors::TEXT_PRIMARY(), ui::colors::BG_SURFACE(), any_causal ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY())); results_layout_->addWidget(hdr); QStringList causal_strs; for (const auto& v : causal_lags) causal_strs << QString::number(v.toInt()); QList sum = { gs_make_card(tr("ANY CAUSAL LAG"), any_causal ? tr("YES") : tr("NO"), this, any_causal ? ui::colors::POSITIVE() : ui::colors::TEXT_PRIMARY()), gs_make_card(tr("CAUSAL LAGS"), causal_strs.isEmpty() ? QStringLiteral("—") : causal_strs.join(", "), this, any_causal ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY()), gs_make_card(tr("LAGS TESTED"), QString::number(d.value("max_lag").toInt()), this), gs_make_card(tr("OBS"), QString::number(d.value("n_observations").toInt()), this), }; results_layout_->addWidget(gs_card_row(sum, this)); const QJsonArray tests = d.value("tests").toArray(); if (!tests.isEmpty()) { auto* table = new QTableWidget(tests.size(), 6, this); table->setHorizontalHeaderLabels( {tr("Lag"), tr("F-stat"), tr("F p-value"), tr("χ² stat"), tr("χ² p-value"), tr("x ⇒ y @ 5%")}); table->verticalHeader()->setVisible(false); table->setEditTriggers(QAbstractItemView::NoEditTriggers); table->setSelectionMode(QAbstractItemView::NoSelection); table->horizontalHeader()->setSectionResizeMode(QHeaderView::Stretch); table->setStyleSheet(table_ss()); table->setMaximumHeight(qMin(tests.size() * 26 + 32, 280)); for (int i = 0; i < tests.size(); ++i) { const auto t = tests[i].toObject(); const bool causal = t.value("x_causes_y_5pct").toBool(); table->setItem(i, 0, new QTableWidgetItem(QString::number(t.value("lag").toInt()))); auto add_num = [&](int col, double v, int dec) { auto* it = new QTableWidgetItem(QString::number(v, 'f', dec)); it->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); table->setItem(i, col, it); }; add_num(1, t.value("f_statistic").toDouble(), 3); auto* fp = new QTableWidgetItem(QString::number(t.value("f_p_value").toDouble(), 'g', 4)); fp->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); fp->setForeground(QColor(causal ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY())); table->setItem(i, 2, fp); add_num(3, t.value("chi2_statistic").toDouble(), 3); auto* cp = new QTableWidgetItem(QString::number(t.value("chi2_p_value").toDouble(), 'g', 4)); cp->setTextAlignment(Qt::AlignRight | Qt::AlignVCenter); cp->setForeground(QColor(causal ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY())); table->setItem(i, 4, cp); auto* yn = new QTableWidgetItem(causal ? tr("YES") : tr("NO")); yn->setForeground(QColor(causal ? ui::colors::POSITIVE() : ui::colors::TEXT_TERTIARY())); yn->setTextAlignment(Qt::AlignCenter); table->setItem(i, 5, yn); table->setRowHeight(i, 24); } results_layout_->addWidget(table); } status_label_->setText(d.value("interpretation").toString()); return; } // ── 7. DESCRIPTIVE + NORMALITY ─────────────────────────────────────────── if (command == "descriptive") { QList central = { gs_make_card(tr("MEAN"), gs_fmt_num(d.value("mean").toDouble(), 4), this), gs_make_card(tr("MEDIAN"), gs_fmt_num(d.value("median").toDouble(), 4), this), gs_make_card(tr("STD DEV"), gs_fmt_num(d.value("std").toDouble(), 4), this), gs_make_card(tr("VARIANCE"), gs_fmt_num(d.value("variance").toDouble(), 4), this), }; results_layout_->addWidget(gs_card_row(central, this)); QList spread = { gs_make_card(tr("MIN"), gs_fmt_num(d.value("min").toDouble(), 4), this, ui::colors::NEGATIVE()), gs_make_card(tr("MAX"), gs_fmt_num(d.value("max").toDouble(), 4), this, ui::colors::POSITIVE()), gs_make_card(tr("RANGE"), gs_fmt_num(d.value("range").toDouble(), 4), this), gs_make_card(tr("IQR"), gs_fmt_num(d.value("iqr").toDouble(), 4), this), }; results_layout_->addWidget(gs_card_row(spread, this)); QList qrt = { gs_make_card(tr("p25"), gs_fmt_num(d.value("p25").toDouble(), 4), this), gs_make_card(tr("p50"), gs_fmt_num(d.value("p50").toDouble(), 4), this), gs_make_card(tr("p75"), gs_fmt_num(d.value("p75").toDouble(), 4), this), gs_make_card(tr("CV"), d.value("coefficient_of_variation").isNull() ? QStringLiteral("—") : gs_fmt_num(d.value("coefficient_of_variation").toDouble(), 4), this), }; results_layout_->addWidget(gs_card_row(qrt, this)); QList shape = { gs_make_card(tr("SKEWNESS"), gs_fmt_num(d.value("skewness").toDouble(), 3), this, gs_pos_neg_color(d.value("skewness").toDouble())), gs_make_card(tr("KURTOSIS (excess)"), gs_fmt_num(d.value("kurtosis_excess").toDouble(), 3), this), gs_make_card(tr("SUM"), gs_fmt_num(d.value("sum").toDouble(), 2), this), gs_make_card(tr("OBS"), QString::number(d.value("n_observations").toInt()), this), }; results_layout_->addWidget(gs_card_row(shape, this)); // Normality auto* nh = new QLabel(tr("NORMALITY (H₀: data is normally distributed; p > 0.05 ⇒ cannot reject normal)")); nh->setStyleSheet(QString("color:%1; font-size:10px; font-weight:700; padding:4px 0 0 2px;") .arg(ui::colors::TEXT_TERTIARY())); results_layout_->addWidget(nh); const QJsonValue jb_normal = d.value("jarque_bera_normal_5pct"); const QJsonValue sw_normal = d.value("shapiro_wilk_normal_5pct"); QList norm = { gs_make_card(tr("JARQUE-BERA STAT"), gs_fmt_num(d.value("jarque_bera_stat").toDouble(), 3), this), gs_make_card(tr("JARQUE-BERA p"), gs_fmt_num(d.value("jarque_bera_p").toDouble(), 4), this, jb_normal.isBool() ? (jb_normal.toBool() ? ui::colors::POSITIVE() : ui::colors::WARNING()) : ui::colors::TEXT_PRIMARY()), gs_make_card(tr("SHAPIRO-WILK p"), gs_fmt_num(d.value("shapiro_wilk_p").toDouble(), 4), this, sw_normal.isBool() ? (sw_normal.toBool() ? ui::colors::POSITIVE() : ui::colors::WARNING()) : ui::colors::TEXT_PRIMARY()), gs_make_card(tr("NORMAL (BOTH)"), (jb_normal.isBool() && sw_normal.isBool() && jb_normal.toBool() && sw_normal.toBool()) ? tr("YES") : tr("NO"), this, (jb_normal.isBool() && sw_normal.isBool() && jb_normal.toBool() && sw_normal.toBool()) ? ui::colors::POSITIVE() : ui::colors::WARNING()), }; results_layout_->addWidget(gs_card_row(norm, this)); status_label_->setText(tr("μ=%1 σ=%2 n=%3 JB p=%4") .arg(d.value("mean").toDouble(), 0, 'f', 3) .arg(d.value("std").toDouble(), 0, 'f', 3) .arg(d.value("n_observations").toInt()) .arg(d.value("jarque_bera_p").toDouble(), 0, 'f', 3)); return; } // ── Fallback ───────────────────────────────────────────────────────────── display_result(d); } } // namespace fincept::screens