// src/services/ai_quant_lab/AIQuantLabTypes.h
#pragma once
#include <QColor>
#include <QJsonObject>
#include <QString>
#include <QVector>

namespace fincept::services::quant {

// ── Module definitions ──────────────────────────────────────────────────────

struct QuantModule {
    QString id;
    QString label;
    QString short_label;
    QString category; // CORE, AI_ML, ADVANCED, ANALYTICS
    QColor color;
    QString script; // Python script to call
    QString description;
};

inline QVector<QuantModule> all_quant_modules() {
    return {
        // CORE
        {"factor_discovery", "Factor Discovery", "FACTOR", "CORE", QColor("#FF6B35"), "ai_quant_lab/qlib_service.py",
         "Discover alpha factors using Qlib framework"},
        {"model_library", "Model Library", "MODEL", "CORE", QColor("#00E5FF"), "ai_quant_lab/qlib_service.py",
         "30+ ML models: LightGBM, XGBoost, LSTM, Transformer"},
        {"backtesting", "Backtesting", "BTEST", "CORE", QColor("#00D66F"), "ai_quant_lab/qlib_advanced_backtest.py",
         "Multi-strategy backtesting with portfolio optimization"},
        {"live_signals", "Live Signals", "SIGNAL", "CORE", QColor("#FFC400"), "ai_quant_lab/qlib_service.py",
         "Real-time trading signal generation"},

        // AI/ML
        {"deep_agent", "Deep Agent", "DAGENT", "AI_ML", QColor("#9D4EDD"), "agents/rdagents/cli.py",
         "Autonomous AI research agent (RD-Agent)"},
        {"rl_trading", "RL Trading", "RL", "AI_ML", QColor("#FF3B8E"), "ai_quant_lab/qlib_rl.py",
         "Reinforcement learning: PPO, DQN, A2C, SAC, TD3"},
        {"online_learning", "Online Learning", "ONLINE", "AI_ML", QColor("#00BCD4"),
         "ai_quant_lab/qlib_online_learning.py", "Incremental learning with concept drift detection"},
        {"meta_learning", "Meta Learning", "META", "AI_ML", QColor("#E040FB"), "ai_quant_lab/qlib_meta_learning.py",
         "Few-shot learning, model selection, AutoML"},
        // REMOVED: {"pattern_intelligence", …, "Analytics/technical_indicators.py"}
        //
        // The module was registered — so it appeared in the AI Quant Lab sidebar
        // and could be opened and run — but it could not work, in two
        // independent ways:
        //
        //   1. No panel or result display is wired for it (see the note at the
        //      top of QuantModulePanel_AIMLDisplays.cpp), so it fell through to
        //      build_generic_panel().
        //   2. Its target, scripts/Analytics/technical_indicators.py, is a
        //      LIBRARY. Its main() accepts exactly one command — "test" — which
        //      fabricates prices with np.random.seed(42) and returns indicators
        //      computed over that synthetic series. Every other command returns
        //      {"error": "Unknown command: …"}.
        //
        // So the module either errored or, on the one command it accepts,
        // returned indicators derived from random numbers with no indication
        // they were synthetic. Removed rather than left shipping.
        //
        // To restore it properly: give technical_indicators.py a real command
        // surface over user-supplied OHLCV (the uniform
        // `python <script> <command> [args…] → JSON` ABI the other 21 modules
        // use), then add a panel + display and re-register here.

        // ADVANCED
        {"hft", "High Frequency Trading", "HFT", "ADVANCED", QColor("#F44336"), "ai_quant_lab/qlib_high_frequency.py",
         "Order book dynamics, market making, latency optimization"},
        {"rolling_retraining", "Rolling Retraining", "ROLL", "ADVANCED", QColor("#8BC34A"),
         "ai_quant_lab/qlib_rolling_retraining.py", "Automated model retraining with rolling windows"},
        {"advanced_models", "Advanced Models", "ADV", "ADVANCED", QColor("#03A9F4"),
         "ai_quant_lab/qlib_advanced_models.py", "LSTM, GRU, Transformer, Localformer, HIST, GAT"},

        // QLIB SUITE
        {"feature_engineering", "Feature Engineering", "FEAT", "ADVANCED", QColor("#26C6DA"),
         "ai_quant_lab/qlib_feature_engineering.py", "Technical indicators, factor selection, expression engine"},
        {"portfolio_opt", "Portfolio Optimization", "PORTOPT", "ADVANCED", QColor("#66BB6A"),
         "ai_quant_lab/qlib_portfolio_opt.py", "Black-Litterman, HRP, Min-Variance, Max-Sharpe, EF"},
        {"factor_evaluation", "Factor Evaluation", "EVAL", "ADVANCED", QColor("#FFA726"),
         "ai_quant_lab/qlib_evaluation.py", "IC metrics, factor returns, risk metrics, factor scoring"},
        {"strategy_builder", "Strategy Builder", "STRAT", "ADVANCED", QColor("#AB47BC"),
         "ai_quant_lab/qlib_strategy.py", "TopK-Dropout, Enhanced Indexing, Risk Parity, Mean-Variance"},
        {"data_processors", "Data Processors", "PROC", "ADVANCED", QColor("#EF5350"),
         "ai_quant_lab/qlib_data_processors.py", "10 normalizers: zscore, winsorize, cs_rank, tanh, fillna"},
        {"quant_reporting", "Quant Reporting", "REPORT", "ANALYTICS", QColor("#78909C"),
         "ai_quant_lab/qlib_reporting.py", "IC reports, cumulative returns, risk analysis, model performance"},

        // ANALYTICS
        {"cfa_quant", "CFA Quant", "CFA", "ANALYTICS", QColor("#FF6B35"), "Analytics/quant_analytics_cli.py",
         "Trend, ARIMA, ML prediction, PCA, bootstrap"},
        {"gs_quant", "GS Quant", "GS", "ANALYTICS", QColor("#9D4EDD"), "Analytics/gs_quant_wrapper/gs_quant_service.py",
         "Risk metrics, portfolio analytics, options Greeks, VaR"},
        {"statsmodels", "Statsmodels", "STATS", "ANALYTICS", QColor("#2196F3"),
         "Analytics/statsmodels_wrapper/statsmodels_service.py",
         "OLS, ARIMA, stationarity, ACF/PACF, Granger causality, descriptive"},
        {"functime", "Functime", "FUNC", "ANALYTICS", QColor("#4CAF50"),
         "Analytics/functime_wrapper/functime_service.py",
         "Forecasting, anomaly detection, seasonality, intervals, stationarity"},
        {"fortitudo", "Fortitudo", "FORT", "ANALYTICS", QColor("#FF5722"),
         "Analytics/fortitudo_tech_wrapper/fortitudo_service.py",
         "Portfolio metrics, MV/CVaR optimization, efficient frontier, exp-decay weights"},
        {"gluonts", "GluonTS", "GLUON", "ANALYTICS", QColor("#795548"), "Analytics/gluonts_wrapper/gluonts_service.py",
         "Probabilistic + quantile forecasts, distribution fits, forecast evaluation, baselines"},
    };
}

inline QStringList quant_categories() {
    return {"CORE", "AI_ML", "ADVANCED", "ANALYTICS"};
}

} // namespace fincept::services::quant