"""
run.py — 主入口：数据更新 → 引擎 → 分析 → 对冲 → 因果 → Dashboard
===================================================================
用法：
  python run.py              # 完整流程（含数据更新+新闻）
  python run.py --skip-update  # 跳过数据更新，直接用现有数据
"""

import os, json, webbrowser, argparse
import pandas as pd

from config import BASE_DIR, OUTPUT_DIR, OUTPUT_FILES, INDUSTRIES, PRICE_COLS
from core.engine import load_panel, run_walk_forward
from core.analysis import apply_industry_rules, generate_all_reports, evaluate_results, run_ablation
from core.hedging import compute_all_industry_hedges, backtest_hedging
from core.feature_selection import run_feature_funnel

os.chdir(BASE_DIR)
os.makedirs(OUTPUT_DIR, exist_ok=True)


def run_benchmark(panel_path, benchmark, price_col, features_override=None):
    """对单个油价基准运行完整 walk-forward 流程。"""
    print(f"\n{'─'*65}")
    print(f"  Benchmark: {benchmark} ({price_col})")
    print(f"{'─'*65}")

    # Load panel
    panel, features = load_panel(panel_path, price_col=price_col)

    # Walk-forward
    results, shap_data = run_walk_forward(panel, features)
    print(f"  测试月数: {len(results)}")

    # Industry rules
    for i, row in results.iterrows():
        rules = apply_industry_rules(row)
        for k, v in rules.items():
            results.at[i, k] = v

    # NLG reports
    reports = generate_all_reports(results)

    # Tag benchmark
    results['benchmark'] = benchmark

    return results, shap_data, reports, panel, features


def main(skip_update=False):
    # ════ Step 0: Data Update ════
    if not skip_update:
        print("═" * 65)
        print("Step 0: 全特征 API 数据更新")
        print("═" * 65)
        try:
            from pipeline.live_data import main as live_main
            live_main()
            panel_path = 'output/panel_monthly_live.csv'
            if os.path.exists(panel_path):
                print(f"✓ 使用更新后的面板: {panel_path}")
            else:
                panel_path = 'output/panel_monthly.csv'
        except Exception as e:
            print(f"⚠ 数据更新跳过: {e}")
            panel_path = 'output/panel_monthly.csv'
    else:
        print("跳过数据更新")
        if os.path.exists('output/panel_monthly_live.csv'):
            panel_path = 'output/panel_monthly_live.csv'
        else:
            panel_path = 'output/panel_monthly.csv'

    # ════ Step 1: Feature Selection Funnel ════
    print("\n" + "═" * 65)
    print("Step 1: 特征筛选漏斗 (329→17)")
    print("═" * 65)
    funnel = run_feature_funnel('output/panel_monthly.csv')
    with open(OUTPUT_FILES['feat_sel'], 'w', encoding='utf-8') as f:
        json.dump(funnel, f, ensure_ascii=False, indent=2)
    print(f"✓ 特征筛选: {OUTPUT_FILES['feat_sel']}")

    # ════ Step 2: Walk-Forward for EACH benchmark ════
    print("\n" + "═" * 65)
    print("Step 2: Walk-Forward 预测 (WTI + Brent)")
    print("═" * 65)

    all_results = {}
    all_shap = {}
    all_reports = {}
    all_panels = {}
    all_features = {}

    for benchmark, price_col in PRICE_COLS.items():
        results, shap_data, reports, panel, features = run_benchmark(
            panel_path, benchmark, price_col)
        all_results[benchmark] = results
        all_shap[benchmark] = shap_data
        all_reports[benchmark] = reports
        all_panels[benchmark] = panel
        all_features[benchmark] = features

    # Use WTI as primary for hedging/evaluation (backward compat)
    primary = 'WTI'
    results = all_results[primary]

    # ════ Step 3: Hedging (based on WTI) ════
    print("\n" + "═" * 65)
    print("Step 3: 对冲决策计算")
    print("═" * 65)
    latest = results.iloc[-1]
    hedging_data = compute_all_industry_hedges(latest)
    hedging_json = {}
    for ind, hd in hedging_data.items():
        hedging_json[ind] = {
            'industry_zh': hd['industry_zh'],
            'exposure': hd['exposure'],
            'elasticity': hd['elasticity'],
            'recommended_ratio': hd['recommended_ratio'],
            'recommended_ratio_pct': hd['recommended_ratio_pct'],
            'recommended_tool': hd['recommended_tool'],
            'rationale': hd['rationale'],
            'matrix': hd['matrix'],
            'tool_comparison': hd['tool_comparison'],
        }
        print(f"  {hd['industry_zh']}: 推荐对冲 {hd['recommended_ratio_pct']}, "
              f"工具={hd['recommended_tool']}")
    with open(OUTPUT_FILES['hedging'], 'w', encoding='utf-8') as f:
        json.dump(hedging_json, f, ensure_ascii=False, indent=2)
    print(f"✓ Hedging: {OUTPUT_FILES['hedging']}")

    # Hedge Backtest
    print("  [回测对冲策略 — 过去60月]")
    backtest_data = backtest_hedging(results)
    backtest_json = {}
    for ind, bt in backtest_data.items():
        backtest_json[ind] = bt
        print(f"  {bt['industry_zh']}: 累计节省${bt['total_saving']:.1f}M, "
              f"波动率降低{bt['vol_reduction']:.0f}%, "
              f"最大回撤改善${bt['dd_improvement']:.1f}M")
    with open(OUTPUT_FILES['backtest'], 'w', encoding='utf-8') as f:
        json.dump(backtest_json, f, ensure_ascii=False, indent=2)
    print(f"✓ Backtest: {OUTPUT_FILES['backtest']}")

    # ════ Step 4: NLG Reports ════
    print("\n" + "═" * 65)
    print("Step 4: NLG 报告生成")
    print("═" * 65)
    for bm, reports in all_reports.items():
        print(f"  {bm}: {len(reports)} 份报告")

    # ════ Step 5: Evaluation ════
    for bm, res in all_results.items():
        print(f"\n--- Evaluation: {bm} ---")
        evaluate_results(res)

    # ════ Step 6: Save ════
    print("\n" + "═" * 65)
    print("Step 6: 保存结果")
    print("═" * 65)

    # Save per-benchmark results
    for bm, res in all_results.items():
        out_path = os.path.join(OUTPUT_DIR, f'v2_results_{bm}.csv')
        res.to_csv(out_path, index=False)
        print(f"✓ 结果 [{bm}]: {out_path}")

    # Also save primary as the main results (backward compat)
    results.to_csv(OUTPUT_FILES['results'], index=False)
    print(f"✓ 结果 [primary]: {OUTPUT_FILES['results']}")

    # SHAP (primary)
    with open(OUTPUT_FILES['shap'], 'w', encoding='utf-8') as f:
        json.dump(all_shap[primary][-12:], f, ensure_ascii=False, indent=2)
    print(f"✓ SHAP: {OUTPUT_FILES['shap']}")

    # NLG (merge all benchmarks)
    merged_reports = {}
    for bm, reps in all_reports.items():
        for dt, report in reps.items():
            key = f"{dt}_{bm}" if bm != primary else dt
            merged_reports[key] = report
        # Also save per-benchmark
        with open(os.path.join(OUTPUT_DIR, f'v2_nlg_{bm}.json'), 'w', encoding='utf-8') as f:
            json.dump(reps, f, ensure_ascii=False, indent=2)
    with open(OUTPUT_FILES['nlg'], 'w', encoding='utf-8') as f:
        json.dump(merged_reports, f, ensure_ascii=False, indent=2)
    print(f"✓ NLG: {OUTPUT_FILES['nlg']}")

    # Scenarios (primary)
    scenario_data = {}
    for _, row in results.tail(12).iterrows():
        dt = pd.Timestamp(row['test_date']).strftime('%Y-%m')
        scenario_data[dt] = {
            'base': round(row['scenario_base'] * 100, 2),
            'vix_shock': round(row['scenario_vix_shock'] * 100, 2),
            'supply_cut': round(row['scenario_supply_cut'] * 100, 2),
            'demand_crash': round(row['scenario_demand_crash'] * 100, 2),
        }
    with open(OUTPUT_FILES['scenarios'], 'w', encoding='utf-8') as f:
        json.dump(scenario_data, f, indent=2)
    print(f"✓ Scenarios: {OUTPUT_FILES['scenarios']}")

    # Regime (primary)
    regime_data = {}
    for _, row in results.iterrows():
        dt = pd.Timestamp(row['test_date']).strftime('%Y-%m')
        regime_data[dt] = {
            'match': row.get('regime_match', 'Unknown'),
            'similarity': row.get('regime_similarity', 0),
            'type': row.get('regime_type', 'normal'),
        }
    with open(OUTPUT_FILES['regime'], 'w', encoding='utf-8') as f:
        json.dump(regime_data, f, ensure_ascii=False, indent=2)
    print(f"✓ Regime: {OUTPUT_FILES['regime']}")

    # ════ Step 7: Ablation (primary only) ════
    print("\n" + "═" * 65)
    print("Step 7: 消融实验")
    print("═" * 65)
    ablation_results = run_ablation(all_panels[primary], all_features[primary])
    with open(OUTPUT_FILES['ablation'], 'w') as f:
        json.dump(ablation_results, f, indent=2)
    print(f"✓ Ablation: {OUTPUT_FILES['ablation']}")

    # ════ Step 7b: Causal Analysis ════
    print("\n" + "═" * 65)
    print("Step 7b: 因果因子网络分析")
    print("═" * 65)
    try:
        from pipeline.causal_analysis import run_full_causal_analysis
        causal_result = run_full_causal_analysis(panel_path)
        print(f"✓ 因果分析: {OUTPUT_FILES.get('causal', 'output/causal_analysis.json')}")
    except Exception as e:
        print(f"⚠ 因果分析跳过: {e}")

    # ════ Step 8: Done ════
    print("\n" + "═" * 65)
    print("✅ 全部完成!")
    print("═" * 65)
    print("  启动前端: cd frontend && npm run dev")
    print("  启动API:  python api_server.py")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='油价风险分析平台 — 一键启动')
    parser.add_argument('--skip-update', action='store_true',
                        help='跳过 FRED/EIA 数据更新，直接使用现有数据')
    args = parser.parse_args()
    main(skip_update=args.skip_update)