import React from 'react';
import { BookOpen } from 'lucide-react';
import 'katex/dist/katex.min.css';
import { BlockMath, InlineMath } from 'react-katex';

interface EconometricsMathModalProps {
  isOpen: boolean;
  onClose: () => void;
}

export default function EconometricsMathModal({ isOpen, onClose }: EconometricsMathModalProps) {
  React.useEffect(() => {
    const handleKeyDown = (e: KeyboardEvent) => {
      if (e.key === 'Escape') {
        onClose();
      }
    };
    if (isOpen) {
      window.addEventListener('keydown', handleKeyDown);
    }
    return () => {
      window.removeEventListener('keydown', handleKeyDown);
    };
  }, [isOpen, onClose]);

  if (!isOpen) return null;

  return (
    <div className="fixed inset-0 z-50 flex items-center justify-center bg-slate-950/85 backdrop-blur-md p-4 sm:p-6 md:p-8">
      <div className="bg-slate-900 border border-slate-800/80 rounded-3xl w-full max-w-4xl h-[80vh] flex flex-col overflow-hidden shadow-2xl relative text-slate-300">
        
        {/* Modal Header */}
        <div className="flex justify-between items-center px-6 py-4 bg-slate-950/40 border-b border-slate-800/60">
          <div>
            <h2 className="text-base font-bold bg-gradient-to-r from-rose-400 to-indigo-400 bg-clip-text text-transparent flex items-center gap-2">
              <BookOpen className="w-5 h-5 text-rose-400" /> Econometrics Workspace - Math & Logic Specification
            </h2>
            <p className="text-[10px] text-slate-500 font-mono">Institutional Specification Manual</p>
          </div>
          <button
            onClick={onClose}
            className="text-slate-400 hover:text-slate-200 bg-slate-950/50 border border-slate-800 hover:border-slate-700 px-3 py-1.5 rounded-lg text-xs font-semibold font-mono transition-all cursor-pointer"
          >
            Schließen (ESC)
          </button>
        </div>

        {/* Modal Body */}
        <div className="flex-1 overflow-y-auto p-6 sm:p-8 space-y-6 text-slate-300 scrollbar-thin">
          <div className="space-y-6">
            <div className="border-b border-slate-800/80 pb-3">
              <h3 className="text-base font-bold text-slate-200">1. Econometrics Workspace Engine</h3>
              <p className="text-xs text-slate-400 mt-1">Estimates asset reactions to macroeconomic shocks using panel regression, predictions accuracy, and survival durability.</p>
            </div>

            <div className="space-y-3">
              <h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">A. Ingestion & Storage Pipeline</h4>
              <p className="text-xs leading-relaxed text-slate-400">
                A background manager checks event parameters against the simulated current workstation local time (<code className="bg-slate-950 px-1 py-0.5 rounded text-[10px] text-purple-400">2026-06-11</code>).
                If an active event's date is in the past:
              </p>
              <ul className="list-disc pl-5 text-xs text-slate-400 space-y-1">
                <li>FMP API fetches relative prices $P_t$ for $t \in [T-30, T+30]$ (60-day historical window).</li>
                <li>Asset curves and the user's manual score are frozen under <code className="bg-slate-950 px-1 py-0.5 rounded text-[10px] text-slate-300">archivedEvents</code> in <code className="bg-slate-950 px-1 py-0.5 text-slate-300 rounded text-[10px]">econometrics_storage.json</code>.</li>
                <li>Future edits to the active matrix will <strong>never</strong> modify archived price vectors.</li>
              </ul>
            </div>

            <div className="space-y-3">
              <h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">B. Endogenous Calibration</h4>
              <p className="text-xs leading-relaxed text-slate-400">
                Active future matrix cells pre-fill suggested scores by looking up the corresponding historical LMM coefficient <InlineMath math="\beta_{asset\_event\_post}" /> and scaling it to our native score scale:
              </p>
              <div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2">
                <BlockMath math="\text{Score}_{\text{suggested}} = \max\left(-3, \min\left(3, \text{Round}(\beta_{\text{estimate}} \times 100)\right)\right)" />
              </div>
            </div>

            <div className="space-y-3">
              <h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">C. Linear Mixed Model (LMM) Panel Regression</h4>
              <p className="text-xs leading-relaxed text-slate-400">
                The engine estimates direct event drift and impact returns, isolating asset-level intercepts as random deviances and purging macro volatility using VIX indices:
              </p>
              <div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2">
                <BlockMath math="Y_{it} = X_{it}\beta + Z_{it}b_i + \varepsilon_{it}" />
                <p className="text-[11px] text-slate-400 mt-2 font-mono leading-relaxed">
                  Where:<br/>
                  - <InlineMath math="Y_{it}" /> is the log-return <InlineMath math="\ln(P_t/P_0)" /> of asset <InlineMath math="i" /> at relative index <InlineMath math="t \in [-30, 30]" />.<br/>
                  - <InlineMath math="X_{it}" /> design matrix elements isolate Pre-Event Drift (<InlineMath math="t < 0" />) and Post-Event Impact (<InlineMath math="t \ge 0" />) while controlling for systemic covariates (VIX).<br/>
                  - <InlineMath math="b_i \sim N(0, \sigma_b^2)" /> random intercept captures unique baseline asset variance.<br/>
                  - <InlineMath math="\varepsilon_{it} \sim N(0, \sigma^2)" /> residuals noise.
                </p>
              </div>
            </div>

            <div className="space-y-3">
              <h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">D. ROC Classifier & Youden Threshold</h4>
              <p className="text-xs leading-relaxed text-slate-400">
                Evaluates prediction accuracy on binary outcomes (rebound return &gt; 0). The Youden index maximizes classifier sensitivity and specificity:
              </p>
              <div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2 space-y-4">
                <div>
                  <p className="text-xs text-slate-400 mb-1">Logistic Probability Projection:</p>
                  <BlockMath math="P(\text{Bullish}) = \frac{1}{1 + e^{-\text{Score}}}" />
                </div>
                <div>
                  <p className="text-xs text-slate-400 mb-1">Optimal Youden Index (J):</p>
                  <BlockMath math="J = \text{Sensitivity} + \text{Specificity} - 1 = \text{TPR} + (1 - \text{FPR}) - 1" />
                </div>
                <div>
                  <p className="text-xs text-slate-400 mb-1">Inverting probability optimal threshold <InlineMath math="P^*" /> back to native score <InlineMath math="S^*" /> via Logit:</p>
                  <BlockMath math="S^* = \ln\left(\frac{P^*}{1 - P^*}\right)" />
                </div>
              </div>
            </div>

            <div className="space-y-3">
              <h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">E. Kaplan-Meier Survival Curve</h4>
              <p className="text-xs leading-relaxed text-slate-400">
                Measures trend durability. Survival rates represent the probability of an asset holding its predicted direction before reversing:
              </p>
              <div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2 space-y-4">
                <div>
                  <BlockMath math="\hat{S}(t) = \prod_{t_i \le t} \left(1 - \frac{d_i}{n_i}\right)" />
                  <p className="text-[11px] text-slate-400 mt-2 font-mono">
                    Where:<br/>
                    - <InlineMath math="n_i" /> is the number of active asset-run observations at risk at day <InlineMath math="t" />.<br/>
                    - <InlineMath math="d_i" /> is the number of trend-reversal events recorded on day <InlineMath math="t" />.
                  </p>
                </div>
                <div>
                  <p className="text-xs text-slate-400 mb-1">Reversal trigger with 1% Volatility Buffer:</p>
                  <BlockMath math="\text{Sign}(\text{Score}) \times \text{Return} \le -0.01" />
                </div>
              </div>
            </div>
          </div>
        </div>
      </div>
    </div>
  );
}
