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Closes #016 - Deploy Native KaTeX Rig & Dual-Handbook System

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Antigravity Agent
2026-06-13 15:16:57 +02:00
parent 59e0a04bfa
commit 94ccf63a38
26 changed files with 1074 additions and 137 deletions
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16
components/modules/crypto/CryptoMathModal.tsx
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@@ -65,11 +65,11 @@ export default function CryptoMathModal({ isOpen, onClose }: CryptoMathModalProp
<span className="block text-rose-400 font-bold">State 1 (S1)</span>
<span>Bearish Squeeze / Crackdown</span>
</div>
<div className="bg-slate-955/40 p-3 rounded-lg border border-slate-800/50">
<div className="bg-slate-950/40 p-3 rounded-lg border border-slate-800/50">
<span className="block text-slate-300 font-bold">State 2 (S2)</span>
<span>Consolidation / Mean Reversion</span>
</div>
<div className="bg-slate-955/40 p-3 rounded-lg border border-slate-800/50">
<div className="bg-slate-950/40 p-3 rounded-lg border border-slate-800/50">
<span className="block text-emerald-400 font-bold">State 3 (S3)</span>
<span>Parabolic Bull Run</span>
</div>
@@ -117,10 +117,10 @@ export default function CryptoMathModal({ isOpen, onClose }: CryptoMathModalProp
<div className="bg-slate-950/40 p-5 rounded-2xl border border-slate-800/60 space-y-3">
<span className="text-xs font-semibold text-slate-200 block">Ensemble Feature Inputs:</span>
<ul className="text-xs text-slate-400 list-disc pl-5 space-y-1">
<li><strong className="text-cyan-400">Funding Rates (\\(FR_t\\)):</strong> Measures leverage imbalance in futures contracts.</li>
<li><strong className="text-cyan-400">Open Interest Volatility (\\(OI_t\\)):</strong> Captures the contract buildup speed.</li>
<li><strong className="text-cyan-400">Long/Short Retail Skew (\\(LS_t\\)):</strong> Identifies retail sentiment extremes.</li>
<li><strong className="text-cyan-400">Whale Accumulation Inflows (\\(W_t\\)):</strong> Proxy for cold-wallet transfer velocities.</li>
<li><strong className="text-cyan-400">Funding Rates (<InlineMath math="FR_t" />):</strong> Measures leverage imbalance in futures contracts.</li>
<li><strong className="text-cyan-400">Open Interest Volatility (<InlineMath math="OI_t" />):</strong> Captures the contract buildup speed.</li>
<li><strong className="text-cyan-400">Long/Short Retail Skew (<InlineMath math="LS_t" />):</strong> Identifies retail sentiment extremes.</li>
<li><strong className="text-cyan-400">Whale Accumulation Inflows (<InlineMath math="W_t" />):</strong> Proxy for cold-wallet transfer velocities.</li>
</ul>
<p className="text-xs text-slate-400 leading-relaxed pt-2">
The Random Forest ensemble evaluates 10 non-linear decision trees to output the short-term and medium-term trend probabilities:
@@ -135,7 +135,7 @@ export default function CryptoMathModal({ isOpen, onClose }: CryptoMathModalProp
<p className="text-xs leading-relaxed text-slate-400">
Raw ML forecasts are susceptible to regime drift. We implement a self-correcting Beta-Binomial update. The continuous probability output from the Random Forest model is mapped into a discrete Binomial likelihood by defining the Trust-Weight Hyperparameter (<InlineMath math="w" />) as the Effective Sample Size (ESS):
</p>
<div className="bg-slate-955/40 p-5 rounded-2xl border border-slate-850 space-y-4">
<div className="bg-slate-950/40 p-5 rounded-2xl border border-slate-850 space-y-4">
<div>
<p className="text-xs text-slate-350 mb-2 font-semibold">1. Prior Distribution (Accuracy History):</p>
<BlockMath math="\\theta \\sim \\text{Beta}(\\alpha_{\\text{prior}}, \\beta_{\\text{prior}})" />
@@ -165,7 +165,7 @@ export default function CryptoMathModal({ isOpen, onClose }: CryptoMathModalProp
<p className="text-xs leading-relaxed text-slate-400">
To resolve a single operational point-estimate from our posterior distribution, we integrate out the continuous parameter <InlineMath math="\\theta" /> to calculate the mathematical expectation of the posterior distribution:
</p>
<div className="bg-slate-955/40 p-5 rounded-2xl border border-slate-850 space-y-4">
<div className="bg-slate-950/40 p-5 rounded-2xl border border-slate-850 space-y-4">
<p className="text-xs text-slate-350 font-semibold">Posterior Expectation Integral Proof:</p>
<BlockMath math="\\mathbb{E}[\\theta \\mid \\text{Data}] = \\int_{0}^{1} \\theta \\cdot P(\\theta \\mid \\text{Data}) \\, d\\theta = \\int_{0}^{1} \\theta \\cdot \\frac{\\theta^{\\alpha_{\\text{post}}-1}(1-\\theta)^{\\beta_{\\text{post}}-1}}{\\text{B}(\\alpha_{\\text{post}}, \\beta_{\\text{post}})} \\, d\\theta" />
<p className="text-xs text-slate-400 leading-relaxed">