Midas/index.html at main · Billy1900/Midas · GitHub

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<title>Midas - Compound Engineering Framework</title>
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<!-- ═══════════════════════════════════════════════════════════ HERO -->
<header class="hero">
  <div class="wrap">
    <div class="logo-mark"><span class="logo-dot"></span>Midas Quant Systems</div>

    <h1>Mi<span>das</span></h1>

    <p class="tagline">
      Compound Engineering Framework for Alpha Feature Research<br>
      <em>Every iteration makes the next one cheaper.</em>
    </p>

  </div>
</header>

<main>
<div class="wrap">

<!-- ═══════════════════════════════════════════════════════════ OVERVIEW -->
<div class="divider"></div>

<h2>Overview</h2>
<h3>Two loops. One compounding knowledge base.</h3>
<p>
  Midas is a dual-loop system for discovering and maintaining alpha features
  in diverse asset classes.
  The <strong style="color:var(--offline)">Offline Loop</strong> searches for new predictive signals using an LLM as a quant researcher;
  the <strong style="color:var(--online)">Online Loop</strong> monitors live features, diagnoses degradation, and fires kill signals.
  Both loops write structured <strong style="color:var(--gold)">Learning Documents</strong> to a shared knowledge base,
  so that every failure and every success permanently improves future research.
</p>

<div class="note-strip">
  <strong>The compound principle:</strong> each unit of work makes subsequent work easier.
  A feature that fails in the offline loop produces a learning that the next session reads before generating candidates.
  A feature that degrades in production produces an online learning the offline loop reads before its next plan.
</div>

<!-- ═══════════════════════════════════════════════════════════ MASTER PIPELINE -->
<div class="divider"></div>
<h2>System Architecture</h2>
<h3>Master pipeline</h3>

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      <stop offset="100%" stop-color="#1e2a5a"/>
    </linearGradient>
    <linearGradient id="grad-online" x1="0%" y1="0%" x2="100%" y2="0%">
      <stop offset="0%" stop-color="#1a4a3d"/>
      <stop offset="100%" stop-color="#0f2d25"/>
    </linearGradient>
    <linearGradient id="grad-kb" x1="0%" y1="0%" x2="100%" y2="100%">
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  <!-- Background lane: Offline -->
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  <rect x="14" y="16" width="832" height="148" rx="10" fill="none" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.25"/>
  <text x="28" y="36" font-size="9" font-weight="600" letter-spacing="2" fill="#7B8FD4" opacity="0.7" text-decoration="none">OFFLINE LOOP — DISCOVERY</text>

  <!-- Background lane: Online -->
  <rect x="14" y="190" width="832" height="148" rx="10" fill="url(#grad-online)" opacity="0.45"/>
  <rect x="14" y="190" width="832" height="148" rx="10" fill="none" stroke="#4ECBA8" stroke-width="1" stroke-opacity="0.25"/>
  <text x="28" y="210" font-size="9" font-weight="600" letter-spacing="2" fill="#4ECBA8" opacity="0.7">ONLINE LOOP — IMPROVEMENT</text>

  <!-- Background: Knowledge Base -->
  <rect x="14" y="364" width="832" height="100" rx="10" fill="url(#grad-kb)" opacity="0.6"/>
  <rect x="14" y="364" width="832" height="100" rx="10" fill="none" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.3"/>
  <text x="28" y="384" font-size="9" font-weight="600" letter-spacing="2" fill="#C9A84C" opacity="0.8">KNOWLEDGE BASE — SHARED COMPOUND LEARNING</text>

  <!-- ─── Offline nodes ─── -->
  <!-- Plan -->
  <rect x="38" y="52" width="100" height="72" rx="7" fill="url(#grad-node-offline)" stroke="#7B8FD4" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="88" y="83" text-anchor="middle" font-size="18" fill="#7B8FD4" opacity="0.9">📋</text>
  <text x="88" y="103" text-anchor="middle" font-size="11" font-weight="600" fill="#C0C8F0">Plan</text>
  <text x="88" y="117" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.65">LLM research plan</text>

  <!-- Write -->
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  <text x="258" y="83" text-anchor="middle" font-size="18" fill="#7B8FD4" opacity="0.9">✍</text>
  <text x="258" y="103" text-anchor="middle" font-size="11" font-weight="600" fill="#C0C8F0">Write</text>
  <text x="258" y="117" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.65">DSL expression gen</text>

  <!-- Assess -->
  <rect x="378" y="52" width="100" height="72" rx="7" fill="url(#grad-node-offline)" stroke="#7B8FD4" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="428" y="83" text-anchor="middle" font-size="18" fill="#7B8FD4" opacity="0.9">🔬</text>
  <text x="428" y="103" text-anchor="middle" font-size="11" font-weight="600" fill="#C0C8F0">Assess</text>
  <text x="428" y="117" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.65">6-agent evaluation</text>

  <!-- Learn (offline) -->
  <rect x="548" y="52" width="100" height="72" rx="7" fill="url(#grad-node-offline)" stroke="#7B8FD4" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="598" y="83" text-anchor="middle" font-size="18" fill="#7B8FD4" opacity="0.9">💡</text>
  <text x="598" y="103" text-anchor="middle" font-size="11" font-weight="600" fill="#C0C8F0">Learn</text>
  <text x="598" y="117" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.65">Write to KB</text>

  <!-- Candidate store (offline) -->
  <rect x="718" y="52" width="110" height="72" rx="7" fill="#0e1428" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.35" stroke-dasharray="4 3"/>
  <text x="773" y="83" text-anchor="middle" font-size="18" fill="#7B8FD4" opacity="0.6">📁</text>
  <text x="773" y="103" text-anchor="middle" font-size="11" font-weight="500" fill="#8891C4">Candidates</text>
  <text x="773" y="117" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.45">features/candidates/</text>

  <!-- Offline arrows: Plan→Write→Assess→Learn→Candidates -->
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  <line x1="309" y1="88" x2="374" y2="88" stroke="#7B8FD4" stroke-width="1.5" stroke-opacity="0.7" marker-end="url(#arr-offline)"/>
  <line x1="479" y1="88" x2="544" y2="88" stroke="#7B8FD4" stroke-width="1.5" stroke-opacity="0.7" marker-end="url(#arr-offline)"/>
  <line x1="649" y1="88" x2="714" y2="88" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.45" stroke-dasharray="4 3" marker-end="url(#arr-offline)"/>

  <!-- Refine back-arc -->
  <path d="M548,110 C520,148 240,148 208,110" fill="none" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.35" stroke-dasharray="3 4" marker-end="url(#arr-offline)"/>
  <text x="378" y="160" text-anchor="middle" font-size="9" fill="#7B8FD4" opacity="0.45" font-style="italic">refine (if failing)</text>

  <!-- ─── Online nodes ─── -->
  <!-- Deploy -->
  <rect x="38" y="226" width="100" height="72" rx="7" fill="url(#grad-node-online)" stroke="#4ECBA8" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="88" y="257" text-anchor="middle" font-size="18" fill="#4ECBA8" opacity="0.9">🚀</text>
  <text x="88" y="277" text-anchor="middle" font-size="11" font-weight="600" fill="#A0E8D4">Deploy</text>
  <text x="88" y="291" text-anchor="middle" font-size="9" fill="#4ECBA8" opacity="0.65">features/deployed/</text>

  <!-- Monitor -->
  <rect x="208" y="226" width="100" height="72" rx="7" fill="url(#grad-node-online)" stroke="#4ECBA8" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="258" y="257" text-anchor="middle" font-size="18" fill="#4ECBA8" opacity="0.9">📊</text>
  <text x="258" y="277" text-anchor="middle" font-size="11" font-weight="600" fill="#A0E8D4">Monitor</text>
  <text x="258" y="291" text-anchor="middle" font-size="9" fill="#4ECBA8" opacity="0.65">rolling IC · decay</text>

  <!-- Diagnose -->
  <rect x="378" y="226" width="100" height="72" rx="7" fill="url(#grad-node-online)" stroke="#4ECBA8" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="428" y="257" text-anchor="middle" font-size="18" fill="#4ECBA8" opacity="0.9">🩺</text>
  <text x="428" y="277" text-anchor="middle" font-size="11" font-weight="600" fill="#A0E8D4">Diagnose</text>
  <text x="428" y="291" text-anchor="middle" font-size="9" fill="#4ECBA8" opacity="0.65">LLM root-cause</text>

  <!-- Learn (online) -->
  <rect x="548" y="226" width="100" height="72" rx="7" fill="url(#grad-node-online)" stroke="#4ECBA8" stroke-width="1.5" stroke-opacity="0.6"/>
  <text x="598" y="257" text-anchor="middle" font-size="18" fill="#4ECBA8" opacity="0.9">💡</text>
  <text x="598" y="277" text-anchor="middle" font-size="11" font-weight="600" fill="#A0E8D4">Learn</text>
  <text x="598" y="291" text-anchor="middle" font-size="9" fill="#4ECBA8" opacity="0.65">Write to KB</text>

  <!-- Kill / Fix -->
  <rect x="718" y="226" width="110" height="72" rx="7" fill="#1a0a0a" stroke="#D4674E" stroke-width="1" stroke-opacity="0.45" stroke-dasharray="4 3"/>
  <text x="773" y="257" text-anchor="middle" font-size="18" fill="#D4674E" opacity="0.7">⚡</text>
  <text x="773" y="277" text-anchor="middle" font-size="11" font-weight="500" fill="#D4A0A0">Kill / Fix</text>
  <text x="773" y="291" text-anchor="middle" font-size="9" fill="#D4674E" opacity="0.5">on_kill callback</text>

  <!-- Online arrows: Deploy→Monitor→Diagnose→Learn→Kill/Fix -->
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  <!-- Improve back-arc -->
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  <text x="378" y="334" text-anchor="middle" font-size="9" fill="#4ECBA8" opacity="0.4" font-style="italic">fix & redeploy</text>

  <!-- ─── Knowledge Base nodes ─── -->
  <!-- Offline learnings -->
  <rect x="50" y="388" width="130" height="58" rx="6" fill="url(#grad-node-kb)" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.4"/>
  <text x="115" y="412" text-anchor="middle" font-size="11" font-weight="600" fill="#E8C96A">Offline Learnings</text>
  <text x="115" y="428" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.6">learnings/offline/</text>
  <text x="115" y="440" text-anchor="middle" font-size="9" fill="#8C6D2F" opacity="0.6">YYYY-MM-DD-*.md</text>

  <!-- Online learnings -->
  <rect x="220" y="388" width="130" height="58" rx="6" fill="url(#grad-node-kb)" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.4"/>
  <text x="285" y="412" text-anchor="middle" font-size="11" font-weight="600" fill="#E8C96A">Online Learnings</text>
  <text x="285" y="428" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.6">learnings/online/</text>
  <text x="285" y="440" text-anchor="middle" font-size="9" fill="#8C6D2F" opacity="0.6">YYYY-MM-DD-*.md</text>

  <!-- Skills -->
  <rect x="390" y="388" width="100" height="58" rx="6" fill="url(#grad-node-kb)" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.4"/>
  <text x="440" y="412" text-anchor="middle" font-size="11" font-weight="600" fill="#E8C96A">Skills</text>
  <text x="440" y="428" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.6">skills/</text>
  <text x="440" y="440" text-anchor="middle" font-size="9" fill="#8C6D2F" opacity="0.6">DSL · patterns</text>

  <!-- Regimes -->
  <rect x="530" y="388" width="100" height="58" rx="6" fill="url(#grad-node-kb)" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.4"/>
  <text x="580" y="412" text-anchor="middle" font-size="11" font-weight="600" fill="#E8C96A">Regimes</text>
  <text x="580" y="428" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.6">regimes/</text>
  <text x="580" y="440" text-anchor="middle" font-size="9" fill="#8C6D2F" opacity="0.6">market docs</text>

  <!-- Thresholds -->
  <rect x="672" y="388" width="154" height="58" rx="6" fill="url(#grad-node-kb)" stroke="#C9A84C" stroke-width="1" stroke-opacity="0.4"/>
  <text x="749" y="412" text-anchor="middle" font-size="11" font-weight="600" fill="#E8C96A">Thresholds + Reports</text>
  <text x="749" y="428" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.6">thresholds.json</text>
  <text x="749" y="440" text-anchor="middle" font-size="9" fill="#8C6D2F" opacity="0.6">reports/daily/</text>

  <!-- ─── Cross-loop arrows ─── -->
  <!-- Offline Learn → KB Offline Learnings -->
  <line x1="598" y1="124" x2="200" y2="388" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.4" stroke-dasharray="3 4" marker-end="url(#arr-offline)"/>

  <!-- Online Learn → KB Online Learnings -->
  <line x1="598" y1="298" x2="338" y2="388" stroke="#4ECBA8" stroke-width="1" stroke-opacity="0.4" stroke-dasharray="3 4" marker-end="url(#arr-online)"/>

  <!-- KB → Plan (offline reads learnings) -->
  <path d="M115,388 C80,360 60,300 60,190" fill="none" stroke="#C9A84C" stroke-width="1.2" stroke-opacity="0.5" stroke-dasharray="3 4" marker-end="url(#arr-gold)"/>

  <!-- KB → Diagnose (online reads learnings) -->
  <path d="M285,388 C310,355 380,320 390,298" fill="none" stroke="#C9A84C" stroke-width="1.2" stroke-opacity="0.5" stroke-dasharray="3 4" marker-end="url(#arr-gold)"/>

  <!-- Candidates → Deploy (promote pipeline) -->
  <line x1="773" y1="124" x2="773" y2="165" stroke="#C9A84C" stroke-width="1.5" stroke-opacity="0.6" marker-end="url(#arr-gold)"/>
  <line x1="773" y1="165" x2="138" y2="220" stroke="#C9A84C" stroke-width="1.5" stroke-opacity="0.5" marker-end="url(#arr-gold)"/>
  <text x="500" y="190" text-anchor="middle" font-size="9" fill="#C9A84C" opacity="0.7" font-weight="600">promote →</text>
</svg>
</div>

<!-- ═══════════════════════════════════════════════════════════ OFFLINE DETAIL -->
<div class="divider"></div>
<h2>Offline Loop</h2>

<span class="loop-label offline">Discovery Pipeline</span>
<h3>Plan → Write → Assess → Learn</h3>
<p>
  Each session begins by reading the knowledge base — past failures, past successes,
  regime docs — before generating a research plan. The LLM then proposes 3 diverse
  DSL expressions. Each is validated, computed against historical data, and scored by
  six evaluation agents running in parallel threads. Failing expressions are refined;
  passing ones are saved as candidates.
</p>

<div class="pipeline-wrap">
<svg width="800" height="280" viewBox="0 0 800 280" xmlns="http://www.w3.org/2000/svg" font-family="DM Mono, monospace">
  <defs>
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    </marker>
    <marker id="a-val" markerWidth="7" markerHeight="7" refX="5" refY="3" orient="auto">
      <path d="M0,0 L0,6 L7,3 z" fill="#D4674E"/>
    </marker>
    <marker id="a-acc" markerWidth="7" markerHeight="7" refX="5" refY="3" orient="auto">
      <path d="M0,0 L0,6 L7,3 z" fill="#C9A84C"/>
    </marker>
  </defs>

  <!-- Step boxes -->
  <!-- 1. Load KB -->
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  <text x="65" y="127" text-anchor="middle" font-size="10" font-weight="600" fill="#C0C8F0">Load KB</text>
  <text x="65" y="142" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">learnings · skills</text>
  <text x="65" y="155" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">thresholds</text>

  <!-- 2. Plan (LLM) -->
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  <text x="215" y="127" text-anchor="middle" font-size="10" font-weight="600" fill="#C0C8F0">Plan</text>
  <text x="215" y="142" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">hypothesis · risks</text>
  <text x="215" y="155" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">target horizon</text>
  <text x="215" y="90" text-anchor="middle" font-size="8" fill="#7B8FD4" opacity="0.45">LLM</text>

  <!-- 3. Generate -->
  <rect x="320" y="100" width="110" height="64" rx="6" fill="#1a1e3a" stroke="#7B8FD4" stroke-width="1.2" stroke-opacity="0.6"/>
  <text x="375" y="127" text-anchor="middle" font-size="10" font-weight="600" fill="#C0C8F0">Generate</text>
  <text x="375" y="142" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">3 candidates</text>
  <text x="375" y="155" text-anchor="middle" font-size="8.5" fill="#7B8FD4" opacity="0.7">DSL expressions</text>
  <text x="375" y="90" text-anchor="middle" font-size="8" fill="#7B8FD4" opacity="0.45">LLM</text>

  <!-- 4. Validate -->
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  <text x="375" y="220" text-anchor="middle" font-size="10" font-weight="600" fill="#D4A0A0">DSLValidator</text>
  <text x="375" y="236" text-anchor="middle" font-size="8.5" fill="#D4674E" opacity="0.65">syntax · depth</text>

  <!-- 5. Evaluate (6 agents) -->
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  <text x="545" y="122" text-anchor="middle" font-size="10" font-weight="600" fill="#C0C8F0">6-Agent Eval</text>
  <text x="545" y="136" text-anchor="middle" font-size="7.5" fill="#7B8FD4" opacity="0.65">predictive · decay · cost</text>
  <text x="545" y="149" text-anchor="middle" font-size="7.5" fill="#7B8FD4" opacity="0.65">diversify · overfit · regime</text>
  <text x="545" y="158" text-anchor="middle" font-size="7" fill="#7B8FD4" opacity="0.4">∥ parallel threads</text>

  <!-- 6. Learn + Save -->
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  <text x="725" y="127" text-anchor="middle" font-size="10" font-weight="600" fill="#E8C96A">Learn</text>
  <text x="725" y="142" text-anchor="middle" font-size="8.5" fill="#C9A84C" opacity="0.7">LearningDocument</text>
  <text x="725" y="155" text-anchor="middle" font-size="8.5" fill="#C9A84C" opacity="0.7">→ KB + candidates/</text>
  <text x="725" y="90" text-anchor="middle" font-size="8" fill="#C9A84C" opacity="0.45">LLM</text>

  <!-- Arrows -->
  <line x1="120" y1="132" x2="156" y2="132" stroke="#7B8FD4" stroke-width="1.3" stroke-opacity="0.7" marker-end="url(#a-off)"/>
  <line x1="270" y1="132" x2="316" y2="132" stroke="#7B8FD4" stroke-width="1.3" stroke-opacity="0.7" marker-end="url(#a-off)"/>

  <!-- Generate → Validate -->
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  <!-- Validate → Generate (invalid) -->
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  <text x="268" y="188" text-anchor="middle" font-size="8" fill="#D4674E" opacity="0.5">invalid</text>

  <!-- Validate → Evaluate (valid) -->
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  <text x="468" y="200" text-anchor="middle" font-size="8" fill="#7B8FD4" opacity="0.5">valid</text>

  <!-- Evaluate → Learn -->
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  <!-- Refine back-arc from Evaluate to Generate -->
  <path d="M545,164 C545,240 375,240 375,164" fill="none" stroke="#7B8FD4" stroke-width="1" stroke-opacity="0.35" stroke-dasharray="3 4" marker-end="url(#a-off)"/>
  <text x="460" y="256" text-anchor="middle" font-size="8" fill="#7B8FD4" opacity="0.4" font-style="italic">refine if failing</text>

  <!-- IC pass/fail -->
  <text x="645" y="110" text-anchor="middle" font-size="8" fill="#C9A84C" opacity="0.5">pass</text>
</svg>
</div>

<!-- ═══════════════════════════════════════════════════════════ EVALUATOR DETAIL -->
<div class="divider"></div>
<h2>Evaluator</h2>

<span class="loop-label offline">Static · Deterministic · Parallel</span>
<h3>Six evaluation agents</h3>
<p>
  <code>MultiAgentEvaluator</code> runs six specialised agents in parallel threads via
  <code>ThreadPoolExecutor</code>. All metric computation is vectorised numpy/pandas —
  no LLM calls in the inner loop. Each agent returns a <code>AgentVerdict</code>
  with a pass/fail, a 0–1 score, and concrete improvement suggestions fed back
  to the LLM refine prompt.
</p>

<div class="metric-row">
  <span class="metric-pill">📈 predictive_power</span>
  <span class="metric-pill">⏱ decay_analysis</span>
  <span class="metric-pill">💸 trading_cost</span>
  <span class="metric-pill">🔀 diversification</span>
  <span class="metric-pill">🔍 overfit_detection</span>
  <span class="metric-pill">🌊 regime_robustness</span>
</div>

<div class="pipeline-wrap">
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  <text x="415" y="10" text-anchor="middle" font-size="8" fill="#7B8FD4" opacity="0.4">∥ ThreadPoolExecutor</text>
</svg>
</div>

<!-- ═══════════════════════════════════════════════════════════ ONLINE DETAIL -->
<div class="divider"></div>
<h2>Online Loop</h2>

<span class="loop-label online">Live Production Monitoring</span>
<h3>Deploy → Monitor → Diagnose → Learn</h3>
<p>
  <code>OnlineMonitor.process_update()</code> is called on every completed bar (typically hourly).
  It maintains rolling buffers for up to 30 days, computes <code>ic_5d / ic_30d</code> ratios,
  realised turnover, and PnL attribution. When thresholds are breached, <code>AlertEngine</code>
  fires an <code>Alert</code>. Critical alerts trigger an async LLM diagnosis that writes a
  structured learning to the knowledge base — the core compound-learning hook.
</p>

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</div>

<!-- ═══════════════════════════════════════════════════════════ FEATURE LIFECYCLE -->
<div class="divider"></div>
<h2>Feature Lifecycle</h2>
<h3>Candidate → Deployed → Archived</h3>

<div class="pipeline-wrap">
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</div>

<!-- ═══════════════════════════════════════════════════════════ MODULES -->
<div class="divider"></div>
<h2>Modules</h2>
<h3>What's in the package</h3>

<table class="mod-table">
  <thead>
    <tr>
      <th>File</th>
      <th>Responsibility</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><span class="mod-name">models.py</span></td>
      <td class="mod-desc">All shared dataclasses — <code>EvaluationResult</code>, <code>MultiAgentResult</code>, <code>Alert</code>, <code>FeatureMetrics</code>, <code>LearningDocument</code>, <code>DailyReport</code>, <code>DiagnoseResult</code>. No external imports beyond stdlib.</td>
    </tr>
    <tr>
      <td><span class="mod-name">kb.py</span></td>
      <td class="mod-desc">Filesystem abstraction for the knowledge base. All reads and writes go through typed helpers. Seeds default Midas DSL skill, prompt templates, and threshold JSON on first run.</td>
    </tr>
    <tr>
      <td><span class="mod-name">evaluator.py</span></td>
      <td class="mod-desc"><code>AlphaEvaluator</code> computes all <code>EvaluationResult</code> fields (vectorised). <code>MultiAgentEvaluator</code> runs six specialised agents in <code>ThreadPoolExecutor</code> and synthesises verdicts.</td>
    </tr>
    <tr>
      <td><span class="mod-name">proposer.py</span></td>
      <td class="mod-desc"><code>DSLValidator</code> - fast syntax, depth, and lookback checks with no LLM calls. <code>ExpressionProposer</code> handles the Plan, Generate, and Refine phases through the configured provider, including OpenAI and Anthropic.</td>
    </tr>
    <tr>
      <td><span class="mod-name">loops.py</span></td>
      <td class="mod-desc">The offline compound loop. Orchestrates Plan -&gt; Write -&gt; Assess -&gt; Learn, handles refinement, and writes <code>LearningDocument</code> plus candidate markdown to the knowledge base every run.</td>
    </tr>
    <tr>
      <td><span class="mod-name">monitor.py</span></td>
      <td class="mod-desc"><code>MonitorEngine</code> (rolling buffers), <code>AlertEngine</code> (threshold rules), <code>DiagnoseAgent</code> (async LLM diagnosis + kill signal), <code>OnlineMonitor</code> (top-level orchestrator).</td>
    </tr>
    <tr>
      <td><span class="mod-name">promoter.py</span></td>
      <td class="mod-desc"><code>FeaturePromoter</code> manages candidate -&gt; deployed -&gt; archived transitions through markdown state updates. Exposes <code>pipeline_summary()</code> and <code>print_pipeline()</code>.</td>
    </tr>
    <tr>
      <td><span class="mod-name">factory.py</span></td>
      <td class="mod-desc"><code>create_midas()</code> bootstraps the package. The <code>Midas</code> container wires all components together. The CLI entry point (<code>python -m midas</code>) supports status, promote, demote, reject, learnings, and demo commands.</td>
    </tr>
  </tbody>
</table>

<!-- ═══════════════════════════════════════════════════════════ KB STRUCTURE -->
<div class="divider"></div>
<h2>Knowledge Base</h2>
<h3>Directory structure</h3>

<div class="kb-tree">
<span class="dir">midas-kb/</span><br>
|-- <span class="dir">skills/</span><br>
|   |-- <span class="file">midas-dsl.md</span>         <span class="note"># full DSL operator reference - seeded automatically</span><br>
|   `-- <span class="file">factor-patterns.md</span>   <span class="note"># common alpha pattern catalogue</span><br>
|-- <span class="dir">knowledge/</span><br>
|   |-- <span class="dir">features/</span><br>
|   |   |-- <span class="dir">deployed/</span>         <span class="note"># live in production - monitored every bar</span><br>
|   |   |-- <span class="dir">candidates/</span>       <span class="note"># passed backtest, awaiting promote()</span><br>
|   |   `-- <span class="dir">archived/</span>         <span class="note"># retired, failure analysis attached</span><br>
|   |-- <span class="dir">learnings/</span><br>
|   |   |-- <span class="dir">offline/</span>          <span class="note"># YYYY-MM-DD-&lt;pattern&gt;.md</span><br>
|   |   `-- <span class="dir">online/</span>           <span class="note"># YYYY-MM-DD-&lt;feature&gt;-&lt;alert_type&gt;.md</span><br>
|   |-- <span class="dir">regimes/</span><br>
|   `-- <span class="file">thresholds.json</span>      <span class="note"># pass/fail thresholds for all agents</span><br>
|-- <span class="dir">proposer/prompts/</span><br>
|   |-- <span class="file">plan.md</span>              <span class="note"># seeded on first run, fully editable</span><br>