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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Slipstream</title>
<link rel="stylesheet" href="/static/app.css?v=20260820a">
</head>
<body>
<header>
  <div class="brand">
    <h1>Slipstream</h1>
    <span class="brand-tag">smart-money Β· quality Β· trend-adaptive</span>
  </div>
  <nav class="tabs">
    <button class="tab active" data-tab="fund">Fund</button>
    <button class="tab" data-tab="about">Strategy &amp; Research</button>
  </nav>
  <span id="env-badge" class="badge">checking…</span>
</header>

<main>
  <!-- ============ FUND TAB ============ -->
  <div id="tab-fund" class="tab-panel active">
    <div id="statusline" class="statusline">β€”</div>
    <div id="healthstrip" class="healthstrip" hidden></div>

    <section class="card fund">
      <div class="card-head">
        <h2>Fund Portfolio &amp; Performance</h2>
        <span id="regime" class="regime">β€”</span>
      </div>
      <div id="kpis" class="kpis"></div>
      <div class="perf">
        <div class="perf-head">
          <span class="legend"><i class="lg-fund"></i> Slipstream</span>
          <span class="legend"><i class="lg-spy"></i> SPY</span>
          <span id="perf-note" class="muted"></span>
        </div>
        <canvas id="navchart" height="240"></canvas>
      </div>
      <div class="fund-cols">
        <div>
          <h3>Holdings <small id="holdings-n"></small></h3>
          <div id="holdings">Loading…</div>
        </div>
        <div>
          <h3>Target book <small>Slipstream rule</small></h3>
          <div id="target">Loading…</div>
          <div class="rebal">
            <button id="rebal-preview">Preview rebalance</button>
            <div id="rebal-out"></div>
          </div>
        </div>
      </div>
      <div id="riskscreen" class="riskscreen"></div>
    </section>

    <div class="picks-zone">
      <h2 class="zone-title">Top AI Picks of the Month</h2>
      <p class="zone-sub">Informational signals β€” the fund follows a validated, risk-controlled subset of these. Not individual trade advice.</p>
      <div class="picks-row">
        <section class="card picks" id="panel-superinvestor">
          <h3><span class="dot"></span>Super Investor Activity<a class="top20" href="/picks?panel=superinvestor">Top 20 β€Ί</a></h3>
          <p class="hint">Top 5 by net guru activity last quarter (2Γ— new β‰₯$10M positions + adds βˆ’ trims βˆ’ 2Γ— exits) β€” SEC 13F via Sharadar, curated guru list</p>
          <div class="picks-body">Loading…</div>
          <button class="refresh" data-panel="superinvestor">↻ Refresh</button>
        </section>
        <section class="card picks" id="panel-insider">
          <h3><span class="dot"></span>Insider Activity<a class="top20" href="/picks?panel=insider">Top 20 β€Ί</a></h3>
          <p class="hint">Top 5 by insider conviction, last 30 days β€” SEC Form 4 open-market buys by officers/directors, LLM-screened</p>
          <div class="picks-body">Loading…</div>
          <button class="refresh" data-panel="insider">↻ Refresh</button>
        </section>
        <section class="card picks" id="panel-news">
          <h3><span class="dot"></span>News Action<a class="top20" href="/picks?panel=news">Top 20 β€Ί</a></h3>
          <p class="hint">Top 5 by positive S&amp;P 500 news, last 30 days, LLM-validated</p>
          <div class="picks-body">Loading…</div>
          <button class="refresh" data-panel="news">↻ Refresh</button>
        </section>
      </div>
    </div>
  </div>

  <!-- ============ ABOUT / RESEARCH TAB ============ -->
  <div id="tab-about" class="tab-panel">
    <article class="doc">
      <p class="doc-lede">Slipstream is a quality, trend-adaptive equity paper fund that <b>drafts the smart money</b> β€” it holds the large-caps that concentrated, high-conviction 13F managers are collectively buying, keeps only the most profitable companies in each sector, caps any one stock at 10%, and rotates into momentum when the market trend breaks. What follows is the rule, the backtest, and an honest account of everything we tried on the way here.</p>

      <h2 class="doc-h">1 Β· Strategy &amp; approach</h2>
      <p>The core question: does <em>following the smart money</em> actually work? Our October 2026 re-validation, run from a fresh data pull, found that the original rule (curated super-investors + insiders, ROE/debt quality gate) had <b>no demonstrated edge</b> over SPY. Eight pre-registered follow-up studies then identified what does carry information: <b>breadth of conviction buying among concentrated managers</b>, filtered for <b>profitability within each sector</b>. The rule (v2, live since 5 October 2026), applied monthly:</p>
      <table class="doc-table">
        <tbody>
          <tr><td class="k">Managers</td><td>Every 13F filer that behaves like a concentrated stock-picker <em>that quarter</em>: 10–150 positions, $250M–$100B reported equity, β‰₯40% in its top 10, and a filing the previous quarter. Rule-based, ~900–1,500 managers a quarter β€” new managers join automatically, nobody curates the list</td></tr>
          <tr><td class="k">Signal</td><td><b>Consensus:</b> for each stock, 2 Γ— managers opening a conviction position + managers adding βˆ’ 2 Γ— managers exiting (a conviction position is β‰₯$1M and β‰₯0.5% of the manager's portfolio). Buy candidates have a positive count. 13F only β€” insider buying is no longer used</td></tr>
          <tr><td class="k">Universe</td><td>Top ~1,000 US stocks by market cap, point-in-time</td></tr>
          <tr><td class="k">Quality</td><td>Return on assets in the <b>top 30% of its own sector</b> (latest filing public at the time) β€” so every sector contributes its most profitable companies, and banks or utilities aren't screened out wholesale</td></tr>
          <tr><td class="k">Construction</td><td>Top 20 by consensus, weighted by the dollars managers committed, each stock <b>1–10%</b>, at most 5 per sector</td></tr>
          <tr><td class="k">AI event-risk overlay</td><td>A second-stage AI screen reads recent news on the final candidates and <b>hard-excludes only critical events</b> β€” threats to the company itself: going-concern / bankruptcy, fraud or restatement, criminal indictment, loss of the license to operate, sanctions cutting off a major share of revenue. Lesser events are shown and held. Excluded names are replaced by the next-ranked candidate</td></tr>
          <tr><td class="k">Dynamic sector cap</td><td>Each sector capped at 30% <b>Β± its 12-1m relative strength vs SPY</b> (band 15–45%)</td></tr>
          <tr><td class="k">Regime overlay</td><td>When SPY is in a <b>confirmed downtrend</b> (below its 200-day MA, confirmed a month earlier), the whole book rotates into the 50 large-caps with the strongest 12-month momentum</td></tr>
          <tr><td class="k">Rebalance</td><td>Monthly (~3.5Γ— one-way turnover a year), 1% cash buffer, 0.5% trade deadband</td></tr>
        </tbody>
      </table>
      <p class="doc-note">Why these pieces: counting <em>how many</em> conviction managers are buying works better than following any curated few β€” no 13F manager's individual record proved persistent enough to pick on. The within-sector ROA filter is the single biggest improvement we found; it lifted every manager list we tested. The 10% stock cap keeps the same risk-adjusted return as 15% with a less concentrated book. The regime overlay is what carries the fund through bear markets.</p>
      <p class="doc-note"><b>On the AI overlay β€” deliberately narrow.</b> Selection stays 100% systematic; the LLM is <em>not</em> a stock-picker. Because no backtest can include it, it now fires <b>only on critical events</b>, so the live fund deviates from the tested rule only when a company itself is under threat. It is capped (≀3 exclusions/rebalance), every exclusion cites its headline, and if the news feed or model is unavailable the fund trades the pure quant book unchanged.</p>

      <h2 class="doc-h">2 Β· Backtest results</h2>
      <p>Survivorship-free backtest, 2014-03 β†’ 2026-10, rebuilt from a fresh Sharadar pull (4,323 delisted names included; delisting returns from corporate actions). Trades use only data available the day before, net of 20 bp per side, 1% cash; Sharpe and Sortino are net of T-bills. The AI overlay is not included (it can't be backtested).</p>
      <div class="doc-kpis">
        <div class="dk"><span>CAGR</span><b>19.9%</b><small>SPY 13.9%</small></div>
        <div class="dk"><span>Excess / yr</span><b class="pos">+5.9%</b><small>vs SPY</small></div>
        <div class="dk"><span>Sharpe</span><b>0.85</b><small>SPY 0.73</small></div>
        <div class="dk"><span>Sortino</span><b>1.04</b><small></small></div>
        <div class="dk"><span>Alpha (ann)</span><b class="pos">+3.9%</b><small>t 1.8 Β· Ξ² 1.17</small></div>
        <div class="dk"><span>Max drawdown</span><b class="neg">-37.5%</b><small>SPY -33.7%</small></div>
        <div class="dk"><span>Years beat SPY</span><b>6 / 11</b><small>2015–2025</small></div>
        <div class="dk"><span>In momentum</span><b>24%</b><small>of months</small></div>
      </div>
      <figure class="doc-chart">
        <div class="dc-head"><span class="dc-title">Growth of $100 β€” Slipstream vs SPY</span>
          <span class="dc-leg"><i class="lg-fund"></i> Slipstream <i class="lg-spy" style="margin-left:12px"></i> SPY <span class="faint" style="margin-left:12px">2014-03 β†’ 2026-10 Β· net of cost Β· log scale</span></span></div>
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387.1,207.6 393.4,215.5 399.5,217.0 405.8,228.2 412.0,214.2 417.7,208.2 424.0,207.3 430.1,200.7 436.4,208.4 442.5,202.6 448.8,199.9 455.0,199.6 461.1,199.8 467.4,196.1 473.5,190.8 479.8,186.6 486.1,184.9 492.0,190.6 498.2,210.7 504.3,192.7 510.6,183.7 516.7,173.6 523.0,167.4 529.3,155.5 535.4,159.8 541.6,161.6 547.7,154.8 554.0,152.3 560.3,153.7 566.0,152.1 572.3,150.0 578.4,142.7 584.6,144.7 590.7,138.3 597.0,135.5 603.3,133.2 609.4,139.3 615.7,131.5 621.8,130.4 628.0,127.0 634.3,131.7 640.0,140.9 646.3,136.0 652.4,144.7 658.7,137.4 664.7,154.6 671.0,145.0 677.3,142.7 683.4,156.8 689.7,141.6 695.8,142.1 702.1,151.0 708.4,147.7 714.0,154.6 720.3,144.1 726.4,143.0 732.7,136.5 738.8,131.0 745.1,125.9 751.3,127.1 757.4,131.3 763.7,132.7 769.8,118.1 776.1,112.7 782.4,109.6 788.3,101.8 794.5,100.2 800.6,102.4 806.9,94.8 813.0,87.9 819.3,90.1 825.6,84.5 831.7,79.0 837.9,78.3 844.0,75.6 850.3,77.3 856.6,70.7 862.3,72.0 868.6,80.8 874.7,77.0 880.9,66.5 887.0,57.3 893.3,56.2 899.6,54.7 905.7,49.8 912.0,46.2 918.1,45.1 924.3,46.4 930.6,46.5 936.3,49.4 942.6,55.8 948.7,35.9 955.0,20.4 961.1,27.2 967.3,22.4 973.6,19.6 979.7,17.5 986.0,16.5" fill="none" stroke="#37b8a4" stroke-width="2.4"/><circle cx="986.0" cy="16.5" r="3.5" fill="#37b8a4"/><text x="982.0" y="8.5" fill="#3ed88f" font-size="11" text-anchor="end" font-family="monospace">$973</text><text x="982.0" y="99.9" fill="#8695ad" font-size="11" text-anchor="end" font-family="monospace">$515</text><text x="109.7" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2015</text><text x="257.9" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2017</text><text x="406.0" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2019</text><text x="554.2" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2021</text><text x="702.3" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2023</text><text x="850.5" y="294" fill="#5d6b83" font-size="10" text-anchor="middle" font-family="monospace">2025</text></svg>
      </figure>
      <table class="doc-table wide">
        <thead><tr><th>Period</th><th>Fund CAGR</th><th>SPY</th><th>Excess</th><th>Sharpe (SPY)</th></tr></thead>
        <tbody>
          <tr><td>2014–2019 (calm bull)</td><td>13.1%</td><td>12.2%</td><td class="pos">+0.9%</td><td>0.77 (0.88)</td></tr>
          <tr><td>2020–2022 (COVID + rate shock)</td><td>11.5%</td><td>7.6%</td><td class="pos">+3.9%</td><td>0.49 (0.39)</td></tr>
          <tr><td>2023–2026 (AI mega-cap rally)</td><td>39.1%</td><td>22.1%</td><td class="pos">+17.0%</td><td>1.46 (1.11)</td></tr>
        </tbody>
      </table>
      <p class="doc-note">How the rule was chosen: settings were selected on 2016-03 β†’ 2021-12 only (Sharpe 1.08 vs SPY 1.00). 2022 onward was then reviewed across several studies, so it is not a clean out-of-sample test (Sharpe 0.86 vs 0.52). The two years before selection, 2014-03 β†’ 2016-02, were never used β€” and the fund <b>trailed SPY</b> there (βˆ’1.8% vs +4.4% a year, hurt by risk-off whipsaws in 2015–16).</p>
      <p class="doc-warn"><b>Read honestly.</b> This is a <em>simulated backtest</em>, not a live track record, and it is the best of ~210 configurations tested β€” so expect live results below it. Over the full period the alpha is positive but only borderline significant (t 1.8); the fund carries more market risk than SPY (Ξ² 1.17, volatility ~22%, drawdowns to -37.5%); roughly half the book is usually tech-related; and much of the excess return came after 2023. Candidate rules are tracked in a forward "shadow book" whose books are recorded before their returns exist β€” that record, not this chart, is the real test. Not investment advice.</p>

      <h2 class="doc-h">3 Β· Everything we explored</h2>
      <p>We stress-tested the idea from every angle before settling on the rule above. Each row is a real backtest; the pattern that emerged: <b>every "buy it cheap / beaten-down" tilt hurt, and every "quality / large-cap / diversify / adapt" control helped.</b></p>
      <table class="doc-table wide">
        <thead><tr><th>What we tried</th><th>Result</th><th>Verdict</th></tr></thead>
        <tbody>
          <tr><td>Raw 13F + insider, whole market</td><td><b>0 of 120</b> constructions beat SPY; insider buying actively destructive (βˆ’63% drawdown, worse than random)</td><td class="neg">βœ— no edge</td></tr>
          <tr><td>Prior IC studies (cross-sectional)</td><td>Insider IC β‰ˆ βˆ’0.02, guru β‰ˆ +0.02 (not significant); only institutional breadth marginally positive ("size proxy")</td><td class="neg">βœ— confirms weak</td></tr>
          <tr><td>Restrict to large caps (top-500 / 1,000)</td><td>An edge appears (+2–3% excess) β€” but it was <b>concentrated sector risk</b>, not skill</td><td class="warn">~ misleading</td></tr>
          <tr><td>Sector cap / sector-neutral</td><td>The "edge" collapses to β‰ˆ market once the sector bet is removed β†’ proves it wasn't stock-picking</td><td class="neg">βœ— revealing</td></tr>
          <tr><td><b>Dynamic sector cap (relative-strength)</b></td><td>Beats a hard cap: lets leaders run, trims faders; full-period Sharpe 0.82 β†’ 0.86</td><td class="pos">βœ“ adopted</td></tr>
          <tr><td>Dip / value entry (below 52w-high or 200DMA)</td><td>Made it <b>worse</b> (18 β†’ 1 SPY-beaters) β€” the falling-knife effect; buying strength beats buying dips here</td><td class="neg">βœ— rejected</td></tr>
          <tr><td><b>Quality screen (ROE, debt/equity)</b></td><td>The least-bad overlay β€” quality is a real factor; nudged more configs above SPY</td><td class="pos">βœ“ adopted</td></tr>
          <tr><td>Hold-until-sell (mirror guru holding periods)</td><td>Sell signals are rare β†’ book ballooned to ~190 names and diluted to market; underperformed</td><td class="neg">βœ— rejected</td></tr>
          <tr><td>Monthly vs quarterly churn</td><td>Monthly β‰ˆ quarterly return but more turnover β€” 13F updates quarterly, so churning adds cost, not signal</td><td class="warn">~ no gain</td></tr>
          <tr><td>Momentum factor (in the 2020–22 window)</td><td>The one thing that beat the fund <em>and</em> SPY in that stretch β€” because it's regime-adaptive</td><td class="pos">βœ“ informed overlay</td></tr>
          <tr><td><b>Regime switch β†’ momentum (200DMA, confirmed)</b></td><td>Fixes 2020–22 (βˆ’2.8% β†’ +6.0%), lifts full-period excess to +5.0%, same drawdown</td><td class="pos">βœ“ adopted</td></tr>
          <tr><td>Multi-signal regime consensus (5 signals, K-of-N)</td><td>No better than the simple single 200DMA filter; more knobs = more overfitting risk</td><td class="warn">~ kept it simple</td></tr>
          <tr><td>Dynamic cap + regime overlay (v1, Aug 2026)</td><td>Reported 19.5% CAGR / Sharpe 0.92 β€” <b>not reproducible</b> (its 13F input was overwritten); rebuilt on fresh data with live execution: 15.5% CAGR, Sharpe 0.67 vs SPY 0.73, no significant alpha</td><td class="warn">~ superseded</td></tr>
          <tr><td>Wider / different manager lists (102, 210 curated; top-200 track records; "emerging" managers; top inflows)</td><td>No list beat the simple rule-based concentrated-manager list; past 13F skill doesn't persist (rank correlation β‰ˆ 0 across 44 quarters)</td><td class="neg">βœ— rejected</td></tr>
          <tr><td>Accumulation Radar (volume footprint nowcast of 13F buying)</td><td>Detects institutional accumulation well (AUC 0.76) but adds no return on top of 13F consensus</td><td class="neg">βœ— rejected</td></tr>
          <tr><td><b>Quality = ROA, top 30% within sector</b></td><td>Lifted every manager list (e.g. Sharpe 0.89 β†’ 1.07 on 2016–21); smart-money book now beats SPY in risk-on months</td><td class="pos">βœ“ adopted</td></tr>
          <tr><td>Add debt/equity screen</td><td>Lowered return in all 4 variants, no drawdown benefit β€” mostly removes high-ROA buyback compounders</td><td class="neg">βœ— rejected</td></tr>
          <tr><td><b>10% stock cap (vs 15%)</b></td><td>Same Sharpe, top-5 weight 56% β†’ 48%</td><td class="pos">βœ“ adopted</td></tr>
          <tr><td>Tech cap (Technology + Communication ≀ 30–40%)</td><td>Costs 1.4–2.5%/yr, no drawdown benefit β€” a view on tech, not a free risk reduction</td><td class="warn">~ optional, not used</td></tr>
          <tr><td><b>Concentrated-manager consensus + ROA-30 + 10% cap (v2)</b></td><td>19.9% CAGR vs SPY 13.9%, Sharpe 0.85 vs 0.73, -37.5% max DD (2014–2026)</td><td class="pos">β˜… Slipstream</td></tr>
        </tbody>
      </table>
      <p class="doc-note">A recurring lesson worth keeping: the flashiest backtest numbers came from the configs taking the most <em>uncompensated</em> risk (concentration, single regime). Each time we tightened the methodology β€” survivorship, costs, sub-period robustness, sector control, multiple-testing thresholds β€” the "edge" shrank toward the market. Slipstream is what was left standing after all of that.</p>

      <h2 class="doc-h">4 Β· Data &amp; honesty notes</h2>
      <ul class="doc-list">
        <li><b>Data:</b> SEC 13F (Sharadar SF3, every filer), fundamentals (SF1), prices (SEP), corporate actions (delistings) via Nasdaq Data Link. Survivorship-free. Sector labels are current, not historical. Form 4 insider data (SF2) feeds the Insider picks panel only.</li>
        <li><b>Signal freshness:</b> 13F is quarterly and ~45 days lagged by law β€” so the smart-money signal is inherently stale; the fund is built to work <em>despite</em> that, not to pretend otherwise.</li>
        <li><b>Picks panels</b> are informational and LLM-screened for manipulation/pump patterns; they are <em>not</em> individual buy recommendations. The fund trades a disciplined, risk-controlled subset automatically.</li>
        <li><b>Paper only.</b> Slipstream runs on an Alpaca paper account. Nothing here is investment advice.</li>
      </ul>

      <h2 class="doc-h">5 Β· Operations &amp; monitoring</h2>
      <p>The fund runs fully automated in the cloud, on Hugging Face scheduled Jobs, with a flag-only supervisor watching it daily.
        Three independent scheduled loops:</p>
      <table class="doc-table">
        <tbody>
          <tr><td><b>Weekly refresh</b> (Sun)</td><td>A private worker Space re-pulls all source data from scratch
            (13F, insider, fundamentals, prices, macro), rebuilds the signal panel, and a <em>freshness gate</em>
            asserts every dataset actually advanced β€” a stale input fails the run loudly instead of silently
            trading on old data.</td></tr>
          <tr><td><b>Monthly rebalance</b> (1st)</td><td>Trades the freshest weekly signal row through the
            construction rules above (ROA quality gate β†’ AI critical-event screen β†’ 10% name cap alternating with dynamic
            sector caps). Idempotent β€” a re-run trades only target-minus-actual.</td></tr>
          <tr><td><b>Daily supervisor</b> (every day)</td><td>A deterministic health monitor β€” it <em>never
            trades</em> β€” checking three things: <b>tracking</b> (trailing 4-week fund-vs-SPY gap; WATCH &lt;βˆ’3pp,
            ALERT &lt;βˆ’6pp), <b>guardrails</b> (paper-only invariant, no margin, exposure ≀100.5%, and position
            drift β€” the 10% cap applies <em>at</em> rebalance, so intra-month drift only flags above 13%, alerts
            above 17%), and <b>freshness</b> (signals ≀10 days old, last rebalance ≀35 days). Results appear as
            the OK / WATCH / ALERT chips at the top of the Fund tab, and an ALERT opens an alert on the Space so it
            can't be missed.</td></tr>
        </tbody>
      </table>
      <p class="doc-note">Design choice: monitoring is <b>flag-only</b>. The supervisor observes and reports;
        only the monthly rebalance ever places orders, and only on the paper account. An autonomous
        recalibration agent (self-adjusting strategy parameters under strict acceptance gates) was evaluated and
        deliberately deferred until the fund has a walk-forward variant-testing harness and at least a quarter of
        live tracking history to calibrate against.</p>
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