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  <title>Finance & Quantitative Analysis — Amresh Verma</title>
  <meta name="description" content="Notebook-driven finance portfolio: fixed-income modeling, VaR methodologies, and FinBERT sentiment pipelines. Reproducible notebooks and TL;DR for hiring managers." />
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    <a class="brand" href="portfolio.html">Amresh Verma — Quant / ML</a>
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      <p class="hero-tag">Finance Portfolio</p>
      <h1 id="hero-title">Models, backtests, and research across rates, equities, crypto and derivatives.</h1>

      <p class="hero-subtitle">
        Notebook-driven portfolio demonstrating yield-curve & fixed-income analysis, comparative VaR methods, and a FinBERT sentiment pipeline. Notebooks are linked below — quick runnable instructions included.
      </p>

      <ul class="exec-summary">
        <li><strong>Elevator:</strong> reproducible notebooks for risk & signal research — suitable for quant research or quant engineering teams.</li>
        <li><strong>Why this matters:</strong> clear metrics, backtests, and diagnostic tooling to evaluate model reliability and production readiness.</li>
      </ul>

      <div class="hero-actions">
        <a class="button primary" href="https://github.com/meamresh" target="_blank" rel="noopener">View code on GitHub</a>
        <a class="button" href="https://github.com/meamresh/Finance">Open notebooks</a>
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    </section>

    <!-- KPI section -->
    <section class="section" aria-labelledby="kpi-title">
      <h2 id="kpi-title">Key results (quick)</h2>
      <div class="card-grid three">
        <article class="card">
          <h3>1-day 95% VaR</h3>
          <p class="section-intro">Calculated for a Treasury portfolio</p>
          <p class="plot-card formula"><strong>$291,342</strong></p>
          <p class="small-note">Source: <a href="https://github.com/meamresh/Finance/blob/main/1_FixedIncome/ValueAtRisk.ipynb">ValueAtRisk.ipynb</a> — README-stated value shown; verify by running the notebook if needed.</p>
        </article>

        <article class="card">
          <h3>News articles processed</h3>
          <p class="section-intro">FinBERT sentiment pipeline (sample)</p>
          <p class="plot-card formula"><strong>6,250+</strong></p>
          <p class="small-note">Source: <a href="https://github.com/meamresh/Finance/blob/main/3_SentimentAnalysis/SentimentAnalysis.ipynb">SentimentAnalysis.ipynb</a></p>
        </article>

        <article class="card">
          <h3>PCA for yield curve</h3>
          <p class="section-intro">Dimensionality reduction result</p>
          <p class="plot-card formula"><strong>2 factors — 97% variance</strong></p>
          <p class="small-note">See: <a href="https://github.com/meamresh/Finance/blob/main/1_FixedIncome/Nelson_Siegel_Model.ipynb">Nelson_Siegel_Model.ipynb/</a></p>
        </article>
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    <!-- Projects -->
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      <h2 id="projects-title">Featured projects</h2>
      <p class="section-intro">Three focused notebooks: fixed income, market risk, and alternative-data sentiment. Each card links directly to the notebook for quick review.</p>

      <div class="card-grid three">
        <!-- Fixed Income -->
        <article class="card">
          <h3>Fixed Income — Nelson-Siegel & VaR</h3>
          <p class="section-intro">Objective: model US Treasury curves and compute portfolio VaR using factor decomposition and PCA.</p>
          <ul>
            <li>FRED data fetch (notebook contains fetch + modeling cells; FRED API key required to run fresh).</li>
            <li>Nelson-Siegel parameter estimation and factor decomposition (level, slope, curvature).</li>
            <li>VaR computed for a multi-bond portfolio; backtest cells included.</li>
          </ul>
          <p class="plot-card"><em>Result (notebook):</em> 1-day 95% VaR = <strong>$291,342</strong> (see notebook for backtest details).</p>
          <p class="small-note">
            <a href="https://github.com/meamresh/Finance/blob/main/1_FixedIncome/ValueAtRisk.ipynb">Open notebook</a> ·
            <a href="https://github.com/meamresh/Finance/tree/main/1_FixedIncome#readme">Notebook notes</a>
          </p>
        </article>

        <!-- Market Risk -->
        <article class="card">
          <h3>Market Risk — VaR methodologies</h3>
          <p class="section-intro">Objective: implement and compare Historical, Parametric (normal & t), and Monte Carlo VaR methods (example asset: BTC-USD).</p>
          <ul>
            <li>Distributional fits (normal & Student’s t) and tail diagnostics.</li>
            <li>Monte Carlo simulation with configurable iteration count (notebook includes examples).</li>
            <li>Backtesting comparisons across confidence levels (90%, 95%, 99%).</li>
          </ul>
          <p class="plot-card"><em>Notebook summary:</em> Historical 95% VaR ≈ −6.0% · Monte Carlo 95% VaR ≈ −3.23% (sample wallet)</p>
          <p class="small-note">
            <a href="https://github.com/meamresh/Finance/blob/main/2_Crypto_Equities/MarketRisk.ipynb">Open notebook</a>
          </p>
        </article>

        <!-- Sentiment -->
        <article class="card">
          <h3>Alternative Data — Financial news sentiment</h3>
          <p class="section-intro">Objective: ingest GDELT news, extract article text, run FinBERT, and correlate weekly sentiment with returns.</p>
          <ul>
            <li>Article extraction (newspaper3k) → FinBERT inference (Hugging Face); batch processing included.</li>
            <li>Weekly aggregation, bootstrap CIs, structural break detection, and robustness checks.</li>
            <li>Resume-capable pipeline with Parquet persistence for large runs.</li>
          </ul>
          <p class="plot-card"><em>Notebook notes:</em> 6,250+ articles processed in sample runs — see notebook for sampling & inference cells.</p>
          <p class="small-note">
            <a href="https://github.com/meamresh/Finance/blob/main/3_SentimentAnalysis/SentimentAnalysis.ipynb">Open notebook</a>
          </p>
        </article>
      </div>
    </section>

    <!-- Skills & TLDR -->
    <section class="section" aria-labelledby="skills-title">
      <h2 id="skills-title">Technical skills (quick)</h2>
      <p class="section-intro">Quant: yield-curve modelling, VaR, risk attribution · Stats: PCA, bootstrap CIs, structural-break detection · ML/NLP: FinBERT, PyTorch inference · Engineering: Python, pandas, matplotlib, reproducible notebooks</p>
    </section>

    <section class="section" aria-labelledby="tldr-title">
      <h2 id="tldr-title">Hiring manager TL;DR</h2>
      <p class="section-intro"><strong>30 days:</strong> deliver reproducible backtests and dashboardable metrics for candidate models. <strong>90 days:</strong> productionise the best risk pipeline with CI, monitoring and scheduled retraining.</p>
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      <div class="small-note">Last updated: 2026-02-25</div>
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