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<!DOCTYPE html>
<html lang="en" data-theme="dark">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, viewport-fit=cover" />
<title>RLAlphaLabs — RL-Powered Trading Platform for Indian Equities</title>
<meta name="description" content="A research-stage reinforcement learning trading system for NSE & BSE. Rigorously validated. Currently in research." />
<meta property="og:title" content="RLAlphaLabs — RL Trading Platform" />
<meta property="og:description" content="Research-stage RL trading for Indian equities. Rigorously validated. Currently in research — no live capital deployed." />
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<body>
<a class="skip-link" href="#hero">Skip to content</a>
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<!-- ════════════════════ TICKER STRIP ════════════════════ -->
<div id="ticker">
<div class="ticker-inner">
<span class="ticker-label">SAMPLE UNIVERSE</span>
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<!-- ════════════════════ NAV ════════════════════ -->
<nav id="navbar">
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<a href="#hero" class="nav-logo">
<span class="logo-mark">◈</span>
RLAlphaLabs
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<ul class="nav-links">
<li><a href="#features">Features</a></li>
<li><a href="traction/">Traction</a></li>
<li><a href="research/">Research</a></li>
<li><a href="experiments/">Experiments</a></li>
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<a href="#features">Features</a>
<a href="traction/">Traction</a>
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<a href="#contact" class="nav-cta">Get in Touch</a>
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<!-- Section dots rail (desktop) -->
<div id="sectionDots" aria-hidden="true"></div>
<!-- ════════════════════ HERO ════════════════════ -->
<section id="hero" data-dot="Intro">
<canvas id="heroParticles"></canvas>
<div class="hero-inner">
<div class="hero-left">
<div class="hero-pills reveal">
<span class="pill pill-stage">● Paper Trading Phase</span>
<span class="pill pill-market">NSE · BSE</span>
</div>
<h1 class="hero-h1 reveal">
Reinforcement Learning<br />
for <span class="grad">Indian Markets</span>
</h1>
<p class="hero-p reveal">
A research-stage reinforcement learning system for NSE & BSE.
Rigorously validated — not just backtested.
Currently in research. Not claiming live alpha yet.
</p>
<div class="hero-stats reveal">
<div class="hstat">
<span class="hstat-val mono" data-target="700" data-dec="0" data-sfx="+">—</span>
<span class="hstat-lbl">NSE Stocks Covered</span>
</div>
<div class="hstat-sep"></div>
<div class="hstat">
<span class="hstat-val mono" data-target="8" data-dec="0">—</span>
<span class="hstat-lbl">Time Granularities</span>
</div>
<div class="hstat-sep"></div>
<div class="hstat">
<span class="hstat-val mono" data-target="5000" data-dec="0" data-sfx="+">—</span>
<span class="hstat-lbl">Data Files</span>
</div>
<div class="hstat-sep"></div>
<div class="hstat">
<span class="hstat-val mono" data-target="6" data-dec="0">—</span>
<span class="hstat-lbl">Market Indices</span>
</div>
</div>
<div class="hero-ctas reveal">
<a href="#contact" class="btn-primary">Request Access</a>
<a href="research/" class="btn-ghost">Explore Research →</a>
</div>
</div>
<div class="hero-right reveal">
<div class="chart-shell">
<div class="chart-shell-bar">
<div class="shell-dots"><span></span><span></span><span></span></div>
<span class="shell-title">Illustrative Equity Curve — Shape Only</span>
</div>
<canvas id="heroChart"></canvas>
<div class="chart-note">
Illustrative curve shape for visual context — not a return figure, not a specific episode, not a performance claim
</div>
</div>
</div>
</div>
<div class="scroll-cue">↓</div>
</section>
<!-- ════════════════════ PROBLEM ════════════════════ -->
<section id="problem" data-dot="Problem">
<div class="container">
<div class="sec-label reveal">The Problem</div>
<h2 class="sec-h2 reveal">Algo Trading is Hard.<br /><span class="grad">Most Fail in Production.</span></h2>
<p class="sec-p reveal">Building a strategy that looks great in backtests is easy. Making it survive live markets is not.</p>
<div class="prob-grid">
<div class="prob-card reveal">
<div class="prob-num">01</div>
<h3>Curve Fitting</h3>
<p>A model optimized on historical data captures noise, not signal. Without walk-forward validation, backtest performance is fiction.</p>
</div>
<div class="prob-card reveal">
<div class="prob-num">02</div>
<h3>Unrealistic Simulation</h3>
<p>Ignoring NSE transaction taxes (STT, stamp duty, exchange fees), slippage, and minimum lot sizes inflates paper returns by 30–50%.</p>
</div>
<div class="prob-card reveal">
<div class="prob-num">03</div>
<h3>Slow Iteration</h3>
<p>Training RL agents for days per experiment destroys research velocity. You can't build intuition about what works without rapid feedback.</p>
</div>
<div class="prob-card reveal">
<div class="prob-num">04</div>
<h3>No Risk Model</h3>
<p>Agents that maximize raw returns without drawdown constraints become reckless. Risk must be part of the objective, not an afterthought.</p>
</div>
</div>
</div>
</section>
<!-- ════════════════════ FEATURES ════════════════════ -->
<section id="features" data-dot="Features">
<div class="container">
<div class="sec-label reveal">What We Built</div>
<h2 class="sec-h2 reveal">Six Core Capabilities<br /><span class="grad">That Address Each Failure Mode</span></h2>
<div class="feat-grid">
<div class="feat-card reveal" data-color="cyan">
<div class="feat-icon cyan-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M12 2L2 7l10 5 10-5-10-5zM2 17l10 5 10-5M2 12l10 5 10-5"/></svg>
</div>
<h3>RL-Powered Agents</h3>
<p>Deep reinforcement learning agents trained on real NSE data with realistic transaction costs and risk constraints.</p>
<div class="feat-meta">
<span class="mono-tag">Actor-Critic · JAX · PyTorch</span>
<span class="mono-tag">Multi-stock portfolio</span>
</div>
</div>
<div class="feat-card reveal" data-color="blue">
<div class="feat-icon blue-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><polyline points="22 12 18 12 15 21 9 3 6 12 2 12"/></svg>
</div>
<h3>Walk-Forward Validation</h3>
<p>Multi-window rolling validation. The agent must generalize across time periods — not just memorize one regime.</p>
<div class="feat-meta">
<span class="mono-tag">Rolling windows</span>
<span class="mono-tag">Cross-regime consistency</span>
</div>
</div>
<div class="feat-card reveal" data-color="green">
<div class="feat-icon green-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><rect x="2" y="3" width="20" height="14" rx="2"/><path d="M8 21h8M12 17v4"/></svg>
</div>
<h3>GPU-Accelerated Training</h3>
<p>XLA-compiled training loops for rapid iteration. Experiments complete in minutes, not days.</p>
<div class="feat-meta">
<span class="mono-tag mono-real">>9K steps/sec</span>
<span class="mono-tag mono-real">Consumer GPU</span>
</div>
</div>
<div class="feat-card reveal" data-color="amber">
<div class="feat-icon amber-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M22 12h-4l-3 9L9 3l-3 9H2"/></svg>
</div>
<h3>NSE-Realistic Simulation</h3>
<p>Full Indian brokerage cost model: STT, exchange fees, SEBI charges, GST, stamp duty. Slippage and market impact included.</p>
<div class="feat-meta">
<span class="mono-tag">Full NSE fee structure</span>
<span class="mono-tag">Broker API integration</span>
</div>
</div>
<div class="feat-card reveal" data-color="cyan">
<div class="feat-icon cyan-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M18 20V10"/><path d="M12 20V4"/><path d="M6 20v-6"/></svg>
</div>
<h3>Multi-Granularity Data</h3>
<p>Extensive coverage across timeframes and indices. From minute bars to daily, spanning large-cap to small-cap universes.</p>
<div class="feat-meta">
<span class="mono-tag mono-real">Multi-timeframe</span>
<span class="mono-tag mono-real">NIFTY 50 to Smallcap</span>
</div>
</div>
<div class="feat-card reveal" data-color="blue">
<div class="feat-icon blue-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z"/></svg>
</div>
<h3>Risk-Aware Reward Design</h3>
<p>Multi-objective reward function combining returns, drawdown penalties, concentration limits, and turnover costs. Agent learns risk discipline, not just return maximization.</p>
<div class="feat-meta">
<span class="mono-tag">Multi-component</span>
<span class="mono-tag">Risk-adjusted</span>
</div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ PORTFOLIO SAMPLE ════════════════════ -->
<section id="portfolio-sample">
<div class="container">
<div class="psample-wrap reveal">
<div class="psample-left">
<div class="sec-label">Live Data</div>
<h3>Trained on Real NIFTY Constituents</h3>
<p>Not synthetic data. Historical data fetched directly via broker API, stored in columnar format with quality validation.</p>
<div class="psample-stats">
<div class="pstat"><span class="mono" id="stockCount">700+</span><span>Stocks Tracked</span></div>
<div class="pstat"><span class="mono">5000+</span><span>Data Files</span></div>
<div class="pstat"><span class="mono">8</span><span>Granularities</span></div>
<div class="pstat"><span class="mono">6</span><span>Indices Covered</span></div>
</div>
</div>
<div class="psample-right">
<div class="stock-grid" id="stockGrid"></div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ PIPELINE ════════════════════ -->
<section id="pipeline" data-dot="Pipeline">
<div class="container">
<div class="sec-label reveal">How It Works</div>
<h2 class="sec-h2 reveal">Six-Stage Pipeline<br /><span class="grad">Every Stage Gated</span></h2>
<p class="sec-p reveal">An agent only advances when it clears measurable criteria. No exceptions. This is what separates research-grade from production-grade.</p>
<div class="pipeline">
<div class="pipe-step reveal done">
<div class="pipe-left">
<div class="pipe-status done">✓</div>
<div class="pipe-line"></div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 01</span><span class="pipe-state done-tag">Complete</span></div>
<h3>Data Ingestion</h3>
<p>Extensive NSE/BSE coverage across multiple granularities via broker API. Checkpointed parallel downloads, quality validation, multi-source fallback.</p>
</div>
</div>
<div class="pipe-step reveal done">
<div class="pipe-left">
<div class="pipe-status done">✓</div>
<div class="pipe-line"></div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 02</span><span class="pipe-state done-tag">Complete</span></div>
<h3>Feature Engineering</h3>
<p>Technical indicators and auxiliary signal models for regime detection, volatility forecasting, and trend classification.</p>
</div>
</div>
<div class="pipe-step reveal done">
<div class="pipe-left">
<div class="pipe-status done">✓</div>
<div class="pipe-line"></div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 03</span><span class="pipe-state done-tag">Complete</span></div>
<h3>RL Agent Training</h3>
<p>Agents trained with NSE-realistic frictions and strict promotion gates. Only agents that clear all criteria advance to the next stage.</p>
</div>
</div>
<div class="pipe-step reveal active">
<div class="pipe-left">
<div class="pipe-status active">●</div>
<div class="pipe-line"></div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 04</span><span class="pipe-state active-tag">In Progress</span></div>
<h3>Walk-Forward Validation</h3>
<p>Multi-window rolling test proving the agent generalizes — not just memorizes one period. Adversarial stress tests under extreme market conditions.</p>
</div>
</div>
<div class="pipe-step reveal active">
<div class="pipe-left">
<div class="pipe-status active">●</div>
<div class="pipe-line"></div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 05</span><span class="pipe-state active-tag">In Progress</span></div>
<h3>Paper Trading</h3>
<p>30+ day live simulation against real market data. Realistic order fills, portfolio tracking, real-time P&L. An agent that cannot pass paper trading does not go live.</p>
</div>
</div>
<div class="pipe-step reveal planned">
<div class="pipe-left">
<div class="pipe-status planned">○</div>
</div>
<div class="pipe-body">
<div class="pipe-meta"><span class="pipe-id">Stage 06</span><span class="pipe-state planned-tag">Planned</span></div>
<h3>Live Deployment</h3>
<p>Low-latency inference via broker API. Live risk controls: position limits, drawdown circuit breakers, concentration guards. Real capital only after validation and paper trading clear.</p>
</div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ PERFORMANCE ════════════════════ -->
<section id="performance" data-dot="Results">
<div class="container">
<div class="sec-label reveal">Performance</div>
<h2 class="sec-h2 reveal">Real Experiment Results.<br /><span class="grad">Not Illustrations.</span></h2>
<p class="sec-p reveal">
These numbers come directly from experiment records.
Test metrics are on held-out data the agent never saw during training.
<span class="fine">Past simulation results do not guarantee future live performance.</span>
</p>
<div class="metric-row">
<div class="mcard reveal">
<div class="mcard-label">Test Sharpe</div>
<div class="mcard-val cyan mono" data-target="0.49" data-dec="2">—</div>
<div class="mcard-sub">Risk-adjusted return on held-out test data.</div>
</div>
<div class="mcard reveal">
<div class="mcard-label">Test Win Rate</div>
<div class="mcard-val green mono" data-target="65" data-dec="0" data-sfx="%">—</div>
<div class="mcard-sub">% of test episodes with positive return.</div>
</div>
<div class="mcard reveal">
<div class="mcard-label">Val Sharpe</div>
<div class="mcard-val blue mono" data-target="1.30" data-dec="2">—</div>
<div class="mcard-sub">Validation set used for checkpoint selection.</div>
</div>
<div class="mcard reveal">
<div class="mcard-label">Training Speed</div>
<div class="mcard-val amber mono" data-target="9700" data-dec="0" data-sfx="+ SPS">—</div>
<div class="mcard-sub">Steps per second on GPU with JAX XLA.</div>
</div>
</div>
<div class="perf-chart-wrap reveal">
<div class="perf-chart-hdr">
<div>
<h3>Illustrative Equity Curve — Representative Test Episode</h3>
<p>Generated using real test statistics from held-out evaluation.</p>
</div>
<div class="chart-legend">
<span class="leg cyan-leg">● RL Portfolio</span>
<span class="leg muted-leg">⋯ NIFTY 50 (est.)</span>
</div>
</div>
<canvas id="perfChart" height="110"></canvas>
<p class="chart-disc">
Curve parameterised from real test statistics, not cherry-picked. Benchmark uses estimated NIFTY 50 historical parameters.
This is one representative episode from multi-episode evaluation — not the best one.
</p>
</div>
</div>
</section>
<!-- ════════════════════ STAGE ════════════════════ -->
<section id="stage" data-dot="Status">
<div class="container">
<div class="sec-label reveal">Honest Status</div>
<h2 class="sec-h2 reveal">Where We Actually Are</h2>
<p class="sec-p reveal">
Transparency is a feature. Here is the current development state, without spin.
</p>
<div class="stage-grid">
<div class="stage-card done-card reveal">
<div class="stage-icon done-icon">✓</div>
<h3>What's Done</h3>
<ul>
<li>Full data pipeline for Indian equities</li>
<li>Dual RL training backends for research flexibility</li>
<li>Validated training pipeline with reproducible results</li>
<li>NSE-realistic transaction cost model</li>
<li>GPU-accelerated training infrastructure</li>
<li>Research dashboard for monitoring and analysis</li>
<li>CLI tooling for data, training, and backtesting</li>
<li>Initial backtesting suite across multiple strategies</li>
</ul>
</div>
<div class="stage-card prog-card reveal">
<div class="stage-icon prog-icon">●</div>
<h3>In Progress</h3>
<ul>
<li>Walk-forward validation across multiple windows</li>
<li>Adversarial stress testing framework</li>
<li>Paper trading (live simulation, no real capital)</li>
<li>Unified architecture across training backends</li>
<li>Curriculum learning for exploration</li>
<li>Expanded universe coverage</li>
</ul>
</div>
<div class="stage-card plan-card reveal">
<div class="stage-icon plan-icon">○</div>
<h3>Planned</h3>
<ul>
<li>Live deployment via broker API (post paper trading gate)</li>
<li>Automated hyperparameter search with risk adaptation</li>
<li>Multi-agent portfolio coordination</li>
<li>Institutional API / white-label deployment</li>
<li>Regulatory compliance pathway</li>
</ul>
</div>
</div>
<div class="stage-note reveal">
<span class="stage-note-icon">ℹ</span>
<p>We are a research-stage project. No real capital is deployed. We are seeking collaborators and investors who understand the difference between a validated research prototype and a live product — and who see the value in what's been built so far.</p>
</div>
</div>
</section>
<!-- ════════════════════ ENGINEERING RIGOR ════════════════════ -->
<section id="engineering" data-dot="Rigor">
<div class="container">
<div class="sec-label reveal">Engineering Rigor</div>
<h2 class="sec-h2 reveal">Built Like Infrastructure,<br /><span class="grad">Not Like a Demo.</span></h2>
<p class="sec-p reveal">
A trading system that cannot trust its own data and code cannot be trusted with capital.
The unglamorous engineering underneath the research:
</p>
<div class="feat-grid">
<div class="feat-card reveal" data-color="green">
<div class="feat-icon green-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M9 11l3 3L22 4"/><path d="M21 12v7a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h11"/></svg>
</div>
<h3>Tested Before Every Merge</h3>
<p>A regression suite of thousands of automated tests gates every change. Behavior-sensitive paths — order execution, cost models, feature matrices — are pinned by parity tests so silent drift cannot ship.</p>
<div class="feat-meta">
<span class="mono-tag mono-real">6,300+ tests</span>
<span class="mono-tag">Zero-regression merges</span>
</div>
</div>
<div class="feat-card reveal" data-color="blue">
<div class="feat-icon blue-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z"/><path d="M9 12l2 2 4-4"/></svg>
</div>
<h3>Adversarially Reviewed</h3>
<p>The stack has been exercised by an internal red team: order tampering, data corruption, API impersonation, unsafe agent actions. Findings were fixed and locked behind permanent regression tests.</p>
<div class="feat-meta">
<span class="mono-tag">Red-team tested</span>
<span class="mono-tag">Hardened order path</span>
</div>
</div>
<div class="feat-card reveal" data-color="cyan">
<div class="feat-icon cyan-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><ellipse cx="12" cy="5" rx="9" ry="3"/><path d="M21 12c0 1.66-4 3-9 3s-9-1.34-9-3"/><path d="M3 5v14c0 1.66 4 3 9 3s9-1.34 9-3V5"/></svg>
</div>
<h3>Certified Data Only</h3>
<p>Training runs are gated on signed, certified dataset builds. The store is continuously profiled for anomalies — timezone-shifted bars, phantom rows, split discontinuities — and quarantined data never reaches an agent silently.</p>
<div class="feat-meta">
<span class="mono-tag mono-real">Signed dataset builds</span>
<span class="mono-tag">Automated store audit</span>
</div>
</div>
<div class="feat-card reveal" data-color="amber">
<div class="feat-icon amber-bg">
<svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M10.29 3.86L1.82 18a2 2 0 0 0 1.71 3h16.94a2 2 0 0 0 1.71-3L13.71 3.86a2 2 0 0 0-3.42 0z"/><line x1="12" y1="9" x2="12" y2="13"/><line x1="12" y1="17" x2="12.01" y2="17"/></svg>
</div>
<h3>Fails Loud, Never Silent</h3>
<p>When data quality checks fail, the pipeline stops and names the blockers — it does not fabricate values, silently patch gaps, or train on suspect data. A loud stop is a feature, not an inconvenience.</p>
<div class="feat-meta">
<span class="mono-tag">Blocking quality gates</span>
<span class="mono-tag">No silent patching</span>
</div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ RESEARCH LOG ════════════════════ -->
<section id="research" data-dot="Research">
<div class="container">
<div class="sec-label reveal">Research Log</div>
<h2 class="sec-h2 reveal">Experiment History<br /><span class="grad">Every Run Logged</span></h2>
<p class="sec-p reveal">
Real experiment IDs, real timestamps, real metrics from disk.
Every run is reproducible from its checkpoint.
</p>
<div class="exp-note reveal">
<p>Selected metrics from representative training runs. All results on held-out test data the agent never saw during training.</p>
</div>
<div class="progression reveal">
<h3>Next Experiments</h3>
<div class="prog-items">
<div class="prog-item">
<span class="prog-label">Active</span>
<span class="prog-plan">Walk-forward validation · Adversarial stress testing · Curriculum learning</span>
</div>
<div class="prog-item">
<span class="prog-label">Planned</span>
<span class="prog-plan">Multi-seed statistical validation · Minute-bar pilots · NIFTY 100 expansion</span>
</div>
</div>
</div>
<div style="margin-top:32px">
<a href="experiments/" class="btn-ghost">Read Full Experiment Write-ups →</a>
</div>
</div>
</section>
<!-- ════════════════════ TECHNOLOGY ════════════════════ -->
<section id="technology" data-dot="Tech">
<div class="container">
<div class="sec-label reveal">Technology</div>
<h2 class="sec-h2 reveal">Built on Production-Grade<br /><span class="grad">Open-Source Foundations</span></h2>
<div class="tech-row">
<div class="tech-card reveal"><div class="tech-badge py">Py</div><div><b>Python</b><span>Core language</span></div></div>
<div class="tech-card reveal"><div class="tech-badge jax">JAX</div><div><b>Google JAX</b><span>XLA compilation</span></div></div>
<div class="tech-card reveal"><div class="tech-badge pt">PT</div><div><b>PyTorch</b><span>GPU training</span></div></div>
<div class="tech-card reveal"><div class="tech-badge gym">Gym</div><div><b>Gymnasium</b><span>RL environment interface</span></div></div>
<div class="tech-card reveal"><div class="tech-badge kite">ZK</div><div><b>Broker API</b><span>Live market access</span></div></div>
<div class="tech-card reveal"><div class="tech-badge st">AN</div><div><b>Analytics</b><span>Data exploration</span></div></div>
<div class="tech-card reveal"><div class="tech-badge cu">CU</div><div><b>CUDA</b><span>GPU acceleration</span></div></div>
<div class="tech-card reveal"><div class="tech-badge pq">PQ</div><div><b>Parquet + SQLite</b><span>Hybrid storage</span></div></div>
</div>
</div>
</section>
<!-- ════════════════════ ABOUT ════════════════════ -->
<section id="about" data-dot="About">
<div class="container">
<div class="sec-label reveal">About</div>
<h2 class="sec-h2 reveal">The Builder</h2>
<div class="about-wrap">
<div class="about-card reveal">
<div class="about-avatar" id="aboutAvatar">
<div class="avatar-placeholder">◈</div>
</div>
<div class="about-bio">
<h3 class="about-name">Solo Researcher & Engineer</h3>
<div class="about-role">RL / Quantitative Systems · Indian Markets</div>
<p class="about-desc">
Building reinforcement learning systems for Indian equities from first principles.
Background spans machine learning research, systems engineering, and quantitative analysis.
This project exists because RL for Indian markets is severely underexplored — and because
most "AI trading" products are backtested fiction. I believe rigorous validation, honest metrics,
and realistic cost models are prerequisites for any credible claim of alpha.
</p>
<div class="about-links">
<a href="https://github.com/pctablet505" class="about-link" target="_blank" rel="noopener">GitHub</a>
<a href="mailto:pctablet505@gmail.com" class="about-link">Email</a>
</div>
</div>
</div>
<div class="about-facts reveal">
<div class="fact-card">
<div class="fact-num mono">Substantial</div>
<div class="fact-lbl">Modern research codebase</div>
</div>
<div class="fact-card">
<div class="fact-num mono">1</div>
<div class="fact-lbl">Published experiment result (more in pipeline)</div>
</div>
<div class="fact-card">
<div class="fact-num mono">6</div>
<div class="fact-lbl">Phase development roadmap</div>
</div>
<div class="fact-card">
<div class="fact-num mono">5000+</div>
<div class="fact-lbl">Real market data files on disk</div>
</div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ USE CASES ════════════════════ -->
<section id="usecases" data-dot="For You">
<div class="container">
<div class="sec-label reveal">Who This Is For</div>
<h2 class="sec-h2 reveal">Four Paths to Engagement</h2>
<div class="uc-grid">
<div class="uc-card reveal">
<div class="uc-n">01</div>
<h3>Investors</h3>
<p>Early-stage opportunity in a validated RL trading research platform built for Indian equities. Pipeline is clear. Risk gates are real. Results published, more in pipeline.</p>
<div class="uc-ask">→ Request research materials and methodology</div>
</div>
<div class="uc-card reveal">
<div class="uc-n">02</div>
<h3>Quant Researchers</h3>
<p>Looking for a serious collaborator on RL for Indian markets? The infrastructure is built. The hard problems remaining are algorithmic: walk-forward consistency, multi-agent coordination, curriculum learning.</p>
<div class="uc-ask">→ Discuss research collaboration on specific open problems</div>
</div>
<div class="uc-card reveal">
<div class="uc-n">03</div>
<h3>ML / RL Engineers</h3>
<p>Interested in contributing to a production-bound RL system? Phases 3–6 of the roadmap involve architecture consolidation, AutoRL, and NIFTY 100+ universe expansion.</p>
<div class="uc-ask">→ Review the open roadmap and reach out with your background</div>
</div>
<div class="uc-card reveal">
<div class="uc-n">04</div>
<h3>FinTech / Institutions</h3>
<p>Need an AI trading engine for Indian equities that you can run in-house? This is modular: swap broker backends, risk profiles, and asset universes. Licensing and white-label options available post-Stage 5.</p>
<div class="uc-ask">→ Discuss licensing and integration after paper trading gate</div>
</div>
</div>
</div>
</section>
<!-- ════════════════════ CONTACT ════════════════════ -->
<section id="contact" data-dot="Contact">
<div class="container">
<div class="contact-wrap">
<div class="contact-left reveal">
<div class="sec-label">Contact</div>
<h2 class="sec-h2">Let's Talk.<br /><span class="grad">Seriously.</span></h2>
<p>If you've read this far, you understand what's been built and what stage it's at. That's the kind of conversation worth having.</p>
<div class="contact-items">
<div class="citem">
<span class="citem-icon cyan">◈</span>
<div>
<b>Investors</b>
<span>Research materials and detailed methodology available under NDA.</span>
</div>
</div>
<div class="citem">
<span class="citem-icon blue">◉</span>
<div>
<b>Collaborators</b>
<span>Tell me what problem you want to work on. Open problems listed in the research section.</span>
</div>
</div>
<div class="citem">
<span class="citem-icon green">▲</span>
<div>
<b>FinTech / Institutional</b>
<span>Licensing discussions welcome post-Stage 5. Let's establish a relationship now.</span>
</div>
</div>
</div>
</div>
<div class="contact-right reveal">
<form class="cform" id="contactForm" onsubmit="handleSubmit(event)">
<div class="cform-group">
<label>Name</label>
<input type="text" name="name" placeholder="Your name" required />
</div>
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<label>Email</label>
<input type="email" name="email" placeholder="you@organisation.com" required />
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<div class="cform-group">
<label>I am a…</label>
<select name="type">
<option value="">Select one</option>
<option>Investor / Fund Manager</option>
<option>Quant Researcher</option>
<option>ML / RL Engineer</option>
<option>FinTech Company</option>
<option>Other</option>
</select>
</div>
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<label>Message</label>
<textarea name="message" rows="4" placeholder="What are you looking for? What's your background?"></textarea>
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<h3>Your email client should now be open</h3>
<p>We've pre-filled a message to pctablet505@gmail.com — just review and hit send. If nothing opened, email us directly at pctablet505@gmail.com.</p>
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<!-- ════════════════════ FOOTER ════════════════════ -->
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<p>© 2026 RLAlphaLabs. Research Stage. Not Financial Advice.</p>
<p class="footer-disc">Past simulation results do not guarantee future live performance. No real capital is currently deployed. Trading involves significant risk of loss. RLAlphaLabs is a research project and is not registered with SEBI as an investment adviser or research analyst; nothing here is investment advice or a solicitation to trade.</p>
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