// ======= DESIGN TOKENS ======= const TYPE_SCALE = { hero: 132, title: 68, subtitle: 44, body: 32, small: 26, mono: 22, label: 20, }; const SPACING = { paddingTop: 100, paddingBottom: 80, paddingX: 100, titleGap: 48, itemGap: 28, }; const COLORS = { bg: '#05070D', bgGrad1: '#070B16', bgGrad2: '#0A1225', panel: 'rgba(255,255,255,0.035)', panelBorder: 'rgba(255,255,255,0.08)', panelStrong: 'rgba(255,255,255,0.06)', line: 'rgba(255,255,255,0.08)', text: '#EAF1FB', textDim: '#8A9CB8', textFaint: '#55687F', cyan: '#22D3EE', green: '#10F4B1', amber: '#FFB020', red: '#FF5577', blue: '#60A5FA', violet: '#A78BFA', }; const FONT_MONO = '"JetBrains Mono", "SF Mono", ui-monospace, Consolas, monospace'; const FONT_SANS = '"Inter", system-ui, -apple-system, Helvetica, Arial, sans-serif'; // ======= SHARED CHROME ======= function Frame({ children, style = {} }) { return (
{/* Ambient glow */}
{/* subtle grid */}
{children}
); } function Chrome({ n, section }) { return ( <>
ALPHASTREAM / {section}
LIVE ET GENAI · 26 APR 2026 {n}/17
RICKARYA DAS · PS-06 · AI FOR THE INDIAN INVESTOR github.com/ridash2005/AlphaStream_Final
); } function Body({ children, topPad = 88, botPad = 60 }) { return (
{children}
); } function Title({ eyebrow, children, style = {} }) { return (
{eyebrow && (
{eyebrow}
)}

{children}

); } // Glass card function Card({ children, accent, style = {} }) { return (
{children}
); } // Icon primitives, simple SVG glyphs (no external dep) const Icon = { Data: (p={}) => , Stream:(p={}) => , Brain: (p={}) => , DB: (p={}) => , Screen:(p={}) => , Alert: (p={}) => , Bolt: (p={}) => , Target:(p={}) => , Clock: (p={}) => , Trend: (p={}) => , Users: (p={}) => , Shield:(p={}) => , Code: (p={}) => , Search:(p={}) => , Arrow: (p={}) => , X: (p={}) => , Check: (p={}) => , Github:(p={}) => , }; // ======= SLIDE 1 ======= function Slide1() { return (
{/* Left */}
STREAMING · LIVE
AlphaStream
INDIA
Real-time AI investment intelligence for the Indian retail investor.
TEAM
RICKARYA DAS
BUILD
v1.0 · FINALE
TARGET
PS-06 · RETAIL
{/* Right: contrast panel */}
INSTITUTIONS
$32,000 /yr
Bloomberg-class terminals
RETAIL
WhatsApp tips
Stale data · hallucinated bots
We close the gap.
); } // ======= SLIDE 2 ======= function Slide2() { const stats = [ { num: '21 Cr+', unit: 'DEMAT ACCOUNTS', sub: '4.5 Cr active · NSDL+CDSL, Jan 2026', color: COLORS.cyan, icon: Icon.Users }, { num: '80%', unit: 'RELY ON TIPS', sub: 'Informal, unverified', color: COLORS.amber, icon: Icon.Alert }, { num: '2 to 3 h',unit:'WASTED DAILY', sub: 'Per active trader', color: COLORS.green, icon: Icon.Clock }, { num: '₹2.19 L',unit: 'LOST / USER / YR',sub: 'Productivity foregone', color: COLORS.red, icon: Icon.Trend }, ]; return ( An information gap at nation scale.
{stats.map((s, i) => { const IC = s.icon; return (
{s.unit}
{s.num}
{s.sub}
); })}
THE AI TRAP
Free AI chatbots answer from static training weights, hallucinate prices, and miss real-time earnings, dangerous anywhere near capital.
); } // ======= SLIDE 3 ======= function Slide3() { const rows = [ { dim: 'Data freshness', bad: 'Static weights · training cutoff', good: 'Streaming · sub-2s latency', bi: Icon.Clock, gi: Icon.Bolt }, { dim: 'Reasoning', bad: 'Single LLM call', good: '13-agent fusion · 0-100 Alpha Score', bi: Icon.Brain, gi: Icon.Brain }, { dim: 'Numerical answers', bad: 'Generated / hallucinated', good: 'SQL on DuckDB · deterministic', bi: Icon.X, gi: Icon.Check }, { dim: 'Evidence', bad: 'Plausible prose', good: 'Source-cited · link-backed', bi: Icon.Search,gi: Icon.Shield }, { dim: 'History', bad: 'Opinion', good: 'Backtested across 5 years', bi: Icon.Search,gi: Icon.Target }, ]; return ( A live AI paradigm, not a chatbot wrapper.
DIMENSION
LLM WRAPPER
ALPHASTREAM
{rows.map((r, i) => { const Bi = r.bi, Gi = r.gi; return (
{r.dim}
{r.bad}
{r.good}
); })}
); } // ======= SLIDE 4 ======= function Slide4() { const layers = [ { n: '01', name: 'INGESTION', color: COLORS.cyan, icon: Icon.Data, items: ['NSE · BSE · NSDL', 'Groww · yfinance', 'Macro + news feeds'] }, { n: '02', name: 'STREAMING', color: COLORS.green, icon: Icon.Stream, items: ['Pathway engine', 'Incremental index', 'Zero batch rebuilds'] }, { n: '03', name: 'REASONING', color: COLORS.amber, icon: Icon.Brain, items: ['13-agent swarm', 'Gemini 2.0 Flash', 'Adaptive RAG'] }, { n: '04', name: 'ANALYTICS', color: COLORS.blue, icon: Icon.DB, items: ['DuckDB OLAP', 'ChromaDB vectors', 'Text2SQL pipeline'] }, { n: '05', name: 'DELIVERY', color: COLORS.violet, icon: Icon.Screen, items: ['React 19 terminal', 'WebSocket stream', 'LangGraph NLQ'] }, ]; return ( Five layers, one deterministic pipeline.
{layers.map((L, i) => { const IC = L.icon; return (
LAYER {L.n}
{L.name}
{L.items.map((it) => (
▸{it}
))}
{i < layers.length - 1 && (
)} ); })}
RAW DATA → STREAM → AGENTS → SQL → UI End-to-end latency: < 2,000 ms ); } // ======= SLIDE 5 ======= function Slide5() { const tabs = ['OVERVIEW', 'SIGNALS', 'GLOBAL INTEL', 'NLQ CHAT', 'PORTFOLIO']; return (
▸ DEMONSTRATION · 04

Switching to the live system.

ENTER TERMINAL
{/* Mock terminal */} {/* Tab row */}
{tabs.map((t, i) => (
{t}
))}
RELIANCE.NS · 2,847.20 +1.42%
{/* Body */}
PRICE · 1D CANDLES
{[40, 80, 120, 160, 200].map(y => )} {[[30,130,155,120],[80,150,170,140],[130,115,130,100],[180,95,120,85],[230,105,115,88],[280,80,100,72],[330,90,105,78],[380,65,85,58],[430,55,75,50],[480,65,75,55],[530,40,55,32],[580,35,50,28]].map((c,i) => { const [x, o, l, h] = c; const up = h < o; return ( ); })}
ALPHA SCORE · 13 AGENTS
82/100
STRONG BUY · conviction 0.84
{[['Filing', 88], ['Technical', 79], ['Insider', 84], ['Sentiment', 76], ['Backtest', 82]].map(([l, v]) => (
{l} {v}
))}
{/* Flow strip */}
{['OVERVIEW', 'SIGNALS', 'GLOBAL INTEL', 'NLQ CHAT', 'SENTIMENT SHIFT'].map((step, i, arr) => (
{String(i+1).padStart(2, '0')} {step}
{i < arr.length - 1 && ▶} ))}
); } // ======= SLIDE 6 ======= function Slide6() { const moats = [ { label: 'STREAMING', color: COLORS.cyan, metric: '< 2s', sub: 'news → UI', icon: Icon.Bolt, title: 'Pathway sub-2-second latency', body: 'Ingest, embed, and push a breaking article to the React dashboard via WebSocket, no refresh, no batch rebuild.' }, { label: 'RETRIEVAL', color: COLORS.amber, metric: '−40%', sub: 'token cost', icon: Icon.Search, title: 'Adaptive geometric RAG', body: 'Start with 2 documents. Double only when the model signals insufficient context. Cheap when easy, rigorous when it matters.', viz: 'rag' }, { label: 'REASONING', color: COLORS.green, metric: '8', sub: 'node graph', icon: Icon.Code, title: 'LangGraph NLQ with SQL correction', body: 'Guardrail → route → SQL → execute on DuckDB → narrate. A correction loop retries on errors.', viz: 'graph' }, ]; return ( Three things a wrapper cannot copy.
{moats.map((m, i) => { const IC = m.icon; return (
{m.label}
{m.metric}
{m.sub}
{m.viz === 'rag' && (
{[2, 4, 8].map((n, i) => (
{n} DOCS
{i < 2 && } ))}
)} {m.viz === 'graph' && (
{['Guard', 'Route', 'SQL', 'Exec', 'Fix', 'Narrate'].map((s, i, a) => (
{s}
{i < a.length - 1 && ▶} ))}
)}
{m.title}
{m.body}
); })}
REGULATORY MOAT Perplexity Finance is free, but lacks NSE SAST/PIT & NSDL FII/DII feeds. India-specific regulatory data isn't in any global API. ); } // ======= SLIDE 7 ======= function Slide7() { const weights = [ { name: 'CORPORATE FILINGS', w: 0.25, color: COLORS.cyan, desc: 'BSE regulatory disclosures' }, { name: 'TECHNICAL TRENDS', w: 0.25, color: COLORS.green, desc: 'RSI, MACD, chart patterns' }, { name: 'INSIDER / FII FLOW', w: 0.20, color: COLORS.amber, desc: 'NSDL streak detection' }, { name: 'NEWS SENTIMENT', w: 0.15, color: COLORS.blue, desc: 'Multi-source NLP' }, { name: 'BACKTEST AGENT', w: 0.15, color: COLORS.violet, desc: '5-yr historical validation' }, ]; const maxW = 0.25; return ( 13 agents, one Alpha Score.
{/* LEFT: weights */}
SIGNAL CATEGORY CONTRIBUTION WEIGHT
{weights.map((w, i) => (
{w.name}
{w.desc}
{w.w.toFixed(2)}
))}
{/* RIGHT: score gauge */}
ALPHA SCORE
{/* Arc gauge */}
{/* Tick marks */} {[0, 0.25, 0.5, 0.75, 1].map(t => { const a = Math.PI * (1 - t); const x1 = 100 + Math.cos(a) * 72, y1 = 100 - Math.sin(a) * 72; const x2 = 100 + Math.cos(a) * 80, y2 = 100 - Math.sin(a) * 80; return ; })}
82/100
STRONG BUY
The Decision Agent fuses 13 independent signals, preventing single-point failure and mode collapse.
); } // ======= SLIDE 8 ======= function Slide8() { return ( Time saved. Alpha earned.
{/* Time card */}
RECOVERED PRODUCTIVITY
₹2.19L
per user / year
{/* Visual bar: 2h -> 15min */}
2h+ daily
15 min
1.75h × 250 days × ₹500/hr
{/* Alpha card */}
POTENTIAL ALPHA
₹54K
per user / year
{[['2-3', 'signals/mo'], ['60%', 'hist. accuracy'], ['₹3K', 'avg gain']].map(([n, l]) => (
{n}
{l}
))}
RISK MITIGATION
Portfolio-aware alerts and FII/DII streak cross-references significantly reduce behavioral panic selling.
); } // ======= SLIDE 9 ======= function Slide9() { const rows = [ { y: 'YEAR 1', users: '50,000', rev: '59.9', mult: 0.10, color: COLORS.blue }, { y: 'YEAR 3', users: '2,00,000', rev: '239.8', mult: 0.40, color: COLORS.cyan }, { y: 'YEAR 5', users: '5,00,000', rev: '599.4', mult: 1.00, color: COLORS.green }, ]; return ( The path to ₹599 Cr ARR. {/* Bar chart */}
{rows.map((r, i) => (
{r.y}
₹{r.rev} Cr
annual revenue
{/* Growing bar */}
{r.users}
USERS
))}
MODEL ASSUMPTIONS
₹999/mo premium tier · SOM 1.5L users (0.5% of 4.5 Cr active)
ET MARKETS SAVINGS
30% less manual analyst work · ₹5–10 Cr / yr
BENCHMARK Tickertape (₹5K/yr screener) does ₹114 Cr ARR (FY25). We're real-time at ₹12K/yr targeting half their userbase, so ₹599 Cr at Year 5 is conservative. ); } // ======= SLIDE 10 ======= function Slide10() { const team = ['Rickarya Das']; return (
▸ SESSION CLOSING · 09

Open for
architectural
review.

A live-streaming, multi-agent, source-cited terminal, end-to-end for Problem Statement 6.
REPOSITORY
github.com/
ridash2005/AlphaStream_Final
RICKARYA DAS
{team.map((n, i) => (
{String(i+1).padStart(2, '0')} {n}
))}
> awaiting_jury_input_
); } // ======= SLIDE 11 — HERD OF KNOWLEDGE (animated) ======= function Slide11() { return ( {/* Full-bleed Manim video */}
{/* Overlay badge + legend — fades out 3s after slide activates so video plays clean */}
HERD OF KNOWLEDGE
News (5) · Market (3) · Institutional (3) · Global (3) = 14 Connectors · Pathway Unified Stream
); } // ======= SLIDE 12 — NLQ PIPELINE (animated, dedicated) ======= function Slide12() { return (
{/* Overlay badge — fades out 3s after slide activates so video plays clean */}
NLQ AGENT
Market ChatGPT · Next Gen
8-Node LangGraph · Text2SQL · Self-Correction Loop · 3 MCP Servers · Session Memory
); } // ======= SLIDE 13 — AGENT CONSENSUS (animated) ======= function Slide13() { const legend = [ { color: COLORS.cyan, label: 'Market Analysis' }, { color: COLORS.violet, label: 'Data Intelligence' }, { color: COLORS.amber, label: 'Decision Agent' }, { color: COLORS.green, label: 'NLQ Agent' }, ]; return (
13-AGENT CONSENSUS
Each specialist contributes a weighted score to the Decision Agent
{legend.map(({ color, label }) => ( {label} ))}
); } // ======= SLIDE 14 — APPENDIX A: Accurate System Architecture ======= function Slide14() { return (
APPENDIX A Full System Architecture
AlphaStream India Full System Architecture
Verified against backend/src/ · 15 connectors · 14 agents · 10 DuckDB tables · 60+ FastAPI endpoints
); } // ======= SLIDE 15 — APPENDIX B: DuckDB Analytics Schema ======= function Slide15() { const tables = [ { name: 'dim_stocks', desc: 'Nifty 50/200 master universe' }, { name: 'fact_daily_prices', desc: 'OHLCV per ticker+date' }, { name: 'fact_signals', desc: 'Agent outputs + Alpha Score' }, { name: 'fact_insider_trades', desc: 'SAST/PIT/bulk/block deals' }, { name: 'fact_fii_dii_flows', desc: 'NSDL daily institutional flows' }, { name: 'fact_filings', desc: 'BSE corporate announcements' }, { name: 'fact_bulk_deals', desc: 'Bulk/block transactions' }, { name: 'fact_quarterly_results',desc: 'Revenue, PAT, EBITDA margins' }, { name: 'fact_articles', desc: 'News + threat classification' }, { name: 'insights', desc: 'Ambient alerts + severity' }, ]; const views = [ { name: 'v_signal_summary', desc: 'Latest 7d signals by ticker' }, { name: 'v_insider_activity_30d', desc: '30d insider trades + sector' }, { name: 'v_fii_dii_trend', desc: '5d/20d rolling flows' }, { name: 'v_sector_heatmap', desc: 'Signal count + avg alpha' }, { name: 'v_stock_screener', desc: 'Latest price + latest signal' }, { name: 'v_recent_news', desc: 'Last 200 news articles' }, ]; return (
APPENDIX B DuckDB Analytics Schema
{/* Fact Tables */}
FACT TABLES (10)
{tables.map((t) => (
▸{t.name} {t.desc}
))}
{/* Views */}
PRE-AGGREGATED VIEWS (6)
{views.map((v) => (
▸{v.name} {v.desc}
))}
Star schema · sub-100ms aggregation queries via DuckDB in-process OLAP
); } // ======= SLIDE 16 — APPENDIX C: Online Anomaly Detection ======= function Slide16() { const detectors = [ { name: 'Half-Space Trees', color: COLORS.red, what: 'Price Anomaly Detection', how: 'Online random forest for streaming price ticks. Fires alert when anomaly score > 0.7.', tech: 'River ML · HalfSpaceTrees', }, { name: 'Z-Score Rolling', color: COLORS.amber, what: 'Volume Spike Detection', how: 'Rolling standard deviation on intraday volume. Alert threshold: |z| > 2.5.', tech: 'River ML · rolling stats', }, { name: 'ADWIN', color: COLORS.violet, what: 'Sentiment Drift Detection', how: 'Adaptive Windowing algorithm detects sudden shifts in article sentiment per ticker.', tech: 'River ML · ADWIN drift', }, ]; return (
APPENDIX C Online Anomaly Detection · River ML
{detectors.map((d, i) => (
{d.tech}
{d.name}
{d.what}
{d.how}
))}
Incremental online ML · models update on each price tick without full retraining (River library)
); } // ======= SLIDE 17 — REFERENCES ======= function Slide17() { const refs = [ { cat: 'Market Data', color: COLORS.cyan, items: [ { label: 'NSDL Depository Statistics · 21 Cr+ demat accounts (Oct 2025)', url: 'nsdl.co.in/depository-monthly-statistics.php' }, { label: 'CDSL Depository Statistics', url: 'cdslindia.com/Publications/DepositoryStatistics.html' }, { label: 'Active demat 4.5 Cr (Jan 2026) · AngelOne / Choice India', url: 'choiceindia.com/blog/total-number-of-demat-accounts-in-india' }, ], }, { cat: 'Competitive Landscape', color: COLORS.amber, items: [ { label: 'Bloomberg Terminal pricing $31,980/yr (2026) · PricingNow', url: 'pricingnow.com/question/bloomberg-terminal-pricing/' }, { label: 'Tickertape Pro ₹4,999/yr & ₹114 Cr ARR (FY25) · Tracxn', url: 'tracxn.com/d/companies/tickertape' }, { label: 'Perplexity Finance · free for Indian markets (2026)', url: 'perplexity.ai/finance' }, { label: 'Helm Terminal · AI Bloomberg alternatives ($0 to $150/mo)', url: 'helmterminal.dev/blog/best-bloomberg-terminal-alternatives' }, ], }, { cat: 'Technical Stack', color: COLORS.violet, items: [ { label: 'Pathway streaming engine · Adaptive RAG with geometric retrieval', url: 'pathway.com / pathway/pathway' }, { label: 'LangGraph · multi-step agent orchestration', url: 'langchain-ai.github.io/langgraph' }, { label: 'DuckDB · in-process OLAP analytics', url: 'duckdb.org' }, { label: 'River ML · online learning (Half-Space Trees, ADWIN)', url: 'riverml.xyz' }, { label: 'Manim Community · animation engine for the slides', url: 'manim.community' }, ], }, { cat: 'Regulatory Data Sources', color: COLORS.green, items: [ { label: 'NSE SAST/PIT · insider trading disclosures (regulated)', url: 'nseindia.com' }, { label: 'NSDL FII/DII · institutional flow data (regulated)', url: 'nsdliw.nsdl.com' }, { label: 'BSE · corporate filings, bulk/block deals', url: 'bseindia.com' }, { label: 'FRED · US Federal Reserve macro indicators', url: 'fred.stlouisfed.org' }, ], }, ]; return ( Sources & citations <span style={{ display: 'block', fontSize: 18, fontWeight: 400, fontFamily: FONT_MONO, color: COLORS.textFaint, letterSpacing: '0.04em', marginTop: 10 }}> Every numeric is verified against the linked sources and implemented in our code base. </span>
{refs.map((r, i) => (
{r.cat.toUpperCase()}
{r.items.map((it) => (
▸ {it.label}
{it.url}
))}
))}
); } Object.assign(window, { Slide1, Slide2, Slide3, Slide4, Slide5, Slide6, Slide7, Slide8, Slide9, Slide10, Slide11, Slide12, Slide13, Slide14, Slide15, Slide16, Slide17, COLORS, TYPE_SCALE, SPACING, FONT_MONO, FONT_SANS, });