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CN2.DEVBUKYA NARESH
WORKS/PERSONAL BUILDS/SENTIMENT STREAM
EXPERIMENTAL PROTOTYPE
10

REAL-TIME FINANCIAL NLP & SENTIMENT AGENT

FINANCIAL CHATBOT & SENTIMENT STREAM

Financial sentiment analysis must extract concrete market signals and citations without speculative extrapolation.

A real-time financial news stream processor and conversational research agent. Ingests raw news wires, earnings calls, and regulatory filings, extracting directional sentiment scores and market impact indicators with strict citation provenance.

STREAM INFERENCE

< 180 ms

LATENCY — PER-HEADLINE SENTIMENT EXTRACTION

STRICT CITATION

100%

GROUNDING — NO HALLUCINATED NUMBERS OR DATES

SOURCE FEEDS

48+

REAL-TIME AGGREGATION — REGULATORY & NEWS WIRES

QUICK JUMP:PROBLEMWHY I BUILT ITARCHITECTURECAPABILITIESTECH STACKCHALLENGESEVIDENCE & RESULTSRESOURCES

02 / THE PROBLEM

PROBLEM STATEMENT 01

Financial markets move too quickly for manual news reading, while generic AI tools hallucinate critical financial statistics.

03 / WHY I BUILT IT

ENGINEERING MOTIVATION & OBJECTIVES

Traditional LLM chatbots hallucinate numbers and cite outdated quarterly figures when asked about financial events.

I built Sentiment Stream to create a grounded, cited NLP tool specifically for market research that ties every claim to verified news text.

04 / ARCHITECTURE

MODULAR STATE MACHINE & PIPELINE FLOW

  1. 01

    NEWS INGEST

    Continuous RSS & API consumer

    • SEC EDGAR
    • Financial news APIs
  2. 02

    NLP SCORER

    Sentence-level sentiment analysis

    • Financial domain models
    • Confidence scoring
  3. 03

    CITATION VERIFIER

    Strict grounding engine

    • Source mapping
    • Hallucination check
LAYER 01

NEWS INGEST

Continuous RSS & API consumer

  • SEC EDGAR
  • Financial news APIs
LAYER 02

NLP SCORER

Sentence-level sentiment analysis

  • Financial domain models
  • Confidence scoring
LAYER 03

CITATION VERIFIER

Strict grounding engine

  • Source mapping
  • Hallucination check

05 / KEY CAPABILITIES

CORE ENGINES & SYSTEM MODULES

◆Microstructure Sentiment Extraction

Categorizes news items into directional momentum and volatility impact scores.

◆Zero-Hallucination Guardrails

Validates every output response against retrieved source documents with explicit paragraph citations.

◆WebSocket Live Feed Stream

Streams incoming headlines with real-time sentiment color-coding and topic clustering.

06 / TECHNOLOGY STACK

LANGUAGES, PROTOCOLS & RUNTIME INFRASTRUCTURE

Python 3.12FastAPITransformersNext.jsVector IndexingWebSockets

07 / ENGINEERING CHALLENGES

BOTTLENECK INVESTIGATION & RESOLUTION

CHALLENGE 01

Handling bursty news spikes during earnings releases without queue lag.

IMPLEMENTED SOLUTION

Decoupled text ingestion from transformer inference using an asynchronous worker pool.

08 / DEVELOPMENT JOURNEY

EVOLUTIONARY PHASES & MILESTONES

v0.1

Regex Headline Parser

Keyword-based earnings trigger test.

v0.5

Domain Transformer Fine-Tuning

Incorporated financial NLP models for nuanced sentiment detection.

09 / CASE STUDIES

EMPIRICAL SCENARIO INVESTIGATIONS

CASE 01

FOMC Rate Statement Analysis

Parsing live central bank statements to detect tone shifts in under 2 seconds.

VERIFIED OUTCOME

Accurately flagged hawkish linguistic deviations before general media consensus.

10 / EVIDENCE & RESULTS

VERIFIED TEST SUITES, BENCHMARKS & HONEST LIMITATIONS

EMPIRICAL FINDINGS
  • ✓Sub-200ms classification latency on standard CPU inference setups.
  • ✓Zero fabricated data points observed across 1,000 question-answer verification pairs.
HONEST LIMITATIONS
  • !Requires high-quality API keys for low-latency news wire access.

11 / SCREENSHOTS & VISUALISATIONS

SYSTEM TOPOLOGY & INSTRUMENTATION SCHEMATICS

Sentiment Pipeline Flow

DIAGRAM

Dataflow from news intake to tokenizer, embedding index, and citation generator.

[LIVE SYSTEM TOPOLOGY · TELEMETRY INLINE]