FinTech Multi-Agent AI LangGraph Stock Market NSE/BSE

StockAI India

Client
Codevally
Industry
FinTech / AI
Duration
2 Months
Team count
2
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Overview

Indian retail investors face an information overload problem — technical indicators, quarterly filings, FII/DII flows, and breaking news all move stock prices, but no consumer-facing tool fuses them across NSE and BSE coverage. Most traders end up copying tips from Telegram channels or watching lagging broker terminals, with no visibility into why a stock should be bought or sold.


Codevally built StockAI India, a multi-agent AI platform that turns any NSE/BSE ticker into an institutional-grade BUY, SELL, or HOLD recommendation in around 25 seconds. A LangGraph orchestrator runs four specialist agents in parallel, and a master agent synthesizes their signals into a single weighted decision — with full transparency over the evidence and risk flags behind it.


The Solution delivers institutional-grade equity analysis by providing:

  • Technical Agent: Multi-timeframe RSI, MACD, EMA stacks, ADX, OBV, and automatic candlestick pattern detection.
  • Fundamental Agent: Sector-relative P/E, P/B, ROE, debt-to-equity, earnings and revenue trends with LLM-generated interpretation.
  • Market Context Agent: FII/DII flows, Nifty/Sensex/BankNifty regimes, India VIX, global indices, and sector relative strength.
  • News Sentiment Agent: NewsAPI, Economic Times, and Reuters feeds processed through VADER pre-filtering plus LLM event detection.
  • Master Agent: Weighted synthesis of all four agents into a conviction-scored BUY/SELL call with target price, stop loss, key evidence, and risk flags.

This Codevally product condenses what would take a research analyst hours of cross-referencing into a single 25-second pipeline — every recommendation ships with explicit evidence and risk flags, so users always see why the model called BUY or SELL, no black-box scores. The fuzzy ticker resolver, free public access, and Next.js + Tailwind interface remove the last barriers between retail investors and institutional-grade decision support.

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Bringing institutional-grade equity analysis to every Indian retail trader.

01. Discovery & Architecture

Mapped Indian retail trader pain points, defined the four-agent decomposition, and selected LangGraph for parallel orchestration with fall-through guarantees.

02. Data & Agent Engineering

Wired yFinance, NSE/BSE feeds, NewsAPI, and Economic Times RSS into typed agent contracts. Built indicator stacks, VADER pre-filtering, and LLM prompt chains per agent.

03. Master Synthesis & UX

Designed the master agent's weighted scoring, conviction model, and risk flag schema. Built a Next.js interface that surfaces evidence first, score second.

04. Performance & Launch

Optimized parallel agent latency to ~25 seconds, added fuzzy ticker resolution, and shipped a public-facing analyzer free for NSE/BSE users.
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