I need a connector that ingests real-time stock market data for an LLM financial analyst.

For an LLM financial analyst, I’d structure the connector as a market-data ingestion layer + normalized tool interface, rather than letting the LLM call a raw stock API directly.

Good provider choices

• twelvedata.com — particularly attractive if you want one API for equities, ETFs, FX, crypto, fundamentals, technical indicators, historical data, and real-time streaming. It supports REST plus WebSocket, with coverage across U.S. and 90+ international exchanges.
• polygon.io — a strong option if your analyst is primarily focused on U.S. equities and granular real-time market data. Its WebSocket infrastructure can stream real-time stock events.
• support.twelvedata.com — useful if you specifically need server-push quotes rather than polling.

Architecture I'd use

             Exchange / Market Data Provider
                         │
                  WebSocket + REST
                         │
                         ▼
              ┌─────────────────────┐
              │ Market Data Gateway │
              │                     │
              │ • authentication    │
              │ • reconnects        │
              │ • rate limiting     │
              │ • normalization     │
              │ • timestamps        │
              │ • deduplication     │
              └──────────┬──────────┘
                         │
                  normalized events
                         │
              ┌──────────▼──────────┐
              │ Market Data Store   │
              │ Redis / TimescaleDB │
              │ PostgreSQL          │
              └──────────┬──────────┘
                         │
                         ▼
                 LLM Tool / MCP
                         │
             ┌───────────┼───────────┐
             ▼           ▼           ▼
          quote       history    fundamentals
          tool          tool         tool
             │           │           │
             └───────────┼───────────┘
                         ▼
                    LLM Analyst

The key design decision is not to dump the live feed into the model's context window. Keep the stream in your backend, and expose targeted tools such as:

get_quote(symbol)
get_quotes(symbols[])
get_intraday(symbol, interval)
get_historical_prices(symbol, start, end)
get_company_fundamentals(symbol)
get_earnings(symbol)
get_dividends(symbol)
get_corporate_actions(symbol)
get_market_status()

Then the LLM can ask for exactly the data it needs.

For example:

"Compare AAPL and MSFT's intraday performance and explain today's divergence." The agent would call get_intraday(["AAPL","MSFT"]), retrieve structured data, perform calculations, and produce the analysis—rather than trying to reason from an enormous stream of raw ticks.

For truly real-time analysis

I'd have the gateway maintain something like:

{
  "symbol": "AAPL",
  "timestamp": "2026-09-18T19:32:14.183Z",
  "price": 251.42,
  "bid": 251.41,
  "ask": 251.43,
  "volume": 48219321,
  "source": "massive",
  "market_status": "regular"
}

And separately maintain derived state:

{
  "symbol": "AAPL",
  "last": 251.42,
  "change_pct": 1.84,
  "vwap": 249.87,
  "day_high": 252.18,
  "day_low": 246.91,
  "volume_vs_20d": 1.37
}

That second layer is particularly valuable for an LLM because it turns millions of market events into compact analytical facts.

My default choice: Twelve Data if you want broad/global coverage and a relatively unified API; Massive if your priority is high-granularity U.S. market streaming. Twelve Data explicitly supports WebSocket streaming and REST, while its current documentation says full WebSocket access starts at its Pro plan.

If you're building this as an MCP connector specifically, I can also lay out the exact MCP tool schema, event pipeline, and a Python/TypeScript implementation.

To power a real-time LLM financial analyst, you need a connector that handles streaming data (via WebSockets) or low-latency polling (via REST), formats the output for tokenization, and ideally supports tool-use frameworks or Model Context Protocol (MCP).

Top options for real-time stock market data connectors tailored to LLM and agentic workflows include:

1. Alpha Vantage (MCP Server) : Offers an official Model Context Protocol (MCP) server integration, making it plug-and-play for LLMs and agentic development environments to pull real-time and historical data natively. You can check out their setup details via Alpha Vantage.
2. Financial Datasets API : A modern, high-performance REST and streaming API built specifically for AI agents, providing clean JSON payloads for ticker data, financials, and SEC filings that fit neatly into LLM context windows. Explore the documentation at Financial Datasets.
3. Financial Modeling Prep (FMP) : Provides robust real-time WebSockets and REST endpoints covering live quotes, deep fundamental statements, and market sentiment calendars formatted cleanly for machine consumption. View their endpoint specs at Financial Modeling Prep.
4. Twelve Data : Excellent for multi-asset real-time streaming with comprehensive Python and JavaScript SDKs that easily wrap into custom LangChain or LlamaIndex tool definitions. Review their developer plans on Twelve Data.
5. Polygon.io : The gold standard for raw, tick-by-tick low-latency WebSocket streaming data from US exchanges, ideal if your LLM analyst needs second-by-second trade and quote data. Access their developer portal at Polygon.io.

To help narrow down the ideal connector, let me know:

• Are you building an agentic loop (like LangChain/LlamaIndex) or a streaming dashboard?
• Do you need tick-by-tick WebSockets or just on-demand REST polling?
• What is your target budget and expected request volume?

Yes. For an LLM financial analyst, I’d build the connector around a WebSocket market-data feed + normalized event stream + tool/API layer for the LLM.

Recommended starting point: Alpaca

docs.alpaca.markets is a strong fit because it provides both historical REST APIs and real-time WebSocket streams for equities, options, crypto, and news.

Its stock stream can deliver:

• Trades
• Quotes / bid-ask
• Minute bars
• Symbol subscriptions, including wildcard subscriptions
• Real-time updates over WebSockets

One important caveat: the free/basic equity feed is IEX-only; full U.S. exchange coverage via SIP requires the higher-tier market-data subscription.

Architecture I'd use

                 ┌─────────────────────┐
                 │   Market Data Feed  │
                 │ Alpaca WebSocket    │
                 └──────────┬──────────┘
                            │
                     trades/quotes/bars
                            │
                            ▼
                 ┌─────────────────────┐
                 │  Market Data        │
                 │  Normalizer         │
                 └──────────┬──────────┘
                            │
             ┌──────────────┼──────────────┐
             ▼              ▼              ▼
        Redis/Kafka      TimescaleDB    Object Store
        live state       history        raw events
             │              │
             └──────────────┼──────────────┘
                            ▼
                 ┌─────────────────────┐
                 │ Financial Analyst   │
                 │ Tool / MCP Server   │
                 └──────────┬──────────┘
                            │
                            ▼
                         LLM

The key design decision is not to dump the raw tick stream directly into the LLM. Instead, expose structured tools such as:

get_quote("NVDA")
get_intraday_bars("NVDA", "5m", "1d")
get_price_change("NVDA", "1h")
get_volume_profile("NVDA", "1d")
get_market_snapshot(["AAPL","MSFT","NVDA"])
get_recent_trades("NVDA")

The LLM can then request precisely the data it needs while your connector maintains the high-frequency stream independently.

If you need broader global coverage

twelvedata.com is another interesting option. It advertises coverage of hundreds of thousands of instruments across stocks, ETFs, forex, crypto and other asset classes, with REST and WebSocket APIs. Its WebSocket access starts at the Pro tier.

For a U.S.-equity-focused analyst, I'd start with Alpaca; for a globally diversified analyst, I'd investigate Twelve Data alongside it.

What I'd make the connector expose

For an LLM analyst, I'd define a small canonical schema:

{
  "symbol": "NVDA",
  "timestamp": "2026-09-15T14:59:31.123Z",
  "price": 182.41,
  "bid": 182.40,
  "ask": 182.42,
  "volume": 18392041,
  "source": "alpaca",
  "feed": "sip"
}

And separate analytical endpoints for fundamentals, news, corporate actions, historical prices, technical indicators, and market-wide snapshots. This keeps the model's context compact while preserving the underlying real-time data.

If you're building this as an actual connector/MCP server, I can also lay out the MCP tool schema, WebSocket ingestion service, Redis/Postgres data model, and Python implementation for it.

To build a real-time financial analyst powered by an LLM, you need a connector that bridges live market feeds (WebSockets or high-frequency REST APIs) with an LLM's context or agent framework.

Top-tier options for streaming market data into LLM pipelines vary based on architecture, ease of integration, and data depth:

1. Polygon.io
   • Best For: Ultra-low latency WebSocket streaming (≈ 10 ms) of raw trades, quotes, and aggregates (OHLCV).
   • Why it fits LLMs: Excellent developer documentation, structured JSON output, and clean client libraries that let you stream ticks directly into a vector database or an active event-driven agent loop.
   • Access: Check out documentation and plans on Polygon.io.
2. Alpha Vantage (with MCP Support)
   • Best For: Plug-and-play AI agent integration via the Model Context Protocol (MCP).
   • Why it fits LLMs: Alpha Vantage provides an official MCP server, allowing LLMs and AI development environments to query real-time and historical data natively through standardized agent tools without custom boilerplate parsing.
   • Access: Explore implementation details via Alpha Vantage.
3. Alpaca API
   • Best For: Combining real-time market data streaming with execution capabilities.
   • Why it fits LLMs: If your financial analyst needs to transition from analyzing the market to acting on signals (paper trading or live execution), Alpaca offers robust real-time WebSockets alongside a clean trading API.
   • Access: Review specifications on Alpaca Markets.
4. Financial Modeling Prep (FMP)
   • Best For: Blending real-time price feeds with deep fundamental data (financial statements, analyst estimates, SEC filings).
   • Why it fits LLMs: Pure price data isn't always enough for a holistic analyst. FMP provides WebSockets and REST endpoints that easily cross-reference live quotes with deep historical fundamentals.
   • Access: View endpoint structures on Financial Modeling Prep.
5. Finnhub
   • Best For: Lightweight, multi-asset real-time streaming (stocks, crypto, forex) with social sentiment tracking.
   • Why it fits LLMs: Provides free-to-low-tier WebSocket streaming for US stock ticks and news sentiment, which is useful if your LLM analyst relies on a mix of price action and real-time news chatter.
   • Access: Get started through Finnhub.

To help narrow down the ideal connector, tell me:

• Are you building an event-driven streaming architecture (WebSockets) or a scheduled polling architecture (REST)?
• Does your analyst need just equity pricing , or also fundamentals, news sentiment, and SEC filings?
• Are you using a specific agent framework (like LangChain, LlamaIndex, or AutoGen)?

For an LLM financial analyst, you’ll usually want a market-data connector layer rather than just a raw API wrapper. The connector should convert streaming market feeds into structured tool calls, embeddings, alerts, and context windows that an LLM can consume.

A good architecture:

Exchange feeds
     |
     v
Market Data Connector
     |
     +--> Normalizer (symbols, timestamps, schemas)
     |
     +--> Stream processor (ticks, quotes, news)
     |
     +--> Feature builder (VWAP, RSI, volatility, spreads)
     |
     +--> Vector store / time-series DB
     |
     +--> LLM tool interface (MCP/function calling)
     |
     v
Financial Analyst Agent

Recommended connector capabilities

1. Real-time price ingestion

Support:

• Trades
• Bid/ask quotes
• Level 1 and Level 2 order book
• OHLCV bars
• Market status
• Corporate actions

Examples of providers with streaming APIs:

• Massive provides US stock trades, quotes, aggregates, WebSocket streaming, and APIs designed for developer/model use cases.
• Alpaca offers WebSocket market-data streams for stocks, crypto, options, and news.
• LSEG provides enterprise real-time pricing APIs including WebSocket-based distribution.
• Benzinga provides streaming feeds including quotes, trades, and news.

2. Normalize into an LLM-friendly schema

Example event:

{
  "type": "quote",
  "symbol": "AAPL",
  "timestamp": "2026-09-11T09:30:00.123Z",
  "price": 234.18,
  "bid": 234.16,
  "ask": 234.19,
  "volume": 1203400,
  "market": "NASDAQ"
}

For trades:

{
  "type": "trade",
  "symbol": "NVDA",
  "price": 128.33,
  "size": 500,
  "side": "buy",
  "timestamp": "..."
}

3. Expose the connector as LLM tools

Example tool definitions:

{
  "name": "get_live_quote",
  "description": "Returns current market quote",
  "parameters": {
    "symbol": "string"
  }
}

{
  "name": "get_market_events",
  "description": "Returns unusual price, volume, and news events",
  "parameters": {
    "symbols": ["string"],
    "lookback_minutes": "integer"
  }
}

{
  "name": "screen_stocks",
  "description": "Find stocks matching quantitative criteria",
  "parameters": {
    "criteria": "string"
  }
}

4. Add an event intelligence layer

Raw ticks are too noisy for an LLM. Create derived signals:

• Price movement:

• 1m / 5m / 1h returns

• Gap up/down

• Breakouts

• Volume:

• Relative volume

• Volume spikes

• Risk:

• Volatility

• Beta

• Drawdown

• Market microstructure:

• Bid/ask spread

• Order imbalance

• Liquidity

• News:

• Earnings releases

• SEC filings

• Analyst revisions

• Sentiment

The LLM should see:

"AAPL is up 2.1% today, volume is 180% of 20-day average, earnings are in 5 days, and options implied volatility increased 14%." Not:

"AAPL tick #4389201 price 234.18 size 100."

5. Storage layer

A practical stack:

6. Connector implementation pattern

A minimal service:

market_connector/
├── feeds/
│   ├── massive.py
│   ├── alpaca.py
│   └── lseg.py
├── normalize/
│   └── quotes.py
├── analytics/
│   ├── indicators.py
│   └── anomaly.py
├── tools/
│   └── llm_tools.py
└── api/
    └── server.py

The connector should publish:

• quote.updated
• trade.executed
• volume.anomaly
• news.breaking
• earnings.event

Then your LLM agent subscribes only to meaningful events.

My suggested MVP

For a first production version:

1. Data: Massive or Alpaca WebSocket feed
2. Streaming: Kafka/Redpanda
3. Processing: Python + Polars
4. Storage: TimescaleDB
5. Agent interface: MCP server or OpenAI tool/function calling
6. Analytics: custom factor engine
7. Guardrails: attach timestamps, data source, and confidence to every market fact

This gives the LLM a reliable "market perception layer" instead of forcing it to interpret raw exchange data.

{
  "name": "get_market_events",
  "description": "Returns unusual price, volume, and news events",
  "parameters": {
    "symbols": ["string"],
    "lookback_minutes": "integer"
  }
}

{
  "name": "screen_stocks",
  "description": "Find stocks matching quantitative criteria",
  "parameters": {
    "criteria": "string"
  }
}

4. Add an event intelligence layer

Raw ticks are too noisy for an LLM. Create derived signals:

• Price movement:

• 1m / 5m / 1h returns

• Gap up/down

• Breakouts

• Volume:

• Relative volume

• Volume spikes

• Risk:

• Volatility

• Beta

• Drawdown

• Market microstructure:

• Bid/ask spread

• Order imbalance

• Liquidity

• News:

• Earnings releases

• SEC filings

• Analyst revisions

• Sentiment

The LLM should see:

"AAPL is up 2.1% today, volume is 180% of 20-day average, earnings are in 5 days, and options implied volatility increased 14%." Not:

"AAPL tick #4389201 price 234.18 size 100."

5. Storage layer

A practical stack:

6. Connector implementation pattern

A minimal service:

market_connector/
├── feeds/
│   ├── massive.py
│   ├── alpaca.py
│   └── lseg.py
├── normalize/
│   └── quotes.py
├── analytics/
│   ├── indicators.py
│   └── anomaly.py
├── tools/
│   └── llm_tools.py
└── api/
    └── server.py

The connector should publish:

• quote.updated
• trade.executed
• volume.anomaly
• news.breaking
• earnings.event

Then your LLM agent subscribes only to meaningful events.

My suggested MVP

For a first production version:

To feed real-time stock market data directly into an LLM financial analyst, the best approach depends on whether you are using an agentic desktop app (like Claude or Cursor) or building a custom streaming pipeline.

Top options range from plug-and-play protocol connectors to institutional-grade streaming APIs:

• Alpha Vantage MCP Server (Best for plug-and-play LLM apps)
  • What it is: An official Model Context Protocol (MCP) Server built specifically to connect LLMs (like Claude Desktop, VS Code, or Cursor) to live and historical stock data via natural language tools.
  • How it works: You configure your LLM interface with the remote MCP URL and a free Alpha Vantage API Key , allowing the model to dynamically call stock quotes and technical indicators on demand.
• Polygon.io WebSocket API (Best for high-frequency custom AI agents)
  • What it is: An institutional-grade market data platform offering low-latency WebSocket streams for live trades, quotes, and aggregate bars.
  • How it works: You establish a persistent WebSocket connection in Python, authenticate with your key, and subscribe to ticker feeds. The JSON payloads are then injected straight into your LLM’s context window or vector memory buffer as events occur.
• Financial Modeling Prep (FMP) API (Best for deep fundamental + real-time mix)
  • What it is: A comprehensive financial data provider combining real-time pricing feeds with 30+ years of deep fundamental data (financial statements, SEC filings, and DCF models).
  • How it works: Excellent if your LLM analyst needs to cross-reference live intraday ticks against historical balance sheets or earnings transcripts on the fly.

To help narrow down the ideal choice, tell me:

• Are you building a custom Python application/agent or connecting to a desktop LLM client (like Claude/Cursor)?
• Do you need tick-by-tick WebSocket streaming or just on-demand REST queries when the LLM asks for a price?

Yes. For an LLM financial analyst, I’d prioritize a connector that supports both real-time streaming and tool-callable historical/reference data, rather than a simple quote API.

My first choice: Massive

Massive (formerly Polygon.io) is particularly well suited to an LLM analyst because it now offers:

• Real-time WebSockets for trades, quotes, and aggregates.
• REST APIs for historical trades, bars, snapshots, reference data, fundamentals, and filings.
• A first-party remote MCP server, specifically designed to let LLM agents query the market directly.
• Historical tick data going back to 2003, useful for backtesting and contextual analysis.
• Official SDKs for Python, JavaScript/TypeScript, Go, and Kotlin.

That makes the architecture fairly clean:

             ┌──────────────────────┐
             │   Stock exchanges    │
             └──────────┬───────────┘
                        │
                  Real-time feed
                        │
                        ▼
             ┌──────────────────────┐
             │      Massive         │
             │  REST + WebSocket    │
             │      + MCP           │
             └───────┬───────┬──────┘
                     │       │
              live stream    │ tool calls
                     │       │
                     ▼       ▼
              ┌─────────────────────┐
              │ Market-data layer   │
              │ normalization/cache │
              └──────────┬──────────┘
                         │
                         ▼
                 ┌───────────────┐
                 │  LLM analyst  │
                 │               │
                 │ • price       │
                 │ • volume      │
                 │ • fundamentals│
                 │ • filings     │
                 │ • news        │
                 └───────────────┘

Alternative: Twelve Data

Twelve Data is attractive if you need global/multi-asset coverage. It provides a unified API and WebSocket streaming, with coverage across many markets and asset classes. Twelve Data Support Its WebSocket feed supports subscribing to multiple symbols and pushes updates as they become available.

One important caveat: Twelve Data's US equity real-time feed is sourced from venues representing about 5% of total US trading volume, so I would not choose it if comprehensive US market coverage is the primary requirement.

What I'd build

For an LLM analyst, I wouldn't pipe every tick directly into the model. Instead:

1. WebSocket ingestion → receive trades/quotes.
2. Stream processor → aggregate into 1m/5m/1h bars, VWAP, volume anomalies, etc.
3. Time-series store → retain recent and historical market state.
4. Fundamental/document store → filings, earnings, company metadata.
5. LLM tool layer → expose functions such as: - get_quote(ticker)

• get_intraday(ticker, interval)
• get_trades(ticker, start, end)
• get_company(ticker)
• get_fundamentals(ticker)
• get_market_snapshot()
• get_unusual_volume(ticker)

6. LLM → calls tools only when it needs fresh information, rather than consuming the continuous stream itself.

For this use case, Massive + an MCP/tool layer is probably the shortest path to a production-grade LLM financial analyst. Its native MCP offering is unusually relevant here because you don't have to invent an adapter between the market-data API and the agent.

If by “connector” you mean an actual connector/plugin for your existing LLM platform (e.g. an MCP server, OpenAI connector, LangChain tool, or a custom REST connector), tell me which platform you're using and I can specify the exact connector architecture and API schema.

One important caveat: Twelve Data's US equity real-time feed is sourced from venues representing about 5% of total US trading volume, so I would not choose it if comprehensive US market coverage is the primary requirement.

What I'd build

For an LLM analyst, I wouldn't pipe every tick directly into the model. Instead:

To build a robust AI financial analyst that operates on real-time stock market data, you should implement a Model Context Protocol (MCP) server or an Agent Toolset connected to a financial streaming API. Because LLMs cannot naturally ingest continuous raw WebSocket streams, the connector must expose the real-time data as tools that the LLM can call on-demand.

The top real-time stock market connectors and integration paths for an LLM financial analyst are detailed below.

💡 1. Turnkey AI & Agent-Native Connectors

If you are using orchestration frameworks like LangChain, LlamaIndex , or an MCP client (such as Claude Desktop or Cursor), use these pre-built, agentconfigured tools:

• Alpha Vantage MCP Server:
  • Best for : General agent workflows and technical indicators.
  • Capability : Provides an official Model Context Protocol (MCP) server. It natively exposes real-time and historical stock endpoints as standardized tools that LLM agents can call dynamically.
• OpenBB Platform (AI Integration):
  • Best for : Cross-asset research and multi-source structuring.
  • Capability : Acts as a unified data connector layer. It normalizes ingestion across hundreds of data venues (stocks, crypto, macroeconomics) into clean, machine-readable JSON formats optimized for LLM prompting.
• Composio (Twelve Data / Alpaca Integration):
  • Best for : Fast plug-and-play setup in LangChain or LlamaIndex.
  • Capability : Bridges real-time market data providers directly into agent tools. The Twelve Data MCP toolkit handles real-time stock prices, currency conversions, and ETF data effortlessly.

📊 2. Direct Streaming APIs (Custom Tool Ingestion)

For an institutional-grade AI analyst, you will want to build custom tools using a Python wrapper around a reliable market data API.

⚙️ How to Implement the Architecture

Do not pipe raw, continuous real-time data directly into the LLM prompt window; this will quickly exhaust token limits and cause hallucinations. Instead, use an Agentic Tool-Calling Workflow:

1. Build a Financial Database Cache : Direct your real-time WebSocket connection into a fast cache database (like Redis or TimescaleDB).
2. Expose Python Functions as Tools : Create deterministic Python functions to fetch the latest price, moving averages, or order book health.
3. Equip the LLM : Use LangChain or LlamaIndex to pass these functions to the LLM as tools. The LLM can then reason: "To analyze Tesla's current momentum, I will call get_realtime_quote(ticker='TSLA') and get_technical_indicators()."

➡️ Next Steps to Proceed

If you would like to narrow down the implementation, please let me know:

• What AI framework are you using? (e.g., LangChain/LangGraph, LlamaIndex, or building vanilla Python agents?)
• What is your budget and frequency need? (e.g., Free-tier sub-minute updates, or paid institutional sub-second streaming?)
• Does the LLM only need to analyze data , or should it also execute trades?

To build a robust AI financial analyst that operates on real-time stock market data, you should implement a Model Context Protocol (MCP) server or an Agent Toolset connected to a financial streaming API. Because LLMs cannot naturally ingest continuous raw WebSocket streams, the connector must expose the real-time data as tools that the LLM can call on-demand.

The top real-time stock market connectors and integration paths for an LLM financial analyst are detailed below.

💡 1. Turnkey AI & Agent-Native Connectors

If you are using orchestration frameworks like LangChain, LlamaIndex , or an MCP client (such as Claude Desktop or Cursor), use these pre-built, agentconfigured tools:

• Alpha Vantage MCP Server:
  • Best for : General agent workflows and technical indicators.
  • Capability : Provides an official Model Context Protocol (MCP) server. It natively exposes real-time and historical stock endpoints as standardized tools that LLM agents can call dynamically.
• OpenBB Platform (AI Integration):
  • Best for : Cross-asset research and multi-source structuring.
  • Capability : Acts as a unified data connector layer. It normalizes ingestion across hundreds of data venues (stocks, crypto, macroeconomics) into clean, machine-readable JSON formats optimized for LLM prompting.
• Composio (Twelve Data / Alpaca Integration):
  • Best for : Fast plug-and-play setup in LangChain or LlamaIndex.
  • Capability : Bridges real-time market data providers directly into agent tools. The Twelve Data MCP toolkit handles real-time stock prices, currency conversions, and ETF data effortlessly.

📊 2. Direct Streaming APIs (Custom Tool Ingestion)

For an institutional-grade AI analyst, you will want to build custom tools using a Python wrapper around a reliable market data API.

⚙️ How to Implement the Architecture

Do not pipe raw, continuous real-time data directly into the LLM prompt window; this will quickly exhaust token limits and cause hallucinations. Instead, use an Agentic Tool-Calling Workflow:

1. Build a Financial Database Cache : Direct your real-time WebSocket connection into a fast cache database (like Redis or TimescaleDB).
2. Expose Python Functions as Tools : Create deterministic Python functions to fetch the latest price, moving averages, or order book health.
3. Equip the LLM : Use LangChain or LlamaIndex to pass these functions to the LLM as tools. The LLM can then reason: "To analyze Tesla's current momentum, I will call get_realtime_quote(ticker='TSLA') and get_technical_indicators()."

➡️ Next Steps to Proceed

If you would like to narrow down the implementation, please let me know:

• What AI framework are you using? (e.g., LangChain/LangGraph, LlamaIndex, or building vanilla Python agents?)
• What is your budget and frequency need? (e.g., Free-tier sub-minute updates, or paid institutional sub-second streaming?)
• Does the LLM only need to analyze data , or should it also execute trades?

Yes. For an LLM financial analyst, I’d build the connector around a streaming market-data provider + normalized event layer, rather than having the LLM call a quote API directly.

My top choice: Massive (formerly Polygon.io)

Massive is particularly well suited because it offers:

• Real-time WebSocket streams for trades, quotes, and aggregates.
• REST endpoints for historical data, snapshots, fundamentals, filings, and other on-demand context.
• A first-party MCP interface with 60+ tools, specifically designed to expose its market-data API to AI agents.
• One API key across streaming, REST, bulk files, and agent tooling.

That makes it a good fit for an architecture like:

Exchange / SIP data
        │
        ▼
┌─────────────────────┐
│ Market Data         │
│ Connector           │
│                     │
│ WebSocket           │
│ REST backfill       │
│ Reconnection        │
│ Deduplication       │
│ Timestamping        │
└─────────┬───────────┘
          │
          ▼
   Normalized events
          │
    ┌─────┴─────┐
    ▼           ▼
Time-series   Event store
DB            / cache
    │
    └─────┬─────┘
          ▼
   Financial Analyst
        LLM / Agent
          │
          ├── "What's moving?"
          ├── "Analyze AAPL"
          ├── "Why did NVDA move?"
          └── "Compare today's action
               with historical earnings"

Other strong options

• Alpaca — excellent if you want a straightforward WebSocket implementation. Its stock stream supports SIP, IEX, delayed SIP, overnight, and other feeds depending on subscription.
• Webull OpenAPI — useful if you also want US stocks, ETFs, futures, crypto, and options; its streaming interface uses MQTT over WebSocket/TCP.
• LSEG — the better direction if this is an institutional-grade analyst requiring broad professional market-data coverage and infrastructure. Its WebSocket API supports real-time pricing streams from its real-time distribution systems.
• Alpha Vantage — attractive when you need a broad collection of market, fundamental, economic, FX, and technical-indicator APIs, but I'd favor a true streaming feed for the real-time ingestion layer.

What I'd expose to the LLM

Don't expose raw WebSocket messages directly. Normalize them into tools/events such as:

get_quote(symbol)
get_trades(symbol, start, end)
get_bars(symbol, interval, start, end)
get_market_snapshot(symbols)
get_company_fundamentals(symbol)
get_filings(symbol)
get_news(symbol)
get_market_status()

subscribe_quotes(symbols)
subscribe_trades(symbols)
subscribe_bars(symbols, interval)

Then add an LLM-specific semantic layer:

get_price_move(symbol, window)
get_volume_anomaly(symbol)
get_relative_performance(symbol, benchmark)
get_earnings_context(symbol)
explain_price_move(symbol, timestamp)

That last layer is important: the LLM should reason over clean, timestamped observations, not try to interpret thousands of individual quote events.

If you're building this as an actual connector/plugin for an existing LLM agent, I can also design the connector interface—including WebSocket ingestion, normalized schema, MCP tools, Redis/Kafka buffering, Postgres/Timescale storage, and the LLM tool definitions.