Real-Time Market Data in Excel Using LLMs: A Practical Implementation Guide

You implement real-time market data feeds in Excel using LLMs by treating the language model as your technical translator. It writes the Power Query M code or VBA scripts that connect to market data APIs, parses JSON responses into Excel-compatible tables, and handles the error logic you’d otherwise code manually. The process follows four stages: connect to a market data API using LLM-generated code, transform raw responses into structured tables, configure automated refresh, and layer in dashboards with analytical summaries.

This compresses weeks of development work—or waiting on IT—into hours of guided configuration. The LLM handles syntax; you keep control of logic and output.

This guide is for Excel-proficient financial professionals who need real-time data without becoming developers. If you can write a VLOOKUP and understand what an API does conceptually, you have the foundation. For broader strategic context, we’ve covered LLM integration strategies separately.

Key Takeaways

LLMs function as technical translators that convert plain-language requests into functional Power Query M code or VBA scripts—eliminating the need to learn API authentication protocols, JSON parsing, or programming syntax.
Four-stage implementation framework: (1) Connect to market data APIs via LLM-generated code, (2) Transform JSON responses into Excel-compatible tables, (3) Configure automated refresh cycles, (4) Build dashboards with analytical overlays.
“Real-time” exists on a spectrum: True real-time (sub-second) serves algorithmic trading; near real-time (1-60 seconds) handles most portfolio monitoring; delayed data (15-20 minutes) works for research and EOD reporting at lower cost.
Free API tiers enable proof-of-concept: Alpha Vantage (25 calls/day), Massive/Polygon (end-of-day equities, forex, crypto), and Finnhub (60 calls/minute) offer accessible starting points before scaling to paid tiers.
Prompt structure determines output quality: Use Context → Task → Format → Constraints framework for implementation-ready code rather than generic responses.
Validation is non-negotiable: LLM-generated code requires testing against known data before deployment. Build verification habits early—cross-check prices, dates, and volumes against trusted sources.
Enterprise scaling requires infrastructure beyond Excel: For teams managing dozens of API connections with compliance and reliability requirements, purpose-built platforms eliminate DIY maintenance overhead.

Why Real-Time Market Data in Excel Still Matters

The Persistent Excel Reality in Financial Services

The trading floor runs on terminals. The back office runs on Excel.

Despite decades of specialized platforms entering the market, spreadsheets remain the analytical backbone of financial services. Excel remains central to analyst workflows—building models, running scenarios, preparing the deliverables that inform investment decisions.

Excel persists because it offers something specialized platforms cannot: flexibility without permission. An analyst can build a custom model, test a thesis, and iterate in real-time without submitting feature requests or waiting for software release cycles. The spreadsheet adapts to the analyst’s thinking rather than constraining it.

The Manual Data Workflow Problem

Picture this: 7:15 AM, and a portfolio manager needs to update a 50-stock watchlist before the 8 AM investment committee meeting.

The workflow: navigate to multiple data sources, download CSV files, copy from web interfaces, paste into the master workbook, reformat to match existing structure, validate that nothing broke. Forty-five minutes gone before any actual analysis begins.

The cost isn’t just time. It’s analytical opportunities missed while wrestling with logistics, and decision quality degraded by inconsistent inputs.

Where LLMs Change the Equation

Large Language Models introduce a different approach entirely. Instead of learning API authentication protocols, JSON parsing syntax, or VBA programming patterns, you describe what you want in plain English and receive functional code in return.

The LLM serves as middleware—a translation layer between your intent (“I want daily closing prices for these 50 tickers refreshed every hour”) and the technical implementation (Power Query M code with authentication headers, error handling, transformation logic).

This isn’t replacing Excel with AI. It’s removing the technical barriers that historically required developer support or significant self-taught programming skill. You control what gets built; the LLM accelerates how.

For broader exploration of this intersection, exploratory financial data analysis with LLMs opens additional analytical possibilities beyond data retrieval.

Understanding Real-Time Market Data Infrastructure

What “Real-Time” Actually Means in Practice

A hedge fund’s execution desk and a portfolio manager’s watchlist have different definitions of “real-time.” Understanding this spectrum prevents over-engineering—and overpaying—for freshness you don’t need.

True real-time updates within milliseconds. This matters for algorithmic trading where microseconds affect execution quality. The infrastructure costs are substantial, and the requirements extend beyond Excel’s architecture.

Near real-time updates within seconds to minutes. This serves most portfolio monitoring, risk management, and analytical use cases. When tracking position exposure or monitoring threshold breaches, a 15-second delay is functionally identical to instantaneous.

Delayed data lags 15-20 minutes—the standard free tier from most exchanges. According to NYSE’s official market data policy, delayed data is disseminated at least 15 minutes after real-time release.¹ For historical analysis, end-of-day reporting, and research where you’re examining patterns rather than reacting to prices, delayed data works fine.

Data Freshness	Typical Latency	Primary Use Cases
True Real-Time	Sub-second	Algorithmic execution, HFT
Near Real-Time	1-60 seconds	Portfolio monitoring, risk alerts, intraday analysis
Delayed	15-20 minutes	Research, historical analysis, EOD reporting

For most Excel-based workflows, near real-time hits the sweet spot of freshness and cost.

Market Data APIs: Your Connection Points

Market data APIs are the programmatic bridges between data providers and your workbook. Several offer accessible entry points for individual analysts and small teams looking to automate market data refresh in Excel.

Alpha Vantage provides a free tier with 25 API calls per day and 5 per minute, covering equities, forex, and crypto.² Good for learning and proof-of-concept; production typically requires paid tiers for adequate rate limits.

Massive (formerly Polygon) provides comprehensive coverage including options and forex, with websocket support for streaming and access to free end-of-day U.S. equities, forex, and crypto data, including two years of historical data.³

Finnhub covers global equities, forex, and crypto with a generous free tier for non-commercial use—60 calls per minute for free users, making it suitable for development and prototyping.⁴

When evaluating, consider these six factors:

Data coverage: Does it include your asset classes, markets, and historical depth?
Rate limits: How many calls does your workflow actually require?
Response format: JSON is standard, but structure varies significantly.
Documentation quality: Poor docs dramatically increase implementation time.
Authentication method: Most use API keys in headers or query parameters.
Pricing trajectory: Free tiers change; understand costs if usage scales.

Excel’s Native Tools for Data Integration

Excel provides three mechanisms for external data connections, each with distinct capabilities for financial data automation.

Power Query (called “Get & Transform” in some versions) is the most versatile built-in option. It connects to web APIs, transforms JSON or XML into tabular format, and stores transformation logic for repeatable refresh. Power Query handles nested data structures and type conversion well. One limitation: desktop refresh scheduling requires manual triggering or third-party automation.

VBA provides programmatic control for complex workflows: HTTP requests, response parsing, cell writing, timer-based refresh. Steeper learning curve and maintenance overhead, but this is exactly where LLMs help.

Office Scripts represent Microsoft’s cloud-native alternative for Microsoft 365 commercial subscribers. According to Microsoft’s platform documentation, Office Scripts require Excel on the web (or desktop Version 2210+), OneDrive for Business, and a qualifying Microsoft 365 subscription license.⁵ Integrates with Power Automate for cloud scheduling. Works well for Excel Online but requires appropriate subscription tier.

Each tool can connect to market data APIs. Historically, doing so effectively required significant technical skill or IT involvement. LLMs change the accessibility equation by generating implementation code from plain-language descriptions.

How LLMs Function as an Orchestration Layer

The LLM as Technical Translator

Think of the LLM as a competent technical colleague available around the clock. You describe your goal—”I need Power Query code that pulls daily closing prices from Alpha Vantage for tickers in column A”—and it generates functional code implementing your specification.

What LLMs handle well:

Boilerplate code for API connections (authentication headers, endpoint construction, request formatting)
Parsing nested JSON into flat Excel-ready tables
Transformation logic (date formatting, calculated fields, type conversion)
Error handling for common failures (timeouts, rate limits, malformed responses)
Explaining existing code when you need to modify it
Debugging when implementations throw errors

What requires your oversight:

Output validation: LLMs generate plausible-looking code that sometimes contains subtle errors. Test against known data before trusting.
Credential security: Never paste API keys into prompts. Keys belong in Excel’s credential manager, referenced by code but never exposed.
Performance at scale: Generated code optimizes for correctness and clarity, not necessarily large dataset performance.
Compliance: Your organization may restrict external AI tools for internal data. Verify before sending anything sensitive through LLM APIs.

Prompt Engineering for Excel Workflows

Output quality tracks prompt quality directly. Vague requests produce generic code; specific, structured prompts produce implementation-ready solutions.

Effective prompts follow a consistent structure: Context → Task → Format → Constraints

Example:

Context: I’m working in Excel 365 desktop and need to pull stock price data from Alpha Vantage’s TIME_SERIES_DAILY endpoint.

Task: Generate Power Query M code that:

Connects to the Alpha Vantage API
Accepts a ticker symbol as a parameter
Retrieves daily closing prices for the past 100 trading days
Returns a table with columns: Date, Open, High, Low, Close, Volume

Format: Return only M code, formatted for direct paste into Power Query Advanced Editor. Include comments explaining each major section.

Constraints:

API key referenced as parameter “ApiKey” (configured separately)
Include error handling for failed responses
Convert date strings to Excel date format

This structure eliminates ambiguity. The LLM knows what you’re building, what format you need, and what guardrails to respect.

Refinement tips:

First output not quite right? Provide the error message as context for follow-up.
Need to understand logic? Ask the LLM to explain its code before running.
Want alternatives? Request different approaches if the first doesn’t fit.

Selecting an LLM for Financial Data Tasks

Multiple LLMs handle Excel code generation effectively. Practical differences for this use case are smaller than marketing suggests.

ChatGPT produces reliable Power Query and VBA code with good contextual understanding of financial data structures.

Claude handles longer context windows effectively—useful for complex existing code or lengthy specifications.

Gemini offers comparable capabilities with strong Google ecosystem integration.

Open-source models (Llama, Mistral variants) run locally for data sensitivity requirements, though code generation quality varies.

For most implementations, LLM choice matters less than prompt quality and validation discipline. Any mainstream model generates functional API connection code; your verification ensures it works for your specific case.

For deeper comparison, see our guide on choosing the right LLM for data analysis.

Step-by-Step Implementation Guide

This section walks through connecting Excel to a market data API using LLM-generated code. The framework applies regardless of which API or LLM you choose.

Step 1: Setting Up Your API Connection

Objective: Establish authenticated connectivity between Excel and your market data API.

You’ll need:

Excel 2016 or later (Power Query required)—Microsoft confirms Power Query is available in all Excel 2016+ Windows standalone versions and Microsoft 365 subscription plans.⁶
API key from your data provider (Alpha Vantage in examples)
Access to an LLM (ChatGPT, Claude, etc.)

Process:

First, get your API credentials. Alpha Vantage registration provides a free key immediately. Store it securely—reference it in connections but never expose it in shared workbooks or prompts.

Next, construct your prompt for connection code:

Generate Power Query M code for Excel that connects to Alpha Vantage’s

TIME_SERIES_DAILY_ADJUSTED endpoint. The code should:

Accept parameters for: ticker symbol, API key
Construct the API URL dynamically based on ticker
Make the HTTP request with appropriate headers
Parse the JSON response
Return a clean table with Date, Open, High, Low, Close, Adjusted Close, Volume
Handle API errors gracefully with informative messages

Return only the M code with inline comments.

The LLM generates code like this (simplified):

// Power Query M Code for Alpha Vantage Daily Prices

// Parameters: Ticker (text), ApiKey (text)

let

// Construct the API endpoint URL

BaseUrl = “https://www.alphavantage.co/query”,

QueryParams = [

#”function” = “TIME_SERIES_DAILY_ADJUSTED”,

symbol = Ticker,

apikey = ApiKey,

outputsize = “compact”

// Make the API request

Source = Json.Document(Web.Contents(BaseUrl, [Query = QueryParams])),

// Navigate to the time series data

TimeSeries = Source[#”Time Series (Daily)”],

// Convert to table format

ToTable = Record.ToTable(TimeSeries),

// Expand the nested price data

ExpandedData = Table.ExpandRecordColumn(ToTable, “Value”,

{“1. open”, “2. high”, “3. low”, “4. close”,

“5. adjusted close”, “6. volume”}),

// Rename columns for clarity

RenamedColumns = Table.RenameColumns(ExpandedData, {

{“Name”, “Date”},

{“1. open”, “Open”},

{“2. high”, “High”},

{“3. low”, “Low”},

{“4. close”, “Close”},

{“5. adjusted close”, “Adjusted Close”},

{“6. volume”, “Volume”}

}),

// Convert data types

TypedData = Table.TransformColumnTypes(RenamedColumns, {

{“Date”, type date},

{“Open”, type number},

{“High”, type number},

{“Low”, type number},

{“Close”, type number},

{“Adjusted Close”, type number},

{“Volume”, Int64.Type}

})

TypedData

To implement: Open Power Query Editor (Data → Get Data → Launch Power Query Editor), create a blank query, open Advanced Editor, paste the code. Configure parameters, test with a known ticker.

Validation checkpoint: Before proceeding, verify returned data against a trusted source. Confirm dates, prices, and volumes match for at least 3-5 data points.

Step 2: Parsing and Transforming Market Data

Objective: Convert raw API responses into analysis-ready tables.

Market data APIs return JSON that doesn’t map directly to Excel’s tabular format. The transformation layer bridges this gap.

Common transformations:

Flattening nested structures: API responses nest related data within parent objects. The code above handles primary nesting; complex endpoints may need additional expansion.
Date standardization: APIs return dates in various formats. Prompt for explicit parsing:
Add a transformation step that converts the Date column from “YYYY-MM-DD” string format to Excel date serial numbers for proper sorting and arithmetic.
Handling missing values: Market data has gaps (holidays, halts). Specify handling:
- For missing price values, retain null rather than substituting zero.
- Add a “DataQuality” column flagging rows with any missing values.
Calculated fields: Daily returns, moving averages, percentage changes—added in Power Query or as Excel formulas.

The transformation visualized:

Raw JSON:

{

“Time Series (Daily)”: {

“2024-01-15”: {

“1. open”: “185.2200”,

“4. close”: “186.1200”,

“6. volume”: “48234521”

}

Becomes:

Date	Open	Close	Volume	Daily Return
1/15/2024	185.22	186.12	48,234,521	0.48%

Step 3: Automating Data Refresh

Objective: Establish refresh cycles matching your analytical needs.

Your method depends on freshness requirements and environment:

Method	Refresh Frequency	Environment	Complexity
Manual (Refresh All)	On-demand	Any Excel	None
Power Query Background	At workbook open	Desktop	Low
VBA Timer	Seconds to minutes	Desktop (must stay open)	Medium
Power Automate + Office Scripts	Hourly minimum	Excel Online + M365	Medium
External scheduler	Any interval	Server/cloud required	High

For most analytical workflows, VBA timer-based refresh offers practical middle ground:

Generate VBA code for Excel that:

Refreshes all Power Query connections every 5 minutes
Logs each refresh timestamp to a designated cell
Includes a toggle button to start/stop automatic refresh
Handles errors without crashing Excel

Include comments explaining the timer mechanism.

Critical consideration: API rate limits. If your workbook refreshes 50 tickers every 5 minutes, that’s 600 calls per hour. Most free tiers won’t support this. Calculate call frequency before implementing automation.

Step 4: Building Your Real-Time Dashboard

Objective: Transform live feeds into actionable visual displays.

Dashboard design for real-time market data differs from static reporting. Prioritize information hierarchy and rapid scanning.

Essential components:

Summary metrics panel: Current prices, daily change (absolute and percentage), portfolio value. Headline figures visible without scrolling.
Conditional formatting: Color-code by thresholds—red when daily loss exceeds 2%, green for positions hitting targets. Use Excel’s built-in formatting over VBA for performance.
Sparklines: Mini-charts in cells showing 30-day price trends without consuming dashboard space.
Interactive filtering: Slicers for quick filtering by sector, position size, or custom categories.
LLM-assisted formulas for complex logic. Create Excel formula that:
- Calculates portfolio-weighted average daily return
- Skips positions with missing price data
- Works as dynamic array for variable-size portfolio
- Positions in A2:A50, Weights in B2:B50, Returns in C2:C50

Step 5: Adding LLM-Powered Analysis

Objective: Layer analytical intelligence on top of data infrastructure.

Beyond retrieval, LLMs enable analytical workflows that would otherwise require specialized software.

Analytical summaries: Feed portfolio data to an LLM:

Given this portfolio performance data for the past week:

[paste data]

Generate a 3-paragraph investment memo covering:

Overall performance vs. benchmark
Notable position movements
Areas requiring attention

Professional investment committee style.

Anomaly detection:

Review this daily return data for irregularities:

[paste returns]

Flag days where:

Volume exceeded 2x the 20-day average
Price moved more than 3 standard deviations
Unusual correlation breaks occurred

Brief explanation for each flag.

Sentiment integration (advanced): Pull news headlines via API, use LLMs to score sentiment—adding qualitative layers to quantitative displays.

For sophisticated applications, explore LLM-powered analytics approaches.

Navigating Common Implementation Challenges

Managing Latency and Performance

Excel’s calculation engine isn’t built for streaming data. As feeds scale, performance degrades—sluggish response, delayed updates, occasional freezing during refresh.

Optimization strategies:

Limit calculation scope: Set calculation to Manual (Formulas → Calculation Options → Manual). Trigger recalculation only after data refresh completes.
Minimize volatile functions: According to Microsoft’s Excel recalculation documentation, NOW(), TODAY(), INDIRECT(), and OFFSET() are volatile functions that trigger workbook-wide recalculation with every calculation event.⁷ Replace with static references where possible.
Separate data from presentation: Keep raw imports in dedicated sheets; reference from dashboard. This localizes refresh impact.
Use structured tables: Excel Tables (Ctrl+T) optimize memory and formula efficiency versus raw ranges.
Know your limits: Tick-level data for hundreds of symbols exceeds Excel’s design parameters. For portfolios under 200 positions with minute-or-less-frequent refresh, optimization keeps things responsive.

Ensuring Data Quality and Handling Errors

API connections fail. Networks time out. Providers experience outages. Robust implementations anticipate failure.

Common failure points:

Rate limit exceeded (HTTP 429)
Authentication failure (expired/invalid keys)
Malformed responses (schema changes)
Network timeouts
Provider outages

Building resilience:

Enhance this Power Query code with error handling that:

Catches HTTP errors and returns descriptive error table instead of failing
Implements retry logic (3 attempts, exponential backoff) for timeouts
Validates response structure before parsing
Logs errors with timestamps to separate tracking table

Validation layer: Even successful API responses need sanity checks—prices within reasonable bounds, dates in sequence, volumes as positive integers. Cross-check critical points against secondary sources.

Controlling Token Usage and Costs

LLM API calls carry token-based pricing. Understanding the cost model prevents billing surprises.

Token factors:

Input tokens (your prompt)
Output tokens (the response)
Context tokens (conversation history in chat mode)

Cost optimization:

Batch requests: Generate parameterized solutions handling ticker lists in one query rather than per-ticker calls.
Cache outputs: Working code gets saved. Don’t regenerate the same connection code repeatedly.
Optimize prompt length: Specific but concise—longer prompts consume more input tokens.
Match model to task: Code generation doesn’t always need the largest model. Smaller, faster models often produce equivalent results at lower cost.

For analytical workflows running repeatedly, estimate monthly costs before deployment.

Security and Compliance Considerations

Financial data workflows require careful credential management and policy awareness.

Credential security:

Never include API keys in LLM prompts.
Store keys in Excel’s credential manager or protected parameters.
Avoid saving embedded credentials to shared locations.
Rotate keys periodically; immediately if exposure is suspected.

Data handling:

Know what passes through each service in your workflow.
LLM calls typically send prompt content to external servers—consider sensitivity.
Some organizations restrict external AI for non-public data.

Compliance checkpoint: Before implementing LLM workflows with portfolio data, proprietary research, or client information, consult your compliance team. Policies vary; regulatory guidance continues evolving.

Scaling to Enterprise Deployment

From Individual Workflow to Team Standard

Individual implementations boost personal productivity. Scaling across teams introduces coordination challenges.

Documentation requirements (code obvious to its creator becomes opaque to colleagues):

Plain-language description of what the workflow does
Setup instructions for new users
Parameter configuration guide
Known limitations and failure modes
Maintenance procedures

Version control: Multiple team members modifying shared workbooks creates debugging nightmares. Consider template-based approaches where each user maintains their own copy. Alternatively, centralize data with individual views, or establish clear ownership protocols.

Standardized prompts: Team members generating their own code produces inconsistent outputs. Create a prompt library with tested, validated templates.

Cloud Integration and Collaboration

Microsoft 365 integration extends real-time capabilities beyond desktop limitations.

SharePoint/OneDrive hosting enables collaborative access, though refresh scheduling needs additional configuration.

Power Automate provides cloud-based automation including scheduled Office Scripts triggers—removing the “Excel must stay open” constraint.

Teams integration enables alert distribution—threshold breaches can trigger notifications to stakeholders.

For organizations in the Microsoft ecosystem, these provide natural scaling paths. Structured financial data analysis at scale often requires this cloud layer.

Where Purpose-Built Solutions Add Value

The DIY approach works for individual analysts, small teams, and proofs-of-concept. At enterprise scale, limitations compound:

Maintaining dozens of API connections becomes a full-time job
Data quality assurance requires systematic validation infrastructure
Compliance demands audit trails and access controls
Reliability expectations exceed spreadsheet-based solutions

Purpose-built financial data platforms handle this infrastructure complexity—pre-built maintained connections, data quality guarantees, compliance-ready audit trails, dedicated reliability support.

Ready to skip the configuration complexity? Explore how Daloopa’s Model Context Protocol delivers enterprise-grade market data to your Excel workflows—without the infrastructure overhead.

Future-Proofing Your Market Data Workflow

Evolving LLM Capabilities

Language models continue advancing. Near-term developments relevant to Excel integration:

Improved accuracy: Each generation reduces hallucination rates and improves first-attempt correctness.
Larger context windows: Expanding limits enable LLMs to work with complete workbooks, understanding full scope when generating additions.
Multimodal capabilities: Models processing images and documents could analyze charts, parse scanned statements, interpret visualizations directly.

The Expanding Market Data Ecosystem

Data accessibility keeps improving:

Alternative data proliferation: Satellite imagery, social sentiment, web traffic—sources once requiring institutional infrastructure increasingly reach individual analysts.
API standardization: Industry efforts toward common formats reduce per-provider implementation burden.
Cost compression: Competition drives down near real-time data costs, making institutional-grade capabilities accessible to individuals.

Positioning Yourself for the AI-Augmented Future

The valuable skill isn’t coding—it’s workflow architecture. Decomposing analytical needs into automatable components, then orchestrating effectively, becomes increasingly valuable as AI tools mature.

Skills to develop:

Prompt engineering: Communicating effectively with AI systems
Workflow design: Breaking processes into modular, testable steps
Validation thinking: Verifying automated outputs are correct
Edge case awareness: Anticipating where automation fails

The role evolves from manual processor to workflow architect and quality controller. Those developing these skills position themselves for relevance regardless of which tools dominate.

For broader perspective, see how integrating LLMs with traditional analytics reshapes financial workflows.

Getting Started

Implementing real-time market data in Excel using LLMs represents a practical capability available today—no engineering support or programming expertise required. The central insight is straightforward: LLMs function as an orchestration layer that absorbs technical complexity while you retain control over what gets built and how it serves your analysis.

The four-stage framework outlined here—API connection, data transformation, automated refresh, analytical dashboards—scales from single-ticker experiments to multi-asset portfolio monitoring. Start with a free API and a proven prompt template. Validate rigorously. Expand systematically once proof-of-concept works.

For those ready to move beyond the DIY approach, enterprise solutions eliminate infrastructure burden entirely while adding the compliance, reliability, and support that production workflows demand.

Your next steps:

Start small: Register for a free API (Alpha Vantage works well for learning). Use prompt templates from this guide to generate your first Power Query connection. Build a single-ticker dashboard proving the concept works.
Validate thoroughly: Before trusting LLM-generated code for real work, verify against known data. Build the validation habit early.
Expand systematically: Once proof-of-concept works, extend to additional tickers, asset classes, or sources. Each expansion tests robustness.

For teams ready to move beyond DIY, enterprise solutions eliminate infrastructure burden entirely. See about using LLM integration to transform your analytical workflows.

Frequently Asked Questions

Can I get real-time stock data in Excel without coding?

Yes. Power Query combined with LLM-generated connection code pulls real-time data without writing code from scratch. The LLM handles syntax while you configure parameters and validate outputs.

Which free API works well for market data in Excel?

Alpha Vantage offers a solid free tier—25 calls per day, suitable for learning and small implementations. For production requiring higher limits, Massive (formerly Polygon) and Finnhub provide cost-effective options with more generous rate limits.

How often can Excel refresh real-time data?

Depends on method. Power Query supports manual or workbook-open refresh on desktop, with hourly scheduling in Excel Online. VBA timers refresh every few seconds on desktop but require Excel to stay open.

Are there security risks using LLMs with financial data?

Key considerations: never include API keys in prompts; understand organizational policies on external AI; consult compliance before implementing with proprietary data. LLMs typically don’t store conversations, but your policies take precedence.

What’s the difference between real-time and delayed market data?

Real-time updates within seconds of market activity. Delayed lags 15-20 minutes (standard free tier per NYSE policy). For most analytical and monitoring use cases, near real-time (1-60 second delay) provides sufficient freshness at lower cost.

Implementing Real-Time Market Data Feeds in Excel Using Large Language Models