DEV Community: Kevin Meneses González

How to Connect Claude to Real Financial Data with FMP MCP

Kevin Meneses González — Wed, 22 Apr 2026 06:09:28 +0000

Most investors using AI are still stuck analyzing screenshots.

They copy numbers from a brokerage. Paste charts into ChatGPT. Ask questions about data that's already 3 hours old.

If you're:

building a portfolio monitoring system,
automating weekly research reports,
or trying to cut 4 hours of manual work down to 20 minutes,

This matters.

The real problem isn't the AI. It's the pipeline. And MCP fixes that.

Why Financial AI Tools Break at the Data Layer

The pattern repeats constantly.

A developer sets up a solid Claude workflow. The prompts are good. The logic is there. Then they hit the wall: getting real, structured financial data into the model without building and maintaining a custom API layer.

The options until recently were ugly:

Scrape Yahoo Finance and pray it doesn't break
Pay for a premium API and write integration code from scratch
Use CSV exports and process everything manually

None of these scale. All of them break.

The issue is infrastructure, not intelligence.

What FMP MCP Actually Does

Financial Modeling Prep launched an official MCP server that exposes its entire financial data catalog directly to Claude.

No middleware. No custom connectors. No parsing logic.

Claude reads the available tools, calls them when needed, and returns structured answers — the same way a senior analyst would query a Bloomberg terminal, except you don't need to pay $24,000/year for the hardware.

FMP's MCP server gives you access to:

Real-time and historical stock prices
Income statements, balance sheets, and cash flow data
Financial ratios and valuation metrics
Earnings calendars and analyst estimates
Sector performance and market movers
Crypto, forex, and commodities data

One server. One configuration. Everything your research workflow needs.

Setup in Under 10 Minutes

1. Get your FMP API key

👉 Get access to FMP's data plans here

The free tier covers a solid range of endpoints — enough to test and build the core of your workflow.

2. Install the MCP server

npm install -g @financialmodelingprep/mcp-server

3. Configure Claude Desktop

Open your claude_desktop_config.json file and add:

{
  "mcpServers": {
    "fmp": {
      "command": "npx",
      "args": ["-y", "@financialmodelingprep/mcp-server"],
      "env": {
        "FMP_API_KEY": "your_api_key_here"
      }
    }
  }
}

Restart Claude Desktop. The FMP tools will appear automatically in the interface.

4. Test the connection

Ask Claude directly:

"What is Apple's current P/E ratio and how does it compare to the sector average?"

Claude will call the FMP tools, retrieve live data, and return a structured response. No copy-paste. No manual lookup.

3 Systems You Can Build This Week

This is where the time savings become concrete.

System 1 — Morning Portfolio Brief (15 minutes saved daily)

Instead of checking 6 different tabs every morning, set up a prompt that pulls everything in one shot:

Retrieve the current price, daily change, and P/E ratio for AAPL, MSFT, NVDA, and AMZN. 
Then summarize which position has moved most relative to its 52-week range 
and flag any that have dropped more than 2% today.

Claude queries FMP, compares positions, and gives you a ranked brief.

What used to take 20 minutes of tab-hopping takes 90 seconds.

System 2 — Earnings Prep Autopilot (2 hours saved per company)

Before an earnings call, most investors pull the same data every time: revenue trend, margin evolution, analyst consensus, year-over-year comparisons.

Claude can do it all in one call:

For $TSLA: retrieve the last 4 quarters of revenue, gross margin, and EPS. 
Compare against analyst consensus estimates. 
Highlight the biggest deviation between reported and estimated figures.

You get a structured earnings preview in under a minute.

From here you can build:

Pre-earnings briefing templates for your full watchlist
Automated flags when a company is expected to miss consensus by >10%
Comparative analysis across sectors before reporting season

System 3 — Fundamental Screening on Demand

Screening tools are useful. But they're fixed. They show you what the tool was designed to show.

With FMP MCP + Claude, you ask in natural language:

Find companies in the S&P 500 with a P/E below 15, 
debt-to-equity under 0.5, and revenue growth above 10% YoY. 
Sort by free cash flow yield descending.

Claude translates that into FMP queries, aggregates the results, and returns a clean list.

Your own screener. No SQL. No filters to configure.

What This Looks Like in a Python Workflow

If you're building automated pipelines rather than interactive sessions, FMP's REST API integrates cleanly alongside the MCP layer.

import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://financialmodelingprep.com/api/v3"

def get_income_statement(ticker: str, limit: int = 4) -> list:
    url = f"{BASE_URL}/income-statement/{ticker}"
    params = {"apikey": API_KEY, "limit": limit}
    response = requests.get(url, params=params)
    return response.json()

def get_ratios(ticker: str) -> dict:
    url = f"{BASE_URL}/ratios-ttm/{ticker}"
    params = {"apikey": API_KEY}
    response = requests.get(url, params=params)
    return response.json()

# Pull last 4 quarters of financials + current ratios
ticker = "AAPL"
income = get_income_statement(ticker)
ratios = get_ratios(ticker)

print(f"Revenue (last quarter): ${income[0]['revenue']:,}")
print(f"Net income margin: {ratios[0]['netProfitMarginTTM']:.2%}")
print(f"P/E ratio (TTM): {ratios[0]['peRatioTTM']:.2f}")

Sample output:

Revenue (last quarter): $124,300,000,000
Net income margin: 23.97%
P/E ratio (TTM): 28.43

From here you can build:

Automated weekly reports delivered to Slack or email
Portfolio dashboards that refresh with real data on a schedule
Watchlist monitors that alert you when a ratio crosses a threshold

Practical Tips for Investors Using This Stack

Before building anything, a few lessons that save time.

Start with one workflow, not three. The most common mistake is trying to automate everything on day one. Pick your highest-friction task — for most investors it's the morning check — and make that seamless first. Once it runs reliably, extend it.

Use specific tickers, not vague questions. Claude performs better when you anchor queries to concrete symbols. "How is Apple doing?" produces a generic summary. "Retrieve AAPL's current P/E, 52-week range, and analyst consensus for Q2" produces structured, actionable data.

Chain queries, don't stack them. Instead of asking Claude to do everything in one giant prompt, build multi-step conversations:

Step 1: Pull the raw data
Step 2: Ask for the analysis
Step 3: Ask for the formatted output

This keeps the model focused and makes errors easier to debug.

Set a daily schedule for automated queries. If you're using the Python pipeline, run it at a fixed time — 7:30 AM before markets open works well. Use a simple cron job or a task scheduler like schedule:

import schedule
import time

def run_morning_brief():
    # Your FMP data pull + report generation here
    print("Morning brief generated.")

schedule.every().day.at("07:30").do(run_morning_brief)

while True:
    schedule.run_pending()
    time.sleep(60)

Store your API key as an environment variable, never in code. A small discipline that matters:

export FMP_API_KEY="your_key_here"

import os
API_KEY = os.getenv("FMP_API_KEY")

Use FMP's free tier to prototype, then upgrade only when you hit a wall. The free plan covers quotes, financial statements, and ratios for most common tickers. You'll only need a paid plan when you require real-time data at high frequency, access to smaller/international stocks, or advanced endpoints like institutional holdings.

How to Build a Portfolio Dashboard with FMP + Python

This is where the stack becomes a genuine time-saving system.

The goal: a single HTML file that opens in any browser and shows your portfolio's key metrics, refreshed every morning without manual effort.

Architecture Overview

Claude Desktop + MCP handles interactive queries and one-off analysis
Python Pipeline handles scheduled, automated data pulls via REST API
FMP is the single source of truth for all financial data
Outputs are either Claude responses, Python-generated files, or Slack/email alerts

Step 1 — Define your dashboard metrics

Pick 5–8 metrics that actually drive your decisions. A solid starting set:

PORTFOLIO = {
    "AAPL": 15,   # shares
    "MSFT": 10,
    "NVDA": 8,
    "AMZN": 12,
}

METRICS = [
    "price",
    "changesPercentage",
    "pe",        # P/E ratio
    "eps",
    "52WeekHigh",
    "52WeekLow",
]

Step 2 — Pull data for all positions

import requests
import os

API_KEY = os.getenv("FMP_API_KEY")
BASE_URL = "https://financialmodelingprep.com/api/v3"

def get_quotes(tickers: list) -> list:
    symbols = ",".join(tickers)
    url = f"{BASE_URL}/quote/{symbols}"
    response = requests.get(url, params={"apikey": API_KEY})
    return response.json()

portfolio_tickers = list(PORTFOLIO.keys())
quotes = get_quotes(portfolio_tickers)

Step 3 — Calculate portfolio value and build the report

from datetime import datetime

def build_report(quotes: list, portfolio: dict) -> dict:
    report = {
        "date": datetime.now().strftime("%Y-%m-%d %H:%M"),
        "positions": [],
        "total_value": 0,
        "total_daily_change": 0,
    }

    for q in quotes:
        ticker = q["symbol"]
        shares = portfolio.get(ticker, 0)
        value = q["price"] * shares
        daily_pnl = q["change"] * shares

        report["positions"].append({
            "ticker": ticker,
            "price": q["price"],
            "change_pct": q["changesPercentage"],
            "pe": q.get("pe", "N/A"),
            "shares": shares,
            "value": round(value, 2),
            "daily_pnl": round(daily_pnl, 2),
        })

        report["total_value"] += value
        report["total_daily_change"] += daily_pnl

    report["total_value"] = round(report["total_value"], 2)
    report["total_daily_change"] = round(report["total_daily_change"], 2)
    return report

report = build_report(quotes, PORTFOLIO)

Step 4 — Export to HTML dashboard

def export_html(report: dict, filename: str = "dashboard.html"):
    rows = ""
    for p in report["positions"]:
        color = "#22c55e" if p["daily_pnl"] >= 0 else "#ef4444"
        sign = "+" if p["daily_pnl"] >= 0 else ""
        rows += f"""
        <tr>
            <td><strong>{p['ticker']}</strong></td>
            <td>${p['price']:,.2f}</td>
            <td style="color:{color}">{p['change_pct']:.2f}%</td>
            <td>{p['pe']}</td>
            <td>{p['shares']}</td>
            <td>${p['value']:,.2f}</td>
            <td style="color:{color}">{sign}${p['daily_pnl']:,.2f}</td>
        </tr>"""

    html = f"""<!DOCTYPE html>
<html>
<head>
  <title>Portfolio Dashboard</title>
  <style>
    body {{ font-family: Arial, sans-serif; padding: 20px; background: #0f172a; color: #e2e8f0; }}
    h1 {{ color: #94a3b8; font-size: 18px; }}
    table {{ width: 100%; border-collapse: collapse; margin-top: 20px; }}
    th {{ background: #1e293b; padding: 10px; text-align: left; color: #94a3b8; }}
    td {{ padding: 10px; border-bottom: 1px solid #1e293b; }}
    .total {{ margin-top: 20px; font-size: 22px; }}
  </style>
</head>
<body>
  <h1>Portfolio Dashboard — {report['date']}</h1>
  <table>
    <tr><th>Ticker</th><th>Price</th><th>Change</th><th>P/E</th><th>Shares</th><th>Value</th><th>P&L Today</th></tr>
    {rows}
  </table>
  <div class="total">
    Total: <strong>${report['total_value']:,.2f}</strong> &nbsp;|&nbsp;
    Today: <strong style="color:{'#22c55e' if report['total_daily_change'] >= 0 else '#ef4444'}">${report['total_daily_change']:,.2f}</strong>
  </div>
</body>
</html>"""

    with open(filename, "w") as f:
        f.write(html)
    print(f"Dashboard saved: {filename}")

export_html(report)

Open dashboard.html in any browser. Schedule the full script at 7:30 AM and your portfolio brief is ready before you pour your coffee.

From here you can extend it with:

A Slack webhook to push the summary to a channel automatically
A second tab comparing current P/E ratios against 5-year historical averages
An alert row that highlights any position down more than 3% in red

Key Takeaways

MCP removes the integration layer — Claude connects to FMP directly, without custom code or API wrappers
The time savings are real — systems like these compress hours of manual research into minutes of structured output
The free tier is enough to start — you don't need a premium plan to build and test the core workflows

Start Building

FMP's MCP server is one of the cleanest financial data integrations available for Claude today.

If you're building research workflows, portfolio tools, or automated briefings — this is the infrastructure layer that makes it practical.

👉 Explore FMP's data plans and get started

FAQs

Do I need to know how to code to use FMP MCP with Claude?

No. The Claude Desktop setup requires only editing a JSON config file — no coding involved. You can query financial data in plain English from day one. The Python examples are optional, for those who want to build automated pipelines.

Is FMP MCP available for free?

FMP offers a free tier that includes access to a solid range of endpoints — enough to test and build core workflows. Paid plans unlock higher rate limits, more historical data, and premium endpoints like institutional ownership or advanced DCF models.

What's the difference between using FMP's REST API directly vs using MCP?

The REST API requires you to write integration code: authentication, request formatting, response parsing. MCP exposes those same endpoints as tools Claude can call autonomously. For interactive workflows and natural language queries, MCP is significantly faster to set up and easier to extend.

Can I use FMP MCP with Claude Code, not just Claude Desktop?

Yes. The same MCP server configuration works with Claude Code. You can reference FMP data inside your development environment and build AI-assisted financial tooling without switching contexts.

How current is the data?

FMP provides real-time quotes with minimal delay on paid plans. Historical data, financial statements, and ratios are updated following each earnings release. For intraday trading decisions, always verify against your broker's live feed.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

How to Get Real-Time Crypto Prices with the Mobula API in Python

Kevin Meneses González — Sun, 19 Apr 2026 10:59:42 +0000

Most crypto APIs are either too expensive, too slow, or designed for traders — not developers.

If you're:

building a portfolio tracker,
automating a DeFi monitoring bot,
or just need live onchain data without paying for an enterprise plan,

This is for you.

The Problem with Getting Crypto Data in 2026

Developers hit the same wall every time.

CoinGecko throttles free-tier requests. CoinMarketCap requires manual API key approval. Binance only covers exchange-listed tokens. And most "real-time" endpoints return data that's 60 seconds stale.

You need price. You need 24h change. You need volume. You need market cap.

What you don't need is a rate limit after 30 requests.

The real issue isn't access. It's that most crypto data APIs weren't built with developers in mind.

What Is the Mobula API?

Mobula is an onchain data API that provides structured market data — including real-time prices, volume, market cap, and liquidity — for thousands of tokens across multiple chains.

It works through a standard REST API with JSON responses.

Developers get access to:

Real-time price data (sub-second updates)
24h price change percentage
24h trading volume
Fully diluted market cap
Multi-chain token support (Ethereum, BSC, Polygon, Solana, and more)

No WebSocket setup required for basic use cases. A single HTTP GET request is enough.

Quick note: if you're a developer tool or fintech company looking for this kind of technical content — tutorials, integration guides, API comparisons — feel free to reach out on LinkedIn.

Setup: Install the Dependencies

No official SDK is needed. Mobula's API is REST-based, so requests is all you need.

pip install requests python-dotenv

Create a .env file to store your API key:

MOBULA_API_KEY=your_api_key_here

Get your free API key at mobula.io.

Example 1 — Fetch Real-Time Price for a Single Token

This is the core use case. One token, one request, live data.

import requests
import os
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("MOBULA_API_KEY")
BASE_URL = "https://api.mobula.io/api/1"

def get_token_price(symbol: str) -> dict:
    url = f"{BASE_URL}/market/data"
    params = {"symbol": symbol}
    headers = {"Authorization": API_KEY}

    response = requests.get(url, params=params, headers=headers)
    response.raise_for_status()

    data = response.json().get("data", {})
    return {
        "symbol": symbol.upper(),
        "price": data.get("price"),
        "price_change_24h": data.get("price_change_24h"),
        "volume_24h": data.get("volume"),
        "market_cap": data.get("market_cap"),
    }

result = get_token_price("BTC")
print(result)

Sample output:

{
  "symbol": "BTC",
  "price": 64312.47,
  "price_change_24h": 2.34,
  "volume_24h": 28450000000.0,
  "market_cap": 1264000000000.0
}

Clean. Structured. No parsing gymnastics.

Example 2 — Fetch Multiple Tokens at Once

When you're building a dashboard or tracker, you need batch data — not 10 separate requests.

def get_multiple_tokens(symbols: list[str]) -> list[dict]:
    results = []
    for symbol in symbols:
        try:
            data = get_token_price(symbol)
            results.append(data)
        except requests.HTTPError as e:
            results.append({"symbol": symbol, "error": str(e)})
    return results

tokens = ["BTC", "ETH", "SOL", "MATIC", "LINK"]
portfolio = get_multiple_tokens(tokens)

for token in portfolio:
    if "error" not in token:
        print(
            f"{token['symbol']:6} | "
            f"${token['price']:,.2f} | "
            f"{token['price_change_24h']:+.2f}% | "
            f"Vol: ${token['volume_24h']:,.0f}"
        )

Sample output:

BTC    | $64,312.47 | +2.34% | Vol: $28,450,000,000
ETH    | $3,121.88  | +1.12% | Vol: $14,200,000,000
SOL    | $148.50    | -0.78% | Vol: $3,800,000,000
MATIC  | $0.87      | +0.45% | Vol: $420,000,000
LINK   | $14.23     | +3.10% | Vol: $510,000,000

Five tokens. Five lines. Ready to pipe into a Telegram alert, a Google Sheet, or a Notion dashboard.

Example 3 — Search by Contract Address (Onchain Tokens)

Not every token has a clean ticker symbol. For newer or less-listed tokens, use the contract address instead.

def get_token_by_address(address: str, blockchain: str = "ethereum") -> dict:
    url = f"{BASE_URL}/market/data"
    params = {
        "asset": address,
        "blockchain": blockchain
    }
    headers = {"Authorization": API_KEY}

    response = requests.get(url, params=params, headers=headers)
    response.raise_for_status()

    data = response.json().get("data", {})
    return {
        "name": data.get("name"),
        "symbol": data.get("symbol"),
        "price": data.get("price"),
        "price_change_24h": data.get("price_change_24h"),
        "market_cap": data.get("market_cap"),
        "liquidity": data.get("liquidity"),
    }

# Example: PEPE token on Ethereum
pepe_address = "0x6982508145454Ce325dDbE47a25d4ec3d2311933"
result = get_token_by_address(pepe_address, blockchain="ethereum")
print(result)

This is where Mobula stands out from traditional exchange APIs.

Most CEX-based APIs don't index long-tail or newly launched tokens. Mobula does — because it reads directly from onchain liquidity pools.

Example 4 — Build a Simple Price Alert

Combine the fetcher with a basic threshold check. Useful for bots and automations.

import time

def price_alert_loop(symbol: str, threshold_pct: float = 5.0, interval: int = 60):
    """
    Polls the API every `interval` seconds.
    Triggers an alert if 24h change exceeds `threshold_pct`.
    """
    print(f"Monitoring {symbol} every {interval}s | Alert threshold: ±{threshold_pct}%\n")

    while True:
        try:
            data = get_token_price(symbol)
            change = data["price_change_24h"]
            price = data["price"]

            print(f"[{symbol}] ${price:,.2f} | 24h: {change:+.2f}%")

            if abs(change) >= threshold_pct:
                print(f"🚨 ALERT: {symbol} moved {change:+.2f}% in 24h — price is ${price:,.2f}")

        except Exception as e:
            print(f"Error fetching {symbol}: {e}")

        time.sleep(interval)

# Run the alert loop for ETH
price_alert_loop("ETH", threshold_pct=5.0, interval=60)

From here you can extend this into:

Telegram notifications via python-telegram-bot
Slack alerts via webhooks
n8n automations with an HTTP trigger node
Discord bots for trading communities

Mobula API — Quick Reference

Endpoint	Method	Description
`/market/data`	GET	Price, volume, market cap, 24h change
`/market/history`	GET	Historical OHLCV data
`/market/multi-data`	GET	Batch price data by symbols
`/metadata`	GET	Token metadata (logo, description, socials)

Authentication: Authorization: YOUR_API_KEY header on all requests.

Free tier includes several thousand requests/day — enough for personal projects and prototypes.

What You've Built

A working Python module that:

Fetches live price, 24h change, volume, and market cap
Handles both ticker symbols and contract addresses
Supports batch queries across any token list
Runs as a polling alert system

No SDKs. No bloated dependencies. Just requests and clean JSON.

Frequently Asked Questions

Is the Mobula API free?
Yes. Mobula offers a free tier with several thousand requests per day — enough for personal projects, prototypes, and low-frequency automations. Paid plans unlock higher rate limits, priority support, and access to premium endpoints like institutional-grade OHLCV history.

Does Mobula support tokens not listed on major exchanges?
Yes, and this is one of its key advantages. Because Mobula indexes onchain liquidity pools directly, it covers long-tail tokens and newly launched assets that CoinGecko and CoinMarketCap often lag behind on. You can query any token by contract address across supported chains.

Which blockchains does Mobula support?
As of 2026, Mobula covers Ethereum, BNB Chain, Polygon, Solana, Avalanche, Arbitrum, Base, and several other EVM-compatible chains. The /market/data endpoint accepts a blockchain parameter to scope results.

How fast is the price data?
Price updates are sub-second for major assets. For long-tail tokens with lower liquidity, refresh rates depend on onchain activity. For most use cases — dashboards, bots, alerts — the latency is negligible compared to alternatives.

Can I use Mobula in production?
Yes. The REST API is stable and versioned. For high-throughput production systems, use the /market/multi-data endpoint to batch requests and stay within rate limits. If you're building something at scale, check the paid plans for SLA guarantees.

Do I need a special library or SDK?
No. Mobula's API is standard REST — just requests in Python is enough. There's no proprietary SDK required.

Start Building with Mobula

Crypto data doesn't need to be complicated.

One endpoint, one request, one structured response — and you have everything you need to build monitoring tools, portfolio trackers, and trading automations without paying for an enterprise contract.

👉 Get your free Mobula API key here

You'll get access to:

Real-time prices across thousands of tokens
Onchain data including contract address lookup
Multi-chain support out of the box
A free tier that covers most personal and side-project use cases

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

I Asked Claude to Analyze My Portfolio. Here’s What Happened.

Kevin Meneses González — Fri, 17 Apr 2026 11:02:31 +0000

A Bloomberg Terminal costs $24,000 a year.

For that price, you get real-time market data, financial statements, earnings history, analyst estimates, and a research interface that institutional traders have used for decades.

Most developers, indie investors, and fintech builders can't justify that.

But here's what changed in 2026: you don't need Bloomberg anymore.

I connected Claude to real-time financial data using FMP's MCP server, asked it to analyze a portfolio of five stocks, and documented the entire session. No Python. No API calls. No JSON parsing.

Just questions. And real answers backed by live data.

If you're:

building AI-powered finance tools,
managing your own portfolio and tired of switching between tabs,
or just curious about what AI agents can actually do with real market data,

This is worth reading.

The Problem Nobody Talks About

Let me tell you about a conversation I had with a buy-side analyst last year.

She covers 15 companies. Every Monday morning, her routine looks like this:

Open Bloomberg, pull the weekly movers
Export to Excel, reformat the columns
Cross-reference with last quarter's earnings in a separate tab
Check FactSet for analyst revisions
Manually note any insider filings flagged in SEC EDGAR
Build a summary for the 9am team call

The whole process takes about two hours. Every week. For fifteen companies.

I asked her: "How much of that time is actual thinking versus data gathering?"

She didn't hesitate. "Maybe 20 minutes of thinking. The rest is plumbing."

That's the problem. Financial analysts aren't being paid to copy numbers between systems. They're being paid for the judgment that comes after. But most of their time gets consumed before they ever get there.

The bottleneck isn't intelligence.

It's infrastructure.

Why Data Quality Is the Foundation of Everything

Before getting into the setup, this point is worth making explicitly — because it's the one that gets glossed over in most AI + finance content.

An AI agent is only as good as the data it reasons on.

Claude can structure an argument brilliantly. It can synthesize across multiple data points, flag inconsistencies, and produce analysis that reads like a well-prepared research note. But if the underlying data is stale, incomplete, or sourced from unofficial endpoints that break under load — the output is worthless. Worse than worthless, because it looks credible.

This is why the choice of data provider matters more when you introduce AI into the equation, not less.

With a traditional workflow, an analyst can visually spot when a number looks wrong. They have the context to flag it. An AI agent acting on bad data doesn't have that safety net — it reasons confidently on whatever it's given.

FMP solves this by providing institutional-grade financial data through a clean, reliable API: 70,000+ data points, direct sourcing from exchanges and SEC filings, consistent update cycles, and full coverage across income statements, balance sheets, cash flow, earnings estimates, insider transactions, and macro indicators.

When you connect Claude to FMP via MCP, you're not connecting it to a scraper or a free feed. You're connecting it to the same underlying data infrastructure that powers serious financial tools.

That distinction changes what the AI can actually do.

The Setup: FMP + MCP + Claude in 3 Steps

FMP (Financial Modeling Prep) is a financial data API with 70,000+ data points covering everything from real-time quotes to earnings transcripts. MCP (Model Context Protocol) is the open standard introduced by Anthropic that lets AI agents connect directly to external data sources — no custom integration code, no glue scripts.

FMP now has an official MCP server. Connect it once, and Claude has direct access to FMP's entire dataset in natural language.

Here's how:

Step 1 — Get your FMP API key

Create a free account at financialmodelingprep.com. The free tier gives you 250 requests/day — enough to run all the experiments in this article.

Step 2 — Build your connection URL

https://financialmodelingprep.com/mcp?apikey=YOUR_FMP_API_KEY

Step 3 — Add it to Claude

In Claude.ai: Settings → Connectors → Add custom connector → paste the URL → name it "FMP" → Add.

Start a new chat. FMP's tools are now available to Claude automatically.

The diagram below shows exactly how the pieces connect — from your question to the structured insight that comes back.

The Session: Real Questions, Real Data

I built a portfolio of five stocks representing different sectors and risk profiles: Apple (AAPL), NVIDIA (NVDA), JPMorgan (JPM), Pfizer (PFE), and Tesla (TSLA).

Then I started asking questions as if I were talking to an analyst.

Question 1: "Give me a quick overview of each company in my portfolio"

Claude pulled live company profiles for all five tickers simultaneously — sector, market cap, P/E ratio, 52-week range, and a one-line business description for each.

What would normally take 10 minutes of tab-switching took about 8 seconds.

The output was structured, comparable, and current. Not a cached summary from training data — actual live data from FMP's API, called in real time.

Time saved for a typical analyst: ~25 minutes per week just on the Monday morning overview pull.

Question 2: "Which of these companies has the strongest balance sheet right now?"

Claude pulled the latest balance sheets for all five companies, compared cash positions, total debt, and debt-to-equity ratios, and ranked them with a clear explanation of its reasoning.

The answer: Apple, with a net cash position that dwarfs the others. But the analysis also flagged that NVIDIA's balance sheet has strengthened considerably over the past two years, while Pfizer's debt load increased significantly post-pandemic acquisitions.

Not just numbers — interpretation.

Think about what this replaces. Pulling five balance sheets, normalizing the figures, building a comparison table, writing the summary. That's 40 minutes of work for a single analyst. Claude does it in one query.

Question 3: "Show me how earnings have trended for NVDA over the last 8 quarters"

Claude fetched NVIDIA's quarterly earnings history from FMP, organized it chronologically, and presented a clear progression of EPS and revenue growth.

It also noted the quarters where results beat analyst estimates versus where they came in below — data it pulled from FMP's analyst estimates endpoint without me asking for it.

It went further than the question asked. Because it had the tools to do so.

This is the compounding effect of quality data infrastructure. When the underlying data is complete and reliable, the AI makes connections you didn't think to ask for. When it's not, you get confident-sounding summaries built on gaps.

Question 4: "Which of these stocks has the most insider selling activity recently?"

Claude queried FMP's insider trades endpoint for all five tickers and returned a ranked summary.

The result surfaced something I hadn't thought to check: a notable cluster of executive sell transactions at one of the portfolio companies in the prior 90 days. Not necessarily a red flag — executives sell for many reasons — but exactly the kind of signal that gets buried when you're managing a portfolio manually.

For analysts covering a broader universe, systematic signal monitoring like this is the difference between catching something early and reading about it in a post-mortem.

Question 5: "If I had to cut this portfolio to 3 stocks based purely on fundamentals, which would you keep?"

Claude synthesized everything it had already pulled — balance sheets, earnings trends, analyst ratings, insider activity — and made a structured argument for three positions, with explicit reasoning for each exclusion.

It wasn't financial advice. It was structured analytical reasoning applied to real data.

This is what analysts spend hours producing for their morning call notes. With FMP + Claude, the raw material for that synthesis is assembled in seconds. The analyst's job shifts from data gathering to judgment — which is where their expertise actually lives.

Practical Use Cases for Financial Analysts

The portfolio session above is a starting point. Here's how this setup maps to real analyst workflows:

Earnings season preparation
Before a company reports, pull the last 8 quarters of EPS and revenue vs. estimates in one query. Ask Claude to identify the trend, flag any pattern of beats or misses, and summarize the setup going into the print. What used to take 30 minutes of spreadsheet work becomes a 2-minute prompt.

Peer group comparison
"Compare the operating margins of the five largest US semiconductor companies over the last 3 years." Claude queries FMP for each income statement, normalizes the figures, and returns a structured comparison. No export to Excel, no manual table building.

Macro sensitivity analysis
"How has JPM's net interest income correlated with Fed rate changes over the last 10 quarters?" FMP has both financial statements and macro indicators. Claude connects them. This kind of cross-dataset analysis typically requires a quant setup — here it's a natural language question.

Pre-meeting research brief
Heading into an earnings call or management meeting? Ask Claude to pull the company profile, recent earnings history, analyst consensus, and notable insider activity, structured as a one-page brief. Five minutes instead of forty-five.

Watchlist monitoring
Set up a weekly prompt that pulls the latest price, P/E, and analyst rating changes for your entire coverage list. Claude flags what moved, what changed in estimates, and what warrants a closer look. The cognitive overhead of monitoring 20 names drops dramatically.

What This Actually Means

Most AI tools work with static knowledge. They know what they learned during training — with a cutoff date and no access to real-time data.

FMP's MCP server changes that equation.

Claude connected to FMP doesn't hallucinate earnings numbers. It fetches them. It doesn't estimate balance sheet ratios. It calculates them from live statements. It doesn't guess at analyst consensus. It reads it directly from FMP's database.

The result is an AI that reasons about markets the way a well-prepared analyst would — with access to the same raw data, applied in real time, in natural language.

For developers, this removes weeks of integration work. For investors, it removes the cognitive overhead of data aggregation. For fintech teams, it collapses the distance between idea and working demo.

And for analysts — it gives back the two hours every Monday that were going to plumbing.

Getting Started with FMP

FMP's free tier covers 250 API requests per day — enough to replicate everything in this article. Paid plans unlock higher limits, real-time data, and premium endpoints like earnings transcripts and institutional holdings.

👉 Start free on FMP here

Connection URL once you have your key:

https://financialmodelingprep.com/mcp?apikey=YOUR_FMP_API_KEY

Settings → Connectors → Add custom connector. Two minutes. No code.

What You Can Build From Here

From here you can build:

automated weekly portfolio reviews that pull fresh data every Monday
earnings surprise trackers that flag when actual EPS deviates significantly from estimates
sector rotation monitors comparing relative performance across holdings
custom screening sessions where you describe criteria and Claude queries FMP for matches
pre-earnings research briefs assembled in minutes, not hours

The infrastructure is already there. The data is live. The interface is natural language.

Most analysts don't need a better instinct for markets.

They need to stop spending 80% of their time on plumbing — and start spending it on the 20% that actually matters.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

Top 5 Crowdlending Platforms in Europe (2026): Where to Invest Your Money

Kevin Meneses González — Thu, 16 Apr 2026 08:29:27 +0000

Most people still believe generating passive income requires either a lot of capital or accepting a lot of risk.

Crowdlending breaks that assumption.

If you're:

tired of savings accounts paying 1–2% while inflation erodes your balance,
looking for fixed-income alternatives outside volatile stock markets,
or building a diversified portfolio that generates monthly cash flow,

This matters.

The European P2P lending sector has matured significantly. Regulation is catching up. Platforms are getting stricter about borrower selection. And the gap in quality between good platforms and bad ones is now very clear.

Here's a curated comparison of the five platforms worth your attention in 2026 — with a breakdown of what each one does well and where it falls short.

The Criteria

Before getting into the list, the comparison uses five dimensions:

Expected returns (annual yield, realistic range)
Risk protection (buyback guarantee, collateral, provision fund)
Regulation (licensing, jurisdiction, compliance framework)
Usability (minimum investment, auto-invest, secondary market)
Track record (default history, transparency, audits)

No platform is perfect. The goal is to understand the trade-offs.

1. Maclear — The Structured SME Platform from Switzerland

Maclear isn't the biggest name in European crowdlending. It's the most serious one.

Founded in Switzerland and operating as a P2B (peer-to-business) marketplace, Maclear connects investors with EU-based SMEs seeking capital for real industrial and commercial projects — manufacturing, logistics, retail, renewable energy. Not consumer loans. Not payday debt.

That distinction matters.

Returns: 13.5% to 16.5% annually, with monthly interest payments and no cash drag reported by investors.

Risk protection:

Provision Fund financed by 2% of every funded project
Collateral on every loan (equipment, assets, property)
Maclear acts as Collateral Agent, meaning it can legally enforce recovery on behalf of investors — not just hand you paperwork
More than 60% of loan applications are rejected before reaching the platform
One default recorded to date, resolved with full principal recovery

Regulation: Member of PolyReg SRO (Switzerland), with European crowdfunding license application filed. The platform is registered in the official Swiss company registry and has published a 2023 financial audit.

Usability: €50 minimum investment. AutoInvest launched in July 2025. Secondary market available. No deposit fees. KYC in approximately 5 minutes.

Track record: Growing at 6M€ per month invested as of early 2026. Surpassed platforms like Swaper in total registered users. No missed investor payments on record.

Pros

Real collateral behind every loan — not just a buyback promise from the same originator issuing the loan
Swiss regulatory framework adds structural credibility
Transparent project pages with financial scoring, business plans, and collateral details
Loyalty program with bonus rates on top of standard returns

Cons

Smaller loan supply than volume-driven marketplaces like Mintos
2024 financial audit delayed (expected Q1 2026)
Fewer projects available simultaneously compared to larger platforms

Best for: Investors who want structured protection, not just high numbers on a screen.

Disclosure: this article contains an affiliate link for Maclear. If you sign up through this link, you receive a 2% cashback bonus on investments in your first 90 days — at no extra cost.

2. Mintos — The European Giant

Mintos is the largest P2P lending marketplace in Europe. That's both its main argument and its main risk.

With over €10 billion in funded loans, 340,000+ investors, and access to 50+ loan originators across multiple countries, Mintos delivers diversification at a scale no other platform matches. The auto-invest tool is robust. The secondary market is active. The reporting is detailed.

The problem: you're not lending directly to borrowers. You're buying Notes issued by loan originators. If an originator fails, the Notes can go into recovery — which has happened before.

Returns: 10–12.25% average annually.

Risk protection: Buyback guarantee on most loans, but dependent on originator solvency. Regulated investment firm license in Latvia. Mintos Ratings system for originator assessment.

Pros

Unmatched diversification — dozens of originators, multiple countries, multiple loan types
Regulated platform with structured Notes product
Active secondary market for liquidity
Includes ETFs and bonds alongside loans

Cons

Originator defaults have happened — the buyback is only as solid as the originator
Complexity can overwhelm new investors
Auto-invest setup requires careful configuration to avoid concentration risk

Best for: Experienced investors building a large, diversified P2P portfolio.

3. PeerBerry — Reliability Over Everything

PeerBerry doesn't chase headlines. It has maintained a zero historical capital loss record since launching in 2017, and that consistency is its entire pitch.

Average return sits around 11.14%. Buyback guarantee on all loans, backed by a group guarantee from the Aventus Group — which paid back over €50 million in war-affected loans during the Russia-Ukraine conflict. That single data point explains why PeerBerry retains its investor base.

Returns: 10–12% annually.

Risk protection: 60-day buyback guarantee + group guarantee from Aventus Group. No capital losses recorded.

Pros

Zero historical capital loss
Group guarantee from a financially solid originator group
Clean, simple interface — low learning curve
No fees on deposits, withdrawals, or investing
Loyalty bonus program for larger portfolios

Cons

Loan supply occasionally insufficient for investor demand (cash drag possible)
No secondary market for early exits
Platform is unregulated
High concentration in Aventus Group loans

Best for: Conservative investors who prioritize reliability and a proven track record.

4. Esketit — Consumer Loans, High Efficiency

Esketit is the clean-room option for consumer loan investors. Backed by the AvaFin Group (formerly known as Creamfinance), the platform offers 12% average returns with 60-day buyback guarantees and a group guarantee layer on top.

The secondary market has no fees. Minimum investment is €10. Auto-invest is functional and easy to configure.

The trade-off is concentration: most loans come from a single originator group. That's efficient until it isn't.

Returns: 10–14% annually.

Risk protection: 60-day buyback guarantee + AvaFin Group guarantee. Low historical default rates.

Pros

Consistently high Trustpilot rating
Group guarantee adds a second protection layer
Secondary market with zero fees
€10 minimum — accessible for very small portfolios
Backed by an originator with 10+ years of experience

Cons

No regulatory license (unregulated platform)
Strong originator concentration — almost all loans are AvaFin-originated
Exposure to consumer loan risk, which behaves differently from SME/collateralized loans

Best for: Investors focused on short-term consumer loans who want simplicity and solid buyback protection.

5. Bondora Go & Grow — Passive Income with Daily Liquidity

Bondora belongs in this list for a different reason than the others. It's not the highest yield. It's the simplest entry point.

Go & Grow offers 6% annually with daily liquidity. You deposit money, it earns 6%, you withdraw whenever you want. No loan selection. No configuration. No complexity.

For someone who wants to park savings at returns above a bank account without thinking about originators, collateral, or buyback mechanics — Bondora is the answer.

Returns: 6% fixed annually.

Risk protection: Regulated in Estonia. No buyback guarantee — Bondora manages the portfolio internally. Investors do not see underlying loans.

Pros

Daily liquidity (rare in crowdlending)
Fully automated — zero management required
Regulated, experienced platform (founded 2008)
Minimum €1 investment

Cons

6% is significantly below what structured platforms like Maclear or Mintos offer
No visibility into underlying loans
Not suitable for investors seeking higher yields or active control

Best for: Beginners or investors who want passive savings above bank rates with instant access to funds.

Side-by-Side Comparison

Platform	Annual Return	Protection Type	Min. Investment	Regulation	Secondary Market
Maclear	13.5–16.5%	Collateral + Provision Fund	€50	Swiss (PolyReg SRO)	✅ Yes
Mintos	10–12.25%	Buyback (originator-dependent)	€50	Latvia (FCMC)	✅ Yes
PeerBerry	10–12%	Buyback + Group Guarantee	€10	Unregulated	❌ No
Esketit	10–14%	Buyback + Group Guarantee	€10	Unregulated	✅ Yes
Bondora G&G	6% fixed	Internal management	€1	Estonia (FSA)	❌ N/A

Why Maclear Ranks First

Most crowdlending platforms offer the same structure: a buyback guarantee backed by the same originator that issued the loan.

That structure has a well-known weakness. If the originator runs into financial trouble, the buyback promise becomes difficult to honor. It's not necessarily fraud — it's counterparty risk that investors often don't fully price in until a problem surfaces.

Maclear is structured differently.

Every loan is backed by real collateral — physical assets that exist regardless of whether the originator is solvent. And because Maclear acts as Collateral Agent, it can enforce recovery on behalf of investors independently, without waiting for borrower cooperation.

The Provision Fund, the 60%+ rejection rate on applications, the Swiss regulatory environment, and zero missed investor payments to date all point in the same direction: a platform designed with downside scenarios in mind, not just upside marketing.

That's not a guarantee of future performance. No platform is. But it's a meaningfully different risk architecture than what most alternatives offer.

If you want to explore it, you can open an account here — new investors get a 2% cashback bonus for the first 90 days.

Frequently Asked Questions

Is crowdlending safe?
No investment is risk-free, and crowdlending is no exception. The key variables are platform structure, borrower vetting, and what happens when a loan defaults. Platforms backed by real collateral (like Maclear) offer a different risk profile than those relying solely on buyback guarantees from loan originators — where the guarantee is only as solid as the originator's solvency.

What returns can I realistically expect from crowdlending?
Conservative platforms like Bondora's Go & Grow offer 6% with high liquidity. Mid-range platforms like PeerBerry and Mintos average 10–12%. Structured SME platforms like Maclear offer 13.5–16.5%, reflecting a more selective borrower pool and collateral-backed structure. Always distinguish between the stated rate and the risk-adjusted return.

What is the difference between P2P lending and P2B lending?
P2P (peer-to-peer) typically refers to lending to individual consumers. P2B (peer-to-business) refers to lending to companies — usually SMEs. P2B platforms like Maclear focus on business projects with defined collateral, which tends to offer more structured risk management than consumer loans.

What happens if a borrower defaults on Maclear?
Maclear acts as Collateral Agent for all loans. In the event of a default, the platform initiates recovery independently on behalf of investors — enforcing collateral rights and managing the legal process. The one default recorded on the platform to date resulted in full principal recovery for investors.

Can I withdraw my money early?
On Maclear, investors can sell loan positions on the secondary market before maturity. Liquidity depends on buyer availability, but the option exists. Platforms like Bondora Go & Grow offer daily liquidity. PeerBerry does not have a secondary market.

What is a Provision Fund?
A Provision Fund is a reserve of capital set aside to cover investor losses in the event of a default. On Maclear, it is funded by transferring 2% of every funded project directly to the fund. It acts as a buffer that operates independently from the borrower — providing an additional protection layer on top of collateral.

Is Maclear regulated?
Maclear is registered in Switzerland and is a member of PolyReg SRO, which requires compliance with Swiss AML regulations. The platform has applied for the European Crowdfunding Service Provider (ECSP) license. It is not yet licensed as an ECSP but operates under Swiss financial oversight — a stronger regulatory environment than many competing platforms registered in less scrutinized jurisdictions.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

Top 5 Claude Code Skills for Algorithmic Trading

Kevin Meneses González — Wed, 15 Apr 2026 11:04:20 +0000

Most traders think the bottleneck is strategy.

It's not.

The bottleneck is the time between "I have an idea" and "this is running in production." Writing boilerplate, wiring APIs, debugging vectorbt errors at 2am. That's where most strategies die.

If you're:

building systematic trading systems in Python,
testing technical indicators before deploying real capital,
or trying to compress weeks of quant work into days,

This matters.

Claude Code isn't the chatbot. It's an agentic environment that runs in your terminal, reads your files, and executes code on your machine. For algorithmic trading, that changes what's possible.

The real power comes from skills — SKILL.md files that function as instruction sets, telling Claude exactly how to approach a specific task. Think of them as recipes: step-by-step workflows Claude follows automatically when the task matches. Install one, and Claude transforms into a specialist for that domain.

Here are 5 skills from public repositories that make Claude Code genuinely useful for trading — with the install commands, the recipes inside, and working Python code using EODHD for market data.

1. Backtesting Expert — Systematic Strategy Testing

Source: tradermonty/claude-trading-skills · ⭐ 16

git clone https://github.com/tradermonty/claude-trading-skills.git
cp -r claude-trading-skills/skills/backtest-expert ~/.claude/skills/

This skill turns Claude Code into a dedicated backtesting analyst. The SKILL.md recipe instructs Claude to follow a structured workflow every time you describe a strategy:

SKILL RECIPE (backtest-expert):
1. Confirm strategy rules and parameters
2. Fetch historical OHLCV data for the specified symbol and period
3. Compute indicator(s) from scratch — no library black boxes
4. Generate entry/exit signals with explicit conditions
5. Run a vectorized backtest: returns, Sharpe, max drawdown, win rate
6. Output equity curve + summary table
7. Flag overfitting risks if in-sample window < 2 years

In practice, you describe your idea in plain English. Claude follows the recipe.

import requests
import pandas as pd
import numpy as np

API_KEY = "YOUR_EODHD_KEY"

# Fetch EOD historical prices via EODHD REST API
url = f"https://eodhd.com/api/eod/AAPL.US"
params = {
    "api_token": API_KEY,
    "from": "2022-01-01",
    "to": "2024-12-31",
    "period": "d",
    "fmt": "json"
}
data = requests.get(url, params=params).json()
df = pd.DataFrame(data)[["date", "adjusted_close", "volume"]]
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)
df.columns = ["close", "volume"]

# RSI from scratch
delta = df["close"].diff()
gain  = delta.clip(lower=0).rolling(14).mean()
loss  = -delta.clip(upper=0).rolling(14).mean()
df["rsi"] = 100 - (100 / (1 + gain / loss))

# Signals
df["signal"] = 0
df.loc[df["rsi"] < 30, "signal"] =  1  # buy
df.loc[df["rsi"] > 70, "signal"] = -1  # sell

# Vectorized strategy returns
df["returns"]  = df["close"].pct_change()
df["strategy"] = df["signal"].shift(1) * df["returns"]

sharpe = df["strategy"].mean() / df["strategy"].std() * np.sqrt(252)
total  = (1 + df["strategy"]).prod() - 1
print(f"Sharpe: {sharpe:.2f} | Total return: {total:.2%}")

From here you can build: walk-forward validation loops, multi-ticker screeners, parameter sensitivity grids.

Pros

Structured workflow prevents skipping the overfitting check
Forces explicit signal logic — no implicit library assumptions

Cons

Requires a clear strategy description upfront; vague inputs produce generic backtests

Best for: Quants who test 5–10 strategy variants per week and need reproducible, comparable results across all of them.

2. Market Data Pipeline — EODHD Integration

Source: JoelLewis/finance_skills · trading-operations plugin

npx skills add JoelLewis/finance_skills --plugin trading-operations

This skill handles the full data ingestion layer. Its recipe standardizes how Claude fetches and structures market data — EOD prices, intraday bars, fundamentals, and real-time quotes — from a single provider.

SKILL RECIPE (market-data-pipeline):
1. Identify data type needed: EOD / intraday / fundamentals / real-time
2. Select the correct EODHD endpoint for that data type
3. Build the request with proper params (symbol format, date range, interval)
4. Normalize response to a DataFrame with standard column names
5. Apply corporate-action adjustments for historical price analysis
6. Cache the result to avoid redundant API calls in the same session
7. Return the DataFrame ready for indicator computation

Bad data is the silent killer of backtests. Survivorship bias, unadjusted prices, missing corporate actions — these don't throw errors. They just make your strategy look better than it is.

import requests
import pandas as pd

API_KEY = "YOUR_EODHD_KEY"

def fetch_eod(symbol: str, start: str, end: str) -> pd.DataFrame:
    r = requests.get(
        f"https://eodhd.com/api/eod/{symbol}",
        params={"api_token": API_KEY, "from": start, "to": end, "period": "d", "fmt": "json"}
    )
    df = pd.DataFrame(r.json())
    df["date"] = pd.to_datetime(df["date"])
    return df.set_index("date")[["open", "high", "low", "close", "adjusted_close", "volume"]]

def fetch_intraday(symbol: str, interval: str = "1m") -> pd.DataFrame:
    r = requests.get(
        f"https://eodhd.com/api/intraday/{symbol}",
        params={"api_token": API_KEY, "interval": interval, "fmt": "json"}
    )
    df = pd.DataFrame(r.json())
    df["datetime"] = pd.to_datetime(df["datetime"])
    return df.set_index("datetime")[["open", "high", "low", "close", "volume"]]

def fetch_fundamentals(symbol: str) -> dict:
    r = requests.get(
        f"https://eodhd.com/api/fundamentals/{symbol}",
        params={"api_token": API_KEY, "fmt": "json"}
    )
    return r.json()

# Usage
aapl     = fetch_eod("AAPL.US", "2023-01-01", "2024-12-31")
fund     = fetch_fundamentals("AAPL.US")
eps      = fund["Highlights"]["EPS"]
print(f"EPS: {eps} | Latest adjusted close: ${aapl['adjusted_close'].iloc[-1]:.2f}")

EODHD covers 70+ exchanges worldwide — US equities, ETFs, forex, crypto — with corporate-action-adjusted prices and a free tier for prototyping.

👉 Get started with EODHD and get 10% off any paid plan: eodhd.com

Pros

Adjusted EOD + intraday (1-minute) + fundamentals + real-time from a single API
Free tier available; paid plans from €19.99/mo

Cons

Real-time non-delayed quotes require a paid plan

Best for: Anyone building a serious backtesting or live system who needs institutional-quality data at indie-developer prices.

3. Signal Generator — Strategy Logic to Executable Code

Source: ScientiaCapital/skills · active/signal-generation

git clone https://github.com/scientiacapital/skills.git
cp -r skills/active/signal-generation ~/.claude/skills/

You have a strategy. It lives in your head, in a research note, or in a TradingView Pine Script. You need it as clean, vectorized Python. This skill handles the translation.

SKILL RECIPE (signal-generation):
1. Parse strategy rules from natural language into explicit conditions
2. Map each condition to a pandas/numpy operation
3. Compute indicators using vectorized operations (no row-by-row loops)
4. Build entry and exit Series separately
5. Apply time-of-day or session filters if specified
6. Output DataFrame: [close, *indicators, signal, position]
7. Verify: no lookahead bias (all indicators shifted before signal comparison)

import requests
import pandas as pd
import numpy as np

API_KEY = "YOUR_EODHD_KEY"

r = requests.get(
    "https://eodhd.com/api/eod/TSLA.US",
    params={"api_token": API_KEY, "from": "2023-01-01", "to": "2024-12-31", "period": "d", "fmt": "json"}
)
df = pd.DataFrame(r.json())
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date")[["high", "low", "adjusted_close"]].rename(columns={"adjusted_close": "close"})

# EMA crossover + ADX filter — no TA-Lib
def ema(s, n):
    return s.ewm(span=n, adjust=False).mean()

def compute_adx(df, n=14):
    tr = pd.concat([
        df["high"] - df["low"],
        (df["high"] - df["close"].shift()).abs(),
        (df["low"]  - df["close"].shift()).abs()
    ], axis=1).max(axis=1)
    dm_pos = (df["high"] - df["high"].shift()).clip(lower=0)
    dm_neg = (df["low"].shift() - df["low"]).clip(lower=0)
    atr    = tr.ewm(span=n, adjust=False).mean()
    di_pos = 100 * dm_pos.ewm(span=n, adjust=False).mean() / atr
    di_neg = 100 * dm_neg.ewm(span=n, adjust=False).mean() / atr
    dx     = (100 * (di_pos - di_neg).abs() / (di_pos + di_neg))
    return dx.ewm(span=n, adjust=False).mean()

df["ema20"] = ema(df["close"], 20)
df["ema50"] = ema(df["close"], 50)
df["adx"]   = compute_adx(df)

cross_up     = (df["ema20"] > df["ema50"]) & (df["ema20"].shift() <= df["ema50"].shift())
adx_confirmed = df["adx"] > 25
df["signal"] = (cross_up & adx_confirmed).astype(int)

print(df[df["signal"] == 1][["close", "ema20", "ema50", "adx"]].tail())

Pros

Lookahead-bias check is baked into the recipe — Claude flags it before delivering code
Produces vectorized pandas operations: production-ready, not tutorial code

Cons

Always validate signal logic manually before connecting to live capital

Best for: Traders who think in strategy rules and want Claude to handle the Python translation.

4. Risk Manager — Position Sizing and Exposure Control

Source: JoelLewis/finance_skills · wealth-management plugin (risk-measurement skill)

npx skills add JoelLewis/finance_skills --plugin wealth-management

Strategy without risk management is speculation with extra steps. This skill gives Claude a structured decision framework for every sizing and exposure calculation.

SKILL RECIPE (risk-management):
1. Calculate ATR-based stop distance for the current setup
2. Apply fixed-fractional sizing (default: 1% account risk per trade)
3. Check portfolio heat: flag if total exposure > 5% of equity
4. Compute Historical VaR (95% confidence) on the current portfolio
5. Output: entry, stop, shares, dollar risk, R-multiple target
6. Define kill-switch threshold: halt new entries if drawdown > X%

import requests
import pandas as pd
import numpy as np

API_KEY = "YOUR_EODHD_KEY"

r = requests.get(
    "https://eodhd.com/api/eod/AAPL.US",
    params={"api_token": API_KEY, "from": "2024-10-01", "to": "2024-12-31", "period": "d", "fmt": "json"}
)
df = pd.DataFrame(r.json())
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date")[["high", "low", "adjusted_close"]].rename(columns={"adjusted_close": "close"})

# ATR (14-period)
tr  = pd.concat([
    df["high"] - df["low"],
    (df["high"] - df["close"].shift()).abs(),
    (df["low"]  - df["close"].shift()).abs()
], axis=1).max(axis=1)
atr = tr.ewm(span=14, adjust=False).mean().iloc[-1]

def size_position(equity, risk_pct, entry, stop):
    risk_per_share = abs(entry - stop)
    if risk_per_share == 0:
        return 0
    return int((equity * risk_pct / 100) / risk_per_share)

equity      = 10_000
entry_price = df["close"].iloc[-1]
stop_price  = entry_price - (2 * atr)
shares      = size_position(equity, 1.0, entry_price, stop_price)

print(f"Entry:         ${entry_price:.2f}")
print(f"Stop (2×ATR):  ${stop_price:.2f}  |  ATR: ${atr:.2f}")
print(f"Position size: {shares} shares")
print(f"Dollar risk:   ${shares * abs(entry_price - stop_price):.2f}")

Pros

Generates reusable, parameterized modules — not hardcoded values per trade
Recipe includes portfolio heat check alongside per-trade sizing

Cons

Sizing models require human review — Claude won't know your personal drawdown tolerance

Best for: Systematic traders running multiple strategies simultaneously who need a consistent, auditable risk framework.

5. Live Signal Monitor — Real-Time Alerts

Source: roman-rr/trading-skills · trading-signals skill

git clone https://github.com/roman-rr/trading-skills.git
cp -r trading-skills/trading-signals ~/.claude/skills/

The final skill closes the loop from research to live monitoring. Instead of running backtests, you're watching for signals in real time and triggering alerts when conditions are met.

SKILL RECIPE (live-signal-monitor):
1. Fetch real-time quote and recent EOD bars via EODHD REST API
2. Maintain a rolling window of bars in memory
3. Recompute indicator(s) on each new bar
4. Evaluate signal conditions — entry, exit, or hold
5. On signal trigger: log timestamp, symbol, price, indicator values
6. Send alert (print / Telegram / email)
7. Never execute orders directly — output signal only

import requests
import pandas as pd
import numpy as np
import time
from datetime import datetime

API_KEY = "YOUR_EODHD_KEY"

def fetch_live_quote(symbol: str) -> float:
    r = requests.get(
        f"https://eodhd.com/api/real-time/{symbol}",
        params={"api_token": API_KEY, "fmt": "json"}
    )
    return r.json().get("close", None)

def fetch_recent_eod(symbol: str, n: int = 30) -> pd.Series:
    r = requests.get(
        f"https://eodhd.com/api/eod/{symbol}",
        params={"api_token": API_KEY, "period": "d", "fmt": "json"}
    )
    df = pd.DataFrame(r.json()).tail(n)
    df["date"] = pd.to_datetime(df["date"])
    return df.set_index("date")["adjusted_close"].rename("close")

def compute_rsi(series, n=14):
    delta = series.diff()
    gain  = delta.clip(lower=0).rolling(n).mean()
    loss  = -delta.clip(upper=0).rolling(n).mean()
    return 100 - (100 / (1 + gain / loss))

symbol = "AAPL.US"
print(f"Monitoring {symbol} | {datetime.now():%Y-%m-%d %H:%M}")

while True:
    close_series = fetch_recent_eod(symbol, n=30)
    live_price   = fetch_live_quote(symbol) or close_series.iloc[-1]

    # Append live price as today's bar
    today = pd.Timestamp.now().normalize()
    close_series[today] = live_price

    rsi    = compute_rsi(close_series).iloc[-1]
    status = "OVERSOLD → BUY WATCH" if rsi < 30 else "OVERBOUGHT → SELL WATCH" if rsi > 70 else "NEUTRAL"

    print(f"[{datetime.now():%H:%M:%S}] {symbol} | ${live_price:.2f} | RSI: {rsi:.1f} | {status}")

    if rsi < 30:
        print(f"  🔔 ALERT: RSI oversold at {rsi:.1f} — review entry")

    time.sleep(60)

Pair this with a Telegram bot and you have automated signal detection running 24/7 — no manual chart watching.

👉 Get started with EODHD and unlock 10% off any paid plan: eodhd.com

Pros

Polling approach works with the free EODHD tier — no WebSocket subscription required
Skill enforces the signal/execution separation — no accidental live orders

Cons

60-second polling isn't suitable for sub-minute strategies (use EODHD's WebSocket feed for those)

Best for: Swing and intraday traders who want automated signal detection without building a full execution system.

The shift

Claude Code skills aren't magic. They're structured recipes that eliminate the "figure it out from scratch" tax on every new project.

Five skills. One reliable data layer. The bottleneck shifts from implementation to what it should always have been: deciding which strategies are worth testing.

Most quants don't lack ideas. They lack the bandwidth to implement fast enough to find the ones that actually work.

That's what changes.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

5 Best Onchain Data APIs for Developers in 2026

Kevin Meneses González — Tue, 14 Apr 2026 08:38:42 +0000

Most developers underestimate the data problem until it's too late.

You've built the logic. The strategy is solid. The architecture makes sense. Then you connect a price feed and realize it's cached. Or that the token you need isn't listed. Or that you're paying $99/month for a provider that covers 80% of what you actually need — and the missing 20% is exactly the part that matters.

The onchain data layer is where crypto products quietly break.

If you're:

building a trading bot that needs to react in seconds, not minutes,
designing a wallet dashboard that tracks PnL across multiple chains,
or shipping a DeFi tool that depends on newly listed tokens,

the provider you choose will define how far you can go before hitting a wall.

The problem isn't that good APIs don't exist. The problem is that most comparisons treat them as interchangeable — as if "blockchain data API" means the same thing regardless of what you're building.

It doesn't.

An indexer optimized for historical compliance data is a completely different product from a real-time trading infrastructure layer. A decentralized protocol built for protocol-specific subgraphs solves a different problem than a multi-chain wallet analytics API. Choosing the wrong one doesn't mean your product fails immediately — it means you hit the ceiling at exactly the wrong moment.

This guide cuts through that.

We compare five of the best onchain data APIs available to developers in 2026 — Mobula, Moralis, Bitquery, The Graph, and GoldRush (Covalent) — focusing on features, data coverage, pricing, free tiers, and what each one is actually built for.

By the end, you'll know which one fits your stack.

Overview of the Top 5 Onchain Data API Providers

1. Mobula — Real-Time Onchain Data + Execution in One Platform

Platform: mobula.io

Mobula is the only provider on this list that combines a real-time data layer with a built-in execution layer. You can fetch live prices, stream wallet activity, and route onchain swaps through the same API — no second provider needed.

At the core is the Octopus engine: a multi-chain, composable pricing system that aggregates prices using volume weighting and liquidity ponderation directly from DEX pools. It covers everything from Bitcoin and Ethereum down to tokens listed on obscure DEXs in the last hour, with prices refreshing every 5 seconds and no caching on any tier — including free.

Data Coverage and Features

Mobula's API surface is wide. The platform exposes 50+ REST endpoints and 10+ real-time WebSocket streams, covering:

Live token prices, 24h change, volume, and market cap for any asset on any supported chain
OHLCV historical data for backtesting and charting
Wallet portfolio analytics: balances, historical PnL, DeFi positions, NFT holdings, and wallet labels across chains
Pair-level and pool-level data: trades, liquidity, pool creation, newly listed tokens
Token security checks and holder distribution
Execution layer: swap quotes and onchain trade routing via a single API call

All of this is accessible via REST, WebSocket streams, a TypeScript SDK, and a GraphQL interface. The team responds to support queries in under an hour across Telegram, Slack, and Discord.

Beyond standard data retrieval, the Octopus engine specifically handles what most providers fail at: long-tail tokens. New listings on any DEX, low-liquidity assets, freshly deployed contracts — the engine picks them up as soon as they have trading activity.

Chain Coverage

Ethereum, Solana, BNB Chain, Base, Arbitrum, Optimism, Polygon, Avalanche, and many others. Multi-chain wallet queries aggregate across all supported networks in a single call.

Pricing and Limits

Mobula offers a free API key with immediate access to the full endpoint suite, including no-cache pricing. No credit card required to start.

Paid plans scale with usage through the Mobula dashboard, priced by request volume rather than a flat monthly subscription. For teams running lean bots or early-stage apps, this model is significantly more cost-efficient than paying $49–$99/month flat. Enterprise and custom plans available via Telegram or email.

Pros

No-cache 5-second price refresh on all tiers, including free
Data + execution in one API — fetch price and route the swap without switching providers
Broadest long-tail token coverage via the Octopus aggregation engine
Full wallet analytics: PnL, DeFi positions, NFT holdings, wallet labels
REST + WebSocket + TypeScript SDK + GraphQL — developer-first from day one
Sub-1h support response time

Cons

Requires coding — no visual interface or no-code builder
Execution layer is newer than the data layer
Smaller community than Moralis or The Graph

Best for: Developers building trading bots, price aggregators, wallet dashboards, or any product that needs real-time onchain data and the ability to act on it — from a single provider.

👉 Get your free Mobula API key

2. Moralis — Enterprise-Grade Web3 Backend Infrastructure

Platform: moralis.com

Moralis is the most complete Web3 backend platform on this list. It started as a Firebase-for-blockchain and has evolved into a full-stack data infrastructure layer trusted by MetaMask, Blockchain.com, and hundreds of production applications processing billions of API requests per month.

Data Coverage and Features

The platform covers multiple distinct product lines:

Data APIs: token prices, wallet history, NFT metadata, DeFi positions, token holders, swap data
Streams API: real-time blockchain event monitoring for any wallet, contract, or address — with a 100% delivery guarantee and webhook delivery
Datashare: bulk export of blockchain datasets to Snowflake, BigQuery, and S3 for data warehousing and AI pipelines
Cortex API: an AI-native, LLM-ready crypto data layer for building onchain AI agents

Moralis uses a Compute Units (CUs) pricing model. Every API request consumes a different number of CUs depending on complexity — a simple token balance check costs fewer CUs than a multi-chain portfolio aggregation.

The Streams API deserves special attention. If your product needs to react the moment a wallet receives a token, a smart contract emits an event, or a liquidation threshold is crossed — Moralis Streams handles it reliably at scale with a 100% delivery guarantee.

Chain Coverage

Ethereum, Solana, BNB Chain, Base, Polygon, Avalanche, Arbitrum, Optimism, and many more EVM-compatible chains.

Pricing and Limits

Moralis offers a free Starter plan with limited CUs per month. Paid plans start at approximately $49/month and scale to $199/month for higher CU allowances, premium endpoints, and auto-scaling. Enterprise plans with SOC 2 Type II compliance and ISO 27001 certification are available.

Pros

Streams API: 100% delivery guarantee for real-time event monitoring
Broadest EVM + Solana coverage among enterprise providers
SOC 2 Type II and ISO 27001 certified
Cortex AI layer for LLM-ready data pipelines
Trusted by MetaMask, Blockchain.com, and other large-scale apps

Cons

CU pricing can be unpredictable for complex or high-frequency workloads
No built-in execution layer — data only
Higher entry price for serious production use

Best for: Teams building production-grade dApps, portfolio trackers, AI agents, or compliance tools that need enterprise reliability and event-driven architecture.

3. Bitquery — Deep Blockchain Analytics via GraphQL and Kafka

Platform: bitquery.io

Bitquery is the go-to choice when you need granular, custom queries across 40+ blockchains. Its GraphQL API lets you filter, aggregate, and stream data at a depth that REST-based APIs simply don't expose. Since July 2025 it also offers a dedicated multi-chain Price Index API and Kafka streams for ultra-high-throughput pipelines.

Data Coverage and Features

Using its GraphQL interface, developers can query:

Real-time and historical DEX trade data across all supported chains
Token prices via the Price Index: OHLCV, SMA, WMA, EMA, cross-chain and cross-DEX aggregations at 1-second granularity
Wallet transactions, token transfers, internal calls, and failed transactions
Mempool data — useful for MEV detection, front-running analysis, and liquidation bots
NFT trades, smart contract events, and protocol-level analytics
Kafka streams and WebSocket subscriptions

Chain Coverage

40+ chains including Ethereum, BNB Chain, Solana, Polygon, Arbitrum, Bitcoin (non-EVM), and Tron.

Pricing and Limits

Bitquery uses a points-based pricing system where queries consume points based on infrastructure resource usage. A free community tier is available for development and light use. Paid plans are usage-based and customized through the sales team — no publicly listed flat-rate tiers for production.

Pros

40+ chains including non-EVM networks like Bitcoin
1-second price granularity via Kafka streams
Mempool data access — rare among providers, critical for MEV research
Extremely flexible querying: aggregations, filters, cross-chain joins in one call
OHLCV + SMA + WMA + EMA pre-calculated in the Price Index

Cons

Steep GraphQL learning curve — not beginner-friendly
Pricing is opaque: no clear public flat-rate tiers, requires sales contact
No execution layer

Best for: Blockchain analytics, MEV research, algorithmic trading requiring tick-level data, compliance tools, and deeply customized cross-chain queries.

4. The Graph — Decentralized Indexing Protocol

Platform: thegraph.com

The Graph takes a fundamentally different architectural approach. Rather than a centralized API, it's a decentralized protocol where developers deploy subgraphs — custom indexing schemas tied to specific smart contracts. Query fees are paid in GRT tokens to a network of indexers.

Data Coverage and Features

The value of The Graph depends on what subgraphs already exist. For major DeFi protocols — Uniswap, Aave, Compound, Curve, Balancer — there are well-maintained, community-curated subgraphs that deliver highly efficient, protocol-specific data.

For anything outside supported protocols, developers must build their own subgraph: define a schema, write mapping logic in AssemblyScript, deploy to the decentralized network. Significant setup overhead.

Chain Coverage

Primarily EVM chains: Ethereum, Arbitrum, Optimism, Polygon, Base, Avalanche, Gnosis, and others.

Pricing and Limits

The Graph uses a token-economic model where consumers pay GRT for queries. No flat monthly fee — costs scale directly with usage. The Graph Foundation offers grants and subsidies for education and public good infrastructure projects.

Pros

Censorship-resistant and decentralized — no vendor dependency
Highly efficient for protocol-specific indexed data
Large library of existing subgraphs for major DeFi protocols
Open source — full visibility into data pipelines
No vendor lock-in

Cons

Significant setup overhead for custom subgraphs
Not suitable for real-time arbitrary token price feeds
No wallet-level analytics or cross-protocol coverage out of the box
GRT costs are less predictable than flat subscriptions
No execution layer

Best for: dApp developers building on specific, well-supported DeFi protocols who need reliable, decentralized, protocol-specific indexed data.

5. GoldRush (Covalent) — Historical Depth Across 100+ Chains

Platform: goldrush.dev

GoldRush, the rebranded product from Covalent, specializes in structured historical onchain data with a clean, standardized REST interface. It covers 100+ blockchains — the broadest chain support of any provider on this list — and delivers every endpoint in a consistent schema, making it fast to integrate and easy to switch chains without rewriting queries.

Data Coverage and Features

GoldRush focuses on making historical blockchain data accessible and structured:

Full transaction history for any wallet address across all supported chains
Token balances and portfolio snapshots at any block height
NFT metadata, floor prices, and ownership history
Token approval tracking — useful for security audits and revoke tools
DEX liquidity and swap data
Historical PnL and unrealized/realized gains per token via the upnlForWallet GraphQL endpoint
A terminal-first interface for developers and AI agents to query data without writing code

Particularly strong for audit-grade historical accuracy: compliance tools, tax reporting, portfolio analytics, and any product that needs to reconstruct wallet state at an arbitrary point in the past. GoldRush maintains a full archival copy of every supported blockchain from the genesis block — every balance, transaction, and log event.

Chain Coverage

100+ chains including Ethereum, BNB Chain, Polygon, Avalanche, Base, Optimism, Arbitrum, and a large number of smaller EVM-compatible networks.

Pricing and Limits

GoldRush offers a 14-day free trial with access to all APIs and 25,000 API credits. After the trial:

Vibe Coding: $10/month — 10,000 credits, community Discord support, full historical data
Professional: $250/month — 300,000 credits, auto-scaling flex credits, production-grade performance
Inner Circle (Enterprise): Custom pricing — dedicated support, SLAs, NDAs

Pros

100+ chains — the widest coverage of any provider on this list
Consistent REST schema across all chains — one integration pattern for everything
Strong historical depth: full transaction history from genesis block
upnlForWallet endpoint for PnL and cost basis — ideal for tax reporting tools
Transparent, predictable credit-based pricing
Terminal-first interface for AI agents and developer tooling

Cons

Not optimized for real-time price feeds or active trading use cases
No execution layer
Significant pricing gap between $10 and $250 tiers

Best for: Portfolio trackers, compliance and audit tools, tax reporting platforms, and any product that needs structured historical onchain data across a wide range of chains.

Side-by-Side Comparison

	Mobula	Moralis	Bitquery	The Graph	GoldRush
Real-time prices	✅ 5s, no cache	✅ Block-level	✅ 1s via Kafka	⚠️ Protocol-specific	⚠️ Not focused
Execution layer	✅	❌	❌	❌	❌
Long-tail tokens	✅ Octopus engine	⚠️ Major tokens	✅ DEX trades	⚠️ Subgraph needed	✅ Historical
Chain coverage	✅ Multi-chain	✅ EVM + Solana	✅ 40+ chains	✅ EVM-focused	✅ 100+ chains
Wallet analytics	✅	✅	⚠️ Custom queries	❌	✅ Historical
Historical data	✅	✅	✅	✅	✅ Deep
Real-time streams	✅ WebSocket	✅ Streams API	✅ Kafka/WS	❌	❌
Mempool data	❌	❌	✅	❌	❌
Free tier	✅ Free API key	✅ Limited CU	✅ Limited	✅ GRT-based	✅ 14-day trial
Pricing model	Usage-based	CU-based	Points-based	GRT tokens	Credit-based
Entry price	Free → usage	Free → ~$49/mo	Free → custom	Free → GRT	Free trial → $10/mo
Learning curve	Low-Medium	Low	High	High	Low
Execution API	✅	❌	❌	❌	❌

Ready to Build with Onchain Data That Moves at Market Speed?

If you want real-time token prices with no caching, multi-chain wallet analytics, and the ability to execute swaps — all from a single provider, without stitching together three different APIs — Mobula is the practical pick.

Why Mobula wins for most developer teams:

All-in-one: data + execution in one API — no second provider for swap routing
Honest refresh rate: 5-second no-cache pricing on every tier, including free
Long-tail coverage: Octopus engine tracks any token the moment it has liquidity
Developer-first: REST + WebSocket + TypeScript SDK + GraphQL, sub-1h support
Start free: full API access, no credit card, no sandbox limitations

What you can ship this week:

Live price monitors for any token across any supported chain
Multi-wallet PnL dashboards with historical balance tracking
Bot triggers based on real-time price thresholds
Swap execution flows without switching providers

👉 Start for free with Mobula — grab your API key and make your first request in minutes.

FAQs

What is the best onchain data API for building a crypto trading bot?

Mobula is the strongest option for trading bots. It provides real-time prices with 5-second no-cache refresh, covers long-tail tokens via the Octopus engine, and includes a swap execution layer — all from a single REST API. Most other providers require combining a data API with a separate execution provider, which adds latency and complexity.

Which onchain API has the best free tier?

Mobula offers a free API key with immediate access to the full endpoint suite including no-cache pricing — no credit card required. GoldRush offers a 14-day free trial with 25,000 credits and access to all APIs. Moralis has a free Starter plan with limited CUs. Bitquery provides a free community tier with limited daily query allowances.

What is the difference between an onchain data API and a blockchain RPC node?

A blockchain RPC node gives you raw access to chain state — but you need to parse and index everything yourself. An onchain data API does that indexing and aggregation for you, delivering structured, application-ready data through clean endpoints. For most product teams, onchain data APIs are significantly faster to integrate and easier to scale.

Which onchain API covers the most chains?

GoldRush (Covalent) leads with 100+ supported chains. Bitquery covers 40+ chains including non-EVM networks like Bitcoin. Mobula and Moralis focus on the most-used chains with deep per-chain coverage rather than maximum breadth.

Can I use an onchain data API without coding experience?

Most providers require development knowledge. Moralis is the most accessible for Web3 newcomers. GoldRush offers a terminal interface for AI agents and non-coders. For production bot-building or custom analytics, you will need to write code regardless of provider.

Onchain data is infrastructure. The provider you choose defines your latency, your coverage, and your cost structure as you scale.

The ceiling you hit six months from now is the decision you make today.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

Top 5 Cryptocurrency Data APIs: Comprehensive Comparison (2026)

Kevin Meneses González — Mon, 13 Apr 2026 19:56:09 +0000

Choosing the right API means finding a balance between data coverage, update speed, reliability, and cost.

In this article, we compare five of the most popular crypto data API providers:

EODHD
CoinMarketCap
CoinGecko
CryptoCompare
Glassnode

We evaluate each on data types (historical, real-time, on-chain), rate limits, documentation quality, and pricing plans.

1. Mobula — The Developer-First Onchain Data & Execution Layer

Platform: Mobula Official Site | Standout Feature: Stream-based, real-time onchain API infrastructure with sub-second latency, multi-chain coverage, and built-in execution — the data backbone for building serious trading bots.

Most trading bot platforms give you a pre-packaged interface and call it a day. Mobula takes a different approach: it gives you the raw infrastructure to build exactly the bot you want.

Mobula is a real-time onchain data and execution platform designed for developers building automated trading tools, crypto dashboards, AI agents, and DeFi products. At its core is the Octopus engine — a multi-chain pricing system that tracks assets from major tokens down to the latest listings on any DEX, aggregating prices using volume weighting and liquidity ponderation. Prices update every 5 seconds with no caching, even on free plans, and streams are available via both REST and WebSocket (WSS).

Where Mobula stands apart is its breadth. The API covers over 50 endpoints and 10+ real-time streams: token prices, wallet balances, historical PnL, pair trades, pool states, OHLCV data, wallet labels, token security checks, and more. It also includes an execution layer — meaning you can not only pull live market data but also route swaps and execute onchain trades programmatically. That combination (data + execution in one API) is what makes it especially powerful for bot builders who want to skip stitching together multiple providers.

Mobula supports a wide range of chains including Ethereum, Solana, BNB Chain, Base, and many others, with a TypeScript SDK and a GraphQL interface available alongside the REST API.

Pros

Blazing fast, no-cache data: Prices refresh every 5 seconds with no cache on any tier — your bot reacts to the real market, not stale snapshots.
Data + execution in one platform: Pull prices, wallet states, and pair-level data, then route swaps through the same API. No need to juggle five providers.
Broadest onchain coverage: The Octopus engine covers not just top-cap coins but long-tail tokens, newly listed DEX pairs, and low-liquidity assets — essential for strategies that go beyond BTC/ETH.
Developer-first tooling: REST, WebSocket streams, TypeScript SDK, GraphQL, and detailed documentation with real-world cookbooks (Telegram bots, liquidation alerts, AI agents). Free API key to get started immediately.
Multi-chain wallet analytics: Track PnL, historical balance, trades, DeFi positions, and NFT holdings across chains in a single call — gold for portfolio-aware bots.

Cons

Requires coding: Mobula is an API, not a no-code interface. If you're not comfortable with Python, JavaScript, or TypeScript, you'll need to pair it with another tool.
No visual strategy builder: Unlike Cryptohopper or Coinrule, there's no drag-and-drop interface. You build your own logic.
Execution layer is newer: The swap execution and spot/perps features are more recent additions; mature no-code platforms have more battle-tested execution track records at scale.

Pricing

Mobula offers a free tier with a free API key — queries without an API key are available for testing but not production-ready. Paid plans are available through the Mobula dashboard, with pricing scaled to usage and rate limits. Given that most competitors charge $50–$100+/month for flat subscriptions, Mobula's usage-based model can be significantly more cost-efficient for builders running lean bots or early-stage projects. Contact the team via Telegram or email for custom enterprise plans.

Best For

Developers and technically proficient traders who want to build custom bots, dashboards, or AI agents on top of institutional-grade onchain data. If your ideal setup is writing your own strategy in Python or TypeScript — not clicking through a UI — Mobula is the infrastructure layer you've been looking for. It's also the natural choice when your strategy involves DeFi tokens, newly listed assets, or cross-chain wallet tracking that most no-code platforms simply don't cover.

2. EODHD — All-in-One Multi-Asset Data

EODHD is a versatile financial data provider covering stocks, forex, and cryptocurrencies under a single API. It supports 2,600+ crypto pairs against USD and offers data types that most providers split across multiple products.

Key Features

Historical Price Data
Daily OHLCV (open-high-low-close-volume) going back to 2009 for major coins — essentially Bitcoin's entire history. Ideal for long-term backtesting.

Real-Time Market Data
Live crypto price quotes via REST API and WebSocket, with up to 1,000 requests/minute on paid plans. Bulk endpoints available for efficient multi-asset pulls.

Fundamental Metrics
Market cap (actual and diluted), circulating/total/max supply, all-time highs/lows, whitepaper links, block explorer links. Not a full on-chain analytics platform, but fundamentals are solid.

Developer Experience
Clean JSON responses (CSV optional), Excel and Google Sheets add-ons, an API Academy with code examples, and 24/7 live customer support.

Pricing

Plan	Price	Calls/Day	Notes
Free	$0	20	Basic exploration
Starter	$19.99/mo	100,000	End-of-day + live data
Real-Time	$29.99/mo	100,000	Adds WebSocket streaming
All-in-One	$99.99/mo	Unlimited	Everything incl. fundamentals, news

Students get 50% off. Enterprise/commercial licenses available.

Verdict: Best price-to-performance ratio in the list. Especially powerful if you need crypto and equities/FX under one API key.

3. CoinMarketCap — Industry-Standard Market Data

CoinMarketCap is the most recognized crypto data aggregator, tracking 10,000+ assets from hundreds of exchanges. It's the default choice for current prices, rankings, and global market stats.

Key Features

Real-Time Quotes
Prices, market caps, volumes, rankings, and global metrics (total market cap, BTC dominance). Data refreshes every 1–2 minutes — no WebSocket streaming.

Historical Data
Available on paid tiers. Daily and intraday historical OHLCV back to 2013. The free tier only returns latest data.

Exchange & Derivatives Data
Exchange listings, trading pair metadata, liquidity scores, and futures/options prices on higher plans. No on-chain metrics.

Pricing

Plan	Price	Monthly Credits	Notes
Basic	Free	10,000 (~333/day)	Current data only, 11 endpoints
Hobbyist	~$29/mo	~50,000	More endpoints
Startup	~$79/mo	Higher	Intraday history
Standard	~$199/mo	Higher	Derivatives, more history
Enterprise	$699+/mo	Millions	Full dataset + SLA

Verdict: Very robust but expensive at scale. The free tier lacks historical data, which limits it for research use cases.

4. CoinGecko — Broad Coverage & Developer-Friendly

CoinGecko tracks 13,000+ cryptocurrencies, including small-caps, DeFi tokens, and NFTs. It's the go-to for breadth of coverage and a genuinely useful free tier.

Key Features

Wide Asset Coverage
Any asset trading on a major exchange or DEX is likely listed. DeFi protocols, NFT collections, and emerging tokens included.

Beyond Price Data
GitHub repo stats (forks, stars, commits), social media followers, Reddit subscribers — useful for gauging community health alongside price.

REST Only
No WebSocket streaming. Free users get data cached every 1–5 minutes; Pro users get 30-second updates.

Pricing

Plan	Price	Calls/Month	Rate Limit
Demo (Free)	$0	~10–30/min dynamic	Lower priority
Analyst	~$129/mo	500,000	500/min
Pro	~$499/mo	2,000,000	500/min
Enterprise	~$999+/mo	Custom	Custom

Verdict: Best free tier in this comparison. If you need heavy usage or guaranteed uptime, costs rise sharply. Great for projects that value breadth over latency.

5. CryptoCompare — Full Market Data + Extras

CryptoCompare covers 5,700+ coins across 260,000+ trading pairs from 170+ exchanges. It aggregates prices into a proprietary index (CCCAGG) that filters out exchange anomalies, making it reliable for backtesting.

Key Features

Market Data
Real-time quotes, order book snapshots, tick-level trade data, OHLCV at multiple intervals. WebSocket available for streaming.

Historical Data
Full daily history on all plans; minute-level history goes up to 7 days on standard plans, up to 1 year on enterprise. Raw trade data available.

Extra Data Layers
News feeds, social sentiment endpoints, and basic on-chain metrics (transaction counts, address data for major chains). Not as deep as Glassnode, but useful for correlation analysis.

Performance
Up to 40,000 API calls/second burst capacity. Built for real-time dashboards.

Pricing

Plan	Price	Notes
Free	$0	Non-commercial, thousands of calls/day
Basic	~$80/mo	~100k calls/mo
Advanced	~$200/mo	More resolution + endpoints
Enterprise	Custom	Unlimited + raw data

Verdict: Best option if you need price data and news/sentiment in one place. Mid-range pricing. Minute-level history beyond 7 days requires enterprise.

6. Glassnode — On-Chain Analytics Leader

Glassnode is the industry standard for blockchain analytics. Its focus is not market prices but the fundamental health of blockchain networks — what's happening inside the chain, not just on the order book.

Key Features

800+ On-Chain Metrics
Active addresses, transaction volumes, exchange inflows/outflows, UTXO distributions, SOPR, realized cap, HODLer stats, mining hash rates, and much more across Bitcoin, Ethereum, Litecoin, and major ERC-20 tokens.

Derivatives Data
Futures open interest, funding rates, and options data on higher tiers — useful for correlating on-chain behavior with derivatives markets.

Time Resolutions

Free: Daily, previous day's data
Advanced: Up to hourly
Professional: Down to 10-minute intervals

Pricing

Plan	Price	Access Level	Resolution
Standard	Free	Basic Tier 1 metrics	Daily only
Advanced	~$29–49/mo	Essential Tier 2	Hourly
Professional	~$79/mo	All metrics	10-minute
Enterprise	Custom	API + custom SLAs	Full

⚠️ API access is officially included only for Professional/Enterprise users.

Verdict: Irreplaceable for on-chain research. Not the right tool if you just need price feeds. Best suited for analysts and quant funds, not everyday app development.

Side-by-Side Comparison

Provider	Historical Data	Real-Time	On-Chain	WebSocket	Free Tier	Starting Price
EODHD	✅ 30 years	✅	Partial	✅	20 calls/day	$19.99/mo
CoinMarketCap	✅ (paid)	✅	❌	❌	Latest only	$29/mo
CoinGecko	✅	Near real-time	❌	❌	~10–30/min	$129/mo
CryptoCompare	✅	✅	Basic	✅	Limited	~$80/mo
Glassnode	✅	❌ (focus is on-chain)	✅ 800+ metrics	❌	Daily, delayed	~$29/mo

Which API Should You Use?

Building a trading bot or portfolio tracker? → EODHD or CoinMarketCap
Need maximum asset coverage on a budget? → CoinGecko free tier
Want price + news + basic on-chain in one? → CryptoCompare
Doing institutional on-chain research? → Glassnode
Need crypto and equities/FX under one key? → EODHD

Getting Started with EODHD (Free)

import requests

API_KEY = "your_api_key"
symbol = "BTC-USD"

url = f"https://eodhd.com/api/real-time/{symbol}"
params = {
    "api_token": API_KEY,
    "fmt": "json"
}

response = requests.get(url, params=params)
data = response.json()

print(f"Symbol: {data['code']}")
print(f"Price:  ${data['close']}")
print(f"Volume: {data['volume']}")

Grab a free API key at eodhd.com and make your first request in minutes.

What can you ship this week?

📈 Real-time dashboards — Live crypto prices and alerts via REST or WebSocket.

🔁 Strategy backtesting — Years of OHLCV data to validate your logic before going live.

🌐 Cross-asset analytics — Correlate BTC vs S&P 500, ETH vs USD from one API key.

📊 Auto-refreshing spreadsheets — Excel and Google Sheets add-ins, no code required.

🚀 Get your free EODHD API key →

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

The 7 Best MCP Servers for Stock Market Data (2026)

Kevin Meneses González — Sun, 12 Apr 2026 11:04:17 +0000

Everyone is building MCP servers.

Almost nobody is talking about data quality.

If you're:

building AI agents that trade or analyze markets,
connecting LLMs to financial workflows,
or replacing a fragile API wrapper with something that actually scales,

The MCP you choose isn't a minor implementation detail. It's your data foundation.

And a bad foundation breaks everything downstream.

The Problem Nobody Mentions

Here's what typically happens.

A developer finds an MCP server, connects it in ten minutes, runs a few queries, gets clean JSON back. Looks great. Ships it.

Then, three weeks later:

Rate limits hit during a live session. Fundamentals are six months stale. The free tier silently returns incomplete data without an error. The endpoint for options chains requires a separate paid plan that wasn't in the docs.

The MCP worked. The data didn't.

That's the real problem with most MCP server comparisons: they evaluate the connector, not what's behind it.

How to Read This Comparison

Each server is evaluated on five criteria:

Data depth — prices, fundamentals, options, macroeconomic data
Real-time quality — latency, update frequency, free vs. paid tier gap
MCP maturity — official support, stability, documentation
Developer experience — setup speed, response structure, Python integration
Best-fit use case — where it actually wins

This is not a "best overall" ranking. It's a best-for-your-job guide.

1. EODHD — Best for Fundamentals + OHLCV in One Call

EODHD (End-of-Day Historical Data) is the most complete financial data provider for developers who need both price data and company fundamentals without juggling multiple APIs.

Its MCP server exposes the same clean REST infrastructure that powers production fintech workflows — covering global equities, ETFs, forex, crypto, macroeconomic indicators, and earnings calendars through a single, predictable interface.

Pros

Fundamentals and OHLCV data in one endpoint — no merging from separate sources
Broad global coverage: US, Europe, Asia, 70+ exchanges
Consistent response structure, automation-friendly (no surprise field changes)
Financial news API with sentiment analysis included

Cons

Free tier limited to 20 API calls/day — sufficient for testing, not for production
Some niche datasets (full options chains) require higher-tier plans
Less brand recognition than Alpha Vantage among beginners

Best for: AI agents doing fundamental analysis, backtesting pipelines, n8n/Python automation workflows that need price + financials in one call.

Here's what a basic integration looks like:

import requests
import pandas as pd

api_key = "YOUR_EODHD_API_KEY"
ticker = "AAPL.US"

# Historical OHLCV
url = f"https://eodhd.com/api/eod/{ticker}?api_token={api_key}&fmt=json"
df = pd.DataFrame(requests.get(url).json())
df["returns"] = df["close"].pct_change()

# Fundamentals in the same session
fund_url = f"https://eodhd.com/api/fundamentals/{ticker}?api_token={api_key}&filter=Highlights,Valuation"
fundamentals = requests.get(fund_url).json()

print(df.tail(5))
print("PE Ratio:", fundamentals["Highlights"]["PERatio"])

From here you can build:

Stock screeners combining price momentum and valuation
AI agent contexts with full financial snapshots
Automated research reports with no manual data merging

👉 Get started with EODHD APIs here: eodhd.com

2. Financial Modeling Prep (FMP) — Best for Deep Fundamental Screening

FMP is the go-to choice when your agent needs to go deeper than prices — income statements, balance sheets, cash flow, DCF valuations, insider transactions, and analyst estimates, all available through a clean REST interface.

Its MCP server makes it straightforward to connect these datasets directly into AI workflows, without building custom parsers for SEC filings.

Pros

Exceptional fundamentals depth: 10+ years of standardized financials per company
Built-in valuation endpoints (DCF, P/E comparison, EV/EBITDA)
Affordable entry point (~$19.99/month for serious access)
Google Sheets add-on for non-code workflows

Cons

Free tier: 250 calls/day — hits limits fast in multi-ticker workflows
Real-time data is on paid plans; free tier is end-of-day only
Not a streaming solution — snapshot-based only

Best for: Equity analysts, AI-powered stock screeners, any workflow that needs multi-year financials without writing a Bloomberg integration.

👉 Explore FMP pricing plans: financialmodelingprep.com

3. Alpaca — Best for Agents That Execute Trades

Alpaca is the only option on this list that combines market data with a commission-free brokerage API. This means your AI agent can go from analysis to order placement in the same workflow, without switching platforms.

The MCP server exposes both the data layer and the trading layer — real-time quotes, historical bars, account management, and order execution — all through the same interface.

Pros

Data + execution in one API: no brokerage integration required
Paper trading environment included — test strategies with live data risk-free
Free core tier with real-time US equity data
Python SDK is excellent; community is active

Cons

US equities and crypto only — no international markets
Live trading requires KYC and US residency (or select other countries)
Data depth is narrower than dedicated providers (no deep fundamentals)

Best for: Trading bots, strategy automation, AI agents that need to close the loop between signal and execution.

4. Alpha Vantage — Best Free Tier for Beginners

Alpha Vantage is the most widely known financial data provider for developers learning to build with market data. Its MCP server makes the same accessible infrastructure available to AI agents — stocks, forex, crypto, commodities, and built-in technical indicators.

The free tier is genuinely useful for prototyping, which is why it appears in so many tutorials and side projects.

Pros

Built-in technical indicators (RSI, MACD, Bollinger Bands) via direct API call
Official Nasdaq data vendor — data reliability is solid for US equities
Free tier covers real-time US quotes: no credit card required to start
Extensive community examples and documentation

Cons

Free limit: 25 calls/day (standard), 5 calls/minute — hits walls fast
Fundamentals are basic compared to EODHD or FMP
International market coverage is limited on lower tiers
Paid plans start at $29.99/month but rate limits still feel restrictive at entry level

Best for: Learning, prototyping, agents that need quick technical indicators without building them manually.

5. Polygon.io — Best for Real-Time US Market Data

Polygon is built for speed. If your agent needs tick-by-tick price data, WebSocket streaming, or low-latency US equity quotes, Polygon is the specialist choice.

Its MCP server exposes the same infrastructure used by trading desks and fintech startups that can't afford delayed data.

Pros

WebSocket streaming: live trades and quotes with minimal latency
Full tick-level historical data for backtesting high-frequency strategies
Strong documentation with interactive API explorer
Covers stocks, options, forex, and crypto under one account

Cons

Primarily US-focused — limited international equity coverage
Real-time professional-grade data gets expensive quickly (hundreds/month)
Fundamentals and news coverage is thinner than EODHD or FMP
Free tier: 5 requests/minute, 2 years of historical data

Best for: Day trading agents, live market dashboards, HFT-adjacent workflows where latency is the primary constraint.

6. Finnhub — Best for News + Sentiment Integration

Finnhub covers a broad range of financial data types under one API — real-time quotes, fundamentals, earnings calendars, and financial news with sentiment scores. Its free tier is among the most generous: 60 API calls per minute.

For agents that need to monitor news flow alongside price data, Finnhub is the easiest path to a combined signal.

Pros

60 calls/minute on the free tier — highest in this list
Financial news with sentiment analysis included at no extra cost
Corporate calendars: earnings, IPOs, economic events
Multilingual news coverage and alternative data (ESG, COVID-19 stats)

Cons

Full fundamentals and international data require paid plans ($49–50/month per market)
Pricing can stack up fast if you need multiple markets
Response structure is less consistent across endpoints than EODHD or FMP
No WebSocket streaming for real-time price feeds on the free tier

Best for: News-driven agents, sentiment analysis workflows, event-based trading strategies.

7. QuantConnect — Best for Full Algo Trading Pipelines

QuantConnect is a different category entirely. It's not a data provider with an MCP wrapper — it's an end-to-end algorithmic trading platform that exposes its backtesting engine, strategy deployment, and brokerage integration through the MCP protocol.

If you're building AI agents that design, test, and execute trading strategies (not just analyze data), QuantConnect closes the entire loop.

Pros

Full backtesting engine: equities, options, futures, forex, crypto
Research-to-live-trading pipeline in one platform
Open-source strategy library with thousands of community algorithms
Brokerage integrations for real-money execution

Cons

Steep learning curve — this is a serious platform, not a quick integration
Pricing scales with compute usage, not just API calls
Not suitable if you only need data — it's overkill for read-only workflows
Python/C# required for strategy logic; no no-code option

Best for: Serious algo traders who want AI agents to assist in the full strategy lifecycle — from signal generation to live execution.

Quick Selection Guide

Use Case	Best Option
Fundamentals + prices in one call	EODHD
Deep financial statement analysis	FMP
Trading bot with execution	Alpaca
Learning / prototyping	Alpha Vantage
Real-time US streaming	Polygon.io
News + sentiment signals	Finnhub
Full algo trading pipeline	QuantConnect
Global market coverage	EODHD or FMP
Best free tier (calls/min)	Finnhub
Best free tier (data depth)	Alpha Vantage

The Insight Most Guides Miss

An MCP server is not a data source.

It's a protocol layer on top of a data source.

The quality of your financial agent depends almost entirely on what sits behind that layer — the coverage, consistency, and reliability of the underlying data. Two MCP servers can expose identical tool names and return fundamentally different results at production scale.

Choose the connector that matches your data requirements first. Then evaluate the MCP implementation.

FAQs

❓ What is an MCP server in the context of financial data?
✅ MCP (Model Context Protocol) is a standard that lets AI agents connect to external tools and data sources through a consistent interface. A financial MCP server exposes market data, fundamentals, or trading capabilities as tools that an LLM can call directly — without custom API wrappers or manual parsing.

❓ Which MCP server has the best free tier for developers?
✅ Finnhub offers 60 API calls per minute for free, which is the most generous rate limit in this list. Alpha Vantage is the better choice if you need a broader mix of data types (prices + indicators + some fundamentals) for prototyping. For production work, both require paid plans.

❓ Can I use these MCP servers with Claude or other AI agents?
✅ Yes. All servers in this list are compatible with MCP-enabled clients including Claude Desktop, Cursor, and VS Code. EODHD, FMP, and Alpha Vantage also expose standard REST APIs, which means you can integrate them directly into Python-based agent frameworks (LangChain, CrewAI, n8n) regardless of MCP support.

❓ Is EODHD better than Alpha Vantage for financial agents?
✅ For production workflows, yes. EODHD covers both OHLCV data and company fundamentals under a single API with global exchange coverage — which eliminates the need to merge from multiple sources. Alpha Vantage is easier to start with and has excellent built-in technical indicators, but its free tier limits (25 calls/day) and thinner fundamentals make it less practical at scale.

❓ Do I need coding skills to use these MCP servers?
✅ For most of them, basic Python knowledge is enough to get started. EODHD, FMP, and Alpha Vantage all have clear documentation with copy-paste examples. QuantConnect is the exception — strategy development there requires solid Python or C# skills. If you prefer no-code workflows, EODHD and FMP both offer Google Sheets integrations.

Final Thoughts

MCP is changing how financial data connects to AI systems.

But the protocol is not the product. The data is.

Pick the server that solves your actual data problem — not the one with the best GitHub stars or the most tutorials on YouTube.

If you need one place to start, EODHD gives you the widest data surface for the least integration complexity. Fundamentals, prices, macroeconomics, and news — one API key, one clean interface.

👉 Get started with EODHD: eodhd.com

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

How I Built a Daily Stock Digest Using n8n (Financial Automation Guide)

Kevin Meneses González — Fri, 10 Apr 2026 11:40:31 +0000

You open Chrome. One tab turns into eight.

Yahoo Finance. TradingView. Twitter. A random newsletter you barely trust. Back to your watchlist. Copy. Paste. Scroll. Repeat.

Forty-five minutes later, you still don't feel confident about what's actually moving the market.

And the worst part?

You'll do it all again tomorrow.

There's a better way.

What You're Actually Going to Build

A daily email that lands in your inbox at 7 AM with your tracked stocks, clearly labeled movers (🟢 up, 🔴 down, ⚪ flat), plus the 2–3 most relevant news headlines per ticker.

Built with:

n8n workflow
EODHD APIs (market data + news)
Google Sheets (your watchlist)
Gmail (delivery)

Why This Problem Is Bigger Than It Looks

This isn't about saving 30 minutes.

It's about consistency.

Manual research feels productive, but it's chaotic. One day you check everything. The next day you skip half your list because you're in a rush.

That's how you miss entries.

That's how you react instead of plan.

And slowly, your "system" becomes noise.

I realized this the hard way.

I wasn't losing money because I lacked information.

I was losing clarity because I had too much of it — scattered across tabs, tools, and half-finished notes.

The fix wasn't more data.

It was structure.

That's where n8n financial automation changes everything.

Why I Chose EODHD APIs (And Why It Actually Matters)

Most people underestimate this part.

They think: "I just need stock data."

No.

You need reliable, structured, and consistent data — every single day.

Otherwise, your automation breaks silently.

I tested multiple APIs before landing on EODHD APIs. Some had good pricing but poor coverage. Others had great data… but inconsistent endpoints. And a few? They just randomly failed when you needed them most.

That's unacceptable if you're building automated stock alerts.

Here's what changed with EODHD.

1. One API, Multiple Use Cases

With a single provider, I could fetch:

End-of-day prices
Intraday movements
Financial news
Fundamentals

That matters. Because the moment you start stitching multiple APIs together… things break. Different formats. Different latencies. Different failure points.

👉 Try EODHD APIs — one consistent financial data API for prices, news, and fundamentals.

2. Clean, Predictable Responses

This sounds boring. It's not.

If your JSON structure changes unexpectedly, your whole n8n workflow collapses. With EODHD APIs, the responses are stable. Which means your automation stays stable. And that's the real goal.

3. Built for Automation, Not Just Dashboards

Most tools are built for humans — dashboards, charts, interfaces. But when you're building stock market automation, you need something built for machines. Fast responses. Clear endpoints. No unnecessary noise.

That's exactly what EODHD APIs provide.

4. Coverage That Actually Scales

You start with 10 tickers. Then 20. Then 50. Maybe you add crypto. Maybe ETFs. Maybe international stocks.

If your API can't scale with you, you'll rebuild everything later. EODHD handles that from day one.

5. The Hidden Advantage: News Integration

This was the game changer.

Most APIs give you price data. Few give you context.

EODHD APIs let me pull news per ticker, which turns raw numbers into actionable insight. Because +2% without context is meaningless. +2% because of earnings or a product launch? That's signal.

That's why this setup works — not because of n8n alone, but because the data layer is solid. And if you're serious about n8n financial automation, this is the part you don't want to mess up.

The Workflow, Step by Step

This entire system runs on a simple n8n workflow with five nodes. Each one does one job. Nothing fancy. Just clean automation.

1. Schedule Trigger (7 AM daily)

Runs the workflow every morning before you wake up. Because if it depends on you clicking a button, it won't happen consistently.

2. Google Sheets — Read Ticker List

Pulls your watchlist dynamically from a simple spreadsheet. This turns your system into something you can edit in seconds without touching code.

3. HTTP Request → EODHD APIs

Fetches end-of-day price changes and latest news per ticker. This is where your financial data API does the heavy lifting.

4. Code Node (JavaScript)

Classifies each stock as 🟢 / 🔴 / ⚪ and builds the email layout. This is the "brain" — turning raw data into something readable.

5. Gmail Node — Send the Digest

Sends a clean, formatted email straight to your inbox. No dashboards. No logins. Just information where you already are.

That's it. Five nodes. And suddenly you've built your own stock market automation system.

The Output: What the Email Actually Looks Like

Subject line:

📈 Daily Market Movers — March 23

You open it. First thing you see: a clean list of your stocks, each one with a clear signal:

No thinking required.

Then underneath each ticker: 2–3 short headlines. Not 20 links. Just the ones that matter.

Example:

AAPL 🟢 +2.3%

Apple announces new AI chip strategy

Analysts raise price target ahead of earnings

You scan it in under a minute. You understand what's happening in under two.

And for the first time, your morning doesn't start with chaos. It starts with clarity.

That's what n8n financial automation actually buys you.

How to Customize It for Your Strategy

Once this is running, you'll start tweaking it. That's where it gets interesting.

You can also:

Change your tickers directly in Google Sheets — no redeploy needed
Adjust the "mover" threshold (e.g., only show stocks moving ±3%)
Add a Slack or Telegram notification alongside email
Extend it to crypto using the same EODHD APIs

Or go further — turn it into a mini financial dashboard. Combine it with a screener:

That's how simple workflow automation turns into a full system.

FAQ

Do I need coding skills to build this?

Not really. You can build most of this using n8n's visual interface. The only "code" part is the formatting node, and even that can be copied and adjusted.

Is the EODHD APIs free tier enough to run this daily?

Yes, for a small watchlist. If you're tracking 10–20 tickers, the free tier works fine. If you scale beyond that, upgrading makes sense.

Can I run this on n8n cloud or do I need to self-host?

Both work. n8n Cloud is faster to set up. Self-hosting gives you more control if you already run your own stack.

What happens if a ticker returns an error?

You can handle it in the workflow. Add a simple fallback in the code node to skip or flag missing data. Your email still sends — just without breaking everything.

Can I add more data points like earnings dates or analyst ratings?

Yes. That's the beauty of using a proper financial data API. You can enrich the digest with whatever data matters to your strategy.

You know that moment in the morning when you're staring at 8 tabs, trying to piece together what matters?

That used to be me.

Now I wake up, open one email, and I'm done. No friction. No noise. No wasted time. Just signal.

If you're serious about building your own n8n financial automation system, don't overthink it. Set this up this weekend.

Because once it's running, you don't go back.

👉 Get your free EODHD APIs key here — and have this running before Monday.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

How to Connect Real-Time Stock Data to Your AI Agent (MCP Tutorial)

Kevin Meneses González — Thu, 09 Apr 2026 20:20:55 +0000

Most AI agents sound impressive until you ask for something that depends on the present moment:

"What's Apple trading at right now?"
"Show me the latest fundamentals for Microsoft."
"Which stocks are moving today?"

That's when the weakness appears.

A model can be eloquent, persuasive, and fast. But without access to external tools and live data, it is still reasoning in a closed room. Claude's tool use lets it request external functions that your application executes, and MCP is an open standard for connecting AI applications like Claude to tools and data sources.

In finance, that limitation is fatal.

Because in this category, "almost correct" is just another way of being wrong.

The Real Problem: AI Without Data Is Not a Financial Product

A lot of people are building "AI finance apps" that are really just polished chat interfaces.

They have:

a nice prompt
a clean UI
maybe some charts

But they do not have a robust data layer.

That means the assistant cannot reliably access:

live prices
historical candles
fundamental data
market news

And that destroys trust.

If a financial assistant cannot access current market information through tools or APIs, it either refuses, guesses, or answers from stale context. Claude's tool flow is explicitly designed so the model decides when to call a tool, your application runs it, and then the result is sent back for a grounded answer.

That is why the real product is not "the chatbot."

The real product is the combination of: LLM + tool access + reliable financial data

Why MCP Matters Here

MCP matters because it gives your AI a clean way to interact with external capabilities instead of pretending to know everything.

The MCP architecture is client-server based: a host such as Claude or Claude Desktop connects to one or more MCP servers, and those servers expose tools and resources. In practice, that means your AI can ask for a stock price, a fundamentals lookup, or a market-news request through an external tool instead of inventing an answer.

That is the bridge from "smart text generation" to "useful financial system."

Why EODHD APIs Are So Important

This is the part people underestimate.

You do not just need an API. You need a data source that is broad enough, stable enough, and practical enough to power repeated tool calls.

👉 Try EODHD APIs — the financial data layer that turns Claude from a language model into a finance-aware assistant.

For an AI financial assistant, the data layer needs to support use cases like:

current quote retrieval
historical chart context
fundamentals for deeper analysis
news-driven reasoning

If your data is fragmented across multiple providers, integration gets messy fast. If the responses are inconsistent, the AI layer becomes brittle. If the coverage is weak, the user notices immediately.

So the role of EODHD here is not cosmetic. It is the system that turns Claude from a language model into a finance-aware assistant.

Two Practical Ways to Connect Claude

Option 1: Claude API + Tool Calling

This is the easiest path if you are building your own app or backend. Claude supports tool use through the Messages API: you define the tools, Claude decides when to request one, your app executes it, and then Claude uses the returned data in the final answer.

Option 2: Claude + MCP Server

This is ideal if you want Claude clients such as Claude Desktop or Claude Code to connect to your tools through MCP. Claude Code supports MCP connections, and Claude Desktop can connect to local MCP servers; Claude also supports remote MCP servers through custom connectors.

For most projects, start with tool calling — it is simpler to understand. Then graduate to MCP when you want reusability across AI clients.

Part 1 — Simple Claude Integration with Tool Calling

Step 1: Install the SDK

Anthropic provides an official Python SDK for Claude.

pip install anthropic requests

Step 2: Create the EODHD function

This is the function your app will execute when Claude asks for stock data.

import requests

EODHD_API_KEY = "YOUR_EODHD_API_KEY"

def get_stock_price(symbol: str) -> dict:
    url = f"https://eodhd.com/api/real-time/{symbol}?api_token={EODHD_API_KEY}&fmt=json"
    response = requests.get(url, timeout=20)
    response.raise_for_status()
    return response.json()

This function is the bridge between Claude and real market data.

Step 3: Define the tool for Claude

Claude needs a schema so it knows the tool exists, what it does, and which input it requires. Anthropic's tool use docs emphasize defining tool schemas and descriptions clearly so Claude knows when to call them.

tools = [
    {
        "name": "get_stock_price",
        "description": "Get the latest real-time stock price for a public company ticker symbol using EODHD APIs.",
        "input_schema": {
            "type": "object",
            "properties": {
                "symbol": {
                    "type": "string",
                    "description": "Stock ticker symbol, for example AAPL, MSFT, TSLA"
                }
            },
            "required": ["symbol"]
        }
    }
]

Step 4: Send the user question to Claude

from anthropic import Anthropic

client = Anthropic(api_key="YOUR_ANTHROPIC_API_KEY")

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    tools=tools,
    messages=[
        {
            "role": "user",
            "content": "What is the current price of Apple stock?"
        }
    ]
)

Anthropic's Messages API is the standard way to construct turns, manage state, and build the tool loop yourself.

Step 5: Detect Claude's tool request

When Claude decides the question requires live data, it returns a tool-use block instead of answering directly. That is the expected flow in Anthropic's tool use pattern.

for block in response.content:
    print(block)

Typically, Claude will request something equivalent to:

{
  "type": "tool_use",
  "name": "get_stock_price",
  "input": {
    "symbol": "AAPL"
  }
}

Step 6: Execute the function in your backend

tool_result = get_stock_price("AAPL")
print(tool_result)

At this point, your application has done the real work: it queried EODHD APIs and got live data.

Step 7: Send the result back to Claude

Now you continue the conversation and give Claude the tool result so it can answer the user with grounded data.

follow_up = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    tools=tools,
    messages=[
        {
            "role": "user",
            "content": "What is the current price of Apple stock?"
        },
        {
            "role": "assistant",
            "content": response.content
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "tool_result",
                    "tool_use_id": next(block.id for block in response.content if block.type == "tool_use"),
                    "content": str(tool_result)
                }
            ]
        }
    ]
)

print(follow_up.content)

And now Claude can answer with real market information instead of guessing.

Full Working Example (Claude + EODHD)

import requests
from anthropic import Anthropic

ANTHROPIC_API_KEY = "YOUR_ANTHROPIC_API_KEY"
EODHD_API_KEY = "YOUR_EODHD_API_KEY"

client = Anthropic(api_key=ANTHROPIC_API_KEY)

def get_stock_price(symbol: str) -> dict:
    url = f"https://eodhd.com/api/real-time/{symbol}?api_token={EODHD_API_KEY}&fmt=json"
    response = requests.get(url, timeout=20)
    response.raise_for_status()
    return response.json()

tools = [
    {
        "name": "get_stock_price",
        "description": "Get the latest real-time stock price for a public company ticker symbol using EODHD APIs.",
        "input_schema": {
            "type": "object",
            "properties": {
                "symbol": {
                    "type": "string",
                    "description": "Stock ticker symbol, for example AAPL, MSFT, TSLA"
                }
            },
            "required": ["symbol"]
        }
    }
]

user_question = "What is the current price of Apple stock?"

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    tools=tools,
    messages=[
        {
            "role": "user",
            "content": user_question
        }
    ]
)

tool_use_block = None
for block in response.content:
    if block.type == "tool_use" and block.name == "get_stock_price":
        tool_use_block = block
        break

if tool_use_block is None:
    print("Claude answered directly:")
    print(response.content)
else:
    symbol = tool_use_block.input["symbol"]
    tool_result = get_stock_price(symbol)

    follow_up = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=500,
        tools=tools,
        messages=[
            {
                "role": "user",
                "content": user_question
            },
            {
                "role": "assistant",
                "content": response.content
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": tool_use_block.id,
                        "content": str(tool_result)
                    }
                ]
            }
        ]
    )

    print("Claude final grounded answer:")
    print(follow_up.content)

How the Claude Connection Actually Works

Claude does not call your API directly by magic.

The flow is this:

The user asks Claude a question.
Claude sees that it needs external financial data.
Claude requests a tool call.
Your backend executes that tool.
The tool fetches live data from EODHD APIs.
Your backend sends the result back to Claude.
Claude writes the final answer using that live data.

That means Claude is the reasoning layer. Your tool is the execution layer. And EODHD APIs is the data layer that makes the whole thing useful.

If You Want Claude to Access This Through MCP

If you want to expose the same capability through MCP, the idea is simple:

You create an MCP server
That server exposes a tool such as get_stock_price
Claude Desktop, Claude Code, or another MCP-compatible client connects to that server
Claude can then use the tool through the MCP connection

MCP servers are the standard way to expose tools to compatible AI clients, and Claude Code plus Claude Desktop both support MCP-based tool connections.

Conceptually, it looks like this

Claude / Claude Desktop / Claude Code
↓
MCP Client
↓
MCP Server
↓
EODHD API request
↓
Real-time market data

Tool calling is great when you control the app. MCP is great when you want to make your tool reusable across AI clients and workflows.

If you're building a custom finance app, tool calling is the fastest way to connect Claude to EODHD APIs.
If you want a reusable integration that works across AI clients, MCP is the better long-term structure.

Why EODHD APIs Is the Layer That Creates Trust

In AI finance products, trust is not created by the model alone.

It is created by:

data freshness
response consistency
source reliability
breadth of market coverage

That is why the data provider matters so much.

A model can explain. A model can summarize. A model can compare.

But only a strong financial API can provide the live market context that makes those outputs valuable.

👉 Explore EODHD APIs — it gives Claude something far more important than words. It gives Claude evidence.

FAQ

What is the easiest way to connect Claude to a financial API?

The easiest way is to define a tool in the Claude Messages API, let Claude request it when needed, execute the API call in your backend, and return the result so Claude can answer with grounded data.

What is the difference between Claude tool calling and MCP?

Tool calling is the direct API pattern where your application defines and runs tools for Claude. MCP is an open standard for exposing tools and data sources to compatible AI clients like Claude Desktop or Claude Code.

Why use a financial API with Claude?

Because Claude alone does not have guaranteed access to live market information. A financial API supplies current prices, fundamentals, and other real-world data needed for reliable finance use cases.

Why is EODHD APIs useful for AI agents?

Because it gives your AI agent a practical source of financial data that can be called through tools, which makes the assistant more accurate, more useful, and far more product-ready.

Final Thought

Most AI agents in finance fail for one simple reason: they have intelligence, but they do not have access to reality.

Claude can reason beautifully. MCP can connect tools elegantly. But EODHD APIs is what gives the system real financial substance.

If you want to build an AI finance assistant that does more than generate polished guesses, start with the data layer.

That is where the real product begins.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com

I Tried 33 Data Analytics Courses and These 5 Are the Best

Kevin Meneses González — Sun, 17 Nov 2024 13:21:59 +0000

When I first dipped my toes into the world of data analytics, I was like many others: excited but lost. I remember sitting at my desk, watching hours of tutorials on YouTube, only to realize that half of them were either outdated or too simplistic. My frustration grew, especially one particular night when, after hours of wrestling with a data visualization project, I found myself scrolling through yet another online course catalog, feeling like I’d never find the right path. “Why does learning data analytics have to be so complicated?” I thought.

That night, as I stared at my screen surrounded by coffee cups and crumpled notes, I promised myself I’d make this work. I dove headfirst into dozens of platforms, testing out courses across Google, DataCamp, and even Harvard’s edX, hoping to find something that could make the process clearer and more practical. After months of trial and error, I finally found a few gems that made a real difference in my journey.

Here, I’ll break down the top five data analytics courses that were genuinely worth the time.

1. DataCamp: Practical, Hands-On Learning for All Levels

Purpose: DataCamp provides hands-on training focused on practical skills in data analysis, data science, and machine learning. The platform offers over 23 career tracks, including Data Analyst, Data Scientist, and Machine Learning Specialist, designed to meet users at any skill level — from complete beginners to advanced learners.

Why It Stands Out: DataCamp’s interactive, project-based learning approach makes it feel as if you’re actively working as a data analyst. You’re not just watching tutorials; you’re coding, visualizing, and manipulating data immediately, which reinforces learning. DataCamp also offers specialized tracks for key tools like Python, SQL, and Power BI — essential skills for a data analyst today.

Perks:

Microsoft Certification Discounts: DataCamp has partnered with Microsoft to offer students a 50% discount on certification exams, making it easier to obtain recognized credentials.
Flexible and Interactive: Courses are highly interactive, allowing you to work on real-world datasets and complete challenges.
Tell me and I forget, teach me and I may remember, involve me and I learn.” — Benjamin Franklin. DataCamp’s interactive style truly embodies this principle.

Drawbacks:

Subscription-based, which may not appeal to those looking for a single-course purchase.
If you’re looking to start your analytics career with practical, job-ready skills and certification discounts, DataCamp is a stellar choice.

2. Google Data Analytics Professional Certificate

Purpose: Google’s Data Analytics Professional Certificate is aimed at absolute beginners, covering fundamental skills and preparing you for entry-level positions in data analysis.

Why It Stands Out: Developed by Google, the course teaches practical skills like data cleaning, data visualization, and basic statistical analysis. It’s a great introduction for those who are new to the field and provides a broad overview of core concepts that every data analyst should know.

Perks:

Beginner-Friendly: Structured in a way that makes complex concepts easy to grasp.
Practical Knowledge: Includes various projects and case studies that give students hands-on experience.
Limitations:

Not an Industry-Recognized Certification: The certificate is not widely acknowledged by recruiters as an official qualification, but it serves as an excellent starting point.

“The journey of a thousand miles begins with one step.” — Lao Tzu. Google’s certificate is that first step for anyone entering the data analytics world.

If you’re a beginner wanting a foundational overview, this certificate is a solid, approachable option.

3. Microsoft Power BI and SQL Certifications

Purpose: Microsoft offers certifications for specific tools like Power BI (PL-300) and SQL (DP-300), which are highly valued in the data analytics industry.

Why It Stands Out: These certifications are industry-recognized, often required by employers, and cover both data visualization with Power BI and data management with SQL. This makes them essential for those wanting to demonstrate strong technical proficiency and a solid grasp of two in-demand tools.

Perks:

Globally Recognized: Having a Microsoft certification on your resume can set you apart from others and even fulfill job prerequisites.
Free Learning Pathways: Microsoft provides free study resources to prepare for these exams, making it accessible to everyone.
Limitations:

Cost and Difficulty: Exam fees can add up ($165 for Power BI and $99 for Azure Fundamentals), and the exams require in-depth preparation.

“Success is where preparation and opportunity meet.” — Bobby Unser. Preparing for Microsoft certifications is challenging but incredibly rewarding for career growth.

For those serious about a data analytics career, mastering Power BI and SQL with Microsoft’s certifications can significantly enhance your employability.

4. Tableau Certified Data Analyst

Purpose: This certification validates your proficiency with Tableau, one of the top data visualization tools, and is highly valued by companies seeking candidates with advanced data visualization skills.

Why It Stands Out: The Tableau Certified Data Analyst credential proves your ability to analyze data and create visually compelling, interactive dashboards. This certification is particularly suited for business intelligence roles where data presentation is key.

Perks:

Highly Recognized Certification: Validates your ability to use Tableau, which is one of the most in-demand visualization tools.
Practical Exam: The certification requires a hands-on test, so your knowledge is proven through real tasks.
Limitations:

High Exam Cost: The certification exam is expensive at $250 and requires a subscription to Tableau’s learning resources. However, you can also prepare on DataCamp.
“A picture is worth a thousand words.” — Fred R. Barnard. Tableau’s visualization capabilities bring data to life, making it easier for stakeholders to understand complex information.

For those focused on a career in data visualization, Tableau certification is an asset.

5. HarvardX Data Science Professional Certificate (edX

)
Purpose: HarvardX’s Data Science Professional Certificate provides a rigorous academic foundation in data science, covering programming in R, statistical concepts, and machine learning.

Why It Stands Out: Taught by professors from Harvard, this program provides a high-caliber education and is recognized globally. It’s ideal for those interested in a thorough and respected foundation in data science, including data analysis, probability, and machine learning.

Perks:

Academic Prestige: A certification from HarvardX stands out on any resume.
Comprehensive Curriculum: Covers not only analytics but also deeper statistical methods and machine learning.
Limitations:

High Cost and Time Commitment: The certification costs over $1,200 and can take a year or more to complete if studied part-time.

Education is the most powerful weapon which you can use to change the world.” — Nelson Mandela. This certification offers a transformative learning experience, backed by the academic rigor of Harvard.

This is perfect for those willing to invest in an intensive, high-quality education to gain in-depth data science knowledge.

Conclusion

Choosing the right course in data analytics can be daunting with so many options out there. If you’re looking to dive right in and start building skills that you can apply immediately, DataCamp offers an excellent balance of practical skills and recognized certifications. For entry-level fundamentals, Google’s certificate is a great place to begin, while Microsoft and Tableau certifications add significant value for specific technical skills. And if you’re looking for a comprehensive, prestigious academic experience, HarvardX provides exactly that.

To anyone just beginning this journey, remember: “The only limit to our realization of tomorrow is our doubts of today.” — Franklin D. Roosevelt. Dive into learning, and each of these courses can become a stepping stone to your success in data analytics.

Datacamp Plataform
Follow me on Linkedin
https://www.linkedin.com/in/kevin-meneses-897a28127/
Medium
https://medium.com/@kevinmenesesgonzalez/subscribe
Subscribe to the Data Pulse Newsletter
https://www.linkedin.com/newsletters/datapulse-python-finance-7208914833608478720
Join my Patreon Community https://patreon.com/user?u=29567141&utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink

How I Automated My Workflow by Connecting Python to Google Sheets API

Kevin Meneses González — Tue, 12 Nov 2024 12:22:30 +0000

Introduction

A few months ago, I faced a familiar problem. I was working on a project where I needed to manage a large dataset that required frequent updates and collaboration. At first, I kept everything in Excel, thinking it would be simple enough. But the more data I added, the slower Excel became. I’d save my work, try to update a few cells, and watch as it took minutes to respond. It was a nightmare. Every time I tried collaborating with a team member, I’d have to send over a new version of the file, which quickly became chaotic and prone to mistakes. I realized that I needed a way to update and share data in real-time, something that Excel just couldn’t handle effectively with large datasets.

That’s when I discovered the power of Google Sheets combined with Python. Google Sheets offers the flexibility of cloud storage, allowing multiple users to access and update data simultaneously, while Python offers powerful data manipulation capabilities. Using the Google Sheets API, I was able to seamlessly integrate Python with Google Sheets, creating a system that automatically updated my data, managed real-time changes, and eliminated version conflicts. Here’s a guide to how I set it up, with examples to help you get started with your own projects.

The Solution:

Using the Google Sheets API with Python
Connecting Python to Google Sheets allows you to automate tasks, fetch data, and update sheets effortlessly. The Google Sheets API enables programmatic access to Google Sheets, providing endless possibilities for data management.

Step-by-Step Guide to Setting Up Google Sheets API with Python

1. Set Up Your Google Cloud Project

To begin, you’ll need to create a project in the Google Cloud Console:

Go to the Google Cloud Console and create a new project.
Enable the Google Sheets API and the Google Drive API for this project, as you’ll need both for full access.
Go to Credentials and click on Create Credentials. Choose OAuth client ID or Service Account depending on your requirements. For automated scripts without user interaction, Service Account is recommended.
Once the credentials are created, download the JSON file containing your service account key. Keep this file secure, as it provides access to your Google Sheets.

2. Install Required Libraries in Python

To work with the Google Sheets API, install the following libraries:

pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client spread

google-auth and google-api-python-client are essential for connecting with Google’s APIs.
gspread is a Python library that simplifies interactions with Google Sheets.

3. Obtain Permissions to Access Google Sheets

Before you can interact with Google Sheets through the API, you need to configure the permissions properly to allow your service account or OAuth credentials to access specific sheets.

Share Your Google Sheet with the Service Account Email:
If you are using a service account, you’ll notice that the JSON file contains an email address (something like your-service-account@your-project.iam.gserviceaccount.com). For the service account to access your Google Sheets, you must share the sheet with this email address.
Open the Google Sheet you want to use.
Click Share in the top-right corner of the sheet.
Enter the service account email address and set the permissions to Editor.
Click Send to save these changes.

2. Ensure Proper API Scopes:
When setting up your Google Cloud project, make sure you included the necessary API scopes to allow reading and writing to Google Sheets. In your Python code, use these scopes to ensure proper permissions:

scope = [ "https://www.googleapis.com/auth/spreadsheets", # For accessing and editing Google Sheets "https://www.googleapis.com/auth/drive" # For accessing Google Drive ]

4. Authenticate and Connect to Your Google Sheet

Here’s a Python script to authenticate and connect to your Google Sheet:

import gspread
from google.oauth2.service_account import Credentials

# Define the scope and authenticate using the JSON key file
scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
credentials = Credentials.from_service_account_file("path/to/your/credentials.json", scopes=scope)

# Authorize the client and open the Google Sheet
client = gspread.authorize(credentials)
sheet = client.open("Your Google Sheet Name").sheet1  # Access the first sheet
Replace "path/to/your/credentials.json" with the path to your JSON file, and "Your Google Sheet Name" with the name of your Google Sheet.

Examples of Google Sheets API Functionalities

Once connected, you can perform a wide range of operations on your Google Sheet. Here are some useful examples:

Example 1: Read Data from Google Sheets
To retrieve data from a specific range of cells:

Fetch all data from the sheet as a list of lists

data = sheet.get_all_values()
print("All data:", data)

Fetch data from specific cell range

specific_data = sheet.get("A1:C10")  # Adjust the range as needed
print("Specific data:", specific_data)

This code retrieves all the data in the sheet or a specific range, displaying it as a list of lists.

Example 2: Write Data to Google Sheets

To add data to specific cells:

# Update a single cell
sheet.update("B2", "New Data")

# Update a range of cells
sheet.update("A1:C1", [["Header1", "Header2", "Header3"]])

# Append a new row at the end of the sheet
sheet.append_row(["Row1 Data", "Row2 Data", "Row3 Data"])
These commands allow you to write to individual cells, multiple cells, or append entire rows of data.

Example 3: Clear Data from Google Sheets

If you need to clear data in a specific range:

Clear data from a specific range

sheet.batch_clear(["A2:C100"])  # Adjust the range as needed
This code clears all values within the specified range, which is useful for cleaning up data before importing new information.

Example 4: Automate Data Updates

If you want to automate updates to your data, for example, appending daily statistics:

import datetime

# Append a new row with the current date and dummy statistics
today = datetime.date.today().isoformat()
stats = [today, 123, 456, 789]  # Replace with actual data
sheet.append_row(stats)

This script appends a new row with the current date and data points, making it ideal for tracking daily changes or automating periodic updates.

Conclusion

Using Python to interact with the Google Sheets API has transformed the way I handle large datasets, saving time and reducing errors from manual work. Whether you need to automate data updates, retrieve real-time information, or simply make collaborative work easier, connecting Python to Google Sheets opens up a world of possibilities.

With these examples, you should be well-equipped to start automating your own workflows and move away from outdated methods that slow down your productivity.

Follow me on Linkedin
https://www.linkedin.com/in/kevin-meneses-897a28127/
and Medium
https://medium.com/@kevinmenesesgonzalez/subscribe
Subscribe to the Data Pulse Newsletter
https://www.linkedin.com/newsletters/datapulse-python-finance-7208914833608478720

Join my Patreon Community https://patreon.com/user?u=29567141&utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink