Introducing the FXMacroData Economic Data Embed Widget

Robert Tidball — Wed, 03 Dec 2025 13:34:36 +0000

Today, we’re launching a major new feature on FXMacroData — fully embeddable, real-time charts that you can drop into any website, blog, or research portal with a single copy-and-paste.

Each chart on FXMacroData now includes a dedicated “Embed Graph” button. Clicking it reveals a ready-to-paste HTML snippet.

You can view the full data and chart on the dashboard here: https://fxmacrodata.com/dashboard/EUR_USD To embed the chart above on your own site, use this snippet:

<!-- FXMacroData Chart Embed -->
<div
    id="fxmacro-carryChart"
    style="width: 100%; max-width: 800px; margin: 20px auto;"
>
    <iframe
        src="https://fxmacrodata.com/dashboard/embed/EUR_USD/carryChart"
        width="100%"
        height="450"
        frameborder="0"
        style="border: 1px solid #e5e7eb; border-radius: 8px;"
    ></iframe>
</div>

How Embeds Work

Embedding is as simple as copying the snippet and pasting it into your page. Each FXMacroData embed:

Updates in real time as new announcements are released
Requires zero maintenance or API integration
Loads quickly thanks to a lightweight hosting layer
Automatically adapts to your layout and device size
Built for Analysts, Traders, and Educators

Whether you’re building models, writing research, teaching, or running a fintech product, these embeds let you present live macroeconomic data without engineering work.

Designed for Reliability and Performance

Each widget runs on the same architecture powering the FXMacroData dashboards, backed by:

Real-time streaming data from our announcement pipeline
Optimized caching for low latency
Cloud Run auto-scaling for high-volatility events

Your embeds stay fast, accurate, and stable — even during major releases when traffic spikes.

Start Embedding Today

Visit any dashboard on FXMacroData and click “Embed Graph” to generate your snippet instantly.

🐍 Building an FX Trading Edge: Creating a Python Client for the FXMacroData API

Robert Tidball — Tue, 25 Nov 2025 13:45:20 +0000

When building a Python library, the goal is to turn a complex, boilerplate-heavy process (raw API calls) into a simple, elegant one-liner. The FXMacroData API provides real-time macroeconomic indicators for major currency pairs—a goldmine for quant traders and analysts.

Raw API calls force developers to repeat code for authentication, error checking, and URL construction. Embracing the DRY (Don't Repeat Yourself) principle, I set out to build a dedicated Python library on top of it, creating a user-friendly wrapper. This article walks you through the core components of that wrapper, covering synchronous and asynchronous clients, proper exception handling, and utility functions.

1. Project Scaffolding and Handling Authentication

A good library starts with an intuitive entry point. My goal was to turn an HTTP request into a clean Python method call like:

client.get("aud", "inflation")

The Client Constructor

The Client class holds the base URL and API key. The FXMacroData API has a unique feature: USD data is public, but other currencies require an API key. The constructor handles this requirement upfront.

# client.py or async_client.py
from typing import Optional
from .exceptions import FXMacroDataError

class Client:
    BASE_URL = "https://fxmacrodata.com/api"

    def __init__(self, api_key: Optional[str] = None):
        """
        Synchronous FXMacroData Client.
        api_key: Required for non-USD currencies. USD is public.
        """
        self.api_key = api_key

2. Core Logic: The Synchronous Client (`Client`)

The synchronous Client uses the popular requests library. The main logic resides in the get method, which dynamically constructs the URL and enforces the API key requirement.

The `get` Method: Dynamic URL Construction and Key Check

# client.py
def get(
    self,
    currency: str,
    indicator: str,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
) -> dict:
    currency = currency.lower()
    url = f"{self.BASE_URL}/{currency}/{indicator}"

    headers = {}
    if currency != "usd":
        if not self.api_key:
            # Custom exception is crucial for user-friendly errors
            raise FXMacroDataError(f"API key required for {currency.upper()} endpoints.")
        headers["X-API-Key"] = self.api_key

    params = {}
    # ... params and API call logic ...

Robust Error Handling with Custom Exceptions

A robust library must handle failures gracefully. I created a custom exception, FXMacroDataError, to catch network issues and non-200 status codes, returning a clear, actionable message.

# exceptions.py
class FXMacroDataError(Exception):
    """Custom exception for FXMacroData client errors."""
    pass

Core request logic with the error wrapper:

# client.py (continued)
try:
    response = requests.get(url, headers=headers, params=params)
except Exception as e:
    raise FXMacroDataError(f"Request failed: {e}")

if response.status_code != 200:
    # Raise a clear error if the API returns a problem
    raise FXMacroDataError(f"API Error ({response.status_code}): {response.text}")

return response.json()

3. Advanced Feature: The Asynchronous Client (`AsyncClient`)

For automated trading bots or high-traffic dashboards, asynchronous programming is essential for performance. The AsyncClient uses the aiohttp library for non-blocking I/O.

Asynchronous Session Management

I implemented async context managers (__aenter__ and __aexit__) to ensure the aiohttp.ClientSession is created and properly closed, preventing resource leaks.

# async_client.py
import aiohttp
# ... imports ...

class AsyncClient:
    # ... init ...

    async def __aenter__(self) -> "AsyncClient":
        self.session = aiohttp.ClientSession()
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
        if self.session:
            await self.session.close()
            self.session = None

This allows concurrent execution, where the total time is the maximum delay, not the sum:

import asyncio
from fxmacrodata import AsyncClient

async def main():
    async with AsyncClient(api_key="YOUR_KEY") as client:
        # Concurrent calls are now trivial
        data_aud = client.get("aud", "inflation")
        data_eur = client.get("eur", "gdp")

        aud_data, eur_data = await asyncio.gather(data_aud, data_eur)
        # ...

4. Utility: Cleaning Up the Data

Data consumers expect chronologically sorted data, but APIs don't always guarantee it. A small utility function ensures the output is always clean time-series data, checking for either a 'date' or 'release_date' key.

# utils.py
def sort_by_date(data_list):
    """Sorts a list of indicator data dictionaries by 'date' or 'release_date'."""
    return sorted(data_list, key=lambda x: x.get('date') or x.get('release_date'))

Building this wrapper solidified my understanding of Object-Oriented Design, the crucial performance trade-offs between Synchronous vs. Asynchronous networking, and the importance of a great Developer Experience through custom exceptions. You can explore the full source code on GitHub.

The final step was packaging the library and publishing it to PyPI. If you're building a tool to integrate real-time FX macro data, or just want a pattern for creating your own wrapper, the structure of this library is a solid foundation. Happy coding!

— Rob @ FXMacroData

Forem: FXMacroData