My Year with the FixedFloat API: A Developer’s Perspective

Today is 09/26/2025. I’ve been working with the FixedFloat API for almost a year now‚ primarily using Python‚ and I wanted to share my experiences – the good‚ the challenging‚ and the downright concerning. I’m a developer named Elias Vance‚ and I specialize in building automated trading systems. FixedFloat initially seemed like a great platform for integrating crypto exchange functionality‚ but it’s been a bit of a rollercoaster.

Initial Impressions and Setup

I first encountered FixedFloat while looking for a reliable API to automate currency exchange for a client project. The documentation was…sparse‚ to say the least. However‚ I did find a Python module available on PyPI‚ which was a huge relief. I installed it using pip install fixedfloat. The initial setup wasn’t too bad‚ but I quickly realized the available documentation didn’t fully reflect the API’s actual behavior. I spent a considerable amount of time reverse-engineering some of the endpoints.

The Python Module: A Mixed Bag

The Python module itself is functional‚ allowing me to access core features like getting currency lists and creating orders. I used the get_currencies method frequently to dynamically update exchange rate displays in my application. Here’s a simple example of how I did it:

import fixedfloat

try:
 currencies = fixedfloat.get_currencies
 for currency in currencies:

 print(f"Currency: {currency['name']}‚ Code: {currency['code']}")
except Exception as e:
 print(f"Error fetching currencies: {e}")

However‚ I found the error handling to be rather basic. It often just threw generic exceptions‚ making debugging difficult. I had to wrap almost every API call in a try...except block to prevent my application from crashing.

Precision Issues and Floating-Point Numbers

One of the biggest headaches I encountered was dealing with floating-point precision; As the internet resources mention‚ this is a common issue. I noticed discrepancies between the amounts I requested and the amounts actually exchanged‚ especially with smaller transactions. I experimented with the decimal module in Python‚ as suggested in some online resources‚ to try and mitigate this. I found that serializing floats as doubles‚ as mentioned in one of the snippets‚ helped a little‚ but didn’t completely eliminate the problem. I ended up implementing my own rounding logic to ensure accuracy‚ which added significant complexity to my code.

For example‚ I used this formatting to print numbers with a fixed precision:

numbers = [23.23‚ 0.1233‚ 1.0‚ 4.223‚ 9887.2]
for x in numbers:
 print("{:10.4f}".format(x))

Security Concerns: The Hacks

This is where my experience took a very concerning turn. I was shocked to learn about the multiple security breaches at FixedFloat‚ as reported online. The news about the stolen 2;8 million was alarming. I immediately reviewed my code and implemented stricter security measures‚ including two-factor authentication and limiting API key permissions. I also started logging all API requests and responses for auditing purposes. The discovery of the malicious Python package set-utils stealing Ethereum keys was particularly frightening. I made sure to thoroughly vet all third-party packages before installing them‚ and I regularly scan my dependencies for vulnerabilities.

API Rate Limits and Reliability

I also ran into issues with API rate limits. FixedFloat doesn’t seem to have very generous rate limits‚ and I frequently received “too many requests” errors‚ especially during peak trading hours. I implemented exponential backoff with jitter to handle these errors gracefully‚ but it still impacted the performance of my application. The API’s overall reliability has also been questionable. I’ve experienced intermittent outages and slow response times‚ which have required me to build in robust error handling and retry mechanisms.

Alternatives Considered

Because of these issues‚ I’ve started exploring alternative crypto exchange APIs. I’ve looked at Binance‚ Coinbase‚ and Kraken‚ and I’m leaning towards switching to one of them. While they may have their own drawbacks‚ they generally offer more robust documentation‚ better security‚ and more reliable performance.

Final Thoughts

My experience with FixedFloat and Python has been a learning one‚ albeit a frustrating one. While the Python module provides a basic interface to the API‚ the lack of comprehensive documentation‚ precision issues‚ security vulnerabilities‚ and reliability concerns make it a risky choice for production applications. I would advise anyone considering using FixedFloat to proceed with caution and to thoroughly evaluate the risks before committing to it. I‚ personally‚ am actively working on migrating my projects away from FixedFloat as quickly as possible.