Everything You Need to Know about APIs
- Sam Wocks
- Mar 30, 2024
- 7 min read
Decoding the most common API architectures powering AI and Fintech
In the fast-paced world of finance, the buzz around Artificial Intelligence (AI) and Machine Learning (ML) has reached a fever pitch. These cutting-edge technologies are transforming the landscape of quantitative investing, promising unprecedented insights and data-driven decision-making. But have you ever wondered about the unsung heroes working behind the scenes to make this revolution possible? Enter APIs – the backbone of our interconnected digital world.
APIs, or Application Programming Interfaces, are the bridges that allow different software systems to communicate and seamlessly exchange data.
They're the key to unlocking the full potential of new cloud computing capabilities, microservices, and AI/ML investing. In this post, we'll dive into the most common API architectural styles that are enabling these rapid technological advances. By the end of this article, you'll have a clear understanding of the strengths and use cases of each architecture, the importance of API monitoring, and how these technologies are shaping the future of AI and fintech.
The Most Common API architectures:
I'll use a restaurant analogy for each of these to help make the ideas easier to understand!
1. RESTful (REST) APIs: For Most Things - The Web's Backbone
REST = Representational State Transfer.
REST, or RESTful APIs, are the backbone of the modern web. Chances are, most of the web services you interact with daily, from Twitter to YouTube, are powered by REST APIs.
The beauty of REST lies in its simplicity and scalability. By leveraging standard HTTP methods like GET, POST, PUT, and DELETE, REST APIs provide a clean and intuitive way to interact with resources.
So what are the GET, POST, PUT, and DELETE methods?
Method | What it's used for |
GET | Used to retrieve or read data from a server. |
POST | Used to create a new resource on the server by submitting data. |
PUT | Used to update an existing resource on the server by replacing its data. |
DELETE | Used to remove a specified resource from the server. |
REST APIs are similar to a casual, fast-food experience.
REST APIs are like a casual, fast-food experience | Restaurant Analogy | REST API |
 | You can walk in wearing whatever you like | Flexible data formats like JSON |
 | No need for reservations, just order at the counter | No strict contracts |
 | The staff is friendly and adaptable to your needs | Loose communication rules |
 | Your food comes in a simple, disposable wrapper | Lightweight and easy to cache |
Although REST can be used for many applications, it's important to note that if your application requires real-time data or deals with highly connected data models, then there are better alternatives which are explored below.
Here's a code snippet that you can run in Google Colab that uses the GET method to get the past year's stock price for Google. Note that since this is using GET, we're only retrieving data and not editing anything.
Output:
Please visit https://python-rest-framework.readthedocs.io/en/latest/ for a more detailed breakdown of the Python REST API framework.
2. SOAP APIs: For Mission Critical Tasks
SOAP = Simple Object Access Protocol.
This mature and comprehensive XML-based architecture has been around for decades, earning its stripes in industries where security and reliability are paramount, like financial services and payment gateways.
While SOAP's complexity and verbosity might make it overkill for lightweight applications, its robust feature set and strict standards make it a top choice for mission-critical systems.
SOAP is like a formal, sit-down restaurant experience.
SOAP is like a formal, sit-down restaurant experience | Restaurant Analogy | SOAP API |
 | You have to follow a strict dress code | Adhere to XML structure |
 | You must make a reservation in advance | Establish a contract beforehand |
 | The waiter follows a specific script and protocol | Rigid communication rules |
 | The food comes in multiple courses, each with its own plate and cutlery | Extra overhead and bandwidth |
Here's an example of Python code that uses the Zeep library to create a SOAP client and retrieve country information from a web service based on the given ISO country code.
Output:
Please see https://docs.python-zeep.org/en/master/index.html for a more detailed breakdown of Zeep, a fast and modern Python SOAP client.
3. GraphQL APIs: For Efficient, Flexible Requests
Developed by Facebook, GraphQL has taken the API world by storm. It's not just an architectural style, but also a powerful query language that allows clients to request specific data as needed.
With GraphQL, you can say goodbye to over-fetching and under-fetching of data. Clients can ask for exactly what they need, leading to more efficient network communication and faster response times.
This flexibility and efficiency have made GraphQL a go-to choice for applications with complex data requirements, like those used by GitHub and Shopify.
GraphQL is similar to a customizable, build-your-own-dish experience.
GraphQL is like a customizable, build-your-own-dish experience | Restaurant Analogy | GraphQL API |
 | You can pick and choose exactly what ingredients you want | Request specific data fields |
 | The chef combines your selections into a single, personalized dish | Efficient, tailored response |
 | You only get what you asked for, no more, no less | No over-fetching or under-fetching |
 | If you want to try something new, just modify your order | Flexible and adaptable |
Here's an example code that retrieves information about countries from a GraphQL API endpoint, including the country name, capital, currency, and languages spoken, and prints the details for each country:
Output:
Please see https://graphql.org/learn/ for a more detailed overview of using the GraphQL API in Python.
4. gRPC APIs: For High-Performance Microservices
gRPC, developed by Google, is a modern, high-performance framework that uses Protocol Buffers for serialization. It's quickly gaining popularity in microservices architectures, where efficient inter-service communication is crucial.
Giants like Netflix have embraced gRPC to handle their massive scale, leveraging its bidirectional streaming and low latency.
However, if your application primarily deals with browser clients, gRPC might pose some challenges due to limited browser support.
gRPC is similar to a high-tech, conveyor belt sushi restaurant
gRPC is like a high-tech, conveyor belt sushi restaurant | Restaurant Analogy | gRPC API |
 | The dishes are pre-prepared and standardized | Uses Protocol Buffers |
 | The conveyor belt is lightning-fast, delivering dishes in seconds | Low latency, high performance |
 | You can grab dishes from the belt as they pass by | Bidirectional streaming |
 | The restaurant is highly efficient, serving many customers simultaneously | Ideal for microservices |
Running this code is a bit more in depth, so please see https://grpc.io/docs/languages/python/basics/ for a more detailed overview of using gRPC in Python.
5. WebSocket APIs: For Real-Time Data Streaming
When it comes to real-time data streaming, WebSocket is the API style of choice. This protocol enables bidirectional, persistent connections between servers and clients, making it perfect for applications that require instant updates.
From live chat systems to real-time trading platforms, WebSocket has proven its worth in scenarios where low-latency data exchange is essential.
WebSocket is like a live, hibachi grill.
WebSocket is like a live, hibachi grill | Restaurant Analogy | WebSocket API |
 | You take a seat at the chef's table | Establish a persistent connection |
 | The chef prepares your meal right in front of you, explaining each step as they go | Real-time updates |
 | You can ask questions or make requests directly to the chef as they cook | Bidirectional communication |
 | The show continues until you decide to leave the table or the restaurant closes | Connection remains open |
Please see https://websockets.readthedocs.io/en/stable/index.html for a more detailed overview of using the WebSockets API in Python.
6. Webhook APIs: For Event-Driven Simplicity
Last but not least, there's Webhook – a simple yet powerful API style that revolves around event-driven HTTP callbacks.
With Webhook, your application can subscribe to specific events and receive notifications whenever those events occur. This asynchronous approach is ideal for scenarios where you need to trigger actions based on external events.
For example, GitHub uses webhooks to notify your systems whenever a new commit is pushed, allowing you to automate workflows and integrate with other services seamlessly.
Webhook is similar to Uber Eats, a personalized food delivery service.
Webhook is like Uber Eats, a personalized food delivery service | Restaurant Analogy | Webhook API |
 | You provide your address and food preferences | Register a callback URL |
 | Whenever the restaurant has a special discount that matches your preferences, they notify you | Event-driven |
 | You receive the meal as soon as it's ready, without having to constantly check | Asynchronous - the opposite would be something that happens every 10 minutes |
 | If you move to a new address or change your preferences, simply update your delivery profile | Easy to manage and scale |
This code is more in depth, so to keep the article concise please see https://blog.logrocket.com/receive-webhooks-python-flask-or-django/ for a detailed example code implementation using Flask or Django.
In Summary
API Architecture | Restaurant Analogy | Key Characteristics | Common Use Cases |
RESTful | Casual | Flexible, simple, adaptable | General web services, mobile apps, public APIs |
SOAP |  Mission Critical | Strict structure, robust, secure | Financial services, payment gateways |
GraphQL | Customizable | Efficient, flexible, no over/under-fetching | Complex data requirements, social media |
gRPC |  Lightning Fast | High-performance, standardized, bidirectional | Microservices, inter-service communication |
WebSocket |  Real-Time | Real-time, persistent, bidirectional | Live chat, real-time trading platforms |
Webhook | Event Driven | Event-driven, asynchronous, scalable | Notifications, workflows, third-party integration |
Monitoring API Performance
While understanding the various API architecture styles is crucial, it's only half the battle. To truly harness the power of APIs in any strategy, you need to ensure they're performing optimally. That's where API monitoring comes in.
By continuously testing the availability, response time, and data quality of your APIs, you can identify and resolve issues before they become too impactful, and gain valuable insights into API usage and performance trends.
When monitoring your APIs, consider the following key aspects:
Request headers: Ensure your monitoring tool can set custom headers to simulate real-world API interactions.
Authentication: Test APIs with various authentication methods, such as API keys, OAuth, or custom ticket-based systems.
Data validation: Verify that APIs return data in the expected format and within acceptable value ranges.
Powerful tools like Google Cloud's API management platform, Apigee, provides a comprehensive set of tools for designing, securing, analyzing, and scaling APIs. One of the key features of Apigee is its monitoring and analytics capabilities. Apigee allows you to monitor API performance, track usage, and gain insights into how your APIs are being utilized.
Here's an example cloud architecture with Apigee integrated:
Some of the monitoring features include:
Real-time API monitoring: Track API traffic, response times, and error rates in real-time, enabling you to quickly identify and resolve issues.
Detailed analytics: Gain insights into API usage patterns, top developers, top apps, and more through comprehensive analytics dashboards.
Custom reports: Create custom reports to track specific metrics and KPIs relevant to your business.
Alerts and notifications: Set up alerts to notify you when certain thresholds are breached, such as high error rates or unusual traffic patterns.
Integration with Google Cloud Operations (formerly Stackdriver): Apigee integrates with Google Cloud's operations suite, allowing you to monitor API metrics alongside other Google Cloud services.
In addition to Apigee, Google Cloud also offers Cloud Endpoints, a fully managed API gateway that provides monitoring, logging, and authentication capabilities for APIs hosted on Google Cloud.
These tools, combined with the broader Google Cloud ecosystem, provide a robust platform for monitoring and managing APIs, ensuring they perform optimally and deliver value to your users.
Regardless of the API toolset that you use, it's important to always monitor and test your APIs for optimal performance. API testing best practices can be seen in the graphic below.
Conclusion
As the world of finance continues to evolve, APIs will undoubtedly play a central role in enabling the next generation of Fintech, cloud, and AI-powered solutions. By understanding the strengths and use cases of different API architecture styles and implementing robust monitoring, you can position yourself to better capitalize on these opportunities.
In this article, we've explored the most common API architectures, their unique characteristics, and use cases. We've also highlighted the importance of API monitoring and the powerful tools available to ensure optimal performance.
As you embark on your own API journey, remember that by understanding the strengths of REST, SOAP, GraphQL, gRPC, WebSocket, and Webhook architectures, and leveraging powerful monitoring tools, you can unlock the full potential of these technologies to drive innovative solutions.
At Precession AI, we began with a mission:
What if there was a way to leverage state of the art Artificial Intelligence to unlock patterns in financial markets that have remained elusive to traditional techniques?
The approach involves sophisticated algorithms capable of digesting vast datasets, including historical market data, global news, and social media trends, to reveal hidden market patterns that are not explainable by traditional economic logic. These statistical anomalies are difficult to detect yet not random. These 'ghost patterns' unlock a novel way to predict stock movements, and the results speak for themselves:
In initial tests, we've seen 3 AI strategies achieve astonishing 1 yr. returns
From 20-Feb-2023 to 20-Feb-2024
199.5% - on SPDR
630.8% - on TSLA
16,656,072.3% - on BIT/USD
To put these figures into perspective, the S&P 500 ETF returned 21.9% over the same period. The AI generated strategy profits were an incredible
9.1x, 28.8x, and 760.6x higher, respectively.
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