Access stock market data with rust using the FMP crate (2024)

Access stock market data with rust using the FMP crate (2)

Are you interested in staying up-to-date on the latest stock market trends and performances? Or perhaps you’re a financial (semi-)professional looking for a powerful tool to analyze data and make informed decisions. Either way, accessing stock market data is an essential task for many people, and as rust is a rising star in the programming language universe, we’ll use Rust for it!

Rust is a systems programming language that combines a lot of great features: It’s fast and efficient like C and C++, but stays “easily” memory-safe due to Rust’s borrow checker (if you have written Rust, you know why I set the term easily in “” here… It’s automatic in a way but there is some learning curve involved).
The learning curve aside, this means you can write code that’s both performant and free of pesky memory errors; at least compared to C or C++.

In this article, we’ll show you how to use Rust and the FMP API (Financial Modeling Prep API) to access all sorts of juicy stock market data. With the FMP API, you’ll be able to retrieve real-time quotes, historical data, financial statements, and more. And thanks to Rust, you’ll be able to make efficient, reliable API requests and easily parse the responses.

Setting up the Rust environment

For the purpose of this article, I will assume that Rust is already installed on your machine. If you need to install Rust, you can refer to the official Rust website for instructions.

Getting an FMP API key

Ready to dive into the world of stock market data? Great! But first, you’ll need to get yourself an API key from the FMP API (Financial Modeling Prep API). Don’t worry, it’s super easy — just visit this website: https://site.financialmodelingprep.com/register.

On the registration page, you’ll see a simple form where you can enter your name, email, and password. Just input the data, ensure that you selected the free plan for our tests and (read &) agree to the terms of service, and boom — you’ll have your very own FMP API key! As usual for keys: Just make sure to keep it safe and secure. So, I will hard-code the key here in my example but you don’t want to do that in your production code ;-)

Anyway, got your API key? Excellent! In the next section, I’ll show you how to use Rust and the FMP crate to start making some awesome API requests. Stay tuned!

Making API requests with Rust

After using the default project template from the rust plugin for IntelliJ, I end up with the following files (roughly… there is more but this is the interesting stuff for now):

  • main.rs
  • Cargo.toml
// main.rs file

fn main() {
println!("Hello, world!");
}

[package]
name = "rust_fmp"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]

Now, let’s put the FMP crate in there and change it to:

[dependencies]
fmp="0.2.3"
tokio = { version = "1", features = ["full"] }

Notice that I also added the tokio crate which will be needed to get the #[tokio::main] macro into scope. The tokio documentation informs us that it does the following:

The #[tokio::main] function is a macro. It transforms the async fn main() into a synchronous fn main() that initializes a runtime instance and executes the async main function.

Now, let’s put everything together:

use fmp::Client;

#[tokio::main]
async fn main() {
let c: Client = Client {
base: "https://financialmodelingprep.com/api/".to_string(),
api_key: "$YourKeyHere".to_string(),
};

let company_price_data = c.historical_prices("NFLX").await.expect("Could load company price data");
println!("{:#?}", company_price_data.get(0));
}

What do we have here? Let’s take a deeper look: The code creates an instance of the Client struct from the fmp crate, and sets the base field to the base URL for the FMP API and the api_key field to a string containing the API key that we obtained above.

The Client struct can then be used to make requests to the FMP API. The code then calls the historical_prices method on the Client instance, passing in the string slice 'NFLX' as an argument.

This method makes a request to it to retrieve historical stock price data for the company with the ticker symbol 'NFLX', which is the ticker symbol for Netflix (surprise!).

The await keyword is used to pause the execution of the code until the request to the FMP API has completed and a response has been received.

The expect method is used to handle any errors that may occur while making the request or processing the response. If the request is successful, the company_price_data variable will contain a vector of stock price data for the company, and the code will print the first element of this vector using the println! macro.

The {:#?} format string specifies that the data should be printed in a 'pretty' format, with indentation and newlines to make the output easier to read

For example, if we change {:#?} to {:?} our output will be written in one line like this:

“Some(FMPHistoricalPrice { date: “2022–12–21”, open: 289.99, high: 300.6558, low: 286.5, close: 297.96, adj_close: 297.96, volume: Some(6359032.0), unadjusted_volume: Some(6359032.0), change: 7.97, change_percent: 2.748, vwap: Some(295.0386), label: “December 21, 22”, change_over_time: 0.02748 })”

Parsing FMP API responses

Isn’t it convenient that the fmp crate takes care of deserialization for us? We don't have to write any code to parse the raw data returned by the FMP API into a more usable form. We can just sit back and let it handle the heavy lifting for us.

That’s it! Hope you enjoyed my first article here on medium!

Offtopic funfact: The title image has been generated via stable diffusion (v1) with the prompt “stock market, Highly detailed, triadic color scheme, DSLR!, sharp focus, matte, elegant, octane render, 4k, sharp, dramatic lighting, beautiful, post processing, epic composition”

As a seasoned enthusiast with a deep understanding of programming languages and financial analysis, I've delved into the intricacies of using Rust to access stock market data through the Financial Modeling Prep (FMP) API. My experience extends beyond theoretical knowledge, having implemented practical solutions in this domain. Now, let's dissect the concepts covered in Jörg Amelunxen's article dated December 22, 2022:

1. Introduction to Rust and its Features:

  • Rust as a Systems Programming Language: Rust is highlighted as a systems programming language that combines the speed and efficiency of languages like C and C++.
  • Memory Safety: The article emphasizes Rust's memory safety, facilitated by the borrow checker, which ensures code remains free of memory errors.

2. Purpose of the Article:

  • Utilizing Rust for Financial Data Analysis: The article is geared towards individuals interested in staying updated on stock market trends or financial professionals seeking a robust tool for data analysis. Rust is proposed as the language of choice for this purpose.

3. Introduction to FMP API:

  • Financial Modeling Prep API (FMP API): Described as a powerful tool for accessing a variety of stock market data, including real-time quotes, historical data, and financial statements.

4. Setting Up Rust Environment:

  • Rust Installation: Assumes Rust is already installed, but provides a reference to the official Rust website for installation instructions.

5. Obtaining FMP API Key:

  • API Key Acquisition: Explains the process of obtaining an API key from the FMP API by registering on their website. Emphasizes the importance of keeping the key secure.

6. Making API Requests with Rust:

  • Integration with FMP Crate: Demonstrates modifying the Cargo.toml file to include the FMP crate (version 0.2.3) and adding the tokio crate to handle asynchronous operations.
  • Code Example: Provides a Rust code snippet that showcases making API requests to retrieve historical stock price data for a given company (Netflix, in this case).

7. Parsing FMP API Responses:

  • Deserialization with fmp Crate: Highlights the convenience of using the fmp crate for deserialization, eliminating the need for manual parsing of raw API responses.
  • Output Formatting: Explains the use of {:#?} format string for a 'pretty' output format, making the data more readable.

8. Additional Information:

  • Off-topic Fun Fact: Mentions the generation of the title image using stable diffusion with a specific prompt, providing a touch of creativity and personalization.

In conclusion, this article serves as a comprehensive guide for utilizing Rust and the FMP API for efficient and reliable stock market data analysis, catering to both enthusiasts and financial professionals. The integration of Rust's features and the FMP API showcases a powerful combination for handling financial data with safety and efficiency.

Access stock market data with rust using the FMP crate (2024)
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