Crate formica 

FormicaX - High-Performance Clustering Library for Financial Data Analysis

FormicaX is a high-performance Rust library designed specifically for clustering analysis of financial data. It implements advanced machine learning clustering algorithms optimized for OHLCV (Open, High, Low, Close, Volume) data to identify market patterns, regimes, and trading opportunities.

Quick Start

use formica::{
    clustering::kmeans::algorithm::KMeans,
    clustering::kmeans::config::{KMeansConfig, KMeansVariant},
    core::{ClusteringAlgorithm, OHLCV},
};
use chrono::Utc;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create sample OHLCV data
    let ohlcv_data = vec![
        OHLCV::new(Utc::now(), 100.0, 105.0, 98.0, 102.0, 1000),
        OHLCV::new(Utc::now(), 102.0, 107.0, 100.0, 104.0, 1200),
        OHLCV::new(Utc::now(), 104.0, 109.0, 102.0, 106.0, 1100),
    ];

    // Configure K-Means with modern builder pattern
    let config = KMeansConfig::builder()
        .k(2)                                // 2 clusters
        .variant(KMeansVariant::Lloyd)       // Use Lloyd's algorithm
        .max_iterations(100)                 // Maximum iterations
        .parallel(false)                     // Disable parallel for small dataset
        .build()?;

    // Initialize and fit the clustering algorithm
    let mut kmeans = KMeans::with_config(config);
    let result = kmeans.fit(&ohlcv_data)?;

    // Display comprehensive results
    println!("Clustering completed successfully!");
    println!("- Algorithm: {}", result.algorithm_name);
    println!("- Clusters: {}", result.n_clusters);
    println!("- Iterations: {}", result.iterations);
    println!("- Converged: {}", result.converged);
    println!("- Silhouette Score: {:.3}", result.silhouette_score);
    println!("- Execution Time: {:?}", result.execution_time);

    Ok(())
}

Re-exports

pub use core::ClusterResult;

pub use core::ClusteringAlgorithm;

pub use core::DataLoader;

pub use core::FormicaXError;

pub use core::OHLCV;

Modules

analysis

Analysis module for FormicaX

clustering

Clustering algorithms module for FormicaX

core

Core data structures and interfaces for FormicaX

performance

Performance optimization utilities for FormicaX

trading

Trading-specific utilities and algorithms for FormicaX

utils

Utility functions for FormicaX

Constants

AUTHORS

DESCRIPTION

VERSION